Learning from Nature:
Biologically-inspired Sensors
aksono
Learning from Nature: Biologically-inspired Sensors
INVITATION
You are cordially invited to attend the public defence of myPhD dissertation and the reception afterwards
on
Wednesday, 5 November 2008 at 10.00 hours
in the Senaatzaal of the Aula of Delft University of Technology,
Mekelweg 5, Delft
Prior to the defence, there will be a short presentation
about the PhD research, which will be starting at 09.30 hours
Biologically-inspired
Sensors
Learning from Nature:
Biologically-inspired Sensors
Learning from Nature:
Biologically-inspred Sensors
PROEFSCHRIFT
ter verkrijging van de graad van doctor aan de Technische Universiteit Delft,
op gezag van de Rector Magnificus Prof. dr. ir. J. T. Fokkema, voorzitter van het College voor Promoties,
in het openbaar te verdedigen op woensdag 5 november 2008 om 10:00 uur door
Dedy Hermawan Bagus WICAKSONO
Master of Engineering in Biological Information Engineering, Tokyo Institute of Technology, Japan
Samenstelling promotiecommissie:
Rector Magnificus, voorzitter
Prof. dr. P.J. French, Technische Universiteit Delft, promotor
Prof. dr. ir. G.C.M. Meijer, Technische Universiteit Delft
Prof. dr. J.F.V. Vincent, Bath University
Prof. dr. ir. G.J.M. Krijnen, Universiteit Twente
Prof. dr. rer. nat. G. Wachutka, Technische Universität München
Prof. dr. A. van Keulen, Technische Universiteit Delft
Dr. ir. J.F.L. Goosen, Technische Universiteit Delft
This research was supported by the Technology Foundation STW, applied science division of NWO and the technology programme of the Ministry of Economic Affairs. Part of this research (Chapter 5) was also supported by Toyota Motor Europe.
ISBN: 978-90-813316-3-0
Keywords: MEMS, biomimetic sensor, strain sensor, gyroscope, infrared sensor, biomimetics, bionics
Copyright © 2008 by D. H. B. Wicaksono Email: dedy.wicaksono@gmail.com
All rights reserved. No part of the material protected by this copyright notice may be reproduced or utilized in any form or by any means, electronic, or mechanical, including photocopying, recording, or by any information storage and retrieval system, without written premission from the author.
Dedicated for M.S. Nazim, M.S. Hisham, S. Sharief, and all my teachers and guru-s
