Mr. Manish Kumar Sahu Profile

Manish Kumar Sahu

SPIE Involvement:

Author

Publications (1)

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 | 26 May 2022 Presentation + Paper

Methodologies to handle large volumes of design and defect data for improved pre-silicon defect prediction

Parnashri Wankhede, Devansh Singh, Manish Kumar Sahu, Aruna Veluru, Monisa Ramesh Babu, Chenlong Miao, Shenghua Song, Shobhit Malik, Sriram Madhavan

Proceedings Volume 12052, 1205210 (2022) https://doi.org/10.1117/12.2614185

KEYWORDS: Machine learning, Data modeling, Defect detection, Data analysis, Design for manufacturability, Yield improvement

Read Abstract +

The goal of this study is to discuss the findings of experiments we conducted to enhance defect capture rate in the advanced nodes design-for-manufacturing flow. As a metric of manufacturing excellence, wafer fabrication units track and report defect density on a regular basis. Fabrication with a reduced defect density can produce a higher chip yield. In recent years, systematic defects resulting from design and process have been predicted to be the leading cause of yield loss. This could result in a large rise in manufacturing costs and time. As a result, they are highly undesirable, and it is critical to accurately identify them for root analysis of weak patterns. Unlike random process defects, systematic patterning defects may show characteristic design features and our goal is to leverage these to create a machine learning model that can efficiently identify and categorize these defects before they get into production. An inherent problem that we face is class imbalance: high confidence defect data constitutes a few thousand samples for maturing nodes and nondefects of the order of millions. Additionally, millions of records in the train, test, and inference datasets, even with reduced feature sets, can lead to a host of big data issues like insufficient memory, large experiment times, and lack of integrated development. In this paper, we discuss methodologies to handle the large volume, imbalanced data which led to 47% improvement in the defect capture and 100% improvement in the noise to signal ratio.

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