Structural sensitivity to reliability of flexible AMOLED modules using mechanical simulation and machine learning

Abstract

The flexibility and durability of flexible AMOLEDs are considered mutually exclusive, and their structural integrity under severe load conditions can be attained by minimizing the trade-off between these two properties. In this regard, the thickness and elastic modulus of the plastic films comprising flexible AMOLEDs are crucial design variables that determine their reliability. This study proposes a method for predicting the performance sensitivity of an AMOLED to its flexibility and durability using mechanical simulation and machine learning. A combination of 1000 thicknesses and elastic moduli, generated by Latin hypercube sampling, was used for the mechanical simulation. The results of the mechanical simulation were used to train various machine-learning algorithms, and the performance was evaluated using leave-one-out cross-validation (LOOCV). The CatBoost algorithm, which produced the best accuracy, and Kernel Shapley Additive Explanations (SHAP), were utilized to represent the sensitivity of the thickness and elastic modulus and their mutual exclusiveness is experimentally verified. These results will provide crucial information for the optimal design of flexible AMOLED modules.