{"title":"针对尖峰神经网络使用 Ge-source TFET 的高能效漏整合与发射神经元:仿真分析","authors":"Shreyas Tiwari, Rajesh Saha and Tarun Varma","doi":"10.1088/1402-4896/ad76ea","DOIUrl":null,"url":null,"abstract":"The basic building block of neural network is a device, which can mimic the neural behavior. The spiking neural network (SNN) is an efficient methodology in terms of power and area. Due to the excess energy consumption and larger area, various spintronic neural devices are unfit for neuron applications. In this article, we have implemented Ge source based Tunnel FET (TFET) for ultralow energy spike generation using TCAD simulator. It is seen that Ge source TFET has signature spiking frequency in THz range versus input voltage curve of an artificial biological neuron. The simulated device deploy the leaky integrate and fire (LIF) technique for generation of neurons. The simulation result highlights that the energy of device is 1.08 aJ/spike, which is several order less than existing neural based FET devices in literature.","PeriodicalId":20067,"journal":{"name":"Physica Scripta","volume":null,"pages":null},"PeriodicalIF":2.6000,"publicationDate":"2024-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"An energy efficient leaky integrate and fire neuron using Ge-source TFET for spiking neural network: simulation analysis\",\"authors\":\"Shreyas Tiwari, Rajesh Saha and Tarun Varma\",\"doi\":\"10.1088/1402-4896/ad76ea\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The basic building block of neural network is a device, which can mimic the neural behavior. The spiking neural network (SNN) is an efficient methodology in terms of power and area. Due to the excess energy consumption and larger area, various spintronic neural devices are unfit for neuron applications. In this article, we have implemented Ge source based Tunnel FET (TFET) for ultralow energy spike generation using TCAD simulator. It is seen that Ge source TFET has signature spiking frequency in THz range versus input voltage curve of an artificial biological neuron. The simulated device deploy the leaky integrate and fire (LIF) technique for generation of neurons. The simulation result highlights that the energy of device is 1.08 aJ/spike, which is several order less than existing neural based FET devices in literature.\",\"PeriodicalId\":20067,\"journal\":{\"name\":\"Physica Scripta\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.6000,\"publicationDate\":\"2024-09-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Physica Scripta\",\"FirstCategoryId\":\"101\",\"ListUrlMain\":\"https://doi.org/10.1088/1402-4896/ad76ea\",\"RegionNum\":3,\"RegionCategory\":\"物理与天体物理\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"PHYSICS, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Physica Scripta","FirstCategoryId":"101","ListUrlMain":"https://doi.org/10.1088/1402-4896/ad76ea","RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"PHYSICS, MULTIDISCIPLINARY","Score":null,"Total":0}
An energy efficient leaky integrate and fire neuron using Ge-source TFET for spiking neural network: simulation analysis
The basic building block of neural network is a device, which can mimic the neural behavior. The spiking neural network (SNN) is an efficient methodology in terms of power and area. Due to the excess energy consumption and larger area, various spintronic neural devices are unfit for neuron applications. In this article, we have implemented Ge source based Tunnel FET (TFET) for ultralow energy spike generation using TCAD simulator. It is seen that Ge source TFET has signature spiking frequency in THz range versus input voltage curve of an artificial biological neuron. The simulated device deploy the leaky integrate and fire (LIF) technique for generation of neurons. The simulation result highlights that the energy of device is 1.08 aJ/spike, which is several order less than existing neural based FET devices in literature.
期刊介绍:
Physica Scripta is an international journal for original research in any branch of experimental and theoretical physics. Articles will be considered in any of the following topics, and interdisciplinary topics involving physics are also welcomed:
-Atomic, molecular and optical physics-
Plasma physics-
Condensed matter physics-
Mathematical physics-
Astrophysics-
High energy physics-
Nuclear physics-
Nonlinear physics.
The journal aims to increase the visibility and accessibility of research to the wider physical sciences community. Articles on topics of broad interest are encouraged and submissions in more specialist fields should endeavour to include reference to the wider context of their research in the introduction.