Chip surface defect detection is of great significance in semiconductor manufacturing,for the current chip surface defect area is small,defect shape is variable,defect size spanning a large situation,put forward an improved chip surface defect detection algorithm based on YOLOv5,first of all,based on the CnovNext network to improve the feature extraction module,improve the stability of the network and the ability of feature expression,and at the same time put forward the ehanced convolutional block attention module(E_CBAM)module is proposed to embed more detailed location information into convolutional block attention module(CBAM)to improve the detection capability of the whole network for small area and edge defects.For the problem of large size span of chip defects,the study introduces deformable convolution and BiFPN module,on the one hand,the deformable convolution has better extraction ability for irregular shape convolution,on the other hand,the BiFPN in the Neck part simplifies the structure and ensures the accuracy of multi-scale fusion.After the experiments,it is shown that the improved network achieves a mAP@0.5 index of 95.3%on chip surface defect dataset(CDD),which is 3.1%higher compared to the original YOLOv5s network, which is more accurate and robust to the chip surface defects without too much increase in the network parameters.