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.
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.
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 has 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, 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.
Inhaltsangabe
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
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
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