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Plasmonic nanostructure design and characterization via Deep Learning.

ABSTRACT: Nanophotonics, the field that merges photonics and nanotechnology, has in recent years revolutionized the field of optics by enabling the manipulation of light-matter interactions with subwavelength structures. However, despite the many advances in this field, the design, fabrication and characterization has remained widely an iterative process in which the designer guesses a structure and solves the Maxwell's equations for it. In contrast, the inverse problem, i.e., obtaining a geometry for a desired electromagnetic response, remains a challenging and time-consuming task within the boundaries of very specific assumptions. Here, we experimentally demonstrate that a novel Deep Neural Network trained with thousands of synthetic experiments is not only able to retrieve subwavelength dimensions from solely far-field measurements but is also capable of directly addressing the inverse problem. Our approach allows the rapid design and characterization of metasurface-based optical elements as well as optimal nanostructures for targeted chemicals and biomolecules, which are critical for sensing, imaging and integrated spectroscopy applications.

SUBMITTER: Malkiel I

PROVIDER: S-EPMC6123479 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Plasmonic nanostructure design and characterization via Deep Learning.

Malkiel Itzik I Mrejen Michael M Nagler Achiya A Arieli Uri U Wolf Lior L Suchowski Haim H

Light, science & applications 20180905

Nanophotonics, the field that merges photonics and nanotechnology, has in recent years revolutionized the field of optics by enabling the manipulation of light-matter interactions with subwavelength structures. However, despite the many advances in this field, the design, fabrication and characterization has remained widely an iterative process in which the designer guesses a structure and solves the Maxwell's equations for it. In contrast, the inverse problem, i.e., obtaining a geometry for a d ...[more]

PMID: 30863544

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Project description:A long-term reusable sensor that provides the opportunity to easily regenerate the active surface and minimize the occurrence of undesired absorption events is an appealing solution that helps to cut down the costs and improve the device performances. Impressive advances have been made in the past years concerning the development of novel cutting-edge sensors, but the reusability can currently represent a challenge. Direct shielding of the sensor surface is not always applicable, because it can impact the device performance. This study reports an antiadhesive layer (AAL) made of 90 mg/mL DNA sodium salt from salmon testes (ssstDNA) for passivating gold plasmonic sensor surfaces. Our gold two-dimensional (2D) nanostructured plasmonic metasurfaces modified with AAL were used for DNA quantification. AAL is thin enough that the plasmonic sensor remains sensitive to subsequent deposition of DNA, which serves as an analyte. AAL protects the gold surface from unwanted nonspecific adsorption by enabling wash-off of the deposited analyte after analysis and thus recovery of the LSPR peak position (rLSPR). The calibration curve obtained on a single nanostructure (Achiral Octupolar, 100 nm pitch) gave an LOD = 105 ng/mL and an extraordinary dynamic range, performances comparable or superior to those of commercial UV-vis spectrometers for acid nucleic dosage. Two different analytes were tested: ssstDNA (∼2000 bp) in deionized water and double-strand DNA (dsDNA) of 546-1614 bp in 100 mM Tris buffer and 10 mM MgCl2. The two nanostructures (Achiral Octupolar 25 and 100) were found to have the same sensitivity to DNA in deionized water but different sensitivity to DNA in a salt/buffer solution, opening a potential for solute discrimination. To the best of our knowledge, this is the first report on the use of AAL made of several kilobase-pairs-long dsDNA to produce a reusable plasmonic sensor. The working principle and limitations are drawn based on the LSPR and SERS study.

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Plasmonic nanostructure design and characterization via Deep Learning.

Publications

Plasmonic nanostructure design and characterization via Deep Learning.

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