Abstract: Methods and systems for using pre-process measurement data to train post-process, machine learning (ML) based measurement models are described herein. In one aspect, a post-process, ML based measurement model is trained using reference data derived from actual reference measurements and estimated reference data generated by a trained mapping model. The trained mapping model maps measured values of parameters of interest at a pre-process state to estimated reference values at the post-process state. In this manner, the reference data employed to train a ML based measurement model is augmented based on pre-process measurement data. In another aspect, measurements of complex semiconductor structures are based on a combined measurement model including trained pre-process and post-process measurement models. Pre-process measurement data is employed directly as part of a combined ML based measurement and indirectly as part of the training data set for the combined ML based measurement model.

Abstract: Methods and systems for generating measurement models of complex semiconductor structures based on re-useable, parametric models are presented herein. In some embodiments, the re-useable, parametric models enable measurement of high aspect ratio (HAR) structures having complex shape profiles. In these embodiments, a re-useable, parametric model includes multiple geometric sections each characterized by a different shape profile. Each shape profile is parameterized by at least one shape parameter. In a further aspect, at least one of the multiple geometric sections includes a plurality of subsections. In some other embodiments, the re-useable, parametric models enable measurement of nanowire based semiconductor structures. The re-useable, parametric models described herein are useful for generating measurement models for both optical metrology and x-ray metrology, e.g., soft x-ray metrology and hard x-ray metrology.

Abstract: A semiconductor metrology tool inspects an area of a semiconductor wafer. The inspected area includes a plurality of instances of a 3D semiconductor structure arranged periodically in at least one dimension. A computer system generates a model of a respective instance of the 3D semiconductor structure based on measurements collected during the inspection. The computer system renders an augmented-reality or virtual-reality (AR/VR) image of the model that shows a 3D shape of the model and provides the AR/VR image to an AR/VR viewing device for display.

Abstract: A semiconductor metrology tool inspects an area of a semiconductor wafer. The inspected area includes a plurality of instances of a 3D semiconductor structure arranged periodically in at least one dimension. A computer system generates a model of a respective instance of the 3D semiconductor structure based on measurements collected during the inspection. The computer system renders an augmented-reality or virtual-reality (AR/VR) image of the model that shows a 3D shape of the model and provides the AR/VR image to an AR/VR viewing device for display.

Abstract: A semiconductor metrology tool inspects an area of a semiconductor wafer. The inspected area includes a plurality of instances of a 3D semiconductor structure arranged periodically in at least one dimension. A computer system generates a model of a respective instance of the 3D semiconductor structure based on measurements collected during the inspection. The computer system renders an image of the model that shows a 3D shape of the model and provides the image to a device for display.

Abstract: A semiconductor metrology tool inspects an area of a semiconductor wafer. The inspected area includes a plurality of instances of a 3D semiconductor structure arranged periodically in at least one dimension. A computer system generates a model of a respective instance of the 3D semiconductor structure based on measurements collected during the inspection. The computer system renders an image of the model that shows a 3D shape of the model and provides the image to a device for display.

Abstract: Methods and tools for generating measurement models of complex device structures based on re-useable, parametric models are presented. Metrology systems employing these models are configured to measure structural and material characteristics associated with different semiconductor fabrication processes. The re-useable, parametric sub-structure model is fully defined by a set of independent parameters entered by a user of the model building tool. All other variables associated with the model shape and internal constraints among constituent geometric elements are pre-defined within the model. In some embodiments, one or more re-useable, parametric models are integrated into a measurement model of a complex semiconductor device. In another aspect, a model building tool generates a re-useable, parametric sub-structure model based on input from a user.

Abstract: Provided are optimized scatterometry techniques for evaluating a diffracting structure. In one embodiment, a method includes computing a finite-difference derivative of a field matrix with respect to first parameters (including a geometric parameter of the diffracting structure), computing an analytic derivative of the Jones matrix with respect to the field matrix, computing a derivative of the Jones matrix with respect to the first parameters, and computing a finite-difference derivative of the Jones matrix with respect to second parameters (including a non-geometric parameter). In one embodiment, a method includes generating a transfer matrix having Taylor Series approximations for elements, and decomposing the field matrix into two or more smaller matrices based on symmetry between the incident light and the diffracting structure.

Abstract: Methods and tools for generating measurement models of complex device structures based on re-useable, parametric models are presented. Metrology systems employing these models are configured to measure structural and material characteristics associated with different semiconductor fabrication processes. The re-useable, parametric sub-structure model is fully defined by a set of independent parameters entered by a user of the model building tool. All other variables associated with the model shape and internal constraints among constituent geometric elements are pre-defined within the model. In some embodiments, one or more re-useable, parametric models are integrated into a measurement model of a complex semiconductor device. In another aspect, a model building tool generates a re-useable, parametric sub-structure model based on input from a user.

Abstract: Provided are optimized scatterometry techniques for evaluating a diffracting structure. In one embodiment, a method includes computing a finite-difference derivative of a field matrix with respect to first parameters (including a geometric parameter of the diffracting structure), computing an analytic derivative of the Jones matrix with respect to the field matrix, computing a derivative of the Jones matrix with respect to the first parameters, and computing a finite-difference derivative of the Jones matrix with respect to second parameters (including a non-geometric parameter). In one embodiment, a method includes generating a transfer matrix having Taylor Series approximations for elements, and decomposing the field matrix into two or more smaller matrices based on symmetry between the incident light and the diffracting structure.