While electron-beam (e-beam) lithography is widely employed in the pattern transfer, the proximity effect makes features blurred, and the stochastic nature of the exposure and development processes causes the roughness in the feature boundaries. In an effort to reduce the proximity effect and line edge roughness (LER), it is often necessary to estimate the critical dimension (CD) and LER. In our previous study, the e-beam lithographic process was modeled using the information extracted from SEM images for the estimation of CD and LER. This modeling involves several parameters to be determined and tends to require a long computation time. In this study, the possibility of improving the accuracy of the CD and LER estimation using a neural network (NN) is investigated. In the NN-based estimation, the explicit modeling of the e-beam lithographic process can be avoided. This paper describes the method of estimating the CD and LER using a NN, including the issues of training, tuning, and sample reduction and presents results obtained through an extensive simulation.

中文翻译：

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使用神经网络估算临界尺寸和线边缘粗糙度

虽然电子束（e-beam）光刻技术广泛用于图案转移，但邻近效应使特征模糊，并且曝光和显影过程的随机性会导致特征边界不平整。为了减少接近效应和线条边缘粗糙度（LER），通常需要估算临界尺寸（CD）和LER。在我们先前的研究中，使用从SEM图像中提取的信息对电子束光刻过程进行了建模，以估算CD和LER。该建模涉及要确定的几个参数，并且往往需要较长的计算时间。在这项研究中，研究了使用神经网络（NN）提高CD和LER估计准确性的可能性。在基于NN的估算中，可以避免电子束光刻过程的显式建模。本文介绍了使用NN估算CD和LER的方法，包括训练，调整和样本减少的问题，并介绍了通过广泛仿真获得的结果。