5D Opto‐Magnetization Endowed by Physics‐Enhanced Deep Learning

Autor: Weichao Yan, Guoning Huang, Xiaohao Zhang, Jia Zhou, Mengqiang Cai, Ruiming Xiao, Peng Chen, Guohong Dai, Xiaohua Deng, Zhongquan Nie
Jazyk: angličtina
Rok vydání: 2023
Předmět:
Zdroj: Advanced Photonics Research, Vol 4, Iss 3, Pp n/a-n/a (2023)
Druh dokumentu: article
ISSN: 2699-9293
DOI: 10.1002/adpr.202200272
Popis: In the era of big data, all‐optical control of the magnetization is recognized as an alternative scheme that boosts the accelerating advance of multifunctional integrated opto‐magnetization devices with high‐density capacity. The light‐induced magnetizations demonstrated so far are devoted to steering their spatial orientations and structures by engineering the complicated phase, amplitude, and polarization modulations of incident wavefronts, which, however, confront low efficiency, weak flexibility, and limited dimension. To tackle these issues efficaciously, a novel strategy is proposed to first achieve 5D opto‐magnetization composed of 3D spatial location, vectorial orientation as well as magnitude. This relies on physics‐enhanced deep learning incorporating multilayer perceptron (MLP) artificial neural network and opto‐magnetization principles. The preeminent magnetization morphology largely expedites the improvement in multi‐dimensional storage. The proposed facile approach is time‐efficient, flexible, and accurate to attain the prescribed magnetization. Moreover, the presenting findings and proposed route are not only applied for magnetization manipulation, but also applicable to the control of the structured light field.
Databáze: Directory of Open Access Journals