Abstract: A method comprises: generating input data for inputting to a neural network model based on coordinate data of dispersion curve data and preset values of geometric parameters with respect to a reference model; generating simulated dispersion curve data associated with the preset values of the geometric parameters based on the neural network model trained via a plurality of samples; obtaining measured dispersion curve data in relation to an object calculating a distance of the measured dispersion curve data from the simulated dispersion curve data to determine whether the distance meets a predetermined condition; and in response to determining the distance does not meet the predetermined condition, determining a gradient for updating the preset values of the geometric parameters with respect to the reference model based on the distance, to regenerate simulated dispersion curve data via the neural network model based on the updated preset values to recalculate the distance.

Abstract: Embodiments of the present disclosure relate to a metrology method and system for critical dimensions based on a dispersion relation in momentum space.