Title :
EPIC: Efficient prediction of IC manufacturing hotspots with a unified meta-classification formulation
Author :
Ding, Duo ; Yu, Bei ; Ghosh, Joydeep ; Pan, David Z.
Author_Institution :
ECE Dept., Univ. of Texas at Austin, Austin, TX, USA
fDate :
Jan. 30 2012-Feb. 2 2012
Abstract :
In this paper we present EPIC, an efficient and effective predictor for IC manufacturing hotspots in deep sub-wavelength lithography. EPIC proposes a unified framework to combine different hotspot detection methods together, such as machine learning and pattern matching, using mathematical programming/optimization. EPIC algorithm has been tested on a number of industry benchmarks under advanced manufacturing conditions. It demonstrates so far the best capability in selectively combining the desirable features of various hotspot detection methods (3.5-8.2% accuracy improvement) as well as significant suppression of the detection noise (e.g., 80% false-alarm reduction). These characteristics make EPIC very suitable for conducting high performance physical verification and guiding efficient manufacturability friendly physical design.
Keywords :
circuit optimisation; electronic engineering computing; integrated circuit design; integrated circuit manufacture; mathematical programming; pattern classification; photolithography; EPIC effective predictor algorithm; IC manufacturing hotspots; deep sub-wavelength lithography; detection noise suppression; hotspot detection methods; machine learning; mathematical programming-optimization; pattern matching; unified meta-classification formulation; Accuracy; Calibration; Layout; Lithography; Machine learning; Pattern matching; Design for Manufacturability; Lithography Hotspots; Machine Learning; Meta Classification; Pattern Matching;
Conference_Titel :
Design Automation Conference (ASP-DAC), 2012 17th Asia and South Pacific
Conference_Location :
Sydney, NSW
Print_ISBN :
978-1-4673-0770-3
DOI :
10.1109/ASPDAC.2012.6164956
