In subwavelength lithography, the printed patterns on the silicon wafer suffer from geometric distortions and different from the original design. These non-rectangular patterns can affect electrical characteristics and circuit performances seriously. In this work, we extend the verification of location-dependent weighting method and further propose three single conventional equivalent gate length (EGL) extraction methods for representing each non-rectangular gate transistor with a single EGL model. These methods are applied to sub-20nm FDSOI circuits to predict the postlithography performances. An in-house Extreme Ultraviolet Lithography (EUVL) simulation tool is utilized for nonrectangular pattern simulation. Shape information is imported to TCAD to construct 3D non-rectangular FDSOI transistor models. The accuracy of the location-dependent weighting method and EGL extraction methods are verified with TCAD circuit simulations. A 2D EGL circuit simulation method in TCAD is proposed instead of 3D EGL method to reduce the simulation time required. Preliminary simulation results indicate that weighting factors can improve the accuracy of electrical characteristics estimation, especially in leakage current analysis. On average, the off-state EGL (EGL<sub>off</sub>) with weightings is good enough. These methods could be used to simulate the non-rectangular transistors applied to sub-20nm FDSOI circuits including 6T-SRAM caused by non-ideal optical effects in industrial processes.
Model-based optical proximity correction (MPOPC) has been well adopted in subwavelength lithography for integrated-circuit manufacturing. Typical MBOPC algorithms involve with iteratively moving the layout polygon edges to reduce the edge placement errors (EPEs) predicted by the lithography model. At each iteration, the amounts of movement are mainly determined by the values of the EPEs and the correction factors (CFs). Since full-chip lithography simulation is very computation intensive, it is highly desirable to minimize the number of iterations for acceptable run times, by selecting suitable CFs. In practical applications, the CFs are usually heuristically determined and applied globally throughout the correction regions. This approach efficiently reduces the EPEs at most of the target points but the entire convergence can be hampered at a relatively small number of hot-spot locations. This work investigates the effectiveness of improving the overall convergence by introducing both global and local CFs, and approaches to utilize machine-learning techniques to estimate the hot-spot locations and associated local CF values.