Abstract: An optical metrology device uses a multi-wavelength beam of light that has azimuthally varying polarization states and/or phase states, referred to as a vortex beam. The metrology device focuses the vortex beam on a sample under test over a large range of angles of incidence. The metrology device may detect an image of the vortex beam reflected from the sample and measure the polarization state of the return light as function of the angle of incidence and the azimuth angle, which may be further measured at a plurality of different wavelengths. The vortex beam includes azimuthally varying polarization states, thereby enabling measurement of all desired polarization states without requiring the use of moving optical components. The polarization state information detected over multiple angles of incidence and wavelengths provides data with which an accurate determination of one or more characteristics of a sample may be determined.

Abstract: An optical metrology device uses a multi-wavelength beam of light that has azimuthally varying polarization states and/or phase states, referred to as a vortex beam. The metrology device focuses the vortex beam on a sample under test over a large range of angles of incidence. The metrology device may detect an image of the vortex beam reflected from the sample and measure the polarization state of the return light as function of the angle of incidence and the azimuth angle, which may be further measured at a plurality of different wavelengths. The vortex beam includes azimuthally varying polarization states, thereby enabling measurement of all desired polarization states without requiring the use of moving optical components. The polarization state information detected over multiple angles of incidence and wavelengths provides data with which an accurate determination of one or more characteristics of a sample may be determined.

Abstract: Optical metrology is used to calibrate the plane-of-incidence (POI) azimuth error by determining and correcting an azimuth angle offset. The azimuth angle offset may be determined by measuring at least a partial Mueller matrix from a calibration grating on a sample held on a stage for a plurality of POI azimuth angles. An axis of symmetry is determined for a curve describing a value of a Mueller matrix element with respect to POI azimuth angle, for each desired wavelength and each desired Mueller matrix element. The axis of symmetry may then be used to determine the azimuth angle offset, e.g., by determining a mean, median or average of all, or a filtered subset, of the axes of symmetry. If desired, an axis of symmetry may be determined for data sets other than Mueller matrix elements, such as Fourier coefficients of measured signals.

Abstract: Optical metrology is used to calibrate the plane-of-incidence (POI) azimuth error by determining and correcting an azimuth angle offset. The azimuth angle offset may be determined by measuring at least a partial Mueller matrix from a calibration grating on a sample held on a stage for a plurality of POI azimuth angles. An axis of symmetry is determined for a curve describing a value of a Mueller matrix element with respect to POI azimuth angle, for each desired wavelength and each desired Mueller matrix element. The axis of symmetry may then be used to determine the azimuth angle offset, e.g., by determining a mean, median or average of all, or a filtered subset, of the axes of symmetry. If desired, an axis of symmetry may be determined for data sets other than Mueller matrix elements, such as Fourier coefficients of measured signals.

Abstract: Optical metrology is used to calibrate the plane-of-incidence (POI) azimuth error by determining and correcting an azimuth angle offset. The azimuth angle offset may be determined by measuring at least a partial Mueller matrix from a calibration grating on a sample held on a stage for a plurality of POI azimuth angles. An axis of symmetry is determined for a curve describing a value of a Mueller matrix element with respect to POI azimuth angle, for each desired wavelength and each desired Mueller matrix element. The axis of symmetry may then be used to determine the azimuth angle offset, e.g., by determining a mean, median or average of all, or a filtered subset, of the axes of symmetry. If desired, an axis of symmetry may be determined for data sets other than Mueller matrix elements, such as Fourier coefficients of measured signals.

Abstract: The effective spot size of a spectroscopic metrology device is reduced through deconvolution of a measurement spectra set acquired from a measurement target combined with a training spectra set obtained from a training target. The measurement spectra set may be obtained using sparse sampling of a grid scan of a measurement target. The training spectra set is obtained from a grid scan of a training target that is similar to the measurement target. The training spectra set and the measurement spectra set include spectra from different grid nodes. Deconvolution of the measurement spectra and the training spectra sets produces an estimated spectrum for the measurement target that is an estimate of a spectrum from the measurement target produced with incident light having an effective spot size that is smaller than the actual spot size. One or more characteristics of the measurement target may then be determined using the estimated spectrum.

Abstract: The effective spot size of a spectroscopic metrology device is reduced through deconvolution of a measurement spectra set acquired from a measurement target combined with a training spectra set obtained from a training target. The measurement spectra set may be obtained using sparse sampling of a grid scan of a measurement target. The training spectra set is obtained from a grid scan of a training target that is similar to the measurement target. The training spectra set and the measurement spectra set include spectra from different grid nodes. Deconvolution of the measurement spectra and the training spectra sets produces an estimated spectrum for the measurement target that is an estimate of a spectrum from the measurement target produced with incident light having an effective spot size that is smaller than the actual spot size. One or more characteristics of the measurement target may then be determined using the estimated spectrum.

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: The effective spot size of a spectroscopic metrology device is reduced through deconvolution of a measurement spectra set acquired from a measurement target combined with a training spectra set obtained from a training target. The measurement spectra set may be obtained using sparse sampling of a grid scan of a measurement target. The training spectra set is obtained from a grid scan of a training target that is similar to the measurement target. The training spectra set and the measurement spectra set include spectra from different grid nodes. Deconvolution of the measurement spectra and the training spectra sets produces an estimated spectrum for the measurement target that is an estimate of a spectrum from the measurement target produced with incident light having an effective spot size that is smaller than the actual spot size. One or more characteristics of the measurement target may then be determined using the estimated spectrum.

