High-resolution Imaging Research Articles

Deep tomography for the three-dimensional atmospheric turbulence wavefront aberration

Multiconjugate adaptive optics (MCAO) can overcome atmospheric anisoplanatism to achieve high-resolution imaging with a large field of view (FOV). Atmospheric tomography is the key technology for MCAO. The commonly used modal tomography approach reconstructs the three-dimensional atmospheric turbulence wavefront aberration based on the wavefront sensor (WFS) detection information from multiple guide star (GS) directions. However, the atmospheric tomography problem is severely ill-posed. The incomplete GS coverage in the FOV coupled with the WFS detection error significantly affects the reconstruction accuracy of the three-dimensional atmospheric turbulence wavefront aberration, leading to a nonuniform aberration detection precision over the whole FOV. We propose an efficient approach for achieving accurate atmospheric tomography to overcome the limitations of the traditional modal tomography approach. We employed a deep-learning-based approach to the tomographic reconstruction of the three-dimensional atmospheric turbulence wavefront aberration. We propose an atmospheric tomography residual network (AT-ResNet) that is specifically designed for this task, which can directly generate wavefronts of multiple turbulence layers based on the Shack-Hartmann (SH) WFS detection images from multiple GS directions. The AT-ResNet was trained under different turbulence intensity conditions to improve its generalization ability. We verified the performance of the proposed approach under different conditions and compared it with the traditional modal tomography approach. The well-trained AT-ResNet demonstrates a superior performance compared to the traditional modal tomography approach under different atmospheric turbulence intensities, various turbulence layer distributions, higher-order turbulence aberrations, detection noise, and reduced GSs conditions. The proposed approach effectively addresses the limitations of the modal tomography approach, leading to a notable improvement in the accuracy of atmospheric tomography. It achieves a highly uniform and high-precision wavefront reconstruction over the whole FOV. This study holds great significance for the development and application of the MCAO technology.

Mega-NeRF++: An Improved Scalable NeRFs for High-resolution Photogrammetric Images

Abstract. Over the last few years, implicit 3D representation has attracted more and more research endeavors, typified by the so-called Neural Radiance Fields (NeRF). The original NeRF and some relevant variants mostly address on small-scale scene (such as, indoor or tiny toys), which already show good novel views rendering results. It still remains challenging when dealing with wide coverage area that is captured by large number of high-resolution images, the time efficiency and rendering quality is generally limited. To cope with large-scale scenario, recently, Mega-NeRF was proposed to divide the area into several overlapping sub-area and train corresponding sub-NeRFs, respectively. Mega-NeRF adopts the method of parallel training of multiple sub-modules, which means sub-modules are absolutely independent of each other, which might in principle not be an optimal solution, as two sub-NeRFs of adjacent sub-models obtained by parallel training are likely to get different rendering results for the overlapping area, and the final rendering result is supposed to be negative affected. Therefore, we present Mega-NeRF++, and our goal is to improve Mega-NeRF by implementing extra sub-models optimization that alleviate the rendering discrepancy of overlapping sub-NeRFs. More specifically, we further fine tune the original Mega-NeRFs by considering the consistency of adjacent overlapping area, which means the training data used in the optimization are only from the overlapping region, and we also proposed a novel loss, so that it not only takes into account the difference between the prediction of each sub-model and the true value, but also considers the consistency of the predicted results between various adjacent sub-modules in the overlapping region. The experimental results show that, for the overlapping area, our Mega-NeRF++ can qualitatively render better images with higher fidelity and quantitively have higher PNSR and SSIM compare to original Mega-NeRF.

Bridging Languages through Images: A Multilingual Text-to-Image Synthesis Approach

