Circuit design for energy harvesting from digital TV …1339/...Circuit Design for Energy Harvesting...

Northeastern University Thesis by David Lewis 1

Circuit Design for Energy Harvesting From Digital

TV Band

A Thesis Presented

By

David Richard Lewis

To

The Department of Electrical and Computer Engineering

In partial fulfillment of the requirements for the degree of

Master of Science

In

Electrical Engineering

In the field of

Electronic Circuits, Semiconductor Devices, and Microfabrication

Northeastern University Boston, Massachusetts

July 2012


Table of Contents:

Chapter 1: Introduction ................................................................................................................... 8

1.1: Solar: .................................................................................................................................... 9

1.2: Vibrational: ........................................................................................................................ 12

1.3: Thermoelectric: .................................................................................................................. 13

1.4: RF Energy: ......................................................................................................................... 15

1.5: Near Field Communication and RFID (NFC): .................................................................. 15

1.6: Applications: ...................................................................................................................... 16

1.6.1: Trickle Charge Battery usage: .................................................................................... 16

1.6.2: Self Powered Applications: ........................................................................................ 17

Chapter 2: RF Energy Harvesting Design .................................................................................... 19

2.1: Villard Voltage Boost Concept: ......................................................................................... 19

2.2: Diode Selection: ................................................................................................................. 20

2.3: Selection of a Low Power Microcontroller (MCU) and Super Capacitor: ........................ 21

2.4: Power Matching Networks: ............................................................................................... 33

2.5: LC Tank Circuit: ................................................................................................................ 35

2.6: Printed Circuit Board Design:............................................................................................ 36

2.7: Simulation Results: ............................................................................................................ 43

Chapter 3: Design Optimization for Ultra Low Power Applications ........................................... 60

3.1: Using a Harvesting Cell Array to Increase Power: ............................................................ 60

3.2: Using a Multiple Band Antenna to Harvest More Energy: ............................................... 63


Chapter 4: Network Optimization for Ultra Low Power Applications ......................................... 72

4.1: Spread Spectrum Transfer Energy Protocol (SSTEP) ....................................................... 72

4.1.1: Discovery Phase:......................................................................................................... 76

4.1.2: Active Phase: .............................................................................................................. 79

Chapter 5: Integration of the Design Into 45nm CMOS Technology ........................................... 81

Chapter 6: Conclusion and Future Research ................................................................................. 82

6.1: Conclusion ......................................................................................................................... 82

6.2: Future Research ................................................................................................................. 83

6.2.1: Implementation of SSTEP protocol ............................................................................ 83


List of Figures:

Figure 1: Energy Density of Various Energy Harvesting Sources ................................................. 9

Figure 2: Solar Trash Compactor .................................................................................................. 10

Figure 3: Exponential Relationship of Output Voltage to Input Power ........................................ 19

Figure 4: Harvester and Sensor Block Diagram ........................................................................... 20

Figure 5: Protocol Requirements .................................................................................................. 23

Figure 6: Standard 802.11 DCF Protocol ..................................................................................... 24

Figure 7: Super Capacitor Power Requirements ........................................................................... 24

Figure 8: FET Stage Startup Circuit ............................................................................................. 27

Figure 9: MCU Simulated Charging Region ................................................................................ 28

Figure 10: Micca 2 Charging Regions .......................................................................................... 30

Figure 11: Micca 2 Mote Active Harvesting Region .................................................................... 31

Figure 12: TI Sensor Active Region Wireless -5dBm .................................................................. 32

Figure 13: Micca 2 Mote Power Measurements ........................................................................... 33

Figure 14: S11 Sweep for 10 Stage Harvester .............................................................................. 34

Figure 15: LC Tank Circuit Parameters ........................................................................................ 36

Figure 16: DTV Harvester OrCAD Schematic ............................................................................. 37

Figure 17: Harvester Compared to a Quarter. ............................................................................... 37

Figure 18: Harvester Antenna Compared to Lip Balm Container. ............................................... 38

Figure 19: Chaining Harvesters Together in a Harvesting Array ................................................. 39

Figure 20: PCB Resistance Calculation ........................................................................................ 40

Figure 21: PCB Characteristics ..................................................................................................... 41


Figure 22: PCB Trace Characteristics Extracted for Simulation .................................................. 42

Figure 23: 10 Stage Schematic With PCB Parasitics .................................................................... 44

Figure 24: 10 Stage Single Array Efficiency ................................................................................ 45

Figure 25: Ideal 7 Stage Single Array Efficiency ......................................................................... 46

Figure 26: 10-Stage 100kohm Load Using Extracted PCB parasitics .......................................... 47

Figure 27: 10-Stage 100kohm Load Using Extracted PCB Parasitics Matched with 1.9pF and

25nH ...................................................................................................................................... 47

Figure 28: Finding Ideal Trace Length for 641Mhz ..................................................................... 48

Figure 29: Frequency Sweep of Unmatched PCB with 250mil Trace Lengths ............................ 49

Figure 30: Comparison Between Simulation and Measured Voltages at Certain Frequencies .... 50

Figure 31: Frequency Sweep of Unmatched PCB Voltage Versus Frequency ............................ 50

Figure 32: Dual Array 10 Stage Harvesters (Efficiency x2)......................................................... 51

Figure 33: Quad Array 10 Stage Harvester (Efficiency x4) ......................................................... 52

Figure 34: Quad Array 7 Stage Harvester..................................................................................... 53

Figure 35: 10 Stage with 6kohm Load Simulating MPPT Regulator ........................................... 54

Figure 36: 10 Stage with 6kOhm Load 4pF and 10nH Match ...................................................... 55

Figure 37: 10 Stage 6kOhm Load with 2pf and100nH Match...................................................... 55

Figure 38: 7 Stage 6kOhm Load With 5nh and 5.5pF Match ....................................................... 56

Figure 39: 7 Stage 6kOhm Load With 2.6pf and 3uH Match ....................................................... 57

Figure 40: 5 Stage 6kOhm Load With 5pF and 0nH Match ......................................................... 57

Figure 41: 2stage 6kOhm Load With 0.6pF and 15nH Match ..................................................... 58

Figure 42: 1 Stage 6kOhm With 50nH and 5pF ........................................................................... 59

Figure 43: Ideal Harvester Array Concept .................................................................................... 61


Figure 44: Projected Number of Harvesters in Array Versus Power Harvested .......................... 62

Figure 45: Efficiency Versus Number of Harvesters In Array ..................................................... 63

Figure 46: Harvester Open Circuit Voltage Versus Frequency .................................................... 64

Figure 47: Charge Rates from 0v to 1.8v Versus RF Input Power ............................................... 66

Figure 48: Throughput Versus RF Input Power ........................................................................... 67

Figure 49: Antenna Matching Results .......................................................................................... 68

Figure 50: Total System Efficiency .............................................................................................. 69

Figure 51: Charge Rates of Antenna Array Boards from 0 to 1.8v Using 30dbm Output from

Signal Generator and Combined Charge Rate. ..................................................................... 70

Figure 52: Throughput Versus Array Size 641Mhz 0dbm ........................................................... 71

Figure 53: SSTEP Protocol Improving Harvesting Arrays........................................................... 74

Figure 54: SSTEP RF Output Frequency Spread ......................................................................... 76

Figure 55: Voltage Thresholds Used For Node States.................................................................. 79

Figure 56: SSTEP Test Platform................................................................................................... 84

Figure 57: GNURADIO Python Flow Diagram ........................................................................... 85


Abstract:

Radio Frequency (RF) energy harvesting is relatively unexplored research area, owing to

the challenges involved in converting efficiently the low energy density of RF energy in the ambient

environment to useful electrical power. The proposed research is aimed at making a case for self-

sustaining RF energy harvesting sensors powered by the continuous radiation emitted from

external television (TV) stations as part of their scheduled program broadcasts. This thesis

explores the prototype design and investigates the limitations of creating such a self-powered

network using RF energy harvesting circuits operating around 600 MHz in the VHF digital

television broadcast frequency band. A unique feature of our design is the scalability, wherein

more than one circuit can be joined together to enhance the harvesting capability. Our explorations

in optimizing the energy conversion efficiency of this scalable platform has resulted in

demonstrated improvement by activating multiple frequencies at the same time for transmission,

and using the parallel antenna array that results from the extended circuit platform.

There are three major contributions from our work: The first is analyzing the sensor node

selection (i.e., the choice of the microcontroller) and identifying the best supercapacitor that will

power the sensor for a single packet transmission. The second is designing our harvesting circuit,

fabricating on a PCB, and improving its yield by placing a number of harvesters equipped with

individual antennas in a parallel array. The third topic is demonstrating multiple frequency band

energy harvesting, which accounts for any matching errors in the circuit design that shifts the peak

frequency response of the energy receiver from the idea values. Related to this, we outline an

energy scheduling algorithm called as “Spread Spectrum Transfer Energy Protocol” (SSTEP).

This technique will allow energy transfer from a base station to a set of nodes by building a

frequency spreading table to maximize the efficiency of all or certain nodes.


Chapter 1: Introduction

Energy harvesting can be divided into several forms of energy sources. Solar,

vibrational, thermoelectric, and radio frequency (RF) energy harvesting are some of the more

popular harvesting techniques. Energy harvesting is extremely important for wireless age to

make devices and networks more portable, longer lasting, and self-sufficient. There are many

wireless energy sources that allow for sensor devices to be sustainable in certain environments.

