A metrology system may include an optical metrology tool configured to produce an optical metrology output for one or more features on a processed substrate, and a metrology machine learning model that has been trained using a training set of (i) profiles, critical dimensions, and/or contours for a plurality of features, and (ii) optical metrology outputs for the plurality of features. The metrology machine learning model may be configured to: receive the optical metrology output from the optical metrology tool; and output the profile, critical dimension, and/or contour of the one or more features on the processed substrate.

A method including obtaining a measurement result from a target on a substrate, by using a substrate measurement recipe; determining, by a hardware computer system, a parameter from the measurement result, wherein the parameter characterizes dependence of the measurement result on an optical path length of the target for incident radiation used in the substrate measurement recipe and the determining the parameter includes determining dependence of the measurement result on a relative change of wavelength of the incident radiation; and if the parameter is not within a specified range, adjusting the substrate measurement recipe.

A method of determining a measurement recipe for measurement of in-die targets located within one or more die areas of an exposure field. The method includes obtaining first measurement data relating to measurement of a plurality of reference targets and second measurement data relating to measurement of a plurality of in-die targets, the targets having respective different overlay biases and measured using a plurality of different acquisition settings for acquiring the measurement data. One or more machine learning models are trained using the first measurement data to obtain a plurality of candidate measurement recipes, wherein the candidate measurement recipes include a plurality of combinations of a trained machine learned model and a corresponding acquisition setting; and a preferred measurement recipe is determined from the candidate measurement recipes using the second measurement data.

Provided are a method of selecting multi-wavelengths for overlay measurement, for accurately measuring overlay, and an overlay measurement method and a semiconductor device manufacturing method using the multi-wavelengths. The method of selecting multi-wavelengths for overlay measurement includes measuring an overlay at multiple positions on a wafer at each of a plurality of wavelengths within a set first wavelength range, selecting representative wavelengths that simulate the overlay of the plurality of wavelengths, from among the plurality of wavelengths, and allocating weights to the representative wavelengths, respectively.

Samples at a semiconductor wafer that have been reviewed by a review tool may be identified. Furthermore, a candidate sample at the semiconductor wafer that has not been reviewed by the review tool may be identified. A location of the candidate sample at the semiconductor wafer may and a number of the samples that have been reviewed that are at locations proximate to the location of the candidate sample may be determined. The candidate sample may be selected for review by the review tool based on the number of the plurality of samples that are at locations proximate to the location of the candidate sample.

A method including obtaining a measurement result from a target on a substrate, by using a substrate measurement recipe; determining, by a hardware computer system, a parameter from the measurement result, wherein the parameter characterizes dependence of the measurement result on an optical path length of the target for incident radiation used in the substrate measurement recipe and the determining the parameter includes determining dependence of the measurement result on a relative change of wavelength of the incident radiation; and if the parameter is not within a specified range, adjusting the substrate measurement recipe.

An illumination system for a metrology apparatus that can achieve illumination spatial profile flexibility, high polarization extinction ratio, and high contrast. The illumination system includes a polarizing beam splitter (PBS), an illumination mode selector (IMS), and a reflective spatial light modulator (SLM). The PBS divides an illumination beam into sub-beams. The IMS has a plurality of apertures that transmits at least one sub-beam and may be arranged in multiple illumination positions corresponding to illumination modes. A pixel array of the reflective SLM and reflects a portion of the sub-beam transmitted by the IMS back to the IMS and PBS. The PBS, IMS, SLM collectively generates a complex amplitude or intensity spatial profile of the transmitted sub-beam.

A method including obtaining a measurement result from a target on a substrate, by using a substrate measurement recipe; determining, by a hardware computer system, a parameter from the measurement result, wherein the parameter characterizes dependence of the measurement result on an optical path length of the target for incident radiation used in the substrate measurement recipe and the determining the parameter includes determining dependence of the measurement result on a relative change of wavelength of the incident radiation; and if the parameter is not within a specified range, adjusting the substrate measurement recipe.

An inspection apparatus, including: an objective configured to receive diffracted radiation from a metrology target having positive and negative diffraction order radiation; an optical element configured to separate the diffracted radiation into portions separately corresponding to each of a plurality of different values or types of one or more radiation characteristics and separately corresponding to the positive and negative diffraction orders; and a detector system configured to separately and simultaneously measure the portions.

Performance measurement targets are used to measure performance of a lithographic process after processing a number of substrates. In a set-up phase, the method selects an alignment mark type and alignment recipe from among a plurality of candidate mark types by reference to expected parameters of the patterning process. After exposing a number of test substrates using the patterning process, a preferred metrology target type and metrology recipe are selected by comparing measured performance (e.g. overlay) of performance of the patterning process measured by a reference technique. Based on the measurements of position measurement marks and performance measurement targets after actual performance of the patterning process, the alignment mark type and/or recipe may be revised, thereby co-optimizing the alignment marks and metrology targets. Alternative run-to-run feedback strategies may also be compared during subsequent operation of the process.