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.