Project description
Nitrogen-vacancy tech for faster, more sensitive magnetometry
Nitrogen-vacancy (NV) centres in diamonds are a mature and promising quantum imaging technology known for their ease of operation. With this in mind, the EU-funded PROMISE project will develop widefield magnetometer prototypes. These NV-based sensors can generate magnetic field maps without scanning, leading to significantly faster acquisition times, a broader field of view, and improved sensitivity. This will benefit industries such as semiconductors, materials science, aerospace and biotechnology. The project will support market adoption of a compact, affordable, low-power benchtop prototype that maintains high performance. Additionally, it will develop machine learning software to simplify data acquisition and analysis for users without quantum expertise, enabling automated inspection processes.
Objective
PROMISE is a consortium that focuses on the application of Nitrogen Vacancy (NV) in diamond quantum technology for imaging. The aim is to guide the development and use of this mature and promising quantum technology, which is known for its ease of operation.
PROMISE leads the NV based quantum imaging sensors to the next level of development building widefield magnetometer prototypes to measure relevant samples into operational environments (TRL7) to foster its market uptake. The PROMISE widefield NV magnetometer excels in imaging compared to other technologies by generating magnetic field maps without scanning. This results in a faster acquisition time (orders of magnitude faster than scanning protocols), a wide field of view and increased sensitivity. This speed up in the acquisition is the key to open up its use in a wide range of new applications.
The consortium is committed to leveraging the developed NV-based prototypes for a consolidated market uptake by involving relevant partners along the whole value-chain. A unified, compact, affordable and low-consumption benchtop prototype will be designed and developed without impacting performance and functionalities. Machine learning software is being developed to streamline both data acquisition and data analysis, to facilitate for non-quantum expert use of the device and paving the way to automate inspection process. PROMISE also includes expertise that will contribute to standardising designs and methods required for the industry.
During the project, four use cases will validate the prototypes, impacting the semiconductor industry, material science, aerospace and biotechnology. The industry, in general, will benefit from a tool that will enable improvements in their devices, materials and production processes, as well as provide a deeper understanding of mechanisms at the atomic level. It will also monitor events and dynamics to enable more accurate predictions and address pressing challenges in various domains.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques.
- natural sciencescomputer and information sciencessoftware
- engineering and technologyelectrical engineering, electronic engineering, information engineeringelectronic engineeringsensors
- natural sciencesphysical scienceselectromagnetism and electronicssemiconductivity
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HORIZON-IA - HORIZON Innovation ActionsCoordinator
48160 DERIO BIZKAIA
Spain