Roland Pohle, Siemens Corporate Technology … seminar Yole - EMFT2014/pohle... · CO 10 – 70 ppm...

Restricted © Siemens AG 2013. All rights reserved

Applications and trends in gas

sensing for home and health

Roland Pohle, Siemens Corporate Technology

Restricted © Siemens AG 2014. All rights reserved 20.03.2014 Corporate Technology

Siemens is organized in 4 Sectors: Industry,

Energy, Healthcare and Infrastructure & Cities

1) Sales in FY 2013

Siemens sectors

• Sales: ~€ 76 bn.

• Locations: In 190

countries

• Employees: ~362,000

• R&D

expenses: ~€ 4.3 bn.

• R&D

engineers: ~29,800

• Inventions: ~8,400

• Active patents: ~60,000

Key figures FY 2013

Divisions:

• Industry

Automation

• Drive

Technologies

• Customer

Services

Divisions:

• Power

Generation

• Wind Power

• Energy Service

• Power

Transmission

Divisions:

• Imaging &

Therapy Systems

• Clinical Products

• Diagnostics

• Customer

Solutions

Divisions:

• Rail Systems

• Mobility & Logistics

• Low and Medium

Voltage

• Smart Grid

• Building

Technologies

Corporate functions

Corporate Technology Research&Technology Centre

Corp. Finance

…

Corp. Technology Corp. Development

Infrastructure & Cities

Healthcare Energy Industry

~€ 14 bn.1) ~€ 18 bn.1) ~€ 19 bn.1) ~€ 27 bn.1)


Corporate Technology: Sensor Technologies

Chemical Sensors as Cross-sectional Topic

Work-placesecurity

Process-

control

-

,

-

HVAC

Heating

Fire detection

Acetone Lambda

HC HCl H2

HFCH4

Ethanol

CO

CO2

NO

C2H2

Smell

NO2

Humidity/H2O

O2

ViscosityDensity

ParticleLoad

Soot

Health,

WellnessEnvironment

O3

Material Quality

Media

Temp.contact less

Exhaust

Fuel

watercontentFuel

type

Solid state

electrochemistry Near Infrared

spectroscopy

Semiconducting

metal oxide films

Work function

read-out

Micromachined

Ultrasound

Tunable

Laser diode

spectroscopy

Living cells

as sensors Adsorptive

materials


Challenges from demographic change

Growing and aging population

2050: More people aged 60 years and over than under 14 years

Increasing demand for healthcare services

Source: United Nations

Innovation + process optimization

Prevention,

early

detection,

monitoring

Therapy Care In-vitro

Diagnostics (IVD)

In-vivo Diagnostics (Imaging)

Diagnosis


40%

of European energy con-

sumption used in buildings

60% of Europe’s building stock is

over 25 years old

50% of energy requirements

relate to heating / cooling

Air quality regulated HVAC my save ½ of HVAC energy

Potential to save an average of 25% of buildings energy need

Buildings need to save energy

w/o degrading inhabitants comfort and health

EU: Reducing greenhouse gas emissions by 40% until 2030

Nearly Zero-Energy Buildings:norm for all new buildings in the EU by 2020


Green Building Options for Energy Saving, Comfort and Health

and Related Sensor Needs


Green Building Options

have to meet 3 targets

Save Energy

Whole lifecycle

approach

Add performance

@ competitive costs


Example for gas sensors applications

Gases relevant for Comfort, Health and Safety

Breath Analysis

Indoor

Air

gas c (ppm)

CO2 400-2000 ppm

Acetone 0,1-700 ppm

Pentanal ~ 5-10 ppb

HC-Mix ~ 500 ppm

Ethanol 0,2-5.000 ppm

CO 30 ppm

Ethylacetate 0,05-200 ppm

... ppb - ppm

gas c (ppm)

