Data-Driven Approaches for Process Simulation and Optical Proximity Correction

With continuous shrinking of process nodes, semiconductor manufacturing encounters more and more serious inconsistency between designed layout patterns and resulted wafer images. Conventionally, examining how a layout pattern can deviate from its original after complicated process steps, such as optical lithography and subsequent etching, relies on computationally expensive process simulation, which suffers from incredibly long runtime for large-scale circuit layouts, especially in advanced nodes. In addition, being one of the most important and commonly adopted resolution enhancement techniques, optical proximity correction (OPC) corrects image errors due to process effects by moving segment edges or adding extra polygons to mask patterns, while it is generally driven by simulation or time-consuming inverse lithography techniques (ILTs) to achieve acceptable accuracy. As a result, more and more state-of-the-art works on process simulation or/and OPC resort to the fast inference characteristic of machine/deep learning. This paper reviews these data-driven approaches to highlight the challenges in various aspects, explore preliminary solutions, and reveal possible future directions to push forward the frontiers of the research in design for manufacturability.