ADAPTIVE SPATIAL PATTERN RECOGNITION FOR DEFECT DETECTION

    公开(公告)号:US20240281953A1

    公开(公告)日:2024-08-22

    申请号:US18443748

    申请日:2024-02-16

    CPC classification number: G06T7/0004 G06V10/764 G06V10/82 G06T2207/20084

    Abstract: Systems and methods for identifying and classifying defects in a manufactured article, such as an article containing a semiconductor substrate, are provided. Inspection data generated by an inspection tool inspecting the manufactured article is used to generate a defect map. The defect map includes an arrangement of potential defect indicators. Thresholding is performed between potential defect indicators, removing, based on the dynamic thresholding, a subset of the potential defect indicators from the arrangement to generate a modified defect map. Based on the modified defect map, a defect can be identified and classified.

    PERFORMANCE MANAGEMENT OF SEMICONDUCTOR SUBSTRATE TOOLS

    公开(公告)号:US20240295829A1

    公开(公告)日:2024-09-05

    申请号:US18574554

    申请日:2023-05-24

    Inventor: Xin Song

    CPC classification number: G03F7/706841 G03F7/70608 G03F7/706845

    Abstract: Proactive management of semiconductor substrate tools. A machine learning model is used to predict future performance characteristics for such tools. In some examples, the model can diagnose issues with tools or with ambient conditions of the tools' environment. In some examples, the model can recommend one or more remedial actions to maintain adequate performance of the substrate tool.

    DYNAMIC MODELING FOR SEMICONDUCTOR SUBSTRATE DEFECT DETECTION

    公开(公告)号:US20230186461A1

    公开(公告)日:2023-06-15

    申请号:US18062112

    申请日:2022-12-06

    Inventor: Xin Song

    CPC classification number: G06T7/001 H01L21/67288 G06T2207/30148

    Abstract: Dynamic modeling for detecting and classifying defects of fabricated substrates, such as semiconductor substrates. A model includes pixel-by-pixel distributions of pixel data that define a range of known acceptability for substrates based on images of those substrates. The range of acceptability can be defined between upper and lower thresholds. The model is dynamically updated as new imaging data of substrates is obtained, and particularly new imaging data for which an imaging factor not relevant to substrate acceptability has changed. The model can be updated by shifting one or more of the thresholds for one or more of the pixels.

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