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Abstract

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. In this paper, 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.

Keywords

Photoluminescence fluorescence machine learning neural network

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Classification of Semiconductors Using Photoluminescence Spectroscopy and Machine Learning

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