Abstract
Inverse Lithography techniques for mask optimization employ pixel based optimization algorithms and offer superior quality, but are compute intensive. A Machine learning model can be leveraged to replace the compute intensive portion of the ILT flow. In this paper we demonstrate that Machine learning models can be utilized to speed up the turnaround time of ILT flows. A CNN can be trained to compute an initial approximation of the mask, which can then be cleaned up using a few iterations of conventional OPC. We show that a performance gain of about 4X is achievable without any adverse impact on quality.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.