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.
Highlights
Most modern computer architectures are based on the von Neumann principle, which separates the data processing unit from the data storage
The use in artificial neural networks (ANNs) was demonstrated on many network types such as single-layer perceptrons (Alibart et al, 2013; Prezioso et al, 2015) as well as multilayer perceptrons (Moon et al, 2015; Burr et al, 2017; Babu et al, 2018; Go et al, 2019; Wu et al, 2020) and convolutional neural networks (CNNs) (Yakopcic et al, 2017)
We propose an update rule for a specific memristive device based on Pr0.7Ca0.3MnO3 (PCMO) after a thorough investigation of its switching behavior and the influence of different material stacks
Summary
Most modern computer architectures are based on the von Neumann principle, which separates the data processing unit from the data storage. As the performance of processors increased strongly over the last decades, the bandwidth for the communication between processor and data storage became the limiting factor for the overall computational performance This is called the von Neumann bottleneck (Backus, 1978) (Wolf and McKee, 1994). Neuronal Networks With PCMO Devices or vector-matrix multiplications in artificial neural networks (ANNs) during the inference step. More complex tasks like face recognition have been demonstrated (Yao et al, 2020) These similar network performances are often achieved at higher-energy efficiencies and make memristive device-based ANNs most useful for low-energy applications at the edge and in the IoT sector (Chowdhury et al, 2018) (Krestinskaya et al, 2020). We propose an update rule for a specific memristive device based on Pr0.7Ca0.3MnO3 (PCMO) after a thorough investigation of its switching behavior and the influence of different material stacks
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