For my wife, Ida Fauziah
For my daughters, Humayra, Nurfitri, and Labibah In the memory of my mother
1. Introduction ...
11.1 Biomimetics: Biologically-inspired Engineering ... 1
1.2 Evolution in Sensor Development ... 5
1.3 Motivation and objectives ... 7
1.4 Organisation of the Thesis ... 7
1.5 References ... 8
2. Biological Sensors and Biomimetic Sensors ...
112.1 Introduction ... 11
2.2 Biological Mechanical Sensors ... 12
2.2.1 Flow and Vibration Sensor ... 12
2.2.1.1 Insect’s Hair ... 12
2.2.1.2 Twente University’s Artificial Insect Hair for Wind Flow Sensor ... 13
2.2.1.3 Fish’s Lateral Line and Neuromast ... 14
2.2.1.4 University of Illinois at Urbana Champaign’s Artificial Fish Hair for Liquid flow sensor ... 15
2.2.2 Strain and Proprioception Sensors ... 15
2.2.2.1 Insect’s Campaniform Sensillum ... 15
2.2.2.2 Slit Sensilla‘of the Arachnids ... 16
2.2.3 Fly’s Gyroscope: the Haltere ... 19
2.2.4 Tactile Sensor ... 19
2.2.4.1 Insect’s Bristle ... 20
2.2.4.2 Spider’s Hair Sensilla ... 21
2.2.4.3 Lobster’s Antenna and the hooded sensilla ... 22
2.2.4.4 NorthWestern University’s Artificial Lobster’s Hair Fan organ ... 23
2.2.5 Acoustic Sensors ... 24
2.2.5.1 The Stereocilia ... 24
2.2.5.2 Johns Hopkins University’s Biomimetic Acoustic Microsystems ... 27
2.2.5.3 State University of New York’s Biomimetic Directional Microphone Diaphragm ... 27
Table of Contents
2.3 Biological Electric Field Sensor ... 30
2.3.1 Passive Electrosense System of Sharks and Paddle Fish ... 30
2.3.2 Active Electrolocation System of Weakly Electric Fish ... 32
2.3.3 University of Illinois at Urbana Champaign’s Biorobotic Electrosensory System ... 34
2.4 Biological Radiation Sensor ... 34
2.4.1 Visual Radiation Sensor: Insect’s Optomotor Eyes ... 35
2.4.2 Biological Infrared (IR) Sensor ... 36
2.4.2.1 Snake’s IR sensor ... 36
2.4.2.2 Vampire Bats’ IR sensor ... 38
2.4.2.3 Beetle’s IR sensor ... 38
2.4.2.4 Blood-sucking Bugs’ IR sensor ... 39
2.5 Conclusion ... 39
2.6 References ... 40
3. Biomimetic Strain Sensor ...
473.1 Introduction ... 47
3.2 Inspiration from Insect’s Strain Sensor: The Campaniform Sensillum 49
3.3 Design of Si-based Bio-inspired Microstructure for Strain Sensing Application ... 55
3.3.1 Blind-Hole Structure ... 55
3.3.2 Membrane-in-Recess (MiR) Structure ... 56
3.3.3 Dome-shape Membrane Structure ... 57
3.3.4 Transducer Design ... 57
3.4 Strain-Sensing Characteristics of Bio-inspired Membrane-in-Recess Microstructure ... 59
3.4.1 Analytical Modelling of Strain-Amplifying Property of Bioinspired Membrane-in-Recess (MiR) Microstructure ... 59
3.4.1.1 Structure Design ... 59
3.4.1.2 The Analytical Modelling ... 60
3.4.1.3 Analytical Calculation Results ... 66
3.4.2 Finite Element Modelling of Bio-inspired Structures ... 71
3.4.3 Discussion of Simulation Results ... 80
3.4.3.2 Directional Sensitivity ... 81
3.4.3.3 Influence of Structural Parameters Variation on Sensing Characteristics ... 82
3.5 Strain Amplification of Biomimetic Dome-shape Membrane ... 83
3.5.1 Simulation Design and Models ... 83
3.6 Fabrication ... 89
3.6.1 General Process Steps ... 89
3.6.2 Topography of the etching ... 94
3.6.2.1 Process Parameter Investigation ... 95
3.6.2.2 Top-Geometrical-Shape Investigation ... 96
3.6.2.3 Size-dependent Etch Rate Investigation ... 98
3.6.3 Lithography in the recess ... 100
3.6.3.1 Out-of-focus Lithography ... 101
3.6.3.2 Angled-Side Wall Etching ... 105
3.6.3.3 Photoresist patterning inside the recess ... 105
3.6.4 Dome-shape Fabrication Attempt ... 107
3.6.4.1 Dome-shape Membrane Fabrication with PSG Outgassing ... 107
3.6.4.2 Dome-shape Membrane Fabrication with two steps KOH etching ... 110
3.7 Membrane Characterisation ... 115
3.7.1 Optical Characterisation ... 115
3.7.1.1 The Setup ... 115
3.7.1.2 Observation Under Stress ... 117
3.7.1.3 Further Image Processing ... 118
3.7.1.4 Analysis and Interpretation of the Processed Images .... 122
3.7.2 White Light Interferometry Characterisation ... 125
3.7.2.1 WLI Characterisation of Fabrication Results without Loading ... 126
3.7.2.2 WLI Characterisation of Fabrication Results with Loading ... 129
3.8 Towards Piezoresistive Strain Sensor ... 133
3.9 Discussion ... 134
3.11 References ... 135
4. Biomimetic Gyroscope ...
1394.1 Introduction ... 139
4.2 Inspiration from Fly’s Gyroscope Organ: the Haltere ... 141
4.2.1 Mechanoreceptors of Flies ... 141
4.2.2 “Gyroscopes” of Flies ... 143
4.2.2.1 Morphology of Halteres ... 143
4.2.2.2 Neuromotor Control System of Fly’s Flight Mechanism ... 146
4.2.2.3 Theoretical Modelling of Haltere Force ... 147
4.2.3 Summary of Fly’s Gyroscope Organ ... 148
4.3 Some Background on MEMS Vibratory Gyroscope ... 149
4.3.1 Brief History of Gyroscope ... 150
4.3.2 Physical Principles of General Vibratory Gyroscopes ... 151
4.3.2.1 Kinematics of Rotation ... 151
4.3.2.2 Principle of Vibratory Gyroscope ... 153
4.4 Design of a Si-based Bioinspired Gyroscope for Angular Rate Sensing ... 156
4.4.1 Design Strategy ... 156
4.4.2 Structure Design and Principle of Operation ... 157
4.4.3 Structure Design Investigation ... 158
4.4.3.1 Coriolis Force Amplification ... 158
4.4.3.2 Geometrical Variation of Hammer-shaped Pillar ... 161
4.4.3.3 Geometrical Variation of The Suspension Membrane .. 168
4.4.3.4 Discussion on the Design Simulation Results ... 173
4.4.4 Material Properties ... 173
4.5 Structure Modelling of Si-based Bioinspired Gyroscope for Angular Rate Sensing ... 176
4.5.1 Theoretical Modelling ... 177
4.5.2 General Equations of Swinging Motion ... 178
4.5.2.1 Swinging-Mass Kinetic Energy ... 179
4.5.2.2 Strain Energy ... 180
4.5.2.4 Equations of Swinging Motion ... 180
4.5.3 Lumped-Parameter Modelling ... 182
4.5.3.1 Elastic Constants of Suspension Beams ... 182
4.5.3.2 Squeeze-Film Damping ... 183
4.5.3.3 Electrostatic Actuation ... 185
4.5.3.4 Capacitance Computation ... 187
4.5.4 Numerical Modelling ... 190
4.5.4.1 Resonance Frequency Modelling ... 190
4.5.4.2 Performance Estimation ... 194
4.6 Discussion on Design and Modelling ... 197
4.7 Experimental ... 198
4.7.1 The Surface-Micromachining Process ... 199
4.7.2 Experiment with SU-8 Polymers ... 203
4.7.2.1 Processing Guidelines ... 203
4.7.2.2 Results and Discussion ... 208
4.7.3 Prototype Fabrication Results ... 210
4.7.4 Summary on Experimental Work ... 210
4.8 Discussion ... 211
4.8.1 Future Work ... 213
4.8.1.1 Experimental Evaluation of the Prototype Designs ... 213
4.8.1.2 Improved Fabrication Process for SU-8 Polymers ... 214
4.8.1.3 Compensation of Fabrication Imperfections ... 216
4.8.1.4 Further Improvement of Designs ... 216
4.9 Conclusions ... 216
4.10References ... 217
5. Biomimetic Far Infrared Sensor ...
2235.1 Introduction ... 223
5.2 Background of Infrared Sensing ... 224
5.2.1 Photodetector ... 225
5.2.2 Thermal detector ... 226
5.2.2.1 Resistive Bolometer ... 228
5.2.2.2 Pyroelectric detectors, and ferroelectric bolometers ... 229
5.2.2.4 Golay Cell ... 230
5.2.2.5 Bimaterial layers ... 232
5.2.3 Figure of Merit and Important IR Sensing Parameters ... 234
5.2.3.1 Responsivity R ... 234
5.2.3.2 Noise ... 235
5.2.3.3 D, Detectivity, and D*, Specific Detectivity ... 235
5.2.3.4 NETD, Noise Equivalent Temperature Difference ... 236
5.2.3.5 Other Figures of Merit ... 236
5.3 Inspiration from Beetle’s Infrared Sensor ... 236
5.4 Comparison and Benchmarking of Infrared Sensors ... 239
5.5 Design of a Si-based Bio-inspired Far Infrared Sensor for Night Vision Application ... 243
5.5.1 Photo to Thermal Transducer Subsystem ... 247
5.5.1.1 Material and device possibilities for Part 1 (the concentrator) ... 249
5.5.1.2 Material and device possibilities for Part 2 (the absorber) ... 250
5.5.1.3 Optional material and device possibilities for filter ... 251
5.5.2 Thermal to Mechanical Transducer Subsystem ... 252
5.5.3 Mechanical to Electrical Transducer Subsystem ... 256
5.6 Analytical Modelling and Numerical Simulation ... 258
5.6.1 Heat Power Emitted by Humans ... 258
5.6.2 IR Optics ... 260
5.6.3 Radiation to Thermal Transducer Subsystem (Optical Absorber) ... 264
5.6.3.1 Theory of absorption in dielectrics and thin metal films ... 264
5.6.3.2 Modelling the optical constants out of reflection or transmission measurement ... 267
5.6.3.3 Calculation of absorption of a SiN membrane ... 275
5.6.4 Thermal Modelling of the Sensor Structure ... 275
5.6.4.1 Heat conduction theory ... 277
5.6.4.2 Simulation of heat conduction ... 281
5.6.4.4 More Complex Thermal Modelling ... 288
5.6.5 Thermal to Mechanical Transducer Subsystem ... 291
5.6.6 Mechanical to Electrical Transducer Subsystem ... 291
5.6.6.1 Mechanical Amplifier ... 291
5.6.6.2 Piezoresistive Transducer ... 292
5.6.6.3 Tunneling Transducer ... 293
5.7 Experimental Study of Optical Absorptivity and Heat Conduction of SiRN membrane for the Biomimetic Far Infrared Sensor ... 295
5.7.1 Reflection and Transmission Measurement of SiN ... 295
5.7.1.1 Samples used for reflection and transmission measurements ... 296
5.7.1.2 Transmission and Reflection measurement using a monochromator ... 296
5.7.1.3 Transmission measurement using a FTIR spectrometer 299 5.7.1.4 30° reflection measurement using a FTIR spectrometer 300 5.7.2 Fabrication of Test Structure ... 300
5.7.2.1 High TCR resistors ... 300
5.7.2.2 Fabrication Procedure ... 303
5.7.2.3 Mask Design ... 304
5.7.2.4 Results of Fabrication ... 306
5.7.3 Measurement of the Test Structure ... 307
5.7.3.1 Calibration of resistors in oven ... 308
5.7.3.2 Measurement setup for resistor measurements with light irradiation ... 310
5.7.3.3 Measurement Results ... 312
5.7.3.4 Error estimation of the measurements ... 314
5.7.4 Discussion on Bolometer Test Structure ... 316
5.8 Discussion ... 317
5.9 Conclusions ... 319
5.10 References ... 320
6. Conclusions and Future Work ...
3296.1 Conclusions ... 329
6.1.2 A Biomimetic Gyroscope ... 332
6.1.3 A Biomimetic Far-Infrared Sensor ... 334
6.1.4 General Conclusion ... 336
6.2 Future Work ... 337
6.2.1 Practical Implementation ... 337
6.2.2 Elliptical Topography Stress/Strain Sensor ... 338
6.2.3 Array Stress-/Strain- Sensor ... 338
6.2.4 Optimization of the Biomimetic Hammer-head-Pillar shape Gyroscope ... 339
6.2.5 Tunneling Far-Infrared Sensor ... 339
6.3 References ... 339
Appendix 3-A Bending Force ...
3413-A.1 Single-Point Bending Load from Backside ... 341
3-A.2 Three-Point Bending Load ... 342
Appendix 3-B Matlab Programs WLI Image
Processing ...
3433-B.1 M-file to Import ASCII file of Data (Modified from a Program Courtesy of Ir. Alex de Kraker) ... 343
Appendix 4-A Moment of Inertia Calculation
... 347Appendix 4-B Size and Resonance Frequency ...
349Appendix 4-C SU-8 2075 Experimental Data ...
