在台灣，半導體產業被視為最重要的產業之一，在科技及技術大爆炸的時代下，消費性電子產品不斷地推陳出新，促使市場對於半導體有很高之需求，半導體製程複雜且規格要求嚴苛，因此晶圓的製作成本相當昂貴，半導體廠商為提高競爭力，除了製程能力提升外，更著重於數據分析，由於半導體製程高度自動化資訊易於取得，在此狀況下有利於工程師檢測故障模式及識別製造問題，達到成本降低之目的。
在文獻中，常以晶圓探針測試中的缺陷所形成空間之圖案，進行分類或分群，不同類型的空間圖案會反映出生產之信息，藉由這些資訊推斷異常發生之原因。在本研究中將以某封裝廠為例，除了開發在不良率極低的製程下，找出有分析價值之資料的晶圓圖辨識系統外，另外藉由晶圓上缺陷之識別碼，開發品檢覆判系統，目前文獻上尚未發現相同之研究，由於本研究自動光學檢驗系統較舊，為了避免漏篩情形發生，設備調整較為靈敏導致過篩情形發生，所以必須有操作員進行確認的動作，為了解決人員差異及誤判問題，為此，本研究藉由統計學上Jaccard的方法，延伸開發了一個空間分類器，將隨機及特殊群聚區分開來，並針對特殊群聚類別進行資料分析，再藉由捲積自編碼器(Convolutional Autoencoder, CAE)進行資料增生，解決資料不平衡之問題。最後，將人員所判讀後之訊息，搭配深度學習進行預測，找出人員判定異常之區域。在實務上，其優點是保留彈性，藉由AI強大的整合能力，搭配人員實戰經驗進行互補，系統與人員皆可學習成長，實踐具有彈性決策之智慧製造系統。

In Taiwan, the semiconductor industry is regarded as one of the most important industries. The semiconductor manufacturing process is complex and the specifications are strict. Therefore, the cost of manufacturing wafers is quite expensive. In order to maintain competitiveness, semiconductor manufacturers focus on data analysis in addition to the improvement of process capabilities. Because semiconductor processes are highly automated and information is easily available, it is beneficial to engineers in this situation. Detect failure modes and identify manufacturing problems to achieve cost reduction.
In this study, the assembly house will be taken as an example. In addition to developing a Wafer Pattern Reˇcognition System that finds analytically valuable data under a very low failure rate process, this study also developed a Quality Re-Inspection System. Since the Automatic Optical Inspection System in this study is relatively outdated, in order to avoid the occurrence of omissions, the equipment adjustment is more sensitive and overkill occurs. Therefore, the operator must confirm the action to solve the problem. Personnel differences and misjudgments. In this study, by statistically Jaccard's method, a spatial classifier was developed to separate random and systematic, and data analysis was performed for systematic, and then by convolutional autoencoder , CAE) to accumulate data to solve the problem of data imbalance. Finally, the information judged by the personnel is combined with deep learning to make predictions to find out the areas where the personnel judge abnormal. In practice, its advantage is that it retains flexibility. With AI's powerful integration capabilities and complementing the experience of personnel, both the system and personnel can learn and grow, and practice smart manufacturing systems with flexible decisions.

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網頁資源
37. Li(2017) Which machine learning algorithm should I use?. April 12 2017, 取自：https://blogs.sas.com/content/subconsciousmusings/2017/04/12/machine-learning-algorithm-use/ 最後瀏覽日：2020/06/01