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研究生:侯廷俊
研究生(外文):Ting-Chung Hou
論文名稱:以APQP為基於客戶選擇與多資源規劃上之半導體代工廠生產管理系統發展
論文名稱(外文):Development of an APQP based Production Management System with Customer Selection and Multi-Resource Planning in Semiconductor Manufacturing Service Company
指導教授:宮大川宮大川引用關係王孔政王孔政引用關係
指導教授(外文):Dah-Chuan GongKung-Jeng Wang
學位類別:博士
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:2009
畢業學年度:97
語文別:英文
論文頁數:208
中文關鍵詞:先進品質管理基因演算法資源分配與選擇知識管理半導體代工
外文關鍵詞:Knowledge managementResource allocation and selectionGenetic algorithmSemiconductor manufacturing serviceAPQP
相關次數:
  • 被引用被引用:2
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摘要
在瞬息萬變的半導體封測代工產業, 面對同業間強烈競爭, 除了必須增加或獲得代工客戶品質認同與滿意度外,亦須增加獲利。客戶的選擇、資源分配與新增產能投資、 及整體規劃下,新產品或新製程技術從研發、試樣、工程驗證、量產前試產到大規模量產所需的材料、設備、治具、製造方法、技術選用或移轉、及其相關的供應商,皆要納入生產管理系統整體規劃,以構建出相對應之高效益整運作系統架構,以因應增加競爭力。
客戶的選擇、資源分配與新增產能投資,是找出利基提高收益的重要因素;而完善材料、設備、治具選擇與標準化之先期品質規劃,運用於量產時可提高效率,並降低製造成本。本研究以產品先期品質規劃系統(APQP)概念為中心,輔以基因演算法與知識管理,構建出基於客戶選擇與多資源規劃上之半導體代工廠生產管理系統(ABPMS), 以面對同業間強烈競爭。ABPMS 由客戶先期品質規劃系統(ICQP)、資源分配與新增產能先期品質規劃系統(IRQP)、材料先期品質規劃系統(IMQP)、設備與治具先期品質規劃系統(IETQP)、製造方法先期品質規劃系統(IPQP)、技術選用或移轉系統(ITQP)及供應商先期品質規劃系統(IVQP)所構建。
本論文先以客戶品質規劃系統((ICQP)與資源分配與新增產能品質規劃系統(IRQP),選擇出可獲利或對策略性客戶進行產能與資源分配規劃。 其次導入先期品質規劃系統(APQP)與知識管理整合規劃之材料、設備、治具、製造方法、技術選用或移轉,而開發出之材料先期品質規劃系統(IMQP),配合設備與治具先期品質規劃系統(IETQP), 再結合製造方法先期品質規劃系統(IPQP),技術選用或移轉系統(ITQP),及其相關的供應商先期品質規劃系統(IVQP),形成一套完整的架構,以及配套的軟體工具以支援ABPMS。本論文亦舉一使用本研究提出之 ABPMS架構與軟體工具的半導體封測代工公司為例,配合研究架構,將其系統建制流程及其個別子系統績效與整體系統績效,仔細討論.
實際績效顯示,ABPMS架構運用於半導體封測代工產業,不僅可以策厲標準化、簡單化、系統化以達到有效率及效能的精實製造系統發展外,更可提高生產獲利。其他具有類似性質之製造產業應可導入引用。
ABSTRACT

Future and current challenges posed many issues to semiconductor manufacturing service companies. Resource allocation and expansion, customer selection, quality planning process of product development, processes, materials, equipment, tools selections in the stage of R&D, sample parts qualification, engineering run, qualification run and pre-production run all require a system to have soft computing and organizational knowledge integrated together under the concept of simplification, systemization and source control to realize mass production efficiently, effectively and competitively. This dissertation proposes an advanced product quality planning (APQP) based production management system (ABPMS) framework to respond to such challenges. In this dissertation, firstly, intelligent customer selection quality planning is worked out based on weighted rules to select profitable customers and then intelligent resources allocation and expansion quality planning are applied to do resource allocation or strategic investment. Secondly, APQP is integrated with a knowledge management system to optimize products, processes, equipment, tools selection or development, even technical transfer in a systemization way to keep mass production profitable. The proposed ABPMS framework is applied well in a world leading IC assembly and test manufacturing service company in Taiwan. It is demonstrated that the proposed ABPMS framework brings the company with higher profit results.
