Application-driven optimizations of metal stack and PDN (power delivery network) using machine learning framework

Hosoon Shin; Kyoungin Cho; Yongchan Ban

doi:10.1117/12.3010937

10 April 2024 Application-driven optimizations of metal stack and PDN (power delivery network) using machine learning framework

Hosoon Shin, Kyoungin Cho, Yongchan Ban

Author Affiliations +

Proceedings Volume 12954, DTCO and Computational Patterning III; 129540B (2024) https://doi.org/10.1117/12.3010937
Event: SPIE Advanced Lithography + Patterning, 2024, San Jose, California, United States

Abstract

Advanced technology nodes are beginning to adopt various technologies including innovations in transistor structure and MPT (Multi Patterning Technology) to achieve BEOL (Back End of Line) scaling. During DTCO (Design-Technology Co-Optimization) activity, BEOL geometries can be explored to achieve target PPA (Power-Performance-Area). To improve design's PPA, wire delay per unit distance can be reduced by increasing the metal width and spacing with given metal thickness. But increasing metal width and spacing can negatively affect the performance of the design as wire track resources per unit area reduced. Strengthening PDN (Power Delivery Network) can improve IR-drop of design, but also impact design's PPA negatively by consuming BEOL resource more. In this paper, application-driven metal stack and PDN optimization using ML (Machine-Learning) technique presented to address the issue effectively. To optimize metal stack and PDN, parameters of layer sheet count per thickness, pitch, spacing, and PDN horizontal/Vertical pitches are explored by Synopsys DSO.ai ML framework. DSO.ai optimization is constrained to maximize achieved frequency while maintaining certain IR-drop target. The metal stack and PDN optimization improved +2.2% of achieved frequency while 5.5% worse IR-drop. This is better frequency improvement than +1.4% of achieved frequency while 2.5% worse IR-drop from closest space where the DTCO done by human experts.

Conference Presentation

Citation Download Citation

Hosoon Shin, Kyoungin Cho, and Yongchan Ban "Application-driven optimizations of metal stack and PDN (power delivery network) using machine learning framework", Proc. SPIE 12954, DTCO and Computational Patterning III, 129540B (10 April 2024); https://doi.org/10.1117/12.3010937

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