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

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.

Modelling laser machining of nickel with spatially shaped three pulse sequences using deep learning.

Femtosecond laser machining is a complex process, owing to the high peak intensities involved. Modelling approaches for the prediction of final sample quality based on photon-atom interactions are therefore challenging to extrapolate up to the microscale and beyond. The problem is compounded when multiple exposures are used to produce a final structure, where surface modifications from previous exposures must be taken into consideration. Neural network approaches allow for the automatic creation of a model that accounts for these challenging processes, without any physical knowledge of the processes being programmed by a specialist. We present such a network for the prediction of surface quality for multi-exposure femtosecond machining on a 5µm electroless nickel layer deposited on copper, where each pulse is uniquely spatially shaped using a spatial light modulator. This neural network modelling method accurately predicts the surface profile after three, sequential, overlapping exposures of dissimilar intensity patterns. It successfully reproduces such effects as the sub-diffraction limit machining feasible with multiple exposures, and the smoothing effect on edge-burr from previous exposures expected in multi-exposure laser machining.