Capacity allocation with lot splitting in photolithography area using hybrid genetic algorithm based on self-tuning strategy

摘要

In the semiconductor industry with complicated manufacturing processes, a wafer lot passes through hundreds of operations, and the procedure takes a few months to complete. To meet customer deadline, utilizing and allocating machine capacity in a photolithography area efficiently by making suitable lot/order splitting decisions is a critical issue. Therefore, this research initially presents a mixed-integer nonlinear programming model to solve the capacity allocation problem with lot splitting in the photolithography area. Considering various manufacturing restrictions, such as process capability, machine dedication, and reticle constraints, this model can simultaneously determine the optimal lot-splitting and lot-allocation decisions to minimize the loading difference between each machine. Given the complexity of the mathematical model, this research further develops an adaptive hybrid genetic algorithm (HGA) with a local search mechanism and an auto-tuning strategy, namely, fuzzy logic controller, to solve the capacity allocation problem with lot splitting effectively. Finally, numerical experiments are conducted to demonstrate the efficiency of the developed HGA.