Minnekhanov Anton, Matsukatova Anna, Trofimov Andrey, Nesmelov Alexander, Zavyalov Sergey, Demin Vyacheslav, Emelyanov Andrey
National Research Centre Kurchatov Institute, Moscow 123182, Russia.
Lomonosov Moscow State University, Moscow 119991, Russia.
ACS Appl Mater Interfaces. 2023 Nov 29;15(47):54996-55008. doi: 10.1021/acsami.3c13956. Epub 2023 Nov 14.
Memristive devices, known for their nonvolatile resistive switching, are promising components for next-generation neuromorphic computing systems, which mimic the brain's neural architecture. Specifically, these devices are well-suited for functioning as artificial synapses due to their analogue tunability and low energy consumption. However, the improvement of their performance and reliability remains a pressing challenge. In this study, we report the development and comprehensive characterization of memristive devices based on a parylene-MoO (PPX-Mo) nanocomposite layer, which exhibit improved characteristics over their parylene-based counterparts: lower switching voltage and energy, smaller dispersion, and better resistive plasticity. A robust statistical analysis identified the optimal synthesis parameters for these devices, providing valuable insights for future device optimization. The most probable resistive switching mechanism of the devices is proposed. By successfully integrating these memristors into a neuromorphic computing model and showcasing their scalability in crossbar geometry, we demonstrate their potential as functional artificial synapses. The results obtained from this study can be useful for the development of hardware-brain-inspired computational systems.
忆阻器件以其非易失性电阻开关特性而闻名，是下一代神经形态计算系统中很有前景的组件，该系统模仿大脑的神经架构。具体而言，由于其模拟可调性和低能耗，这些器件非常适合用作人工突触。然而，提高其性能和可靠性仍然是一个紧迫的挑战。在本研究中，我们报告了基于聚对二甲苯 - 氧化钼（PPX - Mo）纳米复合层的忆阻器件的开发和全面表征，与基于聚对二甲苯的同类器件相比，这些器件具有改进的特性：更低的开关电压和能量、更小的分散性以及更好的电阻可塑性。一项稳健的统计分析确定了这些器件的最佳合成参数，为未来的器件优化提供了有价值的见解。提出了这些器件最可能的电阻开关机制。通过成功地将这些忆阻器集成到神经形态计算模型中，并展示其在交叉阵列几何结构中的可扩展性，我们证明了它们作为功能性人工突触的潜力。本研究获得的结果可用于开发受大脑启发的硬件计算系统。
