Designing Smart Sensors In Standard CMOS...Convective cooling ⇒temperature gradient ⇒wind speed...

Designing Smart SensorsIn Standard CMOS

Kofi Makinwa

Electronic Instrumentation Laboratory/DIMES

Delft University of Technology

Delft, The Netherlands

Sept 2007 ESSCIRC 07 2

Sensors are Everywhere!


World Sensor Market

Time

US $Billions

1998 2003 2008

50.6

42.2

32.5

Courtesy of InTechno Consulting


Traditional Sensor Systems

Sensor Interfaceelectronics

traditionalwind sensor


Smart Sensors

smartwind sensor

Sensor InterfaceElectronics

• Sensor + Interface electronics in one package• Robust microprocessor compatible interface


Why Smart Sensors?• Standard output format ⇒ plug-and-play!

• Bus interfaces ⇒ multiple sensors, less wiring

• More functionality: self-test, diagnostics, storage of sensor ID and calibration data

• Smaller, cheaper, more reliable …


Smart Sensor Design

Sensors

• Cover many domains ⇒ sensor physics

• Interact with the environment ⇒ package design

• Output small analog signals ⇒ analog design

Smart sensor design is challenging!

InterfaceElectronics

Package


Sensors in Standard CMOSStandard CMOS sensors cover the following domains:• Thermal ⇒ resistors, transistors & thermopiles• Magnetic ⇒ Hall-plates & magFETs• Optical ⇒ photo-diodes• Chemical ⇒ ISFETs• Electrical ⇒ resistors, capacitors & inductors• Mechanical (requires micro-machining!)

⇒ moveable proof mass or diaphragm

Note: Silicon sensors are usually not best in class!Ultimate performance ⇒ “exotic” sensor + CMOS circuitry


Typical Sensor Characteristics In general, sensors

• Output small analog quantities: microvolts (Hall sensors, thermopiles), microamps (photodiodes), atto-farads (inertial sensors)

• Are relatively slow – at least compared to the switching speed of transistors

In addition, silicon sensors

• Are sensitive to process spread, temperature & (packaging) stress


A Design Methodology 1. Do no harm! ⇒ sensor should limits performance

2. Do system design! ⇒ use sensor physics to compensate for sensor non-idealities

3. Digitize early! ⇒ less analog errors, digital signal processing (flexibility, Moore’s Law)

4. Be dynamic! Use DEM, chopping, auto-zeroing and Σ∆ modulation to shift gain errors,1/f noise, offset and quantization noise out of (LF) sensor bandwidth

Sensor BW

Shifted offset, gain error,1/f noise, Q-noise

freq.

dB


A Design Methodology 1. Do no harm! ⇒ sensor should limits performance

2. Do system design! ⇒ use sensor physics to compensate for sensor non-idealities

3. Digitize early! ⇒ less analog errors, digital signal processing (flexibility, Moore’s Law)

4. Be dynamic! Use DEM, chopping, auto-zeroing and Σ∆ modulation ⇒ reduce gain errors,1/f noise, offset and quantization noise in small sensor bandwidth

Three case studies: a smart wind sensor, a smart Hall-effect sensor and a smart temperature sensor


A Smart Wind Sensor!

Convective cooling ⇒ temperature gradient

⇒ wind speed and direction


An Electronic Wind Sensor


Wind Sensor Chip

• On-chip heaters

• PNP: measures chip temperature Tchip

• Thermopiles: measure temperature differences δTNS and δTEW

⇒ wind speed and direction


Sensor Characteristics • Slow (~1s time constant)

• Thermopile output is small (microvolts) and spreads

• Output is proportional to ∆T = Tchip - Tamb ⇒ regulation

• Sensor suffers from packaging offset (chip is not perfectly centered on disc) ⇒ calibration and trimming

• Sensor achieves ~1° angle error ⇒ thermopile outputs must be digitized with > 8-bit resolution

• Characteristics depend on chip size ⇒ same chip area ⇒ simple interface circuitry


Thermal Balancing

• Old principle: measuretemperature difference δT

• New principle: cancel temperature differences

• Measure difference in heater power δP⇒ wind speed & direction


• Heaters are pulsed by bitstream

• Pulses are thermally low-pass filtered⇒ δTNS ~ 0

• Requires only a low-offset comparator!