CO2 3-4%

Ethanol 25–200

Acetone 0.1 - 2

NO 30 – 40

CO 10 – 100

VOC ~ xx ppb

… ~ x ppb

Fire Detection

gas concentration High formation at

CO2 300-5000 ppm flaming fires

NO2 0.05 - 5 ppm flaming fires

CO 10 – 70 ppm Smouldering wood/paper

H2O 2 –40 % rel. flaming fires

H2 4 – 20 ppm flaming fires

Methanol < 10 ppm Smouldering wood

Formic Acid < 5 ppm Smouldering fires

Methane < 10 ppm Smouldering fires

Formaldehyde < 10 ppm Smouldering fires

Ethylene < 10 ppm Cable, cigarettes

Acrolein < 10 ppm Fat fire

…

Analysis of complex gas mixtures required ↔ strong request for low cost solutions


From Sensing to Sensor Systems:

Fire Detection by Optical Particle Sensors

EVENT SENSOR ALARM

da

ta a

na

lys

is

Full Signal Chain has to be developed in a holistic and multidisciplinary way


Different ways to use sensor devices

Classic Operation vs. Innovative Approaches


Principle

Metal oxides show semi-conductivity

at high temperatures (200-800oC)

Reversible change of conductivity

due to reaction with target gases

Technologies for low cost gas sensing

Metal Oxide Gas Sensors: Sensing Principle

R= R(T, PGas)

U

Ga2O3

Electrodes

Substrate

Gas

sensing layer:

Ga2O3

Manufacturers: AppliedSensor, UST, IST, Steinel Solutions,…..


Technologies for low cost gas sensing

Metal Oxide Gas Sensors: Breath Ethanol Analysis

Sensor signal within one breath cycle

Shrinking of sensor sizes

Ethanol-Sensor

Main Flux

Bypassed Flux

Venting

Reliable detection of blood ethanol

content via breath analysis

Sensor smaller than the tip of a match

5690 5700 5710 5720 5730 57401

10

100

0.25 mg/l ethanol

Rse

nsor

(kO

hm)

time (sec)

T = 830°C

300mW

1W


Metal Oxide Gas Sensors

Temperature as key parameter for gas sensing characteristic

H d + Gas

Chemisorption

Surface

defects

Gas

Volume

defects

e -

e - +

metal oxide

e - +

+ O

- Me

- O

- V O

1 / 2

O 2

CH 4

CO 2

, CO, H 2

O

T

200°C

1000°C

metal oxide

metal oxide

metal oxide

• Gas response strongly dependent on sensor temperature

• „Virtual Array“ created by temperature modulation

Power

consumption

~30mW


Metal Oxide Gas Sensors

Temperature Modulation as Key to Multigas Recognition

0 100 200 300 400 500 600 700 800 900

0,0

0,2

0,4

0,6

0,8

1,0

1,2

1,4

1,6

1,8

2,0

2,2

2,4

2,6

2,8

3,0

Uh

ea

ter

(V)

time (msec)

Heater Voltage

R. Pohle Siemens AG 14.11.2007

Temperature Modulation by

Heating voltage profile

• Application of heating pulse leads to change in resistance of sensor

• Shape of response curve depends on present gases

0,0 0,2 0,4 0,6 0,8 1,01k

10k

100k

R(O

hm

)

time (sec)

10ppm CO

10ppm H2

2.5ppm ethanol

0

50

100

150

200

250

300

350

400

Tse

nso

r (°

C)

Tsensor

Sensor response


Transient T modulation

multi-dimensional signal

Transient input parameters: Temperature, Voltage, Current, Light, …..

Chemical/Physical Influence on Sensing Mechanism

Variation of Sensitivity/Selectivity

Multidimensional Signal: scalar vector

Benefit for Specific Application

Continuous operation

one-dimensional signal

5690 5700 5710 5720 5730 57401

10

100

0.25 mg/l ethanol

Rse

nso

r (k

Ohm

)

time (sec)

SnO2, T =const

100ppm ethanol

0 10 20 30 40 50 60 70 80 90

R (

Oh

m)

heating pulse points

10 ppm ethanol

20ppm acetone

SnO2, T =f(t)

Gas Sensors for Multigas Recognition

Basic Idea: Transient Operation provides Multidimensional Signal

µ-Machined Metal

Oxide Sensor


Why Using Gas Sensors for Early Fire Detection ?

