A method including: simulating an image or characteristics thereof, using characteristics of a design layout and of a patterning process, determining deviations between the image or characteristics thereof and the design layout or characteristics thereof; aligning a metrology image obtained from a patterned substrate and the design layout based on the deviations, wherein the patterned substrate includes a pattern produced from the design layout using the patterning process; and determining a parameter of a patterned substrate from the metrology image aligned with the design layout.

Defects of substrates are inspected when executing a job in which a treatment recipe for substrates and the substrates being treatment objects are designated to perform predetermined treatments on the substrates. An imaging step successively images substrates. A first determination step decomposes, in order from the substrate as head of the job, a planar distribution of pixel values in a substrate image captured at the imaging step into pixel value distribution components using a Zernike polynomial, calculates Zernike coefficients of the pixel value distribution components corresponding to defects to be detected, and determines presence or absence of a defect based on the calculated Zernike coefficients. A second determination step determines, from predetermined timing after one or more substrates is determined to have no defect at the first determination step, presence or absence of a defect based on the substrate image determined to have no defect at the first determination step.

The present application relates to a scanning probe microscope comprising a probe arrangement for analyzing at least one defect of a photolithographic mask or of a wafer, wherein the scanning probe microscope comprises: (a) at least one first probe embodied to analyze the at least one defect; (b) means for producing at least one mark, by use of which the position of the at least one defect is indicated on the mask or on the wafer; and (c) wherein the mark is embodied in such a way that it may be detected by a scanning particle beam microscope.

A method includes identifying a first set of a first care area with a first sensitivity threshold, the first care area associated with a first design of interest within a block of repeating cells in design data; identifying an additional set of an additional care area with an additional sensitivity threshold, the additional care area associated with an additional design of interest within the block of repeating cells in design data; identifying one or more defects within the first set of the first care areas in one or more images of a selected region of a sample based on the first sensitivity threshold; and identifying one or more defects within the additional set of the additional care areas in the one or more images of the selected region of the sample based on the additional sensitivity threshold.

A method of processing a wafer is provided. The method includes providing a reference pattern for patterning a wafer. The reference pattern is independent of a working surface of the wafer. A placement of a first pattern on the working surface of the wafer is determined by identifying the reference pattern to align the first pattern. The first pattern is formed on the working surface of the wafer based on the placement.

Methods and systems for determining information for a specimen are provided. Certain embodiments relate to bump height 3D inspection and metrology using deep learning artificial intelligence. For example, one embodiment includes a deep learning (DL) model configured for predicting height of one or more 3D structures formed on a specimen based on one or more images of the specimen generated by an imaging subsystem. One or more computer systems are configured for determining information for the specimen based on the predicted height. Determining the information may include, for example, determining if any of the 3D structures are defective based on the predicted height. In another example, the information determined for the specimen may include an average height metric for the one or more 3D structures.

A defect