Beiträge zum optimierten Entwurf fehler-und …...©2006 Andreas König, Institute of Integrated Sensor Systems Institute of Integrated Sensor Systems Dept. of Electrical Engineering

© 2006 Andreas König, Institute of Integrated Sensor Systems

Institute of Integrated Sensor Systems

Dept. of Electrical Engineering and Information Technology

Beiträge zum optimierten Entwurf fehler-und störungs-toleranter Sensorsysteme für Mess- und Erkennungsaufgaben

Andreas KönigITG-Fachgruppensitzung, 17.11.2006

Übersicht:GruppenvorstellungÜbersicht früherer Arbeiten Selbstüberwachende und –reparierende SensorsystemeDynamisch rekonfigurierbare Sensorelektronik Zusammenfassung und Ausblick


Institute of Integrated Sensor Systems

Dept. of Electrical Engineering and Information Technology

Research:Adaptive Sensor (µ-)ElectronicsSensor Fusion & Feature MappingLearning/Adaptation TechniquesDesign AutomationSensor System Integration

Labs:Computational Int. LabCAD-Lab (IC/MEMS)Test & Measurement LabSensor Technology Lab

Survey:Founded: April 2003Head: Prof. Andreas KönigStaff: 3 internal & 2 externaldoctoral students, 1 HIWI

Teaching:ElectronicsMeasurement TechnologySensor ElectronicsSensor Signal ProcessingNeurocomputing


Prior WorkSample Applications from former Projects

Bio-Inspired Sensors, Circuits

& Systems

Bio-Inspired Sensors, Circuits

& SystemsBiometry

Automotive

Inspection Man-MachineInterface

Robot

Vision


Prior Work Survey of Sample Sensor Chips from former Projects

Selection of Sensorchips &

Systems

Selection of Sensorchips &

SystemsRobot

Vision

Meter Readng

DOG-Chip

LAPIS HDR

LUCOS HDRELAC Chip

LOC

Low-Power Classifier

Electronic Fovea


Prior WorkHolistic Physical Design of Recognition Systems (First Design-Flow)

Behavioral

Geometrical

FunctionalQuickCog

Modelling of Reference System

Stimuli, Parameters (C/C++, HDLs)

Simulation ResultsModels, Constraints

SpectreSimulation

Design: A/D-Partitioning

Assessment &Optimization,DimensionalityReduction


Prior WorkHolistic Modelling and Design Methodology for IES/IMEMS

QuickCog IES DF Top-Level:

Fast & consistent designAssessment and optimization Intra/inter level optimizationHolistic modelling and simulationOpportunistic & parsimoniousAFS salience: physical savings !

AFS

Cla

ssifi

er


Prior WorkOrigins of Adaptive Electronics

I II

III

Mixed-Signal

OC


Self-Monitoring and –Repairing Sensor SystemsApplication for (Adaptive) Sensor Electronics

Em

bedd

ed C

ompu

ting

Ubi

quitu

ous C

ompu

t ing

Perv

asiv

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ompu

ting

Des

ktop

Com

putin

g

Disa

ppea

ring

Com

putin

g

Ambient

Intel

ligen

ce

Reconfigurable Computing Organic Computing (http.www.organic-computing.com)

Static and dynamic (re)configuration based on programmable (logic) device

Mimicking capabilities Mimicking capabilities of of living living beingsbeings//organismsorganisms: : selfself--xx--propertiesproperties

Mai

n Fr

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Self-Monitoring and –Repairing Sensor SystemsPrincipal Sensor System

Sensor(s) AnalogElectronics

DigitalElectronics

BUS/RF-Ifc.

ObservedQuantity

Noise/perturbation:• static (manufacturing/assembly tolerances, ...)•dynamic(T, P, moisture, vibrations, depositions, ...)

electrical signal

energy

processed signal

measurement,decision

hardwiredprocessing

software/algorithms

Sensor systems are susceptible to drift & manufacturing problemsSophisticated design and self-monitoring & -repair features required


Self-Monitoring and –Repairing Sensor SystemsTowards (Intelligent) Sensor System Design Automation


DigitalElectronics

BUS/RF-Ifc.

ObservedQuantity

electrical signal

energy

processed signal


hardwiredprocessing

software/algorithms

Design phase: Assembly of suitable sensors & methods for IES/IMEMS

SensorArray

Signal proc.feature comp.

ClassifierTrain/Test

Classificationresult

Dimensionality Reduction



SensorArray




Emerging alternative: Automated design by suitable optimzation

State of the art: expert driven design, manual, costly, slow, & tedious

observations(re)configuration

SensorArray





Observation & Optimization




Reduction to the following subproblems:Selection/combinationParameter optimizationStructural optimization Function generation (e.g., breeding of texture filters [M. Köppen])

Automation of the design process currently requires limitationsCommonly only feed-forward architecture are consideredSimple graph structures with nodes of fixed, established methodblocks representing quasi ´´standard cells´´

SensorArray





Observation & Optimization


Self-Monitoring and –Repairing Sensor SystemsDesign Methodology for IES/IMEMS

Size, weight,power

Size, weight,power

Real-timeReal-timeTime-to-marketTime-to-market

Reliability,Safety, (FT)Reliability,Safety, (FT)

MultiobjectiveDesign Optimization

MultiobjectiveDesign Optimization

Cost(low/high vol.)

Cost(low/high vol.)

