Deep ultraviolet (DUV) light-emitting diodes (LEDs) have attracted widespread attention in communication because of their lack of background noise interference in day-blind communications, but their low luminous efficiency and low response frequency cannot meet the requirements of non-visual optical communication systems. AlGaN-based micro-LEDs ( $\mu $ -LEDs) can provide high light output power (LOP) due to their smaller chip size, and they can also withstand high current density due to their enhanced current diffusion uniformity. Thus, matrix-type DUV $\mu $ -LEDs are effective candidates to enhance LOP and response frequency. In this work, a matrix-type AlGaN-based DUV communication LED with step-graded Al composition superlattice (SL) electron-blocking layer (EBL) at wavelength of 274 nm was designed, LOP and response frequency reached 45.4 mW and 414 MHz at 212 A/cm2 current density. After this SL EBL applied in the UV communication LED, hole injection capability was enhanced, high LOP and high response frequency make it be an ideal candidate for wireless communication applications.

Raman spectroscopy is a spectral analysis technique based on molecular vibration. It has gained widespread acceptance as a practical tool for the non-invasive and rapid characterization or identification of multiple analytes and compounds in recent years. In fruit quality detection, Raman spectroscopy is employed to detect organic compounds, such as pigments, phenols, and sugars, as well as to analyze the molecular structures of specific chemical bonds or functional groups, providing valuable insights into fruit disease detection, pesticide residue analysis, and origin identification. Consequently, Raman spectroscopy techniques have demonstrated significant potential in agri-food analysis across various domains. Notably, the frontier of Raman spectroscopy is experiencing a surge in machine learning applications to enhance the resolution and quality of the resulting spectra. This paper reviews the fundamental principles and recent advancements in Raman spectroscopy and explores data processing techniques that use machine learning in Raman spectroscopy, with a focus on its applications in detecting fruit diseases, analyzing pesticide residues, and identifying origins. Finally, it highlights the challenges and future prospects of Raman spectroscopy, offering an effective reference for fruit quality detection.