Identification of spatial intensity profiles from femtosecond laser machined depth profiles via neural networks

Abstract

Laser machining involves many complex processes, especially when using femtosecond pulses due to the high peak intensities involved. Whilst conventional modelling, such as those based on photon-electron interactions, can be used to predict the appearance of the surface after machining, this generally becomes unfeasible for micron-scale features and larger. The authors have previously demonstrated that neural networks can simulate the appearance of a sample when machined using different spatial intensity profiles. However, using a neural network to model the reverse of this process is challenging, as diffractive effects mean that any particular sample appearance could have been produced by a large number of beam shape variations. Neural networks struggle with such one-to-many mappings, and hence a different approach is needed. Here, we demonstrate that this challenge can be solved by using a neural network loss function that is a separate neural network. Here, we therefore present a neural network that can identify the spatial intensity profiles needed, for multiple laser pulses, to produce a specific depth profile in 5 µm thick electroless nickel.

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Identifiers

ORCID for Michael, David Tom McDonnell: orcid.org/0000-0003-4308-1165

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

ORCID for Matthew Praeger: orcid.org/0000-0002-5814-6155

ORCID for R.W. Eason: orcid.org/0000-0001-9704-2204

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

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