Methods and factors to optimize OPC run-time

With the increasing complexity of design and the shrinking of technology nodes, optical proximity correction has become an integral part of IC fabrication. Pattern fidelity is the baseline for any accurate OPC model. Calibrated process models are used to make iterative pattern adjustments over a fragmented design to align simulated images and the target layout. More and more advanced modeling techniques are deployed for accurate prediction of complicated 1D and 2D structures. As such, more aggressive layout situations must be taken into consideration for different process and OPC aspects such as optimization of process window, contrast, MEEF, OPC convergence, and many more. However along with different process optimizations, the mask complexity increases and the OPC run-time is also adversely affected. Often, it has been noticed that the OPC model accuracy is restricted by long OPC run-times. The variation in OPC runtime could be due to many factors, including number of finitely sized segments, multiple iterations of edge movements & simulations, convergence, multiple process conditions, step size, sitelength, model complexity, etc. Integration of fast and accurate analysis is needed to address the growing complexity of OPC solutions. We present here different approaches for OPC run-time improvement. A methodology is prepared to investigate accurate OPC models with fast runtimes. Additionally, proper selection of fragmentation, simulation sitelength, and number of iterations can be modified to achieve significant improvement in computation speed. The variation in run-time is assessed for different approaches listed above with an emphasis on OPC accuracy. Statistical analysis is used to measure image parameters and edge placement errors (EPE) for various experiments and the output is the measurement and plotting of accuracy versus run-time. This paper will present those results and suggest best practices for OPC run-time improvement that can be incorporated as a part of an OPC model building and OPC qualification flow.