Machine learning for modeled-TIS and overlay reduction

Katz, Shlomit; Ophir, Boaz; Shusterman, Udi; Golotsvan, Anna; Yerushalmi, Liran; Megged, Efi; Grauer, Yoav; Zhang, Jian; Shih, Alimei; Wei, Shiming; Yep, Judith; Leung, F.H.F.; Ong, Pek Beng

doi:10.1117/12.2550747

Cited by 2 publications

(2 citation statements)

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“…Trigger was applied and tested on Modeled-TIS (mTIS) [1,2] ML algorithm. mTIS uses ML algorithms to predict per-site Tool Induced Shift (TIS) correction on Image-Based Overlay (IBO) measurements.…”

Section: Using Trigger In Hvmmentioning

confidence: 99%

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Machine learning robustness in overlay metrology

Shusterman

Grechin

Ophir

et al. 2023

Metrology, Inspection, and Process Control XXXVII

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Tool-induced shift (TIS) is a common method used to quantify measurement error or accuracy of an overlay (OVL) metrology tool and is often attributed to tool asymmetry issues. In a previous paper, we introduced Modeled-TIS (mTIS), a machine learning (ML) based algorithm to predict per-site TIS correction on Image- Based Overlay (IBO) measurements. Recently, we adapted mTIS to support the requirements of high-volume manufacturing (HVM) in 3D NAND production. During our work in an HVM environment, we observed that the mTIS model is sensitive to complex process variations and many tool states. Such variations can change the measurement distribution and deteriorate the mTIS accuracy and reliability over time. In this paper, we present an automatic retrain triggering mechanism (“Trigger”) to monitor and adaptively update mTIS. The proposed methodology incorporates statistical and unsupervised ML algorithms which automatically detect shifts in measurement distribution, and initiate data collection to continuously adjust the model to process variations and tool states. The proposed mechanism does not require periodic retraining but rather initiates data collection and model updates only when required. Trigger chooses the wafers which possess novel information reducing the number of wafers required for the training and time to the model, critical requirements for our customers. We demonstrate the mTIS results of Trigger on various 3D NAND layers running in production. Finally, our approach can also be applied to future ML-based solutions.

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Section: Using Trigger In Hvmmentioning

confidence: 99%

“…In a previous paper [1,2], we presented an approach (dubbed mTIS) that uses an ML model to predict TIS using a single measurement (at 0ᴼ rotation). The general flow of mTIS is as follows:…”

Section: Mtis -Ml-based Tis Correctionmentioning

confidence: 99%

Machine learning robustness in overlay metrology

Shusterman

Grechin

Ophir

et al. 2023

Metrology, Inspection, and Process Control XXXVII

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Image-based overlay target design using a grating intersection

Lee¹,

Chang²,

Shin³

et al. 2022

J. Micro/Nanopattern. Mats. Metro.

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Background: An overlay target design is essential before performing overlay metrology and has a great impact on overlay performance. In addition, accurate overlay metrology is required to achieve high product yields in semiconductor manufacturing. When asymmetry of the overlay target occurs, it is necessary to increase the area of the pattern existing in the target and stabilize the optical aberration for stable measurement. For this reason, we propose an image-based overlay target design that can secure a larger area compared with the existing overlay target and stabilize the optical aberration by overlapping and exposing the upper and lower patterns of the overlay target.Aim: The effective area of the existing mark design is improved by proposing an overlay target and a measurement method used in image-based overlay measurement.Approach: The proposed overlay target design is configured by arranging different marks orthogonally to the upper and lower layers, and different two-dimensional information can be inserted in the same area. When necessary, single-dimensional information desired by the user can be obtained through a technique called projection, and it has the advantage of using a larger effective area than the existing overlay target.Results: Overlay can be measured using projected signal components, and the possibility and performance of the proposed target can be confirmed through simulation and test wafer. In addition, it was possible to bring about a 15% improvement in total measurement uncertainty performance compared with the existing advanced imaging metrology (AIM) target by taking a wider effective area.Conclusions: An overlay target design is proposed to solve the technical problems of the existing overlay target design. The proposed overlay target has a method that is capable of minimizing the size of the overlay target while maximally maintaining the signal density, a way of reducing the influence of aberration, and various other advantages.In addition, the proposed method provides a design rule that breaks the "lower pattern and upper pattern must not overlap" framework.

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Machine learning for modeled-TIS and overlay reduction

Cited by 2 publications

References 0 publications

Machine learning robustness in overlay metrology

Machine learning robustness in overlay metrology

Image-based overlay target design using a grating intersection

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