Bridging Hotspot Detection and Mask Optimization via Domain-Crossing Masked Layout Modeling

With the rapid development of semiconductors, the size of transistors is continuously scaling down. The shrinking circuit size poses great challenges to optical proximity correction (OPC) and hotspot detection (HSD). Recent advancements in OPC and HSD commonly employ deep neural networks, achieving impressive performance within a limited runtime. Based on these achievements, we observe that deep learning-based models of both HSD and OPC require knowledge of layout structure information. Furthermore, these two tasks are closely related to the lithography process during chip manufacturing. Observing such strong relationships, we propose that integrating OPC and HSD into a unified deep-learning model will contribute to the performance of both tasks. To bridge the relationship between OPC and HSD, we first pre-train a layout understanding model built on the mask modeling technique, which effectively captures the layout geometric information, and then the pre-trained model can be easily fine-tuned on HSD and OPC with limited data. To fully pre-train the layout understanding model (LUM), we create a large layout dataset using layout generation techniques, solving the data-hungry issues. Experimental results show that the fine-tuned LUM model achieves remarkable performance on both OPC and HSD tasks.