关键词: 晶圆良率优化, 粒子群算法, 晶圆允收测试, 预测模型, 局部搜索

Abstract: Wafer yield is an important indicator to measure the quality of wafer products,and achieving stable optimization can effectively control production costs.In view of the numerous factors affecting wafer yield and the huge amount of data,according to the wafer acceptance testing,a wafer yield optimization method based on improved particle swarm algorithm was designed.Based on the preprocessing of wafer acceptance test data and the extraction of key parameters,a wafer yield prediction model was established for evaluating particle fitness,an iterative adaptive particle swarm inertia factor and acceleration factor were designed,and the local search mechanism based on simulated annealing was designed,which achieved the goal of maximizing wafer yield with minimum adjustment cost.In the example test of a wafer manufacturing workshop,the optimization results of improved particle swarm algorithm,genetic algorithm and traditional particle swarm algorithm were compared and analyzed,and the effectiveness of the proposed method was verified.

Key words: wafer yield improvement, particle swarm optimization, wafer acceptance test, prediction model, local search