Collaborative Research: Energy Efficient Voltage Controlled Non-volatile Domain Wall Devices for Neural Networks

合作研究：用于神经网络的节能压控非易失性畴壁器件

基本信息

批准号：
1954589
负责人：
Jayasimha Atulasimha
金额：
$ 22.5万
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：
2020
资助国家：
美国
起止时间：
2020-05-01 至 2024-04-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1954589&HistoricalAwards=false
关键词：
Collaborative Research Energy Efficient Voltage

项目摘要

As Deep Neural Networks (DNNs) are increasingly deployed in low power embedded device and Internet of Things (IoT) applications. They need to be able to learn in real time while also being energy efficient. This necessitates the use of multi-state memory which is more than the conventional binary “0” and “1” states, is non-volatile such that information is retained when power is turned off, and can be programmed with very little energy. The goal of this project is to study and demonstrate synaptic elements of a neural network, which can store the weights updated during learning using voltage-controlled magnetic domain wall (DW) devices. Information is encoded as the position of a DW in a narrow magnetic wire. Specifically, the research will focus on using the strain generated by application of a small voltage to a thin piezoelectric layer and transferred to a magnetic wire deposited on it to control DW position in an extremely energy efficient manner. This research could lead to a dense, energy efficient and robust hardware paradigm for implementing DNNs. Two graduate students, one at Virginia Commonwealth University (VCU) and one at Massachusetts Institute of Technology (MIT), will gain multidisciplinary skills in advanced nanofabrication, nano-characterization and modeling. The VCU-PI and MIT- Co-PI will incorporate domain wall technology for memory and computing in the courses they teach. The PI and Co-PI plan to host research interns in their labs recruited from outreach programs for underrepresented groups in their respective universities. The students will be trained on nanofabrication of nanomagnets and other aspects of magnetic technology. The PI and Co-PI also plans to hold nanomagnetism workshops for high school students and teachers in their Universities collaboratively. This collaborative effort between VCU and MIT work will study and demonstrate the use of racetracks comprised of magnetostrictive metals such as CoFe, where DWs are moved using Spin Orbit Torque (SOT) from an adjoining Pt layer and arrested deterministically using voltage generated strain from a piezoelectric layer underneath that modulate perpendicular magnetic anisotropy (PMA) in different regions of a racetrack. The research team further plan to explore the use of magnetostrictive Rare Earth Iron Garnets (REIG) that have lower saturation magnetization and low damping, allowing for lower SOT applied for lesser time due to large DW velocities in order to improve the energy efficiency of DW devices. The proposed work will consist of complementary materials growth, characterization, nanofabrication, advanced magnetic visualization, modeling and simulation that includes: (i) Growth of metallic ferromagnetic and insulating ferrimagnets (ii) Study of SOT-driven DW velocity in magnetostrictive racetracks and proof-of-concept demonstration of arresting SOT-driven DW motion with a voltage induced strain (iii) Performing micromagnetic modeling of domain wall motion with SOT and its control with voltage-induced strain in the presence of notches, edge effects and room temperature thermal noise and evaluating the overall performance benefit of the proposed device in implementing DNNs. The research in this project will advance the knowledge of DW dynamics under local voltage- induced variations in anisotropy, in heterostructures that exhibit rich physics of SOT and the presence of chiral DWs. It will also provide a proof-of-concept demonstration of synaptic and neuron devices that could pave the way for energy-efficient hardware implementation of DNNs.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

随着深度神经网络 (DNN) 越来越多地部署在低功耗嵌入式设备和物联网 (IoT) 应用中，它们需要能够实时学习，同时还要节能。比传统的二进制“0”和“1”状态更多，是非易失性的，因此在关闭电源时可以保留信息，并且可以用很少的能量进行编程该项目的目标是研究和演示突触。神经元的组成部分网络，可以使用电压控制磁畴壁（DW）设备存储学习过程中更新的权重，信息被编码为窄磁线中 DW 的位置。具体来说，该研究将集中于使用应用产生的应变。这项研究可以为两名研究生实现密集、节能和强大的硬件范例。，弗吉尼亚联邦大学之一VCU 和麻省理工学院 (MIT) 的一名学生将获得先进纳米制造、纳米表征和建模方面的多学科技能。VCU-PI 和 MIT-Co-PI 将在课程中纳入用于存储和计算的畴壁技术。 PI 和 Co-PI 计划在各自大学的外展项目中招募研究实习生，这些实习生将接受纳米磁体纳米制造和磁技术其他方面的培训。弗吉尼亚联邦大学和麻省理工学院的这项合作项目将研究和演示由磁致伸缩金属（例如 CoFe）组成的跑道的使用，其中 DW 是通过使用来移动的。来自相邻 Pt 层的自旋轨道扭矩 (SOT) 并使用来自下方压电层的电压产生的应变来确定性地停止，该压电层以不同的方式调制垂直磁各向异性 (PMA)研究团队进一步计划探索使用具有较低饱和磁化强度和低阻尼的磁致伸缩稀土铁石榴石（REIG），允许由于大DW速度而在较短的时间内应用较低的SOT，以改善拟议的工作将包括补充材料生长、表征、纳米制造、先进的磁可视化、建模和模拟，其中包括：（i）金属铁磁和绝缘材料的生长。 (ii) 磁致伸缩跑道中 SOT 驱动的 DW 速度的研究以及通过电压感应应变阻止 SOT 驱动的 DW 运动的概念验证演示 (iii) 使用 SOT 进行磁畴壁运动的微磁建模及其电压控制 -在存在缺口、边缘效应和室温热噪声的情况下产生应变，并评估所提出的器件在实现 DNN 方面的整体性能优势。该项目的研究将推进 DW 动力学知识。局部电压引起的各向异性变化、异质结构表现出丰富的 SOT 物理特性以及手性 DW 的存在，它还将提供突触和神经设备的概念验证演示，为节能硬件的实现铺平道路。这反映了 NSF 的法定使命，并且通过使用基金会的智力优点和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

期刊论文数量（5）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Secure Logic Locking with Strain-Protected Nanomagnet Logic

具有应变保护纳米磁体逻辑的安全逻辑锁定

DOI：
10.1109/dac18074.2021.9586258
发表时间：
2021
期刊：
2021 58th ACM/IEEE Design Automation Conference (DAC
影响因子：
0
作者：
Hassan, Naimul;Edwards, Alexander J.;Bhattacharya, Dhritiman;Shihab, Mustafa M.;Venkat, Varun;Zhou, Peng;Hu, Xuan;Kundu, Shamik;Kuruvila, Abraham P.;Basu, Kanad
通讯作者：
Basu, Kanad

Voltage modulated magnetic anisotropy of rare earth iron garnet thin films on a piezoelectric substrate