POWER OPTIMIZATION OF HETEROGENEOUS imec 2011 microtech2011 mems-based system solutions and integration approaches power optimization of heterogeneous and mems-based systems ”consume

© IMEC 2011

MICROTECH2011 MEMS-BASED SYSTEM SOLUTIONS AND INTEGRATION APPROACHES

POWER OPTIMIZATION

OF HETEROGENEOUS AND MEMS-BASED

SYSTEMS

”CONSUME MORE TO CONSUME LESS”

CHRIS VAN HOOF

DIRECTOR INTEGRATED SYSTEMS

© IMEC 2011

OVERVIEW

 Introduction / motivation from imec perspective

 Drivers for System Optimization

 3 Optimization Examples

▸  The Self-Powered Health Patch ▸  The iTire – a new TPMS paradigm ▸  The eNose – a Future Smartphone Add-on

 Conclusions

© IMEC 2011

IMEC

imec Belgium

imec The Netherlands

imec Taiwan

imec office Japan

imec office US imec China

Independent R&D center performing leading research in nanoelectronics

© IMEC 2011

IMEC: TECHNOLOGY IS KEY ONE OF THE WORLD’S MOST ADVANCED IC R&D FACILITIES

© IMEC 2011

imec aims to shape the future.

With our global research partners, we will lead the development of nano-enabled solutions that

allow people to have a better life in a sustainable society.

© IMEC 2011

and it must be a SUSTAINABLE FUTURE

Busy lifestyle Aging society Global warming Unlimited mobile access Mobility Energy shortage

and SUSTAINABILITY

implies ENERGY AWARENESS

© IMEC 2011

OVERVIEW

 Introduction

Drivers for System Optimization

 3 Optimization Examples

▸  The Self-Powered Health Patch ▸  The iTire – A new TPMS paradigm ▸  The eNose as Future Smartphone Add-on

 Conclusion - Disruptive

© IMEC 2011

3 DRIVERS FOR SYSTEM OPTIMIZATION

1 2 3 Power Functionality Size

0

100

Gen 4

Gen 3

Gen 2

Gen 1

COST ?

© IMEC 2011

TRADITIONAL SYSTEM OPTIMIZATION

 Manageable System Optimization

 “EVERYBODY PAYS (SAVES)”

0 20 40 60 80

100 120 140 160 180

GEN 1 GEN 2 GEN 3

Sensor

Analog Interface

Digital Control

Signal Processing

Power Management

Radio

Microsystem cpts

© IMEC 2011

DISRUPTIVE SYSTEM OPTIMIZATION  Smart System Optimization

 “CONSUME MORE TO CONSUME LESS”

0 20 40 60 80

100 120 140 160 180

GEN 1 GEN 2' GEN 3'

Sensor

Analog Interface

Digital Control

Signal Processing

Power Management

Radio

© IMEC 2011

OVERVIEW

 Introduction

 Drivers for System Optimization

 3 Optimization Examples

▸  The Self-Powered Health Patch ▸  The iTire – A new TPMS paradigm ▸  The eNose as Future Smartphone Add-on

 Conclusion - Disruptive

© IMEC 2011 12

THE SELF-POWERED HEALTH PATCH

© IMEC 2011

THE IMEC HEALTH PATCH

13

Cardiac patch with arrhythmia analysis and continuous wireless ECG signal transmission

© IMEC 2011

THE SELF-POWERED HEALTH PATCH ?

14

PV cells! TEG!

▸  BiTe based micromachined thermopiles

▸  30uW/cm2 ! 60uW …90uW maximum

TODAY: Battery powered TOMORROW: Self Powered ?

THERMAL ENERGY

HARVESTING

© IMEC 2011

MCU 2%

Radio 50%

ADC 2%

Sensor &ROIC

7%

PM 39%

THE SELF-POWERED HEALTH PATCH ?

15

1112!W

 Today’s ECG patch consumes 1.1mW

▸ In optimum operation mode ▸ Power consumption

should be reduced by 10x - 20x

© IMEC 2011

POWER DIAGNOSIS BY MODELING POWER OPTIMIZATION STRATEGY: STEP 1

16

989!W !=12%"

MCU 68%

Radio 3%

ADC 2%

Sensor &ROIC

8%

PM 20%

MCU 2%

Radio 50%

ADC 2%

Sensor &ROIC

7%

PM 39%

1112!W

NO CIRCUIT CHANGE PURELY SYSTEM CHANGE

© IMEC 2011

POWER DIAGNOSIS BY MODELING POWER OPTIMIZATION STRATEGY: STEP 2

17

MCU 68%

Radio 3%

ADC 2%

Sensor &ROIC

8%

PM 20%

369!W !=63%"

