Summary:
Ultrafast pulsed laser technology presents unique challenges and opportunities in material processing and characterization for precision photonics. Herein, an experiment is conducted involving the use of an ultrafast pulsed laser to irradiate a molybdenum film, inducing oxide formation. A total of 54 experiments are performed, varying the laser irradiation time and per-pulse laser fluence, resulting in a database with diverse oxide formations on the material. This dataset is further expanded numerically through interpolation to 187 samples. Subsequently, eight different deep neural network models, each with varying hidden layers and numbers of neurons, are employed to characterize the laser behavior with different parameters. These models are then validated numerically using three different learning rates, and the results are statistically evaluated using three metrics: mean squared error, mean absolute error, and R2 score.
Spanish layman's summary:
Artículo explora IA (DNNs) para predecir parámetros láser de femtosegundos en formación de óxido de molibdeno en películas finas. Permite pruebas numéricas, optimiza parámetros y reduce experimentos en fotónica de precisión.
English layman's summary:
Paper explores AI (DNNs) to predict femtosecond laser parameters for molybdenum oxide formation on thin films. This allows numerical testing, optimizing parameters & reducing experiments in precision photonics.
Keywords: deep neural networks, material characterization, molybdenum thin films, oxide formation, ultrafast pulsed lasers
DOI reference:
https://doi.org/10.1002/adpr.202400113
In press: April 2025.
Citation:
J.R. Paredes-Miguel, M. Cano-Lara, A.A. Garcia-Granada, A. Espinal, M.J. Villaseñor-Aguilar, L. Martinez-Jimenez, H. Rostro-González, Exploring the Role of Artificial Intelligence in Precision Photonics: A Case Study on Deep Neural Network-Based fs Laser Pulsed Parameter Estimation for MoOx Formation. Advanced Photonics Research.
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