Ms. Xuan Yan Profile

Xuan Yan

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 | 20 May 2022 Poster + Presentation + Paper

Deep convolutional neural network-based surface defect detection for wafer grinding jig

Chao-Ching Ho, Xuan Yan, Sankarsan Mohanty, Yu-wei Lin

Proceedings Volume 12137, 121370P (2022) https://doi.org/10.1117/12.2620287

KEYWORDS: Image processing, Defect detection, Semiconducting wafers, Neural networks, Abrasives

Read Abstract +

Wafer defects are caused by defects on the surface of the Wafer grinding jig. Therefore, this research is aimed at optical inspection of the self-made dataset of the Wafer grinding jig by using the UNet++ network model. Because of the different background distribution of the inner and outer edges in Wafer grinding jig, it is difficult to detect defects solely by Deep learning. Hence, this study uses Image processing to separate the inner and outer edge and then uses UNet++ with transfer learning to detect the location of the defects. In addition, false detection is reduced by tracking the defects as the jig moves regularly. The proposed deep network layer reduction method can decrease the detection time to 48.27% compared to the original network model. The recall rate and IoU of the outer edge are increased by 13.8% and 16.33% through transfer learning. The recall rate and IoU of the inner edge are increased by 2.01% and 1.16% respectively through transfer learning. And the final defect tracking recall rate reached over 75.81%, while the F1 score reached over 77.08% in every frame from 112 to 192 frames.

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