Flexibility,Adaptivity

Flexibility,Adaptivity

Performance(Recognition)Performance(Recognition)

Appropriate methodology and flow for viable & feasible design mandatoryApproach: Bio-inspired adaptive circuits & systems (integrated HW/SW)


Self-Monitoring and –Repairing Sensor SystemsDesign Methodology for IES/IMEMS

Intelligent System Design Framework

Intelligent System Design Framework

ES/MEMS Design Framework

ES/MEMS Design Framework

Feedforward of feasible behavioral IS

Feedback of Constraints

Linking the Frameworks of IS & ES design using multiobjective optimizationMerging SW & HW development by chosen information processing paradigmLong term: Migrate from design-time to run-time optimization/adaptation


Self-Monitoring and –Repairing Sensor SystemsDesign Methodology for IES/IMEMS: Phases of System Life Cycle

Intelligent System Development

Intelligent System Development

Design time

Design time solution development commonly bases on single prototypeDeployment to multiple instances demands for static deviation compensationVarious dynamic perturbation influences demand for dynamic compensation

Operation time Deployment time

IES 1IES 1

IES 2IES 2

IES NIES N

IES 1IES 1

IES 2IES 2

IES NIES N

staticdeviations

dynamicperturbations


Self-Monitoring and –Repairing Sensor SystemsFrom Design to Deployment Time: Instance-Specific Compensation

Machine-In-the-Loop-LearningGeneral system developmentInstance training for compensationof static non-idealities & deviations

Instance OptimizationInstance Optimization

Parameter,Structure

Design Phase

-

Results

TargetvaluesTargetvalues

Sensordata,

Features

DatabaseDatabase

•Synthesis•Compensation

Medical Laboratory Robot DAVID:Task: Tubes sorting & decappingMultiple installation sites in Europe


Self-Monitoring and –Repairing Sensor SystemsDesirable Properties for Autonomous Sensor System


DigitalElectronics

BUS/RF-Ifc.

ObservedQuantity

Noise/perturbation

electrical signal

energy

processed signal


Need: reliability, availability, robustness, predictive maintenance

Self-monitoring: disturbance/defect detection & diagnosis (cause)Self-repairing: self-monitoring & reconfiguration capability

Constant/repeated monitoring of measurement signal validity/integrityMethods: Sensor redundancy, actuator induced reference signal, measurement signal analysis, ... (TUD, EMK; TU Delft, ....)First research step: dynamically reconfigurable sensor electronics


Dynamically Reconfigurable Sensor ElectronicsAvailable Reconfigurable Sensor Electronics

Recent approaches and products support dynamic self-calibration of analog systems/components

ALD2724x(EPAD)ALD2724x(EPAD) AD8555 (DigiTrim)AD8555 (DigiTrim)

Trimming by EEPROM (finite correction cycles)Trimming by DAC (volatile memory/switches, infinite correction cycles)


Dynamically Reconfigurable Sensor ElectronicsAvailable Reconfigurable Sensor Electronics

Zetex TRACZetex TRAC

AnadigmVortexAnadigmVortex

Cypress PSoCCypress PSoC

Lattice ispPAC30Lattice ispPAC30

IMTEK GmC-FiltersIMTEK GmC-Filters

Collection of Reconfigurable Analog Electronics and Evolvable Hardware – Field-Programmable Analog/Transistor Arrays

KIP/JPL FPTAKIP/JPL FPTA

Approaches differ in granularityCommercial versions: Building block level


Dynamically Reconfigurable Sensor ElectronicsAdvanced Architecture of Adaptive/Reconfigurable Mixed-Signal Systems

ProcessingUnit (Rec.)

ProcessingUnit (Rec.) ActuatorsActuatorsDACDAC

FPGA/ASIC

FPGA/ASIC MemoryMemory

Human IfcHuman Ifc DiagnosticsDiagnostics Aux. SystemsWireless

Aux. SystemsWireless

FPMAAdaptive

Sensor Signal Conditioning& ConversionSensorsSensors

Dynamically reconfigurable FPMA meeting industry requirementsRapid-prototyping and flexibility for sensor front-end (freq. limits !)Overall reconfigurable embedded sensor system architecture (SoC) including metrics and learning/optimization featuresInherently, fault-tolerance and self-x-features of OC are provided


Dynamically Reconfigurable Sensor ElectronicsEvolvable Electronics:CAS Optimization in Design Phase & Post Fabrication

Intrinsic EvolutionIntrinsic Evolution

Parameter,Structure

Post Fabrication

-

Results


Sensordata,

Features

DatabaseDatabase

•Compensation•Yield increase

Evolving parameters with thereconfigurable hardware in the loop CHILL (Intel 89, ETANN)Multiobjective CAS Optimization

Extrinsic EvolutionExtrinsic Evolution

Parameter,Structure

Design Phase

-

Results,Modells


Sensordata,

Features

DatabaseDatabase

•Compensation•Centering

Evolution on circuit-levelEvolving new circuits CHILL in design phaseMultiobjective CAS Optimization


Conclusions and Outlook

Activities & interest in sensor signal processing and intelligent system design (Sensor fusion, non linear methods, optimization, …)

Autoconfiguration and design automation appealing under scientificand industrial/commercial aspects (Talks 1 and 2)

Concepts of holistic design can be pursued down to hardware design and physical system integration (Talks 3 and 4)

Incorporating multiobjective optimization and reconfiguration/adap-tation will return viable, robust, and cost effective systems

Automation & automotive seem to be excellents field for application !

Documents

Beiträge zum optimierten Entwurf fehler-und …...©2006 Andreas König, Institute of Integrated Sensor Systems Institute of Integrated Sensor Systems Dept. of Electrical Engineering