Improving overlay manufacturing metrics through application of feedforward mask-bias

Etienne Joubert; Joseph C. Pellegrini; Manish Misra; John L. Sturtevant; John M. Bernhard; Phu Ong; Nathan K. Crawshaw; Vern Puchalski

doi:10.1117/12.485314

1 July 2003 Improving overly manufacturing metrics through application of feedforward mask-bias

Etienne Joubert, Joseph C. Pellegrini, Manish Misra, John L. Sturtevant, John M. Bernhard, Phu Ong, Nathan K. Crawshaw, Vern Puchalski

Author Affiliations +

Proceedings Volume 5044, Advanced Process Control and Automation; (2003) https://doi.org/10.1117/12.485314
Event: Advanced Microelectronic Manufacturing, 2003, Santa Clara, CA, United States

Abstract

Traditional run-to-run controllers that rely on highly correlated historical events to forecast process corrections have been shown to provide substantial benefit over manual control in the case of a fab that is primarily manufacturing high volume, frequent running parts (i.e., DRAM, MPU, and similar operations). However, a limitation of the traditional controller emerges when it is applied to a fab whose work in process (WIP) is composed of primarily short-running, high part count products (typical of foundries and ASIC fabs). This limitation exists because there is a strong likelihood that each reticle has a unique set of process corrections different from other reticles at the same process layer. Further limitations exist when it is realized that each reticle is loaded and aligned differently on multiple exposure tools.A structural change in how the run-to-run controller manages the frequent reticle changes associated with the high part count environment has allowed for breakthrough performance to be achieved. This breakthrough was mad possible by the realization that; 1. Reticle sourced errors were highly stable over long periods of time, thus allowing them to be deconvolved from the day to day tool and process drifts. 2. Reticle sourced errors can be modeled as a feedforward disturbance rather than as discriminates in defining and dividing process streams. In this paper, we show how to deconvolve the static (reticle) and dynamic (day to day tool and process) components from the overall error vector to better forecast feedback for existing products as well as how to compute or learn these values for new product introductions - or new tool startups. Manufacturing data will presented to support this discussion with some real world success stories.

Citation Download Citation

Etienne Joubert, Joseph C. Pellegrini, Manish Misra, John L. Sturtevant, John M. Bernhard, Phu Ong, Nathan K. Crawshaw, and Vern Puchalski "Improving overly manufacturing metrics through application of feedforward mask-bias", Proc. SPIE 5044, Advanced Process Control and Automation, (1 July 2003); https://doi.org/10.1117/12.485314

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
12 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Reticles

Error analysis

Overlay metrology

Manufacturing

Process control

Control systems

Data modeling

Show All Keywords

Keywords/Phrases

Search In:

Publication Years