Improved reticle requalification accuracy and efficiency via simulation-powered automated defect classification

Shazad Paracha; Benjamin Eynon; Ben F. Noyes III; Anthony Nhiev; Anthony Vacca; Peter Fiekowsky; Dan Fiekowsky; Young Mog Ham; Doug Uzzel; Michael Green; Susan MacDonald; John Morgan

doi:10.1117/12.2048622

2 April 2014 Improved reticle requalification accuracy and efficiency via simulation-powered automated defect classification

Shazad Paracha, Benjamin Eynon, Ben F. Noyes III, Anthony Nhiev, Anthony Vacca, Peter Fiekowsky, Dan Fiekowsky, Young Mog Ham, Doug Uzzel, Michael Green, Susan MacDonald, John Morgan

Author Affiliations +

Proceedings Volume 9050, Metrology, Inspection, and Process Control for Microlithography XXVIII; 905031 (2014) https://doi.org/10.1117/12.2048622
Event: SPIE Advanced Lithography, 2014, San Jose, California, United States

Abstract

Advanced IC fabs must inspect critical reticles on a frequent basis to ensure high wafer yields. These necessary requalification inspections have traditionally carried high risk and expense. Manually reviewing sometimes hundreds of potentially yield-limiting detections is a very high-risk activity due to the likelihood of human error; the worst of which is the accidental passing of a real, yield-limiting defect. Painfully high cost is incurred as a result, but high cost is also realized on a daily basis while reticles are being manually classified on inspection tools since these tools often remain in a non-productive state during classification. An automatic defect analysis system (ADAS) has been implemented at a 20nm node wafer fab to automate reticle defect classification by simulating each defect’s printability under the intended illumination conditions. In this paper, we have studied and present results showing the positive impact that an automated reticle defect classification system has on the reticle requalification process; specifically to defect classification speed and accuracy. To verify accuracy, detected defects of interest were analyzed with lithographic simulation software and compared to the results of both AIMS™ optical simulation and to actual wafer prints.

Citation Download Citation

Shazad Paracha, Benjamin Eynon, Ben F. Noyes III, Anthony Nhiev, Anthony Vacca, Peter Fiekowsky, Dan Fiekowsky, Young Mog Ham, Doug Uzzel, Michael Green, Susan MacDonald, and John Morgan "Improved reticle requalification accuracy and efficiency via simulation-powered automated defect classification", Proc. SPIE 9050, Metrology, Inspection, and Process Control for Microlithography XXVIII, 905031 (2 April 2014); https://doi.org/10.1117/12.2048622

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