McDonnell M D T, Grant-Jacob J A, Xie Y, Praeger M, Mackay B S, Eason R W, Mills B
Opt Express. 2020 May 11;28(10):14627-14637. doi: 10.1364/OE.381421.
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.
飞秒激光加工是一个复杂的过程，这是由于其涉及的高峰值强度。因此，基于光子 - 原子相互作用来预测最终样品质量的建模方法很难外推到微观尺度及更大尺度。当使用多次曝光来产生最终结构时，问题变得更加复杂，因为必须考虑先前曝光产生的表面改性。神经网络方法允许自动创建一个模型来解释这些具有挑战性的过程，而无需专家对这些过程有任何物理知识。我们展示了这样一个网络，用于预测在沉积于铜上的5微米化学镀镍层上进行多次曝光飞秒加工的表面质量，其中每个脉冲使用空间光调制器进行独特的空间整形。这种神经网络建模方法能够准确预测在三次连续、重叠的不同强度图案曝光后的表面轮廓。它成功地再现了多次曝光可行的亚衍射极限加工等效果，以及在多次曝光激光加工中预期的对先前曝光产生的边缘毛刺的平滑效果。
