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
- engineering and technologyelectrical engineering, electronic engineering, information engineeringinformation engineeringtelecommunicationsradio technologyradio frequency
- engineering and technologymechanical engineeringvehicle engineeringaerospace engineeringsatellite technology
- engineering and technologyelectrical engineering, electronic engineering, information engineeringinformation engineeringtelecommunicationsmobile phones
- natural sciencesmathematicspure mathematicsarithmeticsprime numbers
- natural sciencescomputer and information sciencesartificial intelligencemachine learning
Programme(s)
Funding Scheme
MSCA-IF - Marie Skłodowska-Curie Individual Fellowships (IF)Coordinator
70013 Irakleio
Greece