355Appendix 5-A Reflection in a Multilayer System
... 361Appendix 5-B Mask Design ...
365Appendix 5-C Measurement Setup ...
369Appendix 5-D Matlab Programs Far Infrared
Sensor ...
3715-D.1Matlab File for Lorentz Oscillator Fit ... 371
5-D.2Matlab Program for Getting Thermal Time Constant ... 374
5-D.3Matlab program for calculating TCR values ... 376
Summary ...
379Samenvatting
... 383Ringkasan ...
387Acknowledgements
... 391Publications
... 3931
Introduction
11.1 Biomimetics: Biologically-inspired Engineering
Nature has long been a source of inspiration for scientists, engineers, and artists in creating artificial structures and inventions. From flying machines and airplane construction by Leonardo da Vinci and the Wright brothers, the Eiffel tower support structure by Gustav Eiffel [1.1], the recently-invented Grä
tzel solar cell by Michael Grä
tzel [1.2], to the genetic algorithm used in optimisation [1.3], all are just few examples of how men have tried to adapt the ideas found in nature to solve their everyday problem [1.4]. Some of them are consciously taken from nature, while others are merely coincidently similar to nature’s solutions to the same engineering problem.Here, the terms biomimetics, bionics, biognosis, and biomimicry came into birth. These words can be defined according to Wikipedia with the following definition [1.5]:
“the application of methods and systems found in nature to the study and design of engineering systems and modern technology“
There are many examples in the engineering world where solutions for particular problems resemble those found in the biological world. And the engineers might find these solutions consciously by observing similar
sytems in nature, or either by pure coincidence, by which they found optimised solutions similar to those of the nature’s biological world.
Among the fields whose engineers have been taking inspirations from nature intensively is in the field of architectural engineering. The construction of the Eiffel tower [1.1] has already been mentioned, whose curvy structure resembles the structure of the head of femur (tigh bone). Eiffel may have calculated the structure as a support against wind pressure. His design results in similar structure as found in the skeleton of animals [1.6]. Many other contemporary architects are then inspired by structures from nature [1.7]. Among these architects are the Spanish engineer-architect Santiago Calatrava [1.8], who built his stuctures’ recurrent motif inspired by bird wings; then Peter Cook and Colin Fournier who architect the Kunsthaus in Graz, Austria [1.7]. An interesting bio-inspired building is the new Singapore Arts Center (Figure 1-1), designed by architects Michael Wilford and others, and helped by Atelier One structural engineers. The building’s
curved surface is covered with V-shaped lozenges of aluminium, angled so as to exclude direct sunlight. Each aluminium panel can be moved using actuators that are triggered by a photoelectric cell [1.7]. The idea of these panels come from the polar bear whose fur is used to regulate the animal’s temperature. The fur that appear to be white is made up of transparent tubular bristles. When these bristles are raised, more light penetrates through to the bear’s skin, and allow the animal to warm itself under the sun. However, when it gets cooler, the bristles lie flatter, to trap an insulating layer of air, while at the same time making it to appear white for camouflage. Each of these hairs are operated by a muscle, and the action of
Fig. 1-1 Singapore Arts Center, designed by architects Michael Wilford and others. Courtesy of Atelier Ten.
Biomimetics: Biologically-inspired Engineering
that muscle is triggered by a nerve signal. Similarly, the V-shape aluminium lozenge is triggered by the signal coming from the photocell [1.7].
Another engineering field that benefits so much from taking its lesson from nature is materials engineering. The biological world is full of examples of materials that have found an optimum solution between lightness and strength. Kevlar, for example, is a very light, yet very strong synthetic fibre, which is inspired by silk fibre [1.4]. Some tough ceramics were also invented by mimicking mother-of-pearl, nacre [1.9]. Fibre orientation in wood has also inspired the development of tough composites [1.10]. New thin materials are also inspired from the mechanical design of skin [1.11], while the folding structure of leaves of monocotyledons inspire new strategy for the development of structural stiffness [1.12]. Vincent in [1.13] described several other smart structures inspired from different biological systems: e.g. from Echinoderms, Holothuria, the spasmoneme (the contractile stalk of certain single-celled organisms), the elastin, the worm skin geometry, the Venus fly trap, the nematocyst capsule, spinning cellulose, etc. All these give inspiration for the development of new actuator materials, self-repairing materials, and light but strong materials.
Surface engineering can also learn a lot from systems found in the biological world. A drag reduction system can be designed based on the dermal riblets found on sharkskin (Figure 1-2 [1.14], [1.15], [1.16]). Such surface structures have been applied to reduce surface drag of an airplanes’s wing, boat hulls, to the latest swimsuit (e.g. Speedo swimsuit). A surface Fig. 1-2 Dermal riblets found on shark skin, an inspiration for drag
reduction system. Left picture, courtesy of field stream magazine. Right picture courtesy of Center of Turbulence Research, Stanford University.
that is self-cleaning can also be engineered based on the inspiration from the lotus leaf [1.17].
A higher level of biomimicry after mimicking the material structure level in the biological world, is to mimic at the device level. Here, nature also provides an abundant engineering solution for scientist and engineer to learn and apply. Deployable structures benefit from the inspiration of the unfolding leaves of Hornbeam (Carpinus betulus) and Beech (Fagus sylvaticus). This and other deployable structures found in nature were explained by Vincent in [1.18]. Wood wasp ovipositor also gives inspiration for a new drilling mechanism [1.19].
Hence, the key to find a solution for an engineering problem is to find the same system(s) within biological world that have similar functionality as the engineering problems to be solved. A systematic way of doing biomimetics is to apply a method such as TRIZ, a Russian acronym for Teoriya Resheniya Izobretatelskikh Zadatch, or the Theory of inventive problem solving. This method was invented by Genrich Altshuller and his colleagues in 1946 ([1.20]-[1.23]). TRIZ was invented to classify inventions based on their functionality. Here, an engineering problems can be solved by searching a database of engineering inventive solutions from other fields applied to solve similar problem. The working mechanism of TRIZ is illustrated in Figure 1-3. Here, TRIZ relies on finding a technical contradiction within the problem, and removing it. Altshuller’s interpretation of the process of inventing is that “Inventing is the removal of a technical contradiction with the help of of certain principles”[1.20].
Vincent and Mann [1.22] argued that the effectiveness of TRIZ as an engineering problem solving tools can be improved and enriched if solutions from the biological world were also incorporated in the functionality database. Systems found in biological worlds are products of billions of years R&D, thus different creative solutions from the biological world might be found for particular problems with specific functionality. They also described in [1.22] four tools used in TRIZ: function, contradictions, ideality, and maximization of resource usage.
In the next section, it will be explained how biomimetic approach can be applied also for sensor development, after mentioning how the sensors have evolved in the past years.
Evolution in Sensor Development
1.2 Evolution in Sensor Development
Sensors have been an important part of instrumentation and control systems as well as in modern information systems. Sensors can be considered as the front-end of any information or instrumentation systems. It is sensors (system) that channel real-world information into the artificial human-made information manipulation system. And this real-word information can be of physical, chemical, or biological form of energy. Sensors play an important role in converting these signals into an electrical signal readable by microprocessor for further processing.
A modern (smart) sensor system can thus be considered as a tryptich information processing block [1.24], consisting of Identification, Modifier, and Presentation units, as illustrated in Figure 1-4. The identification unit usually is a transducer or a combination of transducers that convert(s) the original signal energy form into another form of signal energy. The modifier can be a collection of electronic signal-processing circuitry integrated within the same chip or within the same packaging. The presentation unit can be a
Fig. 1-3 The TRIZ method of inventive problem solving. Adapted from [1.20]
module that makes the signal into a form such that it is readable by another module in the next chain of the information flow.
Sensors or instruments in previous times were considerably bulky [1.25]. With the advent of microelectronics, however, there is a tendency of further integration of computation and information processing into many different new applications, e.g. machinery, personal computer, mobile telecommunications device, etc. These new devices and tools then demand new types of sensors that are smarter, have lower production cost, and lower power consumption. The same manufacturing technology being used in the microelectronic is also enabling the fabrication of such new types of sensors. Thus, the smart microsystem was born, and Silicon-based microelectromechanical system (MEMS) [1.26] integrated with Complementary-Metal- Oxide- Semiconductor (CMOS) electronics, is the emerging sensor technology to fill the sensing needs in the new emerging applications, as well as in complementing the needs in conventional applications.
