Deep learning-based spectral reconstruction on a chip using a scalable plasmonic encoder

Artem Goncharov; Calvin Brown; Zachary Ballard; Mason Fordham; Ashley Clemens; Yunzhe Qiu; Yair Rivenson; Aydogan Ozcan

Deep learning-based spectral reconstruction on a chip using a scalable plasmonic encoder

Artem Goncharov, Calvin Brown, Zachary Ballard, Mason Fordham, Ashley Clemens, Yunzhe Qiu, Yair Rivenson, Aydogan Ozcan

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

We demonstrate a deep learning-based spectroscopy framework using a low-cost on-chip plasmonic encoder. When blindly tested on N=14,648 random spectra our system shows competitive performance, where the reconstruction of an unknown spectrum on average takes ~28µs.

Original language	English
Title of host publication	2021 Conference on Lasers and Electro-Optics, CLEO 2021 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers
ISBN (Electronic)	9781943580910
State	Published - 1 May 2021
Externally published	Yes
Event	2021 Conference on Lasers and Electro-Optics, CLEO 2021 - Virtual, Online, United States Duration: 9 May 2021 → 14 May 2021

Publication series

Name	2021 Conference on Lasers and Electro-Optics, CLEO 2021 - Proceedings

Conference

Conference	2021 Conference on Lasers and Electro-Optics, CLEO 2021
Country/Territory	United States
City	Virtual, Online
Period	9/05/21 → 14/05/21

ASJC Scopus subject areas

Computer Networks and Communications
Electrical and Electronic Engineering
Electronic, Optical and Magnetic Materials
Instrumentation
Atomic and Molecular Physics, and Optics

Cite this

Goncharov, A., Brown, C., Ballard, Z., Fordham, M., Clemens, A., Qiu, Y., Rivenson, Y., & Ozcan, A. (2021). Deep learning-based spectral reconstruction on a chip using a scalable plasmonic encoder. In 2021 Conference on Lasers and Electro-Optics, CLEO 2021 - Proceedings (2021 Conference on Lasers and Electro-Optics, CLEO 2021 - Proceedings). Institute of Electrical and Electronics Engineers.

@inproceedings{61a5e166218f4edca71b1e2b6475ce31,

title = "Deep learning-based spectral reconstruction on a chip using a scalable plasmonic encoder",

abstract = "We demonstrate a deep learning-based spectroscopy framework using a low-cost on-chip plasmonic encoder. When blindly tested on N=14,648 random spectra our system shows competitive performance, where the reconstruction of an unknown spectrum on average takes \textasciitilde{}28µs.",

author = "Artem Goncharov and Calvin Brown and Zachary Ballard and Mason Fordham and Ashley Clemens and Yunzhe Qiu and Yair Rivenson and Aydogan Ozcan",

note = "Publisher Copyright: {\textcopyright} 2021 OSA.; 2021 Conference on Lasers and Electro-Optics, CLEO 2021 ; Conference date: 09-05-2021 Through 14-05-2021",

year = "2021",

month = may,

day = "1",

language = "English",

series = "2021 Conference on Lasers and Electro-Optics, CLEO 2021 - Proceedings",

publisher = "Institute of Electrical and Electronics Engineers",

booktitle = "2021 Conference on Lasers and Electro-Optics, CLEO 2021 - Proceedings",

address = "United States",

}

Goncharov, A, Brown, C, Ballard, Z, Fordham, M, Clemens, A, Qiu, Y, Rivenson, Y & Ozcan, A 2021, Deep learning-based spectral reconstruction on a chip using a scalable plasmonic encoder. in 2021 Conference on Lasers and Electro-Optics, CLEO 2021 - Proceedings. 2021 Conference on Lasers and Electro-Optics, CLEO 2021 - Proceedings, Institute of Electrical and Electronics Engineers, 2021 Conference on Lasers and Electro-Optics, CLEO 2021, Virtual, Online, United States, 9/05/21.

Deep learning-based spectral reconstruction on a chip using a scalable plasmonic encoder. / Goncharov, Artem; Brown, Calvin; Ballard, Zachary et al.
2021 Conference on Lasers and Electro-Optics, CLEO 2021 - Proceedings. Institute of Electrical and Electronics Engineers, 2021. (2021 Conference on Lasers and Electro-Optics, CLEO 2021 - Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Deep learning-based spectral reconstruction on a chip using a scalable plasmonic encoder

AU - Goncharov, Artem

AU - Brown, Calvin

AU - Ballard, Zachary

AU - Fordham, Mason

AU - Clemens, Ashley

AU - Qiu, Yunzhe

AU - Rivenson, Yair

AU - Ozcan, Aydogan

PY - 2021/5/1

Y1 - 2021/5/1

N2 - We demonstrate a deep learning-based spectroscopy framework using a low-cost on-chip plasmonic encoder. When blindly tested on N=14,648 random spectra our system shows competitive performance, where the reconstruction of an unknown spectrum on average takes ~28µs.

AB - We demonstrate a deep learning-based spectroscopy framework using a low-cost on-chip plasmonic encoder. When blindly tested on N=14,648 random spectra our system shows competitive performance, where the reconstruction of an unknown spectrum on average takes ~28µs.

UR - https://www.scopus.com/pages/publications/85120442909

M3 - Conference contribution

AN - SCOPUS:85120442909

T3 - 2021 Conference on Lasers and Electro-Optics, CLEO 2021 - Proceedings

BT - 2021 Conference on Lasers and Electro-Optics, CLEO 2021 - Proceedings

PB - Institute of Electrical and Electronics Engineers

T2 - 2021 Conference on Lasers and Electro-Optics, CLEO 2021

Y2 - 9 May 2021 through 14 May 2021

ER -

Deep learning-based spectral reconstruction on a chip using a scalable plasmonic encoder

Abstract

Publication series

Conference

ASJC Scopus subject areas

Other files and links

Fingerprint

Cite this