With ever shrinking critical dimensions, half nm OPC errors are a primary focus for process improvement in computational lithography. Among many error sources for 2x and 1x nodes, 3D mask modeling has caught the attention of engineers and scientists as a method to reduce errors at these nodes. While the benefits of 3D mask modeling are well known, there will be a runtime penalty of 30-40% that needs to be weighed against the benefit of optical model accuracy improvements. The economically beneficial node to adopt 3D mask modeling has to be determined by balancing these factors. In this paper, a benchmarking study has been conducted on a 20nm cut mask, metal and via layers with two different computational lithography approaches as compared with standard thin-mask approximation modeling. Besides basic RMS error metrics for model calibration and verification, through pitch and through size optical proximity behavior, through focus model predictability, best focus prediction and common DOF prediction are thoroughly evaluated. Runtime impact and OPC accuracy are also studied.
Printing small vias with tight pitches is becoming very challenging and consequently, different techniques are explored to achieve a robust and stable process. These techniques include reverse tone imaging (RTI) process, source optimization, mask transmission (attenuated Phase Shift Masks (attnPSM) versus binary thin OMOG masks), three-dimensional mask effects models, and SRAF printing models. Simulations of NILS, MEEF, DoF and process variability (PV) band width across a wide range of patterns are used to compare these different techniques in addition to the experimental process window. The results show that the most significant benefits can be gained by using attnPSM masks in conjunction with source optimization and RTI process. However, this improvement alone is not enough; every facet of the computational lithography and process must be finely tuned to produce sufficient imaging quality. As technology continues to shrink, Electromagnetic Field (EMF)-induced errors limit the scalability of this process and we will discuss the need for advanced techniques to suppress and correct for them.