Machine Learning for Semiconductor Materials

Neeraj Gupta, Rashmi Gupta, Rekha Yadav, Sandeep Dhariwal, Rajkumar Sarma

• Publisher: CRC Press
• Year: 2025
• eBook ISBN: 9781040398104
• Topic: Technology & Engineering
• Subtopic: Materials Science
• Index: Technology & Engineering

Machine Learning for Semiconductor Materials studies recent techniques and methods of machine learning to mitigate the use of technology computer-aided design (TCAD). It provides various algorithms of machine learning, such as regression, decision tree, support vector machine, K-means clustering and so forth. This book also highlights semiconductor materials and their uses in multi-gate devices and the analog and radio-frequency (RF) behaviours of semiconductor devices with different materials.

Features:

• Focuses on semiconductor materials and the use of machine learning to facilitate understanding and decision-making
• Covers RF and noise analysis to formulate the frequency behaviour of semiconductor devices at high frequency
• Explores pertinent biomolecule detection methods
• Reviews recent methods in the field of machine learning for semiconductor materials with real-life applications
• Examines the limitations of existing semiconductor materials and steps to overcome the limitations of existing TCAD software

This book is aimed at researchers and graduate students in semiconductor materials, machine learning and electrical engineering.

Table of contents

• Cover
• Half Title
• Series
• Title
• Copyright
• Contents
• Preface
• Editor Biographies
• List of Contributors
• List of Figures and Tables
• Chapter 1 Semiconductor Materials: Current Applications and Limitations of Advanced Semiconductor Devices
• Chapter 2 Machine Learning: Introduction and Features
• Chapter 3 Fault Detection in Semiconductor Manufacturing: A Classification Analysis of the SECOM Dataset
• Chapter 4 Predictive Modelling for Yield Enhancement
• Chapter 5 Deep Learning for Image Classification in Semiconductor Inspection
• Chapter 6 Machine Learning for Semiconductor Devices
• Chapter 7 Numerical Simulation-Based Biosensing Performance Exploration of a Cylindrical BioFET Using Machine Learning
• Chapter 8 Semiconductor Materials for EV and Renewable Energy
• Chapter 9 Performance Comparison of Vertical TFET Using Triple Metal Gate Structures and Insights of Machine Learning Approach: A Comprehensive Study
• Chapter 10 Design and Performance Exploration of Macaroni Channel-Based Ge/Si Interfaced Nanowire FET for Analog and High-Frequency Applications Using Machine Learning
• Index