A method for measuring height differences between tops of multiple bumps of an upper surface of a layer, the method may include performing first measurements of the height differences between the bumps and the corresponding areas, by illuminating the bumps and the corresponding areas with first radiation; wherein the first measurements are subjected to first measurement errors; and determining the height differences between the bumps and the corresponding areas based on the first measurements and the first measurements errors.

An optical system configured to measure a raised or receded surface feature on a surface of a sample may comprise a broadband light source; a tunable filter configured to filter broadband light emitted from the broadband light source and to generate a first light beam at a selected wavelength; a linewidth control element configured to receive the first light beam and to generate a second light beam having a predefined linewidth and a predetermined coherence length; collimating optics optically coupled to the second light beam and configured to collimate the second light beam; collinearizing optics optically coupled to the collimating optics and configured to align the collimated second light beam onto the raised or receded surface feature of the sample, and a processor system and at least one digital imager configured to measure a height of the raised surface or depth of the receded surface from light reflected at least from those surfaces.

A system and methods for OCD metrology are provided including receiving training data for training an OCD machine learning (ML) model, including multiple pairs of corresponding sets of scatterometric data and reference parameters. For each of the pairs, one or more corresponding outlier metrics are by calculated and corresponding outlier thresholds are applied whether a given pair is an outlier pair. The OCD MIL model is then trained with the training data less the outlier pairs.

A system and methods for OCD metrology are provided including receiving multiple first sets of scatterometric data, dividing each set into k sub-vectors, and training, in a self-supervised manner, k2 auto-encoder neural networks that map each of the k sub-vectors to each other. Subsequently multiple respective sets of reference parameters and multiple corresponding second sets of scatterometric data are received and a transfer neural network (NN) is trained. Initial layers include a parallel arrangement of the k2 encoder neural networks. Target output of the transfer NN training is set to the multiple sets of reference parameters and feature input is set to the multiple corresponding second sets of scatterometric data, such that the transfer NN is trained to estimate new wafer pattern parameters from subsequently measured sets of scatterometric data.

In a shape measuring method a scattering intensity profile for a first electromagnetic wave is acquired for a substrate having a pattern thereon. A first expected scattering intensity profile for a first virtual structure corresponding to a first parameter group of first parameters including an attention parameter is acquired by a first simulation. A first convergence value is calculated for each of the first parameters in a first fitting process based on the scattering intensity profile and the first expected scattering intensity profile. A second expected scattering intensity profile is then acquired for a second virtual structure corresponding to a second parameter group of second parameters, which includes the attention parameter fixed to the first convergence value. A second convergence value for each of the second parameters is then calculated in a second fitting process based on the scattering intensity profile and the second expected scattering intensity profile.

Analyzing a buried layer on a sample includes milling a spot on the sample using a charged particle beam of a focused ion beam (FIB) column to expose the buried layer along a sidewall of the spot. From a first perspective a first distance is measured between a first point on the sidewall corresponding to an upper surface of the buried layer and a second point on the sidewall corresponding to a lower surface of the buried layer. From a second perspective a second distance is measured between the first point on the sidewall corresponding to the upper surface of the buried layer and the second point on the sidewall corresponding to the lower surface of the buried layer. A thickness of the buried layer is determined using the first distance and the second distance.

Methods and systems for performing overlay and edge placement errors based on Soft X-Ray (SXR) scatterometry measurement data are presented herein. Short wavelength SXR radiation focused over a small illumination spot size enables measurement of design rule targets or in-die active device structures. In some embodiments, SXR scatterometry measurements are performed with SXR radiation having energy in a range from 10 to 5,000 electronvolts. As a result, measurements at SXR wavelengths permit target design at process design rules that closely represents actual device overlay. In some embodiments, SXR scatterometry measurements of overlay and shape parameters are performed simultaneously from the same metrology target to enable accurate measurement of Edge Placement Errors. In another aspect, overlay of aperiodic device structures is estimated based on SXR measurements of design rule targets by calibrating the SXR measurements to reference measurements of the actual device target.

Methods and apparatus for processing a substrate are provided. For example, metrology apparatus configured for use with a substrate processing platform comprise an interferometer configured to obtain a first set of measurements at a first set of points along a surface of a substrate, a sensor configured to obtain a second set of measurements at a second set of points different from the first set of points along the surface of the substrate, an actuator configured to position the interferometer and the sensor at various positions along a measurement plane parallel to the surface of the substrate for obtaining the first set of measurements and the second set of measurements, and a substrate support comprising a substrate support surface for supporting the substrate beneath the measurement plane while obtaining the first set of measurements and the second set of measurements.

Embodiments of the present disclosure relate to a metrology method and system for critical dimensions based on a dispersion relation in momentum space. The method comprises: establishing, in accordance with parameters of incident light and a modeled geometric topography of the target to be measured, a simulation dataset associated with a dispersion curve of the target to be measured in momentum space; training a neural-network-based prediction model based on the simulation dataset; obtaining, based on an actual measurement of the target to be measured by incident light, a dispersion relation pattern of the target to be measured in momentum space, wherein the dispersion relation pattern at least indicates a dispersion curve associated with the critical dimensions of the target to be measured; extracting, based on the dispersion relation pattern, features related to the dispersion curve from the dispersion relation pattern via the trained prediction model, to determine an estimated value associated with at least one critical dimension of the target to be measured. According to the method disclosed herein, at least one critical dimension is measured in a more efficient, economical and accurate way.

A metrology apparatus for determining a characteristic of interest of a structure on a substrate, the apparatus comprising: a radiation source configured to generate illumination radiation; at least two illumination branches comprising at least one optical fiber and configured to illuminate a structure on a substrate from different angles; and a radiation switch configured to receive the illumination radiation and transfer at least part of the radiation to a selectable one of the at least two illumination branches.