Revealing the improved stability of amorphous boron-nitride upon carbon doping

Abstract

We report on 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...

Impact of hydrogenation on the stability and mechanical properties of amorphous boron nitride

“…However, larger H doping worsens the mechanical properties due to fewer sp 3 -hybridized atoms, lower density, and more porous structure. Reduction in mechanical properties with lower density and sp 3 -hybridized atoms has already been shown for α-BN and other amorphous structures [9,10,[34][35][36]. Even though the reported Young's modulus values in this study are lower than hexagonal and cubic BN, α-BN:H still has superior mechanical properties than other ultralow-dielectric materials.…”

“…However, at larger concentrations, H atoms disrupt the stability of the α-BN samples. Kaya et al [10] showed that the thermal stability of C-doped α-BN samples can be improved with larger amounts of sp 3 -hybridized atoms. Similarly, Cloutier et al [31] and Liu et al [32] showed that increasing the density of α-C films and number of sp 3 -hybridized atoms can improve the stability of films.…”

“…Experimental reports on α-BN indicate that it has a low dielectric constant, k-values lower than 2, and exhibits higher stability and mechanical properties compared to other low dielectric materials such as organic polymeric materials [7][8][9]. In addition, theoretical predictions suggest that a certain density of carbon content improves the structural and thermal properties of α-BN:C [10]. More recently there is also a study on the use of α-BN for interconnect capping layers [11].…”

“…However, neither these results nor the previous studies on ALD-grown α-BN [12,13], report on the effects of C and H content. In [10], using machine learning techniques and classical molecular dynamics, we explored the effects of C content on the physical properties of α-BN in an attempt to create an even more stable dielectric [10]. Also, it is well known that PECVD-grown Si 3 N 4 can contain large amounts of hydrogen [14] and it is likely that PECVD-grown α-BN may also contain hydrogen.…”

Atomistic modeling of the mechanical properties: the rise of machine learning interatomic potentials

“…A stable MLIP with an improved accuracy and stability can then be refitted using the second training dataset. This two-step passive training approach has recently been successfully applied in several studies for predicting the uniaxial stress-strain and failure responses of 2D materials 4,44,[47][48][49][50] and amorphous systems 51 and showed outstanding accuracy as compared with DFT results. The two-step passive training approach can substantially facilitate the development of MLIPs, but the stability is not guaranteed and thus the necessity of on-the-fly or active learning cannot be completely omitted.…”