Trong Nhan Nguyen

Background Mental health conditions such as depression, anxiety, and stress are commonly assessed using self-reported questionnaires and limited wearable physiological measures. However, reliance on subjective reporting, restricted sensor modalities such as heart rate variability and electrodermal activity, and small or homogeneous datasets may limit generalizability. We aimed to evaluate whether wearable optical sensing of microcirculation and tissue metabolism enables objective assessment of stress-related mental health states. Methods We conducted a prospective observational study including 132 adults aged 18 to 94 years (58% female) from 19 countries. Participants underwent repeated fingertip measurements using a non-invasive wearable device combining laser Doppler flowmetry and fluorescence spectroscopy to capture microvascular perfusion and metabolic signals. Frequency-domain features were extracted using wavelet analysis. Depression, anxiety, and stress levels were assessed using a standardized 21-item questionnaire. Multiple machine learning models were evaluated under subject-wise validation, and model interpretability was assessed using Shapley-based feature attribution. Results Here we show that ensemble-based models distinguish individuals with stress-related symptoms from those without with a receiver operating characteristic area under the curve of 0.72 and a precision-recall area under the curve of 0.89 under subject-wise validation. Microcirculatory variability and metabolic fluorescence features contribute substantially to prediction performance. Demographic variables, including sex, age, body mass index, and heart rate, are associated with increased stress-related risk. Conclusions Wearable optical sensing combined with interpretable machine learning provides physiological signatures associated with stress-related mental health conditions. This framework supports development of scalable and data-driven tools for objective mental health monitoring.

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.

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.