• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

采用锗硅带隙工程电阻开关晶体管的低能量且可调谐的激光诱导荧光(LIF)神经元

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

作者信息

Kim Yijoon, Kim Hyangwoo, Oh Kyounghwan, Park Ju Hong, Kong Byoung Don, Baek Chang-Ki

机构信息

Department of Convergence IT Engineering, Pohang University of Science and Technology (POSTECH), Pohang, 37673, South Korea.

Future IT Innovation Laboratory, Pohang University of Science and Technology (POSTECH), Pohang, 37673, South Korea.

出版信息

Discov Nano. 2024 Aug 23;19(1):132. doi: 10.1186/s11671-024-04079-5.

DOI:10.1186/s11671-024-04079-5
PMID:39177916
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11343930/
Abstract

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.

摘要

我们提出了一种无需外部电路元件即可实现低能耗和功能可调的漏电积分发放(LIF)神经元。我们的LIF神经元具有简单的结构,仅由三个组件组成:一个带隙工程化电阻开关晶体管(BE-RST)、一个电容器和一个电阻器。在此,关键在于具有硅锗异质结的BE-RST由于空穴存储能力和碰撞电离系数的提高,具有放大的滞后电流开关特性和低闩锁电压。因此,所提出的利用BE-RST的神经元的能量消耗为0.36 pJ/尖峰,比基于纯硅-RST的神经元的2.08 pJ/尖峰低约六倍。此外,通过栅极偏置调制泄漏率和阈值可以调整尖峰特性,这有助于实现节能稀疏活动和高学习精度。结果,我们提出的神经元有望成为执行各种脉冲神经网络应用的候选者。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cf79/11343930/f746c78c1967/11671_2024_4079_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cf79/11343930/4690cc2a3b7b/11671_2024_4079_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cf79/11343930/f95326ae2c0d/11671_2024_4079_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cf79/11343930/8fe01aa361c8/11671_2024_4079_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cf79/11343930/ea19664569aa/11671_2024_4079_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cf79/11343930/9c8ebfeb24c9/11671_2024_4079_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cf79/11343930/b0f6c3fce6ee/11671_2024_4079_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cf79/11343930/f746c78c1967/11671_2024_4079_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cf79/11343930/4690cc2a3b7b/11671_2024_4079_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cf79/11343930/f95326ae2c0d/11671_2024_4079_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cf79/11343930/8fe01aa361c8/11671_2024_4079_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cf79/11343930/ea19664569aa/11671_2024_4079_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cf79/11343930/9c8ebfeb24c9/11671_2024_4079_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cf79/11343930/b0f6c3fce6ee/11671_2024_4079_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cf79/11343930/f746c78c1967/11671_2024_4079_Fig7_HTML.jpg

相似文献

1
Low-energy and tunable LIF neuron using SiGe bandgap-engineered resistive switching transistor.采用锗硅带隙工程电阻开关晶体管的低能量且可调谐的激光诱导荧光(LIF)神经元
Discov Nano. 2024 Aug 23;19(1):132. doi: 10.1186/s11671-024-04079-5.
2
Leaky Integrate-and-Fire Neuron Circuit Based on Floating-Gate Integrator.基于浮栅积分器的漏电积分发放神经元电路
Front Neurosci. 2016 May 23;10:212. doi: 10.3389/fnins.2016.00212. eCollection 2016.
3
Highly biomimetic spiking neuron using SiGe heterojunction bipolar transistors for energy-efficient neuromorphic systems.用于节能神经形态系统的采用硅锗异质结双极晶体管的高度仿生脉冲神经元。
Sci Rep. 2024 Apr 10;14(1):8356. doi: 10.1038/s41598-024-58962-3.
4
A Split-Gate Positive Feedback Device With an Integrate-and-Fire Capability for a High-Density Low-Power Neuron Circuit.一种具有积分触发功能的用于高密度低功耗神经元电路的分裂栅极正反馈器件。
Front Neurosci. 2018 Oct 9;12:704. doi: 10.3389/fnins.2018.00704. eCollection 2018.
5
Mott memristor based stochastic neurons for probabilistic computing.用于概率计算的基于Mott忆阻器的随机神经元。
Nanotechnology. 2024 Apr 30;35(29). doi: 10.1088/1361-6528/ad3c4b.
6
All-Ferroelectric Spiking Neural Networks via Morphotropic Phase Boundary Neurons.通过准同型相界神经元实现的全铁电脉冲神经网络。
Adv Sci (Weinh). 2024 Nov;11(44):e2407870. doi: 10.1002/advs.202407870. Epub 2024 Oct 9.
7
Engineering Spiking Neurons Using Threshold Switching Devices for High-Efficient Neuromorphic Computing.使用阈值开关器件设计脉冲神经元用于高效神经形态计算
Front Neurosci. 2022 Jan 5;15:786694. doi: 10.3389/fnins.2021.786694. eCollection 2021.
8
Low-Voltage Oscillatory Neurons for Memristor-Based Neuromorphic Systems.用于基于忆阻器的神经形态系统的低压振荡神经元。
Glob Chall. 2019 Aug 7;3(11):1900015. doi: 10.1002/gch2.201900015. eCollection 2019 Nov.
9
Spiking Neural Network Integrated with Impact Ionization Field-Effect Transistor Neuron and a Ferroelectric Field-Effect Transistor Synapse.集成冲击电离场效应晶体管神经元和铁电场效应晶体管突触的脉冲神经网络。
Adv Mater. 2024 Sep 5:e2406970. doi: 10.1002/adma.202406970.
10
A Vertical Single Transistor Neuron with Core-Shell Dual-Gate for Excitatory-Inhibitory Function and Tunable Firing Threshold Voltage.一种具有核壳双栅的垂直单晶体管神经元,用于兴奋-抑制功能和可调触发阈值电压。
Micromachines (Basel). 2022 Oct 14;13(10):1740. doi: 10.3390/mi13101740.

