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:
An imaging assembly of a spectral imaging ellipsometer includes an analyzer configured to polarize reflected light reflected from a sample surface, an imaging mirror optical system disposed on an optical path of the reflected light passing through the analyzer and including a first mirror having a concave surface and a second mirror having a convex surface, and a light detector configured to receive light passing through the imaging mirror optical system to collect spectral data. The reflected light is firstly reflected by the first mirror, the firstly reflected light is secondarily reflected by the second mirror and travels toward the first mirror again, and then thirdly reflected by the first mirror to be imaged on a light receiving surface of the light detector.
Abstract:
Systems, methods, apparatuses, and articles of manufacture are provided for recovering a digitized spectrum and may comprise: an optical system configured to transform rays, the optical system including a diffraction grating, a steering mirror, a stage, and an actuator configured to move one of the stage, diffraction grating, or steering mirror according to a movement regime to vary an incidence of the rays on the stage; a sensor array disposed on the stage configured to receive the rays incident from the optical system at a plurality of measurement locations to obtain a plurality of ray spectra; and a processor electrically connected to the sensor array configured to receive the ray spectra, interleave the ray spectra to yield an interleaved spectrum, and deconvolve a point spread function corresponding to the optical system from the interleaved spectrum to yield a recovered digitized spectrum.
Abstract:
Methods and systems of process control and yield management for semiconductor device manufacturing based on predictions of final device performance are presented herein. Estimated device performance metric values are calculated based on one or more device performance models that link parameter values capable of measurement during process to final device performance metrics. In some examples, an estimated value of a device performance metric is based on at least one structural characteristic and at least one band structure characteristic of an unfinished, multi-layer wafer. In some examples, a prediction of whether a device under process will fail a final device performance test is based on the difference between an estimated value of a final device performance metric and a specified value. In some examples, an adjustment in one or more subsequent process steps is determined based at least in part on the difference.
Abstract:
A spectroscopic beam profile metrology system simultaneously detects measurement signals over a large wavelength range and a large range of angles of incidence (AOI). In one aspect, a multiple wavelength illumination beam is reshaped to a narrow line shaped beam of light before projection onto a specimen by a high numerical aperture objective. After interaction with the specimen, the collected light is passes through a wavelength dispersive element that projects the range of AOIs along one direction and wavelength components along another direction of a two-dimensional detector. Thus, the measurement signals detected at each pixel of the detector each represent a scatterometry signal for a particular AOI and a particular wavelength. In another aspect, a hyperspectral detector is employed to simultaneously detect measurement signals over a large wavelength range, range of AOIs, and range of azimuth angles.
Abstract:
A method of applying a reflective optics system that requires the presence of both convex and a concave mirrors that have beam reflecting surfaces. Application thereof achieves focusing of a beam of electromagnetic radiation with minimized effects on a polarization state of an input beam state of polarization that results from adjustment of angles of incidence and reflections from the various mirrors involved.
Abstract:
A reflective optics system that requires the presence of both convex and a concave mirrors that have beam reflecting surfaces. Application thereof achieves focusing of a beam of electromagnetic radiation with minimized effects on a polarization state of an input beam state of polarization that results from adjustment of angles of incidence and reflections from the various mirrors involved. This invention is also a combination of a focusing element and a filtering element that provides an optimum electromagnetic beam cross-sectional area based on optimizing the beam cross-sectional area in view of conflicting effects of aberration and diffraction inherent in said focusing element, which, for each wavelength, vary oppositely to one another with electromagnetic beam cross-sectional area.
Abstract:
Parameters of a sample are measured using a model-based approach that utilizes the difference between experimental spectra acquired from the sample and experimental anchor spectra acquired from one or more reference samples at the same optical metrology tool. Anchor parameters of the one or more reference samples are determined using one or more reference optical metrology tools. The anchor spectrum is obtained and the target spectrum for the sample is acquired using the optical metrology tool. A differential experimental spectrum is generated based on a difference between the target spectrum and the anchor spectrum. The parameters for the sample are determined using the differential experimental spectrum and the anchor parameters, e.g., by comparing the differential experimental spectrum to a differential simulated spectrum, which is based on a difference between spectra simulated using a model having the parameters and a spectrum simulated using a model having the anchor parameters.
Abstract:
Methods and systems for performing broadband spectroscopic metrology with reduced sensitivity to grating anomalies are presented herein. A reduction in sensitivity to grating anomalies is achieved by selecting a subset of available system parameter values for measurement analysis. The reduction in sensitivity to grating anomalies enables an optimization of any combination of precision, sensitivity, accuracy, system matching, and computational effort. These benefits are particularly evident in optical metrology systems having large ranges of available azimuth angle, angle of incidence, illumination wavelength, and illumination polarization. Predictions of grating anomalies are determined based on a measurement model that accurately represents the interaction between the measurement system and the periodic metrology target under measurement. A subset of available system parameter values is selected to reduce the impact of grating anomalies on measurement results. The selected subset of available system parameters is implemented on a configurable spectroscopic metrology system performing measurements.
Abstract:
The present invention includes generating a three-dimensional design of experiment (DOE) for a plurality of semiconductor wafers, a first dimension of the DOE being a relative amount of a first component of the thin film, a second dimension of the DOE being a relative amount of a second component of the thin film, a third dimension of the DOE being a thickness of the thin film, acquiring a spectrum for each of the wafers, generating a set of optical dispersion data by extracting a real component (n) and an imaginary component (k) of the complex index of refraction for each of the acquired spectrum, identifying one or more systematic features of the set of optical dispersion data; and generating a multi-component Bruggeman effective medium approximation (BEMA) model utilizing the identified one or more systematic features of the set of optical dispersion data.