Chapter
1. Foundations of VLSI and Machine Learning

• Optimizing Circuit Synthesis: Integrating Neural Networks and Evolutionary Algorithms for Increased Design Efficiency

• Study of Physical Processes Analysis and Phenomena of Insights of Trapping in the Performance Degradation in AlGaN/GaN HEMTs

• Framework for Design and Performance Evaluation of Memory using Memristor

• Innovative Design and Optimization of High-Power Amplifiers: A Comparative Study with GaN HEMT and CMOS Technologies

• Exploring FPGA Architecture Designs for Matrix Multiplication in Machine Learning

• Silicon Chip Design and Testing

• A Novel Deep Learning Approach for Early Brain Tumour Detection

• TCAD Augmented Machine Learning for the Prediction of Device Behaviour and Failure Analysis

• Opportunities and Challenges for ML-Based FPGA Backend Flow

• Role of Machine Learning Applications in VLSI Design

• Application of Artificial Intelligence/Machine Learning in VLSI Design

• FinFET-Based 9T SRAM for Enhanced Performance in AI/ML Applications

• Power Consumption and SNM Analysis of 6T and 7T SRAM using 90nm Technology

• Transforming Electronics: An Extensive Analysis of Hyper-FET Technological Developments and Utilisation

• VLSI Realization of Smart Systems using Blockchain and Fog Computing