关键词: 多晶硅晶片, 颜色差异, 多分量, 卷积神经网络

Abstract: Color difference detection of polycrystalline wafers with complex textures was a challenge in the manufacturing processes of solar cells. The traditional algorithm in color difference detection was not suitable for situations where the categories of color differences varied greatly, and the classification results were inaccurate. A detection algorithm of multi-component convolution neural network was proposed based on color features of different components. It is found that color features are different in H, S and V components by analyzing the feature distribution of polysilicon wafer images in HSV color spaces. The influences of model depth and convolution kernel size on detection results were evaluated to build the best convolution neural network structure based on full convolution neural network. A multi-component convolution neural network model was constructed based on the best network model to enhance the ability to distinguish different color difference features. The experimental results show that the accuracy, the values of MCC and F1Score of the multi-component convolution neural network are 92.28%, 95.45%, and 94.03% respectively, which has higher detection accuracy than that of other algorithms.

Key words: polycrystalline wafer, color difference, multi-component, convolutional neural network