高速生產的微型電子元件需要快速且準確的檢測方法，自動光學檢測（Automated Optical Inspection, AOI）常應用於此。然而，AOI易於產生檢測篩選不足且/或篩選過度的問題。本研究提出一全新的兩階段集成卷積神經網絡模型來解決AOI檢測瓶頸，基於檢測速度下，該模型包含兩階段品質檢測，即為瑕疵辨識與瑕疵分類，前者的準確度達到92.75％-100％，且每一產品之檢測速度約為0.02-0.03秒；後者的準確度達到85.71％-100％，且每一瑕疵產品之檢測速度約為0.04-0.06秒。本實驗結果不僅優於AOI的檢測表現、又符合生產速度計劃，且接近即時檢測。此外，本實驗可以檢測到AOI無法辨認的瑕疵，並識別因AOI誤判的良品。

Since tiny-scaled electronical components with high-speed production requires fast but accurate inspection. Automatic optical inspection (AOI) is commonly employed in this situation. However, AOI tends to cause under-screening and/or over-screening. This paper proposes a new two-phase integrated convolutional neural network (CNN) model to resolve AOI inspection issue. The proposed model contains two phases of quality detection that are defective identification and defect type classification on the basis of inspection speed. The former reaches 92.75%-100% with detection speed at 0.02-0.03 second per product, and the latter accuracy reaches 85.71%-100% with detection speed at 0.04-0.06 second per product, which are better than AOI, match production schedule and close to real time. In addition, our methods can detect the defects that AOI cannot recognize, and identify good products that AOI misjudges.

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