在晶圓加工過程中，晶柱(Ingot)經切片(Slicing)後需透過圓邊(Edge Grinding)製程來防止後績加工過程中產生碎邊(Chipping)或汙染微粒。然而，因連績且大量的生產過程，以現行的品檢及預防維護的方式，容易造成人為誤判，導致研磨砂輪更換時機的延誤，使得研磨機台容易因問題累積而加工出報廢品或產品需要重工(rework)，進而提高加工成本。本研究透過在加工機台上安裝加速規與參克風，透過訊號擷取與分析，探討矽晶圆圓邊製程中時域、頻域訊號，將時頻域訊號針對機台加工特性擷取重要特徵建立特徵資料集，利用機器學習回歸模型建模，並用晶圓品質最為標籤標記資料，藉預測晶片倒角值，間接監測砂輪磨耗狀況，提早發現機台問題，在下次加工前採取對應手段。

In the process of wafer processing, the ingot must be sliced and then cut through the edge grinding process to prevent chipping or contamination particles during the subsequent processing. However, due to the continuous and large-scale production process, the current quality inspection and preventive maintenance methods are easy to cause human misjudgment, resulting in delays in the replacement of the grinding wheel, and the grinding machine is likely to process scrap or products due to accumulation of problems. Rework is required, which in turn increases processing costs. In this research, through the installation of accelerometers and parameters on the processing machine, through signal acquisition and analysis, the relationship between time domain and frequency domain signals, grinding wheel wear and wafer quality in the silicon wafer rounding process is explored, and early discovery For machine problems, predict the quality of wafers and take corresponding measures before the next processing.

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