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走向智能扫描探针光刻:一个框架加速纳米制造过程,通过机器学习进行现场表征

Yijie Liu^1,2, Xuexuan Li^1,2, Ben Pei^3,4,5

¹State Key Laboratory of Tribology in Advanced Equipment, Department of Mechanical Engineering, Tsinghua University, Beijing, 100084 China.

Microsystems & nanoengineering

|October 13, 2023

概括

这项研究引入了一种机器学习 (ML) 框架,以优化扫描探针光刻 (SPL) 参数,以提高纳米制造质量和特征测量. 机器学习方法可以精确控制关键尺寸和大规模直接写 nano-lithography.

科学领域:

纳米技术纳米技术
材料科学材料科学材料科学
机器学习机器学习

背景情况:

扫描探针光刻 (SPL) 提供纳米制造,但在质量控制和主观特征测量方面面临挑战.
过程参数在SPL中显著影响纳米制造的关键尺寸.

研究的目的:

开发一种新的机器学习 (ML) 框架,以优化 SPL 过程参数和细分纳米制造的特性.
克服SPL传统手动标签基础实验方法的局限性.

主要方法:

利用机器学习框架智能提取全球信息进行统计分析.
应用框架来微调和优化SPL过程参数,以实现增强的纳米制造.

主要成果:

通过优化 SPL 过程参数,实现了较小的关键尺寸的处理.
成功地进行了大规模的直接写入纳米光刻.
经过数据驱动的特征提取和分析,以优化制造质量.

结论:

拟议的ML框架有效优化了SPL过程参数,从而提高了纳米制造的精度和规模.
这种数据驱动的方法为其他表征方法和制造质量提升提供了潜在的指导.

关键词:

工程工程师工程师工程师纳米科学和技术和技术.

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