NEB: Hybrid Spintronics and Straintronics: A New Technology for Ultra-Low Energy Computing and Signal Processing Beyond the Year 2020.

NEB：混合自旋电子学和应变电子学：2020 年以后超低能耗计算和信号处理的新技术。

• 批准号: 1124714
• 负责人: Supriyo Bandyopadhyay
• 金额: $ 155万
• 依托单位: Virginia Commonwealth University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-09-01 至 2017-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1124714&HistoricalAwards=false
• 关键词: NEB; Hybrid; Spintronics; Straintronics; New

Intellectual merit: This project is awarded under the Nanoelectronics for 2020 and Beyond competition, with support by multiple Directorates and Divisions at the National Science Foundation as well as by the Nanoelectronics Research Initiative of the Semiconductor Research Corporation. The complementary metal oxide semiconductor field effect transistor, considered the workhorse of modern computing machinery, is inherently energy-inefficient because it is a charge-based digital switch. In contrast, a single-domain nanomagnet with uniaxial shape anisotropy, that encodes binary bits in its magnetization orientation, is much more energy-efficient because it is a spin-based switch in which the spins internally interact. Therefore, magnetic computing circuits hold a potential advantage over their electronic counterparts. That advantage however will be lost if the methodology used to switch the magnet becomes so energy-inefficient that it adds an exorbitant energy overhead. To this end, a hybrid spintronic/straintronic paradigm for switching magnets has been developed that reduces the energy dissipation by several orders of magnitude and heralds an ultra-energy-efficient magnetic computing and signal processing architecture. This project will: (1) develop all the modeling tools necessary to simulate these devices and their switching dynamics. They will incorporate the effects of device and circuit stochasticity and thermal fluctuations via appropriate models such as stochastic Landau-Lifshitz-Gilbert equations and/or Fokker-Planck equations; (2) demonstrate Bennett clocking and successful logic bit propagation in a digital gate array fabricated with nanolithography, where clocking is carried out with tiny voltages generating strain; (3) design energy-efficient neuromorphic architectures based on multi-state hybrid spintronic/straintronic synapses and neurons that can process analog signals; and (4) demonstrate image processing with straintronic/spintronic nodes communicating via spin waves to implement specific image morphing algorithms. These image processors will be extremely fast since they will rely on the physics of magnetic interactions between spin wave circuits and the collective activity of multiferroic magnetic cells to elicit the required functionality, without requiring any software or execution of instruction sets. Broader Impact: The proposed research will potentially impact all areas of computing and signal processing. Computers employing the hybrid spintronics/straintronics approach can be so energy-efficient that they could operate by harvesting energy from the surroundings, without requiring a battery. Thus, they have unprecedented applications in medical devices implanted in an epileptic patient?s brain to monitor brain signals and warn of an impending seizure. They can run by harvesting energy from the patient?s body motion alone. They also have other applications in areas such as structural health monitoring where they can constantly monitor fatigue and fracture propagation in bridges and buildings, while harvesting energy from vibrations induced by wind or passing traffic. Integration of this research with education will entail traditional graduate and undergraduate student training, while minority enrichment will involve training high-school students recruited through the Richmond Area Program for Minorities in Engineering at Virginia Commonwealth University, minority outreach centers at University of California-Riverside, Office of Engineering Outreach and Engagement at Michigan, and the Center for Diversity in Engineering at University of Virginia. K-12 outreach will leverage the Math and Science Innovation Center at Richmond and the Summer Discovery Program at Virginia Commonwealth University. New graduate course material will be developed at each participating institution and disseminated through textbooks, tutorials and the worldwide web.

智力优点：该项目是在Nanoelectronics for 2020及以后的竞争中获得的，得到了国家科学基金会多个部门和部门以及半导体研究公司Nanoelectronics Research Initiative的支持。互补金属氧化物半导体场效应晶体管，被认为是现代计算机器的主力，本质上是能量效率低下的，因为它是一种基于电荷的数字开关。相比之下，具有单轴形状各向异性的单畴纳米磁体在其磁化方向上编码二进制位，这是更节能的，因为它是一种基于自旋的开关，其中自旋内部相互作用。因此，磁计算电路比其电子对应物具有潜在的优势。然而，如果用于切换磁体的方法变得如此能量效率低下以至于其增加了过高的能量开销，则该优势将丧失。为此，开发了一种用于切换磁体的混合自旋电子/应变电子范例，可将能量耗散降低几个数量级，并预示着超节能磁计算和信号处理架构的出现。该项目将：（1）开发所有必要的建模工具来模拟这些器件及其开关动态。他们将通过适当的模型，如随机Landau-Lifshitz-吉尔伯特方程和/或福克-普朗克方程，结合器件和电路的随机性和热波动的影响;（2）演示班尼特时钟和成功的逻辑位传播在一个数字门阵列制造的纳米光刻，其中时钟是用微小的电压产生应变;（3）设计基于多态混合自旋电子/应变子突触和可以处理模拟信号的神经元的节能神经形态架构;以及（4）演示通过自旋波通信的应变子/自旋电子节点的图像处理，以实现特定的图像变形算法。这些图像处理器将非常快，因为它们将依赖于自旋波电路之间的磁相互作用的物理学和多铁性磁性细胞的集体活动来引出所需的功能，而不需要任何软件或指令集的执行。更广泛的影响：拟议的研究将可能影响计算和信号处理的所有领域。采用混合自旋电子学/应变电子学方法的计算机可以非常节能，以至于它们可以通过从周围环境中收集能量来运行，而不需要电池。因此，它们在植入癫痫患者体内的医疗器械中具有前所未有的应用？监测大脑信号并警告即将发生的癫痫发作。它们能通过从病人身上获取能量来奔跑？的身体运动。它们在结构健康监测等领域也有其他应用，可以持续监测桥梁和建筑物的疲劳和断裂传播，同时从风或过往交通引起的振动中收集能量。这项研究与教育的整合将需要传统的研究生和本科生培训，而少数民族丰富将涉及培训高中学生通过里士满地区计划招募少数民族工程在弗吉尼亚联邦大学，少数民族外展中心在加州大学河滨分校，工程外展办公室和密歇根州的参与，以及弗吉尼亚大学工程多样性中心。K-12外展将利用里士满的数学和科学创新中心以及弗吉尼亚联邦大学的夏季发现计划。将在每个参与机构编制新的研究生课程材料，并通过教科书、教程和万维网传播。