改良之模糊邏輯模式應用於CVD製程

Abstract

本論文主要研究改良之模糊邏輯模式，並應用於 CVD製程模擬。模糊 邏輯模式的鑑定，包括兩個部分：架構鑑定與參數鑑定。在架構鑑定方面 ，我們研究以遺傳演算法來搜尋歸屬函數的最佳架構，以提高模糊邏輯模 式的預測能力。一般模糊邏輯模式在參數鑑定方面，都會遭遇到學習速度 或收斂速度很慢，而必須花很長的時間才能得到模式，有鑑於此，我們研 究適應性學習率，讓學習率隨著輸出誤差平方和的大小而自動調整其值， 來加快學習速度。以幾個數學函數的近似來驗證，並將此改良的模糊邏輯 模式應用於 CVD的製程模擬。而從得到的結果充分說明本論文所改良的模 糊邏輯模式，相較於已提出的模糊邏輯模式，有更好的時效性和準確性。 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.