Results 181 to 190 of about 2,424,657 (202)

Interface engineering modulation of ferroelectric synapses for high-precision neuromorphic computing

Applied Physics Letters
Nb:SrTiO3 (NSTO) are commonly employed as substrate and electrode for BaTiO3-based ferroelectric memristors. These substrates are available in two types.
Hao Liu, Yan Wang, Wenshuo Wu, Minghao Zhang, Jie Su   +4 more
semanticscholar   +1 more source

Self-Rectifying MoS2 Memtransistor via Asymmetry Contact Metal Engineering for Neuromorphic Computing.

Small
Memtransistors, integrating the resistive switching behavior of memristors with the gate tunability of transistors, offer significant promise for neuromorphic computing and in-memory processing. However, their scalability in crossbar arrays is limited by
Tian Tan, M. Sivan, Kai Zhou, Haoyue Guo, Yiting Wu, Linfeng Sun, Yida Li, Xuewei Feng   +7 more
semanticscholar   +1 more source

Transition of short-term to long-term memory of Cu/TaOx/CNT conductive bridge random access memory for neuromorphic engineering

Carbon, 2023
Jihyung Kim, Jin Hyeong Choi, Sunghun Kim, Changsoon Choi, Sungjun Kim   +4 more
semanticscholar   +1 more source

Wafer-Scale Monolayer MoS2 with Tunable Grain Size via Grain Boundary Engineering for Neuromorphic Computing.

ACS Nano
Grain boundaries (GBs) in two-dimensional (2D) materials, once regarded as detrimental defects, are now increasingly recognized as functional features for tailoring material properties.
Mingxi Chen, Xufan Li, Yongli He, Haofei Zheng, Zheren Cai, Xin Ju, Musi Zeng, Shi Wun Tong, Jing Wu, Lingfei Wang, K. Ang, Dongzhi Chi   +11 more
semanticscholar   +1 more source

A Review of MXene Memristive Networks: Atomic‐Scale Engineering to Neuromorphic System Integration

Advanced Materials & Technologies
With the ever‐growing demands of artificial intelligence and big data, the advancement of the conventional von Neumann framework is increasingly hindered by limitations in memory and power consumption. The human brain's energy‐efficient neural mechanisms
Shuai Yang, Jialin Wang, Dongchen Tan, Nan Sun, Haohao Shi, Sheng Bi, Chengming Jiang   +6 more
semanticscholar   +1 more source

How we created neuromorphic engineering

Nature Electronics, 2020
C. Mead
semanticscholar   +1 more source

Towards Reverse-Engineering the Brain: Brain-Derived Neuromorphic Computing Approach with Photonic, Electronic, and Ionic Dynamicity in 3D integrated circuits

arXiv.org
The human brain has immense learning capabilities at extreme energy efficiencies and scale that no artificial system has been able to match. For decades, reverse engineering the brain has been one of the top priorities of science and technology research.
S. Yoo, Luis El Srouji, S. Datta, Shimeng Yu, J. Incorvia, A. Salleo, Volker J. Sorger, Juejun Hu, L. Kimerling, Kristofer E. Bouchard, Joy Geng, Rishidev Chaudhuri, Charan Ranganath, Randall C. O’Reilly   +13 more
semanticscholar   +1 more source

Neuromorphic Analog Implementation of Neural Engineering Framework-Inspired Spiking Neuron for High-Dimensional Representation

Frontiers in Neuroscience, 2021
Elishai Ezra Tsur, Ezra Tsur Elishai
exaly  

Reservoir Computing with Charge‐Trap Memory Based on a MoS2 Channel for Neuromorphic Engineering

Advanced Materials, 2023
Daniele Ielmini
exaly  

Neuromorphic Neural Engineering Framework-Inspired Online Continuous Learning with Analog Circuitry

Applied Sciences (Switzerland), 2022
Elishai Ezra Tsur, Ezra Tsur Elishai
exaly