A Materials Screening Methodology for Scaled Non-Volatile Memory in the AI Era

Abstract

We demonstrate a simulation workflow based on first-principles calculations to rapidly screen candidate materials for viability as ferromagnetic electrodes in magnetic tunnel junctions (MTJs) for the next generation of high-performance magnetic random access memory (MRAM) technology. For a series of Fe-based alloys with a fixed crystal structure, we calculate formation energies, bulk spin polarization, and essential magnetic properties including magnetic anisotropy energy (MAE) and tunneling magnetoresistance (TMR). This work demonstrates a materials optimization strategy that can guide on-wafer experiments