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研究生:林昀萱
研究生(外文):Ynu-Syuan Lin
論文名稱:半導體成品測試廠之產能分配與派工模糊知識探索模型
論文名稱(外文):A Fuzzy-Based Knowledge Discovery Model for Capacity Allocation and Dispatching in Final Test of Semiconductor Industry
指導教授:陳建良陳建良引用關係王孔政王孔政引用關係
指導教授(外文):James ChenKung Jeng Wang
學位類別:碩士
校院名稱:中原大學
系所名稱:工業工程研究所
學門:工程學門
學類:工業工程學類
論文種類:學術論文
論文出版年:2005
畢業學年度:93
語文別:英文
論文頁數:116
中文關鍵詞:基因演算法模糊邏輯運算半導體最終測試派工及產能分配知識探索類神經網路決策樹
外文關鍵詞:neural networkdecision treefuzzy logicgenetic algorithmfinal testing.capacity allocationand dispatchingKnowledge discovery
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在龐大且複雜的資料庫中,隱含許多高利用價值的資訊,應可運用自動化學習技術加以萃取。此外,半導體測試作業需要多種資源的搭配才可進行,績效要求與訂單特性等因素亦影響現場的產能分配及派工。據此,本研究以半導體最終成品測試產業為研究對象,針對其智慧型產能分配及派工之方法,分為兩部份探討之。第一部份,發展一個混合決策樹與類神經的知識探勘模型,生產系統內透過知識萃取,針對不同指標(在製品存量、設置時間、總完工時間、總延遲時間),以決策樹模式決定派工方法,再以類神經網路預測出績效指標之績效值。本研究觀察多種形式派工法則(工廠實務法、啟發式演算法)所建構之決策樹與類神經網路模型,可用於最適派工法,並作為現場派工知識庫的基礎。第二部份,發展一組基因演算法,以延遲時間最短為目標,安排訂單的優先序,其中並導入模糊邏輯推論,將受限於多資源的訂單生產環境,定義為資源模糊集合,計算出分派決策之所屬歸屬函數,以進行產能堆疊及分配。本研究所發展之混合決策樹與類神經的知識探勘模型可使半導體測試產業遂行最適派工的選取,並可對於績效值有正確之預測。其次,本研究所發展之基因演算與模糊邏輯推論模型,可找出延遲時間最短之產能分派方式。
Statistics techniques have been successfully applied to process ample data. However, one of the drawback of statistical approaches is that one may not be able to learn and discover knowledge from a large data base wth noise, as applying the approaches. The purpose of this thesis is thus, in the conext of semiconductor final testing industry, to develop a dispatching knowledge base by using artificial intelligence techniques, to extract useful business rules from the data. One of the most challenging decisions regarding production in semiconductor testing industry is to select the most appropriate dispatching rule that can be employed in the shop floor to achieve high manufacturing performance against a changing environment. Semiconductor testing is characterized by multi-resource constraints and has many performance measures from the perspective of controlling and managing the system. In the study, we develop a hybrid knowledge discovery model, using a conjunction of decision tree and back-propagation neural network, to determine an appropriate dispatching rule using production data with noise information, and to predict its performance. Experiments have shown that the proposed decision tree found the most suitable dispatching rule given a specific performance measure and system status, and the back propagation neural network then predicted precisely the performance of the selected rule. Second, this study presents a knowledge discovery model which uses a genetic algorithm to find the best priority sequence of customer orders for resource allocation and a fuzzy logic model to allocate the resources and determine the order-completion times, following the priority sequence of orders. Experiments showed that by using realistic resource data and randomly generated orders our proposed models have achieved promising results.
中文摘要 i
Abstract ii
誌謝 iii
Contents iv
List of Figures vi
List if Tables i
1. Introduction 1
1.1 Background 2
1.2 Research framework 3
2. A Hybrid Knowledge Discovery Model Using Decision Tree and Neural Network for Selecting Dispatching Rules 6
2.1 Problem definition 6
2.2 Literature survey 8
2.3 The hybrid decision tree and neural network model 9
2.4 Virtual plant modeling 11
2.4.1 The semi-conductor final testing plant under investigation 11
2.4.2 Dispatching rules and performance measures 14
2.4.3 Identifying attributes to represent demands for testing 15
2.5 Modeling a decision tree to discovering knowledge rules for job dispatching 17
2.5.1 The algorithm 17
2.5.2 Experiments setting and results 19
2.5.3 Evaluating accuracy of dispatch rule selection decision trees 21
2.6 Back propagation neural networks for forecasting performance of dispatching rules 24
2.6.1 Developing the neural networks for predicting performance measures 24
2.6.2 Prediction accuracy of the proposed neural networks 26
2.7 Conclusion 26
3. A Fundamental Resource Allocation Model Using Genetic Algorithm under A Fuzzy Environment 28
3.1 Problem definition 28
3.2 Literature survey 33
3.3 Genetic algorithm 34
3.3.1 Chromosome design 35
3.3.2 Algorithm procedure 36
3.3.3 Selection 36
3.3.4 Crossover 37
3.3.5 Mutation 39
3.3.6 Fitness calculation 39
3.4 An integrated GA-FL algorithm with fuzzy inference module 39
3.4.1 Defining Fuzzy set and degree of Inputs in Inference Module 40
3.3.4 Fuzzy logic inference modlue for capacity allocation 44
3.4.4 An illustrtion of jobs with the same due date 47
3.4.5 An illustrtion of jobs with different due dates 52
3.5 Conclusions 56
4 An Advanced Resource Allocation Model Using Genetic Algorithm and Fuzzy Neural Network Inference Module 57
