A simulation-based optimization approach for a semiconductor photobay with automated material handling system

This study addressed the issue of automated material handling systems (AMHS) in the photolithography zone of a 300 mm (12-in.) wafer fab facility. The lithography process accounts for 40–50% of the time required to produce wafers. Therefore, managing the AMHS in the photolithography zone is a challenging task. This paper examines the dispatching rule and the number of vehicles in variable wafer input cases. With a stochastic and complex manufacturing process, a photobay simulation may lead to excessive iterations and wasted computation time. The most frequently used approach for process management in the literature is performance analysis with a model that simulates each alternative for N times. However, this approach becomes time consuming as the number of variables and iterations increases. To address this issue, we use Optimal Computing Budget Allocation (OCBA) and extend OCBA by adding particle swarm optimization (PSO). With this combined approached, the number of iterations of each alternative is determined by OCBA, and the optimal solution in the domain of feasible solutions is identified through PSO. This research provides a useful reference to optimally allocate lithographical resources and the number of iterations with random parameters for both scholars and practitioners. Results demonstrate the superiority of PSOOCBA in terms of searching quality and robustness.