# Improved MSRN-Based Attention Block for Mask Alignment Mark Detection in Photolithography
## 5 Conclusions  
In this study, we proposed an MSRN-based Attention Block. We applied MSRB and focused on specific features. We were able to implement a good feature extraction map. In particular, by improving the resolution of the small-scale image, the object detection result of the alignment mark was improved. For testing, the image scale size was divided into four equal parts. However, object detection performance deteriorated in certain small-size images. To improve the object detection performance, we applied our proposed model. The proposed method resulted in superior performance compared to the existing method. When the image was applied to the SR model, the resulting Scale \(\times\)2 achieved a PSNR of 33.52 and an SSIM of 0.917. With these results, improved accuracy, reproducibility, and prediction results were obtained through the alignment mark detection experiment. These results are believed to help the recognition of alignment marks in semiconductor photolithography. In the semiconductor industry, various convergence technologies in the era of the fourth industrial revolution are being grafted. Through various technologies, it is possible to improve the semiconductor yield. Wafer chips are manufactured using several process technologies. Among them, photolithography is one of the processes of aligning the wafer and scanning the circuit pattern on the wafer on which the photoresist film is formed by irradiating light to the circuit pattern drawn on the mask. As semiconductor technology becomes highly integrated, alignment becomes increasingly difficult due to problems such as reduced alignment margin, transmittance according to level stack structure, increase in wafer diameter, and photolithography processes. Various methods and studies are continuously being utilized and conducted to reduce the misalignment problem, which is directly related to the production yield. Therefore, in this paper, we proposed a model to improve the image resolution quality of marks for accurate alignment as well as improved image super-resolution and object detection performance through experiments. Various experiments were conducted to verify this method, and the performance was improved compared to the previous study.  
Vision technology is converging in various manufacturing industries. We believe that there is still much to be explored in the direction of image processing and computer vision related to process and quality control in the manufacturing industry. This study provides a basis for potential work in this area. In the future, we plan to conduct research focusing on reducing the weight of the model and improving its performance.  
Figure 15: Experimental results of images for each size.  
**Author Contributions:** Conceptualization, J.P. and J.J.; methodology, J.P.; software, J.P.; validation, J.P. and J.J.; formal analysis, J.P.; investigation, J.P.; resources, J.J.; data curation, J.P.; writing--original draft preparation, J.P.; writing--review and editing, J.J.; visualization, J.P.; supervision, J.J.; project administration, J.J.; funding acquisition, J.J. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the MSIT (Ministry of Science and ICT), Korea, under the ICT Creative Consilience Program (IITP-2021-2020-0-01821) supervised by the IITP (Institute for Information & communications Technology Planning & Evaluation), and the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2021R1F1A1060054).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (No. 2021R1F1A1060054).  
**Conflicts of Interest:** The authors declare no conflict of interest.