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研究生:李根全
研究生(外文):Ken-Chuan Lee
論文名稱:使用智慧型多重控制器於半導體晶圓廠現場監控系統之研究
論文名稱(外文):The Study of Shop Floor Control System in Semiconductor Fabs by Intelligent Multi-Controller Approach
指導教授:薛友仁薛友仁引用關係
指導教授(外文):Yeou-Ren Shiue
學位類別:碩士
校院名稱:華梵大學
系所名稱:資訊管理學系碩士班
學門:電算機學門
學類:電算機一般學類
論文種類:學術論文
論文出版年:2007
畢業學年度:95
語文別:中文
論文頁數:50
中文關鍵詞:晶圓製造廠現場監控系統即時排程機器學習自我組織映射類神經網路增強式學習
外文關鍵詞:Semiconductor FABShop Floor Control System (SFCS)Real Time SchedulingMachine LearningSelf-Organizing Map (SOM)Reinforcement Learning (RL)
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在傳統以類神經網路或決策樹等機器學習方法,在現場監控系統中設計智慧型控制器(排程控制器)需經由有限的訓練樣本中歸納排程知識。其主要缺點為訓練樣本的類別(排程決策規則)必須事先已知。然而此一取得訓練樣本的程序在設計多控制器於半導體晶圓廠現場監控系統時會變成無法忍受的耗時工作,其原因為在下一個排程階段需決定多個排程決策規則。除此之外,以類神經網路或決策樹等機器學習方法所歸納排程知識為區域搜尋求解的方法,對全域目標函數(即整體晶圓廠生產績效指標)的達成仍存有問題。亦即每一個排程控制器之最佳決策規則可決定,但組合後之決策規則無法同時滿足全域目標函數。然而目前相關研究文獻甚少專注於如何進行多個控制器協調以達到整體晶圓廠生產績效指標,因此本研究設計智慧型多控制器以達到上述目標。本研究提出智慧型多控制器其內容包含如下五個模組:模擬為基訓練樣本產生機制、資料正規化機制、訓練樣本以兩階段自我組織映射類神經網路(two-level SOM)之群集分析、以SOM為基之即時排程決策類別標籤選擇機制及最重要的單元:增強式學習(RL)為基之多控制器。本研究所提出以RL為基之多控制器為結合SOM及增強式學習RL的方法,可根據不同的系統屬性、目前的排程決策法則及績效指標在一特定的排程決策類別標籤中選擇適當排程決策(多個)規則。在RL為基之多控制器模組中,先經由兩階段SOM群集方法以產生不同的排程決策類別,然後再使用本研究所提RL演算法於特定的排程決策類別中選擇適當排程決策規則。經由本研究所提出的智慧型多控制器的方法,相對於使用單一決策排程機制之經典機器學習為基的方法及啟發式單一派工法則,長期而言在各種生產績效指標,對晶圓廠之生產績效提升具有更顯著的成效。
In the conventional machine learning approach, such as artificial neural network (ANN), decision tree (DT) develop an intelligent controller (i.e. scheduling controller) in shop floor control system (SFCS) to induce scheduling knowledge from a limited set of training examples. It has the main disadvantage that the classes (scheduling decision rules) to which training data are assigned must be given. However, this process becomes an intolerable time-consuming task to develop multi-controller for SFCS in wafer fabrication factories (i.e. FABs) because of multi-decision scheduling rules for next scheduling period horizon must be determined. In addition, the local approach, whether it is based on ANN or DT learning, has the problem of global objective function (i.e. overall production performance in FABs). That is, although the best decision rule can be selected for each scheduling controller, the combination of the selected decision rules would not simultaneously satisfy the global objective function. However, there is still very little research focusing on harmonized multi-controller for SFCS in FABs to achieve global performance criterion. To resolve discussed above problems, in this study, to provide real time scheduling decision rule knowledge base (KB) support to FABs production, multi-decision rules for next scheduling period horizon must be determined. Hence, in this study develops intelligent multi-controller to achieve these goals. The proposed intelligent multi-controller SFCS comprise five components: simulation-based training example generation mechanism, data normalization mechanism, training example clustering by two-level self-organizing map (SOM) approach, SOM-based real time decision rules class selection mechanism and the most important component: reinforcement learning (RL)-based multi-controller mechanism. In this study, the RL-based multi-controller combines SOM clustering and RL approach to select proper of decision rules in specific scheduling decision rules class based on various system statuses, current scheduling decision rules and performance criterion. Here, a number of scheduling decision rules classes are generated by two-level SOM clustering approach. Then, select proper of decision rules in specific decision rules class by proposed RL algorithm for multi-controller. Applying this study approach, the experiments will be demonstrate that the proposed intelligent multi-controller approach will lead to improve semiconductor FAB production performance compared to classical machine learning-based using single decision rule scheduling mechanism and the heuristic individual dispatching rule based on various performance criteria.
誌 謝 I
摘 要 II
Abstract IV
Contents VI
List of Tables VIII
List of Figures IX
Chapter 1 Introduction 1
1.1 Overview 1
1.2 Research motivations 5
1.3 Research Objectives 6
1.4 Organization 7
Chapter 2 Review of related research 8
2.1 Overview of shop floor control in FAB 8
2.2 Related work on real time dispatching rule selection mechanism 9
2.3 Agents-based system 11
2.4 Reinforcement learning approach 13
2.5 Self-Organizing Map (SOM) 15
Chapter 3 Problem statement and formulation 19
3.1 Study case description 19
3.2 Training examples specification 24
Chapter 4 Intelligent multi-controller SFCS 27
4.1 Simulation-based training example generation mechanism 28
4.2 Data normalization mechanism 28
4.3 Training example clustering by two-level SOM approach 29
4.4 SOM-based real time decision rules class selection mechanism 32
4.5 RL-based multi-controller mechanism 33
Chapter 5 Experiment 38
5.1 Constructing a simulation model and generating training examples 38
5.2 SOM + k-means clustering result 39
5.3 System performance measure verification experiment in intelligent multi-controller SFCS 41
5.3.1 Combination of scheduling strategy 42
5.3.2 Heuristic scheduling strategy 42
Chapter 6 Conclusions 44
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