## 4 Discussion  
A comprehensive dataset of 2000 image samples was generated for the study. These images were created by specifying the grayscale color table within the A2G algorithm, based on the parameter values outlined in Table 2. To ensure suitable threshold determination, the threshold level was established according to minimum moisture content criteria. The minimum moisture level criterion was defined based on the observation that, when the moisture within the OLED substrate reaches a certain threshold, the OLED luminance decreases by more than 50%. Consequently, the images generated visually illustrate the spread of moisture, represented by randomly distributed dark spots rendered using grayscale. The generated dataset was then applied to various machine learning algorithms to assess their performance, and the comparative results are summarized in Table 3. In-depth analysis of the results presented in the form of graphical representations can be found in Figure 3. Through these rigorous evaluations and comparisons, the performance of each machine learning algorithm in predicting moisture spread, as represented by dark spot distribution, was assessed. The assessments were aimed at identifying the most effective algorithm for predicting and analyzing moisture distribution and its effects on OLED performance. From the results displayed in Table 3 and Figure 3, it is evident that the Conv-Next Tiny algorithm emerges as a superior performer among its counterparts, exhibiting the lowest loss value and the highest accuracy. These results strongly suggest that the Conv-Next Tiny algorithm possesses an advanced ability to learn and recognize complex dark spot patterns with a high degree of precision. This is a critical attribute, especially considering the intricate structures and behaviors of OLEDs, which demand meticulous handling in analysis and classification tasks. While Conv-Next Tiny outperforms other algorithms, it is equally important to highlight that other convolutional neural network (CNN) algorithms, such as VGG-16 and ResNet50, also deliver impressive accuracy values of 0.98 or above. This not only underscores the overall effectiveness of CNN-based algorithms in defect detection tasks, but also indicates their proficiency in identifying particular anomalies such as dark spots. As we delve deeper into a more nuanced analysis, considering the unique features and characteristics of each algorithm, it becomes clear that lightweight algorithms such as Conv-Next Tiny offer certain distinct advantages. For instance, these algorithms provide faster training time and improved computational performance compared with their more complex counterparts. This is a crucial factor in practical situations where the speed of execution and efficient utilization of computational resources are of utmost importance. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Range** \\ \hline Initial pinhole radius & 1.2 (\(\mu\)m) \\ Number of dark spots & 10–100 (Ea) \\ Degradation time & 10,000 (h) \\ Minimum humidity inflow (for edge detection) & 1 (\%) \\ OLED panel width/length & 1000/1000 (\(\mu\)m) \\ Moisture & 50\% \\ Temperature & 25 (\({}^{\circ}\)C) \\ Absolute pressure & 1 ATM \\ diffusion coefficient & \(2.6\times 10^{-5}\) [\(\mu\)m\({}^{2}\)/s] \\ Max dark spot growth & 10\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Simulation parameter. Figure 3: Machine learning algorithm results graph. A testament to the practicality and effectiveness of the Conv-Next Tiny algorithm is its superior performance on a batch of 100 OLED test data samples, which were classified as either pass or fail. The Conv-Next Tiny model, which boasted the highest accuracy among the trained algorithms, achieved a remarkable recognition rate of 95%. This excellent performance in real-world testing strongly suggests that the Conv-Next Tiny algorithm has great potential to serve as a powerful tool for accurate and efficient OLED defect detection.