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, 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, 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, and 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.

Table of Contents:
About the Authors xi Preface xiii Acronyms xvii 1 Overview and Problem Formulation 1 1.1 Integrated Circuit Design Optimization 1 1.2 The Need for Analog IC Design Optimization 2 1.3 Analog Circuit Sizing Procedure 4 1.3.1 Topology Selection 5 1.3.2 Parameter Selection for Optimization 5 1.3.3 Optimization Design Variables 5 1.3.4 Objective Function and Design Constraints 6 1.4 Problem Modeling for Analog Circuit Sizing Optimization 6 1.4.1 Problem Modeling for Analog Circuit Sizing Optimization 6 1.4.2 Criteria for Optimization Assessment 8 1.4.3 Correlation Between Optimization Algorithms and Analog Circuit Sizing 9 References 9 2 Evolutionary Algorithms in Analog Circuit Sizing Optimization 11 2.1 Genetic Algorithm 11 2.1.1 Population Initialization 13 2.1.2 Decoding 13 2.1.3 Fitness Evaluation 13 2.1.4 Selection 14 2.1.5 Crossover 14 2.1.6 Mutation 15 2.2 Self-Adaptive Differential Evolution 15 2.2.1 Population Initialization and Fitness Evaluation 16 2.2.2 Mutation 17 2.2.3 Crossover 17 2.2.4 Selection 17 2.3 Biogeography-Based Optimization 18 2.3.1 Biogeography: The Science of Evolution 18 2.3.2 Biogeography and Optimization 21 2.3.2.1 Migration 21 2.3.2.2 Mutation 21 2.4 Case Study: Single-Tail Dynamic Comparator 22 2.4.1 Proposed Python–Spectre Model to Optimize Delay and Power of the Dynamic Comparator 23 2.4.2 Results and Discussion 26 References 29 3 Animal-Behavior-Inspired Algorithms in Analog Circuit Sizing Optimization 33 3.1 Particle Swarm Optimization 33 3.2 Firefly Algorithm 36 3.2.1 Algorithm Formulation 37 3.2.2 Implementation 39 3.3 Cuckoo Search 41 3.4 Bat Algorithm 43 3.5 Flower Pollination Algorithm 45 3.5.1 Global Pollination 47 3.5.2 Local Pollination 48 3.5.3 Elitist Selection 48 3.6 Ant Colony Optimization 49 3.7 Case Study: PSO and Cuckoo Search Algorithm Implementation in Band-gap Reference Circuit Design 51 References 53 4 Human-Behavior-Inspired Algorithms in Analog Circuit Sizing Optimization 57 4.1 Ali Baba and the Forty Thieves Algorithm 57 4.1.1 Basic AFT Algorithm 57 4.1.2 Self-Adaptive AFT (SaAFT) 60 4.2 Drawer Algorithm 62 4.3 Political Optimizer 65 4.3.1 Party Formation and Constituency Allocation 68 4.3.2 Election Campaign 69 4.3.3 Party Switching 70 4.3.4 Parliamentary Affairs 72 4.4 War Strategy Optimization 72 4.4.1 Attack Strategy 74 4.4.2 Rank and Weight Update 75 4.4.3 Defense Strategy 76 4.4.4 Replacement of Weak Soldiers 76 4.5 Case Study: Two-Stage Miller-Compensated Operational Amplifier with SaAFT and PO 76 4.5.1 Choice of the Objective Function 77 4.5.2 Initialization Steps for Optimization 77 4.5.3 Results and Discussion 79 References 83 5 Machine Learning in Analog Circuit Sizing Optimization 85 5.1 Machine Learning Overview 85 5.1.1 Machine Learning: A Heuristic Sandbox 85 5.1.2 Procedure of Applying Machine Learning 87 5.1.3 Supervised Learning 89 5.1.4 Unsupervised Learning 90 5.1.5 Reinforcement Learning 92 5.2 Neural Networks 94 5.3 Hyperparameter Search 96 5.4 Deep Reinforcement Learning 97 5.5 Case Study: High-Level Design of Delta–Sigma Analog-to-Digital Converter 99 5.5.1 Delta–Sigma ADC: Topology and Sizing 99 5.5.2 Multi-agent Proximal Policy Optimization 100 5.5.3 Results and Discussion 101 References 104 6 Quantum Mechanics and Analog Circuit Sizing Optimization 105 6.1 Quantum Computing 105 6.1.1 Quantum States 106 6.1.2 The Bloch Sphere 107 6.1.3 Quantum Gates 107 6.1.3.1 Hadamard Gate 107 6.1.3.2 Rotation Gate 108 6.1.3.3 Pauli Gates 109 6.1.4 Hadamard Product 109 6.1.5 Frobenius Norm 109 6.2 Quantum Variational Optimization 110 6.2.1 Initialize the System 110 6.2.1.1 Method 1. Amplitude Embedding 110 6.2.1.2 Method 2. Angle Encoding 111 6.2.1.3 Method 3. Reference State Construction 112 6.2.2 Prepare an Ansatz 112 6.2.2.1 Parameterized Quantum Operator 112 6.2.2.2 Heuristic Ansatze and Trade-Offs 113 6.2.2.3 N-Local Circuits 113 6.2.3 Readout 114 6.2.3.1 Method 1. Sampler 115 6.2.3.2 Method 2. Estimator 115 6.2.4 Evaluate Cost Function 116 6.2.5 Optimize Variational Parameters 116 6.3 Grover Optimizer 117 6.3.1 Initialization 118 6.3.2 Oracle Operator 119 6.3.3 Grover Diffusion Operator 122 6.3.4 Grover Iteration 123 6.4 Quantum Annealing 125 6.4.1 Adiabatic Quantum Computing 125 6.4.2 Quantum Annealing 126 6.5 Quantum Inspiration for Classical Algorithms 129 6.5.1 Quantum Population Initialization 130 6.5.2 Measurement 130 6.5.3 Decoding 131 6.5.4 Fitness Evaluation 131 6.5.5 Quantum Rotation 132 6.5.6 Algorithm-Based Quantum Population Update 133 6.5.7 Quantum Genetic Algorithm 133 6.5.7.1 Selection 134 6.5.7.2 Quantum Crossover 134 6.5.7.3 Quantum Mutation 134 6.5.8 Hybrid Quantum Firefly—Genetic Algorithm 135 6.6 Case Study: Auto-Adjusting Hybrid Quantum Genetic Algorithm for Two-Stage Op-Amp Design 136 6.6.1 Initialization Steps for Optimization 136 6.6.2 Results and Discussion 136 References 141 Index 143

About the Author :
Trang Hoang is an Associate Professor at Ho Chi Minh City University of Technology, Vietnam National University in Ho Chi Minh City, Vietnam. Thinh Quang Do is a Research Assistant and a PhD student at the Memorial University of Newfoundland, Canada. Thang Quoc Nguyen is a Research Assistant and a PhD student at the Memorial University of Newfoundland, Canada. Hoang Trong Nguyen is a Research Assistant and a Master student at the Memorial University of Newfoundland, Canada. Lihong Zhang is a Full Professor with the Department of Electrical and Computer Engineering, Faculty of Engineering and Applied Science at the Memorial University of Newfoundland, Canada. Octavia A. Dobre is a Professor and the Tier-1 Canada Research Chair at Memorial University of Newfoundland, Canada. Trung Q. Duong is the Canada Excellence Research Chair and a Full Professor at the Memorial University of Newfoundland, Canada. He is also an Adjunct Professor at Queen’s University Belfast, UK and a Visiting Professor at Kyung Hee University, South Korea.