Hybrid Automation in Analog Sizing: Synergizing Designer Intuition with Multi-Objective Optimization for Gain-Boosted Amplifiers

This study explores the automated design of operational amplifiers using the Non-dominated Sorting Genetic Algorithm II (NSGA-II) and III (NSGA-III). The research addresses the challenges of optimizing analog circuit dimensions in high-dimensional search spaces. By employing NSGA-II and NSGA-III, the study aims to achieve multi-objective optimization for operational amplifiers, focusing on maximizing gain and bandwidth while minimizing power consumption. The algorithms are applied to designs in TSMC 65 nm technology, with performance metrics including gain, bandwidth, and power consumption. Results indicate that NSGA-III reaches the Pareto frontier faster and provides better optimization outcomes compared to NSGA-II. The proposed approach significantly reduces design time and enhances the efficiency of operational amplifier design.