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Proceedings Paper

Automated defect disposition with AIMS AutoAnalysis
Author(s): Guy Russell; David Jenkins; Arosha Goonesekera; Kay Dornbusch; Vahagn Sargsyan; Hendrik Zachmann; Ute Buttgereit
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Paper Abstract

The ongoing trend to smaller structures and an increasing number of high MEEF patterns in mask design makes defect disposition and repair verification more critical than ever. For AIMS™ as the standard method for defect disposition and repair verification, the requirements are getting tighter. Additionally, the efforts required for defect analysis are steadily increasing. As a result, mask manufacturers are forced to continually find methods to increase productivity and optimize the cost of defect disposition.

Smart solutions for automated defect treatment together with a high degree of tool integration play an increasing role in this challenge. With AIMS™ AutoAnalysis, which provides fully automated analysis capability of AIMS™ aerial images, ZEISS addresses this challenge. Due to direct connection and communication of AutoAnalysis with the AIMS™ system via the FAVOR® platform, the image analysis process runs in parallel to the measurement process. A high degree of automation reduces the influence of human error and provides highly reliable results.

In the following paper a study is presented demonstrating the benefits of the implementation of AutoAnalysis in the production environment at Photronics, Inc. The study was carried out by analyzing defects on pattern sets, varying from simple to very complex patterns. Furthermore, the analysis capabilities of AutoAnalysis have been compared with the capability of operators and engineers.

The performance of AutoAnalysis is presented showing significant time saving in the defect disposition process as well as an overall increase in reliability of analysis results.

Paper Details

Date Published: 16 October 2017
PDF: 9 pages
Proc. SPIE 10451, Photomask Technology 2017, 1045113 (16 October 2017);
Show Author Affiliations
Guy Russell, Photronics, Inc. (United States)
David Jenkins, Photronics, Inc. (United States)
Arosha Goonesekera, Photronics, Inc. (United States)
Kay Dornbusch, Carl Zeiss SMT GmbH (Germany)
Vahagn Sargsyan, Carl Zeiss SMT GmbH (Germany)
Hendrik Zachmann, Carl Zeiss SMT GmbH (Germany)
Ute Buttgereit, Carl Zeiss SMT GmbH (Germany)

Published in SPIE Proceedings Vol. 10451:
Photomask Technology 2017
Peter D. Buck; Emily E. Gallagher, Editor(s)

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