As semiconductor manufacturing advances to smaller process nodes, hotspot detection has become critical for ensuring the manufacturability and reliability of integrated circuit (IC) layouts. However, existing detection methods rely heavily on labeled data tailored to specific nodes, resulting in poor generalizability across nodes due to variations in layout geometries and fabrication processes. Labeling new data at advanced nodes is also costly and time-consuming. To overcome these challenges, we propose Node2Node, the first adaptation framework explicitly designed for cross-node hotspot detection. Node2Node integrates a novel node-invariant encoder with a node-specific encoder to jointly capture transferable and node-dependent features. To further improve robustness, we introduce a bidirectional center alignment strategy, which refines pseudo-labels by leveraging a small amount of labeled data from the target node. Additionally, a cross-node distribution loss is introduced to explicitly align feature distributions between nodes. Extensive experiments demonstrate that Node2Node substantially improves cross-node generalization and achieves state-of-the-art hotspot detection performance.

Unmanned Aerial Vehicle (UAV)/drone vision and navigation require low-power 3D depth-sensing with robustness against strong/weak light and various weather conditions. CMOS image sensor (CIS) and light detection and ranging (LiDAR) can provide high-fidelity imaging. However, CIS lacks depth sensing and has difficulty in low light conditions. LiDAR suffers from heavy fog and is costly. Radar is resilient in poor weather but tends to be bulky and expensive. Ultrasound imaging system (UIS), on the other hand, is robust in various weather and light conditions and is cost-effective; however, it usually has low imaging resolution and low frame rate. Using a higher frequency ultrasound theoretically enables higher resolution, yet this comes at the cost of short range due to greater attenuation and severe grating lobe phenomenon. Compared to prior air-channeled ultrasound imaging systems, this work is first to integrate low-frequency (LF) and high-frequency (HF) dual band ultrasound imaging into SoC, achieving highest angular resolution so far with near-zero frequency switching latency and typical 11.04M focal point/s throughput to enable real-time high resolution 3D imaging. System level validation including signal-to-noise ratio (SNR) VS range characterization, hybrid LF–HF imaging, two-point separation demonstrating 0.5° angular resolution, and in-air measurements on a flying drone confirms reliable operation at a 7 m range and 21 frames per second (fps) for both LF and HF modes. These results establish the hybrid dual-band SoC as an effective and practical 3D depth-sensing solution for UAV navigation under challenging environmental conditions. The SoC is implemented in 180 nm 1P6M Standard CMOS, occupies 30mm${}^{\mathbf {2}}$and consumes 116.1 mW.

Recent advances in deep learning-based layout hotspot detection have made remarkable progress in identifying potential defect patterns at early design stages. However, most existing methods rely on supervised learning, which requires manual identification of pre-defined hotspots and leads to considerable labeling effort. Moreover, design houses often struggle to obtain a sufficient number of labeled hotspot samples, limiting the applicability and scalability of such methods. In this article, we introduce a novel approach, termed you only need non-hotspot (YONN), which to the best of our knowledge, is the first unsupervised and training-free framework for layout hotspot detection. The proposed method mitigates the dependence on labeled hotspot data by leveraging memorized prototypes and a query-based inference mechanism. Specifically, YONN employs a CNN-based prototype generation network to extract multi-scale, fine-grained representations of layouts. During inference, a combination of shape-aware and topology-aware query mechanisms facilitates precise pixel-wise matching between test layout and memorized prototypes. To further enhance YONN’s efficiency and scalability, we propose a prototype sampling strategy that integrates density-based clustering techniques, significantly reducing the scale of the prototypes. Experimental results indicate that YONN achieves performance within 10% of leading state-of-the-art supervised learning methods, despite operating in a fully unsupervised setting without access to hotspot data. As an optional extension, YONN surpasses existing state-of-the-art approaches using only 30% hotspot labels. Notably, YONN is a training-free framework that enables on-the-fly adaptation by directly incorporating novel samples into the prototype bank, thereby supporting efficient and scalable learning within design for manufacturability workflows.

Recently, machine learning-based techniques have been applied for layout hotspot detection. However, existing methods encounter challenges in capturing the decision boundary across the entire dataset and ignore the geometric properties and topology of the polygons. In this paper, we introduce CLI-HD, a novel contrastive learning framework on layout sequences and images for hotspot detection. Our framework improves the ability to distinguish between hotspots and non-hotspots by similarity computations instead of a single decision boundary. To effectively incorporate geometric information into the model training process, we propose Layout2Seq, which encodes polygon shapes as vectors within sequences that are subsequently fed into the CLIHD. Furthermore, to better represent topology information, we develop an absolute position embedding, replacing the standard position encoders used in Transformer architectures. Extensive evaluations on various benchmarks demonstrate that CLI-HD outperforms current state-of-the-art methods, with an accuracy improvement ranging from $0.82 \%$ to $4.77 \%$ and a reduction in false alarm rates by $\mathbf{4. 9 \%}$ to $\mathbf{2 3. 1 8 \%}$.

