This study presents a novel approach for golf swing biomechanics through deep learning-assisted to enhance the sensing, processing, interpretation, and assessment of swing quality. The proposed methodology introduces a Smart Golf Glove (SGG) system, incorporating a deep neural network and the Internet of Things (IoT) capabilities. The key contributions of this study include (1) a comprehensive design framework encompassing both hardware and software aspects of the SGG system, as well as (2) an algorithm of phase and events to segment swing signals as inputs for deep learning models. Moreover, (3) an Unsupervised Learning Model (ULM) architecture is introduced to address the challenges of dataset limitation and effort for data labeling. Additionally, (4) an IoT-assisted SGG platform is proposed, enabling remote monitoring and management. Experimental results indicate that the trained ULM model achieved an average accuracy of 92.4%. The findings highlight the effectiveness of the proposed SGG system in accurately detecting abnormal motion from beginner players. The integration of artificial intelligence (AI) and IoT-based platforms with targeted videos-based self-coaching capabilities represents a significant advancement in golf swing biomechanics analysis.

In recent decades, Scanning Acoustic Tomography (SAT) has become a vital technique for characterizing semiconductor devices in non-destructive evaluation. Precise and efficient segmentation of SAT images is crucial for detecting defects and assessing material properties in the semiconductor industry. However, current manual methods are often expensive and susceptible to human error. This study enhances the segmentation process of SAT images using the deep learning model SemiSA, which is fine-tuned from the Segment Anything model. In our experiments, SemiSA was trained and evaluated on a large-scale dataset from the Ohlabs TSAM-400 system, encompassing various semiconductor devices such as Flip Chip, Power Semiconductor, 6-inch and 12-inch Wafer, Transistor, and Multilayer Ceramic Capacitor. The results demonstrate that SemiSA significantly improves segmentation tasks across all types of SAT images of semiconductor devices. On average, there was a 17.89% enhancement in Dice Similarity Coefficient scores and a 24.26% improvement in Intersection over Union scores across all tasks. Additionally, this work also proposes an efficient framework tailored specifically for SAT images. The main objective of developing this segmentation tool is to provide researchers and experts with a valuable tool for advancing the semiconductor evaluation and quality control field. The code is available at https://github.com/ThuHa96/SemiSA.

Scanning acoustic microscopy (SAM) plays a critical role in nondestructive inspection of semiconductor and industrial materials; however, deep-layer SAM images often suffer from severe attenuation-induced noise and the absence of clean reference data, significantly limiting reliable defect analysis. To address these challenges, this article proposes a hierarchical diffusion-based denoising framework that transfers structural and statistical knowledge from high-quality top-layer SAM images to restore degraded deep-layer images. A physics-guided synthetic data generation pipeline, combining Gaussian degradation modeling and unpaired domain transfer, is introduced to enable effective training. A hybrid Transformer–Mamba diffusion architecture is also constructed to jointly capture local spatial structures and long-range depth-dependent dependences, while a depthwise latent extraction strategy enhances the separation of noise and structural features. Extensive experiments on benchmark datasets and industrial SAM images demonstrate that the proposed approach achieves better performance on both reference-based metrics (peak signal-to-noise ratio, structural similarity index measure, learned perceptual image patch similarity, and Fréchet inception distance) and no-reference evaluation, while significantly improving downstream defect segmentation accuracy using the segment anything model. Although the proposed model has a higher parameter count than competing methods, the results indicate its strong potential for practical SAM-based industrial inspection.

