Using chemical-formula-based generalizable models to expand the search space for viable interconnect materials

The identification of viable interconnect materials with lower effective resistivity is critical in ensuring the further scaling of transistor CMOS technology. Previously, we curated a set of key metrics for over 15,000 previously synthesized systems, obtained from the Materials Project, to identify compounds that are promising metallic bulk conductors. Using this approach, we identify around 20 systems that can potentially outperform traditional interconnect materials such as Cu or Ru. Our selection criteria involves a multi-objective optimization of Fermi velocities, scalability and stability. For our stability metrics, we use the energy with respect to the convex hull, and 0K thermodynamic reaction energies with air, water and SiO2. . To identify more viable candidate materials, we train classification models based on the chemical formulas of these 15,000 systems to predict those with large Fermi velocity and stability metrics. We apply this approach to over 12,039 charge-balanced binary formulas (AxBy) and identify over 500 previously unconsidered chemical formulas with the potential to outperform Ru or Cu interconnects, and validate some of our entries with first-principles transport calculations. Our approach can be broadly utilized to identify suitable materials for specific transport applications.