TABLE OF CONTENTS

摘要 I
ABSTRACT II
ACKOWLEDGEMENTS III
TABLE OF CONTENTS IV
FIGURES INDEX VI
TABLE INDEX IX
CHAPTER 1. INTRODUCTION 1
1.1 Research Background and motivation 2
1.2 Research purpose and scope 3
1.3 Dissertation structure 6
CHAPTER 2. LITERATURE SURVEY 8
2.1 Advanced product quality planning 8
2.1.1 History of APQP and its development 8
2.1.2 What is APQP 9
2.1.3 APQP phases 10
2.1.4 Summary and purpose of APQP 11
2.2 APQP and ABPMS 11
2.3 Multiple resource allocation through GA 12
2.4 ABPMS integrating APQP with KM 13
2.5 Modeling tools by Unified modeling language 14
2.6 Summary 15
CHAPTER 3. RESEARCH FRAMEWORK AND THE CHARACTERISTICS OF ABPMS 16
3.1 The proposed ABPMS framework 24
3.2 The characteristics of ABPMS 26
3.3 Intelligent quality planning process 27
CHAPTER 4. INTELLIGENT CUSTOMER AND RESOURCE QUALITY PLANNING 33
4.1 Intelligent customer quality planning (ICQP) 33
4.2 Resolving resource allocation and capacity expansion issues through GA (IRQP) 36
4.2.1 Defining the problem 36
4.3 A solution based on genetic algorithm 40
4.3.1 Chromosome design 40
4.3.2 Fitness calculation 41
4.3.3 The algorithm 41
4.4 Genetic algorithm VS. Mathematical model 43
4.4.1 Applying the proposed algorithm to a practical example 43
4.4.2 Outcomes of the mathematical model 45
4.4.3 Outcomes of the genetic algorithm 48
4.4.4 Parameter assessment using Taguchi method 50
4.4.5 Performance assessment of the mathematical and genetic algorithm based models 52
4.4.6 GA to be inducted as rules for data extraction 54
4.5 General IXQP implementation process (IXQP 6-step setup procedure) 55
4.6 ICQP and IRQP system implementation 56
4.6.1 Realization case of ICQP and IRQP 56
4.6.2 ICQP/IRQP system KPI and performance Tracking 69
CHAPTER 5. INTELLIGENT MATERIAL, EQUIPMENT AND TOOLS, VENDOR, QUALITY PLANNING 71
5.1 Intelligent Material Quality Planning (IMQP) 73
5.1.1 Implementation procedures for IMQP 78
5.1.2 Realization of IMQP 78
5.2 Intelligent Equipment/Tools Quality Planning (IETQP) 122
5.2.1 IETQP implementation 125
5.2.2 Realization of IETQP 125
5.3 Performance review for IMQP/IETQP 149
5.4 Intelligent Vendor Quality Planning (IVQP) 151
5.5 Intelligent Technology Quality Planning (ITQP) 154
5.6 Intelligent Process Quality Planning (IPQP) 156
5.7 Summary 161
CHAPTER 6. ABPMS SYSTEM IMPLEMENTATION AND ASSESSMENT 163
6.1 Define the APQP process and use the APQP linkage tools 164
6.2 ABPMS framework and IXQP system developed by the APQP linkage tools 172
6.3 Implementation scope, output and expectation with the ABPMS / IXOP system 184
6.4 KM and IXQP system implementation 185
6.5 Assessment of ABPMS 187
CHAPTER 7. CONCLUSION 193
REFERENCES 195
BIBLIOGRAPHY 199

FIGURES INDEX
Figure 1. Major Challenges to the Semiconductor Manufacturing Services Industry 2
Figure 2. Comparison the Proposed ABPMS Process with Original APQP Process 5
Figure 3. Research Framework 6
Figure 4. Research Process 7
Figure 5. APQP Implementation Roadmap 15
Figure 6. AS-IS Model Micro View – Ineffective APQP Tools 17
Figure 7. AS-Is Model Macro View – Ineffective APQP Tools 18
Figure 8. Research Roadmap 21
Figure 9. TO-BE Model Micro View 22
Figure 10. TO-BE Model Macro View 23
Figure 11. The Proposed ABPMS Framework 25
Figure 12. Scope and Expectation of ABPMS 31
Figure 13. Intelligent Quality Planning Process 32
Figure 14. Intelligent Customer Quality Planning Flow Chart 34
Figure 15. Rule-based Agent’s Role in Customer Selection Process. 34
Figure 16. Customer Selection Criteria 35
Figure 17. Simultaneous Resource Conflict in Chip Final Testing 36
Figure 18. Fitness Evolution (60 chromosomes, 60 generations, 40M budget plan) 48
Figure 19. Comparison of Computation Time 53
Figure 20. Capacity Planning Quality Planning 54
Figure 21. GA Results Converted into Rules 54
Figure 22. Structure and Organization of ICQP and IRQP 58
Figure 23. ICQP and IRQP New Business Tracking Flow Chart 58
Figure 24. Main Flow for ICQP and IRQP Implementation 59
Figure 25. Operational Sequence Flow – Business Approach 61
Figure 26. Operational Sequence Flow – Business Tracking 63
Figure 27. Standard Operation Procedure and Criteria for ICQP and IRQP 65
Figure 28. ICQP and IRQP Checklist and SOP 66
Figure 29. Methods for APQP 71
Figure 30. Example of High-Density Lead Frame Design to Gain Productivity and Cost Down 75
Figure 31. BOM Simplified Process by APQP Methodology 76
Figure 32. IMQP Sequence Diagram 77