The precise identification and non-destructive measurement of structural features and defects in semiconductor wafers are essential for ensuring process integrity and sustaining high yield in advanced manufacturing environments. Unlike conventional measurement techniques, scanning acoustic microscopy (SAM) is an advanced method that provides detailed visualizations of both surface and internal wafer structures. However, in practical industrial applications, the scanning time and image quality of SAM significantly impact its overall performance and utility. Prolonged scanning durations can lead to production bottlenecks, while suboptimal image quality can compromise the accuracy of defect detection. To address these challenges, this study proposes LinearTGAN, an improved generative adversarial network (GAN)-based model specifically designed to improve the resolution of linear acoustic wafer images acquired by the breakthrough rotary scanning acoustic microscopy (R-SAM) system. Empirical evaluations demonstrate that the proposed model significantly outperforms conventional GAN-based approaches, achieving a Peak Signal-to-Noise Ratio (PSNR) of 29.479 dB, a Structural Similarity Index Measure (SSIM) of 0.874, a Learned Perceptual Image Patch Similarity (LPIPS) of 0.095, and a Fréchet Inception Distance (FID) of 0.445. To assess the measurement aspect of LinearTGAN, a lightweight defect segmentation module was integrated and tested on annotated wafer datasets. The super-resolved images produced by LinearTGAN significantly enhanced segmentation accuracy and improved the sensitivity of microcrack detection. Furthermore, the deployment of LinearTGAN within the R-SAM system yielded a 92% improvement in scanning performance for 12-inch wafers while simultaneously enhancing image fidelity. The integration of super-resolution techniques into R-SAM significantly advances the precision, robustness, and efficiency of non-destructive measurements, highlighting their potential to have a transformative impact in semiconductor metrology and quality assurance.

C-scan images in scanning acoustic microscopy (SAM) are crucial for ensuring the reliability and accuracy of semiconductor devices. These images provide detailed cross-sectional views of devices, allowing for precise identification and analysis of internal structures and defects without causing any damage. Recent studies have extensively employed C-scan imaging to enhance quality control processes, significantly improving the overall effectiveness of non-destructive testing (NDT). However, most samples typically possess flat surfaces and are examined under optimal scanning conditions. This article addresses the challenge of uneven or deformed sample surfaces that result in low-quality C-scan images. Specifically, we developed a surface segmentation model utilizing B-scan images from the SAM system and a fine-tuned version of the latest You Only Look Once (YOLO) architecture. Subsequently, the A-scan was recalculated to reconstruct the C-scan image. To evaluate the feasibility of this approach, we posed two research questions: (1) Can the improved YOLOv11-seg network effectively perform surface segmentation? and (2) How is the quality of the C-scan image improved after reconstruction? The results indicate that our enhanced model achieves promising performance in surface segmentation of B-scan images, with a mean Average Precision mAP_0.5−0.95 of approximately 0.938 for bounding boxes and 0.901 for masks. This study proposes an effective solution for the NDT of semiconductor samples with uneven surfaces that ensures high-quality C-scan images, supports quality control processes, and enhances the overall effectiveness of non-destructive evaluation methodologies.

Hydrogen‐induced cracking (HIC) presents a significant concern in industries, such as oil and gas, petrochemicals, and aerospace, where high‐strength steel is prevalently used. This phenomenon compromises the structural integrity of steel pipelines and equipment. Accurate detection and monitoring of HIC are crucial for the safety and reliability of these assets. While traditional defect detection methods are usually costly and easily affected by human physiological state, deep learning approaches offer both time and financial benefits along with high accuracy. This study focuses on employing deep learning to segment HIC in steel, utilizing the robust and state‐of‐the‐art YOLOv8‐seg architecture for defect identification and segmentation. The process involves acquiring two‐dimensional B‐scan images through a scanning acoustic microscopy (SAM) system, followed by employing the YOLOv8‐seg architecture to address the segmentation task within these images. The experimental results demonstrate the effectiveness of the YOLOv8‐seg model, achieving a mean average precision (mAP) score greater than ˜0.95. Notably, this research pioneers the development of a framework for reconstructing HIC within steel based on B‐scan image segmentation results, offering researchers and professionals a comprehensive understanding of internal defects in steel blocks. This work underscores the potential of YOLOv8‐seg architecture for accurate detection and segmentation of HIC in steel, providing a valuable tool for inspection and maintenance activities.