A study on device physics of deep ultraviolet light emitting diodes leveraging machine learning

Abstract

Aluminum gallium nitride (AlGaN)-based deep ultraviolet (DUV) light-emitting diodes (LEDs) hold tremendous potential and application prospects. However, DUV LEDs face challenges such as low internal quantum efficiency (IQE) and degraded luminous performance because of properties intrinsic to aluminum-rich group III-nitride materials. To address these challenges, traditional trial-and-error experimental methods are commonly employed. However, with rapid industrial advancements, this approach has become inadequate to meet current demands. In this work, this study demonstrates an effective approach to optimize the luminous performance of DUV LEDs using machine learning (ML). By training 4 typical ML models with a dataset of AlGaN-based LED structures compiled over the past decade and more, we find that the convolutional neural network (CNN) provides the most accurate predictions, with a root mean square error (RMSE) of 1.6995 W cm⁻² and a coefficient of determination (R²) of 0.9812 for the light output power density (LOPD). Using the CNN model, we reveal the key features that influence the luminous performance of DUV LEDs. In addition, we explore the relationships between different features and LOPD, which align with physical mechanisms and are generally consistent with simulation and experimental results. Overall, this work demonstrates that ML is capable of predicting device performance, extracting critical features from complex structures, and significantly aiding in the optimization of DUV LEDs.