Sparsity-based super-resolved coherent diffraction imaging of one-dimensional objects

Pavel Sidorenko (), Ofer Kfir, Yoav Shechtman, Avner Fleischer, Yonina C. Eldar, Mordechai Segev and Oren Cohen ()
Additional contact information
Pavel Sidorenko: Department of Physics and Solid State Institute
Ofer Kfir: Department of Physics and Solid State Institute
Yoav Shechtman: Department of Physics and Solid State Institute
Avner Fleischer: Department of Physics and Solid State Institute
Yonina C. Eldar: Department of Electrical Engineering
Mordechai Segev: Department of Physics and Solid State Institute
Oren Cohen: Department of Physics and Solid State Institute

Nature Communications, 2015, vol. 6, issue 1, 1-8

Abstract: Abstract Phase-retrieval problems of one-dimensional (1D) signals are known to suffer from ambiguity that hampers their recovery from measurements of their Fourier magnitude, even when their support (a region that confines the signal) is known. Here we demonstrate sparsity-based coherent diffraction imaging of 1D objects using extreme-ultraviolet radiation produced from high harmonic generation. Using sparsity as prior information removes the ambiguity in many cases and enhances the resolution beyond the physical limit of the microscope. Our approach may be used in a variety of problems, such as diagnostics of defects in microelectronic chips. Importantly, this is the first demonstration of sparsity-based 1D phase retrieval from actual experiments, hence it paves the way for greatly improving the performance of Fourier-based measurement systems where 1D signals are inherent, such as diagnostics of ultrashort laser pulses, deciphering the complex time-dependent response functions (for example, time-dependent permittivity and permeability) from spectral measurements and vice versa.

Date: 2015
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.nature.com/articles/ncomms9209 Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:6:y:2015:i:1:d:10.1038_ncomms9209

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/ncomms9209

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().