Machine learning aided solution to the inverse problem in optical scatterometry

• Published in: Measurement : journal of the International Measurement Confederation Vol. 191; p. 110811
• Main Authors: Liu, Shuo, Chen, Xiuguo, Yang, Tianjuan, Guo, Chunfu, Zhang, Jiahao, Ma, Jianyuan, Chen, Chao, Wang, Cai, Zhang, Chuanwei, Liu, Shiyuan
• Format: Journal Article
• Language: English
• Published: London Elsevier Ltd 15.03.2022; Elsevier Science Ltd
• Subjects: Artificial neural networks; Critical dimension; Inverse problem; Inverse problems; Machine learning; Manufacturing; Measurement methods; Mueller matrix ellipsometry; Optical measuring instruments; Optical scatterometry; Semiconductors; Signatures; Optical scatterometry; Mueller matrix ellipsometry; Critical dimension; Machine learning; Inverse problem
• ISSN: 0263-2241; 1873-412X
• DOI: 10.1016/j.measurement.2022.110811

•This work proposes a machine learning aided nanostructure reconstruction method.•The surrogate electromagnetic solver predicts the signatures fast and accurately.•A signature dimensionality reduction approach improves the computational efficiency.•The MLER achieves fast extraction compared to conventioanl library search method.•The scale of dataset for the implementation is smaller than conventional method. Optical scatterometry is the workhorse technique for in-line manufacturing process control in the semiconductor industry. However, as manufacturing processes develop, traditional methods for solving the inverse problem in optical scatterometry are struggling to continue improving productivity. To address this problem, machine learning can be a promising method, but it is a challenge to ensure robustness. In this paper, we propose a machine learning method to reconstruct the profile of nanostructures. The proposed method consists of three parts: compressing signature using a dimensionality reduction approach based on the principle component analysis, constructing a surrogate electromagnetic solver (SurEM) based on an artificial neural network mapping from parameters to signatures, and iteratively comparing the SurEM-predicted signatures with measured one to finally determine the results. Experiments have demonstrated that the proposed method can achieve fast and accurate measurement. This method is thus promising as an efficient in-line measurement method for nano- or micro-scale manufacturing.