Probabilistic process window: a new approach to focus-exposure analysis

Mack, Chris A.; Yannuzzi, Jonathan; Lorusso, Gian; Zidan, Mohamed; Simone, Danilo De; Weldeslassie, Ataklti; Vandenbroeck, Nadia; Foubert, Philippe; Béral, Christophe; Charley, Anne-Laure

doi:10.1117/12.2614445

Cited by 2 publications

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“…This paper will describe and demonstrate an approach to process window analysis that addresses the problems inherent to the geometric process window. 4 Called the probabilistic process window (PPW), this approach is rigorously probabilistic by design, takes metrology uncertainty fully into account, does not use data fitting functions, and avoids overly sensitive geometric interpretations of process errors. The result, we expect, will be improvements in both accuracy and precision of process window measurement and analysis.…”

Section: Introductionmentioning

confidence: 99%

Probabilistic process window: a new approach to focus-exposure analysis

Mack¹,

Yannuzzi²,

Lorusso

et al. 2023

J. Micro/Nanopattern. Mats. Metro.

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Background: Focus-exposure process window measurement and analysis is an essential function in lithography, but the current geometric approach suffers from several significant deficiencies.Aim: By clearly identifying the problems with the geometric process window approach, a process window measurement and analysis method will be proposed to address these problems. Approach:The probabilistic process window (PPW) proposed here takes metrology uncertainty into account and rigorously calculates the expected fraction of in-spec features based on settings for the best dose/focus and presumed random errors in dose and focus. Using the fraction of in-spec features thus calculated, a much more rigorous determination of the trade-off between exposure latitude and depth of focus (DOF) can be performed. Results:The PPW approach is demonstrated on focus-exposure data generated from a standard extreme ultraviolet lithography process at three different pitches, showing the value of this method. Conclusions:The PPW approach offers clear advantages in accuracy for both DOF determination and the best dose/focus determination. Consequently, its use is preferred both for process development applications and high-volume manufacturing.

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Section: Introductionmentioning

confidence: 99%

Probabilistic process window: a new approach to focus-exposure analysis

Mack¹,

Yannuzzi²,

Lorusso

et al. 2023

J. Micro/Nanopattern. Mats. Metro.

Self Cite

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“…We focus on two main approaches. The first one is motivated by the need for an accurate process window qualification for CD that includes not only the CD size (like line width or contact hole diameter) but additional metrics related to pattern fidelity and roughness [3,4]. For line/space patternsextraction and analysis of roughness have already been explored quite extensively in the literature, mostly using PSD (power spectral density) analysis [5,6,7].…”

Section: Introductionmentioning

confidence: 99%

Quantifying CD-SEM contact hole roughness and shape combined with machine learning-based pattern fidelity scores for process optimization and monitoring

Lee

Rah

et al. 2023

Metrology, Inspection, and Process Control XXXVII

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Process window qualification using focus-exposure wafers is an essential step in lithography and a key use case for CD-SEM metrology. An automated analysis using the correlation between CD and focus/dose is easily possible but rarely done due to missing safety checks. Pattern fidelity that is analyzed by eye and problematic focus/dose conditions that may cause pattern degradation are excluded by hand. Specifically, when EUV lithography is utilized for exposing the most critical layers, roughness estimation becomes much more important, as it will restrict the process window further. We develop and describe unbiased and stable roughness estimates for contact hole patterns and integrate them into the process window analysis pipeline and inline monitoring routine. The analysis goes beyond simple roughness values and can detect a variety of possible CD-SEM measurement problems and shape deviations as well. Furthermore, we introduce a novel image-based machine-learning approach to detect outliers and quantify defective or abnormal patterns. Notably, the underlying model does not require knowledge of the types of CD features or design information for which outliers should be detected. We demonstrate that the approach can reliably detect local defects and a variety of other pattern anomalies. Using the generated visualizations, images with anomalous features can be flagged automatically and the locations of the defects or deviations are pinpointed. The approach yields not only the final missing piece in automated process window qualification, but also new opportunities to monitor pattern fidelity in lithographical semi-conductor processes.

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Probabilistic process window: a new approach to focus-exposure analysis

Cited by 2 publications

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Probabilistic process window: a new approach to focus-exposure analysis

Probabilistic process window: a new approach to focus-exposure analysis

Quantifying CD-SEM contact hole roughness and shape combined with machine learning-based pattern fidelity scores for process optimization and monitoring

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