FET: Small: Collaborative Research: Integrated Spintronic Synapses and Neurons for Neuromorphic Computing Circuits - I(SNC)^2

当今有许多紧迫的问题，需要实时完成数据密集型任务。从DNA测序，到自动驾驶汽车识别行人，再到预测飞行物体的轨迹。在这些示例中，传统计算面临着性能瓶颈，计算时间和精力受到内存访问的严重限制。如果计算机能更接近大脑的计算方式，记忆和计算就像大脑的神经元（和突触）一样紧密相连，那么这些任务就可以用少一百万倍的能量来完成。这需要研究设计和建造人工神经元和突触，并研究将它们连接成神经形态回路。由于这种新型计算将解决许多不同类型的问题，因此该领域的研究不仅会对半导体工业产生影响，而且会对医学，国防和新技术产生深远的影响。该项目将在这一跨学科领域培养和培养多名在学术界、国家实验室和工业界备受追捧的博士和本科生。它还将对扩大女性和未被充分代表的少数族裔在计算机领域的参与具有重要意义：研究人员试图教育和培训来自德克萨斯州的女性和西班牙裔学生。由磁性材料（如铁）制成的纳米器件具有许多特性，使它们特别适合作为人工神经元和突触来实现这种计算。然而，在使用磁性设备进行神经形态计算方面仍然存在许多技术问题，这也是本项目旨在解决的问题：结合自旋电子神经元和突触的电路的实验研究很少，到目前为止设计的设备和电路并没有捕捉到所有期望的生物行为，并且没有设计出没有外部硅基设备的电路。实验和电路设计之间的跨学科合作努力将通过构建和研究使用三端磁隧道结器件的电路来解决这些挑战。这项研究将导致设计和制造出更准确地代表大脑功能的新型磁性设备，并测量磁性设备在电路中的行为。该项目有可能建立磁性材料作为神经形态计算的平台，类似于硅是传统计算的平台材料。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

There are many pressing problems today where data-intensive tasks are needed to be accomplished in real time. This can range from sequencing DNA, to self-driving cars recognizing a person walking by, to predicting the trajectory of a flying object. In these examples, traditional computing faces a performance wall where the computing time and energy is severely limited by memory access. If computers could be built closer to the way the brain computes, where memory and computation are densely connected together like the neurons (and synapses) of the brain, these tasks could be performed with a million times less energy. This requires doing research on designing and building artificial neurons and synapses, and research on connecting them together into neuromorphic circuits. Due to the many different kinds of problems this new type of computing will address, research in this area will have impact not only in the semiconductor industry, but also far-reaching impact in medicine, defense, and new technologies. This project will educate and train multiple Ph.D.-level and undergraduate students in this interdisciplinary field, with skills highly sought after in academia, national labs, and industry. It will also have significance for broadening participation of women and under-represented minorities in computing: the researchers seek to educate and train women and Hispanic students from their state of Texas.Nanodevices made from magnetic materials (such as iron) have many properties that make them uniquely suitable as artificial neurons and synapses to enable such computing. Nevertheless, a number of technical problems remain in using magnetic devices for neuromorphic computing, which this project aims to address: there has been little experimental study of circuits that combine spintronic neurons and synapses, the devices and circuits designed so far do not capture all the desired biological behaviors, and there have been no circuits designed that operate without external silicon-based devices. This interdisciplinary collaborative effort between experiment and circuit design will address these challenges by building and studying circuits using three-terminal magnetic tunnel junction devices. The research will result in design and fabrication of new types of these magnetic devices that more accurately represent the brain's functions, and in measurements of the magnetic devices' behavior in circuits. The project has the potential to establish magnetic materials as a platform for neuromorphic computing, similar to how silicon is the platform material for traditional 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.