As semiconductordevices shrink and their manufacturing processes advance,accurately measuring in-cell critical dimensions(Cd)becomes increasingly *** test element group(TEG)measurements are becoming inadequate forre...
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As semiconductordevices shrink and their manufacturing processes advance,accurately measuring in-cell critical dimensions(Cd)becomes increasingly *** test element group(TEG)measurements are becoming inadequate forrepresenting the fine,repetitive patterns in cell *** non-destructive metrology technologies like optical critical dimension(OCd)are limiteddue to their large spot diameter of approximately 25μm,which impedes their efficacy fordetailed in-cell structural ***,there is a pressing need for small-spot and non-destructive metrology *** address this limitation,we demonstrate a microsphere-assisted hyperspectral imaging(MAHSI)system,specifically designed for small spot optical metrology with *** microsphere-assisted super-resolution imaging,this system achieves an optical resolution of 66 nm within a field of view of 5.6μm×5.6μ*** approach effectively breaks the diffraction limit,significantly enhancing the magnification of the *** MAHSI system incorporating hyperspectral imaging with a wavelength range of 400–790 nm,enables the capture of the reflection spectrum at each camera *** achieved pixel resolution,which is equivalent to the measuring spot size,is 14.4 nm/pixel and the magnification is *** MAHSI system enables measurement of local uniformity in critical areas like corners and edges of drAM cell blocks,areas previously challenging to inspect with conventional OCd *** our knowledge,this approach represents the first global implementation of microsphere-assisted hyperspectral imaging to address the metrology challenges in complex 3d structures of semiconductordevices.
The development of integrated circuits towards high performance and high integration is an inevitable trend, and the reliability issues brought about by this trend have become increasingly prominent as the electric fi...
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Additive manufacturing (AM) is the industrial production name for 3d printing, a computer-controlledprocess that creates three dimensional objects by deposing materials, usually in layers. The improvement of the tech...
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1.5V PMOS based on the 0.11 μm ULL processtechnology face a reliability problem similar to the Negative Bias Temperature Instability (NBTI) of processes below 90nm due to their high opening voltage. Based on the dev...
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When an employee leaves the job, it leads to issues such as financial losses, loss of productivity, re-employment costs, the adaptation period for the new employee, and loss of time. Many studies are dedicated to pred...
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Optimal network analysis requires advanced techniques to handle the inherent complexity and uncertainty of real-world systems. We have used vertex order coloring on neutrosophic graphs to find the most effective appro...
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The 5G network's control-plane, a critical component of modern telecommunications infrastructure, manages the signaling and control functions that enable seamless connectivity and service delivery. This paper pres...
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Semantic segmentation of remote sensing images is a critical research area in the field of remote *** the success of Convolutional Neural Networks(CNNs),they often fail to capture inter-layer feature relationships and...
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Semantic segmentation of remote sensing images is a critical research area in the field of remote *** the success of Convolutional Neural Networks(CNNs),they often fail to capture inter-layer feature relationships and fully leverage contextual information,leading to the loss of important ***,due to significant intraclass variation and small inter-class differences in remote sensing images,CNNs may experience class *** address these issues,we propose a novel Category-Guided Feature Collaborative Learning Network(CG-FCLNet),which enables fine-grained feature extraction and adaptive ***,we design a Feature Collaborative Learning Module(FCLM)to facilitate the tight interaction of multi-scale *** also introduce a Scale-Aware Fusion Module(SAFM),which iteratively fuses features from different layers using a spatial attention mechanism,enabling deeper feature ***,we design a Category-Guided Module(CGM)to extract category-aware information that guides feature fusion,ensuring that the fused featuresmore accurately reflect the semantic information of each category,thereby improving detailed *** experimental results show that CG-FCLNet achieves a Mean Intersection over Union(mIoU)of 83.46%,an mF1 of 90.87%,and an Overall Accuracy(OA)of 91.34% on the Vaihingen *** the Potsdam dataset,it achieves a mIoU of 86.54%,an mF1 of 92.65%,and an OA of 91.29%.These results highlight the superior performance of CG-FCLNet compared to existing state-of-the-art methods.
In this study, we present a novel colorimetric method for the precise and sensitive detection of Hg ions utilizing silver nanoparticles (AgNPs) produced and stabilized in an aqueous medium using Poly allyl amine hydro...
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