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000894113 1001_ $$0P:(DE-Juel1)173699$$aGutsche, Alexander$$b0$$eCorresponding author
000894113 245__ $$aExploring Area-Dependent Pr0.7Ca0.3MnO3-Based Memristive Devices as Synapses in Spiking and Artificial Neural Networks
000894113 260__ $$aLausanne$$bFrontiers Research Foundation$$c2021
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000894113 520__ $$aMemristive devices are novel electronic devices, which resistance can be tuned by an external voltage in a non-volatile way. Due to their analog resistive switching behavior, they are considered to emulate the behavior of synapses in neuronal networks. In this work, we investigate memristive devices based on the field-driven redox process between the p-conducting Pr0.7Ca0.3MnO3 (PCMO) and different tunnel barriers, namely, Al2O3, Ta2O5, and WO3. In contrast to the more common filamentary-type switching devices, the resistance range of these area-dependent switching devices can be adapted to the requirements of the surrounding circuit. We investigate the impact of the tunnel barrier layer on the switching performance including area scaling of the current and variability. Best performance with respect to the resistance window and the variability is observed for PCMO with a native Al2O3 tunnel oxide. For all different layer stacks, we demonstrate a spike timing dependent plasticity like behavior of the investigated PCMO cells. Furthermore, we can also tune the resistance in an analog fashion by repeated switching the device with voltage pulses of the same amplitude and polarity. Both measurements resemble the plasticity of biological synapses. We investigate in detail the impact of different pulse heights and pulse lengths on the shape of the stepwise SET and RESET curves. We use these measurements as input for the simulation of training and inference in a multilayer perceptron for pattern recognition, to show the use of PCMO-based ReRAM devices as weights in artificial neural networks which are trained by gradient descent methods. Based on this, we identify certain trends for the impact of the applied voltages and pulse length on the resulting shape of the measured curves and on the learning rate and accuracy of the multilayer perceptron.
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000894113 536__ $$0G:(DE-82)BMBF-16ME0398K$$aBMBF-16ME0398K - Verbundprojekt: Neuro-inspirierte Technologien der künstlichen Intelligenz für die Elektronik der Zukunft - NEUROTEC II - (BMBF-16ME0398K)$$cBMBF-16ME0398K$$x1
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000894113 536__ $$0G:(GEPRIS)167917811$$aDFG project 167917811 - SFB 917: Resistiv schaltende Chalkogenide für zukünftige Elektronikanwendungen: Struktur, Kinetik und Bauelementskalierung "Nanoswitches" (167917811)$$c167917811$$x3
000894113 588__ $$aDataset connected to CrossRef, Journals: juser.fz-juelich.de
000894113 7001_ $$0P:(DE-Juel1)174486$$aSiegel, Sebastian$$b1
000894113 7001_ $$0P:(DE-Juel1)179019$$aZhang, Jinchao$$b2
000894113 7001_ $$0P:(DE-Juel1)174430$$aHambsch, Sebastian$$b3
000894113 7001_ $$0P:(DE-Juel1)130620$$aDittmann, Regina$$b4
000894113 773__ $$0PERI:(DE-600)2411902-7$$a10.3389/fnins.2021.661261$$gVol. 15, p. 661261$$p661261$$tFrontiers in neuroscience$$v15$$x1662-453X$$y2021
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