Deep neural network modeling of multiple oxide/nitride deposited dielectric films for 3d-nand flash

Abstract

The fast and accurate measurement of the thickness of multiple oxide/nitride layer deposition (MOLD) films is desirable to improve the quality of the plasma deposition process and potentially simplify the metrology in 3D NAND flash memory devices. In this study, we performed deep neural network modeling of the reflectance spectrum data of two pairs of oxide/nitride films on a silicon substrate. We designed a deep neural network model to estimate the thickness of four stacked thin-film layers and the MOLD film. Principle component analysis of this model was performed to develop another model with 27 features. Finally, a combined model was designed by fine tuning both the models and applying an ensemble algorithm to both. The mean absolute error of the combined predictive model was lower than that of the individual models. We verified the performance of the proposed model by considering the thin-film deposition mechanism with respect to the infrared reflectance metrology of MOLD thin films. This study demonstrates the potential of machine learning for predicting the thickness of multiple layered films, addressing the limitations of optical metrology.