SEMI-PointRend: improved semiconductor wafer defect classification and segmentation as rendering

MinJin Hwang; Bappaditya Dey; Enrique Dehaerne; Sandip Halder; Young-han Shin

doi:10.1117/12.2657555

27 April 2023 SEMI-PointRend: improved semiconductor wafer defect classification and segmentation as rendering

MinJin Hwang, Bappaditya Dey, Enrique Dehaerne, Sandip Halder, Young-han Shin

Proceedings Volume 12496, Metrology, Inspection, and Process Control XXXVII; 1249608 (2023) https://doi.org/10.1117/12.2657555
Event: SPIE Advanced Lithography + Patterning, 2023, San Jose, California, United States

Abstract

In this study, we applied the PointRend (Point-based Rendering) method to semiconductor defect segmentation. PointRend is an iterative segmentation algorithm inspired by image rendering in computer graphics, a new image segmentation method that can generate high-resolution segmentation masks. It can also be flexibly integrated into common instance segmentation meta-architecture such as Mask-RCNN and semantic meta-architecture such as FCN. We implemented a model, termed as SEMI-PointRend, to generate precise segmentation masks by applying the PointRend neural network module. In this paper, we focus on comparing the defect segmentation predictions of SEMI-PointRend and Mask-RCNN for various defect types (line-collapse, single bridge, thin bridge, multi bridge non-horizontal). We show that SEMI-PointRend outperforms Mask R-CNN by up to 18.8% in terms of segmentation mean average precision.

Conference Presentation

Citation Download Citation

MinJin Hwang, Bappaditya Dey, Enrique Dehaerne, Sandip Halder, and Young-han Shin "SEMI-PointRend: improved semiconductor wafer defect classification and segmentation as rendering", Proc. SPIE 12496, Metrology, Inspection, and Process Control XXXVII, 1249608 (27 April 2023); https://doi.org/10.1117/12.2657555

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