# A Study on Defect Detection in Organic Light-Emitting Diode Cells Using Optimal Deep Learning
## 2 Related Research
### Research on Artificial-Intelligence-Based Virtual Image Dataset Generation  
According to a report by [PERSON] et al., dark spots are defects caused by degradation and various factors in the production process of OLED panels [18]. These dark spots can be formed by multiple factors and are particularly influenced by parameters such as the thickness (L) of the organic light emitting layer, temperature (T), humidity (C0), pressure (P), and degradation time (t). The researchers conducted performance experiments under various process conditions and used Monte Carlo simulations and deep learning to generate virtual sample data. In another study on OLED cell simulation and detection phases based on the A\({}^{2}\)G algorithm for artificial intelligence applications, virtual dark spot data were generated because of the challenge of obtaining real datasets. Data were generated based on the defined conditions for dark spot growth in relation to humidity. The researchers established criteria for identifying display defects after the minimum requirement of 10,000 h of OLED lifespan. They analyzed the growth of dark spots over time using an FEM solver program and investigated the factors influencing the distribution and size of dark spots. Virtual dark spot data were generated using AI technology and the consideration of physical phenomena [19; 20]. Furthermore, in another study, the growth of dark spots in OLEDs and factors that accelerate the growth were investigated [21]. OLEDs, as light emitting diodes, consist of organic semiconductors and emitting layers. However, the prolonged usage or application of high voltages in OLEDs form dark spots, which are defects that occur within the emitting layer. These dark spots degrade the performance of the OLED and reduce its lifespan. Therefore, the growth and acceleration factors of dark spots were investigated using simulations. The group also evaluated the growth rate and acceleration factors of dark spots.  
[PERSON] et al. investigated the thermal evaporation process that affects the performance and quality of OLEDs, and they concluded through the simulation of thin-film thickness distribution in the OLED thermal evaporation process that the distribution of thin-film thickness can impact the performance and quality of OLED devices [22].  
According to the report by [PERSON] et al., having an adequate amount of both normal and defective data is necessary to ensure quality and reliability in the directed energy deposition process [23]. However, collecting a large amount of data is challenging owing to the lengthy processing time and high costs of the metal powder associated with the directed energy deposition process. Therefore, this paper proposes a virtual process data generation algorithm for the deposition process. The generated virtual process data were validated using data collected from an actual deposition process monitoring system. The findings of the study suggest that the generated virtual data can be used. In another study by [PERSON] et al., the researchers used AI to generate artificial image data to address the problem through large scale data mining techniques [24]. In the medical field, securing large-scale data is challenging because of constraints such as privacy protection. From the findings of this study, it is concluded that if virtual data can be generated to resemble real data, they can be used as training data for developing data mining models. Based on the aforementioned studies, this study defines and applies virtual dataset generation methods using AI technology and the FEM to create virtual dark spot image data.