 This study demonstrated the viability of applying artificial intelligence, AI, techniques to inspect OLED cells at the cell level. To generate training data, the authors developed their own proprietary algorithm called A2G, which uses the finite element method to simulate OLED parameters and predict its lifespan. They then use three different convolutional neural network models to evaluate the OLED data and determine whether it was pass or fail. The test results showed a high accuracy of 95 percent, suggesting that AI can be used to replace manual inspections. By introducing OLED defect detection models at the cell level instead of the traditional panel level, the authors expect higher classification rates and improved yields. This forward-looking approach indicates a potential shift in industry standards. This article was authored by Dong Hun Hun, Yang Hun Zhang, and Min Su Kang.