• Another modulator regulates ∆T

Thermal Σ∆ Modulation


Smart Wind Sensor


Smart Wind Sensor Chip

• Same area as original sensor

• Even in a 1.6µm CMOS process!

• Thermal Σ∆modulators ⇒ 10-bit resolution

• Bitstream output


Thermal Σ∆ Modulator Spectrum• Thermal LPF

⇒ Noise shaping!

• But its finite gain⇒ Q-noise floor

• Off-center chip ⇒ DC offset

• Auto-zeroing ⇒ No 1/f noise

fclk = 8kHz


Wind Sensor Performance

• After calibration:Speed error: ± 4% Angle error: ± 2°

• Same as for original sensor

• But, with on-chip electronics

• Is being commercialized


Earth’s Magnetic Field

Goal: Hall-sensor based compass with 1° angle error

⇒ Hall-sensor precision < 0.5µT

⇒ Precision of readout electronics < 25nV!

• Compass senses at least two components of earth’s field

• Field strength < 45µT

Sensor

X-sensor

YSensor


Hall Effect+

B

VHall

Ibias

-

VHall = SH IBias B

+

-Wheatstone bridge model

Resistances in bridge model– Are mismatched ⇒ Offset (10mT typical)– Change due to changes in temperature and

packaging stress ⇒ Offset drift


Spinning-Current Hall Plate• Bias current rotated, while

Hall voltages are summed

• Cancels offset due to staticbridge mismatch

⇒10 - 100µT offset

• But thermal settling ⇒ tens of milliseconds per spin cycle

⇒ Time-varying offset e.g. due to temperature and stress remains a problem


Hall Sensor Offset Reduction• Orthogonal coupling

– 4 sensors are biasedin 4 different directions

– Hall voltages are summed

⇒ Instantaneous compensation of time-varying offset

• Stable offset < 10µT ⇒ can be trimmed!

• Also compensates for errors due to nearby metal objects


Spinning-Current Sensor Output

• Typically 10mV worst case offset

• But offset drift < 25nV is required after spinning⇒ Interface electronics with sub-microvolt offset⇒ Good linearity over an 80 - 100dB dynamic range

Time0

10

Out

put (

mV)

Offs

et

Sign

al


System Architecture

Sub-microvolt offset ⇒ nested chopping

• Hall-voltages converted to currents by chopped instrumentation amplifier (fast choppers)

• Σ∆ Modulator digitizes resulting currents

• Entire front-end is again chopped (slow choppers)

• Decimation filter sums and averages Hall-voltages

DecimationCounter &

Summing Spin

SlowChopper

FastChopper

V - IInst.Amp.

Σ∆Modulator

HallSensor

FastChopper

DigitalSlow Chopper


Chip Micrograph

• 0.5µm CMOS

• Area: 2.9 mm²

• Dissipates 21mW (4.2mA @ 5V)

• RS232, SPI/µwireand PWM interface

• Commercial product

Hall Sensor Inst. Amp. ADC

Timing, Control & Interfaces


Sensor Offset Distribution

0

1

2

3

4

5

6

-3 -2 -1 0 1 2 3

Offset (µT) - 19 Samples

Sensor offset (3σ) < 4µT, but offset drift < 5nT per week!


Heading error

-2.5-2

-1.5-1

-0.50

0.51

1.52

2.5

0 30 60 90 120150180210240270300330360

Before calibrationOffset & Gain calibrated

System Response Measurement

• Angle error < 1° after calibration and trimming!

• State-of-the-art performance!

Compass Output

-25-20-15

-10-505

10

152025

0 30 60 90 120150180210240270300330360

Rotation (degrees)

Sens

or o

utpu

ts (µ

T)

X-VectorY-Vector


A Smart Temperature Sensor

• Commercial smart temperature sensorsare not very accurate (±1.0°C from –55°C to 125°C)

• By comparison: class-A Pt100 ±0.5°C

• Our goal: ±0.1°C from –55°C to 125°Cwith only a single-temperature trim


Operating Principle

• substrate PNPs generate:∆VBE proportional to absolute temp. (PTAT)VBE complementary to absolute temp. (CTAT)

• ratiometric measurement:BEBE

BE

REF

TEMPVV

VVV

∆⋅α+∆⋅α

==µ


Dominant Error Sources

• process spread of VBE ⇒ errors of ~3°C

• offset in ∆VBE read-out: 10µV ⇒ 0.1°C error

• mismatch in 1:p current ratioand gain α: 0.1% ⇒ 0.2°C error


Single-Temperature Calibration• process spread

⇒ PTAT error in VBE

• So single-temperaturetrim is sufficient,provided all othererrors are negligible

