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

该项目的目标是对单电子闭锁开关进行详细的多学科研究，并可能将此类开关的二维阵列用于自组织（塑料）神经形态网络的硬件实现。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。根据这些估计，网络也可能具有显著的缩放特性：如果使用10nm技术实现，它们可能具有大约108个神经元的密度，功耗低于100w / cm2，并且具有几秒钟的完整学习周期时间。这种规模给了我们希望，在初始（很大程度上是无监督的）学习之后，网络不仅能够提供复杂的信息处理，包括复杂的图像识别，而且可能在大约6个数量级的时间尺度上再现大脑皮层的生物进化。拟议项目的目标是对这一非凡的机会进行初步研究，在几个结构层面上解决其所有基本方面。具体而言，研究将包括下列组成部分：单电子开关节点设计（D. Averin， K. Likharev， J. Wells）。对所提出的单电子闭锁开关的静力学、动力学和统计学进行了详细的理论分析和建模（在单电子传输理论的两个基本层面上）。低温原型（J. Lukens）。Al/AlOx/Al单电子闭锁开关原型的制造和实验研究，目标是将单电子岛分别缩小到100 nm和隧道结分别缩小到10 nm，这将使可靠的工作温度达到约10 kc。娃)。探索通过分子元件的化学自组装来实现开关的基本元件——单电子晶体管的机会。分子组分将在溶液中沉积在预制金属线结构上，然后使用一套电学，电化学和时间分辨激光光谱方法进行表征。顶层建模和分析（J. Barhen， M. Bender， K. Likharev）。基于这些开关的神经形态网络的生长、动态和自适应的大规模计算机模拟和部分分析研究。希望这个项目能够取得足够的进展，以证明在这个令人兴奋的方向上进行大规模的研发工作是合理的。特别是，在大量无监督学习期间，自适应神经形态网络的自组织的可靠证据肯定会紧随其后的是大规模网络的第一个硬件实现（可能是在使用商用FPGA技术的纯基于cmos的原型设计的初始阶段之后）。这个项目将包含大量的教育内容。具体来说（除了参加石溪大学的普通教育项目外），每年至少有4名全日制研究生参与该项目，大约20名本科生和研究生将在整个4年期间参与该项目。大部分时间至少有一名学生在BNL工作，另一名在ORNL工作。在一个多学科的团队中工作将使这些学生在他们的教育中克服跨部门的障碍。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关于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。这个多学科项目的相互关联方面的工作将由其私家侦探（K. Likharev）不断协调。特别是，所有石溪和布鲁克海文项目团队的参与者（包括博士后助理和学生）的定期会议，以及与橡树岭合作者的年度会议都计划好了。

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.