半導體製造的基本過程之一是切片，這意味著將晶棒切成許多晶片。
在切片過程中，可能會產生有缺陷的晶圓。
不幸的是，識別缺陷晶片的檢查既浪費時間又檢查困難。為了解決這個問
題，我們建立了一個系統，該系統在切片及表面檢查過程中會使用傳感器
收集晶圓特性（例如溫度，厚度，晶片表面上的圖案等）以檢測晶片在製
造過程中是否有缺陷。
我們在此系統中應用兩種不同模型-GRU 神經網絡和XGBoost。此兩種模型
經過微調後，根據實際數據分析的實驗結果顯示在晶圓的缺陷檢測方面，
GRU 神經網絡的預測準確度和模型訓練時間均優於XGBoost

One of the fundamental processes in semiconductor manufacturing is slicing,
which means cutting an ingot into many wafers. During the slicing process, it
is possible to produce defective wafers. Unfortunately, the inspection to identify
defective wafers is time-consuming and dicult. To solve this problem, we build a
system, which uses sensors to collect features (e.g., temperature, thickness, pattern
on wafer surface, etc.) during the slicing process to detect if the wafers are defective
in the manufacturing process. Two dierent models, the GRU neural network and
XGBoost, are implemented in the proposed system. After ne-tuning both models,
experimental results based on real dataset indicate that the GRU neural network
outperforms XGBoost for wafer defective detection in both the prediction accuracy
and model training time.
ii

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