Automated catastrophic optical damage inspection of semiconductor laser chip based on multi-scale strip convolution aggregation

Catastrophic optical damage (COD) is one of the reasons limiting the output power and lifetime of semiconductor lasers. Nevertheless, the automatic defects inspection of the COD is a challenging task due to several factors, including the micron-scale size of the laser chip, poor contrast, the notable similarities in defect features across different categories, and the fact that defects occupy a minimal portion of image pixels in the images. In this work, We first design and implement a data acquisition and inspection system to collect micron-scale laser chip electroluminescence (EL) images. Secondly, we establish a laser chip COD dataset for training. Finally, a novel COD detection network (CODDNet) is proposed to construct an end-to-end defect detection method. To better extract strip-like COD features under poor contrast, a strip convolution is proposed to acquire more discriminative features and reduce the parameters. A multi-scale strip convolution aggregation structure is proposed to extract richer information across different defect categories from the network to obtain multi-scale feature maps. To address the class imbalance issue between defect and the background, an attention module is embedded into the block to emphasize the COD defects. The experimental results demonstrated that the proposed CODDNet could achieve a higher inspection accuracy and faster inference speed with fewer parameters. Based on the proposed method, manufacturers could take corresponding effective measures to improve the stability of laser chips. In the future, we will continue to expand the COD dataset and conduct research on defect detection for different types of laser chips.