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.