## 4 Experiments
### Comparison with a Single Network Model  
We compared the ENN model with a single neural network model as shown in Table 11. The single model (1,84) led to the best accuracy. However, as mentioned earlier, the single model takes much longer training time than the ENN models. The error is propagated to all classes (design rights) during the training for the single model. On the other hand, pretrained backbone models are frozen, and only the backbone models accepting incrementally added datasets are involved in the ENN training. This modeling approach was a design choice to enhance scalability. UP-DETR helped the ENN model produce the lowest accuracy margin with the single neural network among the backbone models. Specifically, using eight UP-DETR backbone models, the ENN showed 1.51% p and 0.71% p lower Top-1 and Top-3 accuracies, respectively. For cost-effectiveness and the need to address the frequent design right updates, the ENN model's quicker and incremental modeling approach seems more practical while not compromising the accuracy significantly. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**4**} & \multicolumn{2}{c}{**5**} & \multicolumn{2}{c}{**6**} & \multicolumn{2}{c}{**7**} & \multicolumn{2}{c}{**8**} \\ \cline{2-11}  & **Top-1** & **Top-3** & **Top-1** & **Top-3** & **Top-1** & **Top-3** & **Top-1** & **Top-3** \\ \hline (2,21) AB & 98.930 & 99.896 & 99.034 & 99.862 & 98.999 & 99.896 & 98.896 & 99.655 & 98.896 & 99.827 \\ (3,32) ABC & 98.370 & 99.502 & 98.370 & 99.592 & 98.392 & 99.660 & 98.211 & 99.298 & 98.211 & 99.434 \\ (4,42) ABCD & 98.447 & 99.551 & 98.568 & 99.500 & 98.447 & 99.500 & 98.464 & 99.603 & 98.413 & 99.482 \\ (5,53) ABCDE & 98.059 & 99.385 & 98.045 & 99.371 & 98.113 & 99.439 & 97.799 & 99.330 & 98.195 & 99.412 \\ (6,63) ABCDEF & 98.447 & 99.195 & 98.114 & 99.126 & 98.263 & 99.218 & 98.022 & 99.195 & 98.298 & 99.264 \\ (7,74) ABCDEFG & 97.993 & 99.128 & 98.159 & 99.266 & 98.374 & 99.256 & 98.208 & 99.187 & 98.296 & 99.226 \\ (8,84) ABCDEFGH & 98.240 & 99.258 & 98.223 & 99.198 & 98.275 & 99.249 & 98.275 & 99.224 & 98.413 & 99.224 \\ \hline Average & 98.355 & 99.417 & 98.359 & 99.416 & 98.409 & 99.460 & 98.268 & 99.356 & 98.389 & 99.410 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Accuracy (%) of the ENN model according to the depth of FC residual block depth. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**AdamW**} & \multicolumn{2}{c}{**Adam**} & \multicolumn{2}{c}{**SGD**} & \multicolumn{2}{c}{**RMSprop**} \\ \cline{2-9}  & **Top-1** & **Top-3** & **Top-1** & **Top-3** & **Top-1** & **Top-3** & **Top-1** & **Top-3** \\ \hline (2,21) AB & 98.999 & 99.896 & 98.792 & 99.896 & 98.413 & 99.758 & 95.169 & 99.965 \\ (3,32) ABC & 98.392 & 99.660 & 98.324 & 99.615 & 97.962 & 99.706 & 92.278 & 99.841 \\ (4,42) ABCD & 98.447 & 99.500 & 98.671 & 99.465 & 98.447 & 99.655 & 85.059 & 99.638 \\ (5,53) ABCDE & 98.113 & 99.439 & 98.141 & 99.330 & 97.676 & 99.398 & 80.476 & 99.316 \\ (6,63) ABCDEF & 98.263 & 99.218 & 98.091 & 99.137 & 97.930 & 99.275 & 76.398 & 98.884 \\ (7,74) ABCDEFG & 98.374 & 99.256 & 98.365 & 99.138 & 98.188 & 99.226 & 72.111 & 98.570 \\ (8,84) ABCDEFGH & 98.275 & 99.249 & 98.378 & 99.146 & 98.240 & 99.180 & 69.117 & 97.559 \\ \hline Average & 98.409 & 99.460 & 98.395 & 99.390 & 98.122 & 99.457 & 81.515 & 99.111 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Accuracy (%) of the ENN model according to the optimizer. Table 12 shows the precision, recall, and F1 score of one of the largest ENN models ((8,84) ABCDEFGH) using various backbone models. UP-DETR was the best performer, while Yolo showed the lowest accuracy. Figure 8 shows the confusion matrix of the (8,84) ABCDEFGH model using UD-DETR. The Top-1 accuracy of this model was 98.275%. Figure 8: Confusion matrix of (8,84) ABCDEFGH using UP-DETR.