Ant Colony Optimization for Analog Integrated Circuit Design: Enhancing Performance on 65nm CMOS Technology

This paper explores the application of the Ant Colony Optimization (ACO) algorithm in optimizing analog circuits, specifically focusing on the two-stage operational amplifier (op-amp) with Miller compensation. The design process adheres to initial technical constraints and employs the Figure of Merit (FoM) as the objective function to assess circuit quality. The ACO algorithm mimics the foraging behavior of ants, utilizing pheromone distribution and probabilistic improvement to guide the search towards the optimal solution (i.e., the highest FoM). Simulation data from Cadence Virtuoso with the 65nm process is used to construct a Python-based implementation of the ACO algorithm to determine the optimal circuit parameters that satisfy the initial design constraints.