Scientific Seminar on structure, architecture, and ...

Scientific Seminar on structure, architecture, and application of

sensor circuitsSummerterm 2021

Lehrstuhl für Schaltungsentwurf Fakultät für Elektrotechnik und Informationstechnik Technische Universität München

Contact for general questions: Eva [email protected].: +4989/289-22930

https://www.ei.tum.de/lse/startseite/

https://www.ei.tum.de/

https://www.tum.de/

mailto:[email protected]

Registration and Grading

If you are interested in participating in the scientific seminar, please contact the supervisors in advance via mail for the topics you are interested in. The topics are assigned by the respective supervisors only!

In case there is no topic available anymore you can still attend the kick-off meeting in case of people not showing up. Moreover, you can contact [email protected] to ask for additional topics in your research area of interest.

Grading:

Regular discussions (online/in presence) with the assigned supervisor about the progress of the work and the procedure

Online presentation of the results (15 min.) with subsequent discussion (5 min.) (50%)

Written elaboration of the results as a term paper in the form of a scientific paper in IEEE style (4 pages) (50%)

LSE – Scientific seminar on structure, architecture and application of sensor circuits - Prof. Dr.-Ing. Ralf Brederlow


Timing & Deadlines

When? What?

Kick-off 14.04.2021 (Wednesday)16.00-17.30

Introduction & How to do a literature researchCompulsory participation

Lecture 19.05.2021 (Wednesday) 15:00-16:30

How to prepare a scientific presentation & article

Paper submission deadline 21.06.2021 (Monday) until 23:00 PM

In Moodle and via email to your supervisor

Presentations 29.06.2021 (Tuesday) & 30.06.2021 (Wednesday) 13:00-15:30


Topics (Overview)Topic Supervisor Available

Towards Fully Integrated Wearable Multi-Ion-Sensors: Role of Microfluidics in Wearables Christian Pfeffer

Towards Fully Integrated Wearable Multi-Ion-Sensors: State-of-the-Art Readout Techniques Eva Korek

Towards Fully Integrated Wearable Multi-Ion-Sensors: Neural Networks for Feature Extraction Matthias Ochs

Towards a Large-Scale Neural Network Accelerator with Address-Event-Representation Lei Zhang

CMOS Compatible Capacitive Biosensors: Literature Research Oleg Sakolski

Time Domain ADCs Florin Burcea

How to detect touch on the skin of a robot? Measuring mechanical strain Carl Riehm

Ultra-thin silicon stress sensors Vartika Verma

Piezoelectric MEMS microphone: Readout techniques Marco Schewa

Automotive Communication Network Tauseef Siddiqui

Comparison of Ring oscillator PLL using Sub Sampling and Closed Loop VCO Markus Dietl


Towards Fully Integrated Wearable Multi-Ion-Sensors (I):Role of Microfluidics in Wearables

Focus:• What microfluidics are used for Sweat-Analysis?• Challenges of Microfluidics? • Fabrication? • Miniaturization / Integration? • Limitations? Packaging Methods?

Background:Bioengineers aim to

monitor the body’s functions by body fluid analysis

“Fully integrated wearable sensor arrays for multiplexed in situ perspiration analysis”, Gao et.al., Nature 2016

"Advanced Wearable Microfluidic Sensors for Healthcare Monitoring," S. Li, et. al. Small, 2020

https://en.wikipedia.org/wiki/Valinomycin

Supervisor: [email protected]


Towards Fully Integrated Wearable Multi-Ion-Sensors (II):State-of-the-Art Readout Techniques

Focus/Goals:

• Alternatives to potentiometry?

Advantages/disadvantages

Different use cases

• Impedance Measurement Method: Circuit Design

• Other interesting aspects:

Temperature and drift compensation

Implementation of multi-ion-sensing


Problem:Potentiometric sensors are commonly used for ion quantification in sweat but suffer from drift, are difficult to miniaturize, and cost expensive.

[1] https://www.sciencedirect.com/science/article/pii/S0165993618303674

Figure 1. Use cases of wearable multi-ion-sensors: a) healthcare and b) performance tracking [1]

[2] https://www.nature.com/articles/s41378-018-0019-0

Figure 2. Example of an impedance readout circuit [2]


[1] Ngoc Tuan, Vu, et al. "Combination of Multivariate Standard Addition Technique and Deep Kernel Learning Model for Determining Multi-Ion in Hydroponic Nutrient Solution." Sensors 20.18 (2020): 5314[2] https://www.foodnavigator.com/Article/2020/10/05/Firmenich-creates-world-s-first-flavour-by-artificial-intelligence[3] https://phys.org/news/2016-12-world-solid-state-multi-ion-sensor-internet.html

Towards Fully Integrated Wearable Multi-Ion-Sensors (III):Artificial Neural Networks for Feature Extraction (FE)

Focus:• Understanding the challenge of Multi-Ion-

Determination• Which features can be extracted?• Overview of different AI solutions • Comparison to analytical methods• Evaluation of the implementations

regarding performance


[3]

[2]

[1]


Towards a Large-Scale Neural Network Accelerator with Address-Event-Representation

Tasks:1. Understand how AER works for silicon spiking neural

network2. Collect the existing solutions for intra-/inter chip

communication with AER3. Find out other alternative solutions4. Compare different solution with and without AER-

Protocol5. Create the state-of-art



or [email protected]

Background:The current neural network accelerator is far from a strong artificial intelligence because of its limited scale and our understanding of neuroscience. Continuously Increasing the scale of silicon neural networks became an option for researchers to increase the accelerator performance further and approach a strong artificial intelligence. AER-Protocol is a candidate for multi-tile/core/chip communication.

