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Outline

With the advancement of laser technology, we can now apply laser fields onto materials, the strength of which is comparable to the interatomic bonds of the materials themselves. These strong and controllable light electric fields are expected to be utilized in precise laser processing and various forms of material state control. However, the physics underlying irreversible processes, such as the fragmentation of a continuous solid into discrete atoms, remains not fully understood. At present, extensive trial and error is necessary to control these processes. In this research project, we aim to develop a method to extract the 'context' of phenomena by extensively observing microscopic processes beginning with photoexcitation, through the complete automation of experiments. Utilizing this technique, we intend to elucidate the governing equations of materials' irreversible transformation and construct a digital twin for optimizing these processes.

Fields

Interdisciplinary science and engineering, Physics, Engineering

Keywords

Precision laser processing, Ultrafast spectroscopy, Deep learning, Digital Twin, Autonomous experiment

Subjects

1. Development of a Digital Twin for Laser-Driven Irreversible Material Transformation
2. Development of In Situ Observation Techniques for Irreversible Phenomena Using Ultrafast Spectroscopy
3. Full Automation and Autonomy of Experiments and Large-Scale Data accumulation
4. Precision Control of Ultra-Short Pulse Laser Processing

Selected Publications

1. Endo, T., Tani, S., Sakurai, H., and Kobayashi, Y.: “Probing thermal dissipation dimensionality to laser ablation in the pulse duration range from 300 fs to 1 µs”, Opt. Express 31, 36027 (2023).
2. Tani, S., and Kobayashi, Y.: "Pulse-by-pulse evolution of surface morphology driven by femtosecond laser pulses", J. Appl Phys. 133, 143104 (2023).
3. Shimahara, K., Tani, S., Sakurai, H., and Kobayashi, Y.: ”A deep learning-based predictive simulator for the optimization of ultrashort pulse laser drilling", Commun. Eng. 2, 1 (2023).
4. Tani, S., Sugiyama, K., Sukegawa, T., Sato, T., Ishizuka, Y., Taya, S., Feng, F., Komeda, O., Suto, H., Saitoh, H., and Kobayashi, Y.: ”Real-time high-spectral-resolution mid-infrared spectroscopy with a signal-to-noise ratio of ten thousand”, Opt. Express 30, 36813 (2022).
5. Tani, S., and Kobayashi, Y.: "Ultrafast laser ablation simulator using deep neural networks”, Sci. Rep. 12, 5837 (2022).

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