EPE contribution analysis method of multiple patterning lithography by Monte Carlo and Sobol sensitivity analysis

Abstract

As critical dimensions shrink in new semiconductor technologies, process margins become tighter. With the introduction of multiple patterning schemes, the edge placement error (EPE) analysis becomes more important than ever to assess and maintain in-line process performance and yield. In this work, the EPE contribution analysis method of multiple litho-etch (LE) patterning process is proposed. A process flow model of quadruple patterning is built to simulate the effect of each step's impact on the after-etch-inspection (AEI) contour. Under different process conditions, profiles with different critical dimensions (CDs) on the resist can be obtained. In the step of spacer deposition, the parameters of the deposition process, such as the thickness and lateral ratio, have a significant impact on the contour of AEI. For the etching process, there is usually a situation where the top and bottom dimensions are not consistent. The distribution of the AEI under these conditions is statistically analyzed by Monte Carlo and Sobol sensitivity analysis. The contribution levels of different parameters on AEI CDs and spaces are analyzed based on Sobol sensitivity analysis. The results indicate that the impact of the after-develop-inspection (ADI) contour is the greatest, and the contribution levels of the parameters in deposition and etching process are relatively close. The impact of the overlay is mainly reflected in the distribution change of the spaces. Under the given EPE budget conditions, the error budget for each parameter can be obtained to guide the direction of improving the processes.

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Article information

Article type
Paper
Submitted
28 Jan 2026
Accepted
11 May 2026
First published
12 May 2026
This article is Open Access
Creative Commons BY-NC license

Nanoscale Adv., 2026, Accepted Manuscript

EPE contribution analysis method of multiple patterning lithography by Monte Carlo and Sobol sensitivity analysis

F. Ai, X. Su, Y. su and Y. Wei, Nanoscale Adv., 2026, Accepted Manuscript , DOI: 10.1039/D6NA00075D

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