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

智力优势：该项目是根据2020年及以后的纳米电子学竞赛授予的，得到了国家科学基金会多个董事和部门的支持，以及半导体研究公司的纳米电子研究计划的支持。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，因此固有的能源效率低下。相比之下，具有单轴形状各向异性的单磁区纳米磁体，在其磁化方向上对二进制位进行编码，能量效率要高得多，因为它是一种基于自旋的开关，其中自旋在内部相互作用。因此，与电子电路相比，磁计算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低效，以至于增加了过高的能源开销，这种优势将会消失。为此，开发了一种用于开关磁体的混合自旋/强子范例，它将能量消耗降低了几个数量级，并预示着一种超节能的磁计算和信号处理体系结构。该项目将：(1)开发所有必要的建模工具，以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-Gilbert方程和/或Fokker-Planck方程，融合器件和电路的随机性以及热波动的影响；(2)展示Bennett时钟和成功的逻辑比特传播在用纳米光刻制造的数字门阵列中，其中时钟是通过产生应变的微小电压来进行的；(3)基于多态混合自旋/内含子突触和可以处理模拟信号的神经元来设计能源高效的神经形态体系结构；以及(4)展示通过自旋波进行通信的电子/自旋电子节点的图像处理，以实施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和多铁磁性单元的集体活动来获得所需的功能，而不需要任何软件或指令集的执行。更广泛的影响：拟议的研究可能会影响计算和信号处理的所有领域。采用混合自旋/应变电子学方法的计算机可以如此节能，以至于它们可以通过从周围收集能量来运行，而不需要电池。因此，它们在植入癫痫患者S大脑的医疗设备中有着前所未有的应用，以监测大脑信号并警告即将到来的癫痫发作。他们可以通过从患者身上获取能量来运行？S单独的身体动作。它们还在结构健康监测等领域有其他应用，在这些领域，它们可以持续监测桥梁和建筑物中的疲劳和裂缝扩展，同时从风或过往交通引起的振动中获取能量。将这项研究与教育相结合将需要传统的研究生和本科生培训，而少数族裔培养将包括培训通过弗吉尼亚联邦大学里士满地区少数族裔工程项目、加州大学河滨分校的少数族裔外联中心、密歇根工程外联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。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写新的研究生课程材料，并通过教科书、教程和万维网进行传播。

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.