Abstract: The present invention provides a scalable digital twin system structure and a scalable digital twin service method that are capable of, based on a digital twin, performing real-time control of the real world while providing information required for the user to determine the optimal countermeasure in solve real-world problems in stages, thereby helping rapidly solve problems of the real-world. In order to preemptively respond to the real-world problems by providing decision support information with improved reliability according to a timeline based on a digital twin of a scalable structure, the operation of the digital twin is divided into several stages according to complexity and a result of each stage is transferred to an application service and the next stage, so that as the stage becomes higher, a more reliable result can be provided.

Abstract: In a method of inspecting an object, a first light is irradiated onto a bare object and a first reflection signal is reflected from the bare object. A second light is irradiated onto a processed object and a second reflection signal is reflected from the processed object. The first and second reflection signals are differentiated, to thereby generate respective first and second differential signals. A defect on the processed object is detected by a comparison between the first and second differential signals. The first and second differential signals overlap with each other and at least one signal-deviation portion is detected. The first and second differential signals are spaced apart out of an allowable error range in the signal-deviation portion. The defect is detected from a portion of the processed object corresponding to the signal-deviation portion.

Abstract: A method of classifying defects of an object includes irradiating multi-wavelength light onto the object, splitting light reflected from the object into light beams, each of the light beams having different wavelengths, obtaining image information of the object based on each of the light beams, forming a characteristic matrix that represent the wavelengths and the image information, and analyzing the characteristic matrix to determine types of the defects on the object. Thus, the defects may be accurately classified using a difference between reactivity of each of the defects in accordance with variations of the wavelengths and inspection conditions.

Abstract: A method of inspecting an inspection pattern using a statistical inference function is disclosed. The inference function is generated in relation to optical reference signal data and reference pattern characteristic data for a plurality of reference patterns formed by a unit process of interest on reference substrates.

Abstract: A method of classifying defects of an object includes irradiating multi-wavelength light onto the object, splitting light reflected from the object into light beams, each of the light beams having different wavelengths, obtaining image information of the object based on each of the light beams, forming a characteristic matrix that represent the wavelengths and the image information, and analyzing the characteristic matrix to determine types of the defects on the object. Thus, the defects may be accurately classified using a difference between reactivity of each of the defects in accordance with variations of the wavelengths and inspection conditions.

Abstract: In a method of inspecting an object, a first light is irradiated onto a bare object and a first reflection signal is reflected from the bare object. A second light is irradiated onto a processed object and a second reflection signal is reflected from the processed object. The first and second reflection signals are differentiated, to thereby generate respective first and second differential signals. A defect on the processed object is detected by a comparison between the first and second differential signals. The first and second differential signals overlap with each other and at least one signal-deviation portion is detected. The first and second differential signals are spaced apart out of an allowable error range in the signal-deviation portion. The defect is detected from a portion of the processed object corresponding to the signal-deviation portion.

Abstract: A method of inspecting an inspection pattern using a statistical inference function is disclosed. The inference function is generated in relation to optical reference signal data and reference pattern characteristic data for a plurality of reference patterns formed by a unit process of interest on reference substrates.