Gutsche Alexander, Siegel Sebastian, Zhang Jinchao, Hambsch Sebastian, Dittmann Regina
Peter Grünberg Institut (PGI-7/10), Forschungszentrum Jülich GmbH & JARA-FIT, Jülich, Germany.
Front Neurosci. 2021 Jul 2;15:661261. doi: 10.3389/fnins.2021.661261. eCollection 2021.
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 PrCaMnO (PCMO) and different tunnel barriers, namely, AlO, TaO, and WO. 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 AlO 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.
忆阻器件是新型电子器件，其电阻可通过外部电压以非易失性方式进行调节。由于其模拟电阻开关行为，它们被认为可模拟神经网络中突触的行为。在这项工作中，我们研究了基于p型导电PrCaMnO（PCMO）与不同隧道势垒（即AlO、TaO和WO）之间场驱动氧化还原过程的忆阻器件。与更常见的丝状开关器件不同，这些面积依赖型开关器件的电阻范围可适应周围电路的要求。我们研究了隧道势垒层对开关性能的影响，包括电流的面积缩放和变异性。对于具有原生AlO隧道氧化物的PCMO，在电阻窗口和变异性方面观察到最佳性能。对于所有不同的层堆叠，我们展示了所研究的PCMO细胞具有类似脉冲时间依赖可塑性的行为。此外，我们还可以通过用相同幅度和极性的电压脉冲重复切换器件，以模拟方式调节电阻。这两种测量都类似于生物突触的可塑性。我们详细研究了不同脉冲高度和脉冲长度对逐步SET和RESET曲线形状的影响。我们将这些测量结果用作多层感知器中用于模式识别的训练和推理模拟的输入，以展示基于PCMO的ReRAM器件在通过梯度下降方法训练的人工神经网络中作为权重的用途。基于此，我们确定了施加电压和脉冲长度对测量曲线的最终形状以及对多层感知器的学习率和准确性的影响的某些趋势。
