Monoclinic gallium oxide (β-Ga2O3) is attracting intense focus as a material for power electronics, thanks to its ultra-wide bandgap (4.5–4.8 eV) and ability to be easily doped n-type. Because the holes self-trap, the band-edge luminescence is weak; hence, β-Ga2O3 has not been regarded as a promising material for light emission. In this work, optical and structural imaging methods revealed the presence of localized surface defects that emit in the near-UV (3.27 eV, 380 nm) when excited by sub-bandgap light. The PL emission of these centers is extremely bright—50 times brighter than that of single-crystal ZnO, a direct-gap semiconductor that has been touted as an active material for UV devices.

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.

Confocal laser scanning microscopy (CLSM) is a preferred method for obtaining optical images with submicrometer resolution. Replacing the pinhole and detector of a CLSM with a digital camera [charge-coupled device (CCD) or complementary metal oxide semiconductor (CMOS)] has the potential to simplify the design and reduce cost. However, the relatively slow speed of a typical camera results in long scans. To address this issue, in the present investigation a microlens array was used to split the laser beam into 48 beamlets that are focused onto the sample. In essence, 48 pinhole-detector measurements were performed in parallel. Images obtained from the 48 laser spots were stitched together into a final image.