Using machine learning technology to accelerate the development of plasma etching processes

A. Derville; G. Gey; J. Baderot; S. Martinez; G. Bernard; J. Foucher

doi:10.1117/12.2514705

20 March 2019 Using machine learning technology to accelerate the development of plasma etching processes

A. Derville, G. Gey, J. Baderot, S. Martinez, G. Bernard, J. Foucher

Proceedings Volume 10963, Advanced Etch Technology for Nanopatterning VIII; 109630C (2019) https://doi.org/10.1117/12.2514705
Event: SPIE Advanced Lithography, 2019, San Jose, California, United States

Abstract

The latest advances in Machine Learning (ML) produce results with unprecedented accuracy, and could signal a new era in the smart manufacturing field. We propose a framework designed to work alongside experts: learning from them and optimizing their knowledge. This framework must be considered as a tool to assist the experts in their daily work. The user creates a measurement recipe which includes an example of the feature as well as the measurements placed by the process engineer. Grouping the measurement recipes of the same object in an entity collection allows the user to train a machine learning recipe which includes a deformation model to handle variations in structure and contrast. The new images are analyzed following the machine learning pipeline which includes the detection of features, repositioning, measurement, quality evaluation and finally the results of measurement are given to the user. We discuss the pipeline and we focus on the metrics to validate the machine learning recipe, providing quantitative results for stability and robustness to variations.

Conference Presentation

Citation Download Citation

A. Derville, G. Gey, J. Baderot, S. Martinez, G. Bernard, and J. Foucher "Using machine learning technology to accelerate the development of plasma etching processes", Proc. SPIE 10963, Advanced Etch Technology for Nanopatterning VIII, 109630C (20 March 2019); https://doi.org/10.1117/12.2514705

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