Chen, W., Gao, Y., Yan, Y., Zhang, Z., Chen, K., Lin, Y., Shen, J., Zhuang, M., Dong, Z., & Zhu, J. (2026). Reality-infused deep learning for angle-resolved quasi-optical Fourier surfaces. PhotoniX, 7(1). https://doi.org/10.1186/s43074-026-00238-2
Abstract:
Optical Fourier surfaces (OFSs), featuring sinusoidally profiled diffractive elements, manipulate light through patterned nanostructures and incident angle modulation. Compared to altering structural parameters, tuning elevation and azimuthal angles offers greater design flexibility for light field control and significantly reduces fabrication cost. However, angle-resolved responses of OFSs are often complex due to diverse mode excitations and couplings, complicating the alignment between simulations and practical fabrication. Here, we present a reality-infused deep learning framework, empowered by angle-resolved measurements, to enable real-time and accurate predictions of angular dispersion in quasi-OFSs. This approach captures critical features, including nanofabrication and measurement imperfections, which conventional simulation-based methods typically overlook. Our framework significantly accelerates the design process while achieving predictive performance highly consistent with experimental observations across broad angular and spectral ranges. Our study supports valuable insights into the development of OFS-based devices, and represents a paradigm shift from simulation-driven to reality-infused methods, paving the way for advancements in optical design applications.
License type:
Attribution 4.0 International (CC BY 4.0)
Funding Info:
This research / project is supported by the Agency for Science, Technology and Research (A*STAR) - Manufacturing, Trade, and Connectivity Individual Research Grant
Grant Reference no. : M22K2c0088
This research / project is supported by the Singapore University of Technology and Design (SUTD) - Kickstarter Initiative (SKI)
Grant Reference no. : SKI 2021_06_05
This research / project is supported by the National Research Foundation - Competitive Research Programme
Grant Reference no. : NRF-CRP30-2023–0003
Description:
This is a post-peer-review, pre-copyedit version of an article published in PhotoniX. The final authenticated version is available online at: http://dx.doi.org/10.1186/s43074-026-00238-2.