Year: 2026
Type: article
Abstract: We present EUV-OVL-SYN, an open and fully reproducible synthetic benchmark dataset for EUV lithography. Overlay error, the misalignment between consecutive patterning layers, is a primary yield-limiting factor at advanced nodes, yet the absence of publicly available, physically realistic datasets impedes systematic progress in data-driven correction research. EUV-OVL-SYN is built on documented scanner physics and replicates the signal hierarchy of real production data. The dataset captures three distinct levels of physical structure: a dominant inter-field Brink/van den Brink polynomial fingerprint (cross-wafer R2 ≥ 0.93); four lot-level temporal drift scenarios modeling linear scanner heating, reticle alignment drift, multi-parameter process drift, and chiller-cycling oscillation; and a structured nonlinear intra-field residual analytically verified to lie outside the Brink polynomial basis. The dataset consists of 4 lots of 50 wafers each, measured at 420 metrology targets per wafer (284 000 measurements in total), formatted identically to industrial automatic process control exports for direct compatibility with existing correction pipelines. The nonlinear residual (1σ ≈ 1.1–1.4 nm per axis) is well above the 0.15 nm measurement noise floor yet non-trivially masked by the dominant Brink fingerprint, providing a detectable but genuinely challenging correction target. Full ground-truth decomposition is provided for every signal component, permitting exact, method-agnostic evaluation of any correction approach. The complete dataset is generated by a single reproducible Python script and released under an open license.
Source: APL Machine Learning
DOI: 10.1063/5.0346054
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: diamond
Award: G3339517739
OpenAlex ID: W7204937851