Adaptive Layout Decomposition With Graph Embedding Neural Networks

Multiple patterning layout decomposition (MPLD) has been widely investigated, but so far there is no decomposer that dominates others in terms of both result quality and efficiency. This observation motivates us to explore how to adaptively select the most suitable MPLD strategy for a given layout graph, which is nontrivial and still an open problem. In this article, we propose a layout decomposition framework based on graph convolutional networks to obtain the graph embeddings of the layout. The graph embeddings are used for graph library construction, decomposer selection, graph matching, stitch removal prediction, and graph coloring. In addition, we design a fast nonstitch layout decomposition algorithm that purely depends on the message passing graph neural network. The experimental results show that our graph embedding-based framework can achieve optimal decompositions in the widely used benchmark with a significant runtime drop even compared with fast but nonoptimal heuristics.