Mr. Yasuki Takeuchi Profile

Yasuki Takeuchi

SPIE Involvement:

Author

Publications (2)

This will count as one of your downloads.

You will have access to both the presentation and article (if available).

DOWNLOAD NOW

This content is available for download via your institution's subscription. To access this item, please sign in to your personal account.

Email or Username Forgot your username?

Password Forgot your password?

Show

Keep me signed in

No SPIE account? Create an account

Proceedings Article | 27 April 2023 Presentation + Paper

Machine learning robustness in overlay metrology

Udi Shusterman, Grechin Sveta, Boaz Ophir, Cindy Kato, Masanobu Hayashi, Shengxun Zhao, Jiehong Ng, Tomohiro Goto, Atsushi Imada, Manabu Miyake, Yasuki Takeuchi, Hiroyuki Mizuochi

Proceedings Volume 12496, 124960M (2023) https://doi.org/10.1117/12.2657438

KEYWORDS: Semiconducting wafers, Education and training, Data modeling, High volume manufacturing, Overlay metrology, Machine learning, 3D modeling, Metrology, Performance modeling, Object detection

Read Abstract +

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.

Proceedings Article | 22 February 2021 Presentation + Paper

Machine learning for Tool Induced Shift (TIS) reduction: an HVM case study

Boaz Ophir, Udi Shusterman, Anna Golotsvan, Cindy Kato, Masanobu Hayashi, Richika Kato, Tomohiro Goto, Taketo Kuriyama, Manabu Miyake, Yasuki Takeuchi, Hiroyuki Mizuochi

Proceedings Volume 11611, 116110E (2021) https://doi.org/10.1117/12.2590247

Read Abstract +

Tool Induced Shift (TIS) is a measurement error commonly used to measure the accuracy of metrology tools. TIS manifests in the difference in overlay (OVL) misregistration between measurements of the same target at 0ᴼ and 180ᴼ rotations. This inaccuracy is attributed to tool asymmetries and is commonly caused by lens aberrations, lens alignment, illumination alignment and the tool’s interaction with target asymmetries. TIS impacts tool Total Measurement Uncertainty (TMU) and tool-to-tool matching. In memory chips, particularly 3D NAND, TMU is limited by TIS distribution across wafer, as it depends on process stability and is amplified by high layer topology. Additionally, TIS is influenced by wafer-to-wafer and lot-to-lot process variation. TIS correction by direct measurement per site (TIS-onLink, ToL) incurs a heavy penalty to measurement throughput as it requires measuring each site twice. Alternatively, measuring TIS on a sparse subset of sites, interpolating to other sites (TIS-on-Parent, ToP), induces a lower throughput penalty but is not accurate enough in many cases. In a previous paper we introduced a new methodology to improve overlay measurement with minimum throughput impact - Modeled-TIS (mTIS). This approach uses Machine Learning (ML) algorithms to predict per-site TIS correction on Image-Based Overlay (IBO) measurements. This method gives near ToL TIS correction performance at ToP throughput penalty, or better, depending on the use case. In this paper, we describe some of the algorithmic adaptations we made to the original algorithm to work in a high-volume manufacturing (HVM) environment and present results of an HVM use case on 3D NAND production lots.

My Library

You currently do not have any folders to save your paper to! Create a new folder below.

Folder Name

Folder Description

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks. You are receiving this notice because your organization may not have SPIE eBooks access.*

*Shibboleth/Open Athens users─please sign in to access your institution's subscriptions.

To obtain this item, you may purchase the complete book in print or electronic format on SPIE.org.

ORGANIZATIONAL
Sign in with credentials provided by your organization.

Organizational Username

Organizational Password

Show/Hide Password

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members:

Non-members: ADD TO CART

Keywords/Phrases

Search In:

Publication Years