Scheduling of multiple in-line steppers for semiconductor wafer fabs

Abstract

A few prior studies noticed that an in-line stepper (a bottleneck machine in a semiconductor fab) may have a capacity loss while operated in a low-yield scenario. To alleviate such a capacity loss, some meta-heuristic algorithms for scheduling a single in-line stepper were proposed. Yet, in practice, there are multiple in-line steppers to be scheduled in a fab. This article aims to enhance prior algorithms so as to deal with the scheduling for multiple in-line steppers. Compared to prior studies, this research has to additionally consider how to appropriately allocate jobs to various machines. We enhance prior algorithms by developing a chromosome-decoding scheme which can yield a job-allocation decision for any given chromosome (or job sequence). Seven enhanced versions of meta-heuristic algorithms (genetic algorithm, Tabu, GA-Tabu, simulated annealing, M-MMAX, PACO and particle swarm optimisation) were then proposed and tested. Numerical experiments indicate that the GA-Tabu method outperforms the others. In addition, the lower the process yield, the better is the performance of the GA-Tabu algorithm.