Architecture of ROLLS neuromorphic processor with Asynchronous AER Communication

Qiao, Ning et al. “A Reconfigurable on-Line Learning Spiking Neuromorphic Processor Comprising 256 Neurons and 128k Synapses.” Frontiers in neuroscience 9 (2015): 141–141. Web.



CMOS Compatible Capacitive BiosensorsLiterature Research

Tasks:

• Give overview of State-of-the-Art

• Present Applications (Clustered)

• Explain Method/Working Principle Briefly

• Point out Chalanges / Limits



Focus:Capacitive SensorsBio-/chemicalsensors compatible with CMOS Tech.Low Frequency Methods (< 500MHz)

[1] CMOS Biosensors • Chapter 1 • Roland Thewes • Chair of Sensor and Actuator Systems, TU Berlin • Winter Semester 2009 / 2010

Figure 1. SEM Photographs of interdigitated transducer structure with 2um-pitch on Wafer in CMOS Process

Figure 2. SEM Photographs of interdigitated sensor gold electrodeson standard 0.5μm, 6“ CMOS Process for DNA Detection. [1]

Figure 1. Cultivated snail cell on chip surface [1]

Focus:

• Voltage-domain vs. time-domain ADCs

• Existing types of time-to-digital converters (TDCs)

• Resolution, linearity and power efficiency of TDCs

• Applications of TDCs

From:https://www.ti.com/lit/ds/symlink/tdc7200.pdf?ts=1615830070203&ref_url=https%253A%252F%252Fwww.ti.com%252Fproduct%252FTDC7200


Time Domain ADCs

Background:With technology scaling, voltage domain resolution becomes increasingly challenging, while time domain resolution continuously improves.


How to detect touch on the skin of a robot?Measuring mechanical strain

Find out what is state of the art in mechanical strain and stress sensing:• Provide an overview of recent sensing approaches• How can they be categorized? (integrated/discrete solutions)• What are the areas of application?

https://www.topnewscorner.com/wp-content/uploads/2019/02/thin-wafer.jpg

https://wonderfulengineering.com/wp-content/uploads/2016/05/Indycar-aerodynamics.jpg

https://assets.newatlas.com/dims4/default/8738003/2147483647/strip/true/crop/3507x2104+0+0/resize/1440x864!/quality/90/?url=http%3A%2F%2Fnewatlas-brightspot.s3.amazonaws.com%2Fcc%2Fa2%2F3b17c8fd47708b8af222e4956884%2Fnews-electronic-skin-masthead.jpg


Background:In emerging applications such as health monitoring, industrial automation and modern robotics there is an increasing need to measure deformation of materials.


Ultra-thin silicon stress sensors

Focus:

• Using piezoresistive property of silicon for stress sensing.

• Investigating the possible use in force based touch sensing, flexible electronics.

• Circuit design aspects:

• Advantages / Disadvantages of various sensor topologies.

• Improving sensor resolution to kPa range by adapting low noise circuit design techniques.


Problem:The performance of silicon stress sensors isprimarily limited by 1/f noise as the resolution approaches kPa range.

Figures: U. Nurmetov et al., "A CMOS Temperature Stabilized 2-D Mechanical Stress Sensor With 11-bit Resolution," in IEEE Journal of Solid-State Circuits, vol. 55, no. 4, pp. 846-855, April 2020, doi: 10.1109/JSSC.2020.2967554.

Figure 1. a) Concept of stress sensing using primary sensing elements and b) response of stress sensing elements to different stress angles at constant value of stress.

Figure 2. Example of sensor readout circuit.


Piezoelectric MEMS microphoneReadout techniques “listening everything and everywhere”

Focus:

• Trivial MEMS microphone model

• Readout circuits

Disadvantages and advantages of different approaches

• Trade-offs


Problem:MEMS microphones have a high variable capacity and should be converted to a electrical signal

[1] https://audioxpress.com/article/mems-microphones-vesper-delivers-new-mems-microphone-technology

Figure 1. Piezoelectric transducer plus the ASIC [1]

Figure 2. Microphone Block diagram


Charge pump

Am

p

Automotive Communication Network

Focus:• Investigating Automotive Bus network and system; i.e.

various topologies, power considerations• Investigating different aspects of the Physical Layer Circuit

design• Impact of high frequency signals upto 500 MHz on

transmission cable and analog front end (AFE)• Investigating various topologies of AFE; discrete and

integrated solutions• Finding better methods for system modelling, including

insertion losses, crosstalks etc.



Figures: https://ieeexplore.ieee.org/document/8393814; https://ieeexplore.ieee.org/document/6513795/; https://ieeexplore.ieee.org/document/6513795

https://ieeexplore.ieee.org/document/6513795/

https://ieeexplore.ieee.org/document/8393814

https://ieeexplore.ieee.org/document/6513795/

https://ieeexplore.ieee.org/document/6513795

Comparison of Ring oscillator PLL using Sub Sampling and Closed Loop VCO

Focus/Tasks/Goals:

• Literature Research

• Compare the 2 approaches

Understand each of them

Describe & discuss the differences & similarities



Background:PLL loop noise is limiting the Performance of manyPLLs. Sub sampling and Closed Loop VCO areapproaches to improve this.

Source Figure 1 & Figure 2: X. Gao, E. A. M. Klumperink, P. F. J. Geraedts and B. Nauta, "Jitter Analysis and a Benchmarking Figure-of-Merit forPhase-Locked Loops," in IEEE Transactions on Circuits and Systems II: Express Briefs, vol. 56, no. 2, pp. 117-121, Feb. 2009

Figure 1. PLL noise with 1/f noise neglected

Figure 2. Classical PLL (a) Architecture and (b) Phase Domaine model

Scientific Seminar on structure, architecture, and ...

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