MANNGA: MAGNONIC ARTIFICIAL NEURAL NETWORKS AND GATE ARRAYS

我们寻求探索和挑战基于旋转的设备及其能源效率的极限。这将通过结合两种固有的节能技术范式来实现：(i)磁能学（使用自旋波-低能量磁激发-处理信号和数据）和（ii）神经形态计算（使用大规模集成系统和模拟电路以类似大脑的方式解决数据驱动的问题）。我们将使用纳米级手性磁谐振器作为人工神经网络的构建模块。网络的力量将通过创建磁控版本的现场可编程门阵列、水库计算机和循环神经网络来展示。器件的最终效率将通过(a)最大化其磁非线性来实现；(b)使用已知磁阻尼最低的外延钇铁石榴石，用于薄膜磁介质和谐振器；(c)使用无线传输电力。这种人工神经网络对谐振器的微磁状态敏感，可以在现有的磁数据存储范式中方便地编程和训练。后者包括磁性随机存取存储器（MRAM），它已经与CMOS兼容，而与其他自旋电子学技术范例的兼容性也将被寻求，探索和开发。因此，我们提出的前瞻性研究计划的关键目标是开发和建立一种新型的、革命性的、节能的基于自旋的组件和设备，用于绿色高科技数据通信、处理和存储技术，从而帮助释放自旋电子学的全部潜力。我们将寻求向感兴趣的欧洲和国际公司传播我们开发的和适当保护的设计、工艺和技术，从而提高欧洲高科技产业的竞争力。

We seek to explore and challenge the limits of spin-based devices and their energy efficiency. This will be achieved by combining two inherently energy-efficient technology paradigms: (i) magnonics (using spin waves – low energy magnetic excitations – to process signals and data) and (ii) neuromorphic computing (using large-scale integrated systems and analog circuits to solve data-driven problems in a brain-like manner). We will use nanoscale chiral magnonic resonators as building blocks of artificial neural networks. The power of the networks will be demonstrated by creating magnonics versions of field programmable gate arrays, reservoir computers, and recurrent neural networks. The ultimate efficiency of the devices will be achieved by (a) maximising their magnetic nonlinearity; (b) using epitaxial yttrium iron garnet, which has the lowest known magnetic damping, for thin film magnonic media and resonators; and (c) using wireless delivery of power. Sensitive to the resonators’ micromagnetic states, such artificial neural networks will be conveniently programmable and trainable within existing paradigms of magnetic data storage. The latter includes magnetic random-access memory (MRAM), which is already compatible with CMOS, while compatibility with other technology paradigms of spintronics will also be sought, explored, and exploited. Thereby, the key ambition of our proposed very forward-looking research programme is to develop and establish a novel, revolutionary class of energy-efficient spin-based components and devices for use in green high-tech data communication, processing, and storage technologies, thereby helping unlock the full potential of spintronics. We will seek dissemination of our developed and appropriately protected designs, processes, and technologies to interested European and international companies, thereby improving the competitiveness of the European high-tech industry.