Act Talk - Gijs Krijnen: "Imitating Cricket mechanosensing: dream or reality?"

Imitating cricket mechanosensing:
Dream or reality?
Gijs Krijnen & Jerôme Casas*
Transducers Science & Technology Group, MESA+/Impact Research Institutes,
University of Twente, Enschede, The Netherlands
* Institut de Recherche en Biologie de l'Insecte
IRBI UMR CNRS 6035, Université de Tours, France
2010, Tuesday May 18

12/02/2010
Imitating cricket mechanosensing: dream or reality?
Overview
● Why biomimetic sensors?
● MEMS (& what it is not)
● Artificial flow-sensitive mechano-sensory hairs
● Sensor Principle, Design,Fabrication & Characterization
● Adaptability, Nonlinearity and Stochastic Resonance
● From MEMS to Biology
● Biomimetic Hairsensors: Dream or Reality?
● Conclusions
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Biomimetic Sensory Research Drivers
● Principles and versatility
● Performance
 Sensitivity
 Directionality
 Accuracy
 Dynamic range
 (Reduction) of cross-sensitivity
● Power consumption / efficiency
● Size / density of sensors
● Robustness
● (Examples for) Multi-modal sensory integration
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The Chase Scene
Courtesy J. Casas, IRBI, Univ. de Tours
Wolfspider chasing a wood-cricket
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Cricket Hair Sensors
Acheta Domestica
Photograph courtesy of J. Casas et. al.
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Cricket Sensory Hairs Make Sense (I)
(T. Shimozawa et al, in Sensors and Sensing in Bio. and Eng. 2003)
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Cricket Sensory Hairs Make Sense (II)
(Adapted from Raangs, 2005)
● Sound:
 Pressure: P(t,r)
 Particle velocity: U(t,r)
● U prevails
 Near source (r < λ/2π)
 Small source (R<λ/2π)
● Crickets & predators:
 f=10 .. 100 Hz λ > 3 m
 few cm source R<0.1 m
 interaction 0 .. 1 m (very) near field (K. Beissner , JASA 71, pg 1406, 1982)
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Flow Mediated Perception
Courtesy J. Casas, IRBI, Univ. de Tours
Movie of Fluid MovementParticle Image Velocimetry
Flow Field Extraction
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Flow Mediated Perception
Spider running speed is 9.4 cm/s. Line is statistical fit.
Crickets can perceive running spiders at several cm’s
(Casas et al., 2008 PLoS ONE 3(5): e2116)
Let's make artificial
hair-sensors
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MEMS (& What it is not)
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Sequential Fabrication Process: 2.5D
0) Si/Glass substrate
1) Add material in thin layer (0.1 – 10 µm thick)
2) Apply a photolithographical mask
3) Etch unnecessary parts (selectively)
4) Remove mask
5) Structure Ready or Start from 10
Haveaparty
From2-12weeks
Failure /
Incompatibility
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Photolithography
● Spin photoresist
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● Etch through holes
and remove photoresist
● Illuminate through mask
● Develop image

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A
B
C
D
E
Surface micromachining
● Sacrificial layer
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● Patterning
● Structural layer
● Patterning
● Selective etching
& sacrificial release

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MEMS
● No free-form technology (2.5D)
● Stiff materials (4 – 300 GPa)
● Limited range of dimensions
100 nm – 1 cm in plane
10 nm – 10 µm out of plane
● Limitation due to stress
● Scaling behaviour
Surface forces dominate body forces
No quantum physics (no nano)
Full 3D
Range of (flexible) materials
Fewer range limitations
0.1 nm – 30 m
?? flexible materials
Scaling behaviour
Scale dependent
all physics
BIOLOGY
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Artificial Flow-Sensitive Hair Sensors

