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Proceedings Paper

Efficiency and throughput improvement on defect disposition through automated defect classification
Author(s): Lin He; Noel Corcoran; Danping Peng; Vikram Tolani; Hsien-Min Chang; Paul Yu; Kechang Wang; C. J. Chen; T. H. Yen; Rick Lai; B. H. Ong; Laurent C. C. Tuo
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Paper Abstract

The routine use of aggressive OPC at advanced technology nodes, i.e., 40nm and beyond, has made photomask patterns quite complex. The high-resolution inspection of such masks often result in more false and nuisance defect detections than ever before. Traditionally, each defect is manually examined and classified by the inspection operator based on defined production criteria. The significant increase in total number of detected defects has made manual classification costly and non-manufacturable. Moreover, such manual classification is also susceptible to human judgment and hence error-prone. Luminescent's Automated Defect Classification (ADC) offers a complete and systematic approach to defect disposition and classification. The ADC engine retrieves the high resolution inspection images and uses a decision-tree flow based on the same criteria human operators use to classify a given defect. Some identification mechanisms adopted by ADC to characterize defects include defect color in transmitted and reflected images, as well as background pattern criticality based on pattern topology. In addition, defect severity is computed quantitatively in terms of its size, impacted CD error, transmission error, defective residue, and contact flux error. The final classification uses a matrix decision approach to reach the final disposition. In high volume manufacturing mask production, matching rates of greater than 90% have been achieved when compared to operator defect classifications, together with run-rates of 250+ defects classified per minute. Such automated, consistent and accurate classification scheme not only allows for faster throughput in defect review operations but also enables the use of higher inspection sensitivity and success rate for advanced mask productions with aggressive OPC features.

Paper Details

Date Published: 13 October 2011
PDF: 10 pages
Proc. SPIE 8166, Photomask Technology 2011, 81661I (13 October 2011); doi: 10.1117/12.896948
Show Author Affiliations
Lin He, Luminescent Technologies, Inc. (United States)
Noel Corcoran, Luminescent Technologies, Inc. (United States)
Danping Peng, Luminescent Technologies, Inc. (United States)
Vikram Tolani, Luminescent Technologies, Inc. (United States)
Hsien-Min Chang, Luminescent Technologies, Inc. (United States)
Paul Yu, Luminescent Technologies, Inc. (United States)
Kechang Wang, Luminescent Technologies, Inc. (United States)
C. J. Chen, Taiwan Semiconductor Manufacturing Co. Ltd. (Taiwan)
T. H. Yen, Taiwan Semiconductor Manufacturing Co. Ltd. (Taiwan)
Rick Lai, Taiwan Semiconductor Manufacturing Co. Ltd. (Taiwan)
B. H. Ong, Taiwan Semiconductor Manufacturing Co. Ltd. (Taiwan)
Laurent C. C. Tuo, Taiwan Semiconductor Manufacturing Co. Ltd. (Taiwan)

Published in SPIE Proceedings Vol. 8166:
Photomask Technology 2011
Wilhelm Maurer; Frank E. Abboud, Editor(s)

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