This research investigates the challenges posed by the predominant focus on English language text-to-image generation (TTI) because of the lack of annotated image caption data in other languages. The resulting inequitable access to TTI technology in non-English-speaking regions motivates the research of multilingual TTI (mTTI) and the potential of neural machine translation (NMT) to facilitate its development. The study presents two main contributions. Firstly, a systematic empirical study employing a multilingual multi-modal encoder evaluates standard cross-lingual NLP methods applied to mTTI, including TRANSLATE TRAIN, TRANSLATE TEST, and ZERO-SHOT TRANSFER. Secondly, a novel parameter-efficient approach called Ensemble Adapter (ENSAD) is introduced, leveraging multilingual text knowledge within the mTTI framework to avoid the language gap and enhance mTTI performance. Additionally, the research addresses challenges associated with transformer-based TTI models, such as slow generation and complexity for high-resolution images. It proposes hierarchical transformers and local parallel autoregressive generation techniques to overcome these limitations. A 6B-parameter transformer pretrained with a cross-modal general language model (CogLM) and fine-tuned for fast super-resolution results in a new text-to-image system, denoted as It, which demonstrates competitive performance compared to the state-of-the-art DALL-E-2. Furthermore, It supports interactive text-guided editing on images, offering a versatile and efficient solution for text-to-image generation.. Keywords: Text-to-image generation, Multilingual TTI (mTTI), Neural machine translation (NMT), Cross-lingual NLP, Ensemble Adapter (ENSAD), Hierarchical transformers, Super- resolution, Transformer-based models, Cross-modal general language model (CogLM).

High-resolution images in seconds.

High-resolution images in seconds.

Enhanced Solar Coronal Imaging: A GAN Approach with Fused Attention and Perceptual Quality Enhancement

The activity of the solar corona has a significant impact on all aspects of human life. People typically use images obtained from astronomical telescopes to observe coronal activities, among which the Atmospheric Imaging Assembly (AIA) of the Solar Dynamics Observatory (SDO) is particularly widely used. However, due to resolution limitations, we have begun to study the application of generative adversarial network super-resolution techniques to enhance the image data quality for a clearer observation of the fine structures and dynamic processes in the solar atmosphere, which improves the prediction accuracy of solar activities. We aligned SDO/AIA images with images from the High-Resolution Coronal Imager (Hi-C) to create a dataset. This research proposes a new super-resolution method named SAFCSRGAN, which includes a spatial attention module that incorporates channel information, allowing the network model to better capture the corona’s features. A Charbonnier loss function was introduced to enhance the perceptual quality of the super-resolution images. Compared to the original method using ESRGAN, our method achieved an 11.9% increase in Peak Signal-to-Noise Ratio (PSNR) and a 4.8% increase in Structural Similarity (SSIM). Additionally, we introduced two perceptual image quality assessment metrics, the Natural Image Quality Evaluator (NIQE) and Learned Perceptual Image Patch Similarity (LPIPS), which improved perceptual quality by 10.8% and 1.3%, respectively. Finally, our experiments demonstrated that our improved model surpasses other models in restoring the details of coronal images.

Deep-FG-DSM: Fine-Grained DSM for High-resolution UAV Imagery Based on Deep Implicit Occupancy Networks

Abstract. Nowadays, DSM (Digital Surface Model) is one of most important products that has been widely applied in digital city or smart city. Over the last decades, the common way is to follow the conventional photogrammetric pipeline for generating DSM, which is often not efficient and yields noise with small geometric details lost. Inspired by the development of deep implicit occupancy network, in this paper, we presented a learning-based method for obtaining fine-grained DSM, i.e., fine Deep-FG-DSM. In particular, high-resolution UAV imagery together with the corresponding original point cloud are employed to improve DSMs preserving higher details, two heads that embed the features of images and 3D points are deployed, and a MLP (Muti-layer Perceptron) is appended to decode these embedding into continuous occupancy probabilities for predicting the existence (or not) of surface point. Our experimental results demonstrate the robustness of our model against both sparse and noisy point clouds. While generating DSMs, it retains high-frequency details from high-resolution UAV images while maintaining relatively high accuracy. For point cloud obtained after simplification with average sampling resolution of d=5m, the MAE (Mean Absolute Residual Error) is 2.15m.

Research on Satellite and Ground Multi-Sensor Collaborative Sensing Method for Floating Plants

Abstract. Dynamic monitoring of water environment is the basis of maintaining urban security and promoting urban sustainable development. To effectively solve the problems caused by the large-scale flooding of floating aquatic invasive plants, such as water environment destruction, clogging river, water quality pollution, water ecological balance destruction, etc., a satellite and ground multi-sensor collaborative sensing method for floating plants is proposed in this paper. For the monitoring of floating plants in macro watershed, the vegetation index and chlorophyll concentration were used to extract floating plants, analyzing, and calculating the distribution and coverage area of floating plants based on 4-band high-resolution satellite images. For the monitoring of floating plants in small watershed, combined with visible light video, multispectral images, and LiDAR data, optimized spectral feature variable processing, semantic segmentation network, and symmetric function neural network are utilized to obtain real-time information on the invasion location, distribution pattern, and coverage area of floating plants. In this paper, the upstream basin of the Huangpu River in Shanghai in 2023 was selected as the experimental area. The distribution of floating plants in different watercourse and different periods was successfully obtained by this method, and the coverage area was accurately calculated with an identification rate of more than 90%, which provided technical support for cross-regional management and efficient cleaning of floating plants.

