Journal Article

FZJ-2021-00315

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png HRS Instability in Oxide-Based Bipolar Resistive Switching Cells

Wiefels, S. (Corresponding author)FZJ* ; Bengel, C. ; Kopperberg, N. ; Zhang, K. ; Waser, R.FZJ* ; Menzel, S.FZJ*

2020
IEEE New York, NY

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Please use a persistent id in citations: doi:10.1109/TED.2020.3018096

Abstract: One of the key challenges in the reliability of valence change [valence change-based memory (VCM)] resistive switching random access memories (ReRAMs) is the short-term instability of the programed state. Due to read noise, program verify or shaping algorithms are ineffective and read current (or resistance) distributions always revert to their intrinsic statistics. In this work, we analyze the instability of the high resistive state (HRS) measured on ZrO 2 -based devices via Factorial Hidden Markov Models. The extracted current jumps are explained by distinct ionic jumps via physics-based kinetic Monte Carlo (KMC) models. The simulation results reveal jumps of oxygen vacancies from the densely packed filament (plug) region to a sparsely packed gap (disc) region as origin of the most critical, large current jumps. These findings are used to extend our compact model (JART v1b) by a read noise module. We demonstrate simulated HRS instability in excellent agreement with our experimental data. Whereas the KMC approach provides a physical understanding of the processes underlying the HRS instability, the compact model enables the simulation of read noise up to industrially relevant array scales.

Note: Kein post-print verfügbar

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Contributing Institute(s):

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

Research Program(s):

1. 521 - Controlling Electron Charge-Based Phenomena (POF3-521) (POF3-521)
2. Modelling the Valency Change Memory Effect in Resistive Switching Random Access Memory (RRAM). (jpgi70_20200501) (jpgi70_20200501)
3. BMBF-16ES1134 - Verbundprojekt: Neuro-inspirierte Technologien der künstlichen Intelligenz für die Elektronik der Zukunft - NEUROTEC - (BMBF-16ES1134) (BMBF-16ES1134)
4. Verbundprojekt: Neuro-inspirierte Technologien der künstlichen Intelligenz für die Elektronik der Zukunft - NEUROTEC -, Teilvorhaben: Forschungszentrum Jülich (16ES1133K) (16ES1133K)
5. Advanced Computing Architectures (aca_20190115) (aca_20190115)

Appears in the scientific report 2020

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Database coverage:
; Clarivate Analytics Master Journal List ; Current Contents - Electronics and Telecommunications Collection ; Current Contents - Engineering, Computing and Technology ; Ebsco Academic Search ; Essential Science Indicators ; IF < 5 ; JCR ; SCOPUS ; Science Citation Index Expanded ; Web of Science Core Collection

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