Main description:

This book describes the essential requirements for the realization of neuromorphic systems, where memristive devices play a key role. A comprehensive description to organic memristive devices, including working principles and models of the function, preparation methods, properties and different applications is presented. A comparative analysis of organic and inorganic systems is given. The author discusses all aspects of current research in organic memristive devices: fabrication techniques, properties, synapse mimicking circuits, and neuromorphic systems (including perceptrons), etc.

Describes requirements of electronic circuits and systems to be considered as neuromorphic systems;
Provides a single-source reference to the state-of-the-art in memristive devices as key elements of neuromorphic systems;
Provides a comparative analysis of advantages and drawbacks between organic and inorganic devices and systems;
Includes a systematic overview of organic memristive devices, including fabrication methods, properties, synapse mimicking circuits, and neuromorphic systems;
Discusses a variety of unconventional applications, based on bio-inspired circuits and neuromorphic systems.

Contents:

Table of contents

Introduction 7

Chapter 1: Memristive devices and circuits 11

Determination of memristor 11

Mnemotrix 13

First mention about the experimental realization of memristor 13

Inorganic memristive devices 15

Memristive devices with organic materials 21

Chapter 2: Organic memristive device 28

2.1. Basic materials 28

2.2. Structure and working principle of the device 30

2.3. Electrical characteristics of the device 32

2.4. Device working mechanism 39

2.4.1. Spectroscopy 40

2.4.2. X-ray fluorescence 49

2.5. Electrical characteristics in a pulse mode 54

2.6. Optimization of properties and stability of the device 58

2.6.1. Stability of organic memristive device properties 59

2.6.2. Optimization of the device architecture 61

2.6.3. Role of the electrolyte 65

2.7. Organic memristive devices with channels, formed by Layer-by-Layer technique 69

Chapter 3: Oscillators based on organic memristive devices 74

Chapter 4: Models 81

4.1. Phenomenological model 82

4.2. Simplified model of the organic memristive device function 90

4.3. Electrochemical model 93

4.4. Optical monitoring of the resistive states 102

Chapter 5: Logic elements and neuron networks 107

5.1. Logic elements with memory 107

5.1.1. Element "OR" with memory 108

5.1.2. Element "AND" with memory 110

5.1.3. Element "NOT" with memory 113

5.1.4. Comparison of logic elements with memory, based on organic and inorganic memristive devices 114

5.2. Perceptrons 118

5.2.1. Single layer perceptron 119

5.2.2. Double layer perceptron 123

Chapter 6: Neuromorphic systems 130

6.1. Learning of circuits, based on a single memristive device 130

6.1.1. DC mode 130

6.1.2. Pulse mode 134

6.2. Training of networks with several memristive elements 137

6.3. Training algorithms 140

6.4. Electronic analog of the part of the nervous system of pond snail Lymnaea stanignalis 149

6.4.1. Biological benchmark 150

6.4.2. Experimentally realized circuit, mimicking the architecture and properties of the pond snail nervous system part 151

6.5. Cross-talk of memristive devices during pathways formation process 156

6.6. Effect of noise 161

6.7. Frequency driven short-term memory and long-term potentiation 164

6.8. Spike Timing Dependent Plasticity (STDP) learning in memristive systems 169

6.8.1. STDP in circuits with polyaniline-based memristive devices 170

6.8.2. STDP in circuits with parylene-based memristive devices 174

6.8.3. Classic conditioning of polyaniline-based memristive devices systems 178

6.8.4. Classic conditioning of parylene-based memristive devices systems 182

6.9. Coupling with living beings 184

Chapter 7: 3D systems with stochastic architecture 192

7.1. Free-standing fibrillar systems 192

7.2. Stochastic networks on frames with developed structure 196

7.3. 3D stochastic networks, based on phase separation of materials 200

7.3.1. Stabilized gold nanoparticles 200

7.3.2. Block copolymer 208

7.3.3. Fabrication of 3D stochastic network 209

7.3.4. Training of stochastic 3D network, based on phase separation of materials 211

7.3.5. Evidence of 3D nature of the realized stochastic system 215

7.4. Modeling of adaptive electrical characteristics of stochastic 3D network 218

7.4.1. Single memristive device 220

7.4.2. Structure of the network 222

7.4.3. Network dynamics 223

7.4.4. Modeling of experimental results, obtained on 3D stochastic networks 224

Conclusions 232

References