Abstract: A thickness prediction network learning method includes measuring spectrums of optical characteristics of a plurality of semiconductor structures each including a substrate and first and second semiconductor material layers alternately stacked thereon to generate sets of spectrum measurement data, measuring thicknesses of the first and second semiconductor material layers to generate sets of thickness data, training a simulation network using the sets of spectrum measurement data and the sets of thickness data, generating sets of spectrum simulation data of spectrums of the optical characteristics of a plurality of virtual semiconductor structures based on thicknesses of first and second virtual semiconductor material layers using the simulation network, each of the first and second virtual semiconductor layers including the same material as the first and second semiconductor material layers, respectively; and training a thickness prediction network by using the sets of spectrum measurement data and the sets

Abstract: Provided are a multilayer structure inspection apparatus and method of inspecting a multilayer structure in a sample without damaging the sample, the multilayer structure inspection apparatus being configured to measure both of reflectance and dispersion without damaging the sample, wherein the reflectance and dispersion are variables which are changed sensitively to a change in a repetitive pattern of the multilayer structure, by measuring values thereof, a structural change of the sample between before and after a process is inspected with high accuracy.

Abstract: A thickness prediction network learning method includes measuring spectrums of optical characteristics of a plurality of semiconductor structures each including a substrate and first and second semiconductor material layers alternately stacked thereon to generate sets of spectrum measurement data, measuring thicknesses of the first and second semiconductor material layers to generate sets of thickness data, training a simulation network using the sets of spectrum measurement data and the sets of thickness data, generating sets of spectrum simulation data of spectrums of the optical characteristics of a plurality of virtual semiconductor structures based on thicknesses of first and second virtual semiconductor material layers using the simulation network, each of the first and second virtual semiconductor layers including the same material as the first and second semiconductor material layers, respectively; and training a thickness prediction network by using the sets of spectrum measurement data and the sets

Abstract: Provided are a multilayer structure inspection apparatus and method of inspecting a multilayer structure in a sample without damaging the sample, the multilayer structure inspection apparatus being configured to measure both of reflectance and dispersion without damaging the sample, wherein the reflectance and dispersion are variables which are changed sensitively to a change in a repetitive pattern of the multilayer structure, by measuring values thereof, a structural change of the sample between before and after a process is inspected with high accuracy.