Superparamagnets for Probabilistic and Reservoir Computing

这项研究计划旨在优化超顺磁性隧道结，以进行低功率概率计算和基于水库的计算。这一结果将对低能量传感器和手持电子设备以及高性能数据加密和概率解密产生影响。超顺磁性隧道结是在两个电阻状态之间自发波动的设备，并且具有时间平均电阻，可以使用比传统开关所需的电压更小的电压进行调谐，从而实现更低的功耗，例如在智能手机中。概率计算基于时间平均信号的组合执行逻辑运算。电阻起伏的随机性和设计高速超顺磁性隧道结的能力是网络安全应用的重要特征。水库计算是一种基于硬件的神经网络加速器，这里将使用交互的超顺磁网来形成不同类型的水库。一个独特的特点是，只有输入和输出将需要电气连接，这可以显著降低功耗。研究计划的这一组成部分的目的是量化不同几何形状的超顺磁性阵列的速度和短期记忆，以评估它们在人工智能应用中的应用。磁性水库计算的影响将来自对实现高能效和复杂处理的算法的更好理解。研究生将发展广泛的纳米制造、高频电子学和机器学习技能。本科研究项目将有多种选择，并将开发纳米制造实验室的教学模块。这个研究计划有两个相互关联的推进器，都是以超顺磁网的电子控制为中心的。首先，对非相互作用的超顺磁性隧道结进行了优化，使其具有较高的平均波动率和较低的时间平均电阻偏置电压可调性。多个隧道结用可变反馈互连，以演示概率逻辑门行为。将确定反馈幅度和平均时间对不同逻辑状态的统计偏好的影响，并测量功耗，以便对基于超顺磁网的逻辑器件进行基准测试。第二个推力涉及研究用于储集层计算的静磁相互作用纳米磁铁组件。它们由超顺磁性隧道结输入端的磁条纹场控制，它们的响应由超顺磁性隧道结输出端产生的条纹场拾取。静磁耦合图案以前曾用于逻辑器件，但由于需要外部磁场，其应用受到限制。这里将使用电子控制和检测，实现高速操作，并使与半导体电子的集成变得更容易。研究静磁驱动的输出耦合可以消除反自旋霍尔效应产生的微弱信号对高性能磁逻辑器件设计的一个重要瓶颈。通过结合超顺磁性隧道结、电子反馈和静磁耦合模式，拟议的研究计划将开发用于机器学习的加速器和用于探索油藏计算的工具包。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

This research program aims to optimize superparamagnetic tunnel junctions for low power probabilistic and reservoir-based computation. The results will have impact on low energy sensors and hand-held electronic devices, as well as in high performance data encryption and probabilistic decryption. Superparamagnetic tunnel junctions are devices that spontaneously fluctuate between two resistance states, and have a time-averaged resistance that can be tuned using a smaller voltage than that needed for conventional switching, enabling lower power consumption, for example in a smart phone. Probabilistic computing performs logic operations based on combinations of the time-averaged signals. The randomness of the resistance fluctuations and ability to design superparamagnetic tunnel junctions for high speed are important features for cyber security applications. Reservoir computing is a type of hardware-based accelerator for neural networks, and here interacting superparamagnets will be used to form different kinds of reservoir. A unique feature is that only the input and output will require electrical connections, which could dramatically reduce power consumption. The aim of this component of the research program is to quantify the speed and short-term memory for different geometries of superparamagnet arrays, in order to evaluate them for use in artificial intelligence applications. The impact of magnetic reservoir computing would come from a better understanding of the algorithms that enable high energy efficiency and complex processing. A graduate student will develop extensive nanofabrication, high frequency electronics, and machine learning skills. There will be multiple options for undergraduate research projects, and a teaching module for a nanofabrication laboratory will be developed. There are two interconnected thrusts to this research program, both centered on electrical control of superparamagnets. In the first, non-interacting superparamagnetic tunnel junctions are optimized for high average fluctuation rate and low bias voltage tunability of the time-averaged resistance. Multiple tunnel junctions are interconnected with variable feedback in order to demonstrate probabilistic logic gate behavior. The effect of the feedback amplitude and averaging time on the statistical preference for different logic states will be determined, and the power consumption measured, in order to benchmark superparamagnet-based logic devices. The second thrust involves investigation of assemblies of magnetostatically interacting nanomagnets for reservoir computing. They are controlled by the magnetic fringe field of a superparamagnetic tunnel junction input, and their response in picked up by the fringe field generated at a superparamagnetic tunnel junction output. Magnetostatically coupled patterns have previously been used for logic devices, but applications have been limited by the need for an external magnetic field. Here electronic control and detection will be used, enabling high speed operation and making integration with semiconductor electronics easier. Investigation of magnetostatically driven output coupling could eliminate an important bottleneck in the design of high performance magnetic logic devices imposed by the weak signal from the inverse spin Hall effect. By combining superparamagnetic tunnel junctions, electronic feedback, and magnetostatically coupled patterns, the proposed research program will develop an accelerator for machine learning and a toolkit for exploration of reservoir computing.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.