Most of these sensors are expected to have a wireless sensor network built around them

to minimize the energy consumption of the node, but yet make the data transfer of the node

possible. Networks that provide maximum throughput at lowest network cost are key for this

type of network environment.

Wireless energy for the most part has much less power capability than that of a typical

wire. Below is a table for Energy Harvesting from various sources and the effective energy

available [11][27].

Source Power Notes

Solar 100 mW/cm^2

(directed toward bright

sun)

100 uW/cm^2

(illuminated office)

Most solar cells have very poor efficiency less

than 30%. Best efficiencies today still are below

50%, but theoretical is 80%.


Vibrational 4 uW/cm^3 (human

motion—Hz)

800 uW/cm^3

(machines—kHz)

Thermoelectric 60 uW/cm^2

RF Energy < 1 uW/cm^2 RF energy can be higher when closer to

transmitter source.

Figure 1: Energy Density of Various Energy Harvesting Sources

1.1: Solar:

Solar cells are very popular today and may be the most commonly used energy harvesting

source. Solar energy harvesting is the most researched harvesting technique today. Solar cells

or photovoltaic cells are solid state p-n junctions that capture incoming photons and convert them

to current. They do this by using band gaps of specific materials at a material junction. Single

material junctions tend to have very low efficiencies of less than 30%. Using multiple material

junctions research has seen around 50% efficiency today. This is still far below the max

theoretical efficiency of 86%[12].

Since the energy density of solar energy is very high around 100 mW/cm^2, solar has a

distinct advantage in energy harvesting. During daylight hours or in an artificially lit

environment solar will provide the most energy for the sensor network it is powering. Solar cells


are typically made to be stackable in that adding more cells in series and parallel increases the

voltage and current outputs. It does have drawback of being less efficient when not facing direct

sunlight. It also suffers substantially at night when light is minimal.

Solar energy has large corporate support and with government tax breaks for using solar

it clearly is an efficient means of energy harvesting. Companies like Solar City will install solar

panels on a consumer's house for free and collect a monthly payment to allow those who want to

have a green energy source, but cannot afford the upfront installation costs. The company then

takes the solar credits from the government to make up their costs.

Solar trash compactors are placed throughout Northeastern University’s campus. This

shows the many use cases of solar power, the general acceptance in the commercial use cases,

and how enough energy can be harvested over a period of time to run even a garbage compactor.

Figure 2: Solar Trash Compactor

From the higher power harvesting capability solar harvesting is ideal for sensor nodes in

wireless networks as they provide relatively a good amount of power during the day. In [15] a

solar power harvesting solution is presented for Micca 2 sensor motes. The description of the


maximum power point of the solar power harvesting is a critical point that must be maintained

for maximum efficiency of the solar cell. This concept can be applied to all energy harvesting

techniques where the load strongly affects the efficiency of the harvesting source. The MPPT

regulation algorithm is one of the key finds in the research field for improving efficiency of self-

sufficient solar energy harvesting nodes.

In [22] the MPP tracking algorithm is improved for wireless sensor network nodes by

decreasing the amount of power required to operate the regulator and increasing the durability of

the regulator in different environmental conditions. Up to this point MPPT regulators were

assumed to have plenty of input power to run the regulator. The authors propose a new feedback

approach that consumes less power and is capable of allowing the maximum power point

tracking algorithm to work. For the most part this design is too bulky for a wireless sensor node,

but new regulators like the BQ25504 from Texas Instruments have improved upon this concept

and have led to great improvement in efficiency since the load is essentially disconnected from

the harvesting source. In [23] the authors further enhance the MPPT regulation scheme for solar

powered wireless network nodes, but the consumption is still too high for use in RF energy

applications and is restricted to solar applications.

In [24] an interesting approach was devised where robots are used to provide updates to

the network and transfer energy between nodes to increase efficiency. The network is divided

into energy zones and is updated by energy equalizing technique by moving certain nodes from

energy plentiful areas to energy-starved zones. This is a novel approach to increase overall

network efficiency, but again is limited to solar energy networks due to the energy requirements

of the moving energy equalizers.

Much of the research done in the solar regime can be applied to other types of energy


harvesting techniques to improve network efficiency.

1.2: Vibrational:

Vibrational energy can be divided into electromagnetic, electrostatic and piezoelectric

energy sources. Typically vibrational energy can gather around 4 uW/cm^3 using human motion

and 800 uW/cm^3 for industrial machinery. The former case allows the most use cases for

commercial applications, but the latter provides much better energy gathering and allows more

useful sensors to be used.

Electromagnetic energy is gathered by magnetic field changes and wire coils. In practice

it can attain 24.8 mJ/cm^3, but can reach 400 mJ/cm^3[27]. This typically is done via magnetics

being moved back and forth by a source of vibration. Several inductive coils are then used to

gather the induced current caused by the magnetic field changes. A famous use case of this is the

―shaker flashlight‖ where a magnet is placed in a flashlight tube. As you shake the flashlight the

magnet is moved back and forth inside a coil of wire. The wire induces an electrical current and

then the voltage is rectified for use by the light bulb.

Electrostatic energy can also be created by using changes in plate capacitance of loose

plates in a capacitor. In practice it can attain 4 mJ/cm^2, but can reach 44 mJ/cm^3[27]. The

electrostatic energy is gathered using changes in capacitance between vibrational dependant

varactors by using the distance of the parallel plates to change the capacitance. Since

capacitance is directly proportional to distance the vibration allows a somewhat linear change in

capacitance, which can be used to generate a charge. In [30] this is implemented and simulated

to have variable capacitor with the range of 1-100pF subject to vibrational energy every 15us

will produce about 38 uW of power. The author uses a three-phase system that separated into


pre-charge, harvest, and recovery phases. The system in package used requires the fabrication of

a MEMS variable capacitor as the critical feature. Unfortunately this system requires a low

power DSP to cover the power management features and also requires the vibrational energy to

be synchronous like that of a engine. This approach is not adaptable to human vibrations.

Piezoelectric energy converts mechanical strain energy into electrical charge. It can in practice

attain 35.4 mJ/cm^3, but can reach 335 mJ/cm^3[27]. As piezoelectric materials contract and

expand they create a charge similar to that of the electrostatic case. Since mechanical strain by

nature is capacitive it can be used in this manner to gather energy. Most cantilever piezoelectric

devices fabricated today can obtain high energy densities, but only at a specific resonant

frequency.

Piezoelectric energy requires optimizations to gather the maximum amount of energy

possible. In [28] the maximum efficiency duty cycle was found for piezoelectric sources using a

discontinuous conduction type regulator. This type of regulation technique can improve the

power harvested by certain sources up to 325%. In [29] a full wave rectifier is fabricated on chip

and allows efficiency up to 93% for the piezoelectric transducer used. The transducer used also

is an improvement from the cantilever approach as it improves the frequency range of use by

allowing multiple resonances. This allows its use to be broader since random vibrational sources

can be used.

1.3: Thermoelectric:

Thermoelectric is another form of energy harvesting used today. With 60 uW/cm^2 it

has more energy harvesting capability than that of typical vibrational and RF energy sources.

Thermoelectric energy harvesting works by using temperature gradients on a conducting material


produce a voltage. This is due to the diffusion of charge carriers based on the temperature

difference. Thomas Seebeck discovered this effect in 1821. Charles Peltier improved upon this

concept when he discovered that a metal junction of two dissimilar conductors could produce a

current.

The best materials to use for thermoelectric energy harvesting sources are highly

conductive metals with low thermal conductivity. The low thermal conductivity allows the

thermal gradient between different points on the metal to be maintained. The high conductivity

allows for more diffusion of carriers and also less loss in the transfer of energy from the source

to the system.

Some of the more common uses for these materials are for thermocouples, which are

used to measure temperature. They do so by converting the temperature measured to a voltage

that a multimeter could convert to temperature.

In [25] the authors show a SOC approach to combining thermoelectric and RF energy

sources together to provide power to a micro battery. This approach of combining two smaller

sources together has a big advantage when one of the sources is non-existent in a particular

environment. This concept of combining energy harvesting sources is not new, but optimizing

some networks with environment susceptibilities is a good optimization to allow more network

robustness.

Thermoelectric energy has many application cases in the biomedical field since the

human body is a good source of heat. In [26] a bismuth and antimony p-n junction was formed

to harness thermal energy from the human body. The fabrication process reveals that these

junctions are somewhat easy to create in current solid-state fabrication technologies. The

experimental results show as high as 10v can be created using the human forehead. This paves


the way for self-sufficient biomedical sensors for patient recovery and monitoring.

1.4: RF Energy:

RF energy can be broken up into smaller sub-topics based on the method the power is

received. RFID and NFC are a similar topic since NFC is an improvement upon the former

RFID standard. In all cases RF energy transfer is very small compared to solar energy. RFID

and NFC for instance rely on the power transmitter to be very close to the harvesting device.

Charging pads like that of the Powermat [14] use very near field inductive coupling to

create a transformer to transfer energy. This is not as much RF as it is a creation of a transformer

with two inductive coils. When a current pushed through the charge pad’s coil a current is

induced on the devices charge case. This is then regulated for the particular voltage that the

device may take. This is typically 5v.

1.5: Near Field Communication and RFID (NFC):

RF energy harvesting becomes more and more popular as NFC applications take hold in

today’s markets. NFC is an improvement on RFID by adding two-way communication. Prior to

this RFID was a one-way communication by the reader. It uses 13.56Mhz as its frequency band

of operation and uses inductive coupling of each devices loop antenna to harvest its power. It is

limited to about 4 centimeters in range and only about 424 kbits/s bit rate.

