Schmid / Goel / Wang Nano-Net

4th International ICST Conference, Nano-Net 2009, Lucerne, Switzerland, October 18-20, 2009, Proceedings
1. Auflage 2009
ISBN: 978-3-642-04850-0
Verlag: Springer
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, Band 20, 299 Seiten

Reihe: Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

This book constitutes the proceedings of the 4th International Conference on Nano-Networks, Nano-Net 2009, held in Lucerne, Switherland, in October 2009. The 36 invited and regular papers address the whole spectrum of Nano-Networks and spans topis like modeling, simulation, statdards, architectural aspects, novel information and graph theory aspects, device physics and interconnects, nanorobotics as well as nano-biological systems. The volume also contains the workshop on Nano-Bio-Sensing Paradigms as well as the workshop on Brain Inspired Interconnects and Circuits.

Autoren/Hrsg.

Schmid, Alexandre

Goel, Sanjay

Wang, Wei

Beiu, Valeriu

Carrara, Sandro

Inhaltsverzeichnis

1;Preface;5
2;Organization;7
3;Table of Contents;10
4;Nano-Net 2009 Full Papers and Invited Papers;10
4.1;The Impact of Persistence Length on the Communication Efficiency of Microtubules and CNTs;14
4.1.1;Introduction;14
4.1.2;The Nanotube Network Simulation;17
4.1.3;Graph Information Theory;23
4.1.4;Conclusion;25
4.1.5;References;25
4.2;Single and Multiple-Access Channel Capacity in Molecular Nanonetworks;27
4.2.1;Introduction;27
4.2.2;Single Molecular Communication Channel;28
4.2.3;Capacity of Molecular Multiple-Access Channel;31
4.2.4;Numerical Results;32
4.2.4.1;Single Molecular Channel;32
4.2.4.2;Molecular Multiple-Access Channel;33
4.2.5;Conclusion;35
4.2.6;References;36
4.3;Timing Information Rates for Active Transport Molecular Communication;37
4.3.1;Introduction;37
4.3.2;System Model;38
4.3.3;Results;40
4.3.4;References;41
4.4;Information Transfer through Calcium Signaling;42
4.4.1;Introduction;42
4.4.2;Information Communication Model;44
4.4.3;Simulation Results;45
4.4.4;References;46
4.5;Quantitative Analysis of the Feedback of the Robust Signaling Pathway Network of Myosin V Molecular Motors on GluR1 of AMPA in Neurons: A Networking Approach for Controlling Nanobiomachines;47
4.5.1;Introduction;47
4.5.2;Methods and Results;48
4.5.3;Conclusion;51
4.5.4;References;51
4.6;RF Control of Biological Systems: Applications to Wireless Sensor Networks;52
4.6.1;Introduction;52
4.6.2;Approach and System Architecture;53
4.6.3;RF Front End;54
4.6.4;Bio-mechanical Signal Interpreter;54
4.6.4.1;Address Recognition Maze;54
4.6.4.2;Biological Component;56
4.6.5;Conclusion and the Future Work;57
4.6.6;References;58
4.7;Sub-micrometer Network Fabrication for Bacterial Carriers and Electrical Signal Transmission;59
4.7.1;Introduction;59
4.7.2;Platform Presentation;59
4.7.2.1;Producing a Fiber;60
4.7.2.2;Annealing a Silver-Doped Fiber;61
4.7.3;Specific Systems;61
4.7.3.1;Dispensing Module;61
4.7.3.2;Curing System;62
4.7.4;Future Work;62
4.7.5;Conclusion;63
4.7.6;References;63
4.8;Pulse-Density Modulation with an Ensemble of Single-Electron Circuits Employing Neuronal Heterogeneity to Achieve High Temporal Resolution;64
4.8.1;Introduction;64
