Abstract: Described herein is a method for determining process drifts or outlier wafers over time in semiconductor manufacturing. The method involves obtaining a key performance indicator (KPI) variation (e.g., LCDU) characterizing a performance of a semiconductor process over time, and data associated with a set of factors associated with the semiconductor process. A model of the KPI and the data is used to determine contributions of a first set of factors toward the KPI variation, the first set of factors breaching a statistical threshold. The contributions from the first set of factors toward the KPI variation is removed from the model to obtain a residual KPI variation. Based on the residual KPI variation, a residual value breaching a residual threshold is determined. The residual value indicates process drifts in the semiconductor process over time or an outlier substrate corresponding to the residual value at a certain time.

Abstract: A method for determining a process window of a patterning process based on a failure rate. The method includes obtaining a plurality of features printed on a substrate, grouping, based on a metric, the features into a plurality of groups, and generating, based on measurement data associated with a group of features, a base failure rate model for the group of features, wherein the base failure rate model identifies the process window related to the failure rate of the group of features. The method can further include generating, using the base failure rate model, a feature-specific failure rate model for a specific feature, wherein the feature-specific failure rate model identifies a feature-specific process window such that an estimated failure rate of the specific feature is below a specified threshold.