Most cell phones on the market already have NFC support. Major manufacturers have

been constantly adding NFC as a required feature in their RFQ’s for future product development.

This is driven by the great potential that NFC has of replacing our current monetary system.

Google wallet leads the way in this field and combined with Paypass has a small backbone in


place already. In the same way that RFID works NFC gathers energy from a transmitter for long

enough to send a data packet that contains its RFID tag. This tag can then be processed by a

network based application to determine what this product or device is. In a simple example a

cell phone is equipped with a NFC reader, which acts as a power transmitter to a device. The

device could be any product in a grocery store for instance. The product must have a NFC

device embedded in the packaging of the product. When the cell phone is brought close to this,

or ―tapped‖ to the product the item’s RFID string is read by the phone. The phone running a

NFC enabled network application then looks up the product id and brings up a picture of the

product on your phone and gives an option to view the price and or just purchase the product.

As one could see this simplifies the consumers experience at the store considerably. It

also allows marketing of the product to directly interact with the consumer at the time of

purchase. If the consumer looks at the product the network application could bring up an

Internet ad for the product and product comparisons with leading competitors. Other external

sources could also be added including coupons for that particular product or sales prices of the

exact same product a competing store.

1.6: Applications:

There are limited applications where such low energy content is capable of being used.

The following are several types of application scenarios that give a good fit for this type of

harvesting.

1.6.1: Trickle Charge Battery usage:

A clear scenario would be using this harvesting mechanism to trickle charge a sensor


node with an existing battery. This would extend the life of the battery and even fully charge the

battery when the sensor is consuming less power than it is receiving. This would typically be in

a sleep state where the consumption of the MCU is minimal and the RF input energy is high

enough to provide enough charge for the battery.

1.6.2: Self Powered Applications:

Self-powered applications are more limited due to the low energy densities of ambient

RF energy. The expected application would hence need to employ a base station that transmits

enough RF power to charge the sensors in range of it. This would imply a one-way network

where the base station is constantly collecting data from sensor nodes only when the sensor

nodes have gathered enough power to transmit. This is not a difficult scenario to manage.

Commercial applications of this already exist created by a company called PowerCast. Their

short-range RF energy sensors require a base station that is constantly transmitting power at

928Mhz. This allows sensors to either be self-powered using a super capacitor as an energy

storage device or a battery. The sensors in the network are capable of short transmission bursts

and then returning to sleep until enough energy is harvested again.

This type of application is best realized in a fixed environment such as an industrial

facility, an office environment, or a home automation environment. The major requirement in

this type of network is the range of the transmitter in the building selected and the sensor node

placement as to maintain enough input power to charge the node.

Home automation is a growing market today and offers a lot of potential for RF energy

harvesting. A possible application case for the home automation is using a central base station in

ones home like an existing Wifi router for an energy harvesting source and a separate base


station, possibly integrated with the Wifi router to conduct packet transactions. The energy

wasted by constant transmission of Wifi routers could be used to slowly charge sensor nodes

throughout a person’s house. The advantage to this system is that the power source already

exists in a lot of US homes today. The main drawback to this approach is the relatively low

output power from Wifi routers and the attenuation of 2.4Ghz in air. Although 4-Watt

transmission is possible typical Wifi routers are only around 200 mW of output power.

This market has already been taken over by many wireless protocol networks including

Zigbee, 6LoPAN and Wifi. Wifi has the upper hand as far as acceptance with the general

market, but Zigbee and other low power protocols tend to have an advantage with devices that

are battery operated including smoke detectors, thermostats, and carbon monoxide detectors.

Using optimization design technique such as multiple arrays, multi-band antennas, and SSTEP

protocol proposed in this thesis, RF harvesters could find their way into devices where very little

power is used the majority of the time and only short bursts are required to gather the

information like temperature in many rooms of a house.


Chapter 2: RF Energy Harvesting Design

2.1: Villard Voltage Boost Concept:

The proposed method of harvesting RF energy is a variant of the design proposed in

[need citation for our first paper]. The design is built on a power over air harvesting concept

proposed by Telsa in 1904 [6]. The proposed circuit design utilizes a Villard cascade voltage

multiplier circuit was invented by Heinrich Greinacher in 1919[need citation]. The multiplier

acts as a charge pump to effectively both rectify and multiply the input voltage. The circuit used

is a half wave version of the circuit and depends heavily upon impedance matching for

maximum power transfer. The design hence is a non-linear system where input voltage and

number of stages have an exponential relationship with output power.

Figure 3: Exponential Relationship of Output Voltage to Input Power


Using the voltage multiplier as a way to rectify small RF input amplitudes gives way to a

circuit design concept below.

Figure 4: Harvester and Sensor Block Diagram

2.2: Diode Selection:

The voltage multiplier works best when the barrier height of the Schottky diodes used are

as low as possible. The HSMS-2852 series diodes are some of the lowest barrier diodes that are

available to purchase with an easy to use surface mount package. The RF diodes surveyed for

construction of this multiplier tend to fall into RF detector application category. Although this

category has similar characteristic requirements, the diodes in these applications tend to not have

a metric for barrier height in their datasheets. The metric used to determine the best diode choice

is the turn on voltage level, which is extrapolated from the given input voltage versus output

curve. The HSMS-2852 series offers the lowest available turn on voltage in a surface mount

package.

Another key metric of the diode is the internal resistance. To minimize power loss this

resistance must be kept to a minimum. Parasitic inductances as well must be kept to a minimum

so packaging choices are key to keep losses in check.


2.3: Selection of a Low Power Microcontroller (MCU) and Super Capacitor:

In [13] it was found that using super capacitors allow for much longer lifetimes than that

of batteries since they offer much higher charge cycle counts over the lifespan. The requirement

then became using the Maximum Power Point Tracking (MPPT) of the solar cell to operate the

voltage reference of a PFM mode buck regulator. This relates strongly to all energy-harvesting

designs and is a requirement to have a PFM mode regulator with MPPT algorithm support. For

this reason a Texas Instruments BQ25504 is used after the harvester stage to charge a super

capacitor for the sensor MCU. Unfortunately there are no MPPT regulators catering to the high

impedance required to operate the energy harvesting circuit so even with this MPPT regulator the

circuit requires to be pre-charged to at least 2.5v to start reliable operation. This problem should

be focused on in future work.

The maximum power point can be found for the RF energy harvester as well by finding

the maximum open circuit output voltage. This point should then be used to feed the reference

of the PFM regulator for the MCU voltage. By using a PFM mode regulator with MPPT support

we are able to sample the open circuit input voltage and adjust the switching of the regulator to

maximize this operating point. This in effect raises the output impedance seen by the harvester

circuit, which would otherwise see close to a dead short from the super capacitor. By allowing

this operation the efficiency of the harvester can somewhat be maintained even with changing

the output loading substantially. This is a must have for charging from a dead node, but also

useful in total efficiency in the active region of operation.

In calculating the worst-case power the sensor node can consume we must first

understand what protocol it uses and how much power it takes to transmit a packet. If we take

the assumptions that the sensor node is following an 802.11 protocol then we can define the


expected transmission time and with a power measurement during constant transmission we can

calculate the joules of energy required to be generated by the harvester during a single packet

transmission. The following table shows the key parameters for the protocol and the time that

each portion of the protocol takes. This assumes that if a packet is lost the transmitter will not

care. Its data is retransmitted from the beginning each time and therefore packet loss is not

calculated here. We do not calculate the throughput of the system here, but rather only the

power required per packet of transmission.

For this particular system we assume that a node is really a data gatherer and mostly

transmitting data to a fixed node. Therefore there is an assumed receiver that is always active in

the system. It is also assumed that the receive time is the same as the transmit time. To receive

data the receiver would have to look for a packet addressed to it at the time of wake up. If this

packet does not occur and the channel is free it is assumed that the node will transmit its gathered

data to the always active receiver and go back to sleep.

The worst-case power consumption during a single packet is when the maximum back off

time and maximum packet size are used simultaneously. In this particular scenario we can

determine the worst-case power consumption of the MCU and from that calculate the minimum

required capacitor for charge storage. The capacitor must be sized as small as possible for the

harvester to maximize its charge time. The harvester also has a minimum required received

power that is variable based on the capacitor used and the MCU load on the rail. For this reason

minimizing the capacitor to the protocol requirements is the best way to ensure that reasonable

charge times and minimum receiver power are used. Below is a table showing a use case for the

requirements of the capacitor.

Protocol Time Requirements


DIFS 50 Us

SIFS 10 Us

Slot Time 20 Us

Number of Slots 64

BPS 192000 b/s

Max Packet Size 512 bytes

Size of Data 21333.33333 us

RTS 666.6666667

CTS 666.6666667

ACK 666.6666667 us

Propagation Delay Round Trip 20m 0.066666667 us

Max Packet TX Time 24673.4 us

Required Seconds 0.0246734

Power Margin 20 %

Required Joules 0.001110303 J

Required Capacitance 0.004996364 F

Figure 5: Protocol Requirements


These protocol timing requirements assume the standard DCF protocol seen below:

Figure 6: Standard 802.11 DCF Protocol

Capacitor Choice

V (Volts) 3 3 3 3 3

C (Farads) 0.000047 0.1 0.02 0.22 0.22

Energy (Joules) 0.0002115 0.45 0.09 0.99 0.99

Power Per 5 Seconds (W) 0.0000423 0.09 0.018 0.198 0.198

Power Per 30 Seconds (W) 0.00000705 0.015 0.003 0.033 0.033

Power Per Minute (W) 0.000003525 0.0075 0.0015 0.0165 0.0165

Power Micca 2 (W) 0.045

Figure 7: Super Capacitor Power Requirements


One problem with the charging circuit is that when the voltage threshold is reached for

the MCU it will turn on and cause inrush current and droop the voltage back out of the operating

region. Since the harvester can only overcome the discharge rate of the MCU when it is in sleep

mode and not in active transmission or at POR time it is important to overcome this start up

problem. If this is not addressed the charger will ramp the voltage until the MCU becomes

active. Then the MCU will drop the voltage back out of the active region and the this process

will repeat with the MCU never seeing enough voltage for a long enough period of time to

process anything.

