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基于神经网络的几何分析算法,用于纳米网格结构的本地化模拟数据,使用穆勒矩阵光谱圆测量.

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    科学领域:

    • 材料科学 材料科学 材料科学
    • 光学物理学的光学物理学
    • 计算科学 计算科学

    背景情况:

    • 穆勒矩阵光谱圆测量 (MMSE) 是分析纳米结构的关键非破坏性技术.
    • 计算能力和神经网络的进步正在改善复杂纳米结构的分析.
    • 对纳米结构尺寸的准确表征对于设备性能至关重要.

    研究的目的:

    • 开发一种基于深度学习的快速准确的方法,用于使用MMSE数据进行纳米结构参数预测.
    • 预测1D格子结构的尺寸 (宽度和高度).
    • 通过实验测量验证深度学习方法.

    主要方法:

    • 使用了两步深度学习算法,将神经网络与有限的模拟数据相结合.
    • 训练神经网络从穆勒矩阵预测纳米结构参数.
    • 使用二氧化 (SiO2) 格子进行实验验证.

    主要成果:

    • 在预测格子宽度和高度方面实现了高精度,平均绝对误差 (MAE) 低于0.1nm.
    • 深度学习模型成功预测了SiO2格子的尺寸.
    • 实验结果与扫描电子显微镜 (SEM) 测量结果有很好的一致性.

    结论:

    • 拟议的深度学习方法通过MMSE为纳米结构分析提供了快速而准确的方法.
    • 这种技术有效地确定1D格子结构的关键尺寸.
    • 这项研究表明了人工智能驱动的MMSE在先进纳米测量学的潜力.