晶圓廠現場控制策略與交期指派之整合性研究

Abstract

本論文的主要目的在於晶圓製造廠現場管理策略之整合性研究，主要包括交期指派、訂單投料、現場派工及不良品重加工等現場控制策略。論文中將針對晶圓廠利用類神經網路及線性迴歸等技術分別發展交期指派模式，並進行四個主要的模擬實驗，以驗證晶圓廠現場控制策略間的交互關係，進而提出能有效改善晶圓廠各項績效指標之策略整合建議。 實驗一主要之重點在於探討不良品重加工需求對晶圓廠現場控制策略的影響。主要納入的現場控制策略以訂單投料及現場派工為主，並以實務上經常應用且具代表性的法則為限。透過模擬驗證以探討在不同程度的不良品重加工需求下，各項績效指標之表現狀況。研究發現重加工對系統績效指標之影響十分顯著，訂單投料及現場派工等控制策略的績效，明顯受重加工需求的影響。若能正確選用現場控制策略，將可有效改善重加工對系統績效的衝擊。在不同重加工需求下，本論文提出相關現場控制策略之建議，以維持系統相關績效。 實驗二則以分析探討訂單投料、現場派工及不良品重加工等晶圓廠現場控制策略間之關係為核心，並探討不同策略組合對晶圓廠系統績效的影響。如何進行現場控制策略組合決策以提升各項績效指標之表現為模擬實驗的主要目的。由模擬及統計分析結果發現，多數投料法則若能選用適當的現場派工及重加工法則來搭配將可大幅改善其績效。但並沒有任何特定法則可兼顧各項績效指標，因此實務上應針對不同系統狀況及管理需求，選用合適的現場控制策略。且各項現場控制策略應整體規劃，包括訂單投料、現場派工及重加工策略等應一併考慮，以合適的策略組合來有效提升系統績效。論文中已針對不同績效指標提出較合適的現場控制策略組合建議。 交期指派是晶圓廠現場管理決策的第一要務，交期指派法則的良莠將直接影響晶圓廠在交期相關績效指標的表現。決定合適的訂單交期並將產品準時送交客戶將可有效提升客戶服務水準並強化競爭優勢。本論文在實驗三將透過類神經網路來預測製造前置時間，進而決定交期，本論文將發展一個以類神經網路為基礎，並結合系統模擬及統計分析技術的交期指派模式，並將同時構建以迴歸技術為基礎的交期指派法則，並納入傳統交期指派法則作為標竿，以進行績效評估。從模擬及統計分析的結果可發現，以類神經網路為基礎的交期指派法則比其他類型的交期指派法則有較高的敏感度與準確性，訂單延誤的狀況亦可大幅改善。因此，若晶圓廠交期預測相關資訊的取得並不困難，則本論文所發展以類神經網路為基礎的交期預測模式在交期預測的表現將優於其他方法。另外，傳統交期預測方法則以JIQ表現較佳，以迴歸技術為基礎的交期預測方法則以SFM_Sep較佳。 由於交期指派法則及現場管理策略（訂單投料及現場派工）間的交互作用十分明顯，因此本論文將透過實驗四探討在不同投料及派工法則下，各類交期指派法則的績效表現狀況。在現場使用特定投料及派工法則的狀況下，選用一個合適的交期指派法則來預估交期，將直接影響系統中與交期相關績效指標的表現。從模擬及統計分析的結果可發現，以類神經網路為基礎的交期預測模式明顯優於其他方法，尤其在平均延遲的表現最佳。本論文並整理出在不同現場管理策略組合及績效指標要求下合適的交期指派法則建議，其中以ANN_Sep的表現最佳，尤其在現場採用WR及TB此類負荷導向投料法則的情況下。

The purpose of this thesis is to study the integration of SFC strategies, like as due date assignment, order review and release, dispatching, and rework rules. The artificial neural network and regression techniques will be adopted to develop the ANN_Based and Reg_Based due date assignment rules for a virtual wafer fabrication plants. There are four simulation experiments in the thesis for testing the interaction of SFC strategies in wafer fabrication. The first experiment is focused on the effect of rework operations on the shop floor control’s strategies, including order review/release and dispatching. It will try to determine the performance of various production control strategies on the system performance indicators under different level of rework rate. Besides, the interaction of order release and dispatching strategies under different rework operations will be investigated. Some representative SFC strategies are considered in our simulation model. We have found the effect of rework operation on the system performance is significant. The order release and dispatching strategies’ performance will be affected by the rework operation. The performance will be improved dramatically if the suitable combination of order release and dispatching strategies are adopted. Under different rework rate and performance indicators the suitable combination of SFC strategy are suggested in this thesis. The second experiment is concerned with the interaction among shop floor control (SFC) strategies (order review/release, dispatching, and rework rules) and its impact on the performance of wafer fabrication. It tried to find the better combination of these rules by specific performance indicators. From the results of simulations and statistic analysis, the performance of most ORR rules will be improved when combined with suitable dispatching and rework strategies. But no single strategy can satisfy all performance indicators. In practice, we should choose SFC strategies carefully based on the system conditions. Furthermore, ORR, dispatching, and rework strategies cannot be separately considered. Instead, they should be combined and integrated for improving the system performance. The suitable combinations of SFC (Shop Floor Control) strategies for different performance indicators are suggested in this thesis. Due date assignment (DDA) is the first important task of shop floor control in wafer fabrication. Due date related performance is impacted by the quality of the DDA rules. Assigning order due dates and timely delivering the goods to the customer will enhance customer service and competitive advantage. A new methodology for lead-time prediction, artificial neural network (ANN) prediction is considered in the third experiment. An ANN_Based DDA rule combined with simulation technology and statistical analysis is developed. Besides, Reg_Based DDA rules for wafer fabrication are modeled as benchmarking. Whether neural networks can outperform conventional and Reg_Based DDA rules taken from the literature is examined. From the simulation and statistical results, ANN_Based DDA rules perform a better job in due date prediction. ANN_Based DDA rules have a smaller tardiness rate than the other rules. ANN_Based DDA rules have better sensitivity and variance than the other rules. Therefore, if the wafer fab information is not difficult to obtain, the ANN_Based DDA rule can perform better due date prediction. The SFM_Sep and JIQ in Reg_Based and conventional rules are better than the others. Owing to the interactions between the DDA and SFC rules order review/release and dispatching are significant. In the fourth experiment, the ANN_Based DDA rules will be discussed under different SFC rules in the simulation model. It is very important to determine a suitable DDA rule under various ORR combinations and dispatching rules. From the simulation and statistical results, ANN_Based DDA rules perform better in due date prediction. ANN_Based DDA rules have a smaller tardiness rate than the other rules. ANN_Based DDA rules have better sensitivity and variance. We provide suggestions for DDA rules under various SFC rule combinations. ANN_Sep is suitable for most of these combinations, especially when ORR, WR and TB, rules are adopted.