Fast inverse lithography approach based on a model-driven graph convolutional network

Inverse lithography technique (ILT) is a leading-edge method to improve the image fidelity of an advanced optical lithography system by performing pixel-wise optimization on the transmission function of photomask. However, traditional ILTs are computationally intensive, which limits their application in high volume manufacturing of integrated circuits. This paper proposes a model-driven graph convolutional network (MGCN) framework combined with the dense concentric circular sampling (DCCS) method to effectively improve computational efficiency and imaging fidelity of current ILTs. Firstly, the DCCS template is used to extract the geometric features from the layout pattern of integrated circuits, which are then input into a GCN-based encoder to predict the optimized mask pattern of the ILT. Then, a model-driven decoder based on the lithography imaging process is developed to retrieve the print image of the predicted ILT mask. By means of the cooperation between the encoder and decoder, an unsupervised training strategy is proposed to avoid the time-consuming labelling process of the training samples. With the help of the parallel computing under GPU framework, the well-trained encoder can make a fast prediction of ILT mask with high-fidelity image results. The results demonstrate the state-of-the-art performance of the proposed MGCN approach compared to some other comparative ILT methods.