Reduction in on-product overlay random error using machine learning algorithm

Gilwoo Song; Sungchai Kim; Sungwon Park; Taeyoung Jo; Euiseok Kum; Jonghyun Hwang; Seonho Lee

doi:10.1117/12.2612771

26 May 2022 Reduction in on-product overlay random error using machine learning algorithm

Gilwoo Song, Sungchai Kim, Sungwon Park, Taeyoung Jo, Euiseok Kum, Jonghyun Hwang, Seonho Lee

Proceedings Volume 12053, Metrology, Inspection, and Process Control XXXVI; 120531O (2022) https://doi.org/10.1117/12.2612771
Event: SPIE Advanced Lithography + Patterning, 2022, San Jose, California, United States

Conference Poster

Abstract

The random error has been increased relative to the systematic error in overlay misalignment, as the Critical Dimension(CD) of semiconductor-design shrinks to under the 20 nm on DRAM and single-digit nanometer on Logic. The random error comprises diverse factors including non-lithography context, which caused by intricate process other than the scanner itself, hence it’s hard to control through conventional control methods using control knobs of scanner . In this study, we show that how effectively control and reduce on product overlay(OPO) error through making the most use of the conventional control knobs aided by machine learning. In addition to showing improved results, we address that conventional overlay feedback control with weighted moving average(WMA) can give rise to fluctuation of OPO error over entire wafer area, especially on the edge of wafer, due to the lack of control capability or flexibility. As a result, we show that 15.7% of OPO error can be trained and predicted for in-fab data and OPO has been improved from 2.29 nm to 2.08 nm or 9.2% on average over 5-steps of 1,201 lots with simulator.

Citation Download Citation

Gilwoo Song, Sungchai Kim, Sungwon Park, Taeyoung Jo, Euiseok Kum, Jonghyun Hwang, and Seonho Lee "Reduction in on-product overlay random error using machine learning algorithm", Proc. SPIE 12053, Metrology, Inspection, and Process Control XXXVI, 120531O (26 May 2022); https://doi.org/10.1117/12.2612771

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