Advanced Techniques for Optimal Sizing of Analog Integrated Circuits

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.

• About the Authors xiPreface xiiiAcronyms xvii1 Overview and Problem Formulation 11.1 Integrated Circuit Design Optimization 11.2 The Need for Analog IC Design Optimization 21.3 Analog Circuit Sizing Procedure 41.3.1 Topology Selection 51.3.2 Parameter Selection for Optimization 51.3.3 Optimization Design Variables 51.3.4 Objective Function and Design Constraints 61.4 Problem Modeling for Analog Circuit Sizing Optimization 61.4.1 Problem Modeling for Analog Circuit Sizing Optimization 61.4.2 Criteria for Optimization Assessment 81.4.3 Correlation Between Optimization Algorithms and Analog Circuit Sizing 9References 92 Evolutionary Algorithms in Analog Circuit Sizing Optimization 112.1 Genetic Algorithm 112.1.1 Population Initialization 132.1.2 Decoding 132.1.3 Fitness Evaluation 132.1.4 Selection 142.1.5 Crossover 142.1.6 Mutation 152.2 Self-Adaptive Differential Evolution 152.2.1 Population Initialization and Fitness Evaluation 162.2.2 Mutation 172.2.3 Crossover 172.2.4 Selection 172.3 Biogeography-Based Optimization 182.3.1 Biogeography: The Science of Evolution 182.3.2 Biogeography and Optimization 212.3.2.1 Migration 212.3.2.2 Mutation 212.4 Case Study: Single-Tail Dynamic Comparator 222.4.1 Proposed Python–Spectre Model to Optimize Delay and Power of the Dynamic Comparator 232.4.2 Results and Discussion 26References 293 Animal-Behavior-Inspired Algorithms in Analog Circuit Sizing Optimization 333.1 Particle Swarm Optimization 333.2 Firefly Algorithm 363.2.1 Algorithm Formulation 373.2.2 Implementation 393.3 Cuckoo Search 413.4 Bat Algorithm 433.5 Flower Pollination Algorithm 453.5.1 Global Pollination 473.5.2 Local Pollination 483.5.3 Elitist Selection 483.6 Ant Colony Optimization 493.7 Case Study: PSO and Cuckoo Search Algorithm Implementation in Band-gap Reference Circuit Design 51References 534 Human-Behavior-Inspired Algorithms in Analog Circuit Sizing Optimization 574.1 Ali Baba and the Forty Thieves Algorithm 574.1.1 Basic AFT Algorithm 574.1.2 Self-Adaptive AFT (SaAFT) 604.2 Drawer Algorithm 624.3 Political Optimizer 654.3.1 Party Formation and Constituency Allocation 684.3.2 Election Campaign 694.3.3 Party Switching 704.3.4 Parliamentary Affairs 724.4 War Strategy Optimization 724.4.1 Attack Strategy 744.4.2 Rank and Weight Update 754.4.3 Defense Strategy 764.4.4 Replacement of Weak Soldiers 764.5 Case Study: Two-Stage Miller-Compensated Operational Amplifier with SaAFT and PO 764.5.1 Choice of the Objective Function 774.5.2 Initialization Steps for Optimization 774.5.3 Results and Discussion 79References 835 Machine Learning in Analog Circuit Sizing Optimization 855.1 Machine Learning Overview 855.1.1 Machine Learning: A Heuristic Sandbox 855.1.2 Procedure of Applying Machine Learning 875.1.3 Supervised Learning 895.1.4 Unsupervised Learning 905.1.5 Reinforcement Learning 925.2 Neural Networks 945.3 Hyperparameter Search 965.4 Deep Reinforcement Learning 975.5 Case Study: High-Level Design of Delta–Sigma Analog-to-Digital Converter 995.5.1 Delta–Sigma ADC: Topology and Sizing 995.5.2 Multi-agent Proximal Policy Optimization 1005.5.3 Results and Discussion 101References 1046 Quantum Mechanics and Analog Circuit Sizing Optimization 1056.1 Quantum Computing 1056.1.1 Quantum States 1066.1.2 The Bloch Sphere 1076.1.3 Quantum Gates 1076.1.3.1 Hadamard Gate 1076.1.3.2 Rotation Gate 1086.1.3.3 Pauli Gates 1096.1.4 Hadamard Product 1096.1.5 Frobenius Norm 1096.2 Quantum Variational Optimization 1106.2.1 Initialize the System 1106.2.1.1 Method 1. Amplitude Embedding 1106.2.1.2 Method 2. Angle Encoding 1116.2.1.3 Method 3. Reference State Construction 1126.2.2 Prepare an Ansatz 1126.2.2.1 Parameterized Quantum Operator 1126.2.2.2 Heuristic Ansatze and Trade-Offs 1136.2.2.3 N-Local Circuits 1136.2.3 Readout 1146.2.3.1 Method 1. Sampler 1156.2.3.2 Method 2. Estimator 1156.2.4 Evaluate Cost Function 1166.2.5 Optimize Variational Parameters 1166.3 Grover Optimizer 1176.3.1 Initialization 1186.3.2 Oracle Operator 1196.3.3 Grover Diffusion Operator 1226.3.4 Grover Iteration 1236.4 Quantum Annealing 1256.4.1 Adiabatic Quantum Computing 1256.4.2 Quantum Annealing 1266.5 Quantum Inspiration for Classical Algorithms 1296.5.1 Quantum Population Initialization 1306.5.2 Measurement 1306.5.3 Decoding 1316.5.4 Fitness Evaluation 1316.5.5 Quantum Rotation 1326.5.6 Algorithm-Based Quantum Population Update 1336.5.7 Quantum Genetic Algorithm 1336.5.7.1 Selection 1346.5.7.2 Quantum Crossover 1346.5.7.3 Quantum Mutation 1346.5.8 Hybrid Quantum Firefly—Genetic Algorithm 1356.6 Case Study: Auto-Adjusting Hybrid Quantum Genetic Algorithm for Two-Stage Op-Amp Design 1366.6.1 Initialization Steps for Optimization 1366.6.2 Results and Discussion 136References 141Index 143