## 1 Introduction  
Semiconductor wafers have experienced significant advances in recent years, becoming a cornerstone of the electronics industry and enabling the large-scale production of high-performance and cost-effective electronic components [1]. As electronic devices continue to shrink in size, the detection and measurement of defects and anomalies at the wafer level are crucial to ensuring product quality. Automated inspection systems often employ advanced imaging techniques such as optical and electron microscopy [2]. Optical microscopy is constrained by its resolution and inability to image subsurface structures, making it less suitable for the detection of nanoscale defects in wafers. While electron microscopy provides higher resolution, it requires complex sample preparation, offers a limited field of view, and can be destructive to delicate materials. To overcome these limitations, scanning acoustic microscopy (SAM) has emerged as a promising alternative, offering non-invasive and high-resolution (HR) imaging of both surface and subsurface features [3]. Obtaining HR images typically requires a considerable increase in scanning time due to the need for smaller step sizes during the scanning process. For instance, using a traditional scanning system such as the TsAM-400 (Ohlab, Busan, Republic of Korea), scanning a 12-inch wafer (\(300\times 300\times 300\times 300\times 300\times 300\times 300\times 300\)) with a step size of 50 micrometers (\(\upmu\)m) takes approximately 2727 seconds (s) [4]. However, increasing the step size to \(100\times 100\times 100\times 100\times 1  
the resolution of linear images acquired from our R-SAM system. This architecture was selected for its synergistic integration of the generative fidelity of GANs with the global context modeling capabilities of transformer blocks, enabling effective reconstruction of both localized textures and long-range spatial dependencies. To improve convergence and generalization with limited training data, transfer learning was employed by initializing the encoder with pretrained weights, leveraging transferable low-level features. Furthermore, a dynamic loss function was introduced to adaptively balance pixel-wise, perceptual, and adversarial components, ensuring a stable trade-off between structural accuracy and perceptual quality across diverse imaging scenarios. Unlike traditional methods that rely solely on image filtering or interpolation, LinearTGAN is specifically adapted to the statistical and structural characteristics of SAM imaging data. The network was trained using a composite dataset comprising authentic LR scan acquisitions alongside synthetically degraded HR samples, allowing it to concurrently learn the noise distributions and degradation artifacts inherent to practical R-SAM imaging, as well as the high-frequency content present in clean HR scans. By training the model with this hybrid dataset, LinearTGAN effectively learned both the noise characteristics present in real-world data and the fine structural details captured in HR images. The main contributions of this research are summarized as follows:  
- We prepared a self-built dataset of linear acoustic images captured from our R-SAM system for 12-inch wafers. The limitation of the small dataset was addressed by utilizing a combination of real LR images obtained from the scanning process and synthetic images generated from HR scans using a degradation model. - LinearTGAN is an optimized GAN-based model incorporating a generator with eight residual Swin Transformer blocks and a U-Net discriminator. This architecture enhances image reconstruction and resolution, improving the precision of defect measurement in acoustic wafer imaging. - A comprehensive evaluation demonstrates that images enhanced by LinearTGAN lead to substantial improvements in defect segmentation accuracy across all key performance metrics, validating its effectiveness in advancing the precision of non-destructive defect detection and segmentation.