Enhancing Parameters Tuning of Overlay Models with Ridge Regression: Addressing Multicollinearity in High-Dimensional Data

Aris Magklaras; Christos Gogos; Panayiotis Alefragis; Alexios Birbas

doi:10.20944/preprints202409.1046.v1

Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Version 1 : Received: 12 September 2024 / Approved: 13 September 2024 / Online: 13 September 2024 (08:24:36 CEST)

Extreme Ultraviolet (EUV) photolithography process, is a cornerstone of semiconductor manufacturing and operates under demanding precision standards realized via nanometer-level overlay (OVL) error modeling. This procedure allows the machine to anticipate and correct OVL errors before impacting the wafer, thereby facilitating near-optimal image exposure while simultaneously minimizing the overall OVL error. Such models are usually high dimensional and exhibit rigorous statistical phenomena such as collinearities that play a crucial role in the process of tuning their parameters. Ordinary Least Squares (OLS) is the most widely used method for parameters tuning of Overlay models but in most cases it fails to compensate for such phenomena. In this paper we propose the usage of Ridge Regression, a widely known Machine Learning (ML) algorithm especially suitable for datasets that exhibit high multicollinearity. The proposed method has been applied in perturbed data from a 300 mm wafer fab and the results show reduced residuals when ridge regression is applied instead of OLS.

Overlay Modeling; Photolithography; Parameters Tuning; EUV lithography; Semiconductor Manufacturing; Yield Results

Computer Science and Mathematics, Other

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Enhancing Parameters Tuning of Overlay Models with Ridge Regression: Addressing Multicollinearity in High-Dimensional Data

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