Non-Destructive Detection of Sub-Micron Imperceptible Scratches On Laser Chips Based On Consistent Texture Entropy Recursive Optimization Semi-Supervised Network

Laser chips, the core components of semiconductor lasers, are extensively utilized in various industries, showing great potential for future application. Smoothness emitting surfaces are crucial in chip production, as even imperceptible scratches can significantly degrade performance and lifespan, thus impeding production efficiency and yield. Therefore, non-destructively detecting these imperceptible scratches on the emitting surfaces is essential for enhancing yield and reducing costs. These sub-micron level scratches, barely visible against the background, are extremely difficult to detect with conventional methods, compounded by a lack of labeled datasets. To address this challenge, this paper introduces TexRecNet, a consistent texture entropy recursive optimization semi-supervised network. The network, based on a recursive optimization architecture, iteratively improves the detection accuracy of imperceptible scratch edges, using outputs from previous cycles to inform subsequent inputs and guide the network's positional encoding. It also introduces image texture entropy, utilizing a substantial amount of unlabeled data to expand the training set while maintaining training signal reliability. Ultimately, by analyzing the inconsistency of the network output sequences obtained during the recursive process, a semi-supervised training strategy with recursive consistency constraints is proposed, using outputs from the recursive process for non-destructive signal augmentation and consistently optimizes the loss function for efficient end-to-end training. Experimental results show that this method, utilizing a substantial amount of unsupervised data, achieves 75.6% accuracy and 74.8% recall in detecting imperceptible scratches, an 8.5% and 33.6% improvement over conventional Unet, enhancing quality control in laser chips.