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CALCE Webinar - Printed Hybrid Electronics (PHEs): Are They Really Ready for Deployment?Tuesday, November 17, 2020 11:00 am US EDT
Several different ink types are being explored, including (i) polymer dielectric inks; (ii) conductive inks consisting of metal nano-particles, micro-particle and micro-flakes; and (iii) specialty carbon-based inks for sensors. Thermal and photonic curing and sintering methods are being studied. The application domains currently under exploration are in logic circuits, power electronics and RF electronics. The applications are either for printing new electronics or for on-site field repairs and replacements of aging and degraded/damaged/failed electronic systems. The implications for system sustainment are truly transformational and revolutionary because of the promise of reduced down-times and reduced repair costs of fielded systems. Successful realization of additively manufactured electronics will require fundamentally new process-structure-performance understanding due to the use of new material sets, new fabrication methods, new resulting microstructures, and new geometric form factors. This generates fundamentally new reliability physics and necessitates comprehensive new qualification and certification methodologies, based on an ecosystem of testing and modeling tools that are firmly based on reliability physics and data-based machine-learning algorithms. Qualification methods currently used for conventional electronics will not be applicable here because printed electronics rely on new material sets, new form factors, new processing methods and will hence experience new degradation mechanisms/modes. This presentation will discuss some of the current studies in progress at UMD within this PHE domain. About the Presenter: Prof. Abhijit Dasgupta is a founding member of the Center for Advanced Life Cycle Engineering. Prof. Dasgupta's research interests include accelerated product qualification, micromechanics of constitutive and damage behavior, properties of 3-D printed structures, fatigue damage modeling, and self-health monitoring in "smart" systems. |
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