Contour-based self-aligning calibration of OPC models

Kusnadi, Ir; Do, Thuy; Granik, Yuri; Sturtevant, John L.; Bisschop, Peter De; Hibino, Daisuke

doi:10.1117/12.848539

Cited by 8 publications

(4 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this article, we have employed the methods in the article [1] as a foundational reference for the comparison of the two contours with articles [6], [5], [2] serving as the point of reference for calculating roughness.…”

Section: Framework For Distance Between Two Contours and Roughnessmentioning

confidence: 99%

“…Deformity in this case can be described as simulation extra prints or detected manufacturing defects.In this paper, we have utilized vector calculations to detect deformity and calculate roughness. Our product, Calibre ContourCal, facilitates the computation of differences between lithographical simulations and measurements [1,3]. By evaluating the Root Mean Square Error (RMSE) value, we can identify defects in the SEM image or unexpected extra prints in simulation [3].…”

mentioning

confidence: 99%

See 1 more Smart Citation

The effect of edge placement error on deformity and roughness calculation

Abaidi,

Kusnadi,

et al. 2024

Metrology, Inspection, and Process Control XXXVIII

View full text Add to dashboard Cite

In semiconductor industry, CD-SEMs (Critical Dimension Scanning Electron Microscopes) play a pivotal role in metrology and process control. Furthermore, additional applications harnessing the potential of CD-SEM equipment have been developed, including contour extraction algorithms, to address the increasing metrological demands. The task of detecting deformity in semiconductor processes has become a formidable challenge due to the continuous reduction in device feature sizes as we progress from one node to another. Deformity in this case can be described as simulation extra prints or detected manufacturing defects.In this paper, we have utilized vector calculations to detect deformity and calculate roughness. Our product, Calibre ContourCal, facilitates the computation of differences between lithographical simulations and measurements [1,3]. By evaluating the Root Mean Square Error (RMSE) value, we can identify defects in the SEM image or unexpected extra prints in simulation [3]. Additionally, we have proficiently positioned the sites in critical areas based on contour curvature. To evaluate the quality of the model's simulated contours, we have introduced a novel indicator based on the difference between the simulated model's Power Spectral Density (PSD) and the fitted PSD. The computation of RMSE for a large FoV (Field of View) containing high site count, exceeding one million sites for example, is a demanding task. For that we parallelized our calculation method using GPUs. With the utilization of GPUs, we have achieved significant time savings, simplifying the processing of high site count. Consequently, we can efficiently detect deformity and calculate roughness for large FoV.

show abstract

Section: Framework For Distance Between Two Contours and Roughnessmentioning

confidence: 99%

mentioning

confidence: 99%

The effect of edge placement error on deformity and roughness calculation

Abaidi,

Kusnadi,

et al. 2024

Metrology, Inspection, and Process Control XXXVIII

View full text Add to dashboard Cite

show abstract

“…Due to the one-dimensional (1D) nature of the cutlines, it is difficult to get a good model that captures fine 2D feature shapes due to a lack of information on the concave or convex regions 14 – 17 Furthermore, in extreme ultraviolet lithography (EUVL), stochastic errors of bridging, pinching, and missing features also become more apparent. It is a challenging task to collect and perform analysis on a huge volume of data to generate reliable OPC models.…”

Section: Introductionmentioning

confidence: 99%

Applications of large field of view e-beam metrology to contour-based optical proximity correction modeling

Wei,

Kang,

Das

et al. 2023

J. Micro/Nanopattern. Mats. Metro.

Self Cite

View full text Add to dashboard Cite

.BackgroundFor complex two-dimensional (2D) patterns, optical proximity correction (OPC) model calibration flows cannot always satisfy accuracy requirements with the standard cutline-based input data. Utilizing after-development inspection e-beam metrology image contours, better model predictions of 2D shapes and wafer hotspots can be realized.AimWe compare model accuracy performance of conventional cutline-based and contour-based OPC models on the regular and hotspots patterns.ApproachBy utilizing image contours that are directly extracted from large field of view (LFoV) e-beam metrology, OPC models were calibrated and verified with both cutline-based and contour-based modeling flows. We also used a wafer sampling plan that contained bridging hotspots. Using that sampling plan, a hotspot-aware three-dimentional resist (R3D) compact model was created.ResultsFirst, a contour-based OPC model was generated with <1 nm root mean square error of contour sites. Compared with cutline-based models, it shows better predictions on 2D feature corners. Second, when combined with a hotspot sampling plan, a hotspot-aware compact model could be generated. The accuracy of hotspot predictions on false positives and false negatives was reduced to around 1% with this approach.ConclusionsOPC model calibration and verification with LFoV image contours provide improved predictions on corner rounding shapes and great potential to increase design space coverage. We also observed improved accuracy of hotspot predictions when using an update hotspot aware model when comparing with that of the OPC model. Furthermore, the combination of R3D and stochastic compact models also demonstrated great potential on predictions of rare wafer failure events.

show abstract

“…Under these circumstances, hot-spot measurement requires twodimensional (2D) contour metrology by critical-dimension scanning-electron microscope (CD-SEM) for the following reasons [1][2][3]. First, the hot spot itself is a 2D feature in the case of complex mask layout.…”

Section: Introductionmentioning

confidence: 99%

Robust edge detection with considering three-dimensional sidewall feature by CD-SEM

et al. 2011

View full text Add to dashboard Cite

A method for achieving high repeatability in edge detection considering a three-dimensional (3D) feature of a pattern sidewall was developed. An edge-detection condition is regarded as the metric representing the z-coordinate (height) of the edge position. The dependences of line-edge or linewidth roughness (LER/LWR) and LER/LWR bias on the edgedetection condition were calculated. The calculated dependence is interpreted as the qualitative z-dependence of the roughness metrics. The 3D feature of the sidewall can therefore be roughly estimated from the dependence. The condition that minimizes the LER/LWR bias to achieve high repeatability for local measurements was determined. First, to check the validity of this method, the feasibility of 3D information extraction was examined. Unbiased LER and LER bias of resist and silicon pattern samples were plotted against the edge-detection condition, and it was found that the characteristics of the graphs of the unbiased LER and bias are related to the 3D feature observed by atomic force microscopy. Next, the robustness of the method against a change in the pattern shape caused by exposure defocus was demonstrated. Finally, it was shown that the method actually improves local measurement repeatability. In the case of the linewidth of a 9-nm-long line, repeatability was improved by 40% compared with that under the conventional edgedetection condition.

show abstract

Contour-based self-aligning calibration of OPC models

Cited by 8 publications

References 0 publications

The effect of edge placement error on deformity and roughness calculation

The effect of edge placement error on deformity and roughness calculation

Applications of large field of view e-beam metrology to contour-based optical proximity correction modeling

Robust edge detection with considering three-dimensional sidewall feature by CD-SEM

Contact Info

Product

Resources

About