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

智力优势：该项目在2020年纳米电子学和超越竞赛下获得奖励，得到了美国国家科学基金会多个部门和部门以及半导体研究公司纳米电子学研究计划的支持。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，由于它是一种基于电荷的数字开关，本质上是能源效率低下的。相比之下，具有单轴形状各向异性的单畴纳米磁铁，在其磁化方向上编码二进制位，由于它是一个基于自旋的开关，其中自旋内部相互作用，因此能效更高。因此，磁计算电路比电子电路具有潜在的优势。然而，如果用于切换磁铁的方法变得如此能源效率低下，从而增加了过高的能源开销，那么这种优势将失去。为此，一种用于开关磁体的自旋电子/应变电子混合模式已经被开发出来，它将能量耗散降低了几个数量级，并预示着一种超节能的磁计算和信号处理架构。该项目将：(1)开发模拟这些器件及其开关动力学所需的所有建模工具。他们将通过适当的模型，如随机Landau-Lifshitz-Gilbert方程和/或Fokker-Planck方程，纳入器件和电路随机性和热波动的影响；(2)在纳米光刻制作的数字门阵列中演示了班尼特时钟和成功的逻辑位传播，其中时钟是在产生应变的微小电压下进行的；(3)设计基于多态混合自旋电子/应变电子突触和神经元的节能神经形态架构，以处理模拟信号；(4)演示了通过自旋波通信的应变/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，因为它们将依靠自旋波电路之间的磁相互作用和多铁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执行指令集。更广泛的影响：拟议的研究将潜在地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非常节能，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设备上有着前所未有的应用。这是为了监测大脑信号，并警告即将发作的癫痫。它们可以通过从病人身上获取能量来运行吗？S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扩展，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，而少数民族充实将包括通过弗吉尼亚联邦大学里士满地区工程少数民族项目、加州大学河滨分校少数民族外展中心、密歇根工程外展和参与办公室以及弗吉尼亚大学工程多样性中心招募的高中生培训。K-12的推广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暑期探索项目。每个参与机构将开发新的研究生课程材料，并通过教科书、教程和全球网络传播。

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.