Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Optical deep learning nano-profilometry   Determining the dimensions of nanostructures is critical to ensuring the
maximum performance of many geometry-sensitive nanoscale functional devices.
However, accurate metrology at the nanoscale is difficult using optics-based
methods due to the diffraction limit. In this article, we propose an optical
nano-profilometry framework with convolutional neural networks, which can
retrieve deep sub-wavelength geometrical profiles of nanostructures from their
optical images or scattering spectra. The generality, efficiency, and accuracy
of the proposed framework are validated by performing two different
measurements on three distinct nanostructures. We believe this work may
catalyze more explorations of optics-based nano-metrology with deep learning.

17
The categories of this paper are: q-fin
25
The categories of this paper are: patt-sol
24
The categories of this paper are: dg-ga
1
The categories of this paper are: cs
9
The categories of this paper are: nlin
31
The categories of this paper are: ao-sci
28
The categories of this paper are: alg-geom
10
The categories of this paper are: q-bio
34
The categories of this paper are: chem-ph
15
The categories of this paper are: nucl-ex
5
The categories of this paper are: gr-qc
8
The categories of this paper are: hep-ex
0
The categories of this paper are: hep-ph
37
The categories of this paper are: supr-con
27
The categories of this paper are: comp-gas
36
The categories of this paper are: cmp-lg