###### Abstract  
In this study, we demonstrate the viability of applying artificial intelligence (AI) techniques to conduct inspections at the OLED cell level using simulated data. The implementation of AI technologies necessitates training data, which we addressed by generating an OLED dataset via our proprietary A\({}^{2}\)G algorithm, integrating the finite element method among concerns over data security. Our A\({}^{2}\)G algorithm is designed to produce time-dependent datasets and establish threshold conditions for the expansion of dark spots based on OLED parameters and predicted lifespan. We explored the potential integration of AI in the inspection phase, performing cell-based evaluations using three distinct convolutional neural network models. The test results yielded a promising 95% recognition rate when classifying OLED data into pass and fail categories, demonstrating the practical effectiveness of this approach. Through this research, we not only confirmed the feasibility of using simulated OLED data in place of actual data but also highlighted the potential for the automation of manual inspection processes. Furthermore, by introducing OLED defect detection models at the cell level, as opposed to the traditional panel level during inspections, we anticipate higher classification rates and improved yield. This forward-thinking approach underscores significant advancement in OLED inspection processes, indicating a potential shift in industry standards. AI; big data; OLED; machine learning +
Footnote †: journal: Medical Intelligence  
worth noting that moisture permeation in OLED devices is not a unidirectional process, but occurs through various routes. These routes encompass potential reliability issues in the encapsulation method, inherent issues associated with the encapsulation materials used, and manufacturing defects, among others. To achieve stable OLED operation and enhance device lifespan, it is paramount to maintain WVTR below a certain threshold value. According to the standards set for white OLEDs, WVTR should remain below the specified limit for over 10,000 operational hours [4]. This stringent standard underscores the critical importance of effective encapsulation processes in OLED manufacturing and the continuous need for innovative solutions to improve device durability and performance. The aforementioned requirement in Table 1 is determined by measuring the time it takes for the magnesium cathode, which serves as an electrode material in OLEDs, to oxidize and become transparent, ultimately transforming into magnesium oxide (MgO). This value was determined by Professor [PERSON] team in 2001 [5]. If the OLED components have higher humidity and oxygen permeability than the recommended levels, oxygen and moisture will continuously infiltrate the components during their operating lifetime. This will cause the OLED components to degrade more quickly owing to external factors. To tackle this problem, the International Technology Roadmap for Semiconductors (ITRS) has established a criterion for the largest permissible size of dark spots during the operational lifespan of the display. For an area equivalent to a 300 mm diameter wafer, there should be no more than five small dark spots, each measuring between 20 and 30 nanometers in size. Major manufacturers of OLED panels, such as Samsung Display and LG Display, have even more stringent requirements for mass production. However, the specifics of these standards are confidential and cannot be disclosed. To detect defects using machine learning, it is necessary to train the model on a large amount of data. However, because of security concerns in the OLED industry, obtaining a substantial amount of data is challenging. Therefore, in this study, we generated a simulated dataset using the finite element method to detect defects using artificial intelligence. The deep convolutional neural network (CNN) is the state-of-the-art solution for large-scale visual recognition [6; 7].