Complex gases (Amines, Amides, Organic Acids,

Ketones, Aromates…)

Particles/

Aerosols

false alarms

sensitivity

Simple inorganic gases

(CO, H2, NOx, CO2, H2O, NH3 ) - Correlation to fire events established

(literature+ own work)

R e l i a b i l i t y of f i r e d e t e c t i o n

M i n i a t u r i s a t i o n


smouldering

cotton fire

35 T-pulsed

metal oxide sensors

Event description Class

TF 1 open wood fire 1

TF 2 smouldering

wood fire

2

TF 3 cotton 3

TF 4 PU 4

TF 5 heptane fire 5

TF 6 EtOH fire 6

TF 7 decaline fire 7

Nuisance smokers 8

Ref 8S aerosol 9

Nuisance ethanol 10

Nuisance hairspray 11

Normal air 12

Gas Sensors for Early Fire Detection

Fire Testing Regarding European Standard EN54


Performance Potential of gas sensors for fire

detection

• Temperature-modulated operation

• Modells for classification of fire and non-fire events

0 100 200 300 400 500 600 700 800 900

10000

100000

1000000

R (

Oh

m)

time (msec)

Sensor Resistance

Raw Sensor Signal

EARLY WARNING

Statistical Data Evaluation


Gas Sensors in Smart Homes

From Fire Detection to Automated Cooking

Benefit

Early Fire detection More comfort and quality control with

baking, roasting and toasting

Approach

Detection of gases emitted during

cooking process using gas sensors

CO2-sensors as approach for bakery

Fire Detection in Fume Hood

Signal transient of

conventional gassensor array


From Gas Detection to Event Detection

20 40 60 80 100 120 140 160 180 200-0.2

0

0.2

0.4

0.6

0.8

1

1.2

1.4

Sample

Y P

redic

ted 1

(C

lass 1

)

Samples/Scores Plot of fire_170408_SBT_S2bisS15_dataset

Y Predicted 1 (Class 1)

1

10

11

12

2

3

4

5

6

7

8

9

Discrim Y 1

x-axis zero

y-axis zero

• reliable detection and

classification of test fires

demonstrated

Detection of open wood fire

20 40 60 80 100 120 140 160 180 200-0.2

0

0.2

0.4

0.6

0.8

1

1.2

Sample

Y P

redic

ted 1

1 (

Cla

ss 1

1)

Samples/Scores Plot of fire_170408_SBT_S2bisS15_dataset

Y Predicted 11 (Class 11)

1

10

11

12

2

3

4

5

6

7

8

9

Discrim Y 11

x-axis zero

y-axis zero

Detection of human activities

Brew coffee

• Classification of human

activities?

training validation


Live independently at home for

longer. Get well and stay healthy.

Be safe on the go.

SmartSenior - Intelligent services for senior citizens The various scenarios in SmartSenior are derived from

known basic needs.

• http://www1.smart-senior.de/


SmartSenior - Intelligent services for senior citizens The various scenarios in SmartSenior are derived from

known basic needs.

Live independently at home for

longer.

Live independently at home for longer.

• Assistance with everyday domestic life, integration of social and other services in the neighborhood.

• Safety in the home, prevention and identification of emergency situations.

• Integrated, easy-to-use communication facilities with social network and service providers.

Early Fire Detection to safe people, not buildings

Additional potential for Activity Monitoring (cooking, cleaning, open windows..)


