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Programmable Fading Memory in Atomic Switch Systems for Error Checking Applications

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Reservoir Computing

Part of the book series: Natural Computing Series ((NCS))

Abstract

Disruptive technology in computational devices is required as the universal computing machines approach quantum mechanical limits. Integration of state-of-the-art memristive devices provides optimal scaling of current technologies beyond this limit through the adoption of neuromorphic models. Universal computing machines pioneered by Alan Turing are strictly based on top-down intelligent design. Neuromorphic models instead engage in bottom-up programmability by emulating mammalian brain design and characteristics. Here we show the design, characterization, and implementation of a massively parallel memristor neuromorphic network based on metal chalcogenide atomic switch network (ASN) systems with key characteristics such as short- and long-term potentiation, power-law dynamics, and scale-free topology.

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Authors and Affiliations

  1. Department of Chemistry and Biochemistry, University of California, Los Angeles 607 Charles E. Young Drive East, Los Angeles, CA, 90095, USA

    Renato Aguilera, Henry O. Sillin & James K. Gimzewski

  2. California NanoSystems Institute, 570 Westwood Plaza, Los Angeles, CA, 90095, USA

    Adam Z. Stieg & James K. Gimzewski

  3. National Institute for Materials Science (NIMS), WPI Center for Materials Nanoarchitectonics (MANA), 1-1 Namiki, Tsukuba, Ibaraki, 305-0044, Japan

    Adam Z. Stieg & James K. Gimzewski

Authors
  1. Renato Aguilera
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  2. Henry O. Sillin
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  3. Adam Z. Stieg
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  4. James K. Gimzewski
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Corresponding author

Correspondence to James K. Gimzewski .

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Editors and Affiliations

  1. University of Tokyo, Tokyo, Japan

    Kohei Nakajima

  2. Consejo Superior de Investigaciones Científicas, IFISC (UIB-CSIC), Palma, Spain

    Ingo Fischer

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Aguilera, R., Sillin, H.O., Stieg, A.Z., Gimzewski, J.K. (2021). Programmable Fading Memory in Atomic Switch Systems for Error Checking Applications. In: Nakajima, K., Fischer, I. (eds) Reservoir Computing. Natural Computing Series. Springer, Singapore. https://doi.org/10.1007/978-981-13-1687-6_12

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  • DOI: https://doi.org/10.1007/978-981-13-1687-6_12

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  • Online ISBN: 978-981-13-1687-6

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