Machine learning aided solution to the inverse problem in optical scatterometry

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.

Automated summary

In this paper, a machine learning method is proposed to reconstruct the profile of nanostructures. The method involves compressing the signature, constructing a surrogate electromagnetic solver, and iteratively comparing the predicted signatures with measured ones. Experimental results demonstrate that the proposed method can achieve fast and accurate measurement.