Advanced Photonics, Volume. 7, Issue 3, 036004(2025)
Toward the meta-atom library: experimental validation of machine learning-based Mie-tronics
Fig. 1. Schematic illustration of ML-based meta-atom design. The training dataset contains regular (expressed with the equation in the blue box) and irregular meta-atom shapes with a fixed height of 320 nm. The optical response of the complete training dataset is shown in the blue box, with the dots denoting the mean of the specific moments and shaded bands representing their corresponding standard deviation. The desired optical response at a specified operating wavelength (red box) is then fed to the developed IDM that predicts the shape producing the required response (shown in the magenta box). The meta-atoms with the optimized shapes are then fabricated and placed in an array with a large enough spacing to minimize the coupling effects between the neighboring meta-atoms in the white light spectroscopy-based experiments, shown in the orange color box.
Fig. 2. ML-driven meta-atom design. The scattering spectra of the designed meta-atoms for supporting (a) ED, (b) MD, and (c) MQ Mie-type resonant modes, with their contribution reaching 67%, 61%, and 50% of the total response at the operating wavelengths of
Fig. 3. ML-based meta-atom design results based on the QNM expansion theory. The QNM-based total (black line) and eigenmode (colored lines) scattering cross-sections of the predicted shapes correspond to (a) ED, (b) MD, and (c) MQ meta-atoms. The solid line represents the dominant QNM, whereas the dashed lines denote the background modes. The negative values of the scattering cross-section are attributed to the energy exchange between the dominant and the background QNM fields. The normalized field distributions of the first two dominant and relevant modal contributions in the close vicinity of (d)
Fig. 4. Optical response of the predicted meta-atoms arranged in a periodic array. Numerically calculated linear-optical zeroth diffracted order transmittance spectra for arrays of embedded meta-atoms corresponding to (a) ED, (b) MD, and (c) MQ resonant modes with a lattice constant of
Fig. 5. Experimental verification of ML-based Mie-tronics. (a) The experimental workflow of ML-based multipole engineering method. (b) The steps taken to fabricate the predicted shapes by the developed IDM. Using a multi-step process, a 320-nm
Fig. 6. Application of ML-based Mie-tronics for the VUV generation. (a) The schematic of the THG in the meta-atom designed using the ML approach. The generated harmonics consist of homogeneous and inhomogeneous components. The inhomogeneous component is phase-locked with the pump and, as a result, experiences the refractive index of the material at the pump wavelength of 570 nm (corresponding to the transparent wavelength range of the
Get Citation
Copy Citation Text
Hooman Barati Sedeh, Renee C. George, Fangxing Lai, Hao Li, Wenhao Li, Yuruo Zheng, Dmitrii Tstekov, Jiannan Gao, Austin Moore, Jesse Frantz, Jingbo Sun, Shumin Xiao, Natalia M. Litchinitser, "Toward the meta-atom library: experimental validation of machine learning-based Mie-tronics," Adv. Photon. 7, 036004 (2025)
Category: Research Articles
Received: Dec. 12, 2024
Accepted: Feb. 27, 2025
Posted: Feb. 27, 2025
Published Online: Apr. 27, 2025
The Author Email: Litchinitser Natalia M. (natalia.litchinitser@duke.edu)