Summary
Speckle patterns are distinct features of disordered materials. While often regarded as mere noise, the intricate and responsive speckle patterns to minor fluctuations in external factors render them an ideal candidate for applications in hyperspectral imaging and sensing. The MetaSpectrometer harnesses the high information capacity of the speckle pattern of resonant disordered metasurfaces for compact, machine learning-assisted, non-invasive, and efficient near-infrared spectroscopy. An ultra-compact footprint, portability, cost-efficiency, minimal losses, machine-learning assisted recognition, angle-insensitivity, suppressed specular scatterance, significant memory effect, and ability to accommodate low coherence fields are the main features that cannot be currently obtained with any technology proposed so far and would represent a huge step forward in the state of the art. MetaSpectrometer brings together the complementary expertise of the postdoctoral researcher (PR) in metasurface engineering; the incoming host (CNRS-C2N, France) in silicon photonics, optoelectronics and nanofabrication; the associated the outgoing host (MIT, USA) in spectroscopy and machine learning, the secondment host (Rice, USA) in lensless imaging and the industrial partner (ZEISS, Germany) in spectroscopy and market analysis. The project allows PR to acquire expertise in silicon photonics technology's design, fabrication, characterization, and commercialization while giving back his expertise in metasurface design. The developed NIR spectrometers will find direct potential applications in wearable health monitoring systems, food screening, agriculture, and environmental screening that a remarkable global market size. The distribution of the work packages with distinct and clearly defined milestones and deliverables makes sure that the progress of the project will be monitored and matched with the work plan.
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More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/101146245 |
| Start date: | 01-04-2025 |
| End date: | 30-09-2028 |
| Total budget - Public funding: | - 353 380,00 Euro |
Cordis data
Original description
Speckle patterns are distinct features of disordered materials. While often regarded as mere noise, the intricate and responsive speckle patterns to minor fluctuations in external factors render them an ideal candidate for applications in hyperspectral imaging and sensing. The MetaSpectrometer harnesses the high information capacity of the speckle pattern of resonant disordered metasurfaces for compact, machine learning-assisted, non-invasive, and efficient near-infrared spectroscopy. An ultra-compact footprint, portability, cost-efficiency, minimal losses, machine-learning assisted recognition, angle-insensitivity, suppressed specular scatterance, significant memory effect, and ability to accommodate low coherence fields are the main features that cannot be currently obtained with any technology proposed so far and would represent a huge step forward in the state of the art. MetaSpectrometer brings together the complementary expertise of the postdoctoral researcher (PR) in metasurface engineering; the incoming host (CNRS-C2N, France) in silicon photonics, optoelectronics and nanofabrication; the associated the outgoing host (MIT, USA) in spectroscopy and machine learning, the secondment host (Rice, USA) in lensless imaging and the industrial partner (ZEISS, Germany) in spectroscopy and market analysis. The project allows PR to acquire expertise in silicon photonics technology's design, fabrication, characterization, and commercialization while giving back his expertise in metasurface design. The developed NIR spectrometers will find direct potential applications in wearable health monitoring systems, food screening, agriculture, and environmental screening that a remarkable global market size. The distribution of the work packages with distinct and clearly defined milestones and deliverables makes sure that the progress of the project will be monitored and matched with the work plan.Status
SIGNEDCall topic
HORIZON-MSCA-2023-PF-01-01Update Date
18-11-2024
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