Journal Article

FZJ-2021-03046

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Exploring Area-Dependent Pr0.7Ca0.3MnO3-Based Memristive Devices as Synapses in Spiking and Artificial Neural Networks

Gutsche, A. (Corresponding author)FZJ* ; Siegel, S.FZJ* ; Zhang, J. ; Hambsch, S.FZJ* ; Dittmann, R.FZJ*

2021
Frontiers Research Foundation Lausanne

This record in other databases:      

Please use a persistent id in citations: http://hdl.handle.net/2128/28304  doi:10.3389/fnins.2021.661261

Abstract: Memristive devices are novel electronic devices, which resistance can be tuned by an external voltage in a non-volatile way. Due to their analog resistive switching behavior, they are considered to emulate the behavior of synapses in neuronal networks. In this work, we investigate memristive devices based on the field-driven redox process between the p-conducting Pr0.7Ca0.3MnO3 (PCMO) and different tunnel barriers, namely, Al2O3, Ta2O5, and WO3. In contrast to the more common filamentary-type switching devices, the resistance range of these area-dependent switching devices can be adapted to the requirements of the surrounding circuit. We investigate the impact of the tunnel barrier layer on the switching performance including area scaling of the current and variability. Best performance with respect to the resistance window and the variability is observed for PCMO with a native Al2O3 tunnel oxide. For all different layer stacks, we demonstrate a spike timing dependent plasticity like behavior of the investigated PCMO cells. Furthermore, we can also tune the resistance in an analog fashion by repeated switching the device with voltage pulses of the same amplitude and polarity. Both measurements resemble the plasticity of biological synapses. We investigate in detail the impact of different pulse heights and pulse lengths on the shape of the stepwise SET and RESET curves. We use these measurements as input for the simulation of training and inference in a multilayer perceptron for pattern recognition, to show the use of PCMO-based ReRAM devices as weights in artificial neural networks which are trained by gradient descent methods. Based on this, we identify certain trends for the impact of the applied voltages and pulse length on the resulting shape of the measured curves and on the learning rate and accuracy of the multilayer perceptron.

---

Contributing Institute(s):

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

Research Program(s):

1. 5233 - Memristive Materials and Devices (POF4-523) (POF4-523)
2. BMBF-16ME0398K - Verbundprojekt: Neuro-inspirierte Technologien der künstlichen Intelligenz für die Elektronik der Zukunft - NEUROTEC II - (BMBF-16ME0398K) (BMBF-16ME0398K)
3. ACA - Advanced Computing Architectures (SO-092) (SO-092)
4. DFG project 167917811 - SFB 917: Resistiv schaltende Chalkogenide für zukünftige Elektronikanwendungen: Struktur, Kinetik und Bauelementskalierung "Nanoswitches" (167917811) (167917811)

Appears in the scientific report 2021

---

Database coverage:
; ; ; ; Article Processing Charges ; BIOSIS Previews ; Biological Abstracts ; Clarivate Analytics Master Journal List ; Current Contents - Clinical Medicine ; DOAJ Seal ; Essential Science Indicators ; Fees ; IF < 5 ; JCR ; PubMed Central ; SCOPUS ; Science Citation Index Expanded ; Web of Science Core Collection

---