4.1 Problem definition 58
4.2 Literature survey 61
4.3 Defining the system for semiconductor final testing industry and its fuzzy sets of attributes 63
4.3.1 Manufacturing environment under investigation 65
4.3.2 Developing fuzzy sets 68
4.3.3 Building a fuzzy inference system--FNNIS 79
4.3.4 Performance evaluation of FNNIS 90
4.4 Evaluation of the proposed GA-FNNIS modle 93
4.4.1 Designing of Experiments for model parameters 93
4.4.2 An Illustration of semiconductor testing industry 96
4.4.3 Convergence property of the model 98
4.4.4 Solution quality of the model 99
4.5 Conclusions 100
5. Conclusions 102
5.1 Summary of the research 102
5.2 Future research direction 103
Reference 105
Appendix I. Data used in chapter 4 111
Appendix II. Rules producde by FNNIS 112
Appendix III. Decision tree rule 113
Appendix IV. Order sets used in evaluating GA-FNNIS and GA-DTR 114

LIST OF FIGURES
Figure 1.1 Relationship of the simultaneous resources in semiconductor final testing industry 2
Figure 1.2 Simultaneous resource conflicts in semiconductor testing. (Hou 2001) 3
Figure 1.3 Research framework 5
Figure 2.1 Framework of the proposed hybrid decision tree and neural network model. 10
Figure 2.2 System architecture. 12
Figure 2.3 Object-oriented simulation modeling of the example system. 13
Figure 2.4 A refined decision tree for Makespan performance measure. 20
Figure 2.5 The prediction model of Makespan measure 25
Figure 2.6 MSE of testing for Makespan. 25
Figure 3.1 A basic resource allocation model using genetic algorithm under a fuzzy environment 30
Figure 3.2 Resource requirement (Wang et al. 1999) 32
Figure 3.3 GA Overview 35
Figure 3.4 Chromosome structure 35
Figure 3.5 Multiple-point random crossover 38
Figure 3.6 A two-point mutation 39
Figure 3.7 Membership function of fuzzy factors for , , 43
Figure 3.8 Membership function of fuzzy factors for , , 43
Figure 3.9 Job-machine relationship 47
Figure 3.10 Convergence tendency of GA-FL for the same-due-date case 49
Figure 3.11 Resource allocation of initial and resultant states in the same-due-date case 51
Figure 3.12 Convergence tendency of GA-FL for the different -due-date case 52
Figure 3.13 Resource allocation of initial and resultant states in the different-due-date case 55
Figure 4.1 An advanced resource allocation model using genetic algorithm under a fuzzy environment 60
Figure 4.2 Research overview of fuzzy logic inference modules 66
Figure 4.3 Manufacturing system architecture. 68
Figure 4.4 Attribute distributions 71
Figure 4.5 Definition of attribute fuzzy number using quartile statistic 73
Figure 4.6 Computation of clustering center using fuzzy c-means algorithm 75
Figure 4.7 Computation of membership functions by degree of Euclidean norm with truncated tails 76
Figure 4.8 The FNNIS network framework with a focus on the first two layers. 77
Figure 4.9 Definition of attribute fuzzy number using fuzzy c-means algorithm 79
Figure 4.10 Internal layers of FNNIS 82
Figure 4.11 The node from first layer to second layer 83
Figure 4.12 Functional relationship of rule node layer 83
Figure 4.13 Back propagation to adjust the weight of links 87
Figure 4.14 Pesudo code of back propagation for renewing weight of links 89
Figure 4.15 A conceptual control loop using the proposed FNNIS 90
Figure 4.16 GA-FNNIS performance under different parameter setting 97
Figure 4.17 Comparison of GA-FNNIS and GA-DTR 99
Figure 4.18 Convergence property of the GA-FNNIS and GA-DTR models 100

LIST IF TABLES
Table 2.1 Examples of chip testing orders. 13
Table 2.2 Priority rule for the lot to be tested next. 14
Table 2.3 Factors and their levels. 16
Table 2.4 Accuracy of the decision tree for proformances measure 22
Table 2.5 Error of the network to predict completion time. 26
Table 3.1 Notations used in the problem definition 31
Table 3.2 Input of fuzzy sets 41
Table 3.3 Fuzzy degree of decisions 45
Table 3.4 Example data 47
Table 3.5 The resultant resource allocation plan in the same-due-date case 52
Table 3.6 The resultant resource allocation plan in the same-due-date case 55
Table 4.1 Data of the test case 67
Table 4.2 Input-Output Attributes of sample Factory 69
Table 4.3 Fuzzy numeric attributes of example factory 71
Table 4.4 Notations used in the first two layers. 74
Table 4.5 The parameter setting of fuzzy numbers 78
Table 4.6 Notations used in FNNIS 81
Table 4.7 The accuracy of FNNIS based on Takagi-Sugeno fuzzy inference concept 92
Table 4.8 The accuracy of Mamdani fuzzy inference system using rules induced by decision tree 93
Table 4.9 ANOVA analysis 95
Table 4.10 Experiment design 96
Table 4.11. Factor combinations 97
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