# Improved MSRN-Based Attention Block for Mask Alignment Mark Detection in Photolithography
## 4 Experiment and Results
### Results
#### 4.4.4 Alignment Mark Detection for Our Model  
An experiment to improve the image resolution of the 256 \(\times\) 192 size was performed. The test was performed in the same environment as specified in Section 4.4.2. Using our model, a 256 \(\times\) 192 size \(\times\)3 scale resulted in a 768 \(\times\) 576 size SR image. Figure 14 shows the result of detecting an alignment mark of the 768 \(\times\) 576 size. There is \begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Scale** & **PSNR** & **SSIM** \\ \hline Bicubic & & 30.56 & 0.871 \\ A+ [27] & & 32.29 & 0.895 \\ SRCNN [29] & & 32.45 & 0.903 \\ ESPCN [33] & & 32.91 & 0.911 \\ MSRN [32] & & 33.26 & 0.914 \\ Ours & & 33.52 & 0.917 \\ Bicubic & & 27.73 & 0.783 \\ A+ [27] & & 28.97 & 0.807 \\ SRCNN [29] & \(\times\)3 & 29.28 & 0.820 \\ ESPCN [33] & & 29.51 & 0.825 \\ MSRN [32] & & 29.64 & 0.831 \\ Ours & & 29.87 & 0.839 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison with other models. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **Image Size** & **mAP@50** & **Precision** & **Recall** & **F1-Score** & **IOU** \\ \hline YOLOv4 & \(1024\times 768\) & 99.93 & 0.99 & 1 & 1 & 92.39 \\  & \(687\times 512\) & 98.89 & 0.99 & 1 & 0.99 & 91.2 \\ YOLOv4 & \(512\times 384\) & 99.82 & 0.99 & 1 & 0.99 & 89.88 \\  & \(256\times 192\) & 46.13 & 0.89 & 0.21 & 0.34 & 73.89 \\  & \(1024\times 768\) & 99.90 & 0.75 & 0.94 & 0.83 & 69.78 \\ YOLOv4-csp-swish & \(687\times 512\) & 99.89 & 0.77 & 0.95 & 0.85 & 71.22 \\  & \(512\times 384\) & 92.86 & 0.76 & 0.96 & 0.85 & 69.62 \\  & \(256\times 192\) & 92.89 & 0.89 & 0.34 & 0.49 & 74.34 \\  & \(1024\times 768\) & 99.91 & 0.95 & 1 & 0.96 & 84.13 \\ YOLOv4-tiny & \(687\times 512\) & 99.84 & 0.93 & 1 & 0.97 & 84.01 \\  & \(512\times 384\) & 99.72 & 0.93 & 1 & 0.97 & 84.2 \\  & \(256\times 192\) & 80.48 & 0.87 & 0.33 & 0.48 & 66.25 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Test results. no significant difference visually between the \(256\times 192\) and \(768\times 576\) images, but there is a performance improvement. The detection performance was also improved. Table 5 shows detailed performance results of the \(256\times 192\) size and \(768\times 576\) size for the YOLOv4, YOLOv4-csp-swish, and YOLOv4-tiny model. The overall performance of Precision, Recall, _F1-Score_, mAP, and _IOU_ was improved in the improved image by applying super resolution. In particular, the _IOU_ result, which judges the success of detecting alignment marks, increased significantly, from 73.89 to 78.29 in the YOLOv4 model compared to other models. The FI-Score improved from 0.34 to 0.46, Recall from 0.21 to 0.30, and Precision from 0.89 to 0.93. Overall, all three models showed that Recall was improved, and Precision and mAp increased. The calculated result suggests it is effective in improving image resolution and is related to object detection. Figure 15 shows the graph of the results for the entire experiment. Figure 15 is a graph of the results of accuracy, precision, and recall for the entire image. First, the accuracy was excellent when the size was \(1024\times 768\), \(687\times 512\), and \(512\times 384\). However, when it was \(256\times 192\), the accuracy was significantly reduced. In size \(768\times 576\), to which our proposed model is applied, the accuracy is slightly improved in the YOLOv4-csp-swish and YOLOv4-tiny models compared to \(256\times 192\). Precision was improved when we used the YOLOv4 model based on size \(256\times 192\). In addition, the reproducibility was excellent when the recall was \(1024\times 768\), \(687\times 512\), and \(512\times 384\) size. However, in size \(256\times 192\), the reproducibility was significantly lowered. The recall increased by applying the SR image \(768\times 576\) for our proposed model. In the case of low quality due to the low image resolution, this model was applied to enlarge the image size and to produce the effective results of super resolution.