Séminaire 10.07.2014 à 14h

Publié le : 10/07/2014

Alejandro Strachan (School of Materials Engineering and Birck Nanotechnology Center, Purdue University, Indiana, Etats-Unis), présentera un séminaire le 10 juillet 2014 à 14h00 dans la bibliothèque du laboratoire PHENIX (7e étage, bâtiment F, porte 754) intitulé :

Predictive modeling of materials and devices with quantified uncertainties


Predictive, physics-based modeling with quantified uncertainties has the potential to revolutionize design and certification of materials and devices. Accomplishing this requires not only advances in modeling and simulation across scales but also their synergistic combination with experiments via rigorous methods to quantify uncertainties and arrive at the desired decision in an optimal manner. I will illustrate our recent progress in the field with two applications :

Atomistic simulations of nanoscale electrometallization cells for nanoelectronics. These resistance-switching devices operate via the electrochemical formation and disruption of metallic filaments and our simulations predict switching timescales ranging from hundreds of picoseconds to a few nanoseconds for device dimensions corresponding to the scaling limit. The simulations provide the first atomic-scale picture of the operation of these devices and show that stable switching proceeds via the formation of small metallic clusters and their progressive chemical reduction as they become connected to the cathode.

Multiscale predictions of the performance and degradation of a RF MEMS switch. The performance of these microdevices is governed by complex and coupled physics including solid and fluid mechanics, the contact between surfaces with nanoscale asperities and dielectric charging. I will focus on multiscale modeling of dielectric charging and show how capturing atomic scale variability via first principles modeling is critical for accurate device-level predictions. I will also discuss how variability and uncertainties at the atomic, nanostructural and geometrical levels as well as in materials properties affect device performance. Such probabilistic predictions are key for simulation-informed decision-making in device design.