4.8.2;Model, Circuit Structure and Simulation Results;65
4.8.3;References;69
4.9;Carbon Nanotube Nanorelays with Pass-Transistor for FPGA Routing Devices;70
4.9.1;Introduction;70
4.9.2;Novel Nanorelay-CMOS Routing Switch;71
4.9.2.1;Modeling;72
4.9.3;cFPGA Design;73
4.9.3.1;Proposed CB Designs;73
4.9.3.2;Operation of 2T1N;73
4.9.3.3;Proposed SB Designs;74
4.9.4;Performance Evaluation;75
4.9.5;Conclusion;75
4.9.6;References;75
4.10;Quantum-Like Computations Using Coupled Nano-scale Oscillators;77
4.10.1;Introduction;77
4.10.2;Classical Formalism for Quantum Dynamics;78
4.10.2.1;Quantum Gates;78
4.10.3;Quantum-Like Computations with Classical Oscillators;79
4.10.3.1;Quantum-Like Qubit (QLB);79
4.10.3.2;Single-QLB Operations;79
4.10.3.3;Two-QLB Operations;80
4.10.4;Conclusions;81
4.10.5;References;81
4.11;Optimization of Nanoelectronic Systems Reliability by Reducing Logic Depth;83
4.11.1;Introduction;83
4.11.2;Dependency of Reliability on Logic Depth;84
4.11.3;Reliability Improvement by Logic Depth Reduction;86
4.11.4;References;88
4.12;Coherent Polarization Transfer through Sub-wavelength Hole Arrays;89
4.12.1;Hole Arrays in Metal Films;89
4.12.2;Plasmon-Assisted Transmission of Entanglement;91
4.12.3;Angle-Dependent Transmission;93
4.12.4;Beams of Surface Plasmons;94
4.12.5;Summary and Conclusions;95
4.12.6;References;96
4.13;Study on Electrical and Optical Properties of the Hybrid Nanocrystalline TiO_{2} and Conjugated Polymer Thin Films;97
4.13.1;Introduction;97
4.13.2;Experimental;98
4.13.3;Results and Discussion;98
4.13.3.1;Morphology of the Hybrid TiO2 Nanocrystals – MEH-PPV Thin Film;98
4.13.3.2;Photoluminescence of Polymeric Composites;99
4.13.3.3;Electrical Properties of the Hybrid Structures;100
4.13.4;Conclusion;102
4.13.5;References;102
4.14;Through Silicon Via-Based Grid for Thermal Control in 3D Chips;103
4.14.1;Introduction;103
4.14.2;Configuration of the 3D Stack;104
4.14.3;Thermal Model;105
4.14.4;Electrical Measurements;106
4.14.5;Conclusion;110
4.14.6;References;110
4.15;Can SG-FET Replace FET in Sleep Mode Circuits?;112
4.15.1;Introduction;112
4.15.2;SG-FET Background;113
4.15.3;Design Space Exploration;113
4.15.4;SG-FET vs. FET;115
4.15.5;Conclusions;116
4.15.6;References;116
4.16;Functional Model of Carbon Nanotube Programmable Resistors for Hybrid Nano/CMOS Circuit Design;118
4.16.1;Introduction;118
4.16.2;Functional Model of OG-CNTFET for Hybrid Circuit Design;119
4.16.2.1;Physical Structure of OG-CNTFET and Its Spice Symbol;119
4.16.2.2;Switching Behaviors and Equivalent Electrical Circuits;119
4.16.2.3;Intrinsic Random Initial Effect of Nanocomponents;120
4.16.3;Conclusions;122
4.16.4;References;122
4.17;Designing Reliable Digital Molecular Electronic Circuits;124
4.17.1;Introduction;124
4.17.2;Device Model;125
4.17.3;Implementation and Results;126
4.17.4;Conclusions;127
4.17.5;References;128
4.18;Creating Nanotechnicians for the 21st Century Workplace;129
4.18.1;Potential for Workforce Shortages in the Near Future;129
4.18.2;Problems Associated with the Current Educational Model;130
4.18.3;Our Training Model;131
4.18.4;Core Curriculum;135
4.18.5;Primary and Secondary School Outreach;136
