Resistive Switching Systems for In-Memory Computation and Artificial Intelligence

— —

Shaibal Mukherjee (Indian Institute of Technology Indore (India))

Resistive Switching Systems for In-Memory Computation and Artificial Intelligence

Name: Resistive Switching Systems for In-Memory Computation and Artificial Intelligence
Price: 283.36 AUD
Availability: OutOfStock
ISBN: 9780750361675

Shaibal Mukherjee (Indian Institute of Technology Indore (India))

$353.95 $283.36

Hardback

Not in-store but you can order this
How long will it take?

Availability Information

We source books from suppliers in Australia and overseas. For books we don't currently have in stock, the time it takes to get them from our suppliers can vary widely - from a few days to a few months - so we check each book with each supplier to determine the expected time it will take to be supplied to us.

We then advise you accordingly. If the time taken to get any book is too long for you, you can let us know and we will cancel or adjust your order, and refund as required.

To find out the anticipated arrival time for specific items prior to ordering, please contact us by phone or email:

Phone +61 2 9264 3111, or 1800 4 BOOKS (1800 4 26657) if outside Sydney:
option 1 Abbey's Bookshop (Crime, History, Science, Kids & more) • info@abbeys.com.au
option 2 Language Book Centre (ESL & Foreign Languages) • language@abbeys.com.au
option 3 Galaxy Bookshop (Sci-fi, Fantasy, Romance, Graphic Novels) • sf@galaxybooks.com.au

QTY:

English

Institute of Physics Publishing
06 January 2026

Electronic devices & materials; Machine learning

Series: IOP Series in Next Generation Computing

This book provides a detailed review on neuromorphic system theory and its practical realization along with hardware-level implementations. This book will allow readers to understand the new fundamental concepts related to memristive systems and their ongoing and futuristic applications. In summary, this is a comprehensive description of emerging memristor memory technology with its fundamentals, behaviour modeling, physical modeling and potential applications. This book will facilitate classroom adaptations, and its content makes it suitable for advanced diploma, under-graduate and post-graduate courses in various universities.

Key Features:

Includes fundamental theoretical concepts as well as materials and device properties, physical and analytical modelling, algorithmic aspects, circuits, and architectures. Encompasses a broad range of applications such as brain-inspired computing, in-memory computation tasks, logic circuit realization and image computation and processing. Interdisciplinary approach embracing material science, VLSI circuit and systems, electrical and computer engineering, mathematics and physics.

By: Shaibal Mukherjee (Indian Institute of Technology Indore (India))
Imprint: Institute of Physics Publishing
Country of Publication: United Kingdom
Dimensions: Height: 254mm, Width: 178mm, Spine: 17mm
Weight: 732g
ISBN: 9780750361675
ISBN 10: 0750361670
Series: IOP Series in Next Generation Computing
Pages: 296
Publication Date: 06 January 2026
Audience: Professional and scholarly , Undergraduate
Format: Hardback
Publisher's Status: Active

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.

Shaibal Mukherjee completed his PhD in Electrical and Computer Engineering, University of Oklahoma, USA in 2009 followed by his postdoctoral research work in the Center of Quantum Devices, Electrical Engineering and Computer Science, Northwestern University, USA. In September 2010, he joined IIT Indore and currently is a Professor in the Department of Electrical Engineering at IIT Indore. The Hybrid Nanodevice Research Group (HNRG) led by Shaibal at IIT Indore explores new physics of micro- and nano-structured materials, and to apply this knowledge in realizing advanced tools and devices for chemical, biological, optical, electronic and energy applications. He has published 145+ research articles in peer-reviewed journals, 110+ international conference proceedings, 11 book/book chapters and 16 patents (Granted: 14 and Filed/Published: 2). He is the recipient of various prestigious awards such as “2025 Friedrich Wilhelm Bessel Research Award by Alexander von Humboldt Foundation”, “2024 Microelectronic Engineering Journal Middle Career Investigator Award and Lectureship”, “2024 TIH-IoT CHANAKYA Faculty Fellowship, IIT Bombay”, “2023 Japan Society for the Promotion of Science (JSPS) Invitational Fellowship Award”, “2021 JSPS Invitational Fellowship Award”, “2020 DUO-India Professor Fellowship Award”, “2019 DAAD Fellowship Award”, “2018 Materials Research Society of India (MRSI) Medal”, “2016 Young Faculty Research Fellowship (YFRF) under Visvesvaraya PhD Scheme for Electronics and IT”. He is an Associate Editor for IEEE Sensors Journal, a senior member of IEEE, a regular member of Optical Society of America and Life Fellow of MRSI and Optical Society of India. He is a fellow of IoP, JSPS, Humboldt Foundation, RSC and IET. He is the founding Chair of IEEE Madhya Pradesh (MP) Section Electron Devices Society (EDS) chapter. He is also the Director, Co-Founder and Mentor of QuanTechL2M Innovations Pvt. Ltd.