Amorphous Boron Nitride as an Ultrathin Copper Diffusion Barrier for Advanced Interconnects

TL;DR

This paper explores amorphous boron nitride ($\rm \alpha$-BN) as an effective ultrathin diffusion barrier for copper in semiconductor interconnects, combining theoretical modeling with experimental diffusion studies.

Contribution

It introduces a machine learning-based simulation approach to analyze $\rm \alpha$-BN$'$s atomic structure and evaluates its barrier performance against copper diffusion.

Findings

$\rm \alpha$-BN shows excellent copper diffusion resistance.

Theoretical models accurately predict barrier properties.

Experimental results confirm $\rm \alpha$-BN$'$s potential in nanoelectronics.

Abstract

This study focuses on amorphous boron nitride ($\rm \alpha$-BN) as a novel diffusion barrier for advanced semiconductor technology, particularly addressing the critical challenge of copper diffusion in back-end-of-line (BEOL) interconnects. Owing to its ultralow dielectric constant and robust barrier properties, $\rm \alpha$-BN is examined as an alternative to conventional low-k dielectrics. The investigation primarily employs theoretical modelling, using a Gaussian Approximation Potential, to simulate and understand the atomic-level interactions. This machine learning-based approach allows the performance of realistic simulations of amorphous structure of $\rm \alpha$-BN, enabling the exploration of the impact of different film morphologies on barrier efficacy. Furthermore, we studied the electronic and optical properties of the films using a simple Tight-Binding model. In addition to the theoretical studies, we performed diffusion studies of copper through PECVD $\rm \alpha$-BN on Si. The results from both the theoretical and experimental investigations highlight the potential of $\rm \alpha$-BN as a highly effective diffusion barrier, suitable for integration in nanoelectronics. This research shows that $\rm \alpha$-BN is a promising candidate for BEOL interconnects but also demonstrates the synergy of advanced computational models and experimental methods in material innovation for semiconductor applications.