Here, taking a biomimetic approach in the microsensor engineering world would be very useful. Many of these biological sensors are microsystems and even nanosystems. This emerging biomimetic approach can be seen among others from a recent publication [1.27] where biologists and sensor engineers met and tried to solve the same sensing problem using different approaches.
Fig. 1-4 Tryptich Information Processing model of a sensor, adapted from [1.24].
Motivation and objectives
1.3 Motivation and objectives
As has been mentioned at the end of the previous section, new miniaturised low-cost, low-power, microsensors are needed for new emerging consumer electronics applications. Here, the biological world, especially the animal kingdom, can provide a huge database of sensor engineering problems. Thus, a biomimetic approach of taking inspirations from this collection of biological sensors, in order to solve similar problems in the artificial man-made world, would be very interesting to pursue. By learning from natural biological sensors, we hope that novel sensors with better performance to cost ratio can be designed and implemented using existing technology to solve current and future sensing problems.
This thesis presents an attempt to study a few examples of biological physical sensors found in the animal kingdom, especially in insects. The first objective of learning and studying these sensors is to find out which sensing mechanisms are being utilised in those sensors. By knowing the working sensing mechanisms in those sensors, we would also like to know which structural parameters influence its sensing characteristics. In this study, the investigation will be limited only to mechanical structural parameters, keeping in mind that other parameters, e.g. neuron systems, may also be involved in the sensing process.
The second objective of this study, is to investigate the feasibility of implementing similar structures, hence biomimetics, using existing Silicon-based MEMS fabrication technology. In one or two cases, the sensing characteristics of the bio-inspired or biomimetic fabricated MEMS will also be investigated.
Three examples of biomimetic sensors will be elaborated in this thesis: biomimetic stress-/strain- sensor, biomimetic gyroscope, and biomimetic infrared sensor.
1.4 Organisation of the Thesis
The thesis is structured into six chapters. The first chapter contains the present introduction about the background, motivation, and objectives of the research presented in this thesis.
Chapter 2 presents different examples of biological sensors in the physical signal domains, as well as artificial engineered sensors inspired from those natural sensors. Different types of known sensors are presented according to its main working signal form: mechanical sensors (flow, vibration, force, angular rate, tactile, and acoustic), electromagnetic field sensors, and radiation sensors (visual and infrared).
Chapter 3 reports the development of biomimetic stress-strain sensor inspired from insect’s Campaniform sensillum. Aspects regarding the biological sensors inspiration source, design, modelling and simulation, fabrication, and experimental work, of this bio-inspired strain sensor will be explained.
Chapter 4 presents the development of a biomimetic gyroscope inspired by the fly’s haltere. The biological sensor’s working principle is explained, along with the working principle of a vibratory gyroscope. Then, the design, the modelling and simulation, the fabrication and experimental work is presented in detail.
Chapter 5 explains a feasibility study on the development of a biomimetic far infra-red (IR) sensor, inspired by a beetle’s infrared pit organ. The proposed design of a Si-based Bio-inspired Far Infrared MEMS Sensor is presented, followed by initial modelling of signal transduction flow. Experimental study of the optical and thermal properties of the IR absorbing membrane is described.
In the last chapter of the book, Chapter 6, the conclusions of the presented work are drawn and some aspects that need further completion and investigation are presented.
1.5 References
[1.1] http://en.wikipedia.org/wiki/Eiffel_Tower
[1.2] B. O’Regan, and M. Gr
ä
tzel, A low-cost, high-efficiency solar cell based on dye-sensitized colloidal TiO2 films, Nature, 1991, 353: 737-740[1.3] N.A. Barricelli, Symbiogenetic evolution processes realized by artifical methods, Methodos, 1957, 143-182.
[1.4] P. Ball, Life’s lesson in design, Nature, 2001, 409: 413-416 [1.5] http://en.wikipedia.org/wiki/Biomimicry
References
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[1.7] H. Aldersey-Williams, Towards biomimetic architecture, Nature Materials, 2004, 3: 277-279
[1.8] http://en.wikipedia.org/wiki/Santiago_Calatrava
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[1.11] J.F.V. Vincent, G. Jeronimidis, B.H.V. Topping, A.I. Khan, The mechanical Design of Skin – towards the Development of new Materials, 1991, 1-9
[1.12] M.J. King, J.F.V. Vincent, and W. Harris, Curling and folding of leaves of monocotyledons – a strategy for structural stiffness, New Zealand Journal of Botany, 1996, 34: 411-416
[1.13] J.F.V. Vincent, Smart by name, smart by nature, Smart Material Structure, 2000, 9: 255-259
[1.14] D.W. Berchert, G. Hoppe, and W.E. Reif, On the drag reduction of the shark skin, 1985 AIAA Shear Flow Control Conference, Boulder, CO., 1985.
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[1.16] D.W. Berchert, Surface for a wall subject to a turbulent flow sharing a main direction of flow, 1997, US Patent, No. 5971326
[1.17] C. Neinhuis, and W. Barthlott, Characterization and distribution of water-repellent, self-cleaning plant surface, Annals of Botany, 1997, 79: 667-677
[1.18] J.F.V. Vincent, Deployable Structures in Nature, in Deployable Structures, S. Pellegrino, ed., Springer-Verlag, Vienna, 2001, 1-14 [1.19] J.F.V. Vincent, and M.J. King, The mechanism of drilling by wood
wasp ovipositor, Biomimetics, 1996, 3: 187-201 [1.20] http://en.wikipedia.org/wiki/TRIZ
[1.21] G. Altshuller, The innovation algorithm, TRIZ, systematic innovation and technical creativity, 1999, Worcester, MA: Technical Innovation Center, Inc.
[1.22] J.F.V. Vincent and D.L. Mann, Systematic technology transfer from biology to engineering, Phil. Trans. R. Soc. Lond. A, 2002, 360: 159-173
[1.23] J.F.V. Vincent and D.Mann, TRIZ in Biology teaching, The TRIZ Journal, 2000, 1-10
[1.24] S. Middelhoek, S.A. Audet, and P.J. French, Silicon Sensors, Lecture Notes of ET4257, Delft University of Technology, 3-4.
[1.25] H.K.P. Neubert, Instrument Transducers, Clarendon Press, Oxford, 1975.
[1.26] Kurt Petersen, Silicon as Mechanical Material, Proceedings of IEEE, 1982, 70(5): 420-469
[1.27] F.G. Barth, J.A.C. Humphrey, and T.W. Secomb (eds.), Sensors and Sensing in Biology and Engineering, Springer-Verlag, Wien, 2003.
2
Biological Sensors and
Biomimetic Sensors
12.1
Introduction
This chapter contains some examples of physical sensors found in nature, especially in the animal kingdom, and to be more specific, in the insect’s world.
Examples of biological mechanical sensors are given in the next section, that come from different sensors found in insects, fish, and arthropods. These sensors function as transducers from a mechanical signal into an ionic signal, which is processed further by the nervous system. Among the mechanical sensors described are flow sensors, both for air and liquid; vibration sensors; strain and proprioception sensors; an angular rotation velocity sensor, i.e. gyroscope; mechanical touch or tactile sensor; then acoustical sensor.
In the subsequent section 2.3 and 2.4, more advanced physical sensor examples is given. This includes an electromagnetic field sensor found in fish, and some mechanically-related radiation sensors found in insects and snakes.
