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

• DOI: 10.1364/oe.381421
• Publication Date: 2020-01-19T23:30:06Z
• Authors: M. D. T. McDonnell; J. A. Grant-Jacob; Y. Xie; M. Praeger; B. S. Mackay; R. W. Eason; B. Mills
• Keywords: Microscale chemistry; Smoothing

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