Abstract:
A method of determining focus of a lithographic apparatus has the following steps. Using the lithographic process to produce first and second structures on the substrate, the first structure has features which have a profile that has an asymmetry that depends on the focus and an exposure perturbation, such as dose or aberration. The second structure has features which have a profile that is differently sensitive to focus than the first structure and which is differently sensitive to exposure perturbation than the first structure. Scatterometer signals are used to determine a focus value used to produce the first structure. This may be done using the second scatterometer signal, and/or recorded exposure perturbation settings used in the lithographic process, to select a calibration curve for use in determining the focus value using the first scatterometer signal or by using a model with parameters related to the first and second scatterometer signals.
Abstract:
A method of determining focus of a lithographic apparatus has the following steps. Using the lithographic process to produce first and second structures on the substrate, the first structure has features which have a profile that has an asymmetry that depends on the focus and an exposure perturbation, such as dose or aberration. The second structure has features which have a profile that is differently sensitive to focus than the first structure and which is differently sensitive to exposure perturbation than the first structure. Scatterometer signals are used to determine a focus value used to produce the first structure. This may be done using the second scatterometer signal, and/or recorded exposure perturbation settings used in the lithographic process, to select a calibration curve for use in determining the focus value using the first scatterometer signal or by using a model with parameters related to the first and second scatterometer signals.
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.