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

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。他们需要能够在真实的时间内学习，同时还要节能。这需要使用多状态存储器，其多于传统的二进制“0”和“1”状态，是非易失性的，使得当电源关闭时信息被保留，并且可以用非常少的能量进行编程。该项目的目标是研究和演示神经网络的突触元件，该元件可以使用电压控制的磁畴壁（DW）设备存储学习期间更新的权重。 信息被编码为DW在窄磁线中的位置。具体而言，研究将集中于使用通过向薄压电层施加小电压产生的应变，并将其转移到沉积在其上的磁线，以极其节能的方式控制DW位置。这项研究可能会导致一个密集，节能和强大的硬件范例，用于实现DNN。两名研究生，一个在弗吉尼亚联邦大学（VCU）和一个在马萨诸塞州理工学院（MIT），将获得先进的纳米纤维，纳米表征和建模的多学科技能。VCU-PI和MIT-Co-PI将在他们教授的课程中纳入内存和计算的域墙技术。PI和Co-PI计划在他们的实验室接待研究实习生，这些实习生是从各自大学的代表性不足的群体的外联项目中招募的。学生将接受纳米磁体的纳米制造和磁性技术的其他方面的培训。PI和Co-PI还计划在他们的大学合作为高中学生和教师举办纳米磁性研讨会。VCU和麻省理工学院之间的这项合作努力将研究和演示由磁致伸缩金属（如CoFe）组成的赛道的使用，其中DW使用自旋轨道扭矩（SOT）从相邻的Pt层移动，并使用下面的压电层产生的电压产生的应变确定性地阻止，该压电层调制赛道不同区域的垂直磁各向异性（PMA）。研究小组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），由于大DW速度，允许在较短时间内应用较低的SOT，以提高DW设备的能效。拟议的工作将包括互补材料生长，表征，纳米纤维，先进的磁性可视化，建模和模拟，其中包括：（i）金属铁磁和绝缘亚铁磁体的生长（ii）磁致伸缩轨道中SOT驱动的DW速度的研究和用电压感应应变阻止SOT驱动的DW运动的概念验证演示（iii）在存在缺口、边缘效应和室温热噪声的情况下，使用SOT执行畴壁运动的微磁建模及其电压诱导应变控制，并评估所提出的器件在实现DNN方面的整体性能优势。 在这个项目中的研究将推进知识的DW动力学下的局部电压引起的各向异性的变化，在异质结构，表现出丰富的物理SOT和手性DW的存在。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.