Key Laboratory of Precision Opto-Mechatronics, Technology of Education Ministry, School of Instrumentation and Opto-Electronic Engineering, Beihang University, Beijing, China.
School of Instrumentation and Opto-Electronic Engineering, Beihang University, Beijing, China.
J Microsc. 2021 Aug;283(2):93-101. doi: 10.1111/jmi.13011. Epub 2021 Apr 14.
Through-focus scanning optical microscopy (TSOM) is a model-based nanoscale metrology technique which combines conventional bright-field microscopy and the relevant numerical simulations. A TSOM image is generated after through-focus scanning and data processing. However, the mechanical vibration and optical noise introduced into the TSOM image during image generation can affect the measurement accuracy. To reduce this effect, this paper proposes a imaging error compensation method for the TSOM image based on deep learning with U-Net. Here, the simulated TSOM image is regarded as the ground truth, and the U-Net is trained using the experimental TSOM images by means of a supervised learning strategy. The experimental TSOM image is first encoded and then decoded with the U-shaped structure of the U-Net. The difference between the experimental and simulated TSOM images is minimised by iteratively updating the weights and bias factors of the network, to obtain the compensated TSOM image. The proposed method is applied for optimising the TSOM images for nanoscale linewidth estimation. The results demonstrate that the proposed method performs as expected and provides a significant enhancement in accuracy.
聚焦扫描光学显微镜（TSOM）是一种基于模型的纳米级计量技术，它结合了传统的明场显微镜和相关的数值模拟。通过聚焦扫描和数据处理后生成 TSOM 图像。然而，在图像生成过程中引入的机械振动和光学噪声会影响测量精度。为了降低这种影响，本文提出了一种基于 U-Net 的深度学习的 TSOM 图像成像误差补偿方法。在这里，模拟的 TSOM 图像被视为真实值，通过监督学习策略，使用实验 TSOM 图像对 U-Net 进行训练。实验 TSOM 图像首先由 U-Net 的 U 形结构进行编码，然后进行解码。通过迭代更新网络的权重和偏置因子，最小化实验和模拟 TSOM 图像之间的差异，从而获得补偿后的 TSOM 图像。该方法应用于优化 TSOM 图像进行纳米线宽估计。结果表明，该方法符合预期，显著提高了精度。
