Prior-information based super-resolution optical metrology of 2D nanoscale objects
Abstract
Previous work has shown that optical metrology of one-dimensional objects, such as slit width, can achieve improved accuracy by using prior information from similar objects to train the metrology estimator. Here, we demonstrate single-shot optical metrology of nanoscale elliptical particles by analysing their diffraction patterns to retrieve length, width and in-plane orientation using a neural-network estimator trained on prior information from nano-ellipses with varied dimensions and orientations. Fisher-information flow analysis was used to optimise the physical parameters of the metrology apparatus and maximise measurement accuracy. Using a 633 nm laser, we measure the dimensions of elliptical particles with accuracy down to λ/128, corresponding to 4.9 nm, and recover their orientation with 5° accuracy. Our results demonstrate the practicality of optical, deep-super-resolution, single-shot, multiparameter measurements of two-dimensional subwavelength objects, with potential relevance to microbiology and nanotechnology applications.
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BibTeX
@misc{so2026priorinformation,
title = {{Prior-information based super-resolution optical metrology of 2D nanoscale objects}},
author = {Jin-Kyu So and Eng Aik Chan and Carolina Rendón-Barraza and Giorgio Adamo and Nikolay I. Zheludev},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.12671},
eprint = {2609.12671},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.12671},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Prior-information based super-resolution optical metrology of 2D nanoscale objects
AU - Jin-Kyu So
AU - Eng Aik Chan
AU - Carolina Rendón-Barraza
AU - Giorgio Adamo
AU - Nikolay I. Zheludev
PY - 2026
DA - 2026/09/11
PB - arXiv
DO - 10.48550/arxiv.2609.12671
UR - https://arxiv.org/abs/2609.12671
AB - Previous work has shown that optical metrology of one-dimensional objects, such as slit width, can achieve improved accuracy by using prior information from similar objects to train the metrology estimator. Here, we demonstrate single-shot optical metrology of nanoscale elliptical particles by analysing their diffraction patterns to retrieve length, width and in-plane orientation using a neural-network estimator trained on prior information from nano-ellipses with varied dimensions and orientations. Fisher-information flow analysis was used to optimise the physical parameters of the metrology apparatus and maximise measurement accuracy. Using a 633 nm laser, we measure the dimensions of elliptical particles with accuracy down to λ/128, corresponding to 4.9 nm, and recover their orientation with 5° accuracy. Our results demonstrate the practicality of optical, deep-super-resolution, single-shot, multiparameter measurements of two-dimensional subwavelength objects, with potential relevance to microbiology and nanotechnology applications.
ER -