TY - JOUR
T1 - Exploring the Role of Artificial Intelligence in Precision Photonics
T2 - A Case Study on Deep Neural Network-Based fs Laser Pulsed Parameter Estimation for MoOx Formation
AU - Paredes-Miguel, Jose R.
AU - Cano-Lara, Miroslava
AU - Garcia-Granada, Andres A.
AU - Espinal, Andres
AU - Villasenor-Aguilar, Marcos J.
AU - Martinez-Jimenez, Leonardo
AU - Rostro-Gonzalez, Horacio
PY - 2025/6
Y1 - 2025/6
N2 - 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.
AB - 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.
KW - Deep neural networks
KW - Material characterization
KW - Molybdenum thin films
KW - Oxide formation
KW - Ultrafast pulsed lasers
UR - https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=pure_univeritat_ramon_llull&SrcAuth=WosAPI&KeyUT=WOS:001463455000001&DestLinkType=FullRecord&DestApp=WOS_CPL
U2 - 10.1002/adpr.202400113
DO - 10.1002/adpr.202400113
M3 - Article
SN - 2699-9293
VL - 6
JO - Advanced Photonics Research
JF - Advanced Photonics Research
IS - 6
ER -