结合来自多个工具的关键维度的基于模型的测量结果
Nien Fan Zhang1, Bryan M Barnes2, Hui Zhou2
1Statistical Engineering Division, National Institute of Standards and Technology, Gaithersburg, MD, 20899, USA.
Measurement science & technology
|October 31, 2024
概括
结合基于模型的测量技术,如混合计量学,可以优化减少定量估计的不确定性. 这种方法提高了在半导体制造等领域的关键测量精度.
科学领域:
- 计量学和测量科学 计量学和测量科学
- 计算物理 计算物理
- 材料科学 材料科学 材料科学
背景情况:
- 基于模型的测量技术将实验数据与物理模拟进行定量分析.
- 这些估计的不确定性源于数据变化,模型参数灵敏度和参数相关性.
研究的目的:
- 证明结合多种基于模型的技术和采用贝叶斯式方法可以优化最小化测量不确定性.
- 将混合计量学作为实现高精度测量的优越方法.
主要方法:
- 使用标本的参数模型从实验数据和模拟中提取测量值.
- 结合补充技术的回归分析,如临界维度小角度X射线散射 (CD-SAXS) 和扫描电子显微镜 (SEM).
- 应用贝叶斯推理来整合信息并减少不确定性.
主要成果:
- 技术和贝叶斯方法的结合在参数估计中产生了尽可能低的不确定性.
- 混合计量学成功地在半导体制造中测量了14nm以下的线条,并提高了精度.
- 与单个测量技术相比,显著降低了不确定性.
结论:
- 混合计量学,集成多种基于模型的技术,是最大限度地减少测量不确定性.
- 这种方法为关键测量应用提供了最先进的精度,特别是在先进制造业.
- 不同物理模型和数据源之间的协同作用提高了定量测量的可靠性.
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