Synaptic behaviors and modeling of a metal oxide memristive device

By Ting Chang, S. Jo, Kuk-Hwan Kim, Patrick Sheridan, Siddharth Gaba, W. Lu. 2011

412 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Memristor-based neural networks (2013, methodology): “A comparable experiment was conducted by Chang et al [87].”
Sparse coding with memristor networks. (2017, methodology): “The devices were fabricated using electron-beam lithography following previously developed procedures 29 ; a completed array is shown in Fig.”
State Dynamics and Modeling of Tantalum Oxide Memristors (2013, neutral): “We can roughly divide these into models using resistance switching elements in series ( [14], [22], [24]) or parallel ( [19] and the present paper).”
Memristor-Based Multilayer Neural Networks With Online Gradient Descent Training (2015, positive): “For example, the values of the memristive timescales range from picoseconds [74] to milliseconds [55].”
High precision tuning of state for memristive devices by adaptable variation-tolerant algorithm (2011, background): “In some applications such variations can be tolerated by the structure of the circuits, such as the case with some versions of artificial neural networks in which memristive devices are implementing synapses [Lik11, Sni07, Yu11b, Seo11, Cha11, Jo10a].”