Some error occured while loading the Quick View. Please close the Quick View and try reloading the page.
In this book a detailed review will be provided on neuromorphic system theory and its practical realization along with hardware-level implementations. The book will consist of fundamental concepts of memristors and their importance in various domains, including neuroscience, complex logics, applied physics, computer science, materials science, and various engineering fields.
It will also provide the comprehensive review of analytical models and simulations of memristive systems with particular emphasis on neuromorphic designs and logic operations. Moreover, it will also cover the hardware implementation with memristive properties and their applications in the field of nanotechnology, intelligent systems, large-scale optimization, and robotics.
Chapter 1: Introduction to Resistive Switching, Growth of various transitional metal oxides (TMOs) and two-dimensional (2D) transition metal dichalcogenides (TMDs) and Characterizations. This chapter will outline the fundamental concept of resistive switching (RS) and detailed discussion on RS in various TMOs and 2D TMD-based systems, material growth and characterizations. A detailed analytical and physical electro-thermal modelling of nanoscale memristor will be discussed along with their corresponding experimental validation. Chapter 2: Integrated Selector-based Resistive Random Access Memory (RRAM) and Memristor This chapter will discuss the selector memory device for crossbar arrays, essential for multibit data storage. Chapter 3: Role of Memristive Devices in Brain-inspired Computing This chapter will introduce various technologies for memristive devices including their physical switching mechanisms and basic operating principles. In this chapter, the synaptic response of the physical modelled nanoscale memristor and the effect of switching speed over synaptic weight response and will be comprehensively discussed along with the role of memcapacitive devices in synaptic learning to perform the brain inspired computation. Chapter 4: Memristive Devices as Computational Memory This chapter will deal with the impact of memristive devices on in-memory computing. Also, this chapter will describe the multilevel current/conductance state programming functionality with its application in training and writing of memrisitve crossbar array for random alphabet. Chapter 5: Memristor-based In-memory Logic Operation and Applications This chapter will discuss different in-memory logic memristive systems and study their application for data intensive and highly parallel systems and implementation of various logic operations via analytical modelling. This chapter will also cover the detailed comparison analysis between memristor-based logic circuits and comparison with the pre-existing contemporary CMOS based logics. Chapter 6: Vector Multiplications using Memristive Devices This chapter will discuss the ability of memristive crossbars to efficiently perform in-memory vector-matrix operations. Chapter 7: Stochasticity in Memristive Systems Application of memristive system in noise-induced synchronization and stochastic computing will be discussed in this chapter. Herein, we will outline the effect of perturbation either top and bottom electrodes on the switching performance of the memristor. Chapter 8: Integration of Neuromorphic Chip at System-Level This chapter will discuss techniques to exploit memristive devices in dense, high speed and low-power signal. Chapter 9: Memristive System in Image Processing and Artificial Intelligence This chapter will explore the computational modelling of memristor for image compression, biomedical and agriculture image processing to identify the various diseases in the living organisms and plants/crops, AI, edge detection and correction, and image construction applications.