2.2
Biological Mechanical Sensors
2.2.1 Flow and Vibration Sensor
In this section several examples of biological sensors and biomimetic/ bio-inspired sensors dealing with flow and vibration signal are described. Flow and vibration sensors are grouped together here because of their similar basic structure, i.e. as hair structure. Hair or hair-like structures can be found in many living things: insects’ filliform sensillum ([2.1],[2.2]), fish’s neuromast [2.3], spider’s hair [2.4], and human’s ear [2.5]. Each of these hair-like sensors has similar yet rather different sensing function. The most similarity we can see is that they all sense the displacement or movement of the fluid surrounding them. This is understandable seeing the form of this sensor as long slender structure protruding from exocuticle, skin, or other epidermal layer. When the displacement sensed is of static nature or has low-frequency dynamics, then the hair-like structure functions as flow sensor, while vibration sensing hair structure detects a dynamic (higher frequency) displacement of the fluid in its surrounding.
In the following subsections, several examples of both natural hair sensor and artificial bioinspired hair sensors will be briefly explained.
2.2.1.1 Insect’s Hair
Trichoid sensilla (hair sensilla) are considered to be the fundamental structure from which all other sensilla in insects are developed [2.6]. There are two types of insect hair-type sensilla: (thick) bristles and the trichobothria [2.2]. The thick bristles are usually used for touch or tactile sensing while the more slender trichobothria respond to air current flow and vibration/sound. A typical illustration of insect’s hair sensor is shown in Figure 2-1, left, and its mechanical model in Figure 2-1, right.
From Figure 2-1, the sensillum is embedded in the epidermis, then the trichogen cell grows the hair, while the tormogen cell forms the socket of the hair [2.6]. A sensory neuron extends its ciliary dendrites to connect to the base of the hair which transmits the stimulus, while its axon extends to interneuron or directly to a motor neuron [2.6]. The dendritic membrane is attached to the cuticle via fine attachment fibers [2.2]. The hair-type sensilla are first-order levers, which transmit the deflection of the hair to the dendrite tip. The dendrite tip consists of microtubule structures which upon lateral
Biological Mechanical Sensors
deflection would open a series of ion channel on their walls [2.2]. The ions will be transmitted as neuron signal to the insect’s nervous system.
Mechanically, the hair structure is described in Fig. 2-1, right, as lever with torsional inertia, spring and damper. The dimensions of the hair, e.g. the length and diameter, determine its sensitivity and dynamic response towards air flow or vibration [2.8],[2.9].
2.2.1.2 Twente University’s Artificial Insect Hair for Wind Flow Sensor
A group, led by Prof. Gijs Krijnen in the Twente University, Enschede, Netherlands has developed a biomimetic hair for air flow sensor ([2.7],[2.10], and [2.11]). The same group has investigated the possibility of building an acoustic sensor based on an array of such air flow structures [2.12]. The sensor is implemented using Microelectromechanical Systems (MEMS) technology, using SU-8 polymer for the hair-like structure.
The project is conducted under CICADA project from the European Union, which explores the development of the biomimetic hair sensor based on cricket’s hair sensor [2.8],[2.9]. In nature, the cricket uses hair sensors for detecting the presence of predators by sensing the vibration of air
Fig. 2-1 Left: An illustration of insect hair sensor (adapted from [2.6]);
Right: its mechanical model (adapted from [2.7]), reproduced with kind permission from the authors of [2.7] and IOP Publishing Ltd., UK.
surrounding them [2.13]. The Twente group’s bioinspired sensor is illustrated in Figure 2-2.
2.2.1.3 Fish’s Lateral Line and Neuromast
Hair-like sensing structure can also be found in fish. Here, the sensory hairs of the hair cell are used to sense liquid flow, and pressure, as well as vibration caused by predator or prey in the vicinity of the fish. The hair structures are located inside a capsule structure called a cupula, which is located in the neuromast. An array of neuromasts is located along the lateral line. Thus, by measuring different deflection responses from different neuromasts, the fish knows the direction and the speed of its swimming. The lateral line and neuromast structure are illustrated in Figure 2-3.
Fig. 2-2 Twente University’s artificial bio-inspired hair sensor for air
flow and acoustic sensing; adapted from [2.7], reproduced with kind permission from the authors of [2.7] and IOP Publishing Ltd., UK.
Fig. 2-3 Lateral line of fish, where array of neuromasts is located (left),
Biological Mechanical Sensors
2.2.1.4 University of Illinois at Urbana Champaign’s Artificial Fish Hair for Liquid flow sensor
A group at University of Illinois at Urbana developed an artificial hair sensor inspired by fish ([2.14]-[2.18]). The structure is a vertical paddle attached to a cantilever structure in whose edge a piezoresistor transducer is deposited and patterned. The sensor has directional sensitivity to flow. The flow of air, ‘captured’ by the vertical paddle deflects the cantilever, and its stress will be transduced into electrical signal by the piezoresistor. The sensor is schematically illustrated in Figure 2-4.
2.2.2 Strain and Proprioception Sensors
The next type of mechanical sensors found in nature is strain sensor, which usually functions as proprioception devices. Proprioception is the sense of the relative position of neighbouring parts of the body. Unlike the other senses, proprioception is a distinct sensory modality that provides feedback solely on the status of the body internally [2.19]. Proprioception devices in nature usually measure the lateral strain (deflection) occuring in the dermal tissue. In this subsection, a short explanation about strain sensor found in insect and spider will be given.
2.2.2.1 Insect’s Campaniform Sensillum
As the name implies campaniform sensillum is a strain sensor that has the shape of a dome (Figure 2-5). The dome-shape membrane is located within Fig. 2-4 Artificial fish-inspired MEMS hair sensor, developed by UIUC
group, adapted from [2.14], reproduced with kind permission from the authors of [2.14] and IOP Publishing Ltd., UK.
a hole, which has a boundary structure called a “collar”. Incoming stress from the surrounding exocuticle will be concentrated in the hole area. This concentrated stress will then be transduced into horizontal and vertical displacement of the membrane which is suspended by a hinge-like structure. The resultant displacement of the membrane will squeeze the dendritic tip attached in the middle of the membrane, located inside a socket septum. The squeezing will result in the deflection of the microtubule array in the middle of the tip, releasing ions through its ion gates to the interneuron networks. Further details of the working mechanism of campaniform sensillum will be explored in chapter 3, Biomimetic Strain Sensor.
2.2.2.2 Slit Sensilla‘of the Arachnids
Slit organs are cuticular mechanoreceptors characteristic of the arachnid exoskeleton [2.20], [2.21]. SEM images of typical slit sense organs are shown in Fig. 2-6. As the name implies, slit organs look like a slit, usually occurring in an array, located in the intersegmental part of arachnid’s legs (Fig. 2-7). Slit sense organs are classified into three types according to the degrees of aggregation [2.20]. The first type is Isolated slit sense organ: it is closer than 100 μm or more to the neighbouring slit. Small isolated slits are less than 30 μm in length, while large isolated slits are 30 μm long or more. The second type is a group of single slits, which comprises at least one slit Fig. 2-5 SEM image of campaniform sensillum on an antennal branch of the
silkmoth, Antheraea polyphemus. From [2.2]. Copyright (1997 Wiley-Liss, Inc.), reprinted with permission of Wiley-Liss, Inc., a subsidiary of John Wiley & Sons, Inc.
Biological Mechanical Sensors
Fig. 2-6 SEM images of slit sense organs on the second leg of the whip spider.
(a): Lyriform organ on the anterior side of the trochanter (Tr) consisting of 19 slits, (b) Group of single slits on the posterior side of the trochanter. From fig. 5 in [2.20]. Copyright (1976 Springer-Verlag.), reprinted with kind permission from Springer Science + Business Media.
measuring 30 μm or more in addition to at least one more slit closer than 100 μm. The third type is the Lyriform or compound slit sense organ, which are recognised by a very close, parallel, and side by side arrangement of two or more slits [2.20]. In spiders, the shortest distance between them typically measures only 5 μm or less, and does not exceed 10 μm [2.20].
Fig. 2-7 Illustrations of slit sensilla, posterior view, in the legs of above: Androctonus australis (scorpion); and below: Cupiennius salei
(Araneae). From fig. 4 in [2.20]. Copyright (1976 Springer-Verlag.), reprinted with kind permission from Springer Science + Business Media.
