Highly Intelligent Forward Design of Metamaterials Empowered by Circuit‐Physics‐Driven Deep Learning

Highly Intelligent Forward Design of Metamaterials Empowered by Circuit‐Physics‐Driven Deep Learning

Laser & Photonics Reviews

10.1002/lpor.202400724

https://doi.org/10.1002/lpor.202400724

Yiming Yan, Fajun Li, Jiaqing Shen, Mingyong Zhuang, Yuan Gao, Wei Chen, Yuyang Li, Zhilin Wu, Zhaogang Dong, Jinfeng Zhu

deep learning, generalization, metamaterials, optical design, physicsinformed

19 November 2024

Yan, Y., Li, F., Shen, J., Zhuang, M., Gao, Y., Chen, W., Li, Y., Wu, Z., Dong, Z., & Zhu, J. (2024). Highly Intelligent Forward Design of Metamaterials Empowered by Circuit‐Physics‐Driven Deep Learning. Laser & Photonics Reviews, 19(5). Portico. https://doi.org/10.1002/lpor.202400724

The field of advanced metamaterial design has witnessed the great progress of artificial intelligence (AI) for science. Typically, the widely used deep learning is a purely data‐driven approach. However, it is often assumed as a black box performing interpolation among training data in a fuzzy way, which suffers from poor generalization outside the training domain of critical physical parameters. Inspired by physics‐guided deep learning, a circuit‐theory‐informed neural network (CTINN) in order to strengthen the generalization of metamaterial design is proposed. A series of plasmonic stack metamaterials (PSMs) are taken as learning paradigms of CTINN. Compared to conventional deep learning, the scheme decreases one‐order lower test loss of spectral prediction beyond the structure training span, and it possesses an extraordinary function to predict spectra of the extrapolated wavelength range. Due to the physics‐informed mechanism, the CTINN only adopts 10% training samples of the pure data‐driven counterpart, while it reduces >50% test loss. Moreover, the CTINN design is used to guide the PSM experiments and demonstrate its great potential for metamaterial development. This work introduces the theory of equivalent circuits into the neural network, empowers physics supervision to AI, and accomplishes the smart and powerful design of PSMs.

Publisher Copyright

This research / project is supported by the Agency of Science, Technology and Research - Manufacturing, Trade, and Connectivity (MTC) Individual Research Grant
Grant Reference no. : M21K2c0116

This research / project is supported by the Agency of Science, Technology and Research - Manufacturing, Trade, and Connectivity (MTC) Individual Research Grant
Grant Reference no. : M22K2c0088

This research / project is supported by the National Research Foundation, Singapore - Quantum Engineering Programme 2.0
Grant Reference no. : NRF2021-QEP2-03-P09

This research is supported by core funding from: Agency of Science, Technology and Research DELTA-Q 2.0
Grant Reference no. : C230917005

This is the peer reviewed version of the following article: Yan, Y., Li, F., Shen, J., Zhuang, M., Gao, Y., Chen, W., Li, Y., Wu, Z., Dong, Z., & Zhu, J. (2024). Highly Intelligent Forward Design of Metamaterials Empowered by Circuit‐Physics‐Driven Deep Learning. Laser & Photonics Reviews, 19(5). Portico, which has been published in final form at https://doi.org/10.1002/lpor.202400724. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions. This article may not be enhanced, enriched or otherwise transformed into a derivative work, without express permission from Wiley or by statutory rights under applicable legislation. Copyright notices must not be removed, obscured or modified. The article must be linked to Wiley’s version of record on Wiley Online Library and any embedding, framing or otherwise making available the article or pages thereof by third parties from platforms, services and websites other than Wiley Online Library must be prohibited.

https://oar.a-star.edu.sg/communities-collections/articles/21758

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Institute of Materials Research & Engineering