Machine Learning for Semiconductor Materials

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

Buch, Englisch, 240 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 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.

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

Inhaltsverzeichnis

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

Über Autor(innen)

Neeraj Gupta is an Associate Professor at Amity University Haryana, specializing in Device Modeling, VLSI Design, Low Power Design, Analog Design, Optimization Techniques, AI & Machine Learning, IoT & Embedded Systems. Dr Gupta received his doctoral degree at Amity University Haryana, India in 2019. He has more than sixteen years of teaching experience in different subjects. With extensive experience in both academic and research domains, Dr. Gupta has contributed significantly to the fields of VLSI circuits and device simulation. His work often bridges cutting-edge technologies with practical applications, making him a key figure in advancing innovations in semiconductor technologies and AI-driven systems. He has published more than 40 international articles in different reputed SCI/SCOPUS indexed journals, conferences, and 2 book chapters. He has also published one book on Microprocessor and five patents. He has also received Best Researcher and Best Teacher award 2024 from Wegrow Pvt. Ltd.

Rashmi Gupta is an Assistant professor at Amity University Haryana, Gurugram, India. Dr Gupta received her doctoral degree at Amity University Haryana, India in 2020. She has more than thirteen years of teaching and research experience. Her current research interests are Artificial Intelligence, Machine Learning, Software Engineering, Soft computing, IoT, etc. She has published more than 20 international articles in the different reputed SCI/SCOPUS journals and 2 book chapters. She has published five patents and has also published one book on Software Testing.

Rekha Yadav is an Assistant professor at DCR University of Science and Technology, Murthal, India. She received her doctoral degree from DCR University of Science and Technology, Murthal, Haryana, India in 2018. She has 15 years of teaching and research experience. Her current research interests are semiconductor device modelling, VLSI design etc. She has published more than 30 international articles in the different reputed SCI/SCOPUS journals and 4 book chapters.

Sandeep Dhariwal is an Associate professor at Alliance University-Bengaluru, India. Dr Dhariwal received his doctoral degree at Banasthali University- Rajasthan, India in 2015. He has more than fourteen years of teaching experience in different subjects. His current research interests are Semiconductor device Modeling, Low power CMOS VLSI Design etc. He has published more than 40 international articles in the different reputed SCI/SCOPUS indexed journals, conferences and 3 books. He published 3 patents in multidisciplinary area of research.

Rajkumar Sarma received his B.E. in Electronics and Communications Engineering from Vinayaka Mission’s University, Salem, India in 2008. He received his MTech as well as Ph.D. degrees from Lovely Professional University, Phagwara, Punjab in the years 2012 and 2020, respectively. He has more than 11 years of teaching and research experience. He is currently working as a postdoctoral researcher at the University of Limerick, Limerick, Ireland under the Automatic Design of Digital Circuits (ADDC) project since September 2022. He is working on a Science Foundation of Ireland (SFI) funded project to generate VHDL/Verilog code for digital circuit design and apply it to the creation of different IP Blocks, specifically FIR and FFT filters. His research interests include (but are not limited to) Analog and Digital VLSI design, Low-power architecture design, Quantum Cellular Automata, Prototype development using FPGA etc. The researcher has around 25+ research publications in SCI, Scopus indexed and other reputed Journals along with national/international conferences. Moreover, the author has 15+ patents published in various engineering fields. Dr Sarma has also published two books on Hardware Description Language (HDL) and FPGA prototyping.