本文引用的文献

1
Intrinsic threshold plasticity: cholinergic activation and role in the neuronal recognition of incomplete input patterns.内在阈限可塑性:胆碱能激活及其在神经元识别不完全输入模式中的作用。
J Physiol. 2023 Aug;601(15):3221-3239. doi: 10.1113/JP283473. Epub 2022 Aug 11.
2
Engineering Spiking Neurons Using Threshold Switching Devices for High-Efficient Neuromorphic Computing.使用阈值开关器件设计脉冲神经元用于高效神经形态计算
Front Neurosci. 2022 Jan 5;15:786694. doi: 10.3389/fnins.2021.786694. eCollection 2021.
3
Three-Terminal Ovonic Threshold Switch (3T-OTS) with Tunable Threshold Voltage for Versatile Artificial Sensory Neurons.
用于多功能人工感觉神经元的具有可调阈值电压的三端双向阈值开关(3T-OTS)
Nano Lett. 2022 Jan 26;22(2):733-739. doi: 10.1021/acs.nanolett.1c04125. Epub 2022 Jan 13.
4
Atomic threshold-switching enabled MoS transistors towards ultralow-power electronics.原子阈值开关使二硫化钼晶体管迈向超低功耗电子学。
Nat Commun. 2020 Dec 4;11(1):6207. doi: 10.1038/s41467-020-20051-0.
5
ZnTe Ovonic Threshold Switching Device Performance and its Correlation to Material Parameters.碲化锌双向阈值开关器件性能及其与材料参数的相关性。
Sci Rep. 2018 Aug 7;8(1):11822. doi: 10.1038/s41598-018-30207-0.
6
Leaky Integrate and Fire Neuron by Charge-Discharge Dynamics in Floating-Body MOSFET.浮体 MOSFET 中基于电荷-放电动力学的漏电积分与放电神经元
Sci Rep. 2017 Aug 15;7(1):8257. doi: 10.1038/s41598-017-07418-y.
7
Artificial brains. A million spiking-neuron integrated circuit with a scalable communication network and interface.人工大脑。具有可扩展通信网络和接口的 100 万个尖峰神经元集成电路。
Science. 2014 Aug 8;345(6197):668-73. doi: 10.1126/science.1254642. Epub 2014 Aug 7.
8
Neuromorphic silicon neuron circuits.神经形态硅神经元电路。
Front Neurosci. 2011 May 31;5:73. doi: 10.3389/fnins.2011.00073. eCollection 2011.
9
Receptive field optimisation and supervision of a fuzzy spiking neural network.感受野优化与模糊尖峰神经网络的监督。
Neural Netw. 2011 Apr;24(3):247-56. doi: 10.1016/j.neunet.2010.11.008. Epub 2010 Dec 7.
10
Spiking neural networks.脉冲神经网络。
Int J Neural Syst. 2009 Aug;19(4):295-308. doi: 10.1142/S0129065709002002.