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Exploring Rule-Free Layout Decomposition via Deep Reinforcement Learning
Bentian Jiang
*
, Xinshi Zang
,
Martin D.F. Wong
, Evangeline F.Y. Young
*
Corresponding author for this work
Office of the Provost
Research output
:
Contribution to journal
›
Journal article
›
peer-review
2
Citations (Scopus)
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Dive into the research topics of 'Exploring Rule-Free Layout Decomposition via Deep Reinforcement Learning'. Together they form a unique fingerprint.
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Keyphrases
Deep Reinforcement Learning (deep RL)
100%
Multiple Patterning Lithography
100%
Decomposers
100%
Layout Decomposition
100%
Free Layout
100%
Deep Learning
50%
Learning-based
50%
Reinforcement Learning
50%
Sweet
50%
Turnaround Time
50%
Rule-based Method
50%
Mathematical Programming
50%
Graphical Approach
50%
Device Features
50%
Shrinking Device
50%
Mask Optimization
50%
Mathematical Graph
50%
Decomposition Optimization
50%
Lithography System
50%
Computer Science
Deep Learning Method
100%
Reinforcement Learning
100%
Mathematical Programming
100%
Deep Reinforcement Learning
100%
Engineering
Lithography
100%
Deep Reinforcement Learning
100%
Deep Learning Method
33%
Reinforcement Learning
33%
Feature Size
33%
Material Science
Lithography
100%
Reinforcement Learning
100%