Abstract: Optical measurement of a sample that includes a structure-of-interest (SOI) optically coupled to an unknown structure is optically measured by extracting from the resulting spectral signal the spectral variation that is correlated to key parameters associated with the SOI and removing the spectral variation from unknown structure that is irrelevant to the key parameters. An offline process is used to generate a physics-based model from a number of calibration measurements using reconstructed spectral signals after removing the spectral variation from the spectral signals that is irrelevant to the key parameters. A machine learning model may be additionally generated using at least the spectral variations correlated to key parameters associated with the SOI. In an in-line process, a sample is measured by filtering the spectral signals from a sample to remove spectral effects from the unknown structure and using the physics-based model or using the trained machine learning model.

Abstract: Various examples include an apparatus to characterize substrate and film thicknesses of the substrate and films formed thereon. The apparatus uses one or more wavelengths of light from a light source (e.g., a swept laser) to interrogate the substrate. The light is directed substantially orthogonally to an upper surface of the substrate. A polarizer and an analyzer element are coupled between the light source and the substrate. Therefore, the polarizer and the analyzer are both located in the beam propagation path from the light source to the substrate. An optical detector is arranged substantially orthogonally to the upper surface of the substrate. The optical detector receives light returned back from the substrate. The apparatus is capable of determining the thickness of the substrate and one or more films contained thereon, regardless of substrates having optical anisotropies, such as chiral characteristics or stress-induced films. Other apparatuses and methods are also disclosed.

Abstract: Various examples include a substrate pre-aligner system that can align substrates by detecting a fiducial on the substrate, determine an amount of bow in the substrate, and determine other characteristics of the substrate. In one example, by imaging both a 0-degree orientation and after a single 180-degree rotation of the substrate, the pre-aligner of the disclosed subject-matter can determine, for example, a location of the fiducial and bow in the substrate. In other embodiments, multiple cameras are used to capture images of the substrate substantially simultaneously and determine, for example, a location of the fiducial and bow in the substrate. The multiple camera embodiment can also allow a higher throughput of substrates as compared with the 0-degree to 180-degree embodiment. Other systems and methods are also disclosed.

Abstract: An optical metrology device characterizes a test object using a phase shift interferometer with synchronous time varying optical frequency shifts. A light source generates a beam having a time varying frequency, which is divided into two collinear, orthogonally polarized beams that differ by a first frequency shift. One or more optical cavities receive the beams and produce a pair of reference beams that differ from each other in frequency by the first frequency shift and a pair of test beams with a second frequency shift induced by the one or more optical cavities. The test beams differ from each other by the first frequency shift and differ from the reference beams by the second frequency shift. The first frequency shift has a pre-defined relationship with respect to the second frequency shift to generate interference between a reference beam and test beam that have frequency shift magnitudes with the pre-defined relationship.

Abstract: Optical measurement of a sample that includes a structure-of-interest (SOI) optically coupled to a section having an unknown structure is optically measured using an influence map of the deviation contribution from the unknown structure. The influence map is generated by obtaining metrology data for a plurality of locations that include the SOI and unknown structure and determining the deviation contribution at each location by decoupling the deviation contribution from base contributions from the SOI and unknown structure. During measurement of a location, the deviation contribution associated with that location may be obtained from the influence map and removed from the measured data. The processed data may be fit with a model that includes a rigorous model for the SOI and an effective model for the base contribution of the unknown structure to determine one or more parameters of the SOI.

Abstract: An optical system and design can image objects under inspection in the ultraviolet (UV) and visible spectrums. This imaging can be used to detect both large defects in the visible spectrum and small defects in the UV spectrum in a single pass while reducing the time and cost of the inspection process. The optical system may include an off-axis reflective focusing system for aberration correction with a beamsplitter to separate the visible spectrum from the UV wavelengths. Cameras may then image visible and UV wavelengths.

