Abstract

Analytical advantages of facile and expeditious spectral data collections from laser-induced breakdown spectroscopy (LIBS) are often offset by the low-accuracy quantitative analyses offered by the technique due to non-equilibrium plasma–matrix interactions. Herein, we developed a one-dimensional (1D) convolutional neural network (CNN) and a least absolute shrinkage and selection operator (LASSO) models for LIBS data analyses to predict trace amounts of interstitial oxygen impurities in commercial Czochralski-silicon (Cz-Si) crystals with known interstitial oxygen concentrations at 0–16 parts per million (ppm). While traditional spectral analyses from O(I) (777.2 nm) atomic lines offer poor accuracy, CNN and LASSO analyses generate excellent predictions for the interstitial oxygen concentrations. Specifically, CNN-based spectral analyses uniquely identified systematic alterations in LIBS fingerprints manifested by laser-matter interactions. Our results pave the path for combining facile and voluminous LIBS data collection with deep learning driven high-fidelity data analytics.

© 2022 The Author(s)

Surface hardness determination of laser cladding using laser-induced breakdown spectroscopy and machine learning (PLSR, CNN, ResNet, and DRSN)

Jiacheng Yang, Linghua Kong, and Hongji Ye
Appl. Opt. 63(10) 2509-2517 (2024)

Small-sample stacking model for qualitative analysis of aluminum alloys based on femtosecond laser-induced breakdown spectroscopy

Qing Ma, Ziyuan Liu, Tong Sun, Xun Gao, and YuJia Dai
Opt. Express 31(17) 27633-27653 (2023)

Quantification of trace elements in molten aluminum with randomized impurity concentrations using laser-induced breakdown spectroscopy

Sveinn H. Gudmundsson, Jon Matthiasson, and Kristjan Leosson
OSA Continuum 3(9) 2544-2552 (2020)

Simultaneous measurement of carbon emission and gas temperature via laser-induced breakdown spectroscopy coupled with machine learning

Dongju Kim, Cheolwoo Bong, Seong-kyun Im, and Moon Soo Bak
Opt. Express 31(4) 7032-7046 (2023)

Simple method for liquid analysis by laser-induced breakdown spectroscopy (LIBS)

D. C. Zhang, Z. Q. Hu, Y. B. Su, B. Hai, X. L. Zhu, J. F. Zhu, and X. Ma
Opt. Express 26(14) 18794-18802 (2018)