Recent news
Publications
Sulagna Sarkar; Anqi Ji; Zachary Jermain; Robert Lipton; Mark L Brongersma; Kaushik Dayal; Hae Young Noh
Physics‐Informed Machine Learning for Inverse Design of Optical Metamaterials Journal Article
In: Advanced Photonics Research, pp. 2300158, 2023.
@article{sarkar2023physics,
title = {Physics‐Informed Machine Learning for Inverse Design of Optical Metamaterials},
author = {Sulagna Sarkar and Anqi Ji and Zachary Jermain and Robert Lipton and Mark L Brongersma and Kaushik Dayal and Hae Young Noh},
doi = {10.1002/adpr.202300158},
year = {2023},
date = {2023-10-11},
urldate = {2023-10-11},
journal = {Advanced Photonics Research},
pages = {2300158},
abstract = {Optical metamaterials manipulate light through various confinement and scattering processes, offering unique advantages like high performance, small form factor and easy integration with semiconductor devices. However, designing metasurfaces with suitable optical responses for complex metamaterial systems remains challenging due to the exponentially growing computation cost and the ill-posed nature of inverse problems. To expedite the computation for the inverse design of metasurfaces, a physics-informed deep learning (DL) framework is used. A tandem DL architecture with physics-based learning is used to select designs that are scientifically consistent, have low error in design prediction, and accurate reconstruction of optical responses. The authors focus on the inverse design of a representative plasmonic device and consider the prediction of design for the optical response of a single wavelength incident or a spectrum of wavelength in the visible light range. The physics-based constraint is derived from solving the electromagnetic wave equations for a simplified homogenized model. The model converges with an accuracy up to 97% for inverse design prediction with the optical response for the visible light spectrum as input, and up to 96% for optical response of single wavelength of light as input, with optical response reconstruction accuracy of 99%.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Optical metamaterials manipulate light through various confinement and scattering processes, offering unique advantages like high performance, small form factor and easy integration with semiconductor devices. However, designing metasurfaces with suitable optical responses for complex metamaterial systems remains challenging due to the exponentially growing computation cost and the ill-posed nature of inverse problems. To expedite the computation for the inverse design of metasurfaces, a physics-informed deep learning (DL) framework is used. A tandem DL architecture with physics-based learning is used to select designs that are scientifically consistent, have low error in design prediction, and accurate reconstruction of optical responses. The authors focus on the inverse design of a representative plasmonic device and consider the prediction of design for the optical response of a single wavelength incident or a spectrum of wavelength in the visible light range. The physics-based constraint is derived from solving the electromagnetic wave equations for a simplified homogenized model. The model converges with an accuracy up to 97% for inverse design prediction with the optical response for the visible light spectrum as input, and up to 96% for optical response of single wavelength of light as input, with optical response reconstruction accuracy of 99%.
Anqi Ji; Jung-Hwan Song; Qitong Li; Fenghao Xu; Ching-Ting Tsai; Richard C Tiberio; Bianxiao Cui; Philippe Lalanne; Pieter G Kik; David AB Miller; Mark L Brongersma
Quantitative phase contrast imaging with a nonlocal angle-selective metasurface Journal Article
In: Nature Communications, vol. 13, iss. 1, pp. 1-7, 2022.
@article{ji2022quantitative,
title = {Quantitative phase contrast imaging with a nonlocal angle-selective metasurface},
author = {Anqi Ji and Jung-Hwan Song and Qitong Li and Fenghao Xu and Ching-Ting Tsai and Richard C Tiberio and Bianxiao Cui and Philippe Lalanne and Pieter G Kik and David AB Miller and Mark L Brongersma},
year = {2022},
date = {2022-12-21},
urldate = {2022-12-21},
journal = {Nature Communications},
volume = {13},
issue = {1},
pages = {1-7},
abstract = {Phase contrast microscopy has played a central role in the development of modern biology, geology, and nanotechnology. It can visualize the structure of translucent objects that remains hidden in regular optical microscopes. The optical layout of a phase contrast microscope is based on a 4 f image processing setup and has essentially remained unchanged since its invention by Zernike in the early 1930s. Here, we propose a conceptually new approach to phase contrast imaging that harnesses the non-local optical response of a guided-mode-resonator metasurface. We highlight its benefits and demonstrate the imaging of various phase objects, including biological cells, polymeric nanostructures, and transparent metasurfaces. Our results showcase that the addition of this non-local metasurface to a conventional microscope enables quantitative phase contrast imaging with a 0.02π phase accuracy. At a high level, this work adds to the growing body of research aimed at the use of metasurfaces for analog optical computing.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Phase contrast microscopy has played a central role in the development of modern biology, geology, and nanotechnology. It can visualize the structure of translucent objects that remains hidden in regular optical microscopes. The optical layout of a phase contrast microscope is based on a 4 f image processing setup and has essentially remained unchanged since its invention by Zernike in the early 1930s. Here, we propose a conceptually new approach to phase contrast imaging that harnesses the non-local optical response of a guided-mode-resonator metasurface. We highlight its benefits and demonstrate the imaging of various phase objects, including biological cells, polymeric nanostructures, and transparent metasurfaces. Our results showcase that the addition of this non-local metasurface to a conventional microscope enables quantitative phase contrast imaging with a 0.02π phase accuracy. At a high level, this work adds to the growing body of research aimed at the use of metasurfaces for analog optical computing.
Anqi Ji
Quantitative Phase Contrast Imaging with A Nonlocal Angle-Selective Metasurface PhD Thesis
Stanford University, 2022.
@phdthesis{anqijithesis,
title = {Quantitative Phase Contrast Imaging with A Nonlocal Angle-Selective Metasurface},
author = {Anqi Ji},
url = {http://purl.stanford.edu/rk000mv7920},
year = {2022},
date = {2022-08-28},
urldate = {2022-08-28},
address = {Stanford, CA, US},
school = {Stanford University},
abstract = {Phase contrast microscopy has played a central role in the development of modern biology, geology, and nanotechnology. It can visualize the structure of translucent objects that remains hidden in regular optical microscopes. The optical layout of a phase contrast microscope is based on a 4f image processing setup and has essentially remained unchanged since its invention by Zernike in the early 1930s. Here, we propose a conceptually new approach to phase contrast imaging that harnesses the non-local optical response of a guided-mode-resonator metasurface. We highlight its benefits and demonstrate the imaging of various phase objects, including biological cells, polymeric nanostructures, and transparent metasurfaces. Our results showcase that the addition of this non-local metasurface to a conventional microscope enables quantitative phase contrast imaging with a 0.02π phase accuracy. At a high level, this work adds to the growing body of research aimed at the use of metasurfaces for analog optical computing.},
keywords = {},
pubstate = {published},
tppubtype = {phdthesis}
}
Phase contrast microscopy has played a central role in the development of modern biology, geology, and nanotechnology. It can visualize the structure of translucent objects that remains hidden in regular optical microscopes. The optical layout of a phase contrast microscope is based on a 4f image processing setup and has essentially remained unchanged since its invention by Zernike in the early 1930s. Here, we propose a conceptually new approach to phase contrast imaging that harnesses the non-local optical response of a guided-mode-resonator metasurface. We highlight its benefits and demonstrate the imaging of various phase objects, including biological cells, polymeric nanostructures, and transparent metasurfaces. Our results showcase that the addition of this non-local metasurface to a conventional microscope enables quantitative phase contrast imaging with a 0.02π phase accuracy. At a high level, this work adds to the growing body of research aimed at the use of metasurfaces for analog optical computing.
