纳米尺度磁振子网络展望
Perspective on Nanoscaled Magnonic Networks
AI总结：
本文综述磁振子器件及其在神经形态、随机和储备池计算中的应用，提出逆向设计磁振子学作为新工具，并预测能耗、提出通用磁振子电路基准。
摘要：
近年来，随着人工智能的快速发展，人类正面临前所未有的数据处理需求。如今，几乎所有数据处理都是利用传统互补金属氧化物半导体（CMOS）电路中的电子来完成的。在过去的几十年里，科学家们一直在寻找更快、更高效的数据处理方法。现在，磁振子（自旋波的量子）在解决某些特定问题方面显示出更高效率和更低能耗的潜力。尽管磁振子学仍主要停留在学术界，但人们正在付出巨大努力来探索该领域的科学和技术挑战。许多概念验证原型已在实验室中成功开发和测试。在本文中，我们回顾了已开发的磁振子器件，并讨论了基于这些构建模块实现磁振子电路所面临的当前挑战。我们考察了自旋波在神经形态网络、随机计算和储备池计算中的应用，并讨论了在这些领域相对于传统电子学的优势。然后，我们介绍了一种新的强大工具——逆向设计磁振子学，它通过能够在短时间内精确设计和优化磁振子器件，有望彻底改变该领域。最后，我们对能耗进行了理论预测，并为通用磁振子电路提出了基准。
英文摘要：
With the rapid development of artificial intelligence in recent years, mankind is facing an unprecedented demand for data processing. Today, almost all data processing is performed using electrons in conventional complementary metal-oxide-semiconductor (CMOS) circuits. Over the past few decades, scientists have been searching for faster and more efficient ways to process data. Now, magnons, the quanta of spin waves, show the potential for higher efficiency and lower energy consumption in solving some specific problems. While magnonics remains predominantly in the realm of academia, significant efforts are being made to explore the scientific and technological challenges of the field. Numerous proof-of-concept prototypes have already been successfully developed and tested in laboratories. In this article, we review the developed magnonic devices and discuss the current challenges in realizing magnonic circuits based on these building blocks. We look at the application of spin waves in neuromorphic networks, stochastic and reservoir computing and discuss the advantages over conventional electronics in these areas. We then introduce a new powerful tool, inverse design magnonics, which has the potential to revolutionize the field by enabling the precise design and optimization of magnonic devices in a short time. Finally, we provide a theoretical prediction of energy consumption and propose benchmarks for universal magnonic circuits.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
