# Accelerated Discovery of Topological Conductors for Nanoscale Interconnects

## TL;DR

This paper introduces a computational method to identify topological materials that could replace copper in nanoscale electronics due to their superior conductivity.

## Contribution

The study presents a scalable computational framework for evaluating surface transmission in topological semimetals, enabling data-driven discovery of interconnect materials.

## Key findings

- TiS, ZrB2, MoC, WC, and nitrides AN show conductance matching or exceeding copper and benchmark TSMs.
- A dataset of 3000 surface transmission values supports machine learning for rapid identification of interconnect compounds.
- Topological conductors are shown to overcome copper's scaling limits in nanoscale interconnects.

## Abstract

The sharp increase in resistivity of copper interconnects at ultra‐scaled dimensions threatens the continued miniaturization of integrated circuits. Topological semimetals (TSMs) with gapless surface states (Fermi arcs) provide conduction channels resistant to localization. Here we develop an efficient computational framework to quantify 0 K surface‐state transmission in nanowires derived from Wannier tight‐binding models of topological conductors that faithfully reproduce relativistic density functional theory results. Sparse matrix techniques enable scalable simulations incorporating disorder and surface roughness, allowing systematic materials screening across sizes, chemical potentials, and transport directions. A dataset of 3000 surface transmission values reveals TiS, ZrB2, MoC, WC, and nitrides AN where A=(Mo,Ta,W) as candidates with conductance matching or exceeding copper and benchmark TSMs NbAs and NbP. This dataset further supports machine learning models for rapid interconnect compound identification. Our results highlight the promise of topological conductors in overcoming copper's scaling limits and provide a roadmap for data‐driven discovery of next‐generation interconnects.

Copper interconnects exhibit a sharp increase in resistivity at ultra‐scaled dimensions, threatening continued miniaturization of integrated circuits. The gapless surface states of topological semimetals provide conduction channels resistant to localization. We develop an efficient computational framework to generate a dataset of 3000 zero‐Kelvin surface transmission values for topological semimetals. This dataset reveals optimal interconnect candidates and supports machine learning models for future discovery workflows.