Abstract: Measuring or inspecting samples through non-destructive systems and methods. Multiple light pulses emitted from a light source. The light pulses are split into pump pulses and probe pulses. A first probe pulse reaches the surface of a sample after a first time duration after a first pump pulse reaches the surface. A second pump pulse reaches the surface after a time duration after the first probe pulse. When the second pump pulse reflects off the sample, the second pump pulse may be altered by an acoustic wave generated by the first probe pulse. The reflected second pump pulse may be analyzed to determine a characteristic of the sample.

Abstract: Various examples include a system and network to map of substrates within a substrate carrier (e.g., such as silicon wafers within a wafer cassette), and a classification of a state of each substrate, as well as the carrier in which the substrates are placed. In various examples provided herein, an image acquisition system, such as a camera, acquires multiple images of the substrates within the carrier. The image or images are then processed with a deep-convolutional neural-network to classify a state of the substrate relative to a substrate slot including empty slots, occupied slots (e.g., properly loaded slots), double-loaded slots, cross-slotted, and protruded (where a substrate is not fully loaded into a slot).

Abstract: A time resolved reflectivity metrology device images structures underlying layers using a pulsed pump beam and pulsed probe beam with at least one time delay between the pulses. One or both beams are modulated. A camera with a multi-pixel array and independent phase locking for each pixel in the multi-pixel array receives and demodulates the reflected probe beam to generate images. The camera may record a change in reflectivity or surface deformation of the target sample at every pixel as a function of at least one time delay between the pump pulses and the probe pulses, with which at least one property of the target sample may be characterized.

Abstract: Systems and methods for improving substrate fabrication are provided. Subsets of dies of substrates may be inspected at various points in the fabrication process to generate spectra data. The spectra data can be used to generate data that are input to a machine learning model to predict yields for the substrates.

Abstract: An opto-acoustic metrology device is configured to measure or inspect structures in a sample using both vertical and lateral transient perturbations. Multiple probe beams that have different locations of incidence may detect both the vertical and lateral transient perturbations produced by a pump beam, or a single probe beam may detect both the vertical and lateral transient perturbations produced by multiple pump beams that have different locations of incidence. The multiple probe beams or multiple pump beams are modulated with orthogonal waveforms, which allow the measurement of the different locations without interference from one another. The received signals are demodulated based on the orthogonal waveforms to recover the contributions to the received signals associated with each of the multiple probe beams or each of the multiple pump beams.

Abstract: Non-contact measurements, such as optical measurements or X-ray measurements, of a structure are supported using transfer learning for training a machine learning (ML) model for predicting key parameters. A first set of metrology data for a first set of structures is obtained and used to train a first ML model. A second set of metrology data for a second one or more structures is obtained. Transfer learning from the first ML model to the second set of metrology data is performed to produce a second ML model for predicting key parameters of the second one or more structures. Domain adaptation may be used in which metrology data is selected from the first set of metrology data and the second set of metrology data using a feature extractor and used to train a ML model for predicting key parameters of the second one or more structures.

Abstract: Contamination of metrology data by non-target signals caused by the measurement spot being incident on target and non-target areas may be reduced or eliminated using local gradient based mixed modeling or a machine learning model. The local gradient based mixed modeling approach uses a mixed model based on a model of the target region and a term of the local gradient of measured signals, which may be determined from scan data. The machine learning approach obtains mixed metrology data from a plurality of locations with respect to the target. The mixed metrology data is a mixture of target signals from the target and non-target signals from a non-target area and is used by a trained machine learning model to determine target metrology data that includes target signals from the target without the non-target signals and is used to determine parameters of interest for the target.

Abstract: Measuring or inspecting samples through non-destructive systems and methods. Multiple light pulses emitted from a light source. The light pulses are split into pump pulses and probe pulses. A first probe pulse reaches the surface of a sample after a first time duration after a first pump pulse reaches the surface. A second pump pulse reaches the surface after a time duration after the first probe pulse. When the second pump pulse reflects off the sample, the second pump pulse may be altered by an acoustic wave generated by the first probe pulse. The reflected second pump pulse may be analyzed to determine a characteristic of the sample.

