Generalized boundary condition memristor model. (3rd March 2015)

• Record Type: Journal Article
• Authors: Ascoli, Alon; Corinto, Fernando; Tetzlaff, Ronald
• Is Part Of: International journal of circuit theory and applications. Volume 44:Number 1(2016:Jan.)
• Page Start: 60
• Page End: 84
• Publication Date: 2015-03-03
• Subjects: memristor -- modeling -- activation -- boundary conditions -- neuromorphic circuits -- programmable analog filters; Electric circuit analysis -- Periodicals; 621.319205
• DOI: 10.1002/cta.2063
• Languages: English
• ISSNs: 0098-9886

Abstract

SUMMARY: A number of resistive switching memories exhibit activation‐based dynamical behavior, which makes them suitable for neuromorphic and programmable analog filtering applications. Because the Boundary Condition Memristor (BCM) model accounts for tunable activation thresholds only at the on and off boundary states, it is not quantitatively accurate in the description of these kinds of memristors and in the investigation of their circuit applications. This paper introduces the Generalized Boundary Condition Memristor (GBCM) model, preserving the features of the BCM model while allowing, further, an ad‐hoc tuning of activation‐based dynamics, which enables an appropriate modulation of the conditions under which memristors may operate as storage elements or data processors. A simple circuit implementation of the novel model is presented, and time‐efficient simulations confirming the improvement in modeling accuracy over the BCM model are shown. As a proof‐of‐concept for the suitability of the GBCM model in the exploration of the full potential of memristors in neuromorphic circuits and programmable analog filters, this paper adopts it to model fundamental synaptic rules governing the mechanisms of learning in neural systems and to gain some insight into key issues in the design of a couple of filters. Copyright © 2015 John Wiley & Sons, Ltd. Abstract : This paper introduces and validates a novel boundary condition memristor model suited to the analysis of memristors with activation‐based state dynamics. Applications of these types of memristors in neuromorphic circuits and analogue programmable filters are investigated through model simulations and analytical derivations.