Advanced Techniques for Optimal Sizing of Analog Integrated Circuits: Quantum Computing, Machine Learning, and Bio-Inspired Optimization

A novel and authoritative approach to quantum machine learning in integrated circuits design optimization

In Advanced Techniques for Optimal Sizing of Analog Integrated Circuits: Quantum Computing, Machine Learning, and Bio-inspired Optimization, a team of distinguished researchers deliver a comprehensive discussion of the theory, models, methodologies, practical implementation, and utilization of integrated circuit (IC) design. The authors explain IC design optimization, demonstrating cost-effective and time-saving design approaches, as well as techniques likely to be impactful in the near future.

The book covers major topics in the field, describing key concepts, recent advances, effective algorithms, and pressing challenges associated with analog circuit sizing optimization. It discusses using both animal and human-inspired optimization algorithms to create basic and quantum machine learning methods.

Readers will also find:

• A novel approach to quantum machine learning in integrated circuit design optimization
• A range of introductory and advanced topics suitable for students and advanced professionals and researchers
• Detailed illustrations that clarify abstract, complicated engineering concepts
• Complete treatments of animal behavior-inspired optimization algorithms, including particle swarm optimization, firefly algorithm, cuckoo search, bat algorithm

Perfect for researchers in engineering, computer scientists, professors, and senior undergraduate and graduate students in integrated circuit design, this book will also benefit students of machine learning, computer science, quantum computing and optimization.