Results 61 to 70 of about 4,441 (253)
Emerging neuromorphic computing offers a promising and energy-efficient approach to developing advanced intelligent systems by mimicking the information processing modes of the human brain.
Weisheng Wang, Liqiang Zhu
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Artificial Perception Built on Memristive System: Visual, Auditory, and Tactile Sensations
The widespread implementation and rapid development of autonomous systems pose stringent performance requirements on emerging sensory systems. In addition to the basic sensing requirements, leading sensory systems are required to process data and extract
Xinglong Ji +3 more
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Algorithm-Hardware Co-design for Ultra-Low-Power Machine Learning and Neuromorphic Computing
The rapid proliferation of the Internet of Things (IoT) devices and the growing demand for intelligent systems have driven the development of low-power, compact, and efficient machine learning solutions. Deep neural networks (DNNs) have become state-of-the-art algorithms in various applications, such as face recognition, object detection, and speech ...
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Opportunities of Semiconducting Oxide Nanostructures as Advanced Luminescent Materials in Photonics
The review discusses the challenges of wide and ultrawide bandgap semiconducting oxides as a suitable material platform for photonics. They offer great versatility in terms of tuning microstructure, native defects, doping, anisotropy, and micro‐ and nano‐structuring. The review focuses on their light emission, light‐confinement in optical cavities, and
Ana Cremades +7 more
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Directional Flow of Confined Polaritons in CrSBr
CrSBr, a layered magnetic semiconductor, naturally channels self‐hybridized excitonpolaritons into highly directional flow. Its intrinsic optical anisotropy, high refractive index, and strong lightmatter coupling enable long‐range guided modes along the a‐axis, with propagation lengths set by their excitonphoton admixture.
Pratap Chandra Adak +10 more
wiley +1 more source
Preparation of MXene-based hybrids and their application in neuromorphic devices
The traditional von Neumann computing architecture has relatively-low information processing speed and high power consumption, making it difficult to meet the computing needs of artificial intelligence (AI). Neuromorphic computing systems, with massively
Zhuohao Xiao +10 more
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Atomistic Mechanisms Triggered by Joule Heating Effects in Metallic Cu‐Bi Nanowires for Spintronics
Bi doped metallic Cu nanowires are promising for spintronics thanks to the stabilization of a giant spin Hall effect. However, heat resulting from current injection forces Bi to leave solution, forcing segregation into monoatomic decorations which evolve into coherent crystalline aggregates.
Alejandra Guedeja‐Marrón +6 more
wiley +1 more source
Ion‐Reconfigurable “N”‐Shaped Antiambipolar Behavior in Organic Electrochemical Transistors
A unique N‐shaped negative differential transconductance (NDT) characteristics is demonstrated in single‐polymer organic electrochemical transistors through a sequential doping–redox–doping process driven by iodide ions. This redox‐driven mechanism enables low‐voltage, ion‐controlled reconfigurability and tunable current modulation, allowing seamless ...
Debdatta Panigrahi +11 more
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Neuromorphic Hardware Systems for Ultra-Low-Power Computing
Neuromorphic computing represents a paradigm shift in computational architecture, offering unprecedented energy efficiency through brain-inspired hardware implementations. This paper provides a comprehensive analysis of neuromorphic hardware systems designed for ultra-low-power computing applications.
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Vertical organic electrochemical transistors (vOECTs) are limited in speed by ion‐impermeable metal electrodes that slow ion injection. Using ion‐permeable PBFDO top electrodes allows direct vertical ion injection into BBL channels, achieving high current densities (>400 A cm−2), large on/off ratios (>106), and ultrafast switching in 28 µs. This sets a
Han‐Yan Wu +14 more
wiley +1 more source