SmartSenior - Intelligent services for senior citizens

Approach for remote activity monitoring

Remote Activity Monitoring Scenario Approach for Automated Assistance Services


From Gas Detection to Event Detection:

Monitoring of human presence and activities

• Significant sensor response to presence of people and human activities

• Database for evaluation of gas sensor based human activity monitoring

12:00 13:00 14:00 15:00 16:00 17:00 18:00 19:00

10000

100000

open all

windows

1 persons in room

2 persons in room

cooking

senso

r re

sist

an

ce (

Ohm

)

time

ceiling

kitchen window

near electric cooker

desk

couch

open kitchen

window

4 persons in room

gas sensor response in test appartment • 6 T-pulsed metal oxide sensors in

test appartment

•100 situations in 9 days

*A. MacWilliams et. al, PervasiveHealth 2012




• Vision: non invasive activity monitoring systems for safety and security

100 systems installed in real homes (data evaluation ongoing)

• flat equipped with sensors

Assitance service

center Activity monitor with T-pulsed gas

sensor, PIR motion detector and

Wi-Fi communication

Gas sensor supported prediction of activities of daily living

Intelligent

wrist watch




sleeping

Increased physical activity

Cooking and eating

Prepare and drinkcoffee

personal hygiene

Increasedphysical activity

Cooking and

eating


sleeping Personal hygiene

prediction

sleeping

Increased physical activity

Cooking and eating


personal hygiene

Increasedphysical activity

Cooking and

eating


sleeping Personal hygiene

prediction

Gas sensor supported prediction of activities of daily living

Preliminary evaluation

from lab test

• promising results from testlab appartment

• Evaluation of real life data:

performance is highly fluctating from flat to flat (i.e. from user to user)

Algorithms has to be adapted to the specific user

0

5

10

15

20

25

30

35

Days o

r E

ven

ts

Flats

Data from 12 Flats Eating Cooking Statistics

AnnotatedDaysEstimableDaysPeriods ofEventsTruePositiveFalseNegativeFalsePositive


Fall Detection

Posture Analysis with 3D Time-of-Flight Visioning

Volumetric and topological

approach for posture analysis


Fall Detection

Posture Analysis with 3D Time-of-Flight Visioning

Robust background

modelling, people

segmentation and tracking

Fall-detection proof-of-

concept provided

Performance cannot be

evaluated without a

specific data collection


State of the Art

industrial

Sensor package:

Semiautomatic mounting

Room air monitor: discrete electronics

MEMS Approach

Si based sensor chip

Low cost

housing

MEMS mike

Low cost

Microcontroller

- readout, interfacing

2010 2014 > 2014

Room air monitoring

state of the art vs. MEMS based approach

State of the Art

consumer

MEMS platform

Special process for

gas sensing layer

Heterogenous integration

2cm

3.3V

70mW CW

10mW pulsed

Target: <<1mW

AppliedSensor 2014

Micronas 2014


Mechanism:

Gas diffusion in air gap

Interaction of gas at sensitive surface: surface potential acts as additional VG

Capacitive coupling to channel

Advantages:

low power consumption (µW -mW) possible

Use of versatile sensor layers e.g. metals, inorganic salts, organic compounds ..

gives wide spectrum of detectable gases.

GAS

VG

layer

p-Si

n-Si n-Si

Source

Drain

passivation

GASFET with air gap

+

-

+

- +

-

+

-

+

- DF

VG = VG + /q

Transitor characteristics

Low-Power Low-Cost Gas Sensors based on

Workfunction Readout (Suspended Gate FET)


31

Suspended Gate FET:

Ability for Multigas Detection

Humidity

0 20 40 60 80 100 120 140 160 180

0

10

20

30

40

50

60

70

80

90

rh(F

ET

)

time

rh(FET)

rel. humidity (%)

Comparison Comforstat light V1 to commercial r.h. sensor

60 120 180 240 300 360 420 480 540 600

-120,0m

-100,0m

-80,0m

-60,0m

-40,0m

-20,0m

0,0

20,0m thinfilm CuPc 330nm

channel 4

UDS = -0,5V

95°C

channel 4

[

V]

time [min]

1E-3

0,01

0,1

1

10

100

rel. humidity %

NO2 ppmp

Gas

[ppm

]

NO2

60 120 180 240 300 360 420 480 540

0,20

0,22

0,24

0,26

0,28

0,30

0,010,1

110

100

Sonde 2 (G46, Ga2O3-Dickschicht + Pt-Resinat auf Platin)

S. Stegmeier CT PS 6 Gast_V_3_070907 7.9.07

CP

D (

) Gold

/ s

en.

