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



Biomimetic Sensory Research Drivers●Principles and versatility●Performance

SensitivityDirectionalityAccuracyDynamic range (Reduction) of cross-sensitivity

●Power consumption / efficiency●Size / density of sensors●Robustness● (Examples for) Multi-modal sensory integration



The Chase SceneCourtesy J. Casas, IRBI, Univ. de Tours

Wolfspider chasing a wood-cricket



Cricket Hair Sensors

Acheta Domestica

Photograph courtesy of J. Casas et. al.



Cricket Sensory Hairs Make Sense (I)

(T. Shimozawa et al, in Sensors and Sensing in Bio. and Eng. 2003)



Cricket Sensory Hairs Make Sense (II)

(Adapted from Raangs, 2005)

●Sound:Pressure: P(t,r)Particle velocity: U(t,r)

●U prevailsNear 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)



Flow Mediated PerceptionCourtesy J. Casas, IRBI, Univ. de Tours

Movie of Fluid MovementParticle Image Velocimetry

Flow Field Extraction



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



MEMS (& What it is not)



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

Have a partyFr

om 2

- 12

wee

ks

Failure /

Incompatibility



Photolithography

●Spin photoresist

12

●Etch through holesand remove photoresist

● Illuminate through mask

●Develop image



A

B

C

D

E

Surface micromachining

●Sacrificial layer

13

●Patterning

●Structural layer

●Patterning

●Selective etching & sacrificial release



MEMS●No free-form technology (2.5D)

●Stiff materials (4 – 300 GPa)

●Limited range of dimensions 100 nm – 1 cm in plane10 nm – 10 µm out of plane

●Limitation due to stress

●Scaling behaviourSurface forces dominate body forcesNo quantum physics (no nano)

Full 3D

Range of (flexible) materials

Fewer range limitations 0.1 nm – 30 m

?? flexible materials

Scaling behaviourScale dependent

all physics

BIOLOGY


12/02/2010 Imitating cricket mechanosensing: dream or reality? 15

Artificial Flow-Sensitive Hair Sensors



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.



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



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



Sensor Principle & Optimization



Sensor considerations●Small displacements (nm-scale):

Requires high sensitivity

●Array application: Low power consumption ⇒ Generator type sensor

Small number of interconnections

Capacitivedifferential

Piezo-Electric

PiezoResistive

ThermoResistive

Power-consumption + + + + +/- - -

Interconnections # 2, 3 2 2/4 2/4

Thermal Xtalk + + + + - - -

Technology +/- - +/- +

Parasitics - - - - +/- +



Conceptual Sensor Structure

(M. Dijkstra et al., J. Micromech. Microeng. 15 (2005) S132–S138)



●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)



Model (I): Mechanical Response

(T. Shimozawa, et al., J. Comp. Physiology A 183, 171-186, 1998)



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)



●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




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)



Model (V): Capacitive Sensing●Capacitance change per unit of rotation:


27

●Rectangular membrane with curvature up to δ:

●Optimization Long membrane Small gapNo curvature (stress)



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



Sensor Fabrication & Characterization



Christiaan Bruinink, MEMS 2009

Fabrication of 3nd Generation Sensors

Optical microscope image



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)



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



Characterization (II)Sensor Interfacing

●Differential capacitive readout●Charge-amplifier = op-amp + capacitive feedback

(charge-to-voltage converter)



Characterization (III)Capacitive Read-out, Acoustic Actuation



Characterization (IV) Frequency Response

Type A: Ls=75 Ws=10 Q=2.3

Type W: Ls=100, Ws=10, Q=1.8



Characterization (V)Sensitivity of 3G Sensors

●Measured with Lock In Amplifier1 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



Characterization (VI): Directivity (3G Sensors)

(Ram. Kottumakulal et.al, Transducers 2009)

●Other modes●Viscous coupling



Adaptability



Adaptability (I): ES Spring Softening

● Add bias Voltage U

(G. Krijnen et al, Nanotechnology, vol 17, pp. 84-89, 2006)

39

● Transduction Theory

● On application of DC bias: Lower resonance frequencyHigher sensitivity



Adaptability (II): Increasing SensitivityAcoustic 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)



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)



Adaptability (IV): Response Curve Predictions



Happy with Nonlinearity & NoiseElectro Mechanical Signal Processing



Happy with noise: Stochastic Resonance●Threshold system

model for flow detection in grayfish



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



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 withconnected tip-links

●Opening of ion-channels adds force tothe moving cilia

●Net effect: negative spring stiffness

(Hundspeth, C. R. Biologies 328 (2005) 155–162)



Happy with Noise: Stochastic Resonance

●Double potential energy well●Skewed energy function lowers

threshold unidirectionally



Negative Spring-Stiffness & Amplification (2)●DC-biasing scheme

causes instability before negative spring-stiffness is obtained

●Solution: displaced comb-like structure

●No pull-in



From MEMS to Biology



Good hints,but difficult to interpret…….back to technology…

How Are Cricket Arrays Optimised?

●Aerodynamics●Viscous Coupling●Directivity

T. Steinmann, J. Casas



From MEMS to Biology: Viscous Coupling●Viscous coupling between hairs

hard to do on crickets suggested for

hair-distances < 10·Dh special structures



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



Biomimetic Mechanosensing:Dream or Reality



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?



Cricket Signal ProcessingNeurons provide:●efficient●fast●Parallel●robust information

collectiontransport &processing

How do we get this?

(Insausti, Lazzari et al. Submitted to Journal of Morphology)



Distributed Arrays (Dreams)

10 Hz

20 Hz

30 Hz



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.



Distributed/Dispersed Sensing

●Arrays of hairs with single hair interfacing

●Detection ofspatio-temporalpatterns (signatures)

●First resultsare promising

(R. Wiegerink et al., Proc. IEEE Sensors 2007, pg 1073)



• 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



Successful simultaneous measurement from (1x4) array

FDM Implementation

(Ahmad Dagamseh et. al, accepted for publication in Sensors & Actuators)



Bio-Inspired Measurements



Work by the Chang Liu Group, Univ. Ill.

●Lateral line●Hot Wire Anemometers●Piezo resistive sensors

61

(Journal on Applied Signal Processing, Volume 2006, Article ID 76593, Pages 1–8)



Source localization

(Results of Ahmad Dagamseh)

Lateral Line System



at : different source-lateral line distance

Virtual lateral line re-constructs dipole field

at : different sphere diameter

different vibration frequencies


Virtual Lateral Line System



Lateral line – source distance is the only effective parameter


Virtual Lateral Line System



Bio-Inspired Measurement

2 x RealTime speed65



Conclusions



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 toFurther improve the sensors by hairs, lower spring-stiffnessesReduce influence of parasitic capacitances, improve electronics

●We canAdapt sensors by DC biasingUse parametric Amplification, Stochastic Resonance

●We should borrow from nature Soft materialsDendrites and neurons

Or be Real Smart



AcknowledgementsDominique 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


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

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