Acoustofluidic scanning fluorescence nanoscopy with a large field of view

Large-field nanoscale fluorescence imaging is invaluable for many applications, such as imaging subcellular structures, visualizing protein interactions, and high-resolution tissue imaging. Unfortunately, conventional fluorescence microscopy requires a trade-off between resolution and field of view due to the nature of the optics used to form the image. To overcome this barrier, we developed an acoustofluidic scanning fluorescence nanoscope that simultaneously achieves superior resolution, a large field of view, and strong fluorescent signals. The acoustofluidic scanning fluorescence nanoscope utilizes the superresolution capabilities of microspheres that are controlled by a programmable acoustofluidic device for rapid fluorescence enhancement and imaging. The acoustofluidic scanning fluorescence nanoscope resolves structures that cannot be resolved with conventional fluorescence microscopes with the same objective lens and enhances the fluorescent signal by a factor of ~5 without altering the field of view of the image. The improved resolution realized with enhanced fluorescent signals and the large field of view achieved via acoustofluidic scanning fluorescence nanoscopy provides a powerful tool for versatile nanoscale fluorescence imaging for researchers in the fields of medicine, biology, biophysics, and biomedical engineering.

PCINet: a Prototype- and Concept-based Interpretable Network for Mutli-scene Recognition

Abstract. With the development of remote sensing techniques, a large number of high-resolution aerial images is now available and benefit many applications. Multi-scene recognition plays a key role in applying remote sensing images to these applications, which refers to predicting multiple scenes coexisted in an aerial image and has attracted an increasing attention. Recently, most researchers tend to invent deep learning-based recognition models and has gained great achievements. However, few efforts have been deployed to explaining the success of deep neural networks in multi-scene recognition. To address this, we introduce concept bottleneck model (CBM) to interpreting model performance and propose a novel network, namely Prototype- and Concept-based Interpretable Network (PCINet), that projects aerial imagery into a prototype-concept memory bank and encode their correlations for explaining how a network can identify coexisting scenes in an aerial image. Specifically, the proposed network mainly consists of two branches: prototype matching that measures similarity scores between image features and scene prototypes, and concept bottleneck branches that aligned image features to textual embeddings and compute their relations with concept embeddings. Afterwards, Outputs are integrated for inferring scene categories. Experimental results show that the model enhances interpretability, providing valuable insights for urban planning and resource management, thereby bridging the gap between deep learning models and practical applications.

Triplet Epipolar Images for Spaceborne Three Linear Cameras

Abstract. Epipolar images can reduce conjugate points searching from a two-dimensional to a one-dimensional space, which significantly improves the robustness and efficiency of dense matching. However, two images matched the lack of redundancy and suffered from occlusions. The third image could introduce two more stereos and improve the accuracy and completeness of the three-dimension reconstruction, which were adopted by spaceborne three linear cameras (TLCs), the ALOS PRSIM and ZY-3. In this study, we propose a new triplet epipolar images generation method for spaceborne TLCs. Triplet epipolar images were defined as any two of the three epipolar image pairs. The triplet epipolar geometry requires the imaging rays of any conjugate point to be coplanar. For high-resolution satellite images, parallel projection can be used to approximate the imaging geometry of satellite images. Therefore, a coplanar principal optical axis is a fundamental requirement for spaceborne TLCs. We propose a general workflow to generate triplet epipolar images (TEIs), which includes free-net bundle adjustment of TLCs, building a triplet epipolar geometry, correcting the y-parallaxes, and generating a rational function model (RFM) for TEIs. The ZY3-02 satellite images were used to validate the proposed method. The root means square error (RMSEs) of the free-net bundle adjustment in the image space was 0.185 pixels, which proved the fine intrinsic accuracy. After compensation, the RMSEs of the y parallaxes of the three epipolar image pairs were 0.295, 0.310, and 0.370 pixels. Owing to the simple geometry of the TEIs, the RMSEs of the RFM replacements were within 0.001 pixels.

