## 7 Conclusions  
This study introduces LinearTGAN, a deep learning-driven SR model designed to enhance the resolution of linear acoustic wafer images while maintaining scanning efficiency. Extensive experimental evaluations demonstrate that LinearTGAN consistently outperforms conventional CNN-based, GAN-based, and Transformer-based approaches, exhibiting superior pixel-level accuracy and perceptual quality performance. Further evaluation using YOLOv8-seg confirms its efficacy in improving defect detection and segmentation, with precision, recall, and mAP scores closely matching those of HR inputs. Moreover, the seamless integration of LinearTGAN with R-SAM effectively mitigates the trade-off between scanning speed and image quality, achieving a 92% reduction in scanning time compared to conventional scanning systems while successfully restoring HR imaging. Ablation studies validate the significance of key architectural components in preserving fine texture details, ensuring structural coherence, and optimizing computational efficiency, reinforcing the robustness of the model in high-fidelity SR reconstruction.  
The present evaluation was conducted using a self-built dataset comprising ten wafers, with LR inputs generated through synthetic degradation of HR scans to ensure controlled benchmarking conditions. To enhance model robustness, the training dataset incorporated authentic LR acquisitions alongside synthetically degraded HR counterparts, capturing a broad range of structural variations and noise characteristics representative of R-SAM imaging. While this hybrid strategy contributes to improved generalization, broader empirical validation remains essential. Future research will focus on expanding the dataset to include a wider variety of wafer materials, geometries, and defect modalities, thereby enhancing the model's adaptability. Additionally, to support integration into real-time semiconductor manufacturing workflows, ongoing work will prioritize the optimization of computational efficiency through lightweight architectures and network pruning. With these advances, LinearTGAN is expected to emerge as an effective solution for NDT wafer inspection and measurement, offering a scalable, high-performance approach to quality control and instrumentation in semiconductor manufacturing while reducing operational complexity and inspection time.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app15126780/s1](https://www.mdpi.com/article/10.3390/app15126780/s1)]([https://www.mdpi.com/article/10.3390/app15126780/s1](https://www.mdpi.com/article/10.3390/app15126780/s1)).  
Conceptualization, T.T.H.V., T.H.V. and T.N.N.; Methodology, T.T.H.V. and T.N.N.; Software, T.H.V.; Validation, T.T.H.V., T.H.V. and T.N.N.; Formal analysis, J.C., L.H.T. and V.H.M.D.; Data curation, V.B.N. and W.L.; Writing--original draft, T.T.H.V.; Writing--review and editing, T.T.H.V.; T.H.V., T.N.N. and S.M.; Visualization, J.O.; Supervision, J.O. and S.M. All authors have read and agreed to the published version of the manuscript.  
This research was supported by a National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (no. 2022R1A5A8023404) and the Pukyong National University Industry-University Cooperation Foundation's 2024 Post-Doc support project.  
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The data used in this article is not made public. If you need to use them, please contact the corresponding author.  
Author [PERSON] was employed by the company Ohlabs Corp. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.