One way to overcome this problem is to implement a power on latch the keeps the MCU

in reset while the charging circuit surpasses the turn on threshold and continues to a targeted set

point. The set point is best to be set at the maximum allowed recommended voltage to power the

MCU. In a lot of cases this is the nominal voltage VDD plus 5%. The power on latch circuit

desired uses a d-flop to create this effect. The clock input is positive edge triggered and the flop

is chosen based on minimum power consumption. Since most MCU resets are active low one

should use a non-inverting d-flop. The clock input should have a hysteretic type input to get a

clean positive edge. Note that this approach depends on having an MCU that either does not

have a built in power on reset circuit or if it does that it is open drain style drive such that there is

no contention on the reset line.

Using a 74LVX series part is a good choice for most low power MCU’s. This part allows

working at 2.0v to 3.6v, which covers most of the low power MCU market. The part also

maintains its state down to 1.2v such that the MCU can use as much of the capacitor energy as

possible during discharge. In the figure below the charge ramp of the capacitor is shown during

charge and discharge phases. The power latch circuit allows the capacitor to charge to 3.15v and


then toggles reset from low to high. When this occurs the MCU wakes up for a brief period of

time and transmits. This is called the discharge time. It is a much steeper discharge rate than

that of the charge rate.

Depending on the MCU used the active region changes. In this example it is only from

the 3.15v to 2.1v. During this time the MCU must wake up and either transmit its data or receive

from the base station. It can either go back to sleep on its own, which will allow a faster charge

time or run until power is low enough for the flop to reset the MCU. If it chooses to sleep on its

own it must remain in sleep for the full charge rate time to get back to 3.15v. To do this it must

either periodically monitor the voltage with an ADC, or it must wait for a known amount of time

that would guarantee a full charge. One could assume 5*RC to get to the full charge time. The

other unknown is in this approach is the RF signal power is assumed to be constant to know the

RC time constant. If the signal power drops during this period then the sensor will not be fully

charged when it wakes up and most likely will dip below the non-active voltage boundary and

the flop will then reset the device and begin a charge cycle.

A latch to hold reset state is a valid approach but does not prevent leakage current flow

from the MCU during power down. The best approach has been found to be that when a voltage

is less than the lower threshold the MCU should be turned completely off to maximize the charge

rate of the super capacitor. This can be done using a two-stage FET configuration that utilizes

the voltage thresholds of the transistors to connect VDD to the MCU. The voltage threshold of

the PFET must be chosen in a way to turn on around -2.5v and the NFET threshold voltage must

be chosen to be around 55% of the VDD used for the MCU. In this case the MCU VDD is 3.3v

so we choose a 1.8v threshold. As the voltage is applied from the harvester the output to the

MCU is nothing. When the harvester input is 2.5v the voltage at the gate of the NFET becomes


1.25v. From here as the voltage increases from the harvester the half of that voltage is applied to

the gate of the NFET. When 1.8v is reached at the gate of the NFET we have approximately

have 3.3v applied to the MCU.

Figure 8: FET Stage Startup Circuit

The proposed MCU charging algorithm requires two threshold levels. One is the

transmission threshold and one is the must sleep threshold. The transmission threshold is best set

at the upper limit of the recommended operating conditions of the MCU. The must sleep

threshold should be set at the lowest recommended operating voltage. As an example for a 3.0v

MCU this would be 3.15v for the transmission level and 2.85v for the must sleep level.

With the power trip switch in place when the lower threshold voltage is reached the MCU

will turn on. At this point in time the MCU should sample the voltage with its ADC and then go

into a low power sleep mode. Using an internal timer the MCU waits 5 seconds and wakes up to

then again sample the VDD voltage. The cycle repeats until the transmit threshold voltage is


reached. In this case it waits until 3.15v is sampled by the ADC. When the upper transmission

level is reached the sensor will transmit its data to the base station.

Below is a simple simulation with the expected behavior during the active region. We confirmed

this theoretical behavior with the Micca 2 mote.

Figure 9: MCU Simulated Charging Region

We programmed both a Micca 2 sensor and a Texas Instruments MSP430 on the eZ430-

RF2500-SEH platform to follow this algorithm. Since the ADC uses an internal band gap

voltage as its reference VDD can be measured without the effect of changes in VDD changing

the ADC codes expected. If this was not the case an external reference must be used for the

ADC. Either using a Zener diode or some other band gap reference. The power on switch

threshold must be higher than that of the ADC band gap reference or else the MCU will turn on


and start sampling before the band gap has been reached. In this scenario the design would not

be able to charge effectively.

The below experiment is the voltage output of the Micca 2 sensor over time without a

power latch using a 100mF super capacitor and a single harvester circuit. An RF signal

generator for precision controls the RF input power. The input frequency used was 641Mhz.

The input power was varied during the charge ramp to see the minimum RF input power levels

required to charge from a dead node as well as to maintain an active state.

All MCU’s have an initial current spike in consumption based on transistor turn on

thresholds. Most MCU’s have a specific minimum power on ramp rate to avoid latch up

conditions. Because the charge rate of the harvester is very slow we run into conditions that

faster charge rate input voltage ramp would not see. Through some experimentation it was found

that 1.2v is a critical region of the Micca 2 sensor and hence needs to be avoided. Our power

latch circuit prevents the MCU from being powered during this period and allows us to use a

much smaller input power to complete charge from dead start.

Without the power latch the MCU required 10dbm RF input power at 641Mhz to

overcome the behavior of the Micca 2 sensor with a slow charge ramp. Once the active region

was reached the RF power to maintain the active region was explored. To maintain charge at the

minimum threshold -10dbm was required. Charging occurs with any input power more than this,

but lower input power takes more time between transmissions.

Below is the logged Micca 2 VDD voltage output from the harvester during the

experiment. The time step is in 500ms intervals. In the first 335 time steps only 0dbm was

applied to the circuit. At 335 to 1003 steps 5dbm was applied to the circuit. At 1003 to 2673

7dbm was applied. After this gets to 1.2v this flattens out and cannot overcome the Micca 2 turn


on current. The input is then increased to 15dbm at the 3341 mark. It is dropped down again to

the 10dbm mark at 3675. The active region begins at the 4009 marker and continues. From the

4009 mark forward the RF input frequency is dropped in steps until a minimum of -10dbm is

found to be a flat line from 5679 to 6013. From 6013 to 7015 the capacitor is shorted out and the

charge cycle begins again.

Figure 10: Micca 2 Charging Regions

Below is Micca 2 during the active region with 10dbm input power. It takes about 20

seconds between packet transmissions, which translates to a 0.01 Volts/Second charge rate in the

active region.


Figure 11: Micca 2 Mote Active Harvesting Region

There will be a defined time between transmissions that is dependant on the MCU used,

the energy storage capacitor used, and the received RF power. Minimizing the MCU power

consumption in turn lowers the storage capacitor size and increases the frequency at which the

node can re-transmit data. The RF power input also increases this frequency as the power

increases. From this retransmit time and with the protocol assumptions made previously we can

compute the throughput of the system at a particular RF power input.


TI Sensor Active Region

0

0.5

1

1.5

2

2.5

3

3.5

1 241 481 721 961 1201 1441 1681 1921 2161 2401 2641 2881 3121 3361

Time Units in 0.5s

Vo

ltag

e

TI Sensor VDD

Figure 12: TI Sensor Active Region Wireless -5dBm

Next the experiment was repeated with a Texas Instruments MSP430 sensor IC’s using

the eZ430-RF2500-SHE platform and an MPA-40-40 4Watt wideband power amplifier from RF

Bay was used to amplify the RF signal generators output and drive a 50 ohm antenna. This is

less accurate as to what power is received by the harvester, but shows the concept. The on board

antenna was used for the harvester. The output shows a 0.001 Volts/second charge rate in the

active region.

From this data one can see the limitations of this type of system. Although the power is

self-sufficient the RF power input required to run the system is substantial. This would limit the

design to a scenario where a base station must transmit power to the devices at 641Mhz and be

active at all times to receive data as it is transmitted on possibly a different frequency band such

as the 915 MHz ISM band.


2.4: Power Matching Networks:

Because the boost circuit is inherently non-linear the output impedance and input

impedances of the circuit have a non-linear effect on the output voltage. The important factors

that go into the matching network selection are input impedance, output impedance, RF input

power, and RF input frequency. Input impedance is dependant on the antenna used. In most

cases we can assume that 50 or 75-ohm antennas are the expected requirement. Output

impedance, which is dependant on the MCU and capacitor chosen from the study above, is best

measured from measuring the voltage and current of the sensor during particular states of

operation. The important states to measure are sleep, idle, RX active, and TX active. Below is a

table of Micca 2 gathered power measurements and its translation to impedance. From this it

was concluded to run the simulations with a 100kohm load to emulate the MCU load in sleep

mode.

