SSDL-ILT: Efficient ILT utilizing a self-supervised deep learning model

Inverse lithography technology (ILT) is an advanced resolution enhancement technique that achieves mask optimization at the pixel level. However, application of ILT is hindered by time-intensive physical simulation. Herein, we propose an efficient ILT algorithm leveraging a deep learning model. A novel loss function is constructed to guide the model training in a self-supervised manner, eliminating the requirement of labelled data that might be nontrivial to acquire. The trained model outputs final mask patterns without further ILT optimization. Sub-resolution assist features (SRAFs) are generated automatically, the complexity of which can be adjusted during the training process to control mask manufacturability. The model was trained and validated on ICCAD-2013 CAD contest dataset. Better pattern fidelity and up to 12,000 times speedup are observed compared to other SOTA models. The trained model also shows good generalization ability to geometrically-different design patterns from another dataset, via a few-shot learning approach.