Machine Learning Assisted Stability Analysis of Blue Quantum Dot Light-Emitting Diodes

Abstract

The operational stability of the blue quantum dot light-emitting diode (QLED) has been one of the most important obstacles to initialize its industrialization. In this work, we demonstrate a machine learning assisted methodology to illustrate the operational stability of blue QLEDs by analyzing the measurements of over 200 samples (824 QLED devices) including current density-voltage-luminance (J-V-L), impedance spectra (IS), and operational lifetime (T95@1000 cd/m²). The methodology is able to predict the operational lifetime of the QLED with a Pearson correlation coefficient of 0.70 with a convolutional neural network (CNN) model. By applying a classification decision tree analysis of 26 extracted features of J-V-L and IS curves, we illustrate the key features in determining the operational stability. Furthermore, we simulated the device operation using an equivalent circuit model to discuss the device degradation related operational mechanisms.