Deep-learning-based compact spectrum analyzer on a chip

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

doi:10.1117/12.2579818

5 March 2021 Deep-learning-based compact spectrum analyzer on a chip

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

Proceedings Volume 11703, AI and Optical Data Sciences II; 117031H (2021) https://doi.org/10.1117/12.2579818
Event: SPIE OPTO, 2021, Online Only

Abstract

We report a deep-learning based compact spectrometer. Using a spectral encoder chip composed of unique plasmonic tiles (containing periodic nanohole-arrays), diffraction patterns created by the transmitted light through these tiles are captured by a CMOS sensor-array, without the use of any lenses or other components between the plasmonic encoder and the CMOS-chip. A neural network rapidly reconstructs the input light spectrum from the recorded lensless image data, which was blindly tested on randomly-generated new spectra to demonstrate the success of this computational on-chip spectrometer, which will find applications in various fields that demand low-cost and compact spectrum analyzers.

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Citation Download Citation

Artem Goncharov, Calvin Brown, Zachary Ballard, Mason Fordham, Ashley Clemens, Yunzhe Qui, Yair Rivenson, and Aydogan Ozcan "Deep-learning-based compact spectrum analyzer on a chip", Proc. SPIE 11703, AI and Optical Data Sciences II, 117031H (5 March 2021); https://doi.org/10.1117/12.2579818

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KEYWORDS

Spectrum analysis

Computer programming

Spectroscopy

Neural networks

Plasmonics

Optical design

Sensors

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