Development of Machine-Learning Techniques for Time-of-Flight Secondary Ion Mass Spectrometry Spectral Analysis: Application for the Identification of Silane Coupling Agents in Multicomponent Films

• Published in: Analytical chemistry (Washington) Vol. 94; no. 5; pp. 2546 - 2553
• Main Authors: Lang, Yusheng, Zhou, Lilin, Imamura, Yutaka
• Format: Journal Article
• Language: English
• Published: United States American Chemical Society 08.02.2022
• Subjects: Artificial neural networks; Automation; Chemistry; Coupling agents; Information processing; Ions; Learning algorithms; Machine Learning; Mass spectra; Mass spectrometry; Mass spectroscopy; Neural networks; Scientific imaging; Secondary ion mass spectrometry; Silanes; Spectral analysis; Spectrometry, Mass, Secondary Ion - methods; Spectroscopy; Spectrum Analysis; Standardization
• ISSN: 0003-2700; 1520-6882
• DOI: 10.1021/acs.analchem.1c04436

Time-of-flight secondary ion mass spectrometry (ToF-SIMS) is an important analysis technique that can gather vast amounts of information from surfaces. Recently, machine learning was combined with ToF-SIMS to successfully extract useful information from mass spectra. However, the descriptor generation required for ToF-SIMS analysis using machine learning remains challenging because it requires a lot of effort, is time-consuming, and significantly limits the versatility and practicality of the machine learning approach for ToF-SIMS analysis. Herein, we proposed a new approach to avoid the descriptor generation: to regard ToF-SIMS spectra as images and apply the convolutional neural network (CNN) to analyze these spectral images. We applied and assessed this approach for the identification of silane coupling agents in multicomponent films. Furthermore, the CNN showed higher accuracy than descriptor-based approaches, suggesting its usefulness in achieving the automation and standardization of the ToF-SIMS analysis.