Enhancing spatial resolution in Fourier transform infrared spectral image via machine learning algorithms.

Owing to the intrinsic signal noise in the characterization of chemical structures through Fourier transform infrared (FT-IR) spectroscopy, the determination of the signal-to-noise ratio (SNR) depends on the level of the concentration of the chemical structures. In situations characterized by limited concentrations of chemical structures, the traditional approach involves mitigating the resulting low SNR by superimposing repetitive measurements. In this study, we achieved comparable high-quality results to data scanned 64 times and superimposed by employing machine learning algorithms such as the principal component analysis and non-negative matrix factorization, which perform the dimensionality reduction, on FT-IR spectral image data that was only scanned once. Furthermore, the spatial resolution of the mapping images correlated to each chemical structure was enhanced by applying both the machine learning algorithms and the Gaussian fitting simultaneously. Significantly, our investigation demonstrated that the spatial resolution of the mapping images acquired through relative intensity is further improved by employing dimensionality reduction techniques. Collectively, our findings imply that by optimizing research data through noise reduction enhancing spatial resolution using the machine learning algorithms, research processes can be more efficient, for instance by reducing redundant physical measurements.

Enhancement in external quantum efficiency of AlGaInP red µ-LED using chemical solution treatment process.

To investigate the effects of their surface recovery and optical properties, extremely small sized (12 µm × 12 µm mesa area) red AlGaInP micro light emitting diodes ([Formula: see text] LED) were fabricated using a diluted hydrofluoric acid (HF) surface etch treatment. After the chemical treatment, the external quantum efficiencies (EQEs) of [Formula: see text]-LED at low and high injection current regions have been improved by 35.48% and 12.86%, respectively. The different phenomena of EQEs have a complex relationship between the suppression of non-radiative recombination originating from the etching damage of the surface and the improvement of light extraction of the sidewalls. The constant enhancement of EQE at a high injection current it is attributed to the expansion of the active region's sidewall surface area by the selective etching of AlInP layers. The improved EQE at a low injection current is related to the minimization of the surface recombination caused by plasma damage from the surface. High-resolution transmission electron microscopy (HR-TEM) revealed physical defects on the sidewall surface, such as plasma-induced lattice disorder and impurity contamination damage, were eliminated using chemical treatment. This study suggests that chemical surface treatment using diluted HF acid can be an effective method for enhancing the [Formula: see text]-LED performance.