Papers by Keyword: Semiconductor Wafer Fabrication

Abstract: Photolithography is usually the bottleneck process with the most expensive equipment in a semiconductor wafer fabrication system. To improve the performances of the photolithography area with dynamic combination rules, a method of Kohonen neural network (KNN)–based performance improvements is proposed. First, a dynamic scheduling framework based on a KNN model and scheduling rules is proposed. A KNN-based sample learning algorithm for improving the performances is presented. Finally, to demonstrate the validity and feasibility of the proposed method, data from a real wafer fabrication system are used to simulate the proposed method. Results of simulation experiments indicate that the proposed method can be used to improve a complex wafer photolithography performance.

Abstract: Due to the increasing scale of scheduling problems, the study is to explore an effective optimize scheduling approach for the semiconductor wafer fab. Semiconductor manufacturing is widely regarded as one of the most complex manufacturing processes in the world. The particular complexity, characterized by reentrant flows, uncertainties, mixed processing styles, etc., leads to a great challenge in its scheduling. Without powerful scheduling techniques it becomes practically impossible to design a manufacturing system. So, by means of a new coding method, and the adaptive mechanism of crossover and mutation, an improved immune algorithm (IA) is presented to solve the scheduling problem. The algorithm is based on clonal selection and affinity maturation for finding optimal solutions. To demonstrate the efficiency of the IA, some numerical experiments are carried out. The results show that the IA will yield a more efficient solution than several other scheduler.