提出了一种基于机器学习参数校正的极紫外光刻三维含缺陷掩模仿真方法。本方法采用随机森林、K近邻等机器学习方法,对基于结构分解法的含缺陷掩模衍射谱快速仿真模型的参数进行动态校正,提高了模型的精度及适应性。以严格仿真为标准值,对随机设定的50组接触孔掩模进行仿真验证,结果表明,经参数校正后,快速模型的空间像仿真精度平均提升了45%,且参数校正前、后的快速模型仿真精度皆优于所对比的改进型单平面近似法(平均仿真精度分别提升4.3倍和8.7倍)。此外,在对像面周期为44 nm掩模的缺陷补偿仿真应用中,在仿真结果较一致(误差0.8 nm)的情况下,校正后的快速模型的单次衍射谱仿真速度比严格仿真提升了约97倍。

This study proposes a fast simulation method that employs machine learning-based parameter calibration for three-dimensional (3D) rigorous simulation of defective masks in extreme ultraviolet lithography. The parameters of the structure-decomposed fast simulation model for defective mask diffraction are calibrated using machine learning methods, such as random forest and K-nearest neighbors, to improve the simulation accuracy and adaptivity. Herein, rigorous simulation is used as a benchmark standard for the calibration of model parameters. Simulation results of 50 validation contact masks set randomly reveal that the average simulation accuracy of aerial images is increased by 45% after calibration; both calibrated and uncalibrated fast models display better simulation accuracy (improved by 4.3 and 8.7 times, respectively) compared with an advanced single-surface approximation model. By applying defect-compensation simulation to a mask of 44-nm period, the simulation speed of single diffraction of the corrected fast model is ~97 times faster than that of the rigorous simulation when the simulation results are consistent (error is 0.8 nm).