Journal Article

FZJ-2020-01019

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Analyses of a 1-layer neuromorphic network using memristive devices with non-continuous resistance levels

Siemon, A. (Corresponding author)FZJ* ; Ferch, S. ; Heittmann, A.FZJ* ; Waser, R.FZJ* ; Wouters, D. J. ; Menzel, S.FZJ*

2019
AIP Publ. Melville, NY

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Please use a persistent id in citations: http://hdl.handle.net/2128/24330  doi:10.1063/1.5108658

Abstract: The emerging nonvolatile memory technology of redox-based resistive switching (RS) devices is not only a promising candidate for future high density memories but also for computational and neuromorphic applications. In neuromorphic as well as in memory applications, RS devices are configured in nanocrossbar arrays, which are controlled by CMOS circuits. With those hybrid systems, brain-inspired artificial neural networks can be built up and trained by using a learning algorithm. First works on hardware implementation using relatively large and high current level RS devices are already published. In this work, the influence of small and low current level devices showing noncontinuous resistance levels on neuromorphic networks is studied. To this end, a well-established physical-based Verilog A model is modified to offer continuous and discrete conduction. With this model, a simple one-layer neuromorphic network is simulated to get a first insight and understanding of this problem using a backpropagation algorithm based on the steepest descent method

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Contributing Institute(s):

1. Elektronische Materialien (PGI-7)
2. JARA-FIT (JARA-FIT)
3. JARA Institut Green IT (PGI-10)

Research Program(s):

1. 521 - Controlling Electron Charge-Based Phenomena (POF3-521) (POF3-521)

Appears in the scientific report 2019

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Database coverage:
; ; ; ; Clarivate Analytics Master Journal List ; Current Contents - Engineering, Computing and Technology ; Current Contents - Physical, Chemical and Earth Sciences ; DOAJ Seal ; IF < 5 ; JCR ; SCOPUS ; Science Citation Index Expanded ; Web of Science Core Collection

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