Nanoscale Single-electron Switching Arrays for Self-evolving Neuromorphic Networks

用于自进化神经形态网络的纳米级单电子开关阵列

基本信息

  • 批准号:
    0103059
  • 负责人:
  • 金额:
    $ 60万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Standard Grant
  • 财政年份:
    2001
  • 资助国家:
    美国
  • 起止时间:
    2001-07-01 至 2003-12-31
  • 项目状态:
    已结题

项目摘要

The goal of this project is to carry out a detailed multi-disciplinary study of single-electronlatching switches and of possible use of 2D arrays of such switches for hardwareimplementation of self-organizing (plastic) neuromorphic networks. Preliminary estimatesshow that such networks may provide unparalleled possibilities for complex informationprocessing. By these estimates, the networks may also have remarkable scaling properties:if implemented using a 10-nm technology, they may have density about 10 8 neurons percm 2 at manageable power dissipation below 100 W/cm 2 , and feature full learning cycle timeof the order of a few seconds. This scaling gives every hope that the networks will be able,after initial (largely unsupervised) learning, not only provide complex information processingincluding complex image recognition, but possibly reproduce biological evolution of thecerebral cortex at a time scale some 6 orders of magnitude shorter.The objective of the proposed project is to carry out a preliminary study of thisremarkable opportunity, addressing all its basic aspects at several structural levels. Inparticular, research will include the following components:A. Single-electron switch node design (D. Averin, K. Likharev, J. Wells).Detailed theoretical analysis and modeling (on two basic levels of single-electron transporttheory) of statics, dynamics, and statistics of the proposed single-electron latching switches.B. Low temperature prototyping (J. Lukens). Fabrication and experimentalstudy of Al/AlOx/Al prototypes of single-electron latching switches, with the goal to scalesingle-electron islands down to 100 nm and tunnel junctions to 10 nm, respectively, whichwould bring the reliable operation temperature up to about 10 K.C. Molecular single-electron device development (B. Brunschwig, J. Lukens,A. Mayr). Exploration of the opportunity to implement the basic component of the switches,the single-electron transistor, by chemical self-assembly of molecular components. Themolecular components will be deposited in solution on the prefabricated metallic wirestructures, and then characterized using a set of electrical, electrochemical, and time-resolvedlaser-spectrometry methods.D. Top level modeling and analysis (J. Barhen, M. Bender, K. Likharev).Large-scale computer simulation and a partial analytical study of the growth, dynamics, andself-adaptation of neuromorphic networks based on these switches.Hopefully, the project will achieve enough progress to justify a large-scale R&D effortin this exciting direction. In particular, a reliable evidence of self-organization of adaptiveneuromorphic networks during largely unsupervised learning would certainly be followed bythe first hardware implementations of sizable networks (possibly, after an initial stage ofpurely-CMOS-based prototyping using commercially available FPGA technology).The project will have a substantial educational component. Specifically (besidesparticipating in general educational Stony Brook initiatives), at least 4 FTE graduatestudents will be involved in the project each year, and some 20 undergraduate andgraduate students will take part in the project during its full 4-year period. At least onestudent will work in BNL and one in ORNL most of the time. Working in a multi-disciplinaryteam will allow these students to overcome inter-departmental barriers in their education.As another specific educational initiative, we plan to organize a Web-based undergraduatecourse on massively parallel supercomputing and neural networks, using the IBM SP3computer at Oak Ridge.Work on the inter-related aspects of this multi-disciplinary project will be constantlycoordinated by its P.I. (K. Likharev). In particular, regular meetings of all Stony Brook andBrookhaven participants of the team working on the project (including postdoctoralassociates and students), and annual meetings with Oak Ridge collaborators, are planned.
该项目的目标是进行详细的多学科研究的单电子闭锁开关和可能使用的二维阵列,这种开关的硬件实现的自组织(塑料)神经形态网络。初步估计表明,这种网络可能为复杂的信息处理提供无与伦比的可能性。通过这些估计,网络也可能具有显着的缩放特性:如果使用10 nm技术实现,它们可以在低于100 W/cm 2的可管理功耗下具有约10 8个神经元/cm 2的密度,并且具有几秒量级的完整学习周期时间。这种扩展给网络带来了希望,在最初的(主要是无监督)学习,不仅提供复杂的信息处理,包括复杂的图像识别,但可能重现大脑皮层的生物进化的时间尺度约6个数量级短。拟议项目的目标是进行初步研究,这一显着的机会,在几个结构层面处理其所有基本方面。具体而言,研究将包括以下组成部分:A。单电子开关节点设计(D. Averin,K.威尔斯)。详细的理论分析和建模(单电子传输理论的两个基本层次)的静态,动态和统计的建议单电子闭锁开关。低温原型(J. Lukens)。制备和实验研究了Al/AlOx/Al单电子锁存开关原型,目标是将单电子岛尺寸缩小到100 nm,将隧道结尺寸缩小到10 nm,使开关的可靠工作温度达到10 K. C左右。分子单电子器件开发(B. Brunschwig,J. Lukens,A. Mayr)。探索通过分子组件的化学自组装来实现开关的基本组件单电子晶体管的机会。分子组分将在溶液中沉积在预制的金属线结构上,然后使用一套电学、电化学和时间分辨激光光谱法进行表征。顶级建模和分析(J. Barhen,M. Bender,K. Likharev)。大规模计算机模拟和基于这些开关的神经形态网络的生长,动力学和自适应的部分分析研究。希望该项目将取得足够的进展,以证明在这个令人兴奋的方向上进行大规模研发的努力是合理的。特别是,在大部分无监督学习期间自适应神经形态网络自组织的可靠证据之后,肯定会出现第一批大型网络的硬件实现(可能是在使用商用FPGA技术的纯CMOS原型设计的初始阶段之后)。具体而言(除了参加普通教育斯托尼布鲁克倡议),每年至少有4名全职研究生将参与该项目,约20名本科生和研究生将在整个4年期间参加该项目。大部分时间至少有一名学生在BNL工作,一名学生在ORNL工作。在一个多学科的团队中工作将使这些学生克服教育中的跨部门障碍。作为另一个具体的教育计划,我们计划组织一个基于网络的大规模并行超级计算和神经网络的本科课程,使用橡树岭的IBM SP3计算机。(K. Likharev)。特别是,计划定期召开该项目团队所有斯托尼布鲁克和布鲁克海文参与者(包括博士后研究员和学生)的会议,以及与橡树岭合作者的年度会议。

项目成果

期刊论文数量(0)
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Konstantin Likharev其他文献

Dragging single electrons
拖动单个电子
  • DOI:
    10.1038/35069173
  • 发表时间:
    2001-03-29
  • 期刊:
  • 影响因子:
    48.500
  • 作者:
    Konstantin Likharev
  • 通讯作者:
    Konstantin Likharev

Konstantin Likharev的其他文献

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{{ truncateString('Konstantin Likharev', 18)}}的其他基金

NIRT: Devices and Architectures for Neuromorphic Circuits with Nanoelectronic Components
NIRT:具有纳米电子元件的神经形态电路的设备和架构
  • 批准号:
    0403618
  • 财政年份:
    2004
  • 资助金额:
    $ 60万
  • 项目类别:
    Standard Grant
BIC: Bio-inspired Information Processing Using Hybrid Nanodevice Arrays
BIC:使用混合纳米器件阵列的仿生信息处理
  • 批准号:
    0432116
  • 财政年份:
    2004
  • 资助金额:
    $ 60万
  • 项目类别:
    Continuing Grant

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