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) predicts material properties by solving the quantum-mechanical equations of the electrons. It 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.

Using our in-house machine-learning framework, you will run large-scale molecular dynamics simulations of ferroelectric switching in HZO. You will study domain nucleation, domain-wall motion and the role of defects, to help interpret experiments and guide materials optimization. You will interact with CEA-Leti teams working on FeRAM fabrication and advanced characterization.

You have a background in materials science, physics or chemistry (Master's or engineering school), and a strong interest in numerical simulation and/or machine learning. Python skills are required; experience with molecular dynamics, DFT or HPC is a plus.