Mat. / [V

]

Zeit / [min]

Kohlenwasserstoff Mix

Aceton

Ethylacetat

Pentanal

r.h. / [%

]

T / [°C

]G

as / [ppm

]

0

50

020406080100

Time/[min]

VOCs

Micronas

MySENS

1 10 100100

150

200

250

300

350

FET NH3 sensor

Sensitivity ~ 170mV/Decade

Detection limit < 1 ppm

se

nso

r re

sp

on

se

(m

V)

NH3 (ppm)

TiN15ch1

TiN15ch2

TiN15ch3

TiN15ch4

TiN15ch5

NH3


From mW to µW

Industrialisation of GasFET: Micronas mySENS®

80

k

Sensor

Production at

Suspended gates

Temperature, Humidity + Additional Gas Sensors (NO2, NH3, VOC, +XXX

Signal conditioning

SPI Interface


From ppm to ppb

Detection of explosive (TNT) with Molecular Imprinted Polymers

Significant, concentration-dependent response to TNT in ppb level

Readout with GasFET feasible

0 ppb <<47 ppb 47 ppb 54 ppb0

-2

-4

-6

-8

-10

-12

-14

-16

-18

TNT imprinted

non imprinted

(m

V)

Response of TNT imprinted and non imprinted

MIP Kelvin Probe to TNT

TNT concentration (ppb)

5410 5415 5420 5425 5430 5435 5440 5445 5450

0.135

0.140

0.145

0.150

50ppb TNT exposure

(V

)

time (min)

delay time response time

Kelvin measurement

with TNT imprinted MIP

200

300

400

500

600 IMR-MS TNT response

IMR

-MS

(co

un

ts)

0

1

2

TNT cell flow state TN

T c

ell

flo

w

Analyte

Sensing


SWING: Siemens wireless fire detector network mesh technology

■ Safe wireless communication –

at least two redundant communication paths

■ No costly business interruptions

– thanks to self-mending

■ mesh network and deception-

free ASA technology

■ radius of 60 m – spanning up to five floors

■ Battery lifetime > 3 years

Building Sensor network

Example for state of the art


Building Sensor network

Example for state of the art: Fire detection

Signal evaluation adapted to environment

-- Situation adapted signal evaluation on sensor node


Lighting/shade: •Monitoring and control

•Natural light •Artificial light •Blind control

Comfort Monitoring and control •Temperature •Humidity •Air flow •Light •Occupancy •TVOC (Total Volatile organic compounds) •CO •CO2

Energy knowledge: •Monitoring and control

•Usage •Profile •Efficiency

Micro-systems for Building Environment

•Improved Indoor Environment Quality •Energy conservation-demand based intelligent usage •Operational efficiency- repair/change

Value


Guardian Angels FET Flagship Proposal

From Low Power to Ultra Low Power

• Targets the next wave in sensing technology.

Autonomous cost-effective systems.

• Deployed in our daily environment, industry,

at/in the body, wireless delivering a quantum

leap of accessible information.

• Enabling sustainable services

Preventive medical monitoring

Safe and secure environment

Preventive stress management

Smart cities &

smart transportation

Optimized industrial production

• Strong industrial participation

29 Universities, 16 Research Institutes, 21 Companies



Guardian Angels are future zero-power smart autonomous

systems featuring sensing, computation communication and

energy harvesting features beyond human aptitudes.