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: The effective spot size of a spectroscopic metrology device is reduced through deconvolution of a measurement spectra set acquired from a measurement target combined with a training spectra set obtained from a training target. The measurement spectra set may be obtained using sparse sampling of a grid scan of a measurement target. The training spectra set is obtained from a grid scan of a training target that is similar to the measurement target. The training spectra set and the measurement spectra set include spectra from different grid nodes. Deconvolution of the measurement spectra and the training spectra sets produces an estimated spectrum for the measurement target that is an estimate of a spectrum from the measurement target produced with incident light having an effective spot size that is smaller than the actual spot size. One or more characteristics of the measurement target may then be determined using the estimated spectrum.

Abstract: Optical metrology is used to calibrate the plane-of-incidence (POI) azimuth error by determining and correcting an azimuth angle offset. The azimuth angle offset may be determined by measuring at least a partial Mueller matrix from a calibration grating on a sample held on a stage for a plurality of POI azimuth angles. An axis of symmetry is determined for a curve describing a value of a Mueller matrix element with respect to POI azimuth angle, for each desired wavelength and each desired Mueller matrix element. The axis of symmetry may then be used to determine the azimuth angle offset, e.g., by determining a mean, median or average of all, or a filtered subset, of the axes of symmetry. If desired, an axis of symmetry may be determined for data sets other than Mueller matrix elements, such as Fourier coefficients of measured signals.

Abstract: An optical metrology device produces a broadband beam of light that is incident on and reflected by a sample and introduces multiple variations in the polarization state of the beam of light induced by an optical chiral element. Using the detected light, the Muller matrix or partial Mueller matrix for the sample is determined, which is then used to determine a characteristic of the sample. For example, simulated spectra for a Mueller matrix for a model is fit to the measured spectra for the Mueller matrix of the sample by adjusting the parameters of the model until an acceptable fit between the simulated spectra and measured spectra from the Mueller matrices is produced. The varied parameters are then used as the sample parameters of interested, which can be reported, such as by storing in memory or displaying.

Abstract: An optical metrology device produces a broadband beam of light that is incident on and reflected by a sample and introduces multiple variations in the polarization state of the beam of light induced by an optical chiral element. Using the detected light, the Muller matrix or partial Mueller matrix for the sample is determined, which is then used to determine a characteristic of the sample. For example, simulated spectra for a Mueller matrix for a model is fit to the measured spectra for the Mueller matrix of the sample by adjusting the parameters of the model until an acceptable fit between the simulated spectra and measured spectra from the Mueller matrices is produced. The varied parameters are then used as the sample parameters of interested, which can be reported, such as by storing in memory or displaying.

Abstract: Non-contact apparatus and methods for evaluating at least one of the DC (or RF) dielectric constant, the hardness, and Young's Modulus of a dielectric material on a microelectronic workpiece under process and for generating a correlation factor that relates a measured IR spectrum to at least one of the dielectric constant, the hardness, and Young's Modulus of the dielectric material. A specific example of a method comprises measuring a thickness of the dielectric material on the process workpiece, irradiating the process workpiece with an IR source, and collecting and measuring an IR spectrum from the process workpiece. The measured thickness and at least a portion of the measured IR spectrum from the process workpiece are used with the correlation factor to determine at least one of the dielectric constant, the hardness, and Young's Modulus of the dielectric material. The determined value from the correlation factor is then stored and/or displayed.

Abstract: Methods and apparatus for assessing a constituent in a semiconductor substrate. Several embodiments of the invention are directed toward non-contact methods and systems for identifying an atom specie of a dopant implanted into the semiconductor substrate using techniques that do not mechanically contact the substrate with electrical leads or other types of mechanical measuring instruments. For example, one embodiment of a non-contact method of assessing a constituent in a semiconductor substrate in accordance with the invention comprises obtaining an actual reflectance spectrum of infrared radiation reflected from the semiconductor substrate, and ascertaining a plasma frequency value (?p) and a collision frequency value (?) for the semiconductor substrate based on the actual reflectance spectrum. This method can further include identifying a dopant type based on a relationship between dopant types and (a) plasma frequency values and (b) collision frequency values.

Abstract: Non-contact apparatus and methods for evaluating at least one of the DC (or RF) dielectric constant, the hardness, and Young's Modulus of a dielectric material on a microelectronic workpiece under process and for generating a correlation factor that relates a measured IR spectrum to at least one of the dielectric constant, the hardness, and Young's Modulus of the dielectric material. A specific example of a method comprises measuring a thickness of the dielectric material on the process workpiece, irradiating the process workpiece with an IR source, and collecting and measuring an IR spectrum from the process workpiece. The measured thickness and at least a portion of the measured IR spectrum from the process workpiece are used with the correlation factor to determine at least one of the dielectric constant, the hardness, and Young's Modulus of the dielectric material. The determined value from the correlation factor is then stored and/or displayed.

Abstract: Methods and apparatus for assessing a constituent in a semiconductor substrate. Several embodiments of the invention are directed toward non-contact methods and systems for identifying an atom specie of a dopant implanted into the semiconductor substrate using techniques that do not mechanically contact the substrate with electrical leads or other types of mechanical measuring instruments. For example, one embodiment of a non-contact method of assessing a constituent in a semiconductor substrate in accordance with the invention comprises obtaining an actual reflectance spectrum of infrared radiation reflected from the semiconductor substrate, and ascertaining a plasma frequency value (?p) and a collision frequency value (?) for the semiconductor substrate based on the actual reflectance spectrum. This method can further include identifying a dopant type based on a relationship between dopant types and (a) plasma frequency values and (b) collision frequency values.