In semiconductor manufacturing, soft sensors play a key role in Advanced Process Control (APC) by enabling realtime wafer-to-wafer monitoring. However, their performance is often limited by sparse labeled data, process variability, and model-specific tuning. To address these challenges, we propose REDM: a Regression-Guided Diffusion Modeling framework designed to boost the accuracy and robustness of soft sensor prediction across diverse fabrication stages. REDM generates highfidelity virtual data guided by predictive regression objectives and incorporates a quality-aware filtering mechanism based on Sliced Wasserstein Distance and intra-subset Cosine Similarity. Through multi-objective selection techniques, REDM identifies informative virtual samples that balance distributional similarity and internal diversity, thereby enhancing downstream model training. We evaluate REDM on real-world datasets from three major semiconductor process stages: Chemical Vapor Deposition (CVD), Etching, and Chemical Mechanical Polishing (CMP). Across various regression models, REDM consistently enhances soft sensor performance, with an average $\mathbf{R}^{2}$ improvement of $3.27 \%$. Its independence from process-specific customization makes REDM a scalable and process-aware solution for soft sensor enhancement in smart manufacturing.

Accurate analysis of nanoscale defects in semiconductor manufacturing is essential for optimizing yield and reliability. Existing methods heavily rely on large, labor-intensive datasets and primarily focus on macroscopic defect distributions rather than finer nanoscale defect morphology. In this study, we introduce a novel hybrid weakly supervised segmentation framework for scanning electron microscope (SEM) images, which significantly reduces labeling demands while maintaining high precision. Our approach consists of two interconnected subnetworks: the first is dedicated to precise defect localization and image cropping, and the second performs detailed segmentation of the localized regions. Additionally, we propose an enhanced H-WSSNet that employs Leaky ReLU and a novel multi-level feature fusion mechanism, addressing gradient vanishing during training and improving the model’s adaptive feature fusion and selection capabilities. Extensive validation on a dataset of 1,328 real-world SEM images shows that our model achieves accuracy comparable to fully supervised methods, but with only 10% of the labeling workload. This advancement opens up new possibilities for efficient and scalable high-precision defect segmentation in semiconductor manufacturing.

In semiconductor manufacturing, pinpointing nanoscale wafer defects is crucial for yield and reliability. Deep learning methods for defect segmentation rely heavily on large, labor-intensive datasets and focus mainly on macroscopic wafer defects, not nanoscale morphology. Our research introduces a hybrid weakly supervised scanning electron microscope (SEM) defect segmentation system with two sub-networks: one for accurate defect localization and image cropping, another for detailed segmentation. Validated on 1,328 SEM image defects from a real facility, our model surpasses existing weakly supervised methods and equals fully supervised models in accuracy, with 10% labeling effort, providing a novel approach for high-precision defect segmentation.

Vehicle re-identification (re-id) is an essential task in the field of intelligent transportation systems (ITS). The main goal of re-id is to find the same vehicle in different scenarios, which can is still a challenging task in both ITS and computer vision (CV). The existing vehicle re-identification methods simply combine the coarse-grained and the fine-grained attributes together with multi-task training. However, such combination may still have limited performance in vehicles with trivial appearance differences, or with rare models and colors. To solve this problem, we propose a simple yet effective framework, called dual domain multi-task model (DDM), that divides the vehicle images into two domains based on the frequency. And then two parallel branches are proposed to recover the two domains. Furthermore, a multi-task method is proposed, which combines the classification loss in color and model together with triplet loss for fine-grained distance measurement. Besides, a progressive strategy is used in the training process. Two public datasets, PKU VehicleID and VeRi are used to validate the proposed DDM. The experimental results demonstrate that the proposed approach outperforms the existing methods on both datasets.

In recent years, deep learning methods have received more attention in epithelial-stroma (ES) classification tasks. Traditional deep learning methods assume that the training and test data have the same distribution, an assumption that is seldom satisfied in complex imaging procedures. Unsupervised domain adaptation (UDA) transfers knowledge from a labelled source domain to a completely unlabeled target domain, and is more suitable for ES classification tasks to avoid tedious annotation. However, existing UDA methods for this task ignore the semantic alignment across domains. In this paper, we propose a Curriculum Feature Alignment Network (CFAN) to gradually align discriminative features across domains through selecting effective samples from the target domain and minimizing intra-class differences. Specifically, we developed the Curriculum Transfer Strategy (CTS) and Adaptive Centroid Alignment (ACA) steps to train our model iteratively. We validated the method using three independent public ES datasets, and experimental results demonstrate that our method achieves better performance in ES classification compared with commonly used deep learning methods and existing deep domain adaptation methods.

Unsupervised domain adaptation (UDA) transfers knowledge from a label-rich source domain to a fully-unlabeled target domain. To tackle this task, recent approaches resort to discriminative domain transfer in virtue of pseudo-labels to enforce the class-level distribution alignment across the source and target domains. These methods, however, are vulnerable to the error accumulation and thus incapable of preserving cross-domain category consistency, as the pseudo-labeling accuracy is not guaranteed explicitly. In this paper, we propose the Progressive Feature Alignment Network (PFAN) to align the discriminative features across domains progressively and effectively, via exploiting the intra-class variation in the target domain. To be specific, we first develop an Easy-to-Hard Transfer Strategy (EHTS) and an Adaptive Prototype Alignment (APA) step to train our model iteratively and alternatively. Moreover, upon observing that a good domain adaptation usually requires a non-saturated source classifier, we consider a simple yet efficient way to retard the convergence speed of the source classification loss by further involving a temperature variate into the soft-max function. The extensive experimental results reveal that the proposed PFAN exceeds the state-of-the-art performance on three UDA datasets.