Advanced Techniques for Optimal Sizing of Analog Integrated Circuits - Trang Hoang

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Résumé :

Preface ix

About the Authors xiii

Acronyms xv

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 andWeight Update 75

4.4.3 Defense Strategy 76

4.4.4 Replacement ofWeak 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 78

4.5.3 Results and Discussion 80

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 ...

Biographie:

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 Optimizat...

Sommaire:

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....