Method, system, computing device and storage medium for optical measurement

A computing device, comprising:

• a memory configured to store one or more computer programs;
• and
• a processor coupled to the memory and configured to execute the one or more programs to cause a measuring apparatus to:
• generate 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;
• extract features of the input data based on the neural network model trained via a plurality of samples, so as to generate simulated dispersion curve data associated with the preset values of the geometric parameters, the simulated dispersion curve data indicating a plurality of optical responses corresponding to a plurality of coordinate data of the dispersion curve data;
• obtain measured dispersion curve data in relation to a measuring object;
• calculate a distance of the measured dispersion curve data from the simulated dispersion curve data, so as to determine whether the distance meets a predetermined condition;
• and
• in response to determining the distance does not meet the predetermined condition, determine a gradient for updating the preset values of the geometric parameters with respect to the reference model based on the distance, so as to regenerate simulated dispersion curve data via the neural network model based on the updated preset values to recalculate the distance.

A measuring system, comprising:

• an angle-resolved spectrometry configured to measure an object based on incident light so as to generate an optical energy band with respect to the measuring object;
• and
• a computing device configured to be operable to:
• generate 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;
• extract features of the input data based on the neural network model trained via a plurality of samples, so as to generate simulated dispersion curve data associated with the preset values of the geometric parameters, the simulated dispersion curve data indicating a plurality of optical responses corresponding to a plurality of coordinate data of the dispersion curve data;
• obtain measured dispersion curve data in relation to a measuring object;
• calculate a distance of the measured dispersion curve data from the simulated dispersion curve data, so as to determine whether the distance meets a predetermined condition;
• and
• in response to determining the distance does not meet the predetermined condition, determine a gradient for updating the preset values of the geometric parameters with respect to the reference model based on the distance, so as to regenerate simulated dispersion curve data via the neural network model based on the updated preset values to recalculate the distance.