Neural‐Network Potential for Defect Formation Induced by Knock‐On Irradiation Damage in 4H‐SiC
Abstract Understanding the microscopic mechanism of the irradiation damage in silicon carbide (SiC) is of great importance for improving the irradiation resistance and the ion implantation processes of SiC‐based devices. Currently, the atomic‐scale simulations of the cascade collisions caused by irr...
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| Format: | Article |
| Language: | English |
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Wiley-VCH
2025-07-01
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| Series: | Advanced Electronic Materials |
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| Online Access: | https://doi.org/10.1002/aelm.202400911 |
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| author | Wei Liu Pengsheng Guo Ziyue Zheng Shiyou Chen Yu‐Ning Wu |
| author_facet | Wei Liu Pengsheng Guo Ziyue Zheng Shiyou Chen Yu‐Ning Wu |
| author_sort | Wei Liu |
| collection | DOAJ |
| description | Abstract Understanding the microscopic mechanism of the irradiation damage in silicon carbide (SiC) is of great importance for improving the irradiation resistance and the ion implantation processes of SiC‐based devices. Currently, the atomic‐scale simulations of the cascade collisions caused by irradiation in SiC are bottlenecked by the low accuracy of molecular dynamics (MD) with classical interatomic potentials and the low efficiency of ab initio MD (AIMD). In this study, a neural network potential (NNP) is constructed for the simulations of irradiation damage in 4H‐SiC using the stochastic surface walking (SSW) for the potential energy surface (PES) exploration. This potential is not only able to provide accurate structural and elastic properties, but also capable of predicting the defect properties and threshold displacement energies (TDEs) that well agree with the first‐principles results. More importantly, using this NNP, the directional dependence of the TDEs can be determined based on a set of high throughput calculations, and the minimal TDEs and the corresponding collision directions for Si and C can be predicted, which are in good agreement with the experimental results. This potential provides an efficient and accurate tool to accurately simulate the cascade collisions and gain fundamental understanding of the irradiation damage mechanisms of 4H‐SiC. |
| format | Article |
| id | doaj-art-fd59f4eb8ea84af68048215dbe763aca |
| institution | Kabale University |
| issn | 2199-160X |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Wiley-VCH |
| record_format | Article |
| series | Advanced Electronic Materials |
| spelling | doaj-art-fd59f4eb8ea84af68048215dbe763aca2025-08-20T03:50:58ZengWiley-VCHAdvanced Electronic Materials2199-160X2025-07-011111n/an/a10.1002/aelm.202400911Neural‐Network Potential for Defect Formation Induced by Knock‐On Irradiation Damage in 4H‐SiCWei Liu0Pengsheng Guo1Ziyue Zheng2Shiyou Chen3Yu‐Ning Wu4Key Lab of Polar Materials and Devices (MOE) and Department of Electronics East China Normal University Shanghai 200062 ChinaKey Lab of Polar Materials and Devices (MOE) and Department of Electronics East China Normal University Shanghai 200062 ChinaKey Lab of Polar Materials and Devices (MOE) and Department of Electronics East China Normal University Shanghai 200062 ChinaSchool of Microelectronics and Key Laboratory of Computational Physical Sciences (MOE) Fudan University Shanghai 200062 ChinaKey Lab of Polar Materials and Devices (MOE) and Department of Electronics East China Normal University Shanghai 200062 ChinaAbstract Understanding the microscopic mechanism of the irradiation damage in silicon carbide (SiC) is of great importance for improving the irradiation resistance and the ion implantation processes of SiC‐based devices. Currently, the atomic‐scale simulations of the cascade collisions caused by irradiation in SiC are bottlenecked by the low accuracy of molecular dynamics (MD) with classical interatomic potentials and the low efficiency of ab initio MD (AIMD). In this study, a neural network potential (NNP) is constructed for the simulations of irradiation damage in 4H‐SiC using the stochastic surface walking (SSW) for the potential energy surface (PES) exploration. This potential is not only able to provide accurate structural and elastic properties, but also capable of predicting the defect properties and threshold displacement energies (TDEs) that well agree with the first‐principles results. More importantly, using this NNP, the directional dependence of the TDEs can be determined based on a set of high throughput calculations, and the minimal TDEs and the corresponding collision directions for Si and C can be predicted, which are in good agreement with the experimental results. This potential provides an efficient and accurate tool to accurately simulate the cascade collisions and gain fundamental understanding of the irradiation damage mechanisms of 4H‐SiC.https://doi.org/10.1002/aelm.202400911irradiation damageneural‐network potentialSiCthreshold displacement energy |
| spellingShingle | Wei Liu Pengsheng Guo Ziyue Zheng Shiyou Chen Yu‐Ning Wu Neural‐Network Potential for Defect Formation Induced by Knock‐On Irradiation Damage in 4H‐SiC Advanced Electronic Materials irradiation damage neural‐network potential SiC threshold displacement energy |
| title | Neural‐Network Potential for Defect Formation Induced by Knock‐On Irradiation Damage in 4H‐SiC |
| title_full | Neural‐Network Potential for Defect Formation Induced by Knock‐On Irradiation Damage in 4H‐SiC |
| title_fullStr | Neural‐Network Potential for Defect Formation Induced by Knock‐On Irradiation Damage in 4H‐SiC |
| title_full_unstemmed | Neural‐Network Potential for Defect Formation Induced by Knock‐On Irradiation Damage in 4H‐SiC |
| title_short | Neural‐Network Potential for Defect Formation Induced by Knock‐On Irradiation Damage in 4H‐SiC |
| title_sort | neural network potential for defect formation induced by knock on irradiation damage in 4h sic |
| topic | irradiation damage neural‐network potential SiC threshold displacement energy |
| url | https://doi.org/10.1002/aelm.202400911 |
| work_keys_str_mv | AT weiliu neuralnetworkpotentialfordefectformationinducedbyknockonirradiationdamagein4hsic AT pengshengguo neuralnetworkpotentialfordefectformationinducedbyknockonirradiationdamagein4hsic AT ziyuezheng neuralnetworkpotentialfordefectformationinducedbyknockonirradiationdamagein4hsic AT shiyouchen neuralnetworkpotentialfordefectformationinducedbyknockonirradiationdamagein4hsic AT yuningwu neuralnetworkpotentialfordefectformationinducedbyknockonirradiationdamagein4hsic |