生產現況與製造資料挖礦為基礎之半導體生產流程時間預測與控管

Semiconductor Cycle Time Prediction and Control Based on Production Status and Manufacturing Data Mining

蕭之維(Chih-Wei Hsiao)

指導教授 ： 簡禎富

國立清華大學/工學院/工業工程與工程管理學系/碩士(2006年)

摘要

半導體製程由於受到動態工件到達、工件迴流、生產流程漫長與瓶頸機台飄移…等等因素之挑戰，是最複雜的生產環境之一。傳統的生產計劃方法受到這些不確定因素與限制條件的影響，對於推估不同在製品(WIP)水位時對應的流程時間與產出之能力難免受到限制。
本研究的目的在於建構一可預測流程時間之資料挖礦架構，以生產線現況包括在製品、產能、產能利用率等做為輸入因子，考量該領域相關知識以推衍實證規則，並且藉由控制輸入因子達成流程時間與產出之控管。本研究研究架構整合不同的資料挖礦技術，包含自我組織映射網路、決策樹分析、導傳遞類神經網路與高斯─牛頓非線性迴歸法等。本研究利用蒐集自一座位於新竹科學園區實際半導體製造廠的生產資料進行實證研究。預測結果顯示本研究所提出之研究架構可在大部分的情況下，以低預測誤差推導出該廠之生產力表現曲線；即便在少部分時間中該廠之生產力變動過於劇烈，本預測模型仍舊可以得到有效的預測結果以降低預測誤差，同時在數日內重新校正，因此證明本研究之效度。

關鍵字

資料挖礦 ； 製造策略 ； 生產計劃 ； 生產週期時間

並列摘要

Semiconductor manufacturing process is one of the most complicated production environments owing to the challenges of dynamic job arrival, job re-circulation, long production length, and bottleneck drifts. Traditional production planning methodologies were limited to estimate the corresponding throughput and cycle time under various WIP levels with uncertain factors and production constraints.
This study aims to develop a data mining framework for cycle time prediction with input factors of production line status such as WIP, capacity, utilization, etc, combining with domain knowledge, to derive empirical rules that the levels of input production factors can be controlled to thus control cycle time and throughput. This approach integrated several data mining techniques in this two-phase research framework including self-organizing maps, decision tree analysis, back propagation neural network, and Gauss-Newton nonlinear regression method. We conducted an empirical study in which real production line data from a semiconductor fabrication factory in Hsinchu Science Park are collected for validation of this framework. The forecast results showed that the proposed framework can derive the productivity performance curves with low forecast error most of times; even sometimes the fab productivity change violently, the forecast models can still obtain an effective result to decrease the forecast error and re-align the forecast models immediately in a few days.

並列關鍵字

data mining ； manufacturing strategies ； production plan ； cycle time

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