Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing.

By Zhongrui Wang, S. Joshi, S. Savel’ev, Hao Jiang, Rivu Midya, Peng Lin, Miao Hu, Ning Ge, John Paul Strachan, Zhiyong Li and 7 more. Nature Materials, 2017

2,057 citations recorded by the source corpus.

Subfields: Cellular and Molecular Neuroscience, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/b049e0d9bccb8e85e03388aa9a6dd6d63208b93a

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

1. J. Joshua Yang
2. Qiangfei Xia
3. John Paul Strachan
4. Zhongrui Wang
5. Huaqiang Wu
6. Mohammed Affan Zidan
7. Hao Jiang
8. Wei D. Lu

In Electrical and Electronic Engineering

1. J. Joshua Yang
2. Qiangfei Xia
3. Keum Do Kim
4. C. S. Hwang
5. John Paul Strachan
6. Zhongrui Wang
7. Huaqiang Wu
8. Mohammed Affan Zidan

Citations in the corpus

• Neuro-Inspired Computing With Emerging Nonvolatile Memorys (2018, neutral): “…various devices, including Ag/Ag 2 S-based CBRAM [56], Cu/Cu 2 S-based CBRAM [57], Ag/GeS 2 -based CBRAM [58], Ag/Ge 30 Se 70 -based CBRAM [59], Ag/SiO x N y -based CBRAM [60], TiO x -based OxRAM [61], HfO x -based OxRAM [55], [62], [63], WO x -based OxRAM [64], [65], TaO x -based OxRAM [66], etc.”
• Fully memristive neural networks for pattern classification with unsupervised learning (2018, methodology): “The relaxation dynamics also leads to the leakiness of the internal Ag dynamics, which gradually dissolves the Ag conducting channel(s) driven by the minimization of interfacial energy between Ag and dielectrics, or Thomson–Gibbs effect 33 .”
• An artificial nociceptor based on a diffusive memristor (2018, methodology): “The response of the device to the quasi-DC voltage sweeps represents the typical threshold switching behavior as previously reported 55,56 .”