Version 1
: Received: 5 August 2024 / Approved: 6 August 2024 / Online: 6 August 2024 (16:00:39 CEST)
How to cite:
Dragas, M.; Galović, S.; Milicevic, D.; Suljovrujic, E.; Djordjevic, K. Solution of Inverse Photoacoustic Problem for Semiconductors via Phase Neural Network. Preprints2024, 2024080436. https://doi.org/10.20944/preprints202408.0436.v1
Dragas, M.; Galović, S.; Milicevic, D.; Suljovrujic, E.; Djordjevic, K. Solution of Inverse Photoacoustic Problem for Semiconductors via Phase Neural Network. Preprints 2024, 2024080436. https://doi.org/10.20944/preprints202408.0436.v1
Dragas, M.; Galović, S.; Milicevic, D.; Suljovrujic, E.; Djordjevic, K. Solution of Inverse Photoacoustic Problem for Semiconductors via Phase Neural Network. Preprints2024, 2024080436. https://doi.org/10.20944/preprints202408.0436.v1
APA Style
Dragas, M., Galović, S., Milicevic, D., Suljovrujic, E., & Djordjevic, K. (2024). Solution of Inverse Photoacoustic Problem for Semiconductors via Phase Neural Network. Preprints. https://doi.org/10.20944/preprints202408.0436.v1
Chicago/Turabian Style
Dragas, M., Edin Suljovrujic and Katarina Djordjevic. 2024 "Solution of Inverse Photoacoustic Problem for Semiconductors via Phase Neural Network" Preprints. https://doi.org/10.20944/preprints202408.0436.v1
Abstract
In this paper, the neural network is developed for solving the inverse photoacoustic problem aiming to estimate thermo-elastic properties and thickness of a semiconductor sample. The idea was that these sample properties be estimated from the phase characteristic because the phase measurements are more sensitive. The neural network has been trained on a large basis of photoacoustic phases calculated from a theoretical Si n-type model in the range of 20Hz to 20kHz, on which random Gaussian noise has been applied. It is shown that high accuracy and precision could be reached in solving of inverse photoacoustic problem using only phase measurement.
Computer Science and Mathematics, Applied Mathematics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
