Share Email Print

Proceedings Paper

Automated Heuristic Defect Classification (AHDC) for haze-induced defect growth management and mask requalification
Author(s): Saghir Munir; Gul Qidwai
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

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: 5 April 2012
PDF: 10 pages
Proc. SPIE 8324, Metrology, Inspection, and Process Control for Microlithography XXVI, 83243C (5 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)

© SPIE. Terms of Use
Back to Top