One of the best methods to increase correction accuracy in model based OPC is to decrease the correction segment length. As design rules shrink, this methodology is becoming more prevalent in model based OPC corrections. Unfortunately, it increases general mask feature complexity, which leads to reticles that are difficult to manufacture and inspect. With current OPC segmentation methodologies, the smallest correction segment length is generally applied uniformly across an entire correction set. A more targeted segmentation approach using the process model to determine sampling rates and locations could be used to confine complex correction features only to regions where they are absolutely necessary. For example, the choice between a hammerhead or dog-ear serif can be made using process model data so that dog-ear serifs are only used when flat aerial images are generated by the layout. This would lead to a more frugal correction that maintains correction accuracy while reducing mask construction complexity. OPC complexity is a key factor driving mask costs higher as design rules are pushed smaller. Methods for effectively reducing OPC complexity, without compromising OPC effectiveness, are being leveraged to help reduce the rate of NRE cost growth. In previous papers we have discussed methods for identifying features in which OPC accuracy can be sacrificed safely to reduce mask complexity. In addition, we have outlined methods for handling process variation effects for simple OPC shapes in complex regions. In this paper we will discuss another method for reducing OPC complexity while optimally preserving OPC accuracy on every feature. The method leverages pre-correction process simulation to predict the most “cost effective” shape for a feature. With simulated pattern characteristics and with consideration of potential mask rule violations, the method establishes an optimum correction shape “template.” For example, the choice between various line end-treatments can be determined up front, thus focusing the OPC computation on the most effective and least complex shape, and removing the need to perform post-OPC mask constraint shape adjustments. The implementation of this methodology leads to a more frugal correction that maintains correction accuracy while reducing mask construction complexity.