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.
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