The Institute for Solid State Physics, The University of Tokyo, Kashiwa, Chiba, 277-8581, Japan.
Sci Rep. 2022 Apr 7;12(1):5837. doi: 10.1038/s41598-022-09870-x.
Laser-based material removal, or ablation, using ultrafast pulses enables precision micro-scale processing of almost any material for a wide range of applications and is likely to play a pivotal role in providing mass customization capabilities in future manufacturing. However, optimization of the processing parameters can currently take several weeks because of the absence of an appropriate simulator. The difficulties in realizing such a simulator lie in the multi-scale nature of the relevant processes and the high nonlinearity and irreversibility of these processes, which can differ substantially depending on the target material. Here we show that an ultrafast laser ablation simulator can be realized using deep neural networks. The simulator can calculate the three-dimensional structure after irradiation by multiple laser pulses at arbitrary positions and with arbitrary pulse energies, and we applied the simulator to a variety of materials, including dielectrics, semiconductors, and an organic polymer. The simulator successfully predicted their depth profiles after irradiation by a number of pulses, even though the neural networks were trained using single-shot datasets. Our results indicate that deep neural networks trained with single-shot experiments are able to address physics with irreversibility and chaoticity that cannot be accessed using conventional repetitive experiments.
基于激光的材料去除，或烧蚀，使用超快脉冲能够对几乎任何材料进行精确的微尺度加工，适用于广泛的应用领域，并且可能在未来制造中提供大规模定制能力方面发挥关键作用。然而，由于缺乏合适的模拟器，目前优化加工参数可能需要数周的时间。实现这样的模拟器的困难在于相关过程的多尺度性质以及这些过程的高度非线性和不可逆性，这些性质可能因目标材料而异。在这里，我们展示了可以使用深度神经网络来实现超快激光烧蚀模拟器。该模拟器可以计算在任意位置和任意脉冲能量下的多个激光脉冲辐照后的三维结构，并且我们将模拟器应用于各种材料，包括电介质、半导体和有机聚合物。该模拟器成功地预测了在多个脉冲辐照后的深度分布，即使神经网络是使用单次数据集训练的。我们的结果表明，使用单次实验训练的深度神经网络能够解决使用传统重复实验无法访问的具有不可逆性和混沌性的物理问题。