Association of Morphology of Lenticulostriate Arteries and Proximal Plaque Characteristics With Single Subcortical Infarction: A Whole-Brain High-Resolution Vessel Wall Imaging Study.

We aimed to investigate the association of characteristics of lenticulostriate artery (LSA) morphology and parental atheromatous disease (PAD) with single subcortical infarction (SSI) and to explore whether the LSA morphology is correlated with proximal plaque features in asymptomatic PAD. Patients with acute SSI were prospectively enrolled and classified as large- and small-SSI groups. The clinical data and imaging features of LSA morphology (branches, length, dilation, and tortuosity) and middle cerebral artery plaques (normalized wall index, remodeling index, enhancement degree, and hyperintense plaques) were evaluated. Logistic regression was performed to determine the association of large SSIs with morphologic features of LSAs and plaques. The Spearman correlation between the morphologic characteristics of LSAs and plaque features in asymptomatic PAD was analyzed. Of the 121 patients recruited with symptomatic PAD, 102 had coexisting asymptomatic contralateral PAD. The mean length of LSAs (odds ratio, 0.84 [95% CI, 0.73-0.95]; P=0.007), mean tortuosity of LSAs (odds ratio, 1.13 [95% CI, 1.05-1.22]; P=0.002), dilated LSAs (odds ratio, 22.59 [95% CI, 2.46-207.74]; P=0.006), and normalized wall index (odds ratio, 1.08 [95% CI, 1.01-1.15]; P=0.022) were significantly associated with large SSIs. Moreover, the normalized wall index was negatively correlated with the mean length of LSAs (r=-0.348, P<0.001), and the remodeling index was negatively correlated with the mean tortuosity of LSAs (r=-0.348, P<0.001) in asymptomatic PAD. Our findings suggest that mean length of LSAs, mean tortuosity of LSAs, dilated LSAs, and normalized wall index are associated with large SSIs. Moreover, plaque features in asymptomatic PAD are correlated with morphologic features of LSAs.

Building Height Extraction Based on Satellite GF-7 High-Resolution Stereo Image

Abstract. High-resolution remote sensing images can distinguish objects of smaller size, so as to more clearly express the texture features and structural information of objects, and provide a data source for the development of large-scale mapping, high-precision stereo measurement and other fields. The purpose of this paper is to complete the height estimation of the buildings by analyzing the stereoscopic observation formed by the front and rear view images of the Gaofen-7 line array CCD. After determining the roof profile of the building on the rear-view image, assuming a series of object elevations of the building, that is, searching for elevations with a certain step distance within a certain elevation search range, adopt the object-based image matching VLL algorithm, Through the RFM imaging model of the Gaofen-7 sensor, the rear-view contour is projected to the front-view image, and then the PSNR is selected as the similarity measure of the window, and the similarity of the image block area corresponding to the front- and rear-view contour is calculated. Corresponds to the hypothetical object elevation as the estimated height of the building. Under the technical route of this paper, the height of buildings on high-resolution images can be estimated to a level within 3 meters of accuracy.

High-resolution vessel wall imaging for quantitatively and qualitatively evaluating in-stent stenosis of intracranial aneurysms

BackgroundIt is critical to accurately and noninvasively evaluate the stented parent artery of intracranial aneurysms (IAs) with endovascular treatment.ObjectiveTo investigate high-resolution vessel wall imaging (HR-VWI) for quantitative and qualitative evaluation of in-stent stenosis (ISS) in IAs treated with stent placement (SP).MethodsFifty-five patients (58 aneurysms) underwent HR-VWI, contrast-enhanced (CE)-HR-VWI, CE-MR angiography (MRA), time-of-flight (TOF)-MRA, and digital subtraction angiography (DSA) six months after SP, and the reliability of quantitative stent lumen measurements was evaluated by intraclass correlation coefficient (ICC) analysis. Agreement and correlation of quantitative evaluation were estimated by comparing the four MR imaging modalities with DSA. The diagnostic performance for &amp;gt;0%, ≥25%, and ≥50% of ISS degrees and overall diagnostic accuracy for the ISS degrees of the four MR imaging modalities were calculated to qualitative evaluation.ResultsThe reliability of CE-HR-VWI and HR-VWI for ISS quantitative measurements was excellent (ICC 0.955–0.989). The agreement and correlation of CE-HR-VWI, HR-VWI versus DSA for ISS quantitative measurements were better than those of CE-MRA and TOF-MRA (p &amp;lt; 0.05). The diagnostic performance for distinguishing the degree of ISS &amp;gt;0%, ≥25%, and ≥50% by CE-HR-VWI and HR-VWI was superior to CE-MRA and TOF-MRA, and their overall diagnostic accuracy was 96.55 and 94.83%, respectively. HR-VWI and CE-HR-VWI were not statistically significant in the quantitative and qualitative evaluation of ISS performance (p &amp;gt; 0.05).ConclusionHR-VWI and CE-HR-VWI have similar performance and value in the quantitative and qualitative evaluation of ISS, and HR-VWI without contrast media could be used as an ideal long-term follow-up approach after SP treatment for IAs.