100aJ/op

to

0.1aJ/op

1nJ/bit

to

1pJ/bit

10mW

to

100nW

100mW/cm2

to

10mW/cm2

http://www.ga-project.eu/



39

Self heated metal oxide nanowires for Ozone detection

Single metal oxide nanowire setup

Heating by resitive heating

On Power < 100nW

Self-heated metal oxide nanowires (CNM Barcelona)

MOX nanowire sensor Response to Ozone



Single Wall Carbon Nanotube transistors (CNTFET)

for NO2 gas detection

*M. Mattmann, Applied Physics Letters (2010)

Active sensing area

On Power < 100nW

Detection limit < 100ppb

Single CNTFET device (ETH Zürich)

schematic SEM image of real device Sub-ppm response to NO2


Horizontal CNT- array-based gas sensors (EPFL, CEA-LITEN)

SEM image (cross section) of the selective CNT growth. The

CNTs nucleate only from the iron catalyst deposited on Al2O3

surfaces since Fe diffuses inside the TiN.

• High-yield, In-situ Fabrication and Integration of Horizontal Carbon

Nanotube Arrays at the Wafer Scale for Robust and Reliable Gas Sensors

Hoël Guerin et. al., ACS 2014 (submitted)

SEM tilted view of the localized, in-situ growth of

horizontal CNT arrays.


Horizontal CNT- array-based gas sensors (EPFL, CEA-LITEN)

Detection limit << 10ppm

High reproducibility

room temperature operation low power operation

High-yield, In-situ Fabrication and Integration of Horizontal Carbon Nanotube

Arrays at the Wafer Scale for Robust and Reliable Gas Sensors


i(t)

Gas

Tunable single-mode

Laser

Photodiode

l(t), IL(t) IP(t)

Transmission

Miniaturitzation of optical sensor by MEMS + Photonics

Tunable Diode Laser Spectrometry

General Principle

Mirror

Transmission measurement of gas allows for determination of C, p, T, etc…


Overview on gases and achievable sensitivity

in Tunable Diode Laser Spectrometry

10ppb m

10ppm m

4 3,5 3 2,5 2 1,5 1 0,5

O2

100ppm m

100ppb m

0.1ppb m

1ppb m

1ppm m

CO2

CH4

SO2 NO2

NH3

HF

HF

HCl

HCl

H2S

N2O

NH3

CH4 CO2

CO2

N2O H2O

H2O

CO

wavelenght m

Für Absorption

10-5

N2O

NH3

C2H2

CxHy

H2O

CO

CH4

NIR MIR

DFB

VCSEL

DFB-QCL

H2S

Optical path limits miniaturization


Tunable Diode Laser Spectroscopy

MEMS based Photonics designs and vertical Integration

Main Targets:

• Reduce the length of the light

absorption path m <1 cm

• Integrating the light source, detector

diode, ASIC with small form factor

• Targeted form factor for selective

gas detection 1 cm3

Photonic integrated design

Diffuse reflector

Detector with lens

laser diodeGas

Electronics

Cable

Light

Source

Detector with Lens

Reflector

Conventional

design


Tunable Diode Laser Spectroscopy

MEMS based Photonics designs and vertical Integration

Laser die

Photodiode

GasGas

Sensing

beam

Photodiodedi

e

Laser die

Photonic

crystal

Sensing

beam

GasGas


Applications and trends in gas sensing for home and health

summary

• Gas sensing is suitable to contribute significantly to

• Reduce age and health associated problems

• Reduce energy consumption in buildings

• Manifold upcoming MEMS/NEMS sensor technologies with high potential

for low cost fabrication

• Main technical issues to be solved for industrialization

• Reduce power consumption mW µW

• Lower detection limits ppm ppb

• Overcome stability problems

Identify and use options from system perspective

Sensor + Operation Mode + Data Evaluation


Trillion Sensors (TSensors) Visions

Meeting in Germany in September

don‘t forget to add gas sensors !

Roland Pohle, Siemens Corporate Technology … seminar Yole - EMFT2014/pohle... · CO 10 – 70 ppm...

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Transcript of Roland Pohle, Siemens Corporate Technology … seminar Yole - EMFT2014/pohle... · CO 10 – 70 ppm...