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

Kaya, Onurcan; Colombo, Luigi; Antidormi, Aleandro; Lanza, Mario; Roche, Stephan

doi:10.1039/d2nh00520d

Cited by 11 publications

(16 citation statements)

References 72 publications

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“…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.…”

Section: Mechanical Properties Of α-Bn:hsupporting

confidence: 47%

“…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.…”

Section: Thermal Stability Of α-Bn:hmentioning

confidence: 99%

“…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].…”

Section: Introductionmentioning

confidence: 99%

“…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.…”

Section: Introductionmentioning

confidence: 99%

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

Kaya,

Colombo,

Antidormi

et al. 2024

J. Phys. Mater.

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Interconnect materials with ultralow dielectric constant, and good thermal and mechanical properties are crucial for the further miniaturization of electronic devices. Recently, it has been demonstrated that ultrathin amorphous boron nitride (aBN) films have a very low dielectric constant, high density (above 2.1 g/cm3), high thermal stability, and mechanical properties. The excellent properties of aBN derive from the nature and degree of disorder, which can be controlled at fabrication, allowing tuning of the physical properties for desired applications. Here, we report an improvement in the stability and mechanical properties of amorphous boron nitride upon hydrogen doping. With the introduction of a Gaussian approximation potential (GAP) for atomistic simulations, we investigate the changing morphology of amorphous boron nitride with varying H doping concentrations. We found that for 8 at\% of H doping, the concentration of $sp^3$-hybridized atoms reaches to a maximum which leads to an improvement of thermal stability and mechanical properties by 20\%. These results will be a guideline for experimentalists and process engineers to tune the growth conditions of amorphous boron nitride films for numerous applications.

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Section: Mechanical Properties Of α-Bn:hsupporting

confidence: 47%

Section: Thermal Stability Of α-Bn:hmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Kaya,

Colombo,

Antidormi

et al. 2024

J. Phys. Mater.

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“…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.…”

Section: Training a Mlip For The Analysis Of Mechanical Propertiesmentioning

confidence: 99%

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

et al. 2023

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Recent Advances in Machine Learning‐Assisted Multiscale Design of Energy Materials

Mortazavi

2024

Advanced Energy Materials

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This review highlights recent advances in machine learning (ML)‐assisted design of energy materials. Initially, ML algorithms were successfully applied to screen materials databases by establishing complex relationships between atomic structures and their resulting properties, thus accelerating the identification of candidates with desirable properties. Recently, the development of highly accurate ML interatomic potentials and generative models has not only improved the robust prediction of physical properties, but also significantly accelerated the discovery of materials. In the past couple of years, ML methods have enabled high‐precision first‐principles predictions of electronic and optical properties for large systems, providing unprecedented opportunities in materials science. Furthermore, ML‐assisted microstructure reconstruction and physics‐informed solutions for partial differential equations have facilitated the understanding of microstructure–property relationships. Most recently, the seamless integration of various ML platforms has led to the emergence of autonomous laboratories that combine quantum mechanical calculations, large language models, and experimental validations, fundamentally transforming the traditional approach to novel materials synthesis. While highlighting the aforementioned recent advances, existing challenges are also discussed. Ultimately, ML is expected to fully integrate atomic‐scale simulations, reverse engineering, process optimization, and device fabrication, empowering autonomous and generative energy system design. This will drive transformative innovations in energy conversion, storage, and harvesting technologies.

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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...

Cited by 11 publications

References 72 publications

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

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

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

Recent Advances in Machine Learning‐Assisted Multiscale Design of Energy Materials

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