Abstract: Chip surface defects can affect the appearance and performance of the chip.Therefore,surface defect detection is an important part of the chip production process.The automatic detection method based on machine vision attracts much attention because of its advantages of low cost and high efficiency.Weak defects such as low contrast between defects and background and small defects,bring challenges to traditional detection methods.Because deep learning has shown strong capabilities in the fields of machine vision in recent years,this paper studied the detection of weak defects on the chip surface by using the method based on deep learning.Chip surface defects were regarded as noise in this menthod.Firstly,convolutional denoising auto-encoders (CDAE) is applied to reconstruct the image without defect.Then,the reconstructed image without defect is used to subtract the input image,thus obtaining the residual image with defect information.Because the influence of background has been eliminated from the residual diagram,it is easier to detect defects based on the residual diagram.Because of the random noise in the process of reconstructing defect-free image from chip background image based on CDAE,the weak defect may be lost in the reconstructed noise.Therefore,this paper proposed an overlapping block strategy to suppress the reconstructed noise,so as to better detect the weak defect.Because CDAE is an unsupervised learning network,there is no need to perform a large amount of manual data annotation during training,which further enhances the applicability of the method.By using the real chip surface data provided by the paper partner,the effectiveness of the proposed method in chip surface detection is verified.

Key words: Chip surface defects, Convolution denoising auto-encoders, Deep learning, Defect detection, Unsupervised learning