Automatic classification and defect verification based on inspection technology with lithography simulation

Masaya Kato; Hideki Inuzuka; Takeshi Kosuge; Shingo Yoshikawa; Kayoko Kanno; Hidemichi Imai; Hiroyuki Miyashita; Anthony Vacca; Peter Fiekowsky; Dan Fiekowsky

doi:10.1117/12.2197814

23 October 2015 Automatic classification and defect verification based on inspection technology with lithography simulation

Masaya Kato, Hideki Inuzuka, Takeshi Kosuge, Shingo Yoshikawa, Kayoko Kanno, Hidemichi Imai, Hiroyuki Miyashita, Anthony Vacca, Peter Fiekowsky, Dan Fiekowsky

Author Affiliations +

Proceedings Volume 9635, Photomask Technology 2015; 96351W (2015) https://doi.org/10.1117/12.2197814
Event: SPIE Photomask Technology, 2015, Monterey, California, United States

Abstract

Even small defects on the main patterns can create killer defects on the wafer, whereas the same defect on or near the decorative patterns may be completely benign to the wafer functionality. This ambiguity often causes operators and engineers to put a mask "on hold" to be analyzed by an AIMS™ tool which slows the manufacturing time and increases mask cost. In order to streamline the process, mask shops need a reliable way to quickly identify the wafer impact of defects during mask inspection review reducing the number of defects requiring AIMS™ analysis.

Source Mask Optimization (SMO) techniques are now common on sub 20nm node critical reticle patterns These techniques create complex reticle patterns which often makes it difficult for inspection tool operators to identify the desired wafer pattern from the surrounding nonprinting patterns in advanced masks such as SMO, Inverse Lithography Technology (ILT), Negative Tone Development (NTD).

In this study, we have tested a system that generates aerial simulation images directly from the inspection tool images. The resulting defect dispositions from a program defect test mask along with numerous production mask defects have been compared to the dispositions attained from AIMS™ analysis. The results of our comparisons are presented, as well as the impact to mask shop productivity.

Citation Download Citation

Masaya Kato, Hideki Inuzuka, Takeshi Kosuge, Shingo Yoshikawa, Kayoko Kanno, Hidemichi Imai, Hiroyuki Miyashita, Anthony Vacca, Peter Fiekowsky, and Dan Fiekowsky "Automatic classification and defect verification based on inspection technology with lithography simulation", Proc. SPIE 9635, Photomask Technology 2015, 96351W (23 October 2015); https://doi.org/10.1117/12.2197814

ACCESS THE FULL ARTICLE

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: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
9 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Photomasks

Inspection

Critical dimension metrology

Opacity

Lithography

Semiconducting wafers

Reticles

Show All Keywords

Keywords/Phrases

Search In:

Publication Years