Predictive visualisation of high repetition rate femtosecond machining of silica using deep learning

Abstract

Whilst femtosecond laser machining can enable extremely high-resolution fabrication, it is a highly nonlinear process that is challenging to model when starting from basic principles and a theoretical understanding. Deep learning offers the potential for modelling complex systems directly from experimental data, and hence is a complementary alternative to traditional modelling approaches. In this work, deep learning is applied to the predictive visualisation of femtosecond laser machining of lines in a silica substrate, in a specific experimental regime where nanofoam is fabricated. The neural networks used for this task are shown to consider both the laser power and the amount of debris on the sample before machining, when predicting the appearance of the line after machining. This predictive capability provides clear evidence of the potential for deep learning to become an important tool in the understanding and optimisation of laser machining, and indeed, other complex physical phenomena.

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Identifiers

ORCID for Ben Mills: orcid.org/0000-0002-1784-1012

ORCID for James A. Grant-Jacob: orcid.org/0000-0002-4270-4247

ORCID for Michalis N. Zervas: orcid.org/0000-0002-0651-4059

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