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Proceedings Paper

Automated Heuristic Defect Classification (AHDC) for haze-induced defect growth management and mask requalification
Author(s): Saghir Munir; Gul Qidwai
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Paper 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.

Paper Details

Date Published: 6 April 2012
PDF: 10 pages
Proc. SPIE 8324, Metrology, Inspection, and Process Control for Microlithography XXVI, 83243C (6 April 2012); doi: 10.1117/12.924329
Show Author Affiliations
Saghir Munir, Reticle Labs. (United States)
Gul Qidwai, Reticle Labs. (United States)


Published in SPIE Proceedings Vol. 8324:
Metrology, Inspection, and Process Control for Microlithography XXVI
Alexander Starikov, Editor(s)

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