What happens at the atomic scale when a bit of information is written in a ferroelectric memory, and how does it determine the behaviour of a real device? This PhD will address these questions by combining machine learning, large-scale atomistic simulations and device-scale modelling.

Ferroelectric Hafnium Zirconium Oxide (HZO) has become a key material for ultra-low-power non-volatile memories such as FeRAM and ferroelectric transistors (FeFETs). It is CMOS-compatible and remains ferroelectric in films only a few nanometres thick. Yet its ferroelectric behaviour is still only partly understood. It depends strongly on composition, defects, microstructure and interfaces, and it is governed by switching mechanisms at the nanoscale. Density functional theory (DFT) is highly accurate but limited to a few hundred atoms, far too few to capture these mechanisms. Machine-learning interatomic potentials, trained on DFT calculations, now overcome this limit. They can simulate the response of materials to an electric field with near-DFT accuracy on systems of up to a million atoms.

Building on our in-house machine-learning framework, you will first run large-scale molecular dynamics simulations of ferroelectric switching in HZO films and in complete TiN/HZO/TiN capacitors under applied voltage. You will study domain nucleation, domain-wall motion and the role of oxygen vacancies and electrode interfaces. You will then use these results to parameterize a device-scale model of the full memory cell, in order to interpret electrical measurements on FeRAM devices fabricated at CEA-Leti. You will work closely with the CEA-Leti teams in charge of FeRAM integration and advanced characterization.