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Journal of Electroceramics

, Volume 39, Issue 1–4, pp 4–20 | Cite as

Memristive computing devices and applications

  • Mohammed A. Zidan
  • An Chen
  • Giacomo Indiveri
  • Wei D. LuEmail author
Article

Abstract

Advances in electronics have revolutionized the way people work, play and communicate with each other. Historically, these advances were mainly driven by CMOS transistor scaling following Moore’s law, where new generations of devices are smaller, faster, and cheaper, leading to more powerful circuits and systems. However, conventional scaling is now facing major technical challenges and fundamental limits. New materials, devices, and architectures are being aggressively pursued to meet present and future computing needs, where tight integration of memory and logic, and parallel processing are highly desired. To this end, one class of emerging devices, termed memristors or memristive devices, have attracted broad interest as a promising candidate for future memory and computing applications. Besides tremendous appeal in data storage applications, memristors offer the potential to enable efficient hardware realization of neuromorphic and analog computing architectures that differ radically from conventional von Neumann computing architectures. In this review, we analyze representative memristor devices and their applications including mixed signal analog-digital neuromorphic computing architectures, and highlight the potential and challenges of applying such devices and architectures in different computing applications.

Keywords

Resistive memory Neuromorphic computing Nanoionics Filament Memristor 

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© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  1. 1.Department of Electrical Engineering & Computer ScienceUniversity of MichiganAnn ArborUSA
  2. 2.Nanoelectronics Research Initiative (NRI)Semiconductor Device CorporationDurhamUSA
  3. 3.Institute of NeuroinformaticsUniversity of Zurich and ETH ZurichZurichSwitzerland

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