Memristive devices for computing.
By J. Joshua Yang, Dmitri B. Strukov, Duncan R. Stewart. Nature Nanotechnology, 2013
3,508 citations recorded by the source corpus.
Subfields: Cellular and Molecular Neuroscience, Electrical and Electronic Engineering
View Paper: https://www.semanticscholar.org/paper/59f65b20279e6153f6caa83c07ada43fac643d34
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Cellular and Molecular Neuroscience
- Shimeng Yu
- Daniele Ielmini
- John Paul Strachan
- Shahar Kvatinsky
- Mohammed Affan Zidan
- H. Philip Wong
- Avinoam Kolodny
- J. Joshua Yang
In Electrical and Electronic Engineering
- Shimeng Yu
- Daniele Ielmini
- H. Philip Wong
- John Paul Strachan
- J. Joshua Yang
- Shahar Kvatinsky
- Mohammed Affan Zidan
- Qiangfei Xia
Citations in the corpus
- Stochastic Memristive Devices for Computing and Neuromorphic Applications (2013, background): “oxygen vacancy in oxides) (6) based memristive systems as well.”
- Organic electronics for neuromorphic computing (2018, methodology): “Conventional inorganic memristive devices have reported writing energies of the order of 0,1–1,000 pJ (refs 13,21 ) with typical device dimensions of 0.1–5 μ m, depending on the specific mechanisms and materials.”
- Efficient and self-adaptive in-situ learning in multilayer memristor neural networks (2018, methodology): “One promising approach is in-memory analog computation based on memristor crossbars 10 – 18 , for which simulations have indicated potentially signi fi cant speed and power advantages over digital complementary metal-oxide-semiconductor (CMOS) 19 – 23 .”
- Towards Oxide Electronics: a Roadmap (2019, background): “Another degree of freedom is the ferroelectric domain structure of the tunnel barrier that can be exploited for analog devices such as memristors [322].”
- Memristor crossbar arrays with 6-nm half-pitch and 2-nm critical dimension (2018, background): “Accordingly, we observed a low switching current corresponding to a current density comparable to normal resistance switching at larger sizes 11,35 , i.e., 1.15 × 10 6 A/cm 2 , in contrast to the four orders of magnitude higher current density reported in previous sub-10 nm devices 13 .”
- Pattern classification by memristive crossbar circuits using ex situ and in situ training (2013, methodology): “In this context, memristors 2–8 are very promising candidates owing to their analogue memory functionality, which is similar to that of biological synapses 8,9 .”