Device-Scale Atomistic Simulations of Heat Transport in Advanced Field-Effect Transistors
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , , , , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2025
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866917103377842176 |
|---|---|
| author | Xu, Ke Wang, Gang Liang, Ting Xiao, Yang Ding, Dongliang Guo, Haichang Gao, Xiang Tong, Lei Wan, Xi Zhang, Gang Xu, Jianbin |
| author_facet | Xu, Ke Wang, Gang Liang, Ting Xiao, Yang Ding, Dongliang Guo, Haichang Gao, Xiang Tong, Lei Wan, Xi Zhang, Gang Xu, Jianbin |
| contents | Self-heating in next-generation, high-power-density field-effect transistor limits performance and complicates fabrication. Here, we introduce NEP-FET, a machine-learned framework for device-scale heat transport simulations of field-effect transistors. Built upon the neuroevolution potential, the model extends a subset of the OMat24 dataset through an active-learning workflow to generate a chemically diverse, interface-rich reference set. Coupled with the FETMOD structure generator module, NEP-FET can simulate realistic field-effect transistor geometries at sub-micrometer scales containing millions of atoms, and delivers atomistic predictions of temperature fields, per-atom heat flux, and thermal stress in device structures with high fidelity. This framework enables rapid estimation of device-level metrics, including heat-flux density and effective thermal conductivity. Our results reveal pronounced differences in temperature distribution between fin-type and gate-all-around transistor architectures. The framework closes a key gap in multiscale device modeling by combining near-quantum-mechanical accuracy with device-scale throughput, providing a systematic route to explore heat transport and thermo-mechanical coupling in advanced transistors. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_18915 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Device-Scale Atomistic Simulations of Heat Transport in Advanced Field-Effect Transistors Xu, Ke Wang, Gang Liang, Ting Xiao, Yang Ding, Dongliang Guo, Haichang Gao, Xiang Tong, Lei Wan, Xi Zhang, Gang Xu, Jianbin Materials Science Self-heating in next-generation, high-power-density field-effect transistor limits performance and complicates fabrication. Here, we introduce NEP-FET, a machine-learned framework for device-scale heat transport simulations of field-effect transistors. Built upon the neuroevolution potential, the model extends a subset of the OMat24 dataset through an active-learning workflow to generate a chemically diverse, interface-rich reference set. Coupled with the FETMOD structure generator module, NEP-FET can simulate realistic field-effect transistor geometries at sub-micrometer scales containing millions of atoms, and delivers atomistic predictions of temperature fields, per-atom heat flux, and thermal stress in device structures with high fidelity. This framework enables rapid estimation of device-level metrics, including heat-flux density and effective thermal conductivity. Our results reveal pronounced differences in temperature distribution between fin-type and gate-all-around transistor architectures. The framework closes a key gap in multiscale device modeling by combining near-quantum-mechanical accuracy with device-scale throughput, providing a systematic route to explore heat transport and thermo-mechanical coupling in advanced transistors. |
| title | Device-Scale Atomistic Simulations of Heat Transport in Advanced Field-Effect Transistors |
| topic | Materials Science |
| url | https://arxiv.org/abs/2511.18915 |