在半導體産業下與探針卡相關的精密元件製造過程中，因製程複雜、不穩定，且影響良率的變因衆多等因素，所製造的産品易出現OOS(Out of Spec)或是外觀異常的問題，致使産品良率不佳，造成損失；另一方面，缺乏異常排除決策及知識管理機制使得工程師往往需要以試誤法或依據個人經驗排除異常問題，以致影響故障排除效率和決策品質，機台參數設定項目衆多、製程模具構造複雜也致使工程師難以從過往的經驗中發覺導致異常狀況的原因並採取有效的問題解決手段，因而造成了異常排除次數頻繁且效率低、耗時長的缺點，大大影響産能。本研究針對上述問題，整合了資料挖礦技術、個案式推理循環機制與簡易多屬性評等模式，並提出了一個異常解決之決策支援系統架構，綜合考量資料面之潛在規則與領域專家知識，實現了彈性決策需求下最優決策方案建議，從而協助工程師快速且有效的排除製程異常狀況並達到了提升製程良率、加強企業競爭力之最終目的。最後，本論文以產學合作研究的廠商為實證研究，以某半導體廠之實際異常處理資料驗證了該系統架構之可行性與效度，並探討未來研究方向。

In the manufacturing process of precision components in the semiconductor industry, due to the complicated and unstable manufacturing process and many factors that affect the yield, the OOS (Out of Spec) or the abnormal appearance of the manufactured product is prone to occur, it is increasingly difficult to enhance the yield. To address potential quality issues, lacking of decision-making and knowledge management mechanisms made it necessary for engineers to use trial-and-error methods or based on personal experience to solve abnormal problems. However, the large number of machine parameter setting items and the complex construction of process molds make it difficult for engineers to discover the causes of abnormal problems from their past experience and to adopt an effective problem solving method. Therefore, the frequency of the solving abnormal problems is frequent, the efficiency is low, and the time consuming is long, which greatly affects the production capacity. Focusing on these issues, this thesis aims to integrate the data mining technology, case-based reasoning cycle mechanism and simple multi-attribute rating model, and thus propose an decision support system (DSS) architecture to effectively address abnormal issues via integrated solutions that comprehensively considers the potential rules derived from database and domain expert knowledge. The proposed solution can provide the optimal decision suggestions under a flexible decision-making requirements, which helps the engineers to quickly and effectively solve the abnormal problems and achieve the ultimate goal of improving the process yield and strengthening the competitiveness of enterprises. Finally, the feasibility and validity of the proposed DSS architecture is estimated with the abnormal data collected in a semiconductor factory.

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