ICMarkingNet: An Ultrafast and Streamlined Deep Model for IC Marking Inspection

This study presents ICMarkingNet, an end-to-end model for integrated circuit (IC) the marking inspection task. The model pinpoints markings against an IC image utilizing a saliency-guided regime under weakly supervised learning. Through the introduction of a novel direction representation alongside a transform-based rotation method, the model achieves improved accuracy in recognizing marking directions. Furthermore, the incorporation of a newly introduced sampling method, namely LinkSampling, enables the model to extract character features with high consistency, and empowers the model to excel in word-level marking recognition tasks. Notably, ICMarkingNet is uniquely engineered to function within a compact and streamlined pipeline, facilitating execution entirely on graphics process units. Experiments on a real-line IC marking dataset exhibit an f1-score of 96.88% and the inspection speed surpassing 100 samples per second, validating the superior performance and efficiency of the proposed model over both general end-to-end text recognition models and existing IC marking inspection frameworks.