Classification of Semiconductors Using Photoluminescence Spectroscopy and Machine Learning

Yu, Yinchuan; McCluskey, Matthew D.

doi:10.1177/00037028211031618

Title: Classification of Semiconductors Using Photoluminescence Spectroscopy and Machine Learning

Journal Article · Tue Aug 03 00:00:00 EDT 2021 · Applied Spectroscopy

DOI:https://doi.org/10.1177/00037028211031618· OSTI ID:1829316

Yu, Yinchuan ^[1];

^[1]

Washington State Univ., Pullman, WA (United States)

Photoluminescence spectroscopy is a nondestructive optical method that is widely used to characterize semiconductors. In the photoluminescence process, a substance absorbs photons and emits light with longer wavelengths via electronic transitions. This paper discusses a method for identifying substances from their photoluminescence spectra using machine learning, a technique that is efficient in making classifications. Neural networks were constructed by taking simulated photoluminescence spectra as the input and the identity of the substance as the output. Here, six different semiconductors were chosen as categories: gallium oxide (Ga₂O₃), zinc oxide (ZnO), gallium nitride (GaN), cadmium sulfide (CdS), tungsten disulfide (WS₂), and cesium lead bromide (CsPbBr₃). The developed algorithm has a high accuracy (>90%) for assigning a substance to one of these six categories from its photoluminescence spectrum and correctly identified a mixed Ga₂O₃/ZnO sample.

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Research Organization:: Washington State Univ., Pullman, WA (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Basic Energy Sciences (BES). Materials Sciences & Engineering Division

Grant/Contract Number:: FG02-07ER46386

OSTI ID:: 1829316

Journal Information:: Applied Spectroscopy, Vol. 76, Issue 2; ISSN 0003-7028

Publisher:: Society for Applied SpectroscopyCopyright Statement

Country of Publication:: United States

Language:: English

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sj-pdf-1-asp-10.1177_00037028211031618 - Supplemental material for Classification of Semiconductors Using Photoluminescence Spectroscopy and Machine Learning Yu, Yinchuan; McCluskey, Matthew D. https://doi.org/10.25384/sage.15145164 The current record is supplemented by this text	text	January 2021
Classification of Semiconductors Using Photoluminescence Spectroscopy and Machine Learning Yu, Yinchuan; McCluskey, Matthew D. https://doi.org/10.25384/sage.c.5554203 The current record is supplemented by this collection	collection	January 2021

Title: Classification of Semiconductors Using Photoluminescence Spectroscopy and Machine Learning

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