Cognitive learning: a machine learning approach for automatic process characterization from design

J. Foucher; J. Baderot; S. Martinez; A. Dervilllé; G. Bernard

doi:10.1117/12.2297348

16 March 2018 Cognitive learning: a machine learning approach for automatic process characterization from design

J. Foucher, J. Baderot, S. Martinez, A. Dervilllé, G. Bernard

Proceedings Volume 10585, Metrology, Inspection, and Process Control for Microlithography XXXII; 105852R (2018) https://doi.org/10.1117/12.2297348
Event: SPIE Advanced Lithography, 2018, San Jose, California, United States

Abstract

Cutting edge innovation requires accurate and fast process-control to obtain fast learning rate and industry adoption. Current tools available for such task are mainly manual and user dependent. We present in this paper cognitive learning, which is a new machine learning based technique to facilitate and to speed up complex characterization by using the design as input, providing fast training and detection time. We will focus on the machine learning framework that allows object detection, defect traceability and automatic measurement tools.

Citation Download Citation

J. Foucher, J. Baderot, S. Martinez, A. Dervilllé, and G. Bernard "Cognitive learning: a machine learning approach for automatic process characterization from design", Proc. SPIE 10585, Metrology, Inspection, and Process Control for Microlithography XXXII, 105852R (16 March 2018); https://doi.org/10.1117/12.2297348

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