Predicting laser energy absorption on nanostructured surfaces with deep learning

Fayad Ali Banna; Rémi Emonet; Anton Rudenko; Marc Sebban; Jean-Philippe Colombier

doi:10.1117/12.3022317

18 June 2024 Predicting laser energy absorption on nanostructured surfaces with deep learning

Fayad Ali Banna, Rémi Emonet, Anton Rudenko, Marc Sebban, Jean-Philippe Colombier

Proceedings Volume PC13017, Machine Learning in Photonics; PC130170Y (2024) https://doi.org/10.1117/12.3022317
Event: SPIE Photonics Europe, 2024, Strasbourg, France

Conference Poster

Abstract

Brief laser pulses can induce autonomous organization of nanostructures pattern without external guidance. This interaction between a laser light and a material is governed by Maxwell's equations. These equations provide the theoretical framework for understanding how electromagnetic waves propagate and interact with matter. The Finite-Difference Time-Domain (FDTD) method models the laser-material interactions, providing insights into absorption, reflection, and scattering over time, ultimately contributing to self-organization within the material. Despite a theoretical understanding, there is no reliable model to predict the self-organization process responsible for the nanostructures. Our work addresses this issue by aiming to predict the surface changes after multiple laser irradiations using neural networks. Deep learning models have undergone advancements and prove suitable for extracting meaningful insights and simulating physical processes. This combination of laser physics and deep learning offer a promising approach to improve our ability to control nanostructures formation on materials.

Citation Download Citation

Fayad Ali Banna, Rémi Emonet, Anton Rudenko, Marc Sebban, and Jean-Philippe Colombier "Predicting laser energy absorption on nanostructured surfaces with deep learning", Proc. SPIE PC13017, Machine Learning in Photonics, PC130170Y (18 June 2024); https://doi.org/10.1117/12.3022317

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