Journal of Micro/Nanolithography MEMS and MOEMS · 2007 · 47 citations · 47 references
EngineeringComputational EngineeringManufacturing EngineeringDigital FabricationMicrofabricationPhotonicsOptical PrintingTolerance ModelingComputational LithographyDigital LithographyEdge Placement ErrorNanometer-scale MetrologyOptical LithographyOptical Proximity CorrectionResolution EnhancementResolution Enhancement Techniques
Optical proximity correction (OPC) is a widely used resolution enhancement technique for nanometer designs, yet conventional model‑based OPC assumes nominal process conditions and ignores variations, and sampling across the process window to improve results is time‑consuming. The study aims to develop a variational lithography model and associated metrics to enable process‑variation aware OPC. The authors calibrate the VLIM to experimental data, define a variational edge placement error metric, and use it within a PVOPC framework that runs only 2–3× slower than conventional OPC. The calibrated VLIM achieves accuracy comparable to nonvariational models while accounting for process variations, and the resulting PVOPC yields more robust geometric and electrical results than conventional OPC, with only a modest 2–3× runtime increase.
Optical proximity correction (OPC) is one of the most widely used resolution enhancement techniques (RET) in nanometer designs to improve subwavelength printability. Conventional model-based OPC assumes nominal process conditions without considering process variations because of the lack of variational lithography models. A simple method to improve OPC results under process variations is to sample multiple process conditions across the process window, which requires long run times. We derive a variational lithography model (VLIM) that can simulate across the process window without much run-time overhead compared to the conventional lithography models. To match the model to experimental data, we demonstrate a VLIM calibration method. The calibrated model has accuracy comparable to nonvariational models, but has the advantage of taking process variations into consideration. We introduce the variational edge placement error (VEPE) metrics based on the model, a natural extension to the edge placement error (EPE) used in conventional OPC algorithms. A true process-variation aware OPC (PVOPC) framework is proposed used the VEPE metric. Due to the analytical nature of VLIM, our PVOPC is only about 2 to 3× slower than the conventional OPC, but it explicitly considers the two main sources of process variations (exposure dose and focus variations) during OPC. Thus our post-PVOPC results are much more robust than the conventional OPC ones, in terms of both geometric printability and electrical characterization under process variations.
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