Automatic defect classification using topography map from SEM photometric stereo

Sergio David Serulnik; Jacob Cohen; Boris Sherman; Ariel Ben-Porath

doi:10.1117/12.532961

29 April 2004 Automatic defect classification using topography map from SEM photometric stereo

Sergio David Serulnik, Jacob Cohen, Boris Sherman, Ariel Ben-Porath

Proceedings Volume 5378, Data Analysis and Modeling for Process Control; (2004) https://doi.org/10.1117/12.532961
Event: Microlithography 2004, 2004, Santa Clara, California, United States

Abstract

As the industry moves to smaller design rules, shrinking process windows and shorter product lifecycles, the need for enhanced yield management methodology is increasing. Defect classification is required for identification and isolation of yield loss sources. Practice demonstrates that an operator relies on 3D information heavily while classifying defects. Therefore, Defect Topographic Map (DTM) information can enhance Automatic Defect Classification (ADC) capabilities dramatically. In the present article, we describe the manner in which reliable and rapid SEM measurements of defect topography characteristics increase the classifier ability to achieve fast identification of the exact process step at which a given defect was introduced. Special multiple perspective SEM imaging allows efficient application of the photometric stereo methods. Physical properties of a defect can be derived from the 3D by using straightforward computer vision algorithms. We will show several examples, from both production fabs and R&D lines, of instances where the depth map is essential in correctly partitioning the defects, thus reducing time to source and overall fab expenses due to defect excursions.

Citation Download Citation

Sergio David Serulnik, Jacob Cohen, Boris Sherman, and Ariel Ben-Porath "Automatic defect classification using topography map from SEM photometric stereo", Proc. SPIE 5378, Data Analysis and Modeling for Process Control, (29 April 2004); https://doi.org/10.1117/12.532961

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