Overlay metrology is crucial to process control in manufacturing semiconductor devices. Diffraction-based overlay (DBO) is an effective overlay measurement approach because it exhibits multiple advantages. This study analyzed measurement errors caused by sidewalls in the bottom gratings of DBO targets. Accordingly, improvement was proposed using a neural network. First, rigorous coupled wave analysis was employed to calculate the pupil images generated by an overlay target. These images were then used as a data set. Next, two-directional two-dimensional principal component analysis was used to reduce the dimension of features in these images. The features were then used to train a neural network and determine weighting coefficients in each network layer to create a DBO model. This study used virtual metrology to analyze 30 types of overlay targets and generated 18900 pupil images to create a data set. Each overlay target model was measured 10 times, and shot noise, dark noise, and quantization noise in the pupil images were accounted for. The simulation results revealed that when the dose level was 1000 mJ/ $\text{s}\cdot $ cm2, the overlay mean square error of the testing data was 0.40nm2, indicating notable improvement in the measurement results of overlay targets with bottom grating sidewalls. Therefore, the proposed neural network-based DBO model can be applied to overlay targets with sidewalls and effectively improve the overlay accuracy.

中文翻译：

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非对称侧壁叠加目标的基于衍射的叠加模型的增强

叠加测量对于制造半导体器件的过程控制至关重要。基于衍射的叠加 (DBO) 是一种有效的叠加测量方法，因为它具有多种优势。本研究分析了 DBO 目标底部光栅侧壁引起的测量误差。因此，建议使用神经网络进行改进。首先，采用严格的耦合波分析来计算由叠加目标生成的瞳孔图像。然后将这些图像用作数据集。接下来，使用二维二维主成分分析对这些图像中的特征进行降维。然后使用这些特征来训练神经网络并确定每个网络层的加权系数以创建 DBO 模型。本研究使用虚拟计量学分析了 30 种重叠目标，并生成了 18900 张瞳孔图像以创建数据集。每个叠加目标模型测量 10 次，并考虑了瞳孔图像中的散粒噪声、暗噪声和量化噪声。仿真结果表明，当剂量水平为1000 mJ/$\text{s}\cdot $ cm2时，测试数据的叠加均方误差为0.40nm2，表明底部光栅叠加目标的测量结果有显着改善侧壁。因此，所提出的基于神经网络的DBO模型可以应用于具有侧壁的叠加目标，有效提高叠加精度。并且考虑了瞳孔图像中的量化噪声。仿真结果表明，当剂量水平为1000 mJ/$\text{s}\cdot $ cm2时，测试数据的叠加均方误差为0.40nm2，表明底部光栅叠加目标的测量结果有显着改善侧壁。因此，所提出的基于神经网络的DBO模型可以应用于具有侧壁的叠加目标，有效提高叠加精度。并且考虑了瞳孔图像中的量化噪声。仿真结果表明，当剂量水平为1000 mJ/$\text{s}\cdot $ cm2时，测试数据的叠加均方误差为0.40nm2，表明底部光栅叠加目标的测量结果有显着改善侧壁。因此，所提出的基于神经网络的DBO模型可以应用于具有侧壁的叠加目标，有效提高叠加精度。