Hardware AI-empowered Ultrasensitive Detection

Qizhou Wang; Ning Li; Zhao He; Arturo Burguete Lopez; Maksim Makarenko; Fei Xiang; Andrea Fratalocchi

doi:10.1117/12.3009163

Hardware AI-empowered Ultrasensitive Detection

Qizhou Wang^*, Ning Li, Zhao He, Arturo Burguete Lopez, Maksim Makarenko, Fei Xiang, Andrea Fratalocchi

^*Corresponding author for this work

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

Abstract

This work proposed a universal platform for ultra-sensitive detection, which integrates sensory data acquisition and spectral feature extraction into a single machine learning (ML) hardware. We fabricated and tested the sensing platform in glucose detection tasks, reaching 5 orders of magnitude higher sensitivity compared to the state-of-the-art. This technology requires no bulky spectral measuring devices such as a spectrum analyzer but a standard off-the-shelf camera to achieve real-time detection of the glucose concentration.

Original language	English (US)
Title of host publication	Machine Learning in Photonics
Editors	Francesco Ferranti, Mehdi Keshavarz Hedayati, Andrea Fratalocchi
Publisher	SPIE
ISBN (Electronic)	9781510673526
DOIs	https://doi.org/10.1117/12.3009163
State	Published - 2024
Event	Machine Learning in Photonics 2024 - Strasbourg, France Duration: Apr 8 2024 → Apr 12 2024

Publication series

Name	Proceedings of SPIE - The International Society for Optical Engineering
Volume	13017
ISSN (Print)	0277-786X
ISSN (Electronic)	1996-756X

Conference

Conference	Machine Learning in Photonics 2024
Country/Territory	France
City	Strasbourg
Period	04/8/24 → 04/12/24

Bibliographical note

Publisher Copyright:
© 2024 SPIE.

ASJC Scopus subject areas

Electronic, Optical and Magnetic Materials
Condensed Matter Physics
Computer Science Applications
Applied Mathematics
Electrical and Electronic Engineering

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10.1117/12.3009163

Cite this

Wang, Q., Li, N., He, Z., Lopez, A. B., Makarenko, M., Xiang, F., & Fratalocchi, A. (2024). Hardware AI-empowered Ultrasensitive Detection. In F. Ferranti, M. K. Hedayati, & A. Fratalocchi (Eds.), Machine Learning in Photonics Article 1301704 (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 13017). SPIE. https://doi.org/10.1117/12.3009163

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title = "Hardware AI-empowered Ultrasensitive Detection",

abstract = "This work proposed a universal platform for ultra-sensitive detection, which integrates sensory data acquisition and spectral feature extraction into a single machine learning (ML) hardware. We fabricated and tested the sensing platform in glucose detection tasks, reaching 5 orders of magnitude higher sensitivity compared to the state-of-the-art. This technology requires no bulky spectral measuring devices such as a spectrum analyzer but a standard off-the-shelf camera to achieve real-time detection of the glucose concentration.",

author = "Qizhou Wang and Ning Li and Zhao He and Lopez, {Arturo Burguete} and Maksim Makarenko and Fei Xiang and Andrea Fratalocchi",

note = "Publisher Copyright: {\textcopyright} 2024 SPIE.; Machine Learning in Photonics 2024 ; Conference date: 08-04-2024 Through 12-04-2024",

year = "2024",

doi = "10.1117/12.3009163",

language = "English (US)",

series = "Proceedings of SPIE - The International Society for Optical Engineering",

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editor = "Francesco Ferranti and Hedayati, {Mehdi Keshavarz} and Andrea Fratalocchi",

booktitle = "Machine Learning in Photonics",

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Wang, Q , Li, N , He, Z , Lopez, AB, Makarenko, M, Xiang, F & Fratalocchi, A 2024, Hardware AI-empowered Ultrasensitive Detection. in F Ferranti, MK Hedayati & A Fratalocchi (eds), Machine Learning in Photonics., 1301704, Proceedings of SPIE - The International Society for Optical Engineering, vol. 13017, SPIE, Machine Learning in Photonics 2024, Strasbourg, France, 04/8/24. https://doi.org/10.1117/12.3009163

Hardware AI-empowered Ultrasensitive Detection. / Wang, Qizhou ; Li, Ning ; He, Zhao et al.
Machine Learning in Photonics. ed. / Francesco Ferranti; Mehdi Keshavarz Hedayati; Andrea Fratalocchi. SPIE, 2024. 1301704 (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 13017).

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

TY - GEN

T1 - Hardware AI-empowered Ultrasensitive Detection

AU - Wang, Qizhou

AU - Li, Ning

AU - He, Zhao

AU - Lopez, Arturo Burguete

AU - Makarenko, Maksim

AU - Xiang, Fei

AU - Fratalocchi, Andrea

PY - 2024

Y1 - 2024

N2 - This work proposed a universal platform for ultra-sensitive detection, which integrates sensory data acquisition and spectral feature extraction into a single machine learning (ML) hardware. We fabricated and tested the sensing platform in glucose detection tasks, reaching 5 orders of magnitude higher sensitivity compared to the state-of-the-art. This technology requires no bulky spectral measuring devices such as a spectrum analyzer but a standard off-the-shelf camera to achieve real-time detection of the glucose concentration.

AB - This work proposed a universal platform for ultra-sensitive detection, which integrates sensory data acquisition and spectral feature extraction into a single machine learning (ML) hardware. We fabricated and tested the sensing platform in glucose detection tasks, reaching 5 orders of magnitude higher sensitivity compared to the state-of-the-art. This technology requires no bulky spectral measuring devices such as a spectrum analyzer but a standard off-the-shelf camera to achieve real-time detection of the glucose concentration.

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DO - 10.1117/12.3009163

M3 - Conference contribution

AN - SCOPUS:85200230544

T3 - Proceedings of SPIE - The International Society for Optical Engineering

BT - Machine Learning in Photonics

A2 - Ferranti, Francesco

A2 - Hedayati, Mehdi Keshavarz

A2 - Fratalocchi, Andrea

PB - SPIE

T2 - Machine Learning in Photonics 2024

Y2 - 8 April 2024 through 12 April 2024

ER -

Hardware AI-empowered Ultrasensitive Detection

Abstract

Publication series

Conference

Bibliographical note

ASJC Scopus subject areas

Access to Document

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