Dynamics of a Memristive Bridge with Valence Change Mechanism (VCM) Devices

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Biological synapses behave as dynamically-rich nonlinear elements, participating in complicated computing tasks through their adaptation due to external stimuli. Such adaptivity constitutes an intrinsic property of non-volatile memristor devices, which are also able to maintain their internal state, under zero input, enabling novel bio-inspired learning operations. In this work, a synaptic element based on a memristive bridge, containing two resistors and two memristors, is studied, aiming to investigate complex memristor-based topologies that may result in rich synaptic dynamics. The proposed memristive bridge allows the realization of both positive and negative synaptic weights, while an asymmetric tuning of a weight, stemming from memristor's features and bridge topology, is demonstrated. In particular, by properly selecting the memristor's position and polarity within the bridge, different tuning behaviors have been observed, showcasing versatile learning properties of the topology. Along with the synaptic weight tuning, the read overall process of the synaptic weight, necessary for inference operations, is also investigated. We explore the dynamics of the bridge via numerical simulations.


Original languageEnglish
Title of host publicationISCAS 2023 - 56th IEEE International Symposium on Circuits and Systems, Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (electronic)9781665451093
Publication statusPublished - 2023

Publication series

SeriesProceedings - IEEE International Symposium on Circuits and Systems


TitleIEEE International Symposium on Circuits and Systems 2023
Abbreviated titleISCAS 2023
Conference number56
Duration21 - 25 May 2023
Degree of recognitionInternational event
LocationMonterey Conference Center
CountryUnited States of America

External IDs

ORCID /0000-0002-1236-1300/work/142239554
ORCID /0000-0001-7436-0103/work/142240395
ORCID /0000-0002-6200-4707/work/145698432


ASJC Scopus subject areas


  • memristive synaptic bridge, memristors, memritive devices