[Purposes]Catastrophic Optical Damage(COD)is a major limiting factor for the reli-ability and lifespan of high-power semiconductor lasers,making effective defect detection crucial for optimizing the manufacturing processes and structural designs of laser chips.In this study,a light-weight laser chip defect detection algorithm based on an improved YOLOv7-Tiny is proposed,aim-ing at addressing the high computational and parameter demands of deep learning applications in defect detection.[Methods]By employing a lightweight convolutional neural network as the feature extrac-tion backbone and integrating multi-branch reparameterized convolution blocks,this algorithm not only significantly reduces resource consumption but also enhances feature representation capabilities.Additionally,the introduced coordinate attention mechanism improves the precision of defect localiza-tion.Pruning experiments and model deployment are conducted to further verify the algorithm practi-cality.[Findings]Experimental results on the electroluminescence dataset demonstrate that this method can accurately detect chip defects with lower parameter and computational costs,showing ex-cellent performance.
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