Lifetime assessment of organic light emitting diodes by compact model incorporated with deep learning technique

Simple and efficient lifetime modeling of organic light emitting diodes (OLED) are suggested by in-situ successive AC/DC measurements with reinforcement assessments of machine learning. AC/DC device parameters of phosphorescent OLED devices with multiple transport layers are monitored and analyzed by third-order parallel R//C circuit model with deep learning algorithm. The prediction efficiency of the lifetime assessment is enhanced by combining in-situ AC/DC device parameters, reducing the assessment time compared to conventional constant-stress test methods.