FULL PDF
Abstract
The semiconductor industry is under continuous pressure to optimize production efficiency while maintaining rigorous quality standards. One of the key challenges lies in reducing test time without compromising yield or product reliability. This paper explores the application of multi-objective optimization using evolutionary artificial intelligence (AI) methods to minimize test time in semiconductor production lines. By framing test time reduction as a multi-objective problem—balancing speed, cost, and quality—evolutionary algorithms such as NSGA-II and MOEA/D demonstrate significant potential. Empirical insights and simulation-based evaluations show improved efficiency and trade-off navigation compared to traditional single-objective methods.
View more >>