MCU 13%

Radio 7%

ADC 5%

Sensor & ROIC

20%

PM 55%

CIRCUIT CHANGE: REPLACE MCU (MSP430 ! CORTEX M3)

989!W

© IMEC 2011

POWER DIAGNOSIS BY MODELING POWER OPTIMIZATION STRATEGY: STEPS 3 & 4

18

MCU 13%

Radio 7%

ADC 5%

Sensor & ROIC

20%

PM 55%

214!W !=42%"

MCU 12%

Radio 12%

ADC 9%

Sensor & ROIC

35%

PM 23%

174!W !=20%"

MCU 27%

Radio 15%

ADC 11%

Sensor & ROIC

21%

PM 26%

REPLACE linear

regulator

REPLACE sensor

interface (lower

voltage)

369!W

© IMEC 2011

MCU 39%

Radio 4%

ADC 1%

Sensor & ROIC

31%

PM 26%

POWER DIAGNOSIS BY MODELING POWER OPTIMIZATION STRATEGY: STEPS 5 & 6

19

MCU 27%

Radio 15%

ADC 11%

Sensor & ROIC

21%

PM 26%

121!W !=30%

96!W !=21%"

MCU 32%

Radio 5%

ADC 1% Sensor

& ROIC 39%

PM 23%

REPLACE RADIO

by custom BAN radio

REPLACE MCU

by custom BIO-DSP

174!W

© IMEC 2011

THE SELF-POWERED HEALTH PATCH !

20

PV cells! TEG!

▸  BiTe based micromachined thermopiles

▸  30uW/cm2 ! 60uW …90uW maximum

TODAY: Battery powered TOMORROW: Self Powered !

THERMAL ENERGY

HARVESTING MCU 32%

Radio 5%

ADC 1% Sensor

& ROIC 39%

PM 23%

96!W

© IMEC 2011

THE iTIRE VEHICLE AND TIRE EVOLUTION

1885

1908

1985

2015

2011

2500 BCE

2020

First pneumatic automobile tire

Tire pressure monitoring mandatory in US

Introduction of the radial tire

Intelligent Autonomous tire Systems

Fully active tires

© IMEC 2011

Board computer

TPMS Transmitter/Receiver IC

TPMS APPLICATION; PRESENT

22 imec Confidential

Pressure sensor

Acceleration sensor (opt.)

ADC

MCU (Memory, Firmware)

RF Transmitter 315/434MHz

LF

Receiver (125KHz) (Wake-up)

Power management

RF Receiver

315/434MHz

MCU

Power management 12V

3V battery LF Transmitter (Wake-up)

Regulated in US and from 2012 in Europe Ultra-low power, low-cost, reliable

EXPLORE DISRUPTIVE SYSTEM OPTIMIZATION POSSIBILITIES

© IMEC 2011

Board computer

TPMS Transmitter/Receiver IC

23 imec Confidential

Pressure sensor

Acceleration sensor

ADC

MCU (Memory, Firmware)

RF Transmitter 315/434MHz

LF

Receiver (125KHz) (Wake-up)

Power management

RF Receiver

315/434MHz

MCU

Power management 12V

3V battery LF Transmitter (Wake-up)

TPMS APPLICATION; NEW ARCHITECTURE

Energy harvester functionality/added value Energy source Wake-up; Takes the LF & accelerometers modules role Reduces capacity consumption by >50%

Harvester+ Capacitor

No need

New

DISRUPTIVE SYSTEM OPTIMIZATION (1): Re-think the TPMS architecture to fit the harvester

© IMEC 2011

CURRENT TPMS; BATTERY CAPACITY ESTIMATION FOR 10 YEARS OPERATION

24

" This explains the 5-7 years battery-powered TPMS life cycle limitation***

Leakage 1%

RF 64%

MCU 14%

Power management

12%

Sensors & ADC 1%

Others 8%

T (15%)

Volt. (15%)

Rx* (94%)

Tx* (6%)

730mAh

* 4/20hours motion/parking cycle **10!A/sec for operations & 10!A/sec Memory for 3/15seconds motion/parking cycle ***500mAh battery

Pressure (58%)

Operations (50%)

Memory (50%)

ADC (12%)

© IMEC 2011

TPMS; POWER / CAPACITY ESTIMATION 10 YEARS OPERATION (WITH HARVESTER)

