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相关概念视频

Imaging Biological Samples with Optical Microscopy01:18

Imaging Biological Samples with Optical Microscopy

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Optical microscopy uses optic principles to provide detailed images of samples. Antonie van Leeuwenhoek designed the first compound optical microscope in the 17th century to visualize blood cells, bacteria, and yeast cells. In 1830, Joseph Jackson Lister created an essentially modern light microscope. The 20th century saw the development of microscopes with enhanced magnification and resolution.
In optical microscopy, the specimen to be viewed is placed on a glass slide and clipped on the stage...
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Optical Scatter Microscopy Based on Two-Dimensional Gabor Filters
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使用素描引导深度学习的反向光学散射计.

Shuo Liu, Xiuguo Chen, Tianjuan Yang

    Optics express
    |June 11, 2024
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    此摘要是机器生成的。

    一个新的素描引导神经网络 (SGNN) 从光学分散计 (OS) 数据中重建纳米结构. 这种方法克服了模型的局限性,使半导体计量学能够准确地绘制配置图.

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    科学领域:

    • 纳米技术纳米技术
    • 计量学 计量学 计量学
    • 人工智能的人工智能

    背景情况:

    • 光学散射计 (OS),或光学临界维度 (OCD) 计量学,对于半导体纳米结构的表征至关重要.
    • 基于模型的操作系统涉及解决复杂的反向问题,通常由预定义的几何模型限制.
    • 现有的方法,包括深度学习,由于模型约束而难以泛化.

    研究的目的:

    • 引入一种新的素描引导神经网络 (SGNN) 用于在光学散射计中进行纳米结构重建.
    • 为了克服预定义的几何模型在反向散射问题的局限性.
    • 为半导体计量学开发一种更准确,更具普遍性的方法.

    主要方法:

    • 开发了一个在通用配置模型上训练的素描引导神经网络 (SGNN).
    • 该SGNN学习散射原理和草图技术,从光学签名中重建纳米结构配置文件.
    • 使用一维网格验证了该方法,并将性能与现有方法进行了比较.

    主要成果:

    • 该SGNN准确地重建了纳米结构配置文件,证明了超出培训模型的强大通用性.
    • 性能与非线性回归可比,优于传统的深度学习方法.
    • 这标志着草图概念在深度学习中首次用于逆分散问题的应用.

    结论:

    • 该SGNN提供了一个新的解决方案,用于在光学散射计中进行纳米结构重建.
    • 该方法可以快速准确地描述半导体制造中的纳米结构.
    • 这种方法提高了光学计量技术的适用性和稳定性.