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Si堆叠纳米板晶体管的热,机械和电性能,使用机器学习的原子间潜力

Mohamed Ahmed Saleh¹, Hamdy M Abdelhamid², Amr M Bayoumi³

¹Nanotechnology and Nanoelectronics Program, Zewail City of Science and Technology, 6th October City Giza, Egypt, Giza, 12578, EGYPT.

|October 4, 2024

概括

机器学习潜力预测了纳米板的热导率和刚度降低. 这些发现对于优化纳米电子设备,如纳米板场效应晶体管至关重要.

科学领域:

材料科学材料科学材料科学
纳米技术纳米技术
计算物理计算物理

背景情况:

优化纳米电子设备性能在很大程度上依赖于了解热和机械性能.
纳米板是先进纳米电子架构中的关键组件.

研究的目的:

为了确定不同厚度的纳米板的晶格导热率和弹性常数.
评估这些特性对纳米板场效应晶体管 (NS-FET) 性能的技术影响.

主要方法:

利用机器学习的原子间潜力 (MLIPs),特别是时刻张量潜力 (MTP) 方法.
训练有素的MLIP使用密度函数理论 (DFT) 计算和潜在能量表面 (PES) 的随机抽样.
采用TCAD设备模拟来评估对NS-FET的影响.

主要成果:

格子的导热率下降到大约7%的散装价值对于薄于6nm.
某些刚性张力元件在6nm以下厚度下降到大约3%的散装值.
随着纳米板厚度的下降,观察到热和机械性能显著减少.

结论:

该研究提供了关于超薄纳米板的热传输和机械行为的关键见解.
这些发现对于下一代纳米电子设备的设计和优化至关重要.
提取的参数为在未来的节点推进NS-FET技术提供了有价值的数据.

关键词:

弹性常量是弹性的常量.网格热导电性网格热导电性机器学习原子间潜力原子间潜力分子动力学分子动力学一个纳米板.语音分散关系是语音分散关系.准2D系统几乎是2D.

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