臺灣博碩士論文加值系統

近年來台灣半導體晶圓廠面臨世界經濟的衝擊，以及來自中國、韓國的強勁競爭對手的挑戰，整個產業必須重新思考未來的策略方向。半導體晶圓廠一向是以低成本策略為一重大策略方向，而生產成本更是總成本當中非常主要的部份。本研究以台灣某晶圓廠為對象，機器環境為彈性流程式工廠，探討其離子植入過程當中存在鋼瓶的劣化與最適化鋼瓶更換數量問題，因而工作時間變長造成產能效率下降，此外本研究擷取實際半導體公司資料，以公司現況所使用之先來先服務之排程方式、基因演算法、蟻群演算法及本研究所提出的ACO-GA 演算法方式進行資料測試，希望進一步為公司提高生產效率降低成本。
實驗結果顯示，本研究所提出的ACO-GA 演算法，能夠在極短時間內求解龐大實務問題、並且改善因鋼瓶劣化與更換鋼瓶問題而造成之工作不效率，ACO-GA 演算法、蟻群演算法與基因演算法相較於個案公司之平均改善率各為15.18%及7.90%與4.21%，有此可知本研究所提出的ACO-GA 演算法之求解能力具突破傳統演算法之求解能力與品質。

In recent years, semiconductor industry in Taiwan faces great impact from the world economical upheaval and fierce competitions from emerging countries in semiconductor industry, such as China and South Korea. It is essential for businesses in semiconductor industry to rethink and develop new strategies in this difficult time. Furthermore, low cost strategy has always been an important part of corporate strategy of semiconductor manufacturing companies, while manufacturing cost account for a major portion in the cost structure.
The production scheduling problems of unrelated parallel machine, and use the data of the semiconductor company, this research investigates the effect of deteriorating gas tanks pressure in the ion implant process, which the deteriorating part leads to prolonged makespan. First come first service, genetic algorithms, ant colony algorithm and ACO-GA algorithm is the way to minimize the maximum completion time test, we prove that dynamic flexibility algorithm can both effectively and efficiently solve the problem. The result will make use for future researches and business practices.
This research proposes a ACO-GA algorithm that can solve the practical scheduling problem of ion implant process for minimizing the maximum completion time in the semiconductor companies. According to the experimental results, the ability and quality of ACO-GA algorithm is better than those of the traditional algorithm like first come first service, ant colony algorithm and genetic algorithms, and is more effective. Last of all, compared to first come first service proposed by the case company, the traditional ant colony algorithm, genetic algorithms, and proposed ACO-GA algorithm reach the average improvement rate 15.18% and 7.90% and 4.21% respectively. As a result, we can know the excellent problem solving ability and high quality of ACO-GA algorithm that supersedes the mentioned traditional ones.

目錄
頁次
第壹章 緒論....................................................................................................1
第一節 問題背景與研究動機…..................................................................1
第二節 研究範圍與限制..............................................................................2
第三節 研究目的..........................................................................................4
第四節 研究流程..........................................................................................4

第貳章 文獻探討................................................................................................6
第一節 平行機流程式排程..........................................................................6
第二節 迴流式生產排程........................................7
第三節 具有機器劣化之排程介紹….........................................8
第四節 蟻群演算法......................................................................................8
第五節 基因演算法....................................................................................12
第參章 半導體晶圓廠離子植入作業排程問題..............................................16
第一節 個案公司生產系統簡介................................................................16
第二節 工廠實際問題描述.................…...................................................18
第三節 求解演算法之設計….............................…...................................20
第肆章 個案公司資料測試與分析..................................................................24
第一節 參數設定....................................................................................24
第二節 有效性分析....................................................................................26
第三節 健全度分析....................................................................................34
第四節 測試結果彙整與分析....................................................................39
第伍章 結論與建議..........................................................................................41
第一節 結論................................................................................................41
第二節 建議...................................................................42
參考文獻.......................................................................................................43

表目錄
頁次
表4-1-1 蟻群演算法之相關參數設定…….……..............................................25
表4-1-2 蟻群演算法之相關參數設定…….……..............................................25
表4-2-1 有效性與最適鋼瓶使用數量分析………...........................................27
表4-3-1 健全度與最適鋼瓶使用數量分析.....................................................35
表4-4-1有效性平均改善率彙整表…..............................................................40


圖目錄
頁次
圖1-4-1研究流程圖…………….…………..…………………….………….5
圖2-4-1自然界螞蟻搜尋最短路徑方式…………....……………………….10
圖2-5-1基因演算流程圖……..………..………….………………………...13
圖3-1-1半導體產業結構上、中、下游之製造流程….…..…..…………...17
圖3-1-2 離子植入機沿革…..…..…..………..……………………………...17
圖3-1-3 離子植入機平面圖……....…..…………...…………..…………...18
圖3-2-1離子植入機迴流概念圖……....…..…………………..…………...19
圖3-2-2 離子植入機鋼瓶效率曲線圖..…………...……………..…………...20
圖3-3-1 ACO-GA演算法流程圖……...……………….…….…..………….23
圖4-2-1工作數300之鋼瓶更換圖...……..………………………..………...31
圖4-2-2 工作數500之鋼瓶更換圖...……..………………………..………...32
圖4-2-3 工作數1,000之鋼瓶更換圖...……..….…………………..………...32
圖4-2-4 工作數1,500之鋼瓶更換圖...……...……………………..………...33

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