2 research outputs found
VLSI Mask Optimization: From Shallow To Deep Learning
VLSI mask optimization is one of the most critical stages in
manufacturability aware design, which is costly due to the complicated mask
optimization and lithography simulation. Recent researches have shown prominent
advantages of machine learning techniques dealing with complicated and big data
problems, which bring potential of dedicated machine learning solution for DFM
problems and facilitate the VLSI design cycle. In this paper, we focus on a
heterogeneous OPC framework that assists mask layout optimization. Preliminary
results show the efficiency and effectiveness of proposed frameworks that have
the potential to be alternatives to existing EDA solutions.Comment: 6 pages; accepted by 25th Asia and South Pacific Design Automation
Conference (ASP-DAC 2020
Machine Learning for Electronic Design Automation: A Survey
With the down-scaling of CMOS technology, the design complexity of very
large-scale integrated (VLSI) is increasing. Although the application of
machine learning (ML) techniques in electronic design automation (EDA) can
trace its history back to the 90s, the recent breakthrough of ML and the
increasing complexity of EDA tasks have aroused more interests in incorporating
ML to solve EDA tasks. In this paper, we present a comprehensive review of
existing ML for EDA studies, organized following the EDA hierarchy.Comment: Accepted by TODAES. The first 10 authors are ordered alphabeticall