Artificial intelligence-assisted auto-optical inspection toward the stain detection of an organic light-emitting diode panel at the backplane fabrication step

Abstract

This paper attempts to implement an auto-optical inspection (AOI) system using artificial intelligence (AI) technology for cost reduction in the production of organic light emitting diode (OLED) panels, specifically at the production stage of the thin film transistor (TFT). Further, to improve the accuracy of mura detection, the possible causes of mura were properly identified, and a model to control and predict mura occurrence was realized based on the sufficient analysis of these causes. More specifically, an explainable AI (XAI) prediction model was developed using the fab image and test element group (TEG) engineering methods, which could be applied as input data for the circuit simulations to improve the accuracy of the overall simulations. Initially, we attempted to predict backplane stain using only the TFT width, length dimension, and resistance–capacitance (RC) extraction data, but the results were not accurate. Consequently, we identified, via sufficient analysis, that the correlation between the dehydrogenation and stain, and introduced an AI model. As a result, the accuracy was improved from 50 to 80%, which is more effective in terms of time and cost, compared to conventional simulation through the TCAD analysis. Overall, by implementing the inspection method described in this paper, it was possible to detect stains at the backplane stage, which was only possible during the final test stage, thereby resulting in significant cost savings.