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

Hyunsoo Kwak; Sungyoon Ryu; Suil Cho; Junmo Kim; Yusin Yang; Jungwon Kim

doi:10.1117/12.2592216

20 June 2021 Angstrom-accuracy multilayer thickness determination using optical metrology and machine learning

Hyunsoo Kwak, Sungyoon Ryu, Suil Cho, Junmo Kim, Yusin Yang, Jungwon Kim

Proceedings Volume 11782, Optical Measurement Systems for Industrial Inspection XII; 117820U (2021) https://doi.org/10.1117/12.2592216
Event: SPIE Optical Metrology, 2021, Online Only

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, threedimensional (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.

Conference Presentation

Citation Download Citation

Hyunsoo Kwak, Sungyoon Ryu, Suil Cho, Junmo Kim, Yusin Yang, and Jungwon Kim "Angstrom-accuracy multilayer thickness determination using optical metrology and machine learning", Proc. SPIE 11782, Optical Measurement Systems for Industrial Inspection XII, 117820U (20 June 2021); https://doi.org/10.1117/12.2592216

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