An Analog Circuit Design and Optimization System With Rule-Guided Genetic Algorithm

Abstract

The developing optimization algorithms provide promising solutions for speeding up analog integrated circuit sizing. However, the optimization of complicated circuits whose solution regions are narrow remains to be a challenge. With a limited number of sampling points due to the restriction of computational resources, it is difficult for traditional algorithms to achieve satisfactory results for such circuits. To solve this problem, this article proposes a rule-guided genetic algorithm (RG-GA) for analog circuit optimization. Different from the random mutation approach in the traditional genetic algorithm (GA), the RG-GA introduces a design rule-guided mutation (RGM) mechanism which helps to find the solution region in a more straightforward fashion. Instead of handing over circuit optimization tasks to pure mathematical algorithms, the proposed method takes advantages of valuable design knowledge to improve searching efficiency. This novel algorithm is implemented and deployed to design a two-stage rail-to-rail operational amplifier (OPA), an LC voltage controlled oscillator (LC-VCO) and a four-stage OPA. Experimental results show that compared to the traditional GA method, the RG-GA achieves about 1.5 and 3.3 times speed enhancement for the two-stage rail-to-rail OPA and the LC-VCO, respectively. For the four-stage OPA, the RG-GA method can find an acceptable point within the given number of iterations while the traditional GA could not.