A reusable framework for data-mining mask shop tools

Dan Meier

doi:10.1117/12.2065574

8 October 2014 A reusable framework for data-mining mask shop tools

Dan Meier

Author Affiliations +

Proceedings Volume 9235, Photomask Technology 2014; 92351G (2014) https://doi.org/10.1117/12.2065574
Event: SPIE Photomask Technology, 2014, Monterey, California, United States

Abstract

Implementations of the Semiconductor Equipment Communications Standard (SECS) are uneven across mask shop tool sets, which often implement only tool control functionality and ignore data collection. Furthermore, data collection, if implemented at all, typically exposes only a fraction of the information available within log files created by the tool. This leaves a veritable wealth of information languishing unused in tool log files – data that could provide key insights toward improvements in tool performance, processes and utilization. This paper discusses a reusable, lightweight framework for mining data from mask shop tool log files. It details the categories of data that can be mined using this framework, as well as different actions that can be triggered based on the data. The paper also proposes a generic log file format that mask shop tool vendors can implement on any tool to facilitate tool troubleshooting and simplify automated data collection.

Citation Download Citation

Dan Meier "A reusable framework for data-mining mask shop tools", Proc. SPIE 9235, Photomask Technology 2014, 92351G (8 October 2014); https://doi.org/10.1117/12.2065574

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