使用硅锗带隙工程电阻开关晶体管的低能耗可调 LIF 神经元。

IF 5.5 3区 材料科学 Q2 MATERIALS SCIENCE, MULTIDISCIPLINARY
Yijoon Kim, Hyangwoo Kim, Kyounghwan Oh, Ju Hong Park, Byoung Don Kong, Chang-Ki Baek
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引用次数: 0

摘要

我们提出的漏电积分发射(LIF)神经元能耗低、功能可调,且无需外部电路元件。我们的 LIF 神经元结构简单,仅由三个元件组成:一个带隙工程电阻开关晶体管(BE-RST)、一个电容器和一个电阻器。其中最关键的一点是,硅锗异质结的 BE-RST 由于提高了空穴存储能力和冲击电离系数,因此具有放大的滞后电流开关和较低的闩锁电压。因此,利用 BE-RST 的神经元所需的能耗为 0.36 pJ/spike,比基于纯硅-RST 的神经元的 2.08 pJ/spike 低约六倍。此外,尖峰特性还可以通过栅极偏置来调节漏率和阈值,从而实现高能效的稀疏活动和高学习精度。因此,我们提出的神经元有望成为执行各种尖峰神经网络应用的候选方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Low-energy and tunable LIF neuron using SiGe bandgap-engineered resistive switching transistor

Low-energy and tunable LIF neuron using SiGe bandgap-engineered resistive switching transistor

We have proposed leaky integrate-and-fire (LIF) neuron having low-energy consumption and tunable functionality without external circuit components. Our LIF neuron has a simple configuration consisting of only three components: one bandgap-engineered resistive switching transistor (BE-RST), one capacitor, and one resistor. Here, the crucial point is that BE-RST with a silicon–germanium heterojunction possesses an amplified hysteric current switching with a low latch-up voltage due to improved hole storage capability and impact ionization coefficient. Therefore, the proposed neuron utilizing BE-RST requires an energy consumption of 0.36 pJ/spike, which is approximately six times lower than 2.08 pJ/spike of pure silicon-RST based neuron. In addition, the spiking properties can be tuned by modulating the leakage rate and threshold through gate bias, which contributes to energy-efficient sparse-activity and high learning accuracy. As a result, our proposed neuron can be a promising candidate for executing various spiking neural network applications.

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来源期刊
Nanoscale Research Letters
Nanoscale Research Letters 工程技术-材料科学:综合
CiteScore
11.30
自引率
0.00%
发文量
110
审稿时长
48 days
期刊介绍: Nanoscale Research Letters (NRL) provides an interdisciplinary forum for communication of scientific and technological advances in the creation and use of objects at the nanometer scale. NRL is the first nanotechnology journal from a major publisher to be published with Open Access.
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