Applications of machine learning in EDA (Conference Presentation)

Paul D. Franzon

doi:10.1117/12.2515268

18 March 2019 Applications of machine learning in EDA (Conference Presentation)

Paul D. Franzon

Proceedings Volume 10962, Design-Process-Technology Co-optimization for Manufacturability XIII; 109620C (2019) https://doi.org/10.1117/12.2515268
Event: SPIE Advanced Lithography, 2019, San Jose, California, United States

Abstract

Machine learning has significant potential to help the human designer produce better outcomes. It can also help manage some of the complexity with chip design in advanced nodes. Projects to be discussed include the following. o Using Bayesian optimization for circuit IP reuse. Analog and custom digital IP reuse is difficult and time-consuming. Professor Franzon's group has built a Bayesian optimization approach involving statistical surrogate models. They have demonstrated by porting a number of designs between nodes, including a BJT to SOI port. The machine learning based approach produced better results than a human designer. o Using Surrogate modeling for Physical design. Professor Franzon's group has used a machine learning approach to predict Global Router and detailed router results as a function of tool input settings. These models can be used to tune the tool setup for specific outcomes. o Using Surrogate Modeling and System Identification Modeling to model digital receiver chains. Digital receiver modeling is predict BER of a receiver in the presence of an input signal with a close eye. Professor Franzon's group has used a combination of Surrogate Modeling and System Identification to build such a modeling capability. o Using deep networks for design rule checking. The team has demonstrated that deep networks can be used instead of Boolen checkers. There is significant potential to close the DFM and custom design productivity gaps.

Conference Presentation

Citation Download Citation

Paul D. Franzon "Applications of machine learning in EDA (Conference Presentation)", Proc. SPIE 10962, Design-Process-Technology Co-optimization for Manufacturability XIII, 109620C (18 March 2019); https://doi.org/10.1117/12.2515268

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
PRESENTATION

WATCH
PRESENTATION SAVE TO MY LIBRARY

GET CITATION

KEYWORDS

Eye models

Machine learning

Electronic design automation

Receivers

System identification

Analog electronics

Eye

Show All Keywords

Keywords/Phrases

Search In:

Publication Years