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

• 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; Collaborative; Research; Energy; Efficient; Voltage

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.

由于深度神经网络（DNN）越来越多地部署在低功率嵌入式设备和物联网（IoT）应用程序中。他们需要能够实时学习，同时又可以节能。这需要使用多状态内存，而多状态内存比传统的二进制“ 0”和“ 1”状态更具挥发性，以便在关闭功率时保留信息，并且可以用很少的能量进行编程。该项目的目的是研究和证明神经元网络的突触元素，该网络可以使用电压控制的磁性域壁（DW）设备来存储在学习过程中更新的权重。信息被编码为DW在狭窄的磁线中的位置。具体而言，该研究将集中于使用将小电压应用于薄的压电层产生的应变，并将其转移到沉积在其上的磁线以以极高的节能方式控制DW位置。这项研究可能导致用于实施DNN的密集，节能和健壮的硬件范式。两名研究生，一名在弗吉尼亚联邦大学（VCU），马萨诸塞州理工学院（MIT）的一名研究生将获得高级纳米制作，纳米特征和建模的多学科技能。 VCU-PI和MIT-CO-PI将在其教学课程中融合用于记忆和计算的域墙技术。 PI和Co-Pi计划在其实验室中接待研究实习生，该实习生是从各自大学中代表性不足的群体的外展计划中招募的。这些学生将接受纳米磁体和磁技术其他方面的纳米化培训。 PI和Co-Pi还计划为大学中的高中生和老师协作举办纳米磁性研讨会。 This collaborative effort between VCU and MIT work will study and demonstrate the use of racetracks included of magnetic dimensional 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.研究团队进一步计划探索磁性稀土石榴石（REIG）的使用，这些石榴石（REIG）具有较低的饱和度磁力化和低舞蹈，从而使SOT较低的SOT由于较大的DW速度而在较小的时间内施加，以提高DW设备的能源效率。拟议的工作将包括完整的材料增长，表征，纳米化，先进的磁性可视化，建模和模拟，其中包括：（i）金属铁磁性和绝缘铁磁铁（II）研究SOT驱动的DW速度在磁性磁磁赛车和范围内的限量性运动DW的DW速度研究，以进行II型DW的运动，以证明II的运动。在存在缺口，边缘效应和室温热噪声的情况下，具有SOT的域壁运动的微磁性建模及其控制，并评估所提出的设备在实现DNN方面的总体性能益处。该项目的研究将在局部电压诱导的各向异性变化下，在暴露于SOT丰富的物理学和手性DWS的存在下，提高DW动力学的知识。它还将提供概念概念的证明，以证明突触和神经退行版，可以为DNN的节能硬件实施铺平道路。该奖项反映了NSF的法定任务，并被认为是通过基金会的智力优点和更广泛的影响标准通过评估来获得的支持。