For the past 20 years, the semiconductor industry has been searching for dielectric materials with low diffusivity to reduce the size of back-end-of-logic (BEOL) interconnects and to further promote the miniaturization of logic and memory devices in electronic circuits.However, most dielectric materials suffer from poor mechanical properties, insufficient chemical stability, and not-enough low metal diffusivity, which leads to reliability failures. Amorphous boron nitride (aBN) films have been discovered in 2020 to offer a new efficient alternative to currently used materials in interconnects technologies, given their ultralow dielectric constant together with its low temperature growth, scalability, and strong barrier property against copper migration.
By using Artificial Intelligence (AI)-based techniques such as machine learning coupled to molecular dynamics, we have predicted that a moderate carbon content could further improve the properties of this material, currently studied by the nanoelectrionics industry such as SAMSUNG, TSMC or INTEL.
Indeed, we have found a large improvement of the thermal stability and mechanical properties of amorphous boron-nitride upon carbon doping. By generating versatile force fields using first-principles and machine learning simulations, we have investigated the structural properties of amorphous boron-nitride with varying contents of carbon (from a few percent to 40%). It was found that for 20% of carbon, the sp3/sp2 ratio reaches a maximum with a negligible graphitisation effect, resulting in an improvement of the thermal stability by up to 20% while the bulk Young’s modulus increases by about 30%.
These results are providing a guide to experimentalists and engineers to further tailor the growth conditions of aBN-based compounds as non-conductive diffusion barriers and ultralow dielectric coefficient materials for a number of applications including interconnect technology.
