Share Email Print
cover

Proceedings Paper

Layout optimization through robust pattern learning and prediction in SADP gridded designs
Author(s): Jen-Yi Wuu; Mark Simmons; Malgorzata Marek-Sadowska
Format Member Price Non-Member Price
PDF $14.40 $18.00

Paper Abstract

In this paper, we study the problem of placement-level layout optimization for designs built from cells with unidirectional self-aligned double patterning (SADP) metal-1 interconnect. Our goal is to minimize the number of potential bridging hotspots in design layouts using predictive, machine learning-based models and applying incremental placement adjustments. In the first part of the paper, we explain how to build layout pattern classification models using machine learning methods. Our support vector machine (SVM)-based model predicts a given layout clip as either robust or non-robust. In the second part of the paper, we apply the predictive models to placement-level optimization. Our algorithm identifies and eliminates potential hotspots in standard cell based layout by modifying local cell position.

Paper Details

Date Published: 15 March 2012
PDF: 7 pages
Proc. SPIE 8327, Design for Manufacturability through Design-Process Integration VI, 832705 (15 March 2012); doi: 10.1117/12.916583
Show Author Affiliations
Jen-Yi Wuu, Univ. of California, Santa Barbara (United States)
Mark Simmons, Mentor Graphics Corp. (United States)
Malgorzata Marek-Sadowska, Univ. of California, Santa Barbara (United States)


Published in SPIE Proceedings Vol. 8327:
Design for Manufacturability through Design-Process Integration VI
Mark E. Mason, Editor(s)

© SPIE. Terms of Use
Back to Top