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

• Published in: Optics express Vol. 29; no. 22; pp. 36469 - 36486
• Main Authors: McDonnell, M. D. T., Grant-Jacob, J. A., Praeger, M., Eason, R. W., Mills, B.
• Format: Journal Article
• Language: English
• Published: 25.10.2021
• ISSN: 1094-4087; 1094-4087
• DOI: 10.1364/OE.431441

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.