Balancing Yield and Makespan in Wafer Fabrication: A Two-Stage Data-Driven Scheduling Approach

Abstract

In semiconductor manufacturing, achieving high quality and productivity remains a challenging task due to the complexity and variability of multistage production processes. This study addresses the hybrid flow shop scheduling problem (HFSP) in wafer fabrication, targeting the inherent trade-off between quality (yield) and productivity (makespan). We propose a two-stage data-driven scheduling framework that integrates historical manufacturing data. In the first stage, sequential patterns are mined using the PrefixSpan algorithm and are statistically validated. Based on their yield, patterns are classified and recombined via rule-based filtering to derive plausible high-quality (PHQ) paths. In the second stage, the PHQ path-based HFSP is formulated and solved using GAInS, a hybrid metaheuristic framework that incorporates Genetic Algorithm (GA), Iterated Local Search, and Simulated Annealing. Computational experiments across various wafer counts ( = 5, 15, 25, 50) demonstrate that GAInS consistently outperforms Mixed Integer Linear Programming, Constraint Programming models, and basic GA approaches in minimizing makespan while maintaining high yield. Compared to an existing method in the literature that combines regression-based yield prediction with GA-based scheduling, the proposed approach achieves superior Pareto solutions by better balancing quality and productivity. These findings highlight the potential of the proposed framework in balancing critical objectives in wafer fabrication.