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Updated: Jan 31, 2026

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新兴半导体设备模型方法的概述:从设备物理到机器学习引擎.
Xufan Li1,2, Zhenhua Wu1,2, Gerhard Rzepa3
1State Key Lab of Fabrication Technologies for Integrated Circuits, Institute of Microelectronics, Chinese Academy of Sciences, Beijing 100029, China.
Fundamental research
|January 30, 2026
概括
机器学习辅助紧建模 (MLCM) 为半导体设备建模提供了一种新的方法. MLCM克服了传统的局限性,为先进的半导体技术提供了更好的设计技术协同优化 (DTCO).
科学领域:
- 半导体设备物理和建模 半导体设备物理和建模
- 机器学习在工程中的应用.
- 材料科学和纳米技术
背景情况:
- 半导体行业的进步引入了新的材料和设备结构,为电路级别的表征提出了复杂的物理挑战.
- 准确的新兴设备建模对于物理驱动的TCAD-to-SPICE流和设计技术协同优化 (DTCO) 是必不可少的.
- 超级尺度设备中的量子效应需要经验参数,将模型与制造过程断开.
研究的目的:
- 为新兴设备建模方法提供全面的概述.
- 分析和结构机器学习辅助紧建模 (MLCM) 的当前研究.
- 展示MLCM如何解决传统紧模型的局限性.
主要方法:
- 审查和综合传统的"白盒"和新兴的"黑盒"建模方法.
- 专注于使用神经网络的机器学习辅助紧建模 (MLCM).
- 在实验和模拟数据上培训MLCM,以便准确的输入输出映射.
主要成果:
- MLCM为复杂的物理学和数学提供了一种通用建模方法.
- 在数据上训练的神经网络生成准确的封闭形式对设备特征的映射.
- MLCM有效地弥合了设备物理和制造过程之间的差距.
结论:
- MLCM克服了传统紧型建模的局限性,提供了一个强大的替代方案.
- MLCM对有效的设计技术协同优化 (DTCO) 作出了重大贡献.
- 这种方法对于推进未来的半导体技术至关重要.
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