Memristors

Properties, models, materials

Olga Krestinskaya, Aidana Irmanova, Alex James Pappachen

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

The practical realization of neuro-memristive systems requires highly accurate simulation models, robust devices and validations on device characteristics. This chapter covers the basics of memristor characteristics, models and a succinct review of practically realized memristive devices. Memristors represent a class of two terminal resistive switching multi-state memory devices that can be compatible with existing integrated circuit technologies. The modeling of memristors for very large scale simulations requires to accurately capture process variations and other non-idealities from real devices for ensuring the validity of deep neural network architecture designs with memristors.

Original languageEnglish
Title of host publicationModeling and Optimization in Science and Technologies
PublisherSpringer Verlag
Pages13-40
Number of pages28
DOIs
Publication statusPublished - Jan 1 2020

Publication series

NameModeling and Optimization in Science and Technologies
Volume14
ISSN (Print)2196-7326
ISSN (Electronic)2196-7334

Fingerprint

Memristors
Equipment and Supplies
Network architecture
Process Variation
Model
Multi-state
Integrated circuits
Network Architecture
Integrated Circuits
Data storage equipment
Simulation Model
Cover
Neural Networks
Technology
Modeling
Simulation

ASJC Scopus subject areas

  • Modelling and Simulation
  • Medical Assisting and Transcription
  • Applied Mathematics

Cite this

Krestinskaya, O., Irmanova, A., & James Pappachen, A. (2020). Memristors: Properties, models, materials. In Modeling and Optimization in Science and Technologies (pp. 13-40). (Modeling and Optimization in Science and Technologies; Vol. 14). Springer Verlag. https://doi.org/10.1007/978-3-030-14524-8_2

Memristors : Properties, models, materials. / Krestinskaya, Olga; Irmanova, Aidana; James Pappachen, Alex.

Modeling and Optimization in Science and Technologies. Springer Verlag, 2020. p. 13-40 (Modeling and Optimization in Science and Technologies; Vol. 14).

Research output: Chapter in Book/Report/Conference proceedingChapter

Krestinskaya, O, Irmanova, A & James Pappachen, A 2020, Memristors: Properties, models, materials. in Modeling and Optimization in Science and Technologies. Modeling and Optimization in Science and Technologies, vol. 14, Springer Verlag, pp. 13-40. https://doi.org/10.1007/978-3-030-14524-8_2
Krestinskaya O, Irmanova A, James Pappachen A. Memristors: Properties, models, materials. In Modeling and Optimization in Science and Technologies. Springer Verlag. 2020. p. 13-40. (Modeling and Optimization in Science and Technologies). https://doi.org/10.1007/978-3-030-14524-8_2
Krestinskaya, Olga ; Irmanova, Aidana ; James Pappachen, Alex. / Memristors : Properties, models, materials. Modeling and Optimization in Science and Technologies. Springer Verlag, 2020. pp. 13-40 (Modeling and Optimization in Science and Technologies).
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