本論文旨為應用渦電流檢測技術建立一套非破壞性金屬薄膜厚度檢測系統，並結合SVR回歸運算以降低於厚度預估時所產生的誤差，進而減少產品生產時的浪費。電路板生產時，表層銅箔厚度為十分重要影響參數，尤其應用於高頻時，如厚度與原訂規格不同，將會導致電路特性改變，故本文以渦電流線圈感測器置於不同厚度銅箔上時將會對應到不同的阻抗值做為發想，採用訊號之相位與振幅做為特徵值，搭配SVR回歸，以推算出待測物之實際厚度。以SVM為基礎延伸而來的SVR回歸，相較於類神經迴歸運算，能在保有相當的精確度下，進一步減少計算所需的時間，而相較於最小二乘法，其能夠有效增加預估精準度，故更為適合應用於線上檢測系統。藉著數學軟體對預測效果進行模擬，結果顯示，SVR 回歸確實能有效減少預估誤差量。最後於實時實驗結果顯示，藉著本文所建立之系統進行厚度預估可有效將相對誤差降低75%以上。

This paper aims at establishing a set of non-destructive copper coating thickness detection system using eddy current technology, and combine Support Vector Regression (SVR) operation to reduce the error caused by thickness estimation, thereby reducing waste in production. When the print circuit board (PCB) is produced, the thickness of the surface copper coating is a very important parameter. Especially when it is applied to high frequency products, if the copper thickness is different from the original specification, the circuit characteristics will change. Therefore, based on the fact that copper coating with different thicknesses will correspond to different impedance values to the probe, this paper takes signal phase and amplitude as characteristic values, and uses SVR to estimate the thickness of the object to be tested. The SVR based on Support Vector Machine (SVM) can reduce the time required for calculations while maintaining considerable accuracy compared with the neural network. On the other hand, SVR also has better accuracy than the least squares method. Hence, it is more suitable for online detection systems. The whole process is simulated and the results show that SVR can effectively reduce the amount of prediction error. The final experimental results also show that the thickness estimation by the system established in this paper can effectively reduce the relatively error by more than 75%.

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