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Accelerate the analysis and optimization of lamellar BCP process using machine learning
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Paper Abstract

This paper presents how to use machine learning to accelerate the development of lamellar block copolymer process. The first part introduces an automated algorithm to measure lamellar CD-SEM images oriented to allow fast images screening, identify and select the most relevant chemical composition. The second part is the use of machine learning in the process development which is a data driven approach. This part is also divided in two sections, the first one for the prediction of the process, where having a set of experiments in a process window, a model is created so that the outcome from is estimated from different parameters. The second part is the estimation of the process window. These tools are oriented to assist process engineers in the process optimization which must be driven under expert supervision.

Paper Details

Date Published: 25 March 2019
PDF: 6 pages
Proc. SPIE 10960, Advances in Patterning Materials and Processes XXXVI, 109600Z (25 March 2019); doi: 10.1117/12.2514955
Show Author Affiliations
A. Derville, POLLEN Metrology (France)
S. Martinez, POLLEN Metrology (France)
G. Gey, POLLEN Metrology (France)
J. Baderot, POLLEN Metrology (France)
G. Bernard, POLLEN Metrology (France)
X. Chevalier, ARKEMA (France)
J. Foucher, POLLEN Metrology (France)


Published in SPIE Proceedings Vol. 10960:
Advances in Patterning Materials and Processes XXXVI
Roel Gronheid; Daniel P. Sanders, Editor(s)

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