本文選題：半導體制造 + 數(shù)據(jù)挖掘FCM算法　； 參考：《北京化工大學》2015年碩士論文

【摘要】：21世紀以來國家大力扶持半導體制造業(yè),在科學技術(shù)不斷發(fā)展的潮流中,半導體制造設備不斷更新?lián)Q代,產(chǎn)品的需求量不斷增加,產(chǎn)品質(zhì)量要求不斷上升。半導體制造系統(tǒng)成為世界各國學者、科學家研究的熱點。半導體制造系統(tǒng)具有多重入、工序工藝復雜、約束條件繁多、不確定性和多目標等特點。在半導體生產(chǎn)線中,特征指標有著重要的作用,他們表示工件的屬性,以及半導體生產(chǎn)線性能好壞。如何在這些特征指標中挖掘出有效的信息,對半導體生產(chǎn)線進行改進優(yōu)化是越來越多學者,科學家研究的課題。在眾多挖掘算法中,聚類算法是較普遍的一種。這種算法基于工件的特征指標,針對工件有效的分類成調(diào)度實例。本文以半導體生產(chǎn)線為背景,重點研究了模糊聚類算法理論以及其面向半導體制造過程數(shù)據(jù)上的應用。本文主要研究內(nèi)容如下：(1)面向半導體制造過程數(shù)據(jù)背景下,對模糊C均值(FCM)算法進行了理論研究。FCM算法是本篇論文的理論基礎(chǔ),通過仿真實驗,發(fā)現(xiàn)該方法有著較好的聚類效果,并將FCM聚類算法用于半導體生產(chǎn)線的工件聚類,利用工件的特征指標,進行聚類分析。(2)通過研究FCM算法,發(fā)現(xiàn)其初始聚類中心是隨機確定的,論文將減法模糊聚類(SUB-FCM)算法應用到半導體生產(chǎn)線背景下。通過仿真實驗,證明SUB-FCM方法準確度和速度都優(yōu)于FCM算法,在半導體生產(chǎn)線工件聚類上得到很好應用。(3)通過研究FCM算法以及半導體生產(chǎn)線的特點,發(fā)現(xiàn)半導體生產(chǎn)線中,動態(tài),不確定性情況較多,從而導致半導體生產(chǎn)線工件的特征指標存在一些結(jié)構(gòu)不一致的異常點,這種異常點對普通FCM聚類有干擾。在半導體制造過程數(shù)據(jù)背景下將二型模糊C均值(Type-2FCM)算法應用在半導體工件聚類分析中,這種算法對結(jié)構(gòu)不一致的異常點有著較強的抗干擾能力。通過仿真實驗以及工件特征指標聚類分析得到較好的效果。
[Abstract]:Since the 21st century, the state has vigorously supported the semiconductor manufacturing industry. In the trend of the continuous development of science and technology, the semiconductor manufacturing equipment has been continuously updated, the demand for products has been increasing, and the requirements of product quality have been rising. Semiconductor manufacturing system has become a hot spot for scholars and scientists all over the world. Semiconductor manufacturing system has the characteristics of multiple re-entry, complex process, various constraints, uncertainty and multi-objective. In semiconductor production line, characteristic index plays an important role, they express the properties of the workpiece and the performance of the semiconductor production line. How to find out the effective information in these characteristic indexes and how to improve and optimize the semiconductor production line are more and more scholars and scientists studying the subject. Among many mining algorithms, clustering algorithm is a common one. This algorithm is based on the characteristic index of the job and classifies the job into scheduling instance effectively. In this paper, the theory of fuzzy clustering algorithm and its application to semiconductor manufacturing process data are studied in the background of semiconductor production line. The main contents of this paper are as follows: (1) in the background of semiconductor manufacturing process data, the fuzzy C-means FCM algorithm is studied theoretically. FCM algorithm is the theoretical basis of this paper. It is found that this method has a good clustering effect, and the FCM clustering algorithm is applied to the job clustering of semiconductor production line. By using the characteristic index of the workpiece, the clustering analysis is carried out. (2) by studying the FCM algorithm, it is found that the initial clustering center is randomly determined. In this paper, subtraction fuzzy clustering algorithm is applied to semiconductor production line. The simulation results show that the accuracy and speed of SUB-FCM method is better than that of FCM algorithm, and it is well applied in the clustering of semiconductor production line. By studying the FCM algorithm and the characteristics of semiconductor production line, we find out the dynamic state in semiconductor production line. There are many uncertainties, which leads to the existence of some abnormal points in the characteristic index of the semiconductor production line, which interfere with the ordinary FCM clustering. In the background of semiconductor manufacturing process data, the type 2 fuzzy C-means Type-2FCMalgorithm is applied to the clustering analysis of semiconductor workpieces. This algorithm has strong anti-interference ability to the abnormal points with inconsistent structure. A good result is obtained by simulation experiment and clustering analysis of feature index of workpiece.
【學位授予單位】：北京化工大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：TN305

【參考文獻】

相關(guān)期刊論文 前1條

1 劉民,吳澄,尹文君;帶特殊工藝約束的并行機器生產(chǎn)線調(diào)度問題的一種遺傳算法[J];自動化學報;2001年03期

相關(guān)博士學位論文 前1條

1 孫志海;視頻運動目標檢測及減法聚類定位技術(shù)研究[D];浙江大學;2009年

，

本文編號：1917207