Application of a genetic algorithm in solving the capacity allocation problem with machine dedication in the photolithography area. (October 2016)

• Record Type: Journal Article
• Authors: Chen, James C.; Chen, Yin-Yann; Liang, Yu
• Is Part Of: Journal of manufacturing systems. Volume 41(2016)
• Page Start: 165
• Page End: 177
• Publication Date: 2016-10
• Subjects: Wafer fabrication -- Capacity allocation -- Capacity planning -- Genetic algorithm; Manufacturing processes -- Periodicals; Production engineering -- Data processing -- Periodicals; Robots, Industrial -- Periodicals; Production, Technique de la -- Informatique -- Périodiques; Robots industriels -- Périodiques; Electronic journals; 670.42
• DOI: 10.1016/j.jmsy.2016.08.010
• Languages: English
• ISSNs: 0278-6125

Abstract

Highlights: Loading balance problem for photolithography area in semiconductor manufacturing. An integer programming model and a heuristic algorithm are proposed. The paper considers process capability, machine dedication, and reticle constraints. Computational performance results show that GA can find the near-optimal solution. Machine capability and reticle flexibility are analyzed to decide the best decision. Abstract: Wafer fabrication is a complicated manufacturing process with high process capability. Hence, maximizing machine capacity to meet customer deadlines is a very important issue in this field. This study proposes an integer programming model and a heuristic algorithm approach to solve the loading balance problem for the photolithography area in the semiconductor manufacturing industry. Considering process capability, machine dedication, and reticle constraints, we aim to minimize the difference in loading between machines. Process capability means that each product must be processed in machines that meet the process specification. Machine dedication means that if the first critical layer of a wafer is assigned to a certain machine, then the following critical layers of such wafer must be processed in this certain machine to ensure wafer quality. This research compares the results of two methods and finds the best parameter settings of the genetic algorithm (GA). The computational performance results of the GA shows that we can find the near-optimal solution within a reasonable amount of time. Finally, this research analyzes machine capability and reticle flexibility to determine the best percentage that can be used as reference for application in the semiconductor industry.