MODE TRANSMIT RECEIVE IDLE SLEEP

CURRENT (mA) 22.00 15.50 3.20 0.03

VOLTAGE (V) 3.00 3.00 3.00 3.00

POWER (mW) 66.00 46.50 9.60 0.09

IMPEDANCE (OHMS) 136.36 193.55 937.50 100000.00

Figure 13: Micca 2 Mote Power Measurements

RF input power changes the impedance of the system as well since when the diodes start

conducting the system lowers in impedance. This means that measuring input power versus


output power is really the best way to get a read on impedance. A S11 sweep was conducted in

simulation using ADS with the results below.

Figure 14: S11 Sweep for 10 Stage Harvester

RF input frequency also affects the impedance of the design. As input frequency

increases the diode switching characteristics breakdown. RF detector diodes are good at

switching in high frequencies, but still break down in the several GHz range. As the switching

characteristics degrade the impedance of the diode changes dramatically. The diode also has a

built in parasitic capacitor that determines these characteristics. As with capacitors as frequency

increases capacitors become short circuits. One may think that lowering impedance would be

good for power transfer, but as this impedance lowers the voltage multiplier ceases to work.

Since the impedance changes dynamically it is impossible to power match the circuit for

all input frequencies, all RF power levels, and all dynamic load states with passive components.


One may then assume that dynamic power matching is the best way to go, but unfortunately to

tune the circuit on the fly requires power that tends to be more consumption than the efficiency

gain it provides it in the first place. Because the design is ultra low power harvesting only

passive components can be used for power matching to assume the best efficiency.

The ideal power-matching network is a LC matching network. Whether it is a step up or

step down network depends on the conditions mentioned prior to this. Using ADS the S11

impedance was simulated. Using this impedance and a tuning assistant in ADS simulation was

done to find the best power matching LC network. Even with these simulations manual tuning

was also required to account for board and component parasitics that are not modeled in ADS.

For a 641Mhz 0dbm RF input the series inductor and shunt capacitor configuration is best. For a

915 MHz 0dbm RF input a series capacitor and shunt inductor is best.

2.5: LC Tank Circuit:

If the design needs to be limited to a very narrow band of harvesting this can be

accomplished with a series LC tank circuit with resonance at the given frequency. A tank circuit

with various bands in the DTV regions is calculated in the table below. The LC tank was tested

and does attenuate signals out of the band of interest, but also degrades the performance of the

circuit in the band of interest. We decided against using this circuit for our data, but the

suggested components are provided below.


Band pass Filter with

LC in series with input

This should be implemented if we only

want 641 MHz power (not everything)

L 0.000001 0.000001 0.000001

C 2.4E-12 2.5E-12 2.3E-12

F 645497224.4 632455532 659380473.4

Figure 15: LC Tank Circuit Parameters

2.6: Printed Circuit Board Design:

The printed circuit board design was to allow for multiple 10 or 7 stage harvesters to be

chained together using board to board interconnections. These board-to-board connectors are

industry standard 0.1‖ headers and plugs that allow for easy connection and disconnection of

each harvester PCB from the other. The board was designed in a way such that each harvester

could be connected on any of the 4 sides of the PCB to another harvester. The connection shares

only the voltage output, which then would be tapped off for the MCU power at some point. To

make sure that each circuit does not interfere with each other’s final stage a blocking diode is

also used. This diode introduces some voltage loss, but is very minimal due to the current flow

in the diode being so low.


Figure 16: DTV Harvester OrCAD Schematic

The PCB schematic was designed in Cadence OrCAD Capture 16.2. The netlist was

exported to Mentor PADS PCB for layout. The PCB was fabricated at Chicago Interconnect.

Figure 17: Harvester Compared to a Quarter.


Figure 18: Harvester Antenna Compared to Lip Balm Container.


Figure 19: Chaining Harvesters Together in a Harvesting Array

One pitfall to LC power matching approach is that the parasitics gathered from the PCB

are on the same order of magnitude as the simulated ideal matching components. Each trace on

the board is a transmission line with RLC components. The resistance is minimal for our short

traces, but the inductance is around 43 nH per inch and capacitance is closer to 216 fF per inch of

trace. When simulated ideal match at 915 MHz is with a less than 5 nH of inductance, and there

is already more than that in parasitic components, one has a problem matching for an ideal case.

Mitigating the parasitics was attempted by fabrication of a second PCB. The PCB was

designed in Cadence OrCAD Schematic Capture, and PADS layout. Footprints for the


components used were drawn as part decals and saved into a reusable library. OrCad symbols

for components were custom drawn for diodes and antenna. Standard symbols used from

Cadence were inductors, and capacitors.

For minimizing component parasitics 0402 sized surface mount inductor and capacitor

sites were used for LC matching network. Using a smaller package size minimizes ESR and

ESL components of matching network capacitors and inductors. High Q RF inductors were

purchased to minimize losses during power and antenna matching stages.

For minimizing trace parasitics the stack up and trace width was chosen to decrease

parasitic capacitance. The trace width was made smaller, but at the same time not so small that

resistance was increased. Picking 25-30 mils on a 62-mil thick board with ½ OZ copper from

simulation seemed to show close to ideal efficiency matching. The inductances were still too

high, but lengths of the traces were minimized as best as possible. To get to 5nH only around

200 mils of total trace would be allowed and this is impossible with the component keep out and

design requirements.

The resistance of the trace can be calculated using the resistivity of copper (1.7E-6 ohm-

cm) and assuming 25C for our calculations. L is length of the trace, W is width of the trace and

T is the thickness of the trace.

Figure 20: PCB Resistance Calculation

Simulation of resistance, inductance, and capacitance values were all done in Mentor

Graphics Hyperlynx and are shown below.


Hyperlynx Extracted PCB Parameters Properties per Inch

Units

Board Thickness 62 62 Mils

Copper Weight 1 1 Oz

Trace Width 30 25 Mils

Trace Length 1 1 Inches

Z0 443.6 449.5 Ohms

Delay 96.8 97.5 pS

L 43 43.8 nH

C 218.3 216.8 fF

R0 0.017 0.02 Ohms

Figure 21: PCB Characteristics

The PCB characteristics were simulated in Mentor Graphics Hyperlynx as a per inch

parameter. These per inch parameters were then multiplied by the total length of the traces of

each section to create a model for simulation of the PCB as seen in Figure 22: PCB Trace

Characteristics Extracted for Simulation.


PCB Trace

Characteristics

From

Antenna to

Match

From Antenna

Match to

Power Match

From Power

Match to

Harvester

(25mils)

Total Units

Trace Length 1.429 0.536 0.222 2.187 inches

Z0 443.6 443.6 449.5 445.5666667 ohms

Delay 138.3272 51.8848 21.645 211.857 pS

L 61.447 23.048 9.7236 94.2186 nH

C 311.9507 117.0088 48.1296 477.0891 fF

R0 0.024293 0.009112 0.00444 0.037845 Ohms

Figure 22: PCB Trace Characteristics Extracted for Simulation

The new PCB design with 641 MHz stimulus showed a 2x improvement in open circuit

voltage from the previous PCB. It was thought at first to shorten the PCB traces as much as

possible to prevent losses so this is how the board was fabricated. It was found after production

of the PCB through simulation that it is actually better to have slightly longer traces to tune to a

particular frequency. A future PCB would use the trace lengths described in simulation to match

to a particular frequency without requiring external matching components to increase efficiency.


2.7: Simulation Results:

Simulations were preformed using Agilent ADS 2009. Spice models were used for the

diodes and passive components. A schematic of the simulation including PCB parasitics is

shown below. To model the transmission line of the PCB an LC network was used. Hyperlynx

simulation environment from Mentor Graphics was then used to extract from the actual layout

the transmission line characteristics. With these extracted values simulation matches real life

measurements in a relatively close manner.

The only major mismatch with simulation and actual measurements are the inductor and

capacitor models used for matching. The components that are purchased from Mouser or

Digikey are far from ideal and their datasheets do not provide a decent model range to use for the

components. Using Kemet spice gets you fairly close for capacitor simulation, but the RF

inductors used did not have models to use for simulation. The component variance tended to be

a problem anyway. The same components on different PCB designs tended to show different

matching frequencies, which show that they cannot be fully relied upon for the matching circuit.

Below is a schematic draw in Agilent ADS used for simulation. There are two harmonic

balance simulations and four sweep parameters for various simulations. It should be noted that

efficiency uses an equation variable that does not change based on the input value, therefore

simulations that use more than one stage show a non-real efficiency percentage. For instance a

dual stage efficiency ideal would have been 200% and a quad stage would have been 400%.


Figure 23: 10 Stage Schematic With PCB Parasitics


Figure 24: 10 Stage Single Array Efficiency

It was found during research in the 915Mhz band that a 10-stage harvester provided the

best efficiency at –10dbm RF input power, while a 5-stage harvester performed better at +5dbm.

With this background information the effort was focused on the harvester that performed the best

at 641Mhz at –5dbm. In simulation this turned out being a 7-stage harvester for a 100kohm load

and a 10-stage harvester for open circuit performance. It can be seen that a 10-stage harvester is

capable of 56% efficiency and a 7-stage is capable of 64% efficiency.

Seeing this trend the PCB was designed in a way to support both 7-stage and 10-stage

harvester designs by removing the extra stages as a PCB assembly option.


