Neuromorphic Devices for Brain-inspired Computing: Artificial Intelligence, Perception, and Robotics

Cover
Title Page
Copyright
Contents
Preface
Chapter 1 Two‐Terminal Neuromorphic Memristors
	1.1 Memristive Devices
		1.1.1 Memristive Device Structure and Materials
			1.1.1.1 Memristive Device Structure
			1.1.1.2 Memristive Materials
		1.1.2 Resistive Switching Behavior
			1.1.2.1 Volatile Resistive Switching
			1.1.2.2 Nonvolatile Resistive Switching
	1.2 Resistive Switching Mechanisms
		1.2.1 Filamentary‐Type Resistive Switching
			1.2.1.1 Cation Migration‐Related Filaments
			1.2.1.2 Anion Migration‐Related Filaments
		1.2.2 Interface‐Type Resistive Switching
	1.3 Memristive Bioinspired Devices
		1.3.1 Memristive Synapses
			1.3.1.1 Short‐Term Memristive Synapses
			1.3.1.2 Long‐Term Memristive Synapses
		1.3.2 Memristive Neurons
			1.3.2.1 Bioplausible Memristive Neurons
			1.3.2.2 Biophysical Memristive Neurons
	1.4 Memristive Neural Networks
		1.4.1 Memristive ANN Computing
		1.4.2 Memristive SNN Computing
	1.5 Summary and Outlook
	References
Chapter 2 Spintronic Neuromorphic Devices
	2.1 Introduction
	2.2 Magnetic Tunnel Junction for Neuromorphic Computing
		2.2.1 Device Structure and the Write/Read Operation
			2.2.1.1 Binary Magnetic Tunnel Junction
			2.2.1.2 Multi‐level Spintronic Memristor
		2.2.2 Working as the Synaptic Device
			2.2.2.1 Stochastic Binary Synaptic Device
			2.2.2.2 Analog‐Like Synaptic Device
		2.2.3 Working as the Neural Device
			2.2.3.1 Spiking Neural Device
			2.2.3.2 Artificial Neural Device
		2.2.4 All‐Spin Neural Network
			2.2.4.1 All‐Spin Artificial Neural Network with Compound Scheme
			2.2.4.2 All‐Spin Spiking Neural Network with Spintronic Memristor
		2.2.5 Summary and Outlook
	2.3 Skyrmion‐Based Neuromorphic Computing
		2.3.1 The Introduction of Skyrmions
		2.3.2 Skyrmion‐Based Synapse Devices
		2.3.3 Skyrmion‐Based Neuron Devices
		2.3.4 Skyrmion‐Based Reservoir Computing
		2.3.5 Skyrmion‐Based Stochastic Computing
		2.3.6 Challenges and Perspectives
	2.4 Spin Torque Oscillators for Neuromorphic Computing
		2.4.1 Introduction to Spin Torque Oscillator
		2.4.2 Associative Memory Based on Injection Locking of STO
		2.4.3 Reservoir Computing Based on STO
		2.4.4 Recurrent Neural Network based on Delayed Feedback of STO
		2.4.5 Neuromorphic Computing Based on the Synchronization of STO
		2.4.6 Problems and Perspectives
	2.5 Conclusion and Outlook
	References
Chapter 3 Multiterminal Neuromorphic Devices with Cognitive Behaviors
	3.1 Introduction
	3.2 Multiterminal Neuromorphic Memristors
		3.2.1 Memristor‐Based Neuromorphic Devices
		3.2.2 Multiterminal Memristor for Neuromorphic System
			3.2.2.1 Synaptic Competition and Cooperation on Multiterminal Memristor
			3.2.2.2 Heterosynaptic Plasticity on Multiterminal Memristor
			3.2.2.3 Multiterminal Memtransistor
	3.3 Multiterminal Neuromorphic Transistors
		3.3.1 Neuron Transistors
		3.3.2 Neuromorphic Devices for Chemical Biosensor Applications
			3.3.2.1 Chemical Biosensors
			3.3.2.2 Neuron Transistors for Chemical Biosensor
			3.3.2.3 Multi‐gate Neuromorphic Transistor for pH Sensor
		3.3.3 Dendritic Algorithm on Multiterminal Neuromorphic Transistors
			3.3.3.1 EGT‐Based Neuromorphic Transistors
