Results 181 to 190 of about 14,142 (285)

A reconfigurable photosensitive split-floating-gate memory for neuromorphic computing and nonlinear activation. [PDF]

Nat Commun
Zhang ZC, Li Y, Yao J, Chen Z, Wang FD, Si SH, Ding Y, Qi HL, Lu TB, Kang L, Liu ZB, Tian JG, Chen XD.   +12 more
europepmc   +1 more source

Electrode‐Engineered Dual‐Mode Multifunctional Lead‐Free Perovskite Optoelectronic Memristors for Neuromorphic Computing

Advanced Electronic Materials, EarlyView.
A lead‐free perovskite memristive solar cell structure that call emulate both synaptic and neuronal functions controlled by light and electric fields depending on top electrode type. ABSTRACT Memristive devices based on halide perovskites hold strong promise to provide energy‐efficient systems for the Internet of Things (IoT); however, lead (Pb ...
Michalis Loizos, Konstantinos Rogdakis, Konstantinos Chatzimanolis, Katerina Anagnostou, Emmanuel Kymakis   +4 more
wiley   +1 more source

PdNeuRAM: forming-free, multi-bit Pd/HfO₂ ReRAM for energy-efficient neuromorphic computing. [PDF]

Commun Eng
Hua E, Spyrou T, Ahmadi M, Syed AM, Xun H, Braic L, Wehbe N, van der Veer E, Elatab N, Gebregiorgis A, Gaydadjiev G, Noheda B, Hamdioui S, Ishihara R, Abunahla H.   +14 more
europepmc   +1 more source

Neuromorphic Computing using Spiking Neural Networks

Neuromorphic computing represents a paradigm shift in computational architecture, drawing inspiration from the biological neural networks of the human brain. This paper presents a comprehensive analysis of neuromorphic computing systems utilizing Spiking
Parth Indulkar, Ganesh Suresh Lagad
core  

Low‐Power Control Of Resistance Switching Transitions in First‐Order Memristors

Advanced Electronic Materials, EarlyView.
Joule losses are a serious concern in modern integrated circuit design. In this regard, minimizing the energy necessary for programming memristors should be handled with care. This manuscript presents an optimal control framework, allowing to derive energy‐efficient programming voltage protocols for resistance switching devices. Following this approach,
Valeriy A. Slipko, Alon Ascoli, Fernando Corinto, Yuriy V. Pershin   +3 more
wiley   +1 more source

Recent Advances in Optoelectronic Synaptic Devices for Neuromorphic Computing. [PDF]

Biomimetics (Basel)
Jang H, Ju S, Lee S, Choi J, Byun U, Min K, Rasheed M, Kim S.   +7 more
europepmc   +1 more source

Monolithic Co‐Integration of Vertical FET and Memristor for 1T1R Cell

Advanced Electronic Materials, EarlyView.
This work demonstrates a vertically integrated one‐transistor–one‐memristor (1T1R) cell by stacking a MoS2 vertical field‐effect transistor (VFET) with a mortise–tenon‐shaped (MTS) memristor. This compact architecture not only exhibits highly uniform resistive switching characteristics but also provides a strategy for constructing densely packed ...
Fubo Jiao, Weiqi Dang, Gong‐Jie Ruan, Jiameng Sun, Xiaoyu Sun, Dehe Kong, Yinzhi Huang, Yuxuan Yuan, Qin Liu, Sicheng Chen, Long Zhao, Cong Wang, Pengfei Wang, Chen Pan, Yajun Fu, Shi‐Jun Liang   +15 more
wiley   +1 more source

Neuromorphic computing for robotic vision: algorithms to hardware advances. [PDF]

Commun Eng
Chowdhury SS, Sharma D, Kosta A, Roy K.
europepmc   +1 more source

Model‐Based Time‐Modulated Write Algorithm for 1R Analog Memristive Crossbar Arrays

Advanced Electronic Materials, EarlyView.
A novel model‐based time‐modulated write algorithm efficiently programs analog 1R memristive crossbar arrays by varying pulse duration at a fixed voltage. By leveraging a physics‐based compact model and a dynamic gain mechanism, this approach overcomes device nonlinearities and parasitic effects.
Richard Schroedter, Ahmet Samil Demirkol, Ioannis Messaris, Christian Bruchatz, Eter Mgeladze, Stefan Slesazeck, Thomas Mikolajick, Ronald Tetzlaff   +7 more
wiley   +1 more source

Neuromorphic computing paradigms enhance robustness through spiking neural networks. [PDF]

Nat Commun
Ding J, Yu Z, Liu JK, Huang T.
europepmc   +1 more source