4.18.6;NANO-Link Project;139
4.18.7;Conclusion;139
4.18.8;References;139
4.19;Chances and Risks of Nanomaterials for Health and Environment;141
4.19.1;Introduction;141
4.19.2;Current Research Challenges;143
4.19.3;Nanomaterials in Research and Companies;145
4.19.4;References;145
4.20;Fabrication of Elastomeric Nanofluidic Devices for Manipulation of Long DNA Molecules;147
4.20.1;Introduction;147
4.20.2;Fabrication/Characterization of Elastomeric Nanofluidic Structures and Analysis of DNA Molecules Stretching;148
4.20.3;Conclusions and Perspectives;152
4.20.4;References;152
4.21;Repeater Insertion for Two-Terminal Nets in Three-Dimensional Integrated Circuits;154
4.21.1;Introduction;154
4.21.2;Delay Model for a 3-D Wire;155
4.21.3;Repeater Insertion Algorithm;157
4.21.3.1;Determine an Initial Solution;158
4.21.3.2;Refinement of the Solution;159
4.21.4;Simulation Results;160
4.21.5;Conclusions;162
4.21.6;References;163
5;Workshop on Nano-Bio-Sensing Paradigms and Application Full Papers and Invited Papers;11
5.1;Nanophotonics for Lab-on-Chip Applications;164
5.1.1;Introduction;164
5.1.2;Fundamentals of Optical Biosensing;165
5.1.2.1;The Basic Measurement Problem in Life Sciences;165
5.1.2.2;Molecular Species and Concentrations of Interest;166
5.1.2.3;Taxonomy of Optical Biosensing Methods;166
5.1.2.4;Principles of Label-Based (Fluorescence) Biosensing Methods;167
5.1.2.5;Principles of Label-Free Optical Biosensing;168
5.1.3;Nanophotonics for High-Sensitivity Label-Free Biosensing;169
5.1.3.1;Optical Micro-resonators for Biosensing with Enhanced Sensitivity;170
5.1.3.2;Whispering Gallery Mode Biosensing for Single-Molecule Detection;171
5.1.3.3;Ultra-Low-Noise Photodetection with CMOS/CCD Image Sensors;171
5.1.4;Outlook: More Nanophotonics in Label-Free Biosensing ?;172
5.1.5;References;172
5.2;Highly Sensitive Arrays of Nano-sized Single-Photon Avalanche Diodes for Industrial and Bio Imaging;174
5.2.1;Introduction;174
5.2.2;Single-Photon Detection in CMOS;176
5.2.3;Industrial and Bio Applications;177
5.2.3.1;Fluorescence Correlation Spectroscopy;177
5.2.3.2;Lifetime Imaging;178
5.2.3.3;Time-of-Flight Imaging;178
5.2.4;Conclusions;179
5.2.5;References;179
5.3;A Cancer Diagnostics Biosensor System Based on Micro- and Nano-technologies;182
5.3.1;Introduction;182
5.3.1.1;CDR Working Principle;182
5.3.2;BioMEMS Device Fabrication and Cartridge Assembly;183
5.3.3;Device Functionalisation: Surface Chemistry and Biopatterning;186
5.3.4;Preliminary CDR Performance Assessment;188
5.3.5;Conclusions;190
5.3.6;References;190
5.4;Nanoelectrochemical Immunosensors for Protein Detection;191
5.4.1;Introduction;192
5.4.2;Experimental Section;193
5.4.2.1;Apparatus and Materials;193
5.4.2.2;Template Fabrication of NEEs;194
5.4.2.3;Detection Schemes and Immobilization Procedures;194
5.4.2.4;Fabrication of NEAs by EBL;195
5.4.3;Results and Discussion;196
5.4.3.1;Tests of NEEs as Nanobiosensors;196
5.4.3.2;Lithographic Tests on NEAs Fabrication on PC and Preliminary Electrochemical Characterization;198
5.4.4;Conclusions;200
5.4.5;References;200
5.5;Quantum Dots and Wires to Improve Enzymes-Based Electrochemical Bio-sensing;202
5.5.1;Introduction;202
