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Improved Conductance Linearity and Conductance Ratio of 1T2R Synapse Device for Neuromorphic Systems

机译:改进的神经形态系统1T2R突触设备的电导线性和电导率

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摘要

We report on a 1-transisor/2-resistor (1T2R) synapse device with improved conductance linearity and conductance ratio under an identical pulse condition for hardware neural networks with high pattern-recognition accuracy. Utilizing an additional series-connected resistor, the conductance linearity of a synapse device was significantly improved owing to the reduced initial voltage drop on an resistive RAM (RRAM) device during depression conditions. Moreover, to maximize the conductance ratio of a synapse device, we utilized a steep subthreshold region of an MOSFET by a parallel connection of an RRAM and a transistor. A small change in voltage on the RRAM directly controlled the gate bias of the MOSFET, which causes a large change in the drain current. Compared with a conventional RRAM synapse device, the 1T2R synapse device shows an improved conductance linearity and conductance ratio ( > ×100). Finally, we confirmed an excellent classification accuracy by using a neural network simulation based on a multilayer perceptron.
机译:我们报告了在具有相同的脉冲条件下,对于具有高模式识别精度的硬件神经网络,具有改进的电导线性度和电导比的1-transisor / 2电阻(1T2R)突触设备。通过使用附加的串联电阻,由于在压抑条件下电阻RAM(RRAM)器件上的初始电压降减小,因此突触器件的电导率线性度得到了显着改善。此外,为了使突触设备的电导率最大化,我们通过RRAM和晶体管的并联连接利用了MOSFET的陡峭亚阈值区域。 RRAM上电压的小幅变化直接控制了MOSFET的栅极偏置,这导致漏极电流发生大变化。与传统的RRAM突触设备相比,1T2R突触设备显示出更高的电导线性度和电导率(>×100)。最后,我们通过使用基于多层感知器的神经网络仿真,确认了出色的分类准确性。

著录项

  • 来源
    《IEEE Electron Device Letters》 |2017年第8期|1023-1026|共4页
  • 作者单位

    Department of Materials Science and Engineering, Pohang University of Science and Technology, Pohang, South Korea;

    Department of Materials Science and Engineering, Pohang University of Science and Technology, Pohang, South Korea;

    Department of Materials Science and Engineering, Pohang University of Science and Technology, Pohang, South Korea;

    Department of Materials Science and Engineering, Pohang University of Science and Technology, Pohang, South Korea;

    Department of Materials Science and Engineering, Pohang University of Science and Technology, Pohang, South Korea;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Linearity; Resistors; Neural networks; Switches; Electrodes; Standards; Neuromorphics;

    机译:线性;电阻器;神经网络;开关;电极;标准;神经形态;

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