OPC model sampling evaluation and weakpoint “in-situ” improvement

One of the major challenges of optical proximity correction (OPC) models is to maximize the coverage of real design features using sampling pattern. Normally, OPC model building is based on 1-D and 2-D test patterns with systematically changing pitches alignment with design rules. However, those features with different optical and geometric properties will generate weak-points where OPC simulation cannot precisely predict resist contours on wafer due to the nature of infinite IC designs and limited number of model test patterns. In this paper, optical property data of real design features were collected from full chips and classified to compare with the same kind of data from OPC test patterns. Therefore sample coverage could be visually mapped according to different optical properties. Design features, which are out of OPC capability, were distinguished by their optical properties and marked as weak-points. New patterns with similar optical properties would be added into model build site-list. Further, an alternative and more efficient method was created in this paper to improve the treatment of issue features and remove weak-points without rebuilding models. Since certain classification of optical properties will generate weak-points, an OPC-integrated repair algorithm was developed and implemented to scan full chip for optical properties, locate those features and then optimize OPC treatment or apply precise sizing on site. This is a named “in-situ” weak-point improvement flow which includes issue feature definition, allocation in full chip and real-time improvement.