基于水平集方法的磁振子器件反设计拓扑优化。

npj Spintronics Pub Date : 2025-01-01 Epub Date: 2025-05-21 DOI:10.1038/s44306-025-00082-3
Andrey A Voronov, Marcos Cuervo Santos, Florian Bruckner, Dieter Suess, Andrii V Chumak, Claas Abert
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引用次数: 0

摘要

磁振学中的逆设计方法利用磁振子的波动特性和机器学习来开发具有超出分析方法能力的功能的逻辑器件。虽然模拟计算、布尔计算和神经形态计算很有前景,但当前的实现面临内存限制,这阻碍了复杂系统的设计。本研究提出了一种用于拓扑优化的水平集参数化方法,并结合伴随状态方法进行磁化动力学的内存效率模拟。该框架在NeuralMag中实现,NeuralMag是一个gpu加速的微磁求解器,具有节点有限差分格式和自动微分工具。为了验证该方法,我们通过对目标函数施加约束来优化磁性纳米颗粒的形状,并设计了一个300 nm宽的钇铁石榴石解复用器,实现了频率选择性自旋波分离。这些结果突出了该算法在探索各种初始配置的局部最小值方面的效率,确立了其作为磁振子逻辑器件逆设计的通用工具的实用性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Inverse-design topology optimization of magnonic devices using level-set method.

The inverse design approach in magnonics exploits the wave nature of magnons and machine learning to develop logical devices with functionalities that exceed the capabilities of analytical methods. While promising for analog, Boolean, and neuromorphic computing, current implementations face memory limitations that hinder the design of complex systems. This study presents a level-set parameterization method for topology optimization, combined with an adjoint-state approach for memory-efficient simulation of magnetization dynamics. The framework is implemented in NeuralMag, a GPU-accelerated micromagnetic solver featuring a nodal finite-difference scheme and automatic differentiation tools. To validate the method, we optimized the shape of a magnetic nanoparticle by applying constraints to the objective function, and designed a 300 nm-wide yttrium iron garnet demultiplexer achieving frequency-selective spin-wave separation. These results highlight the algorithm's efficiency in exploring local minima across various initial configurations, establishing its utility as a versatile tool for the inverse design of magnonic logic devices.

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