Machine Learning for Semiconductor Materials

1. Auflage 2025
ISBN: 978-1-032-79688-8
Verlag: Taylor & Francis Ltd

Buch, Englisch, 226 Seiten, Format (B × H): 156 mm x 234 mm, Gewicht: 453 g

Reihe: Emerging Materials and Technologies

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.

Zielgruppe

Academic and Postgraduate

Autoren/Hrsg.

Gupta, Neeraj

Sarma, Rajkumar

Gupta, Rashmi

Yadav, Rekha

Dhariwal, Sandeep

Fachgebiete

• Technische Wissenschaften Maschinenbau | Werkstoffkunde Technische Mechanik | Werkstoffkunde Materialwissenschaft: Elektronik, Optik
• Technische Wissenschaften Maschinenbau | Werkstoffkunde Technische Mechanik | Werkstoffkunde Materialwissenschaft: Metallische Werkstoffe

Inhaltsverzeichnis

1. Semiconductor Materials: Current Applications and Limitations of Advanced Semiconductor Devices 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

Über Autor(innen)

Neeraj Gupta is an Associate Professor at Amity University Haryana with over 16 years of teaching experience. His expertise includes VLSI Design, Low Power and Analog Design, AI, and Embedded Systems. He has published 40+ papers, two book chapters, one book, and 12 patents, and received the Best Researcher and Best Teacher Award 2024.

Rashmi Gupta is an Assistant Professor at Amity University Haryana with 13+ years of experience. Her research interests include AI, Software Engineering, and IoT. She has authored 20+ papers, two book chapters, one book, and five patents.

Rekha Yadav is an Assistant Professor at DCRUST, Murthal. She specializes in semiconductor device modeling and VLSI design with 15 years of experience and over 30 publications and four book chapters.

Sandeep Dhariwal is an Associate Professor at Alliance University, Bengaluru. With 14+ years of experience, he focuses on low-power CMOS and semiconductor modeling. He has published 40+ articles, three books, and holds three patents.

Rajkumar Sarma is a postdoctoral researcher at the University of Limerick, Ireland. With 11+ years of experience, his research spans digital VLSI, FPGA prototyping, and quantum architectures. He has 25+ publications, 15+ patents, and two books.