Biological Mechanical Sensors
The effect of grouping a number of slits in close distance, as found in the Lyriform organ, is investigated and modelled in [2.21]. A Lyriform organ can take more force than a single slit. The peripheral slits receive more load than intermediate ones. Compression is the adequate deformation of the slit leading to nervous activity. This is mostly achieved by having a load perpendicular to its long axis [2.21]. Recently, finite element model has been used to investigate the behaviour of slit sense organs under mechanical loading [2.22].
2.2.3 Fly’s Gyroscope: the Haltere
While the hair sensors or the slit sensilla and campaniform sensilla receive signal in “passive” state, meaning without any initial movement in the transducer itself, haltere is a unique type of sensor found in the fly, which is working while moving (Fig. 2-8). Together with the eyes, halteres provide stabilization of flight for the fly. Derham [2.24] was the first scientist to note that with their halteres removed, flies cannot keep stable and quickly crash to the ground. Thus, a haltere can be thought of as what gyroscope does for the airplane. Further explanation about how the haltere works will be given in chapter 4.
2.2.4 Tactile Sensor
Tactile sensors are found in insects and other athropods. They are mainly used to “touch” its surroundings solid environment instead of the fluid environment as measured by hair-like sensor. Thus, tactile sensor is similar to force and flow sensor, in terms of the static deflection signal they Fig. 2-8 The arrow is pointing on the right haltere of Musca domestica
measure. In terms of shape, tactile sensors look like hair sensors, but are thicker and stiffer, because they need to detect bigger deflection. In this subsection, two natural tactile sensors will be shortly explained: insect’s bristle and lobster’s antenna. Then an example of artificial lobster antenna’s inspired sensor will be explained.
2.2.4.1 Insect’s Bristle
The insect bristle is actually a type of trichoid or sensory hair. It was briefly mentioned in section 2.2.1.1 as the thick bristle type of trichoid. Insect’s tactile sensors are hair-like projections on the body surface, which are innervated by a single neuron (Figure 2-9). When the hair is bent, an ionic potential signal is sent to the central nervous system.
Fig. 2-9 Insect’s bristle. Top: schematic illustration. Bottom left: Base of a
longitudinally grooved macrochaeta on the thorax of Calliphora. The body cuticle is covered by a lawn of noninnervated microtrichia. Bottom right: Sharply pointed tip of a Calliphora
macrochaeta. From [2.2]. Copyright (1997 Wiley-Liss, Inc.),
reprinted with permission of Wiley-Liss, Inc., a subsidiary of John Wiley & Sons, Inc.
Biological Mechanical Sensors
2.2.4.2 Spider’s Hair Sensilla
Similar to insect, touch sensors in the form of hair-like structures are also found in spider. The front legs of the wandering spiders such as Cupiennius salei are covered with tactile hairs. When wandering at night, Cupiennius uses its front legs as tactile feelers [2.25]. Cupiennius is a spider which is active during night. It uses the so-called “guided-stick walk” tactile orientation behaviour to navigate in the dark [2.25],[2.26]. The tactile hairs protrude from the tarsus and metatarsus regions of the legs. The tactile hairs are seen as the longest hair in comparison with other surrounding hairs (Figure 2-10, top).
Fig. 2-10 Top: Tarsus tactile hair TAD1 (arrow) identified easily from its
surrounding cuticular hairs by its length and steep insertion angle. Bottom: Reconstruction of the basal part. From fig. 1 in [2.27]. Copyright (2004 Springer-Verlag.), reprinted with kind permission from Springer Science + Business Media.
The tactile hair of the spider is first-order lever. The displacement of the hair tip is scaled down by a factor of ca. 750:1. The shaft is illustrated in Figure 2-10, bottom: CF the connecting fibrils; DS dendrite sheath; HB hair base; HS hair shaft; JM joint membrane; S socket; SS socket septum; TC terminal connecting material; 1-3 tubular body of the dendrite [2.27]. The torsional restoring spring constant changes non-linearly with deflection angle, with values in the order of 10-8 Nm/rad. This is ~10,000 times larger than trichobothria, which is the hair sensor of insects used to sense flow [2.25]. Numerical modelling to investigate the mechanical property of this sensor has been conducted by Dechant et.al. [2.28],[2.29].
2.2.4.3 Lobster’s Antenna and the hooded sensilla
Lobsters, or generally crustaceans, have antennae that function as chemosensors (sensing chemicals) and mechanosensors, detecting touch. The antennule of the lobster has four segments. The front two antennules are further bifurcated into lateral flagellum, and medial flagellum [2.30], as illustrated in Fig. 2-11, top. Each flagellum has 500-600 hooded sensilla that are distributed on most annuli (segments), except for the ones located on the tip. An Antennular hooded sensillum has a complex ultrastructure. They are about 55 μm long and 15 μm wide at their base. Two characteristic features of hooded sensilla are the serrate distal end of the shaft (Fig. 2-11 bottom, arrow), and long setules (Fig. 2-11 bottom, double arrow) that appear as a hood over the serrate end. Hooded sensilla have a porous cuticle and are innervated by 9-10 chemosensory and 3 mechanosensory neurons. The dendrites of these mechanosensory neurons project to the distal end of the sensillum. Hooded sensillar chemosensory neurons respond to waterborne chemicals, while the hooded sensillar mechanosensory neurons are not that sensitive, in that they respond to tactile, not waterborne vibrations. For further discussion on hooded sensilla on the spiny lobster Panulirus argus, please refer to ref. [2.30].
Biological Mechanical Sensors
2.2.4.4 NorthWestern University’s Artificial Lobster’s Hair Fan organ
A group in the Northwestern University has developed sensor, inspired from the lobster’s antenna [2.31]. The bioinspired sensor is part of their project to build a bioinspired robot mimicking a lobster [2.32], [2.33]. The lobster robot is aimed at detecting underwater mines. Hence, the antennal sensor Fig. 2-11 Antennular hooded sensilla of spiny lobster Panulirus argus, top:
location of the lateral and medial flagella of the antennules; bottom: SEM image showing ultrastructure of the hooded sensilla. From fig. 1 in [2.30]. Copyright (2002 Wiley-Liss, Inc.), reprinted with (requested) permission of Wiley-Liss, Inc., a subsidiary of John Wiley & Sons, Inc.
designed to mimic lobster’s antenna has the function of ‘touching’ objects assumed to be mines. The bioinspired antenna sensor is made from two halves of machined PVC sheets. Bending sensors and a flexible circuit are sandwiched in the slot between the two halves, serving as a housing to isolate the sensors and the circuit from sea water. The PVC sheets enclosing is designed to have a tapered width mimicking a real lobster antenna. The three bending sensors are located at three different points on the antenna axis [2.31]. It was shown in [2.31] that the tapered width plays a role in distinguishing deflection caused by a touched object and deflection caused by water flow. The bending sensor is a simple switch that is open or closed depending on whether the local curvature exceeds a threshold angle value.
2.2.5 Acoustic Sensors
In section 2.2.1, the sensory hair has been mentioned as a vibration sensor. Given certain length and thickness, a sensory hair will be sensitivite to vibration with a certain frequency, related to its natural resonant frequency. If combined as an array, a number of hair structures could form acoustic sensors which will have a wider dynamic range and wider bandwidth of vibration detection in the surrounding fluid. This subsection will deal with acoustic sensor element found in nature: the stereocilium, as well as several example of biomimetic acoustic sensors.
2.2.5.1 The Stereocilia
The stereocilia are found in most of the biological acoustic sensors functioning as a hearing organ. Stereocilia are hair-like structures attached to the apical side of the hair cells located in the Organ of Corti (see Fig. 2-12, reproduced from [2.34]) within the cochlea of the inner ear of mammals and other types of vertebrate animal. Stereocilia are the basic acoustic sensor/ transducer that transform the mechanical energy of sound pressure or vibration into an electrical signal in the hair cell. This electrical signal will eventually lead to excitation of the auditory nerve.
