Litho friendly via insertion with in-design auto-fix flow using machine learning

Ahmed Mounir Elsemary; Moutaz Fakhry; Janam Bakshi; Nishant Shah; Mohamed Ismail; Fadi Batarseh; Uwe Paul Schroeder; Ahmed Mohyeldin; Jason Cain

doi:10.1117/12.2297499

4 April 2018 Litho friendly via insertion with in-design auto-fix flow using machine learning

Ahmed Mounir Elsemary, Moutaz Fakhry, Janam Bakshi, Nishant Shah, Mohamed Ismail, Fadi Batarseh, Uwe Paul Schroeder, Ahmed Mohyeldin, Jason Cain

Author Affiliations +

Proceedings Volume 10588, Design-Process-Technology Co-optimization for Manufacturability XII; 105880F (2018) https://doi.org/10.1117/12.2297499
Event: SPIE Advanced Lithography, 2018, San Jose, California, United States

Abstract

Via failure has always been a significant yield detractor caused by random and systematic defects. Introducing redundant vias or via bars into the design can alleviate the problem significantly [1] and has, therefore, become a standard DFM procedure [2]. Applying rule-based via bar insertion to convert millions of via squares to via bar rectangles, in all possible places where enough room could be predicted, is an efficient methodology to maximize the redundancy rate. However, inserting via bars can result in lithography hotspots. A Pattern Manufacturability (PATMAN) model is proposed, to maximize the Redundant Via Insertion (RVI) rate in a reasonable runtime, while insuring lithography friendly insertion based on the accumulated DFM learnings during the yield ramp.

Conference Presentation

Citation Download Citation

Ahmed Mounir Elsemary, Moutaz Fakhry, Janam Bakshi, Nishant Shah, Mohamed Ismail, Fadi Batarseh, Uwe Paul Schroeder, Ahmed Mohyeldin, and Jason Cain "Litho friendly via insertion with in-design auto-fix flow using machine learning", Proc. SPIE 10588, Design-Process-Technology Co-optimization for Manufacturability XII, 105880F (4 April 2018); https://doi.org/10.1117/12.2297499

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