Figure 25: Ideal 7 Stage Single Array Efficiency

After simulations with an ideal 80kohm load 100kohm was also simulated to match the

MCU current draw during sleep mode. Using this mode it was found that efficiency could drop

substantially with PCB parasitics included, but did not make much difference if the parasitics

were not included.


Figure 26: 10-Stage 100kohm Load Using Extracted PCB parasitics

Figure 27: 10-Stage 100kohm Load Using Extracted PCB Parasitics Matched with 1.9pF and 25nH

Changing the matching components at open circuit conditions could also strongly

influence the efficiency. Given these statements it is clear that matching must be done in a way

specific to both the input impedance and load impedance a particular frequency. It must also be

mentioned that finding an exact match in impedance does not provide the best power delivery.

One must target a 2:1 ratio to provide best power transfer. With this known it was found that


matching actual passive components was difficult because of their inherent parasitics. In a

design as sensitive as this there needed to be a way to avoid this weakness. SSTEP protocol

described later in this thesis provides a way around this issue.

Figure 28: Finding Ideal Trace Length for 641Mhz

Once the PCB parasitics were extracted it was clear they should be used for matching the

design. A simulation was run to find the ideal trace length for the trace segments used to connect

the stages of the harvester array. The simulation revealed that the 250mils used to minimize the

trace parasitics should have been lengthened to 400mils for ideal matching. In future PCB

designs this metric should be used to hopefully remove the need for extra matching components.


Figure 29: Frequency Sweep of Unmatched PCB with 250mil Trace Lengths

A frequency sweep of the unmatched PCB was run to compare against the measured

results. It was found that in simulation 250Mhz would perform the best on the PCB that was

fabricated and that 650Mhz was actually one of the worst performers. This does not match the

recovered results for the unmatched case perfectly at 650Mhz and 950Mhz, but did correctly

predict the frequency peak effects that were observed. The peaks just appeared to be shifted

upwards slightly. Refer to Figure 46: Harvester Open Circuit Voltage Versus Frequency for a

plot of the measured results. Below is a table of the comparison between the expected voltage

from simulation and the actual voltage at three points of interest.


Frequency Simulation Measured

250 6.57 6.5

650 1.39 3.22

950 5.09 3.38

Figure 30: Comparison Between Simulation and Measured Voltages at Certain Frequencies

Figure 31: Frequency Sweep of Unmatched PCB Voltage Versus Frequency

A comparison of frequency sweep simulations shows that for a sweep of frequencies

from 100Mhz to 1Ghz the best energy transfer point for the unmatched circuit is 250Mhz. The

second best is 900Mhz. In real measurements 300Mhz was the lower peak and the next peak

does not occur until 1.2Ghz. This shows that the simulation is close, but still lacks perfect

correlation with the PCB.


Figure 32: Dual Array 10 Stage Harvesters (Efficiency x2)

It was found that to improve total power consumption that more area needed to be used.

Since the energy density of RF power in air tells us you can harvest more power if you harvest in

a larger area, it seems logical to try to increase the harvesting area to increase the amount of

power harvested. A dual stage and quad stage array of harvesters were simulated to see how

much improvement could be obtained. Unfortunately there is not a 2x improvement for doubling

the amount of harvesters used because of the adverse effects on the other harvesters, but

simulation ideally shows a 1.6x improvement by doubling the amount of harvesters in the array.

The efficiency number used in the graphs is based on the original input RF power for simulation,

therefore the actual efficiency shown in the dual stage simulation is half of the number plotted.


This means that as you increase the number of harvesters the efficiency decreases on each

harvester, but the total power harvested does increase as more harvesters are added.

Figure 33: Quad Array 10 Stage Harvester (Efficiency x4)


Figure 34: Quad Array 7 Stage Harvester

After it was found that there exists a startup current surge that an MCU requires to startup

caused by a minimum voltage ramp rate to prevent transistor latch a MPPT regulator was

researched. The MPPT regulation benefits seem to fit this design perfectly. The only problem

was that the input impedance that the regulator presented to the design was 6kohms.

Unfortunately this is a heavy loading for this type of circuit. Simulations were run to determine

the performance of the design with a 6kohm load.


Figure 35: 10 Stage with 6kohm Load Simulating MPPT Regulator

It was discovered that the efficiency drops from 56% on a 10-stage design to 6% with the

matching used for 100kohm loading. It was then attempted to rematch the circuit via simulation.

After simulation only 27% efficiency at 10-stage circuit could be obtained. This unfortunately is

not enough to power the MPPT regulator.


Figure 36: 10 Stage with 6kOhm Load 4pF and 10nH Match

Figure 37: 10 Stage 6kOhm Load with 2pf and100nH Match


Simulation was then steered in a direction to decrease the number of harvester arrays to

increase the efficiency with this low output load. Decreasing the harvesting arrays tends to

improve heavy loading conditions, but it also tends to the decrease the voltage harvested. The

target voltage to harvest is 2.5v and above for typical sensor MCU’s. Ideally 3.1v-4.1v would be

the best voltage range to harvest at to look similar to a single cell lithium ion battery.

Figure 38: 7 Stage 6kOhm Load With 5nh and 5.5pF Match


Figure 39: 7 Stage 6kOhm Load With 2.6pf and 3uH Match

Figure 40: 5 Stage 6kOhm Load With 5pF and 0nH Match


Figure 41: 2stage 6kOhm Load With 0.6pF and 15nH Match

It was found that the best that could be done was a 1-stage harvester with 62% efficiency.

The power simulation finds that the ideal 1-stage could harvest about 62uW of power for the

MPPT regulator at –10dbm. Unfortunately this provides only 2.181 Volts total voltage at –

10dbm. This is unacceptable for the application of powering a MCU with a 2.5V VDD.


Figure 42: 1 Stage 6kOhm With 50nH and 5pF


Chapter 3: Design Optimization for Ultra Low Power Applications

3.1: Using a Harvesting Cell Array to Increase Power:

A key improvement to this design is increasing the number of antennas used to harvest

more power from the same frequency band. The concept is similar to solar cells in that

increasing the area of cells increases the energy harvested. The energy density of a particular

frequency tells one what type of area is required to harvest a particular amount of energy. The

design must then be created into a cell. Inside this cell is the harvesting circuit proposed as well

as an antenna capable of receiving in the 641 MHz band. A PCB was fabricated for this purpose

by utilizing a Fractus FR01-B1-S-0-047 digital TV antenna. The PCB was built in a way to have

board-to-board connectors on all 4 edges such that additional PCB’s can be attached to build an

array of harvesters in the same plane.

To connect the boards together and not adversely affect the 10th diode stage of the

harvester an additional steering diode was used to block interference from another harvester.

The diode would only allow the highest voltage harvester to conduct to the output, but would

increase the current capability since more harvesters gathering approximately the same amount

of energy are summed together. This does not lead to a voltage increase in the output, but does

increase current capability. Choosing the lowest drop diode here is the key metric, but also

picking a low loss diode must be kept in mind.


Figure 43: Ideal Harvester Array Concept

In the ideal case all the cells of the array are tuned to exactly the same peak frequency,

but in real life due to variances in the components of the design this is not normal. The range of

variance can be up to 50Mhz.

The addition of a cell to the array is not a direct doubling of power harvesting. Each

additional harvester will only add between 10%-50% more power to the overall circuit when

going from one board to two. Because of this the amount of cells required to substantially

increase total power is fairly high. This approach however is required for extremely low ambient

energy density scenarios.


Figure 44: Projected Number of Harvesters in Array Versus Power Harvested

As the number of harvesters in the array increases the power increases, but because of the

degradation of each harvester in the array caused by adding more harvesters the power

relationship is logarithmic. As more harvesters are added the efficiency of the circuit decreases

logarithmically. This is all assuming that all of the harvesters are matched perfectly. In practice

this is not true and each harvester takes even more efficiency loss when a harvester is not

matched properly. A single harvester with poor matching compared to other harvesters could

hurt the array more than it helps.


Figure 45: Efficiency Versus Number of Harvesters In Array

3.2: Using a Multiple Band Antenna to Harvest More Energy:

Another improvement is using multiple frequency bands to harvest energy. The

harvesting circuit proposed naturally has voltage peaks at RF input frequencies of 400Mhz and

2.1Ghz. These peaks are based on resonances of the PCB design and component selections.

When we match the circuit to 641 MHz we are moving the lower voltage peak from 400Mhz to

641 MHz. By using two RF input frequency sources we can almost double the amount of power

to harvest. To test this we insert two tones, one at 400Mhz and one at 2.1Ghz, each at 0dbm into

the harvesting circuit. By doing this we see a close to doubling in voltage at the output. This

relates to doubling the RF input power of the circuit. To fully accommodate this input one must

use a dual band antenna to harvest at several frequencies.


The voltage received at the output is very close to the individual components added. For

instance when 400Mhz at 0dbm is inserted into a circuit one measures 1.59v. When just 641

MHz is provided at 0dbm the open circuit voltage measures 4.26v. When the two frequencies

are combined and provided to the input, each at 0 dBm, the open circuit voltage of the harvester

reaches 5.12v. This means that a similar power amplifier can be used for the output of the base

station instead of a single larger power amplifier for 1 frequency. Because of this we can almost

double our harvested energy.

A survey was constructed to slew the input frequency at 0 dBm power, while measuring

the open circuit output voltage. The results are a shown in the graph below for the various

circuit matches used.