In mammals the hair cells consist of two types which differ functionally: the inner hair cell (IHC) and the outer hair cell (OHC). The outer hair cell functions as a pre-amplifier for a weak sound signal, as well as for wider frequency discrimination. The inner hair cell functions as a mechanosensory transducer. Thus, in the outer hair cell, the stereocilia is
Biological Mechanical Sensors
more of an actuator moved by motor protein (prestin) which makes the OHC motile.
The stereocilia are linked by actin filaments which act as springs. This actin filament is linked to a gated ion channel in each of the stereocilium. The stereocilia are arranged in bundles of 30-300 individual stereocilia (Fig. 2-13, top, arrow). The bundles contain stereocilia arranged in different heights like stairs. One of the cilia is non-motile, and is known as the kinocilium (Fig. 2-13, top, double arrow). When the membranes of the ears receive a sound signal, this is transmitted by the stapes to the endolymphatic fluid inside the cochlea. This causes the organ of Corti to sway and makes the stereocilia tilt. The tilting of the stereocilia will induce tension in the tip links (Fig. 2-13, bottom) that will open the gate of the ion channel, causing more flux of ion channel to the hair cell. The increasing flux of the ions will cause depolarisation of the hair cell which eventually will excite signal to the auditory nerve.
For more discussion on the structure and working mechanism of stereocilia and hair cell in cochlea, the reader is requested to refer to [2.35] and [2.36].
Fig. 2-12 Cross section through the spiral organ of corti inside the cochlea
of the human inner ear. Reproduced from [2.34], which is public domain, reproduced from the 20th U.S. edition of Gray's Anatomy of the Human Body, originally published in 1918.
Fig. 2-13 Top: illustration of a bundle of stereocilia on the apical side of a hair
cell adapted from a SEM image; stereocilium (arrow); and kinocilium (double arrow). Bottom: schematic illustration of parts of stereocilia on hair cell, adapted from [2.35].
Biological Mechanical Sensors
2.2.5.2 Johns Hopkins University’s Biomimetic Acoustic Microsystems
One of the efforts to develop an artificial biomimetic or bioinspired acoustic microsystem is from the group of Johns Hopkins University [2.37]. Here, the group tried to develop a bioinspired acoustic processing microsystem that utilises the state of the art integrated microsystems technologies. The Johns Hopkins group tries to heterogeneously integrate the processing electronics and several micromechanical structures for the purpose of acoustic sensing and acoustic signal processing. The acoustic sensing part is conducted by integrating several mechanical microstructure implemented using MEMS technology. The structures are: (i) arrays of acoustic pressure sensor, (ii) acoustic pressure gradient sensor, (iii) accelerometer, and (iv) air particle flow sensor, e.g. using a hot wire anemometer.
The array of acoustic pressure sensor is made using polysilicon surface micromachining, as a suspended perforated mass on four cantilever springs. The output capacitance change is fed to a readout amplifier using a self-biased floating gate MOS amplifier. In other publications [2.38],[2.39], the group developed mixed-signal architecture and algorithm that make the sensor array able to separate and localize the source, despite the sub-wavelength dimension of the sensor array.
The group also tried to develop a mechanically-coupled acoustic pressure gradient sensor. The mechanically-coupled pressure gradient sensor consists of an array of gradient sensor whose output is fed to independent component analysis described in [2.39]. The coupling is inspired by a biological hearing system found in among others parasitoid fly Ormia ochracea whose model is described in [2.40].
2.2.5.3 State University of New York’s Biomimetic Directional Microphone Dia-phragm
R.N. Miles et al. were the first to introduce a mechanical model of the tympanal hearing in the parasitoid fly Ormia ochracea [2.40]. The fly can detect the direction of the sound despite the very close distance (~500 μm) between the two tympanal membrane ears it has (Fig. 2-14). The arrival time difference between the two ears is approximately only 2 μs. The hearing system is modeled as two rigid bars which are connected to a pivot point with a coupling spring and dash-pot. At the two ends of the two bars there are springs and dash-pots which$ approximately represent the dynamic properties [2.40]. The mechanical model is shown in Fig. 2-15.
In the model shown in Fig. 2-15, the system consists of two distinct beams, the two arms of presternum, that join medially at the fulcrum (pivot). At low frequencies, the presternum will bend and be displaced at the same amplitude at frequencies below 4 kHz. At intermediate frequencies (e.g. 7 kHz), the tympanal membranes oscillate in rocking mode, one displaced outwardly, and the other displaced inwardly. At high frequencies, e.g. > 15 kHz, there appear a combination of both bending and rocking modes, so that contralateral vibrations are minimised [2.40],[2.42].
The bioinspired acoustic sensor is implemented using MEMS technology as two coupled diaphragms. The design and the model are presented in the following references [2.43], and [2.44].
Fig. 2-14 Scanning electron micrographs of the female hearing organ of Ormia ochracea, showing prothoracic coxa (Co); neck insertion on the
prothorax (N); prosternal inflation (PI); prosternal tympanal membrane (PTM); tympanal pit (TP). Scale bar: 200 μm. Magnification: x 100. From fig. 3 in [2.41]. Copyright (1994 Springer-Verlag.), reprinted with kind permission from Springer Science + Business Media.
1 2
Biological Mechanical Sensors
Fig. 2-15 Mechanical model of the tympanal membranes of Ormia ochracea,
shown as double rigid bars with springs and dash-pots, connected with intertympanal bridge at the pivot, modeled as coupling spring and dash-pot. The number 1, 2, and 3 correspond to the parts numbered similarly in Fig. 2-14. Adapted from [2.40]. The lower part of the picture shows the behaviour of the membranes under different frequencies stimuli: Low (2-4 kHz), Medium (6-8 kHz), and High (>15 kHz). Adapted from [2.42].
2.3
Biological Electric Field Sensor
Besides mechanical sensors, there are also biological sensors that work in other physical domains. In this section and the next section 2.4, biological sensors that work in the electromagnetic domain will be explained. Electric field detection is the topic of this section. Electric field sensors, or electroreceptors are found in several fish species. This section gives a brief introduction to the detection of an electric field and its changes using electroreceptor cells. Based on their working mechanism, electroreception in fish can be categorised into two: passive electrosensing/electroreception, and active electrolocation. Both of these sensing mechanisms will be explained in the next two subsections.
2.3.1 Passive Electrosense System of Sharks and Paddle Fish
The elasmobranch fish, e.g. sharks, rays, and skates, have special organs called the ampullae of Lorenzini [2.45]. This organs are usually concentrated in the forward section of the fish. Ampullae of Lorenzini detect weak electric fields in the surrounding aqueous environment. From the organ development point of view, ampulla of Lorenzini is a type of ampullary organ, similar in structure and function to the mechanosensory organs (i.e. the neuromasts) of the lateral line (see section 2.2.1.3, and Fig. 2-3) [2.46]. It is a gel-filled epidermal pit containing sensory hair cells, and synaptic connections with primary afferent neurons at the base of the pit [2.46]. The difference between them is in their spatial distribution. The mechanosensory organs of the lateral line are in tracts on the head and trunk, while the electrosensory ampullae of Lorenzini are organised in clusters in head (Figure 2-16).
Ampulla of Lorenzini detects the changes in the surrounding electric field by ‘measuring’ the potential difference between the body surface and the interior of the body. This potential gradient is applied across the apical membrane of the sensory cells that line the ampullae [2.45]. The firing rate of these sensory cell is a function of this potential difference. The cells are coupled to the neurons. Increasing signal voltage will result in increasing frequency of bursts.
The gel consists of 97% water by weight, sulfated glycoprotein, Na, Ca, Cl ions with similar concentration as the seawater, and a bit more of K ion [2.48].
Biological Electric Field Sensor
Functionally similar electroreceptor organs are also found in the paddle fish (Polyodon spathula) (cf. [2.49]-[2.52]). The passive electroreceptor cells (~104 in number) are located in the rostrum of the paddle fish, which acts as an electrosensory antenna (Fig. 2-17). Paddle fish also uses a mechanism called stochastic resonance to enhance its electrosensory information, especially during prey capture [2.49], [2.50]. Stochastic resonance is the addition of an optimal level of noise to a weak information-carrying input in certain non linear systems. This can enhance the information content at their output [2.49]. Daphnia, a kind of plankton, the prey of paddle fish, reveal their location to paddle fish in two ways. First, individual Daphnia produces electrical signals from their muscular activation during swimming at 8-12 Hz. Second, a population of Daphnia produces background noise that boosts the sensitivity of electroreceptors [2.49].
