Results 21 to 30 of about 6,811 (257)
Transport characteristics and electrochemical properties of Y3+ doped Li4Ti5O12 as anode material
Cailiao gongcheng, 2022 Li4Ti5-xYxO12 (x=0, 0.05, 0.10, 0.15, 0.20) anode materials were synthesized by ball milling assisted solid-state method used Li2CO3 and anatase TiO2 as raw materials and yttrium nitrate (Y(NO3)3·6H2O) as yttrium source.
WU Bing +7 more
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Multiferroic neuromorphic computation devices
APL MaterialsNeuromorphic computation is based on memristors, which function equivalently to neurons in brain structures. These memristors can be made more efficient and tailored to neuromorphic devices by using ferroelastic domain boundaries as fast diffusion paths ...
Guangming Lu, Ekhard K. H. Salje
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Mott Memory and Neuromorphic Devices [PDF]
Proceedings of the IEEE, 2015 Orbital occupancy control in correlated oxides allows the realization of new electronic phases and collective state switching under external stimuli. The resultant structural and electronic phase transitions provide an elegant way to encode, store, and process information.
You Zhou,, Ramanathan, Shriram
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Optoelectronic Synaptic Devices for Neuromorphic Computing [PDF]
Advanced Intelligent Systems, 2020 Neuromorphic computing can potentially solve the von Neumann bottleneck of current mainstream computing because it excels at self‐adaptive learning and highly parallel computing and consumes much less energy. Synaptic devices that mimic biological synapses are critical building blocks for neuromorphic computing.
Yue Wang 0061 +7 more
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Memristive and CMOS Devices for Neuromorphic Computing [PDF]
Materials, 2020 Neuromorphic computing has emerged as one of the most promising paradigms to overcome the limitations of von Neumann architecture of conventional digital processors. The aim of neuromorphic computing is to faithfully reproduce the computing processes in the human brain, thus paralleling its outstanding energy efficiency and compactness.
Milo, Valerio +3 more
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InfoMat, 2023 Biologically inspired neuromorphic sensory memory systems based on memristor have received a lot of attention in the booming artificial intelligence industry due to significant potential to effectively process multi‐sensory signals from complex external ...
Xiaobing Yan +9 more
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Nanotechnology, 2019 Abstract Artificial intelligence (AI) has the ability of revolutionizing our lives and society in a radical way, by enabling machine learning in the industry, business, health, transportation, and many other fields. The ability to recognize objects, faces, and speech, requires, however, exceptional computational power and time, which is ...
Ielmini, Daniele, Ambrogio, Stefano
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Organic neuromorphic devices: Past, present, and future challenges [PDF]
MRS Bulletin, 2020 Abstract
Tuchman, Yaakov +9 more
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Embracing the Unreliability of Memory Devices for Neuromorphic Computing [PDF]
2020 IEEE International Reliability Physics Symposium (IRPS), 2020 The emergence of resistive non-volatile memories opens the way to highly energy-efficient computation near- or in-memory. However, this type of computation is not compatible with conventional ECC, and has to deal with device unreliability. Inspired by the architecture of animal brains, we present a manufactured differential hybrid CMOS/RRAM memory ...
Marc Bocquet +6 more
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Optoelectronic neuromorphic devices and their applications [PDF]
Acta Physica Sinica, 2022 Conventional computers based on the von Neumann architecture are inefficient in parallel computing and self-adaptive learning, and therefore cannot meet the rapid development of information technology that needs efficient and high-speed computing.
Liu-Feng Shen +4 more
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