25

" Scenario with harvester replacing the LF wake-up process

Leakage 3%

RF 7%

MCU 26% Power

management 47%

Sensors & ADC 1% Others

16%

T (15%)

Volt. (15%)

Rx* (0%)

Tx* (100%)

380mAh

* 4/20hours motion/parking cycle **10!A/sec for operations & 10!A/sec Memory for 3/15seconds motion/parking cycle ***90mAh/10 years added for harvester-battery PM2

Pressure (58%)

Operations (50%)

Memory (50%)

ADC (12%)

PM generation

(50%) PM

consumption (50%) May be even reduced to a half or less if

50% (PM + leakage) battery powered 50% harvester powered

© IMEC 2011

Intelligent tire sensor system on inner liner

i TIRE – THE FUTURE

26

Pressure sensor

ADC

MCU (Memory, Firmware)

RF Transmitter 315/434MHz Power management

Super capacitor

Energy harvester functionality/added value Force sensor for improved tire state estimator Energy harvester with sufficient power generation Super capacitor as energy storage Mounted in the inner liner of the tire

Harvester

Energy source Wake-up

Force sensor

RADICAL SYSTEM OPTIMIZATION (2): Re-think the system for improved tire state estimation

© IMEC 2011

REAL iTIRE WITH ENHANCED FUNCTIONALITY A SYSTEM POWER CHALLENGE

27

*Assuming 6kbps data rate

6954!W*

MCU 2%

Radio 82%

ADC 1%

Sensor &R-out

<1% PM 15%

WELL OUT OF RANGE OF BATTERY

POWERED OR HARVESTED SYSTEMS

321!W !=95%"

MCU 7%

Radio 60%

ADC 4%

Sensor &R-out

4%

PM 25%

APPROACHES HARVESTER

CAPABILITIES

© IMEC 2011

REAL iTIRE WITH ENHANCED FUNCTIONALITY MORE THAN JUST NUMBERS … ALSO FIRST HARDWARE

28

First 100’s of Km test drive in June 2011 on a racetrack

 Sensor on the rim and in the inner liner

 MEMS harvester reliability & power

 Instantaneous wireless signal and data storage (8GB)

© IMEC 2011 29

THE eNOSE AS FUTURE SMARTPHONE ADD-ON

Air quality monitoring and indoor air quality control (pollution, malodor emission, toxic gasses..)

Consumer fraud prevention (ingredient confirmation, content standards..)

Ripeness, Food contamination (spoilage, self live..)

Taste, smell characteristics (off flavors , product variety assessment..)

Pathogen identification (patient treatment selection, prognosis..)

Physiological condition (nutritional status, organ failure..)

Personnel and population security (biological and chemical weapons...)

CO2 high levels

© IMEC 2011

 POWER is a challenge but not the main challenge

 PERFORMANCE AND SYSTEM INTEGRATION are key challenges

30

THE eNOSE AS FUTURE SMARTPHONE ADD-ON

© IMEC 2011

Nokia Scentsory Concept

Nokia EcoSensor Concept

31

THE eNOSE AS FUTURE SMARTPHONE ADD-ON

AS THIS CAN BE FULLY INTEGRATED IN A FUTURE SMARTPHONE, A SELF-POWERED SOLUTION IS NOT NEEDED

 POWER is a challenge but not the main challenge

 PERFORMANCE AND SYSTEM INTEGRATION are key challenges

© IMEC 2011 32

THE eNOSE AS FUTURE SMARTPHONE ADD-ON

 PERFORMANCE AND SYSTEM INTEGRATION are key challenges

▸ Device Integration -  Monolithic MEMS integration -  Hybrid MEMS integration -  Very … hybrid integration

▸  Calibration challenges -  Reusable MEMS sensor -  Disposable MEMS sensor

▸  System Integration -  Many applications need to be covered -  One nose fits all vs dedicated eNoses

THE SMARTPHONE ENVIRONMENT OFFERS

UNPRECEDENTED SYSTEM OPTIONS:

-  Calibration by peers

instead of in-situ -  “App” style hardware

additions (micro-SD) -  These are disruptive

integration options

© IMEC 2011

OVERVIEW

 Introduction

 Drivers for System Optimization

 3 Optimization Examples

▸  The Self-Powered Health Patch ▸  The iTire – A new TPMS paradigm ▸  The eNose as Future Smartphone Add-on

 Conclusion: DISRUPTIVE MEMS System Design is key – and it does involve “CONSUME MORE … TO CONSUME LESS”

© IMEC 2011