半導體成品測試廠之產能分配與派工
模糊知識探索模型

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。

關鍵字

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

並列摘要

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.

並列關鍵字

neural network ； decision tree ； fuzzy logic ； genetic algorithm ； final testing. ； capacity allocation ； and dispatching ； Knowledge discovery

參考文獻

陳作琳 (2002)，「以模糊決策樹與適應學習網為基礎之知識探索模型」，私立中原大學工業工程學系研究所碩士論文。

Arzi, Y. and Iaroslavitz, L., (2000),“Operating an FMC by a decision-tree-based adaptive production control system,”International Journal of Production Research, 38(3), 675-697

Bard, J. F., Srinivasan, K. and Tirupati, D. (1999), “An Optimization Approach to Capacity Expansion in Semiconductor Manufacturing Facilities,” International Journal of Production Research, 37(15), pp.3359-3382.

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半導體成品測試廠之產能分配與派工模糊知識探索模型

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