Spectrum adapted expectation-maximization algorithm for high-throughput peak shift analysis. Issue 1 (31st December 2019)

• Record Type: Journal Article
• Authors: Matsumura, Tarojiro; Nagamura, Naoka; Akaho, Shotaro; Nagata, Kenji; Ando, Yasunobu
• Is Part Of: Science and technology of advanced materials. Volume 20:Issue 1(2019)
• Page Start: 733
• Page End: 745
• Publication Date: 2019-12-31
• Subjects: EM algorithm -- peak separation -- spectral data -- XPS analysis -- machine learning; 60 New topics / Others -- 502 Electron spectroscopy; Materials -- Technological innovations -- Periodicals; 620.112
• DOI: 10.1080/14686996.2019.1620123
• Languages: English
• ISSNs: 1468-6996

Abstract

ABSTRACT: We introduce a spectrum-adapted expectation-maximization (EM) algorithm for high-throughput analysis of a large number of spectral datasets by considering the weight of the intensity corresponding to the measurement energy steps. Proposed method was applied to synthetic data in order to evaluate the performance of the analysis accuracy and calculation time. Moreover, the proposed method was performed to the spectral data collected from graphene and MoS2 field-effect transistors devices. The calculation completed in less than 13.4 s per set and successfully detected systematic peak shifts of the C 1 s in graphene and S 2 p in MoS2 peaks. This result suggests that the proposed method can support the investigation of peak shift with two advantages: (1) a large amount of data can be processed at high speed; and (2) stable and automatic calculation can be easily performed. Graphical Abstract: uf0001