Accurate wafer yield prediction is vital for design-for-manufacturability and yield optimization in semiconductor production, enabling early defect detection and proactive process control. However, existing methods are constrained by their heavy dependence on large quantities of yield-labeled dataincurring high costs and limiting scalability. Meanwhile, vast amounts of unlabeled wafer test data remain untapped. We present TabESA (Tabular Enhanced Semi-supervised Architecture), a novel twostage AI framework tailored for manufacturing-aware yield prediction with minimal supervision. In Stage 1, TabESA employs dual self-supervised learning tasks to uncover intrinsic patterns in unlabeled tabular data. In Stage 2, it introduces a consistencybased semi-supervised training scheme that integrates labeled and unlabeled samples to boost prediction robustness. Tested on real-world manufacturing datasets, TabESA achieves over 0.95 in accuracy, precision, F1-score, and AUC using only 128 labeled samples. It surpasses conventional supervised models by 15.7% in F1-score and outperforms state-of-the-art semi-supervised techniques by 19.1 % in AUC. By leveraging unlabeled process data for yield estimation, TabESA provides a label-efficient, scalable, and industry-relevant solution for smart semiconductor manufacturing.

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.

Identifying the root cause of defects in semiconductor manufacturing is crucial for enhancing product yield and reliability. Traditional methods often emphasize defect classification and detection, yet they lack the depth required for comprehensive root cause analysis. This study introduces DefectTrackNet, a pioneering framework designed for automatic root cause analysis through historical similarity image retrieval. Our system employs a novel hybrid CNNTransformer architecture, which excels in the precise extraction and matching of image features pertinent to defect origins. Validated on a dataset of 2,588 real SEM defect images annotated with root causes, DefectTrackNet demonstrates superior performance in both accuracy and retrieval speed compared to existing methodologies. This innovative approach not only offers significant improvements over conventional techniques but also establishes a new benchmark for efficient and accurate defect root cause analysis, thereby advancing the field of semiconductor manufacturing defect analysis.

Time series anomaly detection (TSAD) is critical for ensuring the reliability of equipment in complex industrial environments. However, existing methods face significant challenges, including imbalanced data, lack of labeled samples, reliance on prior knowledge, poor generalization across diverse industrial scenarios, and low sensitivity to subtle anomalies. To address these limitations, we propose KARMAD, a novel framework that integrates Kolmogorov-Arnold Networks (KANs) for bidirectional function learning, adversarial training to enhance sensitivity to minor anomalies, and an adaptive thresholding strategy for improved precision and transferability. Evaluated on five public datasets against 14 state-of-the-art methods, KARMAD achieves state-of-the-art performance on all datasets, with an average F1 score improvement of 13.14%. Further experiments showcase its robustness to noise and adaptability in real-world industrial applications. KARMAD represents a significant advancement in developing scalable and accurate TSAD models suitable for diverse and high-stakes environments.

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.

In the field of integrated circuit manufacturing, the detection and classification of nanoscale wafer defects are critical for subsequent root cause analysis and yield enhancement. The complex background patterns observed in scanning electron microscope (SEM) images and the diverse textures of the defects pose significant challenges. Traditional methods usually suffer from insufficient data, labels, and poor transferability. In this paper, we propose a novel few-shot learning approach, SEM-CLIP, for accurate defect classification and segmentation. SEM-CLIP customizes the Contrastive Language-Image Pretraining (CLIP) model to better focus on defect areas and minimize background distractions, thereby enhancing segmentation accuracy. We employ text prompts enriched with domain knowledge as prior information to assist in precise analysis. Additionally, our approach incorporates feature engineering with textual guidance to categorize defects more effectively. SEM-CLIP requires little annotated data, substantially reducing labor demands in the semiconductor industry. Extensive experimental validation demonstrates that our model achieves impressive classification and segmentation results under few-shot learning scenarios.