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

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.

飞行时间二次离子质谱法（ToF-SIMS）是一项重要的分析技术，能够从表面收集大量的信息。近年来，机器学习技术被引入与ToF-SIMS相结合，成功地从质谱中提取了有用信息。然而，使用机器学习进行ToF-SIMS分析所需的描述符生成过程仍然充满挑战，因为它需要投入大量的人力和时间，并且显著限制了机器学习在ToF-SIMS分析中的灵活性和实用性。本研究中，我们提出了一种新的方法以规避描述符生成：将ToF-SIMS光谱视为图像，并应用卷积神经网络（CNN）对这些光谱图像进行分析。我们应用并评估了该方法在多组分薄膜中硅烷偶联剂识别中的应用。此外，CNN在准确性上优于基于描述符的方法，这表明其在实现ToF-SIMS分析的自动化和标准化方面具有显著的应用价值。