Deep learning-based framework for comprehensive mask optimization

With the dramatically increase of design complexity and the advance of semiconductor technology nodes, huge difficulties appear during design for manufacturability with existing lithography solutions. Sub-resolution assist feature (SRAF) insertion and optical proximity correction (OPC) are both inevitable resolution enhancement techniques (RET) to maximize process window and ensure feature printability. Conventional model-based SRAF insertion and OPC methods are widely applied in industrial application but suffer from the extremely long runtime due to iterative optimization process. In this paper, we propose the first work developing a deep learning framework to simultaneously perform SRAF insertion and edge-based OPC. In addition, to make the optimized masks more reliable and convincing for industrial application, we employ a commercial lithography simulation tool to consider the quality of wafer image with various lithographic metrics. The effectiveness and efficiency of the proposed framework are demonstrated in experimental results, which also show the success of machine learning-based lithography optimization techniques for the current complex and large-scale circuit layouts.