Figure 46: Harvester Open Circuit Voltage Versus Frequency


It can be seen from this data that the unmatched circuit has 3 major peaks in its harvested

energy. When adding matching components we shift and possibly amplify this voltage. The

more capacitance added in shunt shifts the peaks down in frequency. The series inductive

portion allows amplification near the roll-off. It was found that a series inductance of 8.2nH and

a 9.1pF shunt capacitor gave the best results for 641 MHz. It also decreased a large spike of

voltage around 1.4 Ghz. A better choice for multi-band harvesting is using just a 10pF shunt

capacitor and relying on the pre-existing 43nH parasitic in the traces. This choice gives good

voltage at 641 MHz and also amplifies the voltage spike around 1.4Ghz. Using this matching

technique could then be used to amplify several frequencies that can be used for harvesting with

a multiple band antenna.

As this project evolved it was found that someone else has the same idea and proposed an

antenna capable of it in December [17], but does not appear to have done any integration with a

sensor to overcome the many issues faced. The design is however exactly what this work would

have proposed as a future work. By choosing the multi-band antennas for the frequency bands

allowed by the FCC and then following the tuning procedure one can find a best fit harvesting

circuit. Once this circuit is ideal it can be replicated into multiple antenna arrays at half

wavelength distances for board to board connections. This even further increases the energy

gathered and hence shortens the charge cycles. Since -10dbm was measured on the Micca 2

sensor mote without these additions it could be as little as -20dbm input power required to

maintain the MCU in sleep mode. Other MCU’s with even smaller current consumption can

then use smaller supercapacitors and can be used for even better charge times.

Charge times were measured against a 100mF supercapacitor loaded with 100Kohm load

in parallel for a 0v to 1.8v ramp. The input power was introduced by the RF signal generator


connected to the board via the SMA jack to control the input power the harvester sees. The

results are in the table below.

641 Mhz

Measured using a 100 kOhm load and 100 mF capacitor

9 pF capacitor match on site L3

Charge rate (v/s) 8.33333E-05 0.000324254 0.001042753 0.002222222 0.004636069

Target voltage 1.8 1.8 1.8 1.8 1.8

Seconds to charge 21600 5551.2 1726.2 810 388.26

Minutes to charge 360 92.52 28.77 13.5 6.471

Hours to charge 6 1.542 0.4795 0.225 0.10785

Input dBm -10 -5 0 5 10

Measured v/s 8.33333E-05 0.000324254 0.001042753 0.002222222 0.004636069

Figure 47: Charge Rates from 0v to 1.8v Versus RF Input Power

Using this data and creating a working assumption for a typical sensor network using the

10mF super capacitor and 2.5v to 3.4v active range a throughput number was calculated and

projected improvements to the network throughput is compared based on the time in between

charges.


Figure 48: Throughput Versus RF Input Power

The antenna was then matched on a PCB with no load. It was found that the suggested

components from the manufacturer needed adjustments. Below is the matching results based on

maximizing received RF power.

Matching components with 20bm output at antenna 6 inches away from source DB

No match -25

10 nH shunt -33

8.2 nH series + 10nh shunt -28


10 nH series + 8.2nH shunt -30

1.8 nH shunt -42

6.2 nH series + 1.8 nh shunt -44

22 nH shunt -24

12 nH shunt -25

12 nH series + 12 nH shunt -26


1.8 nH series + 22 nH shunt -22








Figure 49: Antenna Matching Results


It can be seen that even with matching the antenna used although small form factor does

not gather much input power. Using a different antenna with more gain would be optimal for

minimizing charge times.

Efficiency Units Notes

Path loss efficiency 2.50% % At 20m 4W 641Mhz

Antenna efficiency 0.0798104 % At 1m 1W 641Mhz

Harvester efficiency 70% %

Regulator efficiency 90% %

Total system efficiency 0.0125685 %

Figure 50: Total System Efficiency

Charge times were then measured with the antenna chosen for four boards. The four

boards were then combined in an array and charge rate increase was measured. It was found that

the arrays increase the charge rate and hence gather more energy. It was found that 3 of the four

boards did the majority of the work, while one board seemed to be less efficient. It was also

found that looping the antenna array had adverse effects on the RF power received and hence

should be avoided.


Figure 51: Charge Rates of Antenna Array Boards from 0 to 1.8v Using 30dbm Output from Signal

Generator and Combined Charge Rate.

641 Mhz Quad

Circuit +

100k

Circuit

without

board 2

Board 1

(has

100k)

Board 2 Board 3 Board 4

Charge

rate

(v/s)

0.00188

7

0.000509 0.000179533 0.000317 3.78E-05 0.00015 0.0000313

Target

voltage

1.8 1.8 1.8 1.8 1.8 1.8 1.8

Seconds 954 3535.2 10026 5673.6 47592 12249 57600

Minutes 15.9 58.92 167.1 94.56 793.2 204.15 960

Hours 0.265 0.982 2.785 1.576 13.22 3.4025 16


From this data we can then determine the improvements to network throughput. Using

the same assumptions from Figure 5: Protocol Requirements we can determine a throughput

number for each board and for quad board configurations.

Figure 52: Throughput Versus Array Size 641Mhz 0dbm

It can be seen from this plot of throughput that board 1 contributes the most to the quad

output at 641Mhz and with 0dbm RF input power. However, it should be noted that use of the

SSTEP energy transfer protocol more evenly distributes the contributions from mostly one board

to all boards.


Chapter 4: Network Optimization for Ultra Low Power Applications

4.1: Spread Spectrum Transfer Energy Protocol (SSTEP)

Noticing the effects of the frequency peaks and tuning effects from board to board led to

a proposed protocol for energy transfer for charging. The protocol leverages spread spectrum

tables from the base station and builds a spreading table for energy transfer to the sensor nodes.

Since each node may have different frequency peaks for maximum efficiency we need to build a

spread table that includes these peaks. The protocol requires two frequency bands. The first

band is a communication carrier, which in our case is the ISM 915 MHz band and an energy-

harvesting band, which is 580-700 MHz. The harvesters have already been optimized based on

MCU selection and capacitor selection to maximize charge ramp rates.

This protocol proposed is implemented only up to the link layer, but is capable of being

scaled for a more complete stack. The protocol is flexible enough to allow a single frequency

band to be both the charging and communication channel using time slots, but this is not

implemented because of the efficiency losses associated with this technique. In [21] the authors

propose a protocol that uses weights for each node in the routing tables that includes a measure

of energy consumption. In our case the consumption is assumed to be the same, but we do want

to assign weights based on the energy gathering rates. If one node is capable of harvesting two

times more energy than that of another node and each node consumes the same amount of energy

to transmit than the weaker charging node must be weighted higher in the spreading table to

ensure it will deliver data at a somewhat reasonable frequency.

The routing protocol is not implemented, but the protocol in [21] still would be a good

candidate for the routing protocol to stack on top of the proposed link layer. AODV is a popular

routing choice for this type of network because of its flexibility. Each node forwards all packets


and the fastest path to reach the destination is the path chosen for return. This is useful and

simple for a constantly changing network of nodes that will be dead or alive at an unknown

probability. This however has a limitation of wasting transmission of nodes that are known to

have poor energy harvesting capabilities.

When building a sensor network there are several conflicting goals. In [31] it was found

that the lifetime of a network of nodes powered by super capacitors is decreased by the amount

of charge and discharge time used. If the network uses short charge times the throughput of the

network increases, but lifetime of the super capacitors is decreased. This design tradeoff can be

accommodated in SSTEP by setting the thresholds of operation for the nodes to the maximum

value for increased lifetime, or decreasing the transmission level to a lower voltage to increase

throughput. Throughout this discussion it is assumed that lifetime of the network is more

important than throughput.

The protocol achieves many new possibilities for a wireless sensor network. Some of the

more interesting possibilities are listed below.

Ability to maximize power harvesting efficiency to all nodes on network.

Provides a way to have as many nodes come alive as possible simultaneously using

charge time prediction. This allows for node-to-node communication with as much of an

active network as possible. This is called charge time synchronization.

Allows for selective node peak efficiencies for nodes that may be of more importance to

the overall network.

Allows multiple base station communication to prevent frequency cancellation between

shared nodes.


Adds a capability to change network throughput on the fly at the cost of network lifetime

losses.

Provides a way to minimize collisions on the network by using charge time prediction to

prevent multiple sensor nodes from coming alive and transmitting simultaneously. This

is preferred for a one-way network where each node is just transmitting to the base

station.

As mentioned earlier it was found that the matching of the arrays limits their ability to be

used together. All the nodes in the array will exhibit different matching based on the

component tolerances. In figure below our ideal harvester array is broken down into a closer

to real life scenario where not all of the harvesters are matched to the same frequency. This

degrades the performance of the overall circuit and hurts efficiency.

Figure 53: SSTEP Protocol Improving Harvesting Arrays


STTEP protocol can be used in this scenario to improve the efficiency of this array. It

can be seen that there are the following cells types and counts:

11 Harvesters Matched to 610Mhz (green)

2 Harvesters Matched to 620Mhz (orange)

6 Harvesters Matched to 630Mhz (red)

4 Harvesters Matched to 640Mhz (blue)

2 Harvesters Matched to 650Mhz (yellow)

Assuming the harvesters are all the same orientation and distance from the RF power source,

when a 610Mhz signal is applied to the array 11 out of 25 harvesters will perform well, while 14

out of 25 may not perform well at all and will actually drive down the performance of the 11 at

peak efficiency. With SSTEP protocol a spread table of 25 values can be created with 1 value

for each harvester. What this would do is create the following frequency percentages.

