Automated Heuristic Defect Classification (AHDC) for haze-induced defect growth management and mask requalification

Saghir Munir; Gul Qidwai

doi:10.1117/12.924329

5 April 2012 Automated Heuristic Defect Classification (AHDC) for haze-induced defect growth management and mask requalification

Saghir Munir, Gul Qidwai

Author Affiliations +

Proceedings Volume 8324, Metrology, Inspection, and Process Control for Microlithography XXVI; 83243C (2012) https://doi.org/10.1117/12.924329
Event: SPIE Advanced Lithography, 2012, San Jose, California, United States

Abstract

This article presents results from a heuristic automated defect classification algorithm for reticle inspection that mimics the classification rules. AHDC does not require CAD data, thus it can be rapidly deployed in a high volume production environment without the need for extensive design data management. To ensure classification consistency a software framework tracks every defect in repeated inspections. Through its various image based derived metrics it is shown that such a system manages and tracks repeated defects in applications such as haze induced defect growth.

Citation Download Citation

Saghir Munir and Gul Qidwai "Automated Heuristic Defect Classification (AHDC) for haze-induced defect growth management and mask requalification", Proc. SPIE 8324, Metrology, Inspection, and Process Control for Microlithography XXVI, 83243C (5 April 2012); https://doi.org/10.1117/12.924329

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