Two-Stage OTA Sizing Optimization Using Bio-Inspired Algorithms

The analog part of a mixed-signal integrated circuit represents a great amount of the circuit sizing effort. It is necessary to size each device separately and, in cases with several variables, the design space becomes quite large. The analog integrated circuit sizing can be modeled as an optimization problem and solved by optimization heuristics. In this work, we compare three bio-inspired heuristics to size a twostage CMOS Miller operational transconductance amplifier: Particle Swarm Optimization (PSO), Cuckoo Search (CS) and Firefly Algorithm (FA). The goal is to evaluate the applicability of these heuristics for the analog sizing problem and to determine the best configuration of the algorithms parameters for optimizing performance of the generated circuit, mainly power consumption and gate area. Results show that PSO and CS are more suitable to find optimized solutions, while FA presents less efficient exploration of the design space. Although PSO is faster and generates good solutions, the best overall solution was achieved with CS algorithm, which resulted in a reduction of 62% in power consumption and 31% in gate area when compared to the results obtained with FA, in addition to meeting all design constraints.

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