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.

光致发光光谱是一种无损光学方法，广泛用于表征半导体。在光致发光过程中，物质吸收光子并通过电子跃迁发射波长更长的光。本文讨论了一种使用机器学习从光致发光光谱中识别物质的方法，这是一种有效的分类技术。以模拟的光致发光光谱为输入，以物质的身份为输出，构建神经网络。在本文中，选择了六种不同的半导体作为类别：氧化镓（Ga ₂ O ₃）、氧化锌（ZnO）、氮化镓（GaN）、硫化镉（CdS）、二硫化钨（WS ₂) 和溴化铅铯​​ (CsPbBr ₃ )。所开发的算法具有很高的准确度（> 90％），可以从其光致发光光谱中将物质分配到这六个类别之一，并正确识别混合的 Ga ₂ O ₃ /ZnO 样品。