Results 121 to 130 of about 716 (180)

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

In-Sensor-Memory Computing for Post-Von Neumann Intelligence: A Perspective. [PDF]

Nanomicro Lett
Tang H, Yu N, Min P, Guo R, Zhang G.
europepmc   +1 more source

Automating multi-task learning on optical neural networks with weight sharing and physical rotation. [PDF]

Sci Rep
Zhou S, Li Y, Gao W, Yu C, Ding C.
europepmc   +1 more source

Fast and robust analog in-memory deep neural network training. [PDF]

Nat Commun
Rasch MJ, Carta F, Fagbohungbe O, Gokmen T.   +3 more
europepmc   +1 more source

Indium Tin Oxide Nanowires: Growth Methods, Applications and Perspectives. [PDF]

Nanomaterials (Basel)
Li Q.
europepmc   +1 more source

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ReRAM: History, Status, and Future

IEEE Transactions on Electron Devices, 2020
This article reviews the resistive random-access memory (ReRAM) technology initialization back in the 1960s and its heavily focused research and development from the early 2000s. This review goes through various oxygen/oxygen vacancy and metal-ion-based ReRAM devices and their operation mechanisms. This review also benchmarks the performance of various
Yangyin Chen
exaly   +2 more sources

Automatic ReRAM SPICE Model Generation From Empirical Data for Fast ReRAM-Circuit Coevaluation

IEEE Transactions on Very Large Scale Integration (VLSI) Systems, 2017
This paper presents an automatic resistive random access memory (ReRAM) SPICE model generator, which enables fast ReRAM circuit evaluation with standard SPICE. Our model generator automatically produces SPICE models of ReRAM devices and selectors from the measured ${I}$ – ${V}$ data to reduce too much time consumption in manual model ...
Kwangmin Kim, Sangheon Lee, Byungsub Kim   +2 more
exaly   +3 more sources

Modeling and design optimization of ReRAM

The 20th Asia and South Pacific Design Automation Conference, 2015
Resistive switching memories (ReRAM) have been widely studied for applications in next-generation data storage and neurormorphic computing systems. To enable device-circuit-system co-design and optimization, a SPICE model of ReRAM that can reproduce the device characteristics in circuit simulations is needed.
Jinfeng Kang, Haitong Li, Peng Huang 0004, Zhe Chen, Bin Gao 0006, Xiaoyan Liu, Zizhen Jiang, H.-S. Philip Wong   +7 more
openaire   +1 more source