Deep Learning Models for Data-Driven Laser Induced Breakdown Spectroscopy (LIBS) Analysis of Interstitial Oxygen Impurities in Czochralski-Si Crystals.

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) (777.2nm) 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.