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Inspection and functionality convergence (Conference Presentation)
Author(s): Matthew Putman; Vadim Pinskiy; Denis Sharoukhov

Paper Abstract

The last several years has been one of rapid adoption of new technologies that use the convergence of super resolution optical microscopy and Artificial intelligence, not only for research but in factory settings. Detection and classification of defects and devices on semiconductors has commonly been used for process control and quality control. Now, utilizing advances in deep learning and other forms of Artificial intelligence, defect inspection framework is extended to be used not only as a reliable tool for identifying problems, but assigning casualty. This presentation will focus on a new convergence of high throughput inspection with Photoluminescence Imaging. Photoluminescence Imaging allows quantification and colocalization of different types of defects by combining multiple spectral snapshots of the sample collected with different output filters. These defects cannot be detected or classified under conventional brightfield microscopy. This method will provide a way to test functionality on the same wafer or product, and potentially at the same time as routine classification, giving a robust single station for solving production problems and leading faster design iteration. Data will be presented that shows this functionality in its early stages.

Paper Details

Date Published: 8 March 2019
PDF
Proc. SPIE 10919, Oxide-based Materials and Devices X, 1091920 (8 March 2019); doi: 10.1117/12.2518129
Show Author Affiliations
Matthew Putman, Nanotronics (United States)
Vadim Pinskiy, Nanotronics (United States)
Denis Sharoukhov, Nanotronics (United States)


Published in SPIE Proceedings Vol. 10919:
Oxide-based Materials and Devices X
David J. Rogers; David C. Look; Ferechteh H. Teherani, Editor(s)

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