Readiness for predictive maintenance of high cost EUV equipment using big data

Hyunjoo Lee; Jaejun Lee; Donggeun Lee; Ilwoo Nam; Sanghee Lee

doi:10.1117/12.3032128

26 August 2024 Readiness for predictive maintenance of high cost EUV equipment using big data

Hyunjoo Lee, Jaejun Lee, Donggeun Lee, Ilwoo Nam, Sanghee Lee

Author Affiliations +

Proceedings Volume 13177, Photomask Japan 2024: XXX Symposium on Photomask and Next-Generation Lithography Mask Technology; 131770N (2024) https://doi.org/10.1117/12.3032128
Event: Photomask Japan 2024, 2024, Yokohama, Japan

Abstract

The EUV (Extreme Ultra Violet) photomask manufacturer is focused on the challenges of high quality and works to photomask industry efforts to reduce defect in mass product operations and in other sectors of YMS (Yield Management System). The Tech Insight official said that the EUV photomask product market looks lucrative business because the mass product has been recording an annual CAGR (Compound Annual Growth Rate) growth rate of 1.8 percent in recent years. Moreover, more than 50% of all wafer scanners are models with ArF scanners or higher model. Therefore, Necessary to contribute to production with efficient management and no loss time for expensive EUV equipment. In a EUV era, every single second management is required and a big data analysis system is needed to detect fine changes. In this paper, we create big data using FDC (Fault Detection & Classification) function and use it to study cases of electron beam writer, process, etch, clean, repair, metrology, inspection, and pellicle equipment’s maintenance and risk management. Photomask process excursion could be a result of one or more of degrading equipment part, or equipment issue of the photomask quality from any of the previous steps. Detecting such FDC data excursions and notifying appropriate fab or assembly/test personnel could result in preventing yield loss, improving cycle-time, OEE (Overall Efficiency Effectiveness) and equipment up-time. So far, we will share the research case that acquiring by accumulated knowledge and the facility maintenance method using FDC will accelerate the resolution of the most urgent problems.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Hyunjoo Lee, Jaejun Lee, Donggeun Lee, Ilwoo Nam, and Sanghee Lee "Readiness for predictive maintenance of high cost EUV equipment using big data", Proc. SPIE 13177, Photomask Japan 2024: XXX Symposium on Photomask and Next-Generation Lithography Mask Technology, 131770N (26 August 2024); https://doi.org/10.1117/12.3032128

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

Sensors

Manufacturing

Vacuum

Data analysis

Extreme ultraviolet

Semiconducting wafers

Show All Keywords

Keywords/Phrases

Search In:

Publication Years