These passive electrosensory organs of sharks and paddlefish play a role in spatial orientation, i.e. in navigation and migration, as well as in prey-capture behaviour, and other social behaviour of the fish [2.54]. The stimulus involved during spatial orientation in a uniform DC geomagnetic field. Here, the fish use angular movement to stimulate the ampullary system. By angular swimming in the uniform geomagnetic field the fish creates an induction field of more than 20 nV.cm-1. Stimuli involved during predatory and social behaviour are non-uniform field and usually near field. The detection limit of the elasmobranch fish in the uniform field is 5
Fig. 2-16 Electroreceptors (Ampullae of Lorenzini) and lateral line canals
nV.cm-1, and in the dipole field is 1 nV.cm-1, respectively. The freshwater fish’s ampullary organs have a detection limit of 30 nV.cm-1 in the non-uniform field, and of 800 nV.cm-1 in the semi-uniform field, although the well documented value is 1 μV.cm-1 [2.54]. The observable lower
detection limit in the marine fish compared to the fresh water fish is due to the lower resistivity of the sea water, 20 Ω..cm, while resistivity of fresh water is about 2000 Ω..cm. Thus, the stimulus comes from the displacement of electric charges across the cell membrane, which then disturbs the electrochemical homeostasis of the receptor cell. Also, stochastic resonance is involved in lowering the threshold, e.g. by swimming in uniform field, and the turning movements of the head of sharks in electric field and magnetic field detection.
2.3.2 Active Electrolocation System of Weakly Electric Fish
Instead of passively detecting the surrounding electric field, some kinds of fresh water fish develop the ability to actively produce a weak electric field, and detecting its change due to the presence or absence of objects in their surroundings (Fig. 2-18). Such ability is an active electrolocation system. And since the electric field produced is weak enough not to kill other living things, the fish type is known as weakly electric fish. Among the fish known to have such ability are Peters' elephantnose fish (Gnathonemus petersii) (cf. [2.56], [2.57]), and the Black ghost knifefish (Apteronotus albifrons) (cf. [2.58]-[2.60]).
Weakly electric fish are nocturnal. They orientate by electrical sense without vision. The electric signals in such fish are produced by specialized electric organs. The signals are called the Electric Organ Discharges (EOD). Fig. 2-17 Paddle fish with its Rostrum that contains electrosensory cells.
Taken from [2.53]. Rostrum
Biological Electric Field Sensor
These electric signals are used by the fish to navigate as well as to perceive and recognise objects in the darkness. Objects within the self-produced electric field alter the electric current at the electroreceptor organs which are distributed over almost the entire body surface of the fish [2.56]. The alteration in the current flow through the epidermal electroreceptor is compared to the current when there is no object present. The location of the object is known by the location of electrical image on the skin surface of the fish. The sign and amplitude of the signal indicates the object’s impedance, which is compared to the water resistance as the reference. EOD phase shift and waveform distortion is interpreted as the object’s capacitive properties, which will show to the fish whether the object is living or inanimate matter [2.56]. The other parameters, however, e.g. size, slope, exact impedance, and distance, are known by calculation using several image parameters. The depth or distance perception, for example, is known by calculating the ratio between maximum image slope and the maximum image amplitude. This derived parameter is independent of size, shape, and material of the detected object [2.57]. This principle of distance perception is unique, since only a single stationary two-dimensional array detector is needed. Compare for example with mammalian stereovision that needs two eyes to have depth perception.
Another interesting point is the use of adaptive tracking strategy by Apteronotus sp. to account for post-detection motion of its prey ([2.58] and [2.59]). During prey detection, Apteronotus will first swim forward to scan its prey. It locates itself under the prey since the electroreceptor organs are concentrated on its dorsal surface of the trunks. Then it will swim backward Fig. 2-18 Active electrolocation, where conductive objects concentrate the
to bring the prey to the mouth [2.59]. The EOD generates quasi-sinusoidal electric field with amplitued of 1 mV.cm-1, and basic frequency of 1 kHz [2.58]. It has two kinds of electrosensors. The first one is a high frequency electrosensor which is sensitive to the field similar to its own EOD. The organ for this type of sensor is the tuberous receptor. The second type of electrosensor is the low frequency electrosensor in the form of an ampullary receptor, sensitive to 0-40 Hz field [2.58]. An adult A. albifrons has 15,000 tuberous receptor organs distributed over its body surface, and 700 ampullary receptor organs. It also has 300 neuromasts for mechanosensory lateral line [2.58].
2.3.3 University of Illinois at Urbana Champaign’s Biorobotic Electrosensory System
Inspired by the active electrolocation of the black ghost knifefish Apteronotus albifrons, a group in University of Illinois at Urbana Champaign is trying to develop an underwater robot having electrosensors with a similar working mechanism to the black ghost knifefish [2.61]. The test setup consists of a small electrosensory array and a robotic platform to control movement. Their aim is to have the setup able to acquire and analyze electrosensory signals similar to the weakly electric fish. Similar to the weakly electric fish, there are two strategies used to influence the strength and the spatiotemporal pattern of the incoming electrosensory signals: first by controlling the velocity and orientation of the body, and second by adjusting the gain and filtering properties of the neuron model. They also tried to model the computational model of the combined receptor organ and afferent nerve fibre [2.61].
Besides its engineering aspect, such work is intended to better understand the mechanism of the natural sensors found in the weakly electric fish [2.62],[2.63].
2.4
Biological Radiation Sensor
Of special interest among different types of electromagnetic (EM) fields is EM fields that have wavelength in the range of 102 nm to tens of μm. These regions of EM field are generally considered as the visible region, and thermal (infrared) region. Many species have equipped themselves with
Biological Radiation Sensor
different types of sensors to work in these regions. This section will described several examples of those sensors, along with an example of a biologically-inspired sensor.
2.4.1 Visual Radiation Sensor: Insect’s Optomotor Eyes
Insect’s eyes are as multi-lens compound eyes. Another characteristic that makes it a sophisticated visual system is how it moves to generate optic flow containing information about the vertical axis rotation (yaw) of the insect’s body, as well as side-translation that encodes information about the spatial layout of the environment [2.64], [2.65].
Part of sensory information is generated actively when animals move. A human, for example, shifts gaze in sequence of saccades. Similarly, insects shift gaze by saccadic turns of body and head, keeping the gaze fixed between saccades. This saccadic self-generated motion generates image flow on the retina of the eyes. Characteristic dynamics of this retinal image flow is efficiently used by the blowfly visual interneuron to compute motion [2.64]. Information about object distance is encoded as low frequency signals of translation by intervals of stable vision from saccadic viewing strategy. A move information about the head rotation (yaw) is encoded as residual intersaccadic higher frequency signal [2.64].
A special type of neuron cell, called the Horizontal System Equatorial (HSE) cell is involved in this real time motion detector. HSE cell is a major output neuron of the visual system and belongs to an identified set of motion-sensitive neurons. It extracts parameters of self-motion from the optic flow field, which consists of rotational/yaw information (high frequency), and side-translational information (low frequency) [2.64].
A group in California Institute of Technology developed motion sensor based on this visual system of the fly, implemented using analog VLSI (Very Large Scale Integration) technology [2.65]. Instead of applying one super-processor to compute algorithmically images produced from the photoreceptor array, the researchers applied parallel architecture. This architecture constructed from an array of elementary motion detectors (EMD), which consists of a pair of photoreceptors, temporal bandpass filters, temporal lowpass filters, multipliers, opponent substraction elements, and spatial integration unit. The pair of photoreceptors form an opponent pair of elementary motion detectors. Lowpass filters delayed incoming signals, which are correlated with non-delayed signals from the adjacent
