Project description
Developing proper reliability testing for radio frequency microelectromechanical systems
In addition to consumer electronics, the impact of high-power wireless systems such as radars and communication satellites throughout our private and work life has been immensely increased. This increase is mainly supported by novel electronic parts capable of dealing with the corresponding signals. Microelectromechanical systems for radio frequency applications (RF MEMS) are one of these parts and could allow for even greater advancements. Unfortunately, their reliability remains an open issue, particularly concerning the high-power implementations. The EU-funded PRIME project aspires to enable predictive reliability assessment by combining machine learning with conventional testing.
Objective
None of us can even imagine spending a day without using a mobile phone or staying far away from a wi-fi area. A deeper insight however reveals that high-power wireless communication systems, such as Radars and Satcoms, have even greater impact on our everyday lives by supporting safe and effective transportation and long-distance communications. This is practically enabled only thanks to electronic components capable to deal with the corresponding signals. Among others, Micro-Electro-Mechanical-Systems for Radio Frequency applications (RF MEMS) are now widely accepted as superior to their counterparts, with their reliability however remaining an open issue and a general concern. This is not only due the demanding scientific nature of the problem but also due to the difficulty to generalize the outcomes of even well-organized studies. Further to these, working in the high-power regime, RF MEMS will have to deal with an additional bunch of issue, presently marginally studied, making failure prediction an even more complicated accomplishment.
PRIME aspires to address this issue by identifying the proper high-power reliability testing and to combine this with the strength of machine learning techniques towards failure prediction. This will be achieved through an interdisciplinary approach relying on placing a fellow with expertise on device reliability physics to a host group working on high power RF electronic devices and systems, supported by two carefully designed secondment, for RF design and for machine learning techniques. Overall, PRIME envisions to equip RF MEMS scientists, engineers and stakeholders with a powerful tool that enables predictive diagnostics paving the way for overcoming the persisting reliability bottleneck, particularly concerning state of art high power applications.
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. See: The European Science Vocabulary.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
- engineering and technology electrical engineering, electronic engineering, information engineering information engineering telecommunications radio technology radio frequency
- engineering and technology mechanical engineering vehicle engineering aerospace engineering satellite technology
- engineering and technology electrical engineering, electronic engineering, information engineering information engineering telecommunications radio technology radar
- engineering and technology electrical engineering, electronic engineering, information engineering information engineering telecommunications mobile phones
- natural sciences computer and information sciences artificial intelligence machine learning
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Keywords
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Programme(s)
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
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H2020-EU.1.3. - EXCELLENT SCIENCE - Marie Skłodowska-Curie Actions
MAIN PROGRAMME
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H2020-EU.1.3.2. - Nurturing excellence by means of cross-border and cross-sector mobility
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Topic(s)
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Funding Scheme
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
MSCA-IF - Marie Skłodowska-Curie Individual Fellowships (IF)
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Call for proposal
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
(opens in new window) H2020-MSCA-IF-2020
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Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.
700 13 IRAKLEIO
Greece
The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.