Advancing VLSI through Machine Learning

Dr. Abhishek Narayan Tripathi is currently an Assistant Professor in the Department of Micro and Nanoelectronics, School of Electronics Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India. He holds a Ph.D. in ECE with a specialization in VLSI Design and Embedded Technology from MANIT, Bhopal. His research work includes the development of methodologies for dynamic power and leakage power estimation in FPGA and ASIC‑based implementations, VLSI system design, AI, deep learning, and microprocessor architecture.Dr. Jagana Bihari Padhy is an Assistant Professor in the Department of Embedded Technology, School of Electronics Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India. He holds a Ph.D. in ECE with a specialization in optical wireless system design from IIIT Bhubaneswar. His research work includes the development of optical system design both in wired and wireless methodologies for the next generation of communication 5G and beyond.Dr. Indrasen Singh is an Assistant Professor (Sr. Grade‑2) in the Department of Embedded Technology, School of Electronics Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India. His research interests are in the areas of cooperative communication, stochastic geometry, modelling of wireless networks, heterogeneous networks, millimetre wave communications, device‑to‑device communication, and 5G/6G communication.Dr. Shubham Tayal is an Assistant Professor in the Department of Electronics and Communication Engineering, SR University, Warangal, India. He has more than 6 years of academic/research experience in teaching at the UG and PG levels. He received his Ph.D. in Microelectronics and VLSI Design from the National Institute of Technology, Kurukshetra; M.Tech. (VLSI Design) from YMCA University of Science and Technology, Faridabad; and B.Tech. (Electronics and Communication Engineering) from MDU, Rohtak. His research interests include simulation and modelling of multi‑gate semiconductor devices, device‑circuit co‑design in digital/analogue domain, ML, and Internet of Things.Prof. Ghanshyam Singh received a Ph.D. degree in Electronics Engineering from the Indian Institute of Technology, Banaras Hindu University, Varanasi, India, in 2000. At present, he is a full Professor with the Department of Electrical and Electronics Engineering, APK Campus, University of Johannesburg, South Africa. His research and teaching interests include RF/microwave engineering, millimetre/THz wave antennas and their applications in communication and imaging, next‑generation communication systems (OFDM and cognitive radio), and nanophotonics. He has more than 19 years of teaching and research experience in electromagnetic/microwave engineering, wireless communication, and nanophotonics.

• Chapter 1. Optimizing Circuit Synthesis: Integrating Neural Networks and Evolutionary Algorithms for Increased Design EfficiencyChapter 2. Study of Physical Processes Analysis and Phenomena of Insights of Trapping in the Performance Degradation in AlGaN/GaN HEMTsChapter 3. Framework for Design and Performance Evaluation of Memory using MemristorChapter 4. Innovative Design and Optimization of High-Power Amplifiers: A Comparative Study with GaN HEMT and CMOS TechnologiesChapter 5. Exploring FPGA Architecture Designs for Matrix Multiplication in Machine LearningChapter 6. Silicon Chip Design and TestingChapter 7. A Novel Deep Learning Approach for Early Brain Tumour DetectionChapter 8. TCAD Augmented Machine Learning for the Prediction of Device Behavior and Failure AnalysisChapter 9. Opportunities and Challenges for ML-Based FPGA Backend FlowChapter 10. Role of Machine Learning Applications in VLSI DesignChapter 11. Application of Artificial Intelligence/Machine Learning in VLSI DesignChapter 12. FinFET-Based 9T SRAM for Enhanced Performance in AI/ML ApplicationsChapter 13. Power Consumption and SNM Analysis of 6T and 7T SRAM using 90nm TechnologyChapter 14. Transforming Electronics: An Extensive Analysis of Hyper-FET Technological Developments and UtilisationChapter 15. VLSI Realization of Smart Systems using Blockchain and Fog Computing