Ultrafast laser ablation simulator using deep neural networks

Abstract

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.