Machine Learning for Semiconductor Materials (Emerging Materials and Technologies)

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予約

Machine Learning for Semiconductor Materials (Emerging Materials and Technologies)

Gupta, Neeraj (EDT)/ Gupta, Rashmi (EDT)/ Yadav, Rekha (EDT)

ウェブストア価格 ¥31,046（本体¥28,224）
CRC Press（2025/08発売）
外貨定価 US$ 150.00
ポイント 282pt

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製本 Hardcover:ハードカバー版／ページ数 240 p.
言語 ENG
商品コード 9781032796888

Full Description

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 the various algorithms of machine learning such as regression, decision tree, support vector machine and k-means clustering and so forth. This book also highlights semiconductor materials and their uses in multi-gate devices, analog and Radio Frequency (RF) behaviour of semiconductor devices with different materials.

Features:

Focuses on the 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 device at high frequency.
Explores pertinent biomolecule detection method.
Reviews recent methods in the field of machine learning for semiconductor materials with real-life application.
Examines 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.

1. Semiconductor Materials: Current Applications and Limitations for Advanced Semiconductor Devices Applications 2. Machine Learning: Introduction and Features 3. Fault Detection in Semiconductor Manufacturing: A Classification Analysis of the SECOM Dataset 4. Predictive Modelling for Yield Enhancement 5. Deep Learning for Image Classification in Semiconductor Inspection 6. Machine Learning for Semiconductor Devices 7. Numerical simulation based biosensing performance exploration of a cylindrical BioFET using machine learning 8. Semiconductor Materials for EV and Renewable Energy 9. Performance Comparison of Vertical TFET using Triple Metal Gate structures and insights of Machine Learning Approach A comprehensive study 10. Design and performance exploration of macaroni channel based Ge/Si interfaced nanowire FET for analog and high-frequency applications using machine learning