Advanced Design of a III-Nitride Light-Emitting Diode via Machine Learning

Abstract

Gallium nitride (GaN)-based light-emitting diodes (LEDs) have obtained great market success in the past 20 years. However, the traditional research paradigm, i.e., experimental trial-and-error method, no longer adapts to the industry development. In this work, an efficient approach is demonstrated to design and optimize GaN-based LED structures via machine learning (ML). By using the dataset of GaN-based LED structures over the past decade to train four typical ML models, it is found that the convolutional neural network (CNN) provides the most accurate prediction, with a root mean square error (RMSE) of 1.03% for internal quantum efficiency (IQE) and 11.98 W cm−2 for light output power density (LOPD). Based on the CNN model, 1) the feature importance analysis is adopted to reveal the critical features for LED performance; 2) the predicted trends of IQE and LOPD match well with the physical mechanism, being consistent with the experimental and simulation results; and 3) a high-throughput screening is demonstrated to predict the properties of over 20 000 structures within seconds to obtain high efficiency LED structures. This ML-based LED design method enables direct guiding of the LED structure optimization in terms of key parameter selection during manufacturing and greatly accelerates the development cycle of GaN-based LEDs.