可靠的记忆突触基于帕利-MoO纳米复合材料用于神经形态应用
Anton Minnekhanov1, Anna Matsukatova1,2, Andrey Trofimov1,3
1National Research Centre Kurchatov Institute, Moscow 123182, Russia.
ACS applied materials & interfaces
|November 14, 2023
概括
使用烯-氧化物 (PPX-Mo) 纳米复合材料的新型记忆设备为神经形态计算中的人工突触提供了增强的性能. 这些memristors显示了大脑启发系统的可靠性和可扩展性的提高.
科学领域:
- 材料科学 材料科学 材料科学
- 神经科学是一个神经科学.
- 计算机工程 计算机工程
背景情况:
- 记忆器件表现出非挥发性电阻切换,这对于神经形态计算至关重要.
- 人工突触需要模拟可调性和低能耗,这是memristors固有的特性.
- 提高memristor性能和可靠性对于推进由大脑启发的计算至关重要.
研究的目的:
- 开发和描述使用烯-氧化物 (PPX-Mo) 纳米复合材料的新型记忆装置.
- 为了评估PPX-Mo记忆器的性能增强,与传统的烯基设备相比.
- 为了证明这些设备作为神经形态系统中的功能性人工突触的潜力.
主要方法:
- 制造和PPX-Mo纳米复合材料记忆器件的全面表征.
- 统计分析以确定设备性能的最佳合成参数.
- 将memristors集成到一个神经形态计算模型中,并评估横条数组中的可扩展性.
主要成果:
- 与基于烯的设备相比,PPX-Mo记忆电阻表现出较低的开关电压和能源消耗.
- 设备显示数据分散减少和增强的电阻可塑性.
- 通过强大的统计分析确定了最佳合成参数.
- 阐明了最可能的电阻开关机制.
结论:
- 开发的PPX-Mo记忆装置为人工突触提供了卓越的特性.
- 这些memristor显示出可扩展,低功耗的神经形态计算应用的前景.
- 这些发现有助于推进基于硬件的脑启发的计算系统.
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