Semiconductor process prediction method and semiconductor process prediction apparatus considering overall features and local features

Abstract

A semiconductor process prediction method and a semiconductor process prediction apparatus considering overall features and local features are provided. The semiconductor manufacturing process prediction method includes the following steps. Several equipment sensing curves are obtained. The equipment sensing curves are filtered to reduce the co-linearity of the equipment sensing curves. A Dynamic Time Warping (DTW) procedure is performed to align the equipment sensing curves. The equipment sensing curves which are aligned are inputted into a Convolutional Neural Network (CNN) model to obtain a first prediction result considering the local features. A statistical analysis procedure is performed on the equipment sensing curves to obtain several statistical data. The statistical data are inputted into an Artificial Neural Network (ANN) model to obtain a second prediction result considering the overall features. According to the first prediction result and the second prediction result, a total prediction result is obtained.