A Memristor Device Model

By C. Yakopcic, Tarek M. Taha, G. Subramanyam, R. Pino, S. Rogers. IEEE Electron Device Letters, 2011

297 citations recorded by the source corpus.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/6564ea2403368d83ea933442f7de0853a7afc6b4

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. Shahar Kvatinsky
2. Avinoam Kolodny
3. Eby G. Friedman
4. Uri C. Weiser
5. Saransh Gupta
6. Pravin S. Mane
7. Nishil Talati
8. Guy Satat

In Cognitive Neuroscience

1. Shahar Kvatinsky
2. Avinoam Kolodny
3. Eby G. Friedman
4. Uri C. Weiser
5. Guy Satat
6. Nimrod Wald

In Electrical and Electronic Engineering

1. Shahar Kvatinsky
2. Avinoam Kolodny
3. Uri C. Weiser
4. Eby G. Friedman
5. Saransh Gupta
6. Pravin S. Mane
7. Nishil Talati
8. Guy Satat

Citations in the corpus

• Logic Design Within Memristive Memories Using Memristor-Aided loGIC (MAGIC) (2016, result): “5% relative root mean square error when compared with the experimental results of resistance switching of memristive materials such as Pt-Hf-Ti based memristor [39], Ferroelectric memristor [40], and metallic nanowire memristor [41].”
• TEAM: ThrEshold Adaptive Memristor Model (2013, background): “Another simplified model has recently been proposed, assuming voltage threshold and an implicit memristance [25].”