Analytical DC model of a TaO memristor

Publikation: Beitrag in Buch/Konferenzbericht/Sammelband/GutachtenBeitrag in KonferenzbandBeigetragenBegutachtung

Beitragende

Abstract

Circuit designers are used to employ analytical formulas and numerically stable expressions for the input-output behaviour of electronic components in preliminary calculations intended to select the most suitable circuit topology to meet prescribed design specifications. Manufactured memristors are highly-nonlinear dynamical circuit elements for new future electronics. However the Differential Algebraic Equation sets, used to capture accurately their nonlinear dynamics, typically consist of involved mathematical expressions, which prevent their analytical integration and the derivation of input-output formulas for circuit design. Adopting certain mathematical techniques, we were recently able to derive for the first time, formulas for the DC behaviour of a real-world memristor exhibiting both non-volatility and fading memory. Particularly, on the basis of an accurate mathematical model, this paper presents a set of analytical expressions for the memory state response of a tantalum oxide resistance switching memory, fabricated at the Palo Alto facilities of Hewlett Packard Labs, to any DC stimulus and for all initial conditions.

Details

OriginalspracheEnglisch
TitelANNA 2018 - Advances in Neural Networks and Applications 2018
Redakteure/-innenValeri Mladenov, Angela Slavova, Vassil Sgurev, Mincho Hadjiski, Kosta Boshnakov
Herausgeber (Verlag)VDE Verlag, Berlin [u. a.]
Seiten8-12
Seitenumfang5
ISBN (elektronisch)9783800747566
PublikationsstatusVeröffentlicht - 2018
Peer-Review-StatusJa

Konferenz

TitelAdvances in Neural Networks and Applications 2018, ANNA 2018
Dauer15 - 17 September 2018
StadtSt. Konstantin and Elena
LandBulgarien

Externe IDs

ORCID /0000-0001-7436-0103/work/142240230
ORCID /0000-0002-2367-5567/work/168720236

Schlagworte

Schlagwörter

  • Fading Memory, Memristor, Non-Volatility