Abstract: A metrology system includes one or more through-focus imaging metrology sub-systems communicatively coupled to a controller having one or more processors configured to receive a plurality of training images captured at one or more focal positions. The one or more processors may generate a machine learning classifier based on the plurality of training images. The one or more processors may receive one or more target feature selections for one or more target overlay measurements corresponding to one or more target features. The one or more processors may determine one or more target focal positions based on the one or more target feature selections using the machine learning classifier. The one or more processors may receive one or more target images captured at the one or more target focal positions, the target images including the one or more target features of the target specimen, and determine overlay based thereon.

Abstract: A metrology system includes one or more through-focus imaging metrology sub-systems communicatively coupled to a controller, where the controller includes one or more processors. The one or more processors may be configured receive a plurality of training images captured at one or more focal positions. The one or more processors may further generate a machine learning classifier based on the plurality of training images captured at one or more focal positions. The one or more processors may further receive one or more target feature selections for one or more target overlay measurements corresponding to one or more target features of a target specimen. The one or more processors may further determine one or more target focal positions based on the one or more target feature selections using the machine learning classifier.

Abstract: A metrology system includes an imaging system. The imaging system may include an objective lens. The metrology system may include one or more detectors. The metrology system may include an objective positioning stage structurally coupled to the objective lens and configured to adjust a focal plane of at least one of the one or more detectors via movement along an optical axis of the metrology system. The metrology system may include one or more proximity sensors configured to measure lateral positions of a stage element as the objective positioning stage moves along the optical axis. The metrology system may be configured to determine a metrology measurement associated with a target on a sample using the images and lateral positions of the stage element when implementing a metrology recipe.

Abstract: A metrology system includes one or more through-focus imaging metrology sub-systems communicatively coupled to a controller having one or more processors configured to receive a plurality of training images captured at one or more focal positions. The one or more processors may generate a machine learning classifier based on the plurality of training images. The one or more processors may receive one or more target feature selections for one or more target overlay measurements corresponding to one or more target features. The one or more processors may determine one or more target focal positions based on the one or more target feature selections using the machine learning classifier. The one or more processors may receive one or more target images captured at the one or more target focal positions, the target images including the one or more target features of the target specimen, and determine overlay based thereon.

Abstract: A system and method for generating a quality parameter value of a semiconductor device wafer (SDW), during fabrication thereof, the method including designating a plurality of measurement site sets (MSSs) on the SDW, each of the MSSs including a first measurement-orientation site (FMS) and a second measurement-orientation site (SMS), the FMS and the SMS being different measurement sites on the SDW, generating a first measurement-orientation quality parameter dataset (FMQPD) by measuring features formed within each the FMS of at least one of the MSSs in a first measurement orientation, generating a second measurement-orientation quality parameter dataset (SMQPD) by measuring features formed within each the SMS of the at least one of the MSSs in a second measurement orientation and generating at least one tool-induced-shift (TIS)-ameliorated quality parameter value (TAQPV), at least partially based on the FMQPD and the SMQPD.

Abstract: An optical metrology tool may include one or more illumination sources to generate illumination having wavelengths both within a short-wave infrared (SWIR) spectral range and outside the SWIR spectral range, illumination optics configured to direct the illumination to a sample, a first imaging channel including a first detector configured to image the sample based on a first wavelength range including at least some wavelengths in the SWIR spectral range, a second imaging channel including a second detector configured to image the sample based on a second wavelength range including at least some wavelengths outside the SWIR spectral range, and a controller. The controller may receive first images of the sample from the first detector, receive second images of the sample from the second detector, and generate an optical metrology measurement of the sample based on the first and second images.

Abstract: A metrology system includes one or more through-focus imaging metrology sub-systems communicatively coupled to a controller having one or more processors configured receive a plurality of training images captured at one or more focal positions. The one or more processors may generate a machine learning classifier based on the plurality of training images. The one or more processors may receive one or more target feature selections for one or more target overlay measurements corresponding to one or more target features. The one or more processors may determine one or more target focal positions based on the one or more target feature selections using the machine learning classifier. The one or more processors may receive one or more target images captured at the one or more target focal positions, the target images including the one or more target features of the target specimen, and determine overlay based thereon.

Abstract: A device and method for expediting spectral measurement in metrological activities during semiconductor device fabrication through interferometric spectroscopy of white light illumination during calibration, overlay, and recipe creation.

Abstract: A device and method for expediting spectral measurement in metrological activities during semiconductor device fabrication through interferometric spectroscopy of white light illumination during calibration, overlay, and recipe creation.

Abstract: A device and method for expediting spectral measurement in metrological activities during semiconductor device fabrication through interferometric spectroscopy of white light illumination during calibration, overlay, and recipe creation.

Abstract: A device and method for expediting spectral measurement in metrological activities during semiconductor device fabrication through interferometric spectroscopy of white light illumination during calibration, overlay, and recipe creation.