Status And Development Of Intelligent Optimization Algorithm For Application In Integrated Circuit Design

With the continuous advancement of integrated circuit (IC) technique, circuit design hasbecome increasingly challenging. Currently, the design of integrated circuits is generally achievedby the manual design, which needs more time and cost. Intelligent optimization algorithms aredesigned on the basis of the intelligent phenomenon which is shown by the biological groups of thenature. IC design technology, which is based on the intelligent optimization algorithms, canresemble the biological evolution which has the characteristics of sef-evolution of environmentalchanges, immune and self-adaption, etc.This article describes the basic principles and operational processes of various intelligentoptimization algorithms, and analyzes their respective strengths and weaknesses. The geneticalgorithm (GA) is selected as the focus of the study for it is most widely used and has strong globalsearch ability. The basic principles and implementation process of the niche genetic algorithm,immune genetic algorithm and elite genetic algorithm are discussed in detail. The application ofthese algorithms in analog integrated circuits, radio frequency integrated circuits (RF ICs) anddigital ICs are studied from their encoding, the fitness function and the circuit topology, etc. Gilbertmixer is used as the design examples of RF ICs, to compare the circuit obtained from the GAoptimization with the manual design one, the simulation results show that when performanceindicators are not high and performance analytic equations are accurate, optimization algorithms isindeed effective to some extent to optimize the circuit design. Finally two operational amplifierwith Miller compensated are used as the design examples of analog ICs, to compare the circuitobtained from GA with the manual design one. The experimental results show that the optimizedperformance of the algorithm is limited to some extent by the performance analytic equation and theaccuracy of the objective function model. Therefore, there are still more researchs in this field to beexpanded.