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 Page 2 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)
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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
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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
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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
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Green Building Options for Energy Saving, Comfort and Health
and Related Sensor Needs
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Green Building Options
have to meet 3 targets
Save Energy
Whole lifecycle
approach
Add performance
@ competitive costs
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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
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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
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Different ways to use sensor devices
Classic Operation vs. Innovative Approaches
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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,…..
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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/
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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..)
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SmartSenior - Intelligent services for senior citizens
Approach for remote activity monitoring
Remote Activity Monitoring Scenario Approach for Automated Assistance Services
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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
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From Gas Detection to Event Detection:
Monitoring of human presence and activities
• 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
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From Gas Detection to Event Detection:
Monitoring of human presence and activities
sleeping
Increased physical activity
Cooking and eating
Prepare and drinkcoffee
personal hygiene
Increasedphysical activity
Cooking and
eating
Prepare and drinkcoffee
sleeping Personal hygiene
prediction
sleeping
Increased physical activity
Cooking and eating
Prepare and drinkcoffee
personal hygiene
Increasedphysical activity
Cooking and
eating
Prepare and drinkcoffee
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
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Fall Detection
Posture Analysis with 3D Time-of-Flight Visioning
Volumetric and topological
approach for posture analysis
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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
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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
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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)
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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
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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
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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
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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
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Building Sensor network
Example for state of the art: Fire detection
Signal evaluation adapted to environment
-- Situation adapted signal evaluation on sensor node
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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
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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
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Guardian Angels FET Flagship Proposal
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/
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Guardian Angels FET Flagship Proposal
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
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Guardian Angels FET Flagship Proposal
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
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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.
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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
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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…
Restricted © Siemens AG 2014. All rights reserved Page 44 20.03.2014 Corporate Technology
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
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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
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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
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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
Restricted © Siemens AG 2014. All rights reserved Page 48 20.03.2014 Corporate Technology
Trillion Sensors (TSensors) Visions
Meeting in Germany in September
don‘t forget to add gas sensors !