Spectrum adapted expectation-maximization algorithm for high-throughput peak shift analysis

• Main Authors: Tarojiro Matsumura, Naoka Nagamura, Shotaro Akaho, Kenji Nagata, Yasunobu Ando
• Format: Article
• Language: English
• Published: Taylor & Francis Group 2019-12-01
• Series: Science and Technology of Advanced Materials
• Subjects: em algorithm; peak separation; spectral data; xps analysis; machine learning
• Online Access: http://dx.doi.org/10.1080/14686996.2019.1620123

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 1s in graphene and S 2p 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.