Abstract: An analyzing apparatus is provided. The analyzing apparatus may include a spectrum unit acquiring a spectrum related to semiconductor characteristics, and a corrector provided with a model that corrects at least one of noise and uncertainty of a measurement parameter related to the spectrum. The analyzing apparatus may include an evaluator provided to evaluate the uncertainty of the parameter corresponding to a controllable factor of measurement equipment that outputs the spectrum, and an attenuator provided to attenuate the spectral noise on the basis of an uncontrollable factor of the measurement equipment.

Abstract: A method for measuring a characteristic of a thin film is disclosed. The method includes a) obtaining a measured spectrum from a target region on the substrate by using a spectroscopic ellipsometer, b) obtaining a physical model capable of obtaining an estimated parameter value related to the characteristic of the thin film through regression analysis of the measured spectrum, c) obtaining a machine learning model capable of obtaining a reference parameter value related to the characteristic of the thin film by using the measured spectrum, and d) obtaining an integrated model which uses an integrated error function capable of considering both of a first error function and a second error function, and obtaining an optimum parameter value through regression analysis of the integrated model.

Abstract: A method for measuring a characteristic of a thin film is disclosed. The method includes a) obtaining a measured spectrum from a target region on the substrate by using a spectroscopic ellipsometer, b) obtaining a physical model capable of obtaining an estimated parameter value related to the characteristic of the thin film through regression analysis of the measured spectrum, c) obtaining a machine learning model capable of obtaining a reference parameter value related to the characteristic of the thin film by using the measured spectrum, and d) obtaining an integrated model which uses an integrated error function capable of considering both of a first error function and a second error function, and obtaining an optimum parameter value through regression analysis of the integrated model.