Abstract: This application discloses a computing system to identify structures of an integrated circuit capable of being fabricated utilizing a lithographic mask described by mask layout data and to generate process windows for the identified structures based, at least in part, on the mask layout data and a failure definition for the identified structures. The computing system utilizes process windows for the identified structures to determine failure rates for the identified structures based on a distribution of the manufacturing parameters. The computing system determines frequency of occurrences for the identified structures from the mask layout data and generates a die yield metric for the integrated circuit by aggregating the failure rates for the identified structures based on the frequency of occurrences for the identified structures in the integrated circuit. These increases in yield of the integrated circuit allow manufacturers to produce more units per fixed processing cost of the wafer.

Abstract: This application discloses a computing system to identify structures of an integrated circuit capable of being fabricated utilizing a lithographic mask described by mask layout data and to generate process windows for the identified structures based, at least in part, on the mask layout data and a failure definition for the identified structures. The computing system utilizes process windows for the identified structures to determine failure rates for the identified structures based on a distribution of the manufacturing parameters. The computing system determines frequency of occurrences for the identified structures from the mask layout data and generates a die yield metric for the integrated circuit by aggregating the failure rates for the identified structures based on the frequency of occurrences for the identified structures in the integrated circuit. These increases in yield of the integrated circuit allow manufacturers to produce more units per fixed processing cost of the wafer.

Abstract: A computing system implementing an optical proximity correction model verification tool can determine parameters for design patterns associated with an integrated circuit described in a layer file, and determine differences between the design patterns and calibration patterns utilized to calibrate an optical proximity correction (OPC) model configured to predict a printed image on a substrate corresponding to a layout design for the integrated circuit by determining distances between the determined parameters for the design patterns and parameters for the calibration patterns. The computing system can classify the design patterns with a modeling capability of the OPC model for the design patterns based on the differences between design patterns and the calibration patterns and possibly error rates of the OPC model associated with the calibration patterns or lithographic difficulty of the calibration patterns. The computing system can modify the layer file to include the classifications of the design patterns.

Abstract: This application discloses a computing system implementing an optical proximity correction model calibration tool to determine parameters for gauges describing features of an integrated circuit. The gauges include values corresponding to measurements collected for a set of the features. The optical proximity correction model calibration tool can ascertain densities of the gauges based on the measurements associated with the parameters for the gauges, and set weights for the gauges based, at least in part, on the densities. The optical proximity correction model calibration tool can calibrate an optical proximity correction (OPC) model using the weights for the gauges. The OPC model calibrated with the weights of the gauges can be utilized to predict of a printed image on a substrate described by a mask layout design corresponding to the integrated circuit.