Results 121 to 130 of about 7,847 (301)

Neuromorphic Near‐Sensor and In‐Sensor Computing Enabled by Next‐Generation Material‐Based Sensors

Advanced Science, EarlyView.
This Review presents a structural framework that classifies neuromorphic sensing into near‐sensor and in‐sensor architectures, clarifying physical coupling between sensing and computation. The framework connects neural and synaptic device functions with recent advances in optical, mechanical, and chemical sensing, compares energy consumption and ...
Su Yeon Jung, Gwang Ya Kim, Sejin Kim, Hyunjong Seo, Se Gi Lee, Sang Min Won, Dongjoon Rhee, Joohoon Kang   +7 more
wiley   +1 more source

Modeling, Fabrication, and Characterization of Memristors

, 2014
The term “memristor” was coined by L. Chua from its two distinct functional characteristics, memory and resistor. From the symmetry argument of the circuit element and circuit variable matrix, memristor is deemed as the fourth fundamental circuit element,
Mazady, Anas
core  

Photonic‐Enabled Energy‐Efficient Transparent Neuromorphic Computing Devices: A Review

Advanced Science, EarlyView.
Transparent photonic neuromorphic computing devices merge optics and brain‐inspired computing to overcome von Neumann bottlenecks with ultrafast, low‐energy processing. By exploiting transparent oxides, 2D materials, phase‐change materials, and hybrid heterostructures, these platforms enable photonic synapses, memory, and logic for see‐through edge ...
Shuvaraj Ghosh, Ki‐bum Lee, Junghyeon Lee, Seunghee Cho, Malkeshkumar Patel, Hangfei Li, Yu Wen, Ye Zhou, Joondong Kim   +8 more
wiley   +1 more source

Memristors: two centuries on

, 2012
Memristors are dynamic electronic devices whose nanoscale realization has led to considerable research interest.
Toumazou, C., Prodromakis, T., Chua, L.
core  

Multiferroic‐Centric Materials and Systems Engineering for Battery Applications: An Insight Into Mechanisms, Strategies, and Characterizations

Advanced Science, EarlyView.
Multiferroic order parameters – polarization, magnetization, and ferroelastic strain – are positioned as dynamic design variables for batteries. Their mechanistic roles, practical tuning through fabrication and external fields, and ferroic‐resolved characterization routes are unified into a closed‐loop framework, revealing how coupled ferroic responses
Jiaqi Su, Yanda Zhu, Hao Peng, Manman Li, Xuzihan Zhang, Meixiao Wu, Siyuan Zhang, Yuhan Zeng, Jiwen Liao, Ming Luo, Hetaishan Huang, Yutong Wang, Sean Li, Wenxian Li   +13 more
wiley   +1 more source

Dual‐Mode Nanoporous SiO2 Memristors with Coexisting Volatile and Nonvolatile Dynamics for Reservoir Computing

Advanced Science, EarlyView.
A nanoporous SiO2 memristor enabling reconfigurable volatile and non‐volatile switching within a single device is demonstrated. The dual‐mode functionality supports both physical reservoir dynamics and synaptic weight storage, allowing unified hardware implementation of reservoir computing for temporal information processing, including image and ...
Bohao Ding, Tongjun Zhang, Li Shao, Nikolay Zhelev, Andrew L. Hector, Ruomeng Huang   +5 more
wiley   +1 more source

Filamentary-based organic memristors for wearable neuromorphic computing systems

Neuromorphic Computing and Engineering
A filamentary-based organic memristor is a promising synaptic component for the development of neuromorphic systems for wearable electronics. In the organic memristors, metallic conductive filaments (CF) are formed via electrochemical metallization under
Chang-Jae Beak, Jihwan Lee, Junseok Kim, Jiwoo Park, Sin-Hyung Lee   +4 more
doaj   +1 more source

Energy-efficient integrated electro-optic memristors

Neuromorphic photonic processors are redefining the boundaries of classical computing by enabling high-speed multidimensional information processing within the memory.
Farmakidis, Nikolaos, Wang, Mengyun, Parmigiani, Francesca, Aggarwal, Samarth, Bhaskaran, Harish, He, Yuhan, Lee, June Sang, Zhang, Yi, Dong, Bowei   +8 more
core   +1 more source

Passivity-based control of networks of memristors and capacitors [PDF]

This paper presents a mathematical framework for modelling networks of memristors and capacitors. Using this framework, we analyse the dynamic behaviour of the fluxes at the memristors in the network. In particular, we show that when no external input is
van der Schaft, A. J.; id_orcid, Huijzer, M. A.; id_orcid, Besselink, B.; id_orcid   +2 more
core   +1 more source

Polarization Dynamics in Ferroelectrics: Insights Enabled by Machine Learning Molecular Dynamics

Advanced Science, EarlyView.
Machine learning molecular dynamics is presented as a route to capture polarization switching, domain wall kinetics, topological polar textures, and polar mechanical coupling beyond the limits of conventional atomistic methods. This Perspective surveys recent progress and identifies key methodological directions, including long‐range electrostatics ...
Dongyu Bai, Ri He, Junxian Liu, Liangzhi Kou   +3 more
wiley   +1 more source