11/25 of the total time the transmission frequency will be 610Mhz





This results in the following spread RF output:


Figure 54: SSTEP RF Output Frequency Spread

Using this spreading function can maximize the power harvesting efficiency by adding

more weight to each point. The protocol can also maximize the time that all the sensor nodes of

the network are alive by weighting certain nodes that take longer to charge higher. This gives

their particular frequency matching more power and lessens the importance of closer nodes until

a time balance is met between the two.

4.1.1: Discovery Phase:

To build a table of frequency values that allow peak efficiency for each sensor we must

first find the charge ramp time that each sensor node is getting from a start frequency. During

the discovery phase the base station will transmit at a fixed frequency of 614 MHz, which is the


low end of the DTV band. The sensor when reaching its min charge threshold starts a timer and

begins sampling its battery voltage until it reaches the max charge threshold. Once the max

active threshold is reached the sensor mote will calculate the time it took to reach its max active

threshold from its minimum active threshold and transmit the data to the base station. This data

will be transmitted every time to ensure that spread tables are constantly updated. During

discovery this is the only information the sensors will transmit.

Using this data the base station will then increase its energy harvesting band transmission

frequency and again wait for all the known motes to update their charge ramps. At each

frequency of transmission the base station will store an array of known motes and their charge

times. A fixed length of time must be used to start with during discovery. Using the delta values

from each mote the base station must build a table of frequencies for each node’s best charge

time. The base station will continue to increase the frequency until the maximum band

frequency is reached (698 MHz). At this point in time a table will have been built with a set of

frequencies that provide best efficiencies for each mote. If there are several motes that exhibit

their best efficiency at the same frequency then that frequency value will receive a higher

periodicity in the spread table.

Since all the nodes must reach max threshold to transmit each node is responsible to clear

its voltage to minimum threshold when the base station changes frequency. The base station is

responsible to broadcast a change frequency beacon for 2 minutes to ensure all sensors see the

change. The beacon will have a change frequency message and a time stamp to allow the sensor

to synchronize with the base station. Since some nodes in the network will be dead or not in an

operational state during discovery they are assumed to be below the operational threshold and

will not be found until they reach at least the minimum threshold. The nodes when reaching the


minimum threshold then are required to turn on their radio receiver every 1 minute to ensure that

if the base station has changed transmission frequency it sees the change and returns to minimum

threshold by doing an energy dissipation routine. This in our case is just enabling an on board

LED to dissipate back down to minimum threshold.

Using the time stamp the sensor node will know how far into the change frequency

broadcast the base station is. It then from that will start a timer to determine when to start

charging again. It does this by dissipating to the minimum threshold, sampling again and

dissipating again to the minimum threshold until the broadcast timer is expected to have expired.

With this method the node’s internal timer is synchronized with the base station’s timer. This is

critical for real time operation.

After the frequency change has been completed the sensor is expected not to transmit

until it reaches max threshold. The base station after the frequency change only listens for

incoming node messages and builds its frequency table. The discovery phase is complete when

the max frequency of the band is hit. The relative sensor thresholds can be seen in the figure

below. The voltage step will change based on the sensors charge ramp time, but the period of

sampling will always be the same at once every 5 seconds.


Figure 55: Voltage Thresholds Used For Node States

4.1.2: Active Phase:

During the active phase the base station will transmit power using its spreading table to

modulate the frequency it is transmitting at. This phase will continue until a new sensor is found

and a discovery phase is required. This strategy also allows the spread table to be updated on the

fly allowing a much more flexible design. If a sensor node suddenly has a large difference in

charge times it can be determined that the node has moved location or a new obstruction was

introduced and a discovery phase can be called again to determine its new best frequency. This


can be useful if nodes periodically will be moved or detrimental environmental factors can occur

periodically.

The base station will apply two values as weights for the spread table. The first is how

many nodes are in that frequency band. The second is the delta charge time, which is used to

determine how long it will take for each node to charge at their peak efficiency frequency. The

goal of the network is to avoid collisions when nodes become active. The protocol can do this

by changing the spread table to space out times when each node becomes active. With expected

time deltas from the sensors discovered in discovery mode the base station can allot slots for

specific sensors it wants to collect data from at a higher periodicity. For our experiment collision

avoidance is the main goal although the protocol would allow for selective charging of nodes.

The sensors in the active phase will be allowed to transmit their data to the base station

when the max threshold is reached and continue to transmit until min threshold is reached.

When min threshold is reached the sensor must return to sleep and wait for max threshold again

before transmitting again. During this period it again measures the amount of time it takes to go

from min threshold to max threshold to sense network changes.


Chapter 5: Integration of the Design Into 45nm CMOS Technology

Since this design incurs many PCB parasitics integration of this design into CMOS

technology seems like an interesting path to pursue.

Unfortunately the current design is not reasonable to design into an ASIC. Assuming that

one would leave the antenna external to the chip is a must. The antenna design is much too large

to integrate into any ASIC. The cascode charge pump as it stands is also not reasonable, because

the 36pF capacitors are much too large to integrate into silicon. This means that the charge

pump would need to be redesigned as a charge redistribution network with much smaller

capacitors or a single external capacitor that can be much larger. The inductor as well is much

too large to integrate on chip and even with simulation of this component using OTA’s the

quality of this component is degraded. The inductance however can be left external to the design

and combined with the bondwire inductance and an on-chip OTA for automatic tuning the series

inductor could be accommodated.

The cascode diode stages can be integrated fairly easily using MOSFETs in a standard

diode configuration. One would use NMOS transistors with gate tied to drain to emulate a diode.

The source would act as the cathode, while the gate and drain would act as the anode.

There are, however, different possible IC approaches made for RF harvesting design like

that of the one in [18]. This type of design is a better fabrication approach than that of the circuit

proposed in [19].


Chapter 6: Conclusion and Future Research

6.1: Conclusion

In this thesis we have made a strong applications case for optimizing RF energy

harvesting nodes in the digital TV band. New concepts of harvesting arrays, multi-band

harvesting, and an energy transfer protocol were proposed.

Harvesting arrays could increase the power harvested enough for RF energy harvesting to

become more useful to applications that cannot work in such a power starved environment. With

even more improvements in efficiency the number of harvesters required in the array to produce

1 watt of power may decrease from the current projection of 300,000 cells.

Multi-band antennas could be implemented with the design to harness the tuned

frequency efficiency peaks and provide the same output power by transmitting less power in two

different frequency bands. This improvement can allow cognitive radio applications for energy

power transfer in selected bands as to no interfere with other nearby networks.

The proposal of SSTEP protocol provides a way to increase the network bandwidth,

while at the same time solve the frequency matching problem that these harvester arrays face.

By using modulation of certain tuned frequencies the protocol can provide weighted power

delivery to all nodes of the network. In this way the protocol can maximize the time that the

majority of the nodes in the network are alive at the same time. SSTEP also provides selectivity

to allow certain nodes to be prioritized to charge either more or less for other more unique node

applications.


6.2: Future Research

Future research would cater to further improving efficiency of the harvester arrays by

exploring antenna design. It would also serve well to create a multi-band antenna for testing

frequency hopping between key bands of interest.

Improving the harvesting array structure as to decrease the efficiency losses sustained by

each node would be a requirement. The spacing of the antennas between each harvester would

also need to be simulated and tested. The array spacing would ideally be as far away from each

other as possible to decrease each others antenna interference, but at the same time a small

distance like a half or quarter of the wavelength is probably enough to accomplish this goal.

The MPPT regulator chosen was too high of a load for a single harvester circuit. In

further research a MPPT regulator should be designed with a very high input impedance, ideally

greater than 80kOhms.

Although the proof concept has been completed, the SSTEP protocol is vastly untested

with actual sensors connected to harvesters or harvester arrays. The MAC protocol must be

finalized and a throughput measurement must be taken to show its true merits.

6.2.1: Implementation of SSTEP protocol

The protocol is currently implemented on Texas Instruments MSP430 sensor IC’s using

the eZ430-RF2500-SHE platform. The base station will be implemented using a sensor node

connected to a host PC via USB. The connection will allow the MSP430 to communicate with

the python application to control the USRP2. The python application is fairly simple and has

already been implemented. It requires a variable frequency source that is accessible to the


MSP430 via USB. It also requires a USRP2 sink with a matching variable input to that of the

frequency source.

Figure 56: SSTEP Test Platform

This program was tested using a simple GUI with control of the amplitude of the output

signal via a slider and a frequency slider to control the frequency range from 600Mhz to

700Mhz. Using a spectrum analyzer the program was verified to change frequency and

amplitude as expected. The missing piece was an external amplifier for the USRP to allow for

higher reception. The USRP2 can only transmit at 1dbm and from across the room this is seen at


the network analyzer as only –30dbm. An MPA-40-40 4Watt wideband power amplifier from

RF Bay was added to fix this issue.

The harvester and eZ430-RF2500-SHE motes were tested using a RF signal generator.

The host PC was connected to one mote via USB to act as the base station and used the TI node

GUI to monitor communication. The second mote, which is used as our test node, was

programmed to charge to a 3.14v threshold then transmit until it reached its lower threshold of

2.5v. This test shows that both motes and the harvester circuits are working as expected.

Figure 57: GNURADIO Python Flow Diagram


The next step is a Linux USB driver must be implemented for the MSP430 to

communicate with the python application. This is currently in development.

The final step is implementing the MAC protocol on the base station MSP430 and on the node

MSP430. The node protocol is close to what has already been implemented for test purposes.

The base station’s protocol implementation is more complicated and requires more work to get

the Linux USB driver implemented properly.


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