Designing and Performance Optimization of the Silicon Wafer Manufacturing System Using PSO and GA Algorithms

There are several subsystems that work together to make silicon wafers. Because quantum computing, power electronics, aerospace, and military are all growing fields, it is important to make manufacturing processes more efficient and cost-effective to improve performance and production efficiency. The goal of this study is to make the silicon wafer system work better by using Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). The Markov birth-death process is used to mathematically represent the system, and differential equations are constructed using the system’s state transition diagram. The Markov approach says that a system will work 98.31% of the time; however, using the PSO and GA algorithms makes it work 99.67% and 99.36% of the time. MATLAB R2023 uses these optimization approaches, and the algorithms change important system settings, such as population size, to get the best results. The suggested technique gives the silicon wafer manufacturing sector useful information that helps them find the best operating parameters and make their production processes more efficient and dependable.