Performance-Driven Optimization of CMOS-Based Two-Stage Operational Amplifier Using Metaheuristic Algorithms

Abstract

Design of CMOS based analog circuits becomes increasingly complex as transistor sizing plays a crucial role due to the trade-offs among power consumption, silicon area, unity gain bandwidth, slew rate, and open loop gain. This sizing challenge is makes analog circuit design inherently multi objective, and traditional analytical approaches based on simplified transistor level equations often fail to deliver globally optimal results. Metaheuristic optimization techniques have emerged as an effective alternative to explore nonlinear and multi-dimensional design spaces. In this work, the design of a two stage CMOS operational amplifier in the Predictive Technology Model (PTM) 45 nm technology node is optimized using four algorithms: Particle Swarm Optimization (PSO), RAO algorithm, Teaching Learning Based Optimization (TLBO), and the proposed Modified TLBO (MTLBO). The algorithms were implemented in Python and verified through Ngspice-26 simulator on an AMD Ryzen™ processor with 16 GB RAM, 64 bit Ubuntu environment. The proposed MTLBO achieved 86.15 dB voltage gain, 94.05 dB CMRR, and 185 MHz unity gain bandwidth. Comparative analysis shows that the proposed MTLBO algorithm achieves faster convergence with fewer iterations and consistently outperforms PSO, RAO, and TLBO making it a strong candidate for efficient analog VLSI design automation.