[1Ep01-05] Development of Data Processing and Data Utilization in Surface Analysis: Surface Analysis Division's Session

[1Ep02] Data analysis contribution to the interpretation of complex data by surface analysis techniques such as time-of-flight secondary ion mass spectrometry (ToF-SIMS) and its future development.

*Satoka Aoyagi¹ (1. Seikei University)

Data analysis is crucial for the interpretation of complex data by sophisticated surface analysis techniques such as time-of-flight secondary ion mass spectrometry (ToF-SIMS). I’d like to talk about machine learning contributions and its future development after I briefly introduce how multivariate analysis support the complex data interpretation. Data analysis learning methods are generally divided into three categories, unsupervised learning, supervised learning, and reinforcement learning. For the analysis of surface analysis data, unsupervised learning is mainly useful for extracting features including those related to unknown materials or unknown factors, while supervised learning is helpful for determination, identification and investigation of the relationship between the results by multiple methods. In addition, machine learning applications to other surface analysis techniques will also be introduced.