Intelligent optimization design of electron barrier layer for AlGaN-based deep-ultraviolet light-emitting diodes

Abstract

AlGaN-based deep-ultraviolet light-emitting diodes (DUV LEDs) are widely used in sterilization, sensing, water purification, medical treatment, non-line of sight (NLOS) communication and many other fields. Especially it has been reported that the global novel coronavirus (COVID-19) can be effectively inactivated by the DUV light with a wavelength below 280 nm (UVC) within a few seconds, which has also attracted great attention. However, the external quantum efficiency (EQE) of UVC LED is still at a low level, generally not more than 10%. As an important component of EQE, internal quantum efficiency (IQE) plays a crucial role in realizing high-performance DUV-LED. In order to improve the IQE of AlGaN-based DUV-LED, this work adopts an electron blocking layer (EBL) structure based on InAlGaN/AlGaN superlattice. The results show that the superlattice EBL structure can effectively improve the IQE compared with the traditional single-layer and double-layer EBL structure for the DUV-LED. On this basis, the optimization method based on JAYA intelligent algorithm for LED structure design is proposed in this work. Using the proposed design method, the InAlGaN/AlGaN superlattice EBL structure is further optimized to maximize the LED’s IQE. It is demonstrated that the optimized superlattice EBL structure is beneficial to not only the suppression of electron leakage but also the improvement of hole injection, leading to the increase of carrier recombination in the active region. As a result, the IQE of the DUV-LED at 200 mA injection current is 41.2% higher than that of the single-layer EBL structure. In addition, the optimized structure reduces IQE at high current from 25% to 4%. The optimization method based on intelligent algorithm can break through the limitation of the current LED structure design and provide a new method to improve the efficiency of AlGaN-based DUV-LED.