  We propose a deep learning-based data-driven framework consisting of two
convolutional neural networks: i) LithoNet that predicts the shape deformations
on a circuit due to IC fabrication, and ii) OPCNet that suggests IC layout
corrections to compensate for such shape deformations. By learning the shape
correspondences between pairs of layout design patterns and their scanning
electron microscope (SEM) images of the product wafer thereof, given an IC
layout pattern, LithoNet can mimic the fabrication process to predict its
fabricated circuit shape. Furthermore, LithoNet can take the wafer fabrication
parameters as a latent vector to model the parametric product variations that
can be inspected on SEM images. Besides, traditional optical proximity
correction (OPC) methods used to suggest a correction on a lithographic
photomask is computationally expensive. Our proposed OPCNet mimics the OPC
procedure and efficiently generates a corrected photomask by collaborating with
LithoNet to examine if the shape of a fabricated circuit optimally matches its
original layout design. As a result, the proposed LithoNet-OPCNet framework can
not only predict the shape of a fabricated IC from its layout pattern, but also
suggests a layout correction according to the consistency between the predicted
shape and the given layout. Experimental results with several benchmark layout
patterns demonstrate the effectiveness of the proposed method.
