13 October 2011 Efficiency and throughput improvement on defect disposition through automated defect classification
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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.
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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, "Efficiency and throughput improvement on defect disposition through automated defect classification", Proc. SPIE 8166, Photomask Technology 2011, 81661I (13 October 2011); doi: 10.1117/12.896948; https://doi.org/10.1117/12.896948
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