Presentation
Generator of constrained test pattern based on multi-scale gradient generative adversarial networks
DescriptionDeep generative models enable the generation of diverse layout patterns.However, existing pattern generators perform poorly in meeting requirements of layout density and large scale which are crucial parameter for process monitoring and evaluation such as etching and CMP. Recognizing that various application scenarios impose specific requirements on test patterns, we propose a regression condition constraints based on multi-scale gradient generative adversarial networks (MSG-GAN), employing vicinal risk minimization loss function and a novel label introduction method to facilitate layout generation for specific conditions. Experiments demonstrate that our generator exhibits the capability to produce layouts of desired densities while satisfying design rule constraints.Furthermore, large continuous layouts without stitches can also be generated due to the multi-scale gradient connections in MSG-GAN.
Event Type
Networking
Work-in-Progress Poster
TimeSunday, June 226:00pm - 7:00pm PDT
LocationLevel 3 Lobby