Full-Stokes polarization detection enabled by a terahertz all-dielectric metasurface

Metasurface has the ability to flexibly modulate the wavefront and detect the polarization states, thus receiving widespread research attention. The combination of polarization multiplexing techniques and focused beams with polarization information provides a new approach to compact polarization detection behavior. Here, an all-dielectric metasurface based on polarization multiplexing encoding technique is demonstrated and assigns the independent phase distributions to x- and y-polarized channels to separate different polarized components. Combining Stokes parameters and visualized polarization ellipses, realizing one-to-one mapping of the incident polarization states with far-field images. We obtain the Poincaré sphere and visualized polarization ellipses based on Stokes parameters to verify the polarization detection ability of the designed metasurface. The proposed scheme is expected to provide potential applications in fields such as full-Stokes polarization detection, high-resolution imaging, and terahertz communication.

Astronomy from the moon in the next decades: from exoplanets to cosmology in visible light and beyond.

We look at what astronomy from the Moon might be like over the next few decades. The Moon offers the possibility of installing large telescopes or interferometers with instruments larger than those on orbiting telescopes. We first present examples of ambitious science cases, in particular ideas that cannot be implemented from Earth. After a general review of observational approaches, from photometry to high contrast and high angular resolution imaging, we propose as a first step a 1-metre-class precursor and explore what science can be done with it. We add a proposal to use the Earth-Moon system to test the quantum physics theory. This article is part of a discussion meeting issue 'Astronomy from the Moon: the next decades (part 2)'.

Evaluating nickel removal efficacy of Filtralite under laboratory conditions: Implications for sustainable urban drainage systems

Our study deals with the critical issue of heavy metal pollution in urban runoff; we focused particularly on the threat posed by heavy metals such as nickel to living organisms and water resources owing to their toxicity at low concentrations and non-biodegradability. In this study, we evaluated the efficacy of Filtralite, a filtration medium with excellent hydraulic properties, in removing nickel from water through laboratory experiments and advanced simulation tools. We analysed the geochemical processes involved in nickel removal, including precipitation and adsorption. Importantly, we implemented an innovative approach for numerical modelling using HP1, by combining HYDRUS-1D and PHREEQC for modelling solute transport in porous media and geochemical reactions, respectively. The experimental results revealed that the Filtralite-laden column achieved a removal efficiency of 93.5 % for injected nickel. We observed a significant increase in the effluent pH from 7.0 to 11.5 during the interaction with Filtralite, affecting the solubility of the nickel phases. The model showed the highest nickel concentration in the initial column layers because of rapid chemical precipitation and adsorption upon contact with Filtralite. High-resolution scanning electron microscopy images confirmed the presence of amorphous precipitates around the soil particles, aligning with the changes in pH. This study contributes significantly to environmental engineering and urban water management by introducing an innovative numerical model in HP1 to accurately simulate the removal of nickel using Filtralite. These findings validate the efficiency of Filtralite in extracting nickel from aqueous solutions. Thus, Filtralite is a strong candidate for inclusion in sustainable urban drainage systems.

