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Handbook of Memristor Networks pp 1005–1020Cite as

Memristive In Situ Computing

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Abstract

The missing link between a nonlinear circuit element that is able to self-adjust its conductance according to the history of applied voltage/current and physical realizations of two-terminal oxide-based resistive memory was discovered in early 2008, and has since then been intensively studied. This class of memory devices is called memristive devices, which includes resistive random access memories (RRAM), phase change memories (PCM) and spin-transfer torque magnetoresistive memories (STT-MRAM). Memristive devices are mostly CMOS and fab friendly, and promise simpler architecture, high scalability and stackability (3D), good selectivity, relatively, low-power consumption, high endurance and retention, and fast operation by utilizing parallelism, and the most important of all, the ability to merge logic and memory. A significantly wide range of material systems show that resistive switching can be categorized under three main redox-related effects, electrochemical metalization effects (ECM), valency change memory effect (VCM) and thermochemical memory effects (TCM). Although, the behavior of these resistive memories can be modeled using high-level finite-state machines (FSMs), the underlying switching mechanisms is yet to be fully understood. Despite this shortage, their application in memory and computing has been constantly improved. These devices can be programmed to exhibit multi-level cell (MLC) and binary cell behavior, thus analog and digital memories can be exists in one device depends on programming. In this chapter, we highlight some of the in situ computational capability of memristive devices.

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Acknowledgements

This work was supported by an Early Career Researcher grant from the Melbourne School of Engineering, University of Melbourne.

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Authors and Affiliations

  1. School of Electrical and Information Engineering, The University of Sydney, Sydney, Australia

    Omid Kavehei & Efstratios Skafidas

  2. Victoria Research Laboratory, National ICT Australia (NICTA), Melbourne, VIC, 3010, Australia

    Efstratios Skafidas

  3. iDataMap Corporation, Eastwood, Australia

    Kamran Eshraghian

Authors
  1. Omid Kavehei
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  2. Efstratios Skafidas
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  3. Kamran Eshraghian
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Correspondence to Omid Kavehei .

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Editors and Affiliations

  1. Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA, USA

    Leon Chua

  2. Democritus University of Thrace, Xanthi, Greece

    Georgios Ch. Sirakoulis

  3. Department of Computer Science and Creative Technologies, University of the West of England, Bristol, UK

    Andrew Adamatzky

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Kavehei, O., Skafidas, E., Eshraghian, K. (2019). Memristive In Situ Computing. In: Chua, L., Sirakoulis, G., Adamatzky, A. (eds) Handbook of Memristor Networks. Springer, Cham. https://doi.org/10.1007/978-3-319-76375-0_35

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  • DOI: https://doi.org/10.1007/978-3-319-76375-0_35

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