Exploring Area-Dependent Pr0.7Ca0.3MnO3-Based Memristive Devices as Synapses in Spiking and Artificial Neural Networks - Gutsche, Alexander; Siegel, Sebastian; Zhang, Jinchao; Hambsch, Sebastian; Dittmann, Regina (Frontiers in Neuroscience, 2021)
Memristive devices are novel electronic devices, which resistance can be tuned by an external voltage in a non-volatile way. Due to their analogue resistive switching behaviour they are considered to emu-late the behaviour 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 dif-ferent tunnel barriers, namely Al2O3, Ta2O5 and WO2. 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 perfor-mance with respect to the resistance window and the variability is observed for Pr0.7Ca0.3MnO3 (PCMO) with a native Al2O3 tunnel oxide. For all different layer stacks, we demonstrate a spike tim-ing dependent plasticity like behavior of the investigated PCMO cells. Furthermore, we can also tune the resistance in an analogue fashion by repeated switching the device with voltage pulses of the same amplitude and polarity. Both measurements resembling the plasticity of biological synapses. We investigate in detail the impact of different pulse heights and pulse lengths on the shape of the step-wise 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 re-sulting shape of the measured curves and on the learning rate and accuracy of the multilayer perceptron.