			3.3.3.2 Multi‐gate Neuromorphic Transistor
			3.3.3.3 Dendrite Neuron and Dendritic Algorithm
			3.3.3.4 Multi‐gate Neuromorphic Transistor for Pattern Recognition
	3.4 Neuromorphic Transistors for Perception Learning Activities
		3.4.1 Artificial Tactile Device
		3.4.2 Artificial Vision Device
		3.4.3 Artificial Auditory Device
	3.5 Conclusion and Outlook
	Acknowledgments
	References
Chapter 4 Neuromorphic Devices Based on Chalcogenide Materials
	4.1 Introduction
	4.2 Ovonic Memory Switching (OMS) and Threshold Switching (OTS) in Chalcogenide Materials
	4.3 Artificial Synapses Based on MS Behaviors
	4.4 Artificial Neurons Based on TS Effects
	4.5 Hardware Neural Networks
	4.6 Summary and Outlook
	References
Chapter 5 Neuromorphic Devices Based on Organic Materials
	5.1 Introduction
	5.2 Two‐Terminal Organic Neuromorphic Devices
		5.2.1 Metal Filament Conducting‐Based Memristors
		5.2.2 Redox Reaction‐Based Memristors
		5.2.3 Ion Migration‐Based Memristors
		5.2.4 Charge Trapping‐Based Memristors
	5.3 Three‐Terminal Organic Neuromorphic Devices
		5.3.1 Floating‐Gate Transistors
		5.3.2 Electrolyte‐Gate Transistors
		5.3.3 Ferroelectric‐Gate Transistors
		5.3.4 Optoelectronic Transistors
	5.4 Innovative Applications of Organic Neuromorphic Devices for Bionic Perception Systems
		5.4.1 Artificial Visualization Systems
		5.4.2 Tactile‐Perception Systems
	5.5 Summary and Outlook
	References
Chapter 6 Neuromorphic Computing Systems with Emerging Devices
	6.1 Introduction
		6.1.1 Background Introduction
		6.1.2 The Motivation for Neuromorphic Computing
		6.1.3 Progress and Challenges of CMOS‐Based Neuromorphic Computing
		6.1.4 Principles of Memristors
	6.2 DNNs Based on Synaptic Devices
		6.2.1 Device Performance Requirements
		6.2.2 Array Demonstrations
		6.2.3 Chip and System Implementations
		6.2.4 Architecture and Algorithm Optimization
			6.2.4.1 Quantization
			6.2.4.2 Nonideal Analog Switching Characteristics
			6.2.4.3 Synaptic Array Size
	6.3 SNNs Based on Neuromorphic Devices
		6.3.1 Learning Rules and Memory Principles
			6.3.1.1 Synapses and Neurons in SNNs
			6.3.1.2 Learning Algorithms and Benchmarks for SNNs
		6.3.2 SNNs with Synaptic Devices and Neuronal Devices
			6.3.2.1 Synaptic Devices with Plasticity
			6.3.2.2 Neuronal Devices
		6.3.3 SNN Implementations with Synaptic Arrays
	6.4 Other Neuromorphic Systems
		6.4.1 Hyperdimensional Computing
		6.4.2 Dendritic Computing
		6.4.3 Reservoir Computing
		6.4.4 Oscillatory Neural Network
		6.4.5 Hopfield Neural Network and Simulated Annealing
	6.5 Summary and Outlook
	References
Chapter 7 Neuromorphic Perceptual Systems with Emerging Devices
	7.1 Background
	7.2 Sensation and Perception
		7.2.1 From Sensation to Perception
		7.2.2 Pursing Artificial Perception by Neuromorphic Devices
	7.3 Implementation of Artificial Perception
		7.3.1 Building Block of Artificial Perception: Artificial Sensory Neuron
		7.3.2 Intelligent Tasks Based on Artificial Perception
			7.3.2.1 Pattern Recognition Tasks
			7.3.2.2 Prosthetics and Robotics Applications
		7.3.3 A Roadmap Toward Neuromorphic Perceptual System
	7.4 Challenges and Perspectives
		7.4.1 Challenges
		7.4.2 Conclusions and Perspectives
	References
Index
EULA