Descrizione del progetto
Sviluppare test di affidabilità adeguati per i sistemi microelettromeccanici a radiofrequenza
Oltre a quello dell’elettronica di consumo, l’impatto nella nostra vita privata e lavorativa dei sistemi wireless ad alta potenza come i radar e i satelliti di comunicazione è aumentato immensamente, principalmente grazie al supporto di nuovi componenti elettronici che sono in grado di gestire i segnali corrispondenti. I sistemi microelettromeccanici per applicazioni a radiofrequenza sono un esempio di ciò, e potrebbero consentire progressi ancora maggiori. Sfortunatamente la loro affidabilità rimane una questione aperta, in particolare per quanto riguarda le implementazioni ad alta potenza. Il progetto PRIME, finanziato dall’UE, intende consentire una valutazione predittiva dell’affidabilità combinando l’apprendimento automatico con i test tradizionali.
Obiettivo
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.
Campo scientifico
- 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
Parole chiave
Programma(i)
Argomento(i)
Meccanismo di finanziamento
MSCA-IF - Marie Skłodowska-Curie Individual Fellowships (IF)Coordinatore
70013 Irakleio
Grecia