Adaptive Test Optimization for Reducing Cost and Improving Coverage in High-Volume Semiconductor Production

Abstract

High-volume semiconductor production requires fast and reliable testing to control cost while protecting product quality. Conventional full testing provides strong fault coverage, but it increases test time, test cost, and production burden when every device follows the same fixed test flow. Adaptive testing offers a better direction by selecting test paths according to device response, waferlevel risk, and coverage importance. Existing test optimization methods often reduce time or cost, but they may not fully control escaped defects when test removal is too aggressive. This article presents an adaptive test optimization framework for reducing test cost and improving coverage in semiconductor production. The study compares conventional full testing, rule-based adaptive testing, machine-learning-assisted adaptive testing, and aggressive test reduction using simulated high-volume production data. The results show that machine-learning-assisted adaptive testing gives the best balance by reducing test time and cost while maintaining high fault coverage, improving screening accuracy, and lowering escaped defects. The framework supports safer and more efficient semiconductor test decision-making.