Fuzzy Logic Models with Adaptive Learning Rates and Genetic Algorithm for Thermally Based Microelectronic Manufacturing Processes

• Main Authors: Lu, Chi-Fu, 盧啟富
• Other Authors: Chiou Jin-Cherng
• Format: Others
• Language: zh-TW
• Published: 1996
• Online Access: http://ndltd.ncl.edu.tw/handle/77342210875456884445

碩士 === 國立交通大學 === 控制工程系 === 84 === This paper presents the improved fuzzy logic models (FLM) to simulate the thermally based microelectronic manufacturing process: the silicon deposition process in a barrel chemical vapor deposition (CVD) reactor. To identify a FLM for a process, there are two major tasks: structure and parameter identifications. In structure identification, the genetic algorithm is used to search for the optimal structure so that the predictive capability of the FLM is increased. In parameter identification, the adaptive learning rate that is based on the sum of square errors between given data and output of the FLM is chosen to increase the convergent speed of the parameters. Several mathematical functions and a CVD process are used to demonstrate the efficiency and accuracy of the improved FLM in comparison with the existing fuzzy models.