Abstract: Characteristics of a standard logic cell, e.g., a random logic cell, are determined using an effective cell approximation. The effective cell approximation is smaller than the standard logic cell and represents the density of lines and spaces of the standard logic cell. The effective cell approximation may be produced based on a selected area from the standard logic cell and include the same non-periodic patterns as the selected area. The effective cell approximation, alternatively, may represent non-periodic patterns in the standard logic cell using periodic patterns having a same density of lines and spaces as found in the standard logic cell. A structure on the sample, such as a logic cell or a metrology target produced based on the effective cell approximation is measured to acquire data, which is compared to the data for the effective cell approximation to determine a characteristic of the standard logic cell.

Abstract: A particle shield for an actuator housing in a semiconductor equipment system includes a ring that mounts to the actuator housing, and a plate that mounts to a Z-axis stage that moves relative to the actuator housing. The ring includes a collar that extends from a flange in the Z coordinate direction. The collar and the plate, or a skirt that extends from the outside perimeter of the plate in the Z coordinate direction, remain within a same plane and do not contact each other during movement in the Z coordinate direction by the first Z-axis stage. The plate and the collar may be coaxial so that plate and the collar do not contact each other during rotation of the Z-axis stage relative to the actuator housing. A second Z-axis stage to which a chuck may be mounted passes through an aperture in the plate.

Abstract: Systems and methods for inspecting or characterizing samples, such as by characterizing patterned features or structures of the sample. In an aspect, the technology relates to a method for characterizing a patterned structure of a sample. The method includes directing a pump beam to a first position on a surface of the sample to induce a surface acoustic wave in the sample and directing a probe beam to a second position on the sample, where the probe beam is affected by the surface acoustic wave when the probe beam reflects from the surface of the sample. The method also includes detecting the reflected probe beam, analyzing the detected reflected probe beam to identify a frequency mode in the reflected probe beam, and based on the identified frequency mode, determining at least one of a width or a pitch of a patterned feature in the sample.

Abstract: Complex three-dimensional structures in semiconductor devices are measured using Mueller matrix paired off-diagonal elements to generate machine learning predictions of asymmetric parameters of the device and determine dimensional parameters based on one or more Mueller matrix elements and the asymmetric parameters. The measurements of the device may be performed at different azimuth angles selected based on sensitivity to the asymmetric parameters and the dimensional parameters. Additionally, the Mueller matrix elements may be generated based on measurements performed at azimuth angles that are 180° apart to eliminate asymmetric noise from the measurement tool. One or more models of the device may be used with the Mueller matrix elements to generate dimensional parameter information and optionally preliminary asymmetrical parameters.

Abstract: A machine learning model that uses composite metrology data determines at least one parameter of a device under test using measured metrology data from the device. The composite metrology data is generated by merging measured metrology data from a reference device with synthetic metrology data calculated from a model of the reference device. The composite metrology data may be generated further based on a synthetic metrology data calculated from a model for a modified reference device. The modified reference device may be generated using variations of at least one parameter of the model to expand the parameter space of the training range.

Abstract: A horizontal Dewar flask is used with an optical metrology device, which may advantageously reduce the vertical height of the device. A thermal transfer member provides thermal transfer between a liquefied gas cooled sensor and liquefied gas in a chamber of the Dewar flask. To compensate for the loss of thermal transfer from the sensor as the liquefied gas evaporates and changes to a gaseous state, the thermal transfer member biases heat transfer to the liquefied gas that is at the bottom of the chamber. The thermal transfer member may have a larger surface area at a bottom portion of the thermal transfer member than the upper portion. For example, the thermal transfer member may include one or more projections that extend into the liquefied gas with greater density at the bottom of the chamber than at the top of the chamber.