Cryo-EM combined with image deconvolution to determine ZIF-8 crystal structure

Abstract Metal-organic frameworks (MOFs) are crystalline porous materials with tunable properties, exhibiting great potential in gas adsorption, separation and catalysis.[1, 2] It is challenging to visualize MOFs with transmission electron microscopy (TEM) due to their inherent instability under electron beam irradiation. Here, we employ cryo-electron microscopy (cryo-EM) to capture images of MOF ZIF-8, revealing inverted-space structural information at a resolution of up to about 1.7 Å and enhancing its critical electron dose to around 20 e-/Å2. In addition, it is confirmed by electron-beam irradiation experiments that the high voltage could effectively mitigate the radiolysis, and the structure of ZIF-8 is more stable along the [100] direction under electron beam irradiation. Meanwhile, since the high-resolution electron microscope images is modulated by contrast transfer function (CTF) and it is difficult to determine the positions corresponding to the atomic columns directly from the images. We employ image deconvolution to eliminate the impact of CTF and obtain the structural images of ZIF-8. As a result, the heavy atom Zn and the organic imidazole ring within the organic framework can be distinguished from structural images.

Learning content-aware feature fusion for guided depth map super-resolution

RGB-D data including paired RGB color images and depth maps is widely used in downstream computer vision tasks. However, compared with the acquisition of high-resolution color images, the depth maps captured by consumer-level sensors are always in low resolution. Within decades of research, the most state-of-the-art (SOTA) methods of depth map super-resolution cannot adaptively tune the guidance fusion for all feature positions by channel-wise feature concatenation with spatially sharing convolutional kernels. This paper proposes JTFNet to resolve this issue, which simulates the traditional Joint Trilateral Filter (JTF). Specifically, a novel JTF block is introduced to adaptively tune the fusion pattern between the color features and the depth features for all feature positions. Moreover, based on the variant of JTF block whose target features and guidance features are in the cross-scale shape, the fusion for depth features is performed in a bi-directional way. Therefore, the error accumulation along scales can be effectively mitigated by iteratively HR feature guidance. Compared with the SOTA methods, the sufficient experiment is conducted on the mainstream synthetic datasets and real datasets, i.e., Middlebury, NYU and ToF-Mark, which shows remarkable improvement of our JTFNet.

Simultaneous Calibration of Multiple Cameras and Generation of Omnidirectional Images

Abstract. Omnidirectional images are increasingly being used in various areas, such as urban mapping, virtual reality, agriculture, and robotics. These images can be generated by different acquisition systems, including multi-camera systems, which can acquire higher-resolution images. Stitching techniques are often used and can be suitable for non-metric applications, but rigorous photogrammetric processing is recommended when having more accurate requirements. The main challenges related to this kind of product are the system calibration and the generation of the final omnidirectional images. When using multi-camera systems, the displacement of the cameras' perspective centres can affect the generation of the omnidirectional images and the resulting accuracy. A common approach to minimising the resulting parallax error is to establish a value for the projection cylinder radius as close as possible to the object's depth. This work proposes a highly accurate simultaneous calibration technique for multiple camera systems using self-calibrating bundle adjustment with constraints of stability of the relative orientation parameters. These parameters are later used to generate a projecting cylindrical surface, maintaining the original camera perspective centres and relative orientation angles. The experiments show that using constraints improved both the calibration results and the final omnidirectional images. Residual mismatches between points in overlapping areas are subpixel.

Microcracking in On-Chip Interconnect Stacks: FEM Simulation and Concept for Fatigue Test

AbstractThe semiconductor industry is continuing the scaling down of both device and on-chip interconnect features, for performance and economic reasons. This trend has implications for the design of guard ring structures, i.e. metallic non-functional structures in the back-end-of-line (BEoL) stack designed to be efficient to stop microcracks. In this work, we present a sample design for an in situ experiment to study mechanical degradation and failure mechanisms of crack stop structures in the BEoL stack, to ensure the mechanical robustness of microchips for future technology nodes. Additional finite element method (FEM) simulations provide supplementary understanding of the crack kinetics. To examine the effects of mechanical loading on crack stop elements of the BEoL stack, a novel sample geometry for an in situ fatigue experiment using x-ray microscopy was developed. The x-ray microscope (ZEISS Xradia 800 Ultra) enables high-resolution imaging of the 3D-patterned sample structures and defects such as microcracks. The tailored sample geometry allows the application of a tensile load to a BEoL specimen by a lever mechanism. The feasibility of the sample design is shown by mode-I loading of a pure interconnect sample. Post-mortem analysis by scanning electron microscopy (SEM), confirming planar microcrack propagation from the notch through the dielectric layer with small deflections of the crack path near cone shaped copper vias. FEM simulations focusing on the stress-strain fields around a crack tip indicate the beginning of copper plasticity as major mechanism starting the redirection of cracks due to resulting material compression in front of the obstacle.