Figure 33. IMQP Team 82
Figure 34. IMQP Functional Procedure and Master Flow 82
Figure 35. IMQP New Material Qualification Main Flow 83
Figure 36. Standard BOM Qualification Main Flow Chart 84
Figure 37. Low Cost Material (BOM) Development Main Flow 85
Figure 38. IMQP Engineer Evaluation and Design Review Flow 86
Figure 39. IMQP Material Prepare Flow 91
Figure 40. IMQP Material Qualification Pre-review Flow 96
Figure 41. IMQP Material Qualification Flow 101
Figure 42. IMQP Material Qualification Review Flow 106
Figure 43. IMQP Material Pre-production Run Flow 111
Figure 44. IMQP SOP Setup for New Material Reliability 116
Figure 45. IMQP New Material Qualification Report 116
Figure 46. Standard BOM Stored at KM Database 117
Figure 47. IMQP BOM Development Flow– STD BOM Creation and Non STD BOM Tracking 118
Figure 48. IMQP IT System Setup 119
Figure 49. IMQP Integrated with KM DB for Material Test Report Query 120
Figure 50. IMQP, IPQP and KM DB Tracking New Material Qualification and STD BOM Development 121
Figure 51. The Flow of Equipment Type Selection 122
Figure 52. IETQP Sequence Diagram 124
Figure 53. IETQP Main Flow 127
Figure 54. Main Flow of New Equipment with Tools Development 128
Figure 55. IETQP New Tools Development Flow Chart 129
Figure 56. IETQP Key Notes 130
Figure 57. New Equipment Pull in Control Procedure – IPO Analysis of Equipment Capability 131
Figure 58. New Equipment Pull in Control Procedure – IPO Analysis of Equipment Evaluation and Survey 133
Figure 59. New Equipment Pull in Control Procedure – IPO Analysis of Equipment Purchase Requirement 135
Figure 60. New Equipment Pull in Control Procedure – IPO Analysis of Equipment Preparation 137
Figure 61. New Equipment Pull in Control Procedure – IPO Analysis of Equipment Qualification 139
Figure 62. New Equipment Pull in Control Procedure – IPO Analysis of Equipment Mass Production Buy-off 141
Figure 63. New Equipment Pull in Control Procedure – Close Loop for Design Rule Update 143
Figure 64. Example of New Tool Qualification Flow 145
Figure 65. Demo Equipment Control Procedure 146
Figure 66. Example of Tools Survey Qualification Flow 147
Figure 67. IETQP Systemization for Tools Management 148
Figure 68. IVQP Sequence Diagram 153
Figure 69. Intelligent Technology Quality Planning 155
Figure 70. IPQP Link with APQP KM DB for IXQP System Development 158
Figure 71. IPQP and KM Database for IXQP System 159
Figure 72. APQP Interactions with other Enterprise Applications 161
Figure 73. ABPMS to Raise the Performance Level to 3S 162
Figure 74. Quality-Driven Five-Phase of APQP Implementation Process 166
Figure 75. Quality-and-Profit-Driven APQP Linkage Tools 167
Figure 76. APQP Linkage Tools Step-1 Plan and Define 168
Figure 77. APQP linkage Tools Step-2 Product Design and Development 169
Figure 78. APQP Linkage Tools Step-3 Process Design and Development 170
Figure 79. APQP linkage Tools Step-4 Product and Process Validation 171
Figure 80. APQP Linkage Tools Step-5 Feedback Loop 172
Figure 81. APQP Development Phase in an IC Manufacturing Service Company 175
Figure 82. APQP Prototype Phase in IC Manufacturing Service Company 176
Figure 83. APQP Trial Run and Pilot Run Phase in IC Manufacturing Service Company 180
Figure 84. APQP Qualification and Pre-production Run Phase in an IC Manufacturing Service Company 182
Figure 85. APQP Cross-Loop Phase (IXQP System) in an IC Manufacturing Service Company 184
Figure 86. KM and IXQP System Implementation 185
Figure 87. APQP Performances for Cost Saving 188


TABLE INDEX
Table 1. QS 7 Pack 9
Table 2. A Sample Problem 44
Table 3. The Availability of Handlers 45
Table 4. Testers Able to Purchase under Constrained Budget 46
Table 5. Outcomes of the Mathematical Model (with a constraint of 40M budget) 47
Table 6. Outcomes of the Proposed Genetic Algorithm(60 chromosomes, 60 evolved generations, 40M budget) 49
Table 7. Factors and Levels 50
Table 8. Signal-to-Noise ANOVA for 20M Budget Case 51
Table 9. Comparison of Mathematical and GA Models 52
Table 10. ICQP and IRQP Scope, Function, Output and Expectation 60
Table 11. KPI and Performance Tracking for ICQP and IRQP 70
Table 12. IMQP Scope and Expectation 81
Table 13. Scope and expectation of IETQP 127
Table 14. KPI Review for IMQP and IETQP 150
Table 15. Scope and Expectation of IPQP 160
Table 16. ABPMS System Performance 189
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