Angstrom-accuracy multilayer thickness determination using optical metrology and machine learning

Abstract

The era of big data and cloud computing services has driven the demand for higher capacity and more compact semiconductor devices. As a result, semiconductor devices are moving from 2-D to 3-D. Most notably, three-dimensional (3D) NAND flash memory is the most successful 3D semiconductor device today. 3D NAND overcomes the spatial limitation of conventional planar NAND by stacking memory cells vertically. Since hundreds of vertically stacked semiconductor materials become the channel length in the final product, accurate thickness characterization is critical. In this paper, we propose a non-destructive multilayer thickness characterization method using optical measurements and machine learning. For a silicon oxide/nitride multilayer stack of >200 layers, we could predict the thickness of each layer with an average root-mean-square error (RMSE) of 1.6 Å. In addition, we could successfully classify normal and outlier devices using simulated data. We expect this method to be highly suitable for semiconductor fabrication processes.