5.5.2;Materials and Methods;204
5.5.2.1;Chemicals;204
5.5.2.2;Sensing Electrodes Preparation;204
5.5.2.3;Electrochemical Measurements;205
5.5.3;Results and Discussion;205
5.5.4;Conclusions;210
5.5.5;References;210
5.6;Ultra Low Energy Binary Decision Diagram Circuits Using Few Electron Transistors;213
5.6.1;Introduction;213
5.6.2;Background and Related Work: SET Circuits;214
5.6.3;Proposed Coupled Nanodot SET Circuits;216
5.6.4;Experimental Evaluation;218
5.6.5;Conclusion;220
5.6.6;References;220
5.7;Organic Memristors and Adaptive Networks;223
5.7.1;Introduction;223
5.7.2;Organic Memristor;224
5.7.3;Adaptive Circuits;227
5.7.4;Composite Organic-Inorganic Structures;228
5.7.5;Statistical Networks of Polymer Fibers;230
5.7.6;Conclusions;232
5.7.7;References;233
5.8;Nanostencil and InkJet Printing for Bionanotechnology Applications;235
5.8.1;Introduction;235
5.8.2;Stencil Lithography;237
5.8.2.1;Cell Patterning by Stencil Lithography;237
5.8.2.2;Nanodot Arrays Fabricated by SL for Biosensing Applications;238
5.8.3;InkJet Printing for Bionanotechnology Applications – Cell and Biomaterials Printing;239
5.8.4;References;240
6;Workshop Toward Brain Inspired Interconnects and Circuits - Full Papers and Invited Papers;12
6.1;A New Method for Evaluating the Dynamics of Human Brain Networks Using Complex-Systems;242
6.1.1;Reference;242
6.2;On Two-Layer Hierarchical Networks How Does the Brain Do This?;244
6.2.1;Introduction;244
6.2.2;Classical Network Topologies;245
6.2.3;Brain’s Connectivity;246
6.2.4;Two-Layer Hierarchical Networks;248
6.2.5;Conclusions;251
6.2.6;References;252
6.3;Reduced Interconnects in Neural Networks Using a Time Multiplexed Architecture Based on Quantum Devices;255
6.3.1;Introduction;255
6.3.2;TMA in Neural Networks;256
6.3.2.1;Implementation Using D-Type Flip-Flops;256
6.3.3;TMA Implementation Using ITD Based Latches;258
6.3.3.1;ITD Based Latches;258
6.3.3.2;TMA Based on ITD Latches;260
6.3.4;Conclusion;262
6.3.5;References;262
6.4;On the Reliability of Interconnected CMOS Gates Considering MOSFET Threshold-Voltage Variations;264
6.4.1;Introduction;264
6.4.2;Reliability;265
6.4.3;Simulation of CMOS Logic Gates;266
6.4.4;Reliability of NAND and MAJ;266
6.4.5;Conclusion;270
6.4.6;References;270
6.5;On Wires Holding a Handful of Electrons;272
6.5.1;Introduction;272
6.5.2;On Scaling the Wires;274
6.5.3;How Often Do Wires Fail?;276
6.5.4;More Accurate Estimates;277
6.5.5;Conclusions;279
6.5.6;References;280
6.6;Improving Nano-circuit Reliability Estimates by Using Neural Methods;283
6.6.1;Introduction;283
6.6.2;Related Research;284
6.6.3;Neural Network for Reliability Estimation;285
6.6.3.1;What Are Neural Networks?;285
6.6.3.2;The Training Database;285
6.6.3.3;Training the Neural Network;286
6.6.3.4;Reliability Estimations;286
6.6.4;Conclusions and Future Work;287
6.6.5;References;287
6.7;A Bayesian-Based EDA Tool for Nano-circuits Reliability Calculations;289
6.7.1;Introduction;289
6.7.2;Reliability/Yield Estimations Tools;291
6.7.3;Reliability Enabled EDA Tool;293
6.7.3.1;Device Module;293
6.7.3.2;Gate Module;294
6.7.3.3;Circuit/Core Module;294
6.7.4;Conclusions;295
6.7.5;References;295
7;Author Index;298