Convolutional neural networks (CNNs) are frequently used to analyze remote sensing images and achieve impressive progress. Limited by the receptive field size of CNNs, small objects tended to lack adequate features to obtain more accurate segmentation results. To address this problem, we introduce a novel CNN model for coarse-to-fine segmentation called C2FNet. C2FNet comprises two stages: the coarse network and the fine network. The coarse network identifies the positions and coarse segmentation outcomes of small objects in the input image. The fine network then takes a closer look at the small objects and re-segments the patches using binary segmentation. The fine network distinguishes small objects from the background to refine small object segmentation. Finally, C2FNet employs an aggregation module that merges the binary segmentation maps and coarse outcomes to obtain accurate small object segmentation. We conducted extensive experiments on three widely accepted datasets for remote sensing image segmentation, namely the ISPRS 2-D semantic labeling Potsdam, Vaihingen, and iSAID. Our approach significantly improves the performance of baseline models, achieving a 0.24%–2.83% increase in IoU per small object class on iSAID.

In the realm of clinical diagnostics and medical research, quantitative assessment of membrane activity in immunohistochemistry (IHC) images is standard practice. Despite a high demand for cell membrane segmentation, only a few algorithms have been developed, and there is a lack of open datasets in this field. In this paper, we propose a three-stage unsupervised framework to accurately segment positive cell membranes in IHC images. Our approach transforms the unsupervised segmentation task into a supervised one by generating pseudo-paired training data using Voronoi diagrams and CycleGAN. Additionally, we introduce a dual encoder segmentation model with domain adaptation modules to mitigate the domain shift between generated images and real images. To our best knowledge, this is the first work focusing on unsupervised learning for IHC cell membrane segmentation. Extensive experiments and ablation studies on our newly built IHC cell membrane segmentation dataset validate the effectiveness of our framework.

Visible-infrared person re-identification (VI-ReID) is a challenging task in computer vision, aiming at matching people across images from visible and infrared modalities. The widely used VI-ReID framework consists of a convolution neural backbone network that extracts the visual features, and a feature embedding network to project heterogeneous features to the same feature space. However, many studies based on the existing pre-trained models neglect potential correlations between different locations and channels within a single sample during the feature extraction. Inspired by the success of the Transformer in computer vision, we extend it to enhance feature representation for VI-ReID. In this paper, we propose a discriminative feature learning network based on a visual Transformer (DFLN-ViT) for VI-ReID. Firstly, to capture long-term dependencies between different locations, we propose a spatial feature awareness module (SAM), which utilizes a single-layer Transformer with a novel patch-embedding strategy to encode location information. Secondly, to refine the representation at each channel, we design a channel feature enhancement module (CEM). The CEM treats the features of each channel as a sequence of Transformer inputs, taking advantage of the Transformer's ability to model long-term dependencies. Finally, we propose a Triplet-aided Hetero-Center (THC) loss to learn more discriminative feature representation by balancing the cross-modality distance and intra-modality distance of the center. The experimental results on two datasets show that our method can significantly improve the VI-ReID performance, outperforming most state-of-the-art methods.

The neural network-based remote sensing image change detection method faces a large amount of imaging interference and severe class imbalance problems under high-resolution conditions, which bring new challenges to the accuracy of the detection network. In this work, to address the imaging interference caused by different imaging angles and times, the siamese strategy and multi-head self-attention mechanism are used to reduce the imaging differences between the dual-temporal images and fully exploit the inter-temporal information. Secondly, a learnable multi-part feature learning module is used to adaptively exploit features from different scales to obtain more comprehensive features. Finally, a mixed loss function strategy is used to ensure that the network converges effectively and excludes the adverse interference of a large number of negative samples to the network. Extensive experiments show that our method outperforms numerous methods on LEVIR-CD, WHU, and DSIFN datasets.

Challenges still exist in the task of object detection in remote sensing images with densely distributed objects due to large variation in scale and neglect of the relative position and correlation. To address these issues, a Correlation Learning Detector based on Transformer (CLT-Det) is proposed for detecting dense objects in remote sensing images. A Transformer Attention Module (TAM) is designed to improve the densely packed objects’ model representation ability by learning pixel-wise attention with Transformer. To alleviate the semantic gap caused by variations in scale, a Feature Refinement Module (FRM) is proposed by improving the multi-scale feature pyramid. A Correlation Transformer Module (CTM) is proposed to extract correlation information and encodes position information of dense objects’ features on the classification branch for fully utilizing the position information and correlation among objects. Extensive experiments compared with several state-of-art methods on two challenging remote sensing datasets, namely DOTA and HRSC2016, demonstrate that the proposed CLT-Det achieves promising and competitive performance.