Generic characterization method for nano-gratings using deep-neural-network-assisted ellipsometry

Abstract

As a non-destructive and rapid technique, optical scatterometry has gained widespread use in the measurement of film thickness and optical constants. The recent advances in deep learning have presented new and powerful approaches to the resolution of inverse scattering problems. However, the application of deep-neural-network-assisted optical scatterometry for nanostructures still faces significant challenges, including poor stability, limited functionalities, and high equipment requirements. In this paper, a novel characterization method is proposed, which employs deep-neural-network-assisted ellipsometry to address these challenges. The method processes ellipsometric angles, which are measured by basic ellipsometers, as functional signals. A comprehensive model is developed to profile nano-gratings fabricated by diverse techniques, by incorporating rounded corners, residual layers, and optical constants into an existing model. The stability of the model is enhanced by implementing several measures, including multiple sets of initial values and azimuth-resolved measurements. A simple compensation algorithm is also introduced to improve accuracy without compromising efficiency. Experimental results demonstrate that the proposed method can rapidly and accurately characterize nano-gratings fabricated by various methods, with relative errors of both geometric and optical parameters well controlled under 5 %. Thus, the method holds great promise to serve as an alternative to conventional characterization techniques for in-situ measurement.

Keywords

• spectroscopic ellipsometry
• deep neural network
• in-situ measurement
• interference lithography
• nanoimprint lithography
• reactive-ion etching