## 5 Conclusions  
OLEDs, known for their requirement of sustained brightness over prolonged periods, pose challenges when traditional physical methods of measurement are employed because of their inherent limitations. Recognizing these challenges, our study showcases the promising potential of using A\({}^{2}\)G-based simulated data generation and artificial intelligence techniques as alternatives for longevity measurements and passive inspections of OLEDs. To combat the scarcity of training data, a dataset revolving around dark spots, which significantly affect the OLED lifespan, was synthesized using the finite element method (FEM) and the A\({}^{2}\)G algorithm. This synthesized dataset served as the foundation to train an AI model. Remarkably, the trained AI model demonstrated high proficiency in recognizing OLED test data, hinting at its valuable application in defining the criteria for defective OLED panels. These findings underline the possibility of automation in the inspection process and project the capability of executing more precise AI inspections at the intricate OLED cell level, shifting away from conventional panel-level inspections. Furthermore, the A\({}^{2}\)G algorithm, which is employed for creating OLED simulation data, enables result confirmation up to 10,000 h through data generation via virtualization post-physical calculation-based design. This methodology deviates from the traditional sequential design-manufacture-evaluation process, highlighting the feasibility of assessing various operational results within a significantly reduced time frame.  
Considering these findings, we advocate for the expansion of future research to delve into potential issues that may arise within the OLED manufacturing process. Incorporating AI throughout the complete process, including the inspection stage, opens up the possibility of crafting research that considers the impact of factors such as organic materials, plastics, and rubbers that occur within the various OLED layers. This holistic approach can lead to better quality control and efficiency in OLED production, advancing the technology to new heights.  
Moreover, this work suggests a roadmap toward the advent of an integrated manufacturing process underpinned by artificial intelligence. Future studies can explore and expand the application of AI models and algorithms not only in OLED manufacturing, but also in other microelectronic devices. Additionally, it would be worth investigating the incorporation of AI-driven feedback mechanisms that can self-improve and self-correct during the manufacturing process. The combination of AI-driven data generation, AI-based predictive modeling, and AI-enabled decision-making and control mechanisms could usher in a new era of smart manufacturing, contributing to the Fourth Industrial Revolution. We also recommend the development of standard testing protocols that can fully exploit the advantages of AI, thereby ensuring reliable and robust manufacturing processes. By using AI in tandem with traditional manufacturing practices, we foresee a future where technology and human expertise coalesce, leading to remarkable advancements in the electronics industry.  
**Author Contributions:** D.-H.H. and Y.-H.J.: Writing--original draft, Data curation and software, Visualization, Data curation. M.-S.K.: Conceptualization, Validation, Writing--review and editing, Project administration. [PERSON]-H.J.: Formal analysis, Funding acquisition. Equal first author contributions. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** All data, models, and code generated or used during the study are available from the corresponding author by request.  
**Conflicts of Interest:** The authors declare no conflict of interest.