Breaking the light extraction limit in AlGaN-based deep-UV LEDs via AI-optimized mesa-sidewall nanoscale reflective structures

A mesa-sidewall nanoscale reflective structure was introduced on the p-GaN surface of flip-chip deep-ultraviolet light-emitting diodes (DUV-LEDs) to enhance the light extraction efficiency (LEE). In contrast to conventional planar reflectors, the proposed structure was specifically engineered to not only minimize DUV absorption in the p-GaN layer but also establish what is believed to be a novel reflective pathway for photon propagation. Utilizing an artificial intelligence (AI)-assisted inverse design approach based on the Jaya algorithm, the reflective structure was systematically optimized to maximize the LEE through precise modulation of the light propagation mechanism. A comprehensive investigation into the light extraction process was conducted, revealing the effects of structural parameters on the light propagation path and LEE. Remarkably, both simulation and experimental results demonstrated that the optimized mesa-sidewall nanoscale structure achieved an enhancement of more than 200% in LEE over the conventional Al reflective layer, showcasing its significant potential for high-performance DUV optoelectronic applications. This work provides a viable and efficient strategy for developing high-power DUV-LEDs and demonstrates the potential of AI-driven design in advanced photonic devices.