Approach:

- reduce all errors except spread to 0.01°C level

- correct spread by trimming the bias current


Block Diagram

• Bipolar core = two PNPs

• Σ∆ modulator produces bitstream bsthat is a digital representation of temperature

• bitstream is filtered and scaled by decimation filterto produce binary reading in °C


Dynamic Element Matching

• Accurate 1:5 current ratio for ∆VBE⇒ rotate current sources

• Accurate 1:8 sampling capacitor ratio ⇒ rotate sampling capacitors


Switched-Capacitor Front-End

• Correlated double-sampling (CDS) cancels offset and 1/f noise of 1st integrator


Chopped Σ∆ Modulator

• After CDS, offset of 1st integrator is still > 10µV⇒ further offset reduction by system-level chopping


Chip Micrograph

• 0.7µm CMOS

• Area: 4.5mm2

• supply voltage:2.5..5.5V

• supply current:75µA

• Bitstream output


Measurement Results

24 samplesfrom 1 batch

inaccuracy (±3σ) after calibration& trimming at 30°C:

±0.1°C –55..125°C

State-of-the-art performance!


New Challenges• Designing ultra-low-power autonomous and biomedical

sensors ⇒ dynamic techniques• Designing smart sensors (e.g. temp sensors)

in nanometer CMOS ⇒ time-domain signal processing• Using dynamic techniques in other analog systems

e.g. amplifiers & ADCs

• Designing smart sensors based on new types of sensors e.g. SPADs and thermal diffusivity sensors


Summary• Smart sensor design is challenging!

• The following design methodology helps

– Do no harm!

– Do system design!

– Digitize early!

– Be dynamic!

• Used to realize a unique wind sensor and state-of-the-art magnetic field and temperature sensors

K.A.A. Makinwa et al, “Smart sensor design: The art of compensation and cancellation,” Proc. ESSCIRC, pp. 76 - 82, Sept 2007.


• Mierij Meteo

• Xensor Integration

• NXP Semiconductors

• Dutch Technology Foundation (STW)

• Thank-You for Your Attention!

• Any questions?

Acknowledgements


Background Reading1. K.A.A. Makinwa and J.H. Huijsing, “A smart wind sensor using thermal sigma-

delta modulation techniques,” Sensors and Actuators A, vol. 97-98, pp. 15 – 20,April 2002.

2. K.A.A. Makinwa and J.H. Huijsing, “A smart CMOS wind sensor,” Digest of Technical Papers ISSCC, pp. 432 – 479, Feb. 2002.

3. J. van der Meer, F.R. Riedijk, K.A.A. Makinwa and J.H. Huijsing, “A fully-integrated CMOS Hall sensor with a 4.5uT, 3σ offset spread for compass applications,”Digest of Technical Papers ISSCC, pp. 246 – 247, Feb. 2005.

4. M. A. P. Pertijs, K. A. A. Makinwa, and J. H. Huijsing, “A CMOS smart temperature sensor with a 3σ inaccuracy of ±0.1°C from −55°C to 125°C,” JSSC, vol. 40, no. 12, pp. 2805 – 2815, Dec. 2005.

5. C.P.L. van Vroonhoven and K.A.A. Makinwa, “A CMOS Temperature-to-Digital Converter with an Inaccuracy of ±0.5°C (3σ) from –55 to 125°C,” Digest of Technical Papers ISSCC, pp. 576 – 577, Feb. 2008.

6. K.A.A. Makinwa and M.F. Snoeij, “A CMOS temperature-to-frequency converter with an inaccuracy of ±0.5°C (3σ) from –40 to 105°C,” J. Solid-State Circuits, vol. 41, is. 12, pp. 2992 – 2997, Dec. 2006.

7. K.A.A. Makinwa, M.A.P. Pertijs, J.C. van der Meer and J.H. Huijsing, “Smart sensor design: The art of compensation and cancellation,” Proc. ESSCIRC, pp. 76 – 82, Sept 2007.

Designing Smart Sensors In Standard CMOS...Convective cooling ⇒temperature gradient ⇒wind speed...

Documents

Transcript of Designing Smart Sensors In Standard CMOS...Convective cooling ⇒temperature gradient ⇒wind speed...