Optical proximity correction with the conditional Wasserstein GAN

Optical proximity correction becomes more and more critical since the technology nodes shrinks nowadays. It usually costs a lot of computational power and days are needed to finish this process. Increasing its speed has become an important research topic. Machine learning technology has been applied to achieve this goal. Generative modelling such as generative adversarial networks appears to be beneficial and applicable in doing the optical proximity correction. We prepare the paired target layout and post OPC layout. The target layout is input into the U net type generator and its output is the calculated post OPC layout. The calculated post OPC layout and corresponding post OPC layout are input into the discriminator of the generative adversarial networks. The discriminator is trained to maximize the discriminative loss function, while the generator is trained to minimize the discriminative loss function. When the whole conditional generative adversarial networks converge, the generator can generate the calculated post OPC layouts quite similar to the prepared ones. The generalization capability of the deep neural network is important here. The generator can also provide good post OPC layout for unseen target layouts. However, the training of generative adversarial networks is difficult and often unstable. To improve this, we use Wasserstein distance as the loss function and stabilize the training and convergence of the conditional generative adversarial networks. We can obtain better results easier this way.