Hexagonal boron nitride (h-BN) memristor arrays for analog-based machine learning hardware
Hexagonal boron nitride (h-BN) memristor arrays for analog-based machine learning hardware
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DOI：
10.1038/s41699-022-00328-2
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发表时间：
2022-07
影响因子：
9.7
通讯作者：
Jing Xie;Sahra Afshari;Ivan Sanchez Esqueda
中科院分区：
文献类型：
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作者：
Jing Xie;Sahra Afshari;Ivan Sanchez Esqueda
最近以六方氮化硼（h-BN）为开关层的电阻开关器件的研究显示了二维（2D）材料在记忆和神经形态计算方面的应用潜力。二维材料的使用允许将电阻开关层厚度缩放到亚纳米尺寸，使器件能够以低开关电压和高编程速度运行，从而大大提高了效率和性能以及超密集集成。这些特性对基于忆阻器交叉棒的神经形态计算和机器学习硬件的实现很有意义。然而，现有的h-BN忆阻器的演示集中在单个隔离器件的开关特性上，缺乏对基本机器学习功能的关注。本文演示了用h-BN忆阻器阵列实现机器学习中普遍存在的基本模拟函数点积运算的硬件实现。此外，我们还演示了h-BN忆阻器阵列上线性回归算法的硬件实现。
Recent studies of resistive switching devices with hexagonal boron nitride (h-BN) as the switching layer have shown the potential of two-dimensional (2D) materials for memory and neuromorphic computing applications. The use of 2D materials allows scaling the resistive switching layer thickness to sub-nanometer dimensions enabling devices to operate with low switching voltages and high programming speeds, offering large improvements in efficiency and performance as well as ultra-dense integration. These characteristics are of interest for the implementation of neuromorphic computing and machine learning hardware based on memristor crossbars. However, existing demonstrations of h-BN memristors focus on single isolated device switching properties and lack attention to fundamental machine learning functions. This paper demonstrates the hardware implementation of dot product operations, a basic analog function ubiquitous in machine learning, using h-BN memristor arrays. Moreover, we demonstrate the hardware implementation of a linear regression algorithm on h-BN memristor arrays.