Streamlining Computational Lithography With Efficient Pattern Database

In the pursuit of advancing computational lithography, this article introduces a novel pattern database framework designed to support related tasks. The proposed framework is built upon three core components: 1) an unsupervised metric learning method for robust pattern embedding; 2) a vector database for swift pattern retrieval; and 3) an efficient algorithm dedicated to pattern clustering. These elements synergize to significantly enhance the efficiency and effectiveness of various computational lithography methods. In downstream tasks, our framework provides accurate lithography hotspot detection through pattern retrieval, streamlines inverse lithography technique (ILT) by leveraging solution reusing, and facilitates the exploration of ILT & source parameters based on the pattern clustering results. Collectively, these advancements culminate in a comprehensive improvement in computational lithography, offering a scalable solution for the ever-evolving demands of this field.