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Why MEMS Hair - Sensors?
● Why hairs?
 can be arranged in high density arrays ⇒ high spatial resolution
 flow pattern measurements ⇒ flow camera
● Why measuring flow or particle velocity?
 near field sensitivity, small sources
 vector ⇒ directionality
● Possibility for Acoustics?
 Frequency resolved flow measurements
 Electro-mechanical signal-processing
● Why MEMS?
 the usual: batchwise & parallel fabrication of many (arrays of) hairs,
small structures, integration, interfacing, etc.
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Artificial Sensory Hairs
● Design considerations
 Transduction principle
 Density of hair-sensors
 Directionality
 Sensitivity
Y. Ozaki et al., Proc. MEMS 2000, pg 531
D.K. KIM et. Al. , Jpn. J. Appl. Phys.
Vol. 39 (2000) pp. 7134–7137
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Artificial Sensory Hairs: Liu Group Illinois
● Sensitivity: 0.7 mm/s
 In water
 @ 50 Hz
 Bandwidth 2 Hz
N. Chen, et. al, Journal of MEMS 16, pp 999 - 1014, 2007
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Sensor Principle & Optimization

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Sensor considerations
● Small displacements (nm-scale):
 Requires high sensitivity
● Array application:
 Low power consumption ⇒ Generator type sensor
 Small number of interconnections
Capacitive
differential
Piezo-
Electric
Piezo
Resistive
Thermo
Resistive
Power-consumption + + + + +/- - -
Interconnections # 2, 3 2 2/4 2/4
Thermal Xtalk + + + + - - -
Technology +/- - +/- +
Parasitics - - - - +/- +
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Conceptual Sensor Structure
(M. Dijkstra et al., J. Micromech. Microeng. 15 (2005) S132–S138)
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● Mechanical system:
⇒ Damped 2nd order (J,S,R)
● Oscillating Flow:
⇒ Stokes/Rayleigh profile
⇒ Boundary layer, Strouhal number,
● Driving torque:
⇒ Stokes drag force
⇒ Hair length, diameter
● Capacitance changes:
⇒ Analytical expressions
⇒ Capacitor geometry
⇒ Stress & curvature
Courtesy J. Casas
Hair-Sensor Physics
(After T. Shimozawa et al, JCPA, 1998)
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Model (I): Mechanical Response
(T. Shimozawa, et al., J. Comp. Physiology A 183, 171-186, 1998)
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Model (II): Predictions
Boundary layer imposes strong
length dependency
⇒ L>400 µm (100 Hz)
Influence hair diameter small
(G. Krijnen et al., Proc. of SPIE Vol. 6592, 65920F, 2007)
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● FoM = usable bandwidth × sensitivity
● Bandwidth proportional to ω0:
● Sensitivity proportional to:
● Figure of Merit:
● FOM Crickets / FOM Artificial Hairs @ 1 mm: 68
 Long, thin, lightweight hairs: D: 8 vs 50 µm
 Soft suspension: S: 2.10-11 vs 8.10-9 Nm/rad
Model (III): Figure of Merit
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Model (IV): FoM and Scaling in Crickets
Allometric scaling: FoM changes by a factor of 7, Q by a factor of < 2
(T. Shimozawa, et al., J. Comp. Physiology A 183, 171-186, 1998)
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Model (V): Capacitive Sensing
● Capacitance change per unit of rotation:
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● Rectangular membrane with curvature up to δ:
● Optimization
 Long membrane
 Small gap
 No curvature (stress)

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Model (VI): Compliant Materials
● How to optimise rotational and vertical stiffness?
● Rotational stiffness given by:
● Vertical stiffness:
● Ratio for given S=S0:
Short beams of low Youngs modulus material!
Use what the crickets use
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Sensor Fabrication & Characterization

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Christiaan Bruinink, MEMS 2009
Fabrication of 3nd Generation Sensors
Optical microscope image
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Fabricated Devices
● Single layer SU-8 (470 µm), Cr electrodes
● Sensors parallel to increase C
● “Cercus” shape
● Double layer SU-8 (980 µm)
● 1 µm gap
● 2 diameter hairs (75% lower J)
● Al electrodes, 0.6 µm gap
(G. Krijnen et al., Proc. EuroSensors 2006)
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Characterization (I)
WLIM (3G Sensor)
● 200 µm x 90 µm Membrane
● Partial electrode areas
● ~ 200 nm downward curvature
(2 - 3 µm upward in 2G)
●Optical effects
Ram. Kottumakulal
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Characterization (II)
Sensor Interfacing
● Differential capacitive readout
● Charge-amplifier = op-amp + capacitive feedback
(charge-to-voltage converter)
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Characterization (III)
Capacitive Read-out, Acoustic Actuation
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Characterization (IV)
Frequency Response
Type A: Ls=75 Ws=10 Q=2.3
Type W: Ls=100, Ws=10, Q=1.8
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Characterization (V)
Sensitivity of 3G Sensors
●Measured with
Lock In Amplifier
1 Hz BW
●100 hairs in parallel
●Sensitivity: 2 mm/s
(⇒ 100 hairs, BW 1 kHz)
●Cricket: 30 µm/s
(single hair, BW ≈ 0.3 - 1 kHz)
Marcel Kolster
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Characterization (VI):
Directivity (3G Sensors)
(Ram. Kottumakulal et.al, Transducers 2009)
● Other modes
● Viscous coupling
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Adaptability

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Adaptability (I): ES Spring Softening
● Add bias Voltage U
(G. Krijnen et al, Nanotechnology, vol 17, pp. 84-89, 2006)
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● Transduction Theory
● On application of DC bias:
 Lower resonance frequency
 Higher sensitivity

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Adaptability (II): Increasing Sensitivity
Acoustic Actuation (2G Sensors)
● Lines ⇒ model
● Fitted for Udc=0
● κ /S0=0.0171 V-2 (fit)
● κ /S0=0.0167 V-2
(calculated)
● Trend predicted well
(J. Floris et al., Proc. Transducers 2007, pg 1267)
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Adaptability (III): Shifting fres
Electrostatic Actuation 2G sensors
● Fitted for Udc=0
● κ /S0=0.0160 V-2 (fit)
● κ /S0=0.0167 V-2
(calculated)
(J. Floris et al., Proc. Transducers 2007, pg 1267)
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Adaptability (IV): Response Curve Predictions
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Happy with Nonlinearity & Noise
Electro Mechanical Signal Processing

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Happy with noise: Stochastic Resonance
● Threshold system
 model for flow detection in grayfish
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Stochastic Resonance in Cricket Perception
● Low signal level:
 Transinformationrate ↑
with noise ↑
 High noise power
⇒ saturation
● High signal level:
 Deterioration
with noise ↓
Levin, Miller, Nat. 380, 1996, p165
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Negative Spring-Stiffness & Amplification (1)
● Lateral line sensors &
hair-cells in the mammalian
cochlea have extended
dynamic range up to 50 dB
● Combination of two (sets of) hairs with
connected tip-links
● Opening of ion-channels adds force to
the moving cilia
● Net effect: negative spring stiffness
(Hundspeth, C. R. Biologies 328 (2005) 155–162)
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Happy with Noise: Stochastic Resonance
● Double potential energy well
● Skewed energy function lowers
threshold unidirectionally
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Negative Spring-Stiffness & Amplification (2)
● DC-biasing scheme
causes instability before
negative spring-stiffness is
obtained
● Solution: displaced comb-like structure
● No pull-in
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From MEMS to Biology

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Good hints,
but difficult to interpret…
….back to technology…
How Are Cricket Arrays Optimised?
● Aerodynamics
● Viscous Coupling
● Directivity
T. Steinmann, J. Casas
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From MEMS to Biology: Viscous Coupling
● Viscous coupling between hairs
 hard to do on crickets
 suggested for
hair-distances < 10·Dh
 special structures
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f=80 Hz f=160 Hz f=320 Hz
MEMS High density Arrays
● Interaction between flow and hairs ⇒ viscous coupling
● Hairs-sensors need to be judiciously spaced
● MEMS helps biologist
T. Steinmann, J. Casas
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Biomimetic Mechanosensing:
Dream or Reality

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MEMS Hairsensors: Dream or Reality?
● Mechanical performance comparable!
 optimised damping (impedance matching)
 small rotational stiffness / moment of inertia
● Mechanical robustness:
 Use non-brittle materials (polymers, metals)
 Prevent rotational / vertical pull-in
● Cross-sensitivity
 Gravitation & Inertial (cross-) effects
 Limitation to the shapes we can make
● Capacitive read-out inferior to neural signal acquisition
 fF changes on pF parasitics: prone to noise / interference
 How to get digital spike like signals (electro-mechanically)
● Sensor arrays:
 Which spatio-temporal signatures?
 Efficient (low-power) signature recognition?
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Cricket Signal Processing
Neurons provide:
●efficient
●fast
●Parallel
●robust

information
collection
transport &
processing
How do we get this?
(Insausti, Lazzari et al. Submitted to Journal of Morphology)
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Distributed Arrays (Dreams)
10 Hz
20 Hz
30 Hz
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Distributed Arrays (Dreams)
● Sensor Array
● Single hair interfacing
● Spatio-temporal flow distribution
● E.g. spatio-frequency distribution 10 Hz
20 Hz
30 Hz
Courtesy of J. Casas et. al.
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Distributed/Dispersed Sensing
● Arrays of hairs with single hair interfacing
● Detection of
spatio-temporal
patterns
(signatures)
● First results
are promising
(R. Wiegerink et al., Proc. IEEE Sensors 2007, pg 1073)
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• 1 × 4 array
• {1.05 - 1.10 - 1.15 - 1.20} MHz
• Single charge amplifier
• 75 Hz air flow source
(Results of Ahmad Dagamseh)
FDM Implementation

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Successful simultaneous measurement from (1x4) array
FDM Implementation
(Ahmad Dagamseh et. al, accepted for publication in Sensors & Actuators)

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Bio-Inspired Measurements

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Work by the Chang Liu Group, Univ. Ill.
● Lateral line
● Hot Wire Anemometers
● Piezo resistive sensors
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(Journal on Applied Signal Processing, Volume 2006, Article ID 76593, Pages 1–8)

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Source localization
Lateral Line System

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at : different source-lateral line distance
Virtual lateral line re-constructs dipole field
at : different sphere diameter
different vibration frequencies
Virtual Lateral Line System

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Lateral line – source distance is the only effective parameter
Virtual Lateral Line System

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Bio-Inspired Measurement
2 x RealTime speed
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Conclusions

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Conclusions
● We have
 successfully fabricated SU-8 sensory hairs up to 1 mm
 shown capacitive hair sensor arrays with good sensitivity
 demonstrated adaptability of sensitivity
 interfacing of arrays-sensors
 shown bioinspired sensing scheme (virtual lateral line)
● We like to
 Further improve the sensors by hairs, lower spring-stiffnesses
 Reduce influence of parasitic capacitances, improve electronics
● We can
 Adapt sensors by DC biasing
 Use parametric Amplification, Stochastic Resonance
● We should borrow from nature
 Soft materials
 Dendrites and neurons
Or be Real Smart
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Acknowledgements
Dominique Altpeter, John van Baar, Erwin Berenschot, Rick de Boer, Meint de Boer,
Christiaan Bruininck, Ahmad Dagamseh, Marcel Dijkstra, Michiel van Dijk, Harmen
Droogendijk Arjan Floris, Bjorn Hagendoorn, Nima Izadi, Theo Lammerink , Marcel
Kolster, Winfred Kuipers, Claudio Lazzaro, Remco Sanders, Satya Shankar Siripurapu,
Thomas Steinmann, Vitaly Svetovoy, R. Jaganatharaja (Ram), Bas Verlaat, Remco
Wiegerink, Henk van Wolferen.
The EU for funding the Cicada/Cilia projects
NWO for the BioEARS Vici grant
The Cicada/Cilia teams
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Act Talk - Gijs Krijnen: "Imitating Cricket mechanosensing: dream or reality?"

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Act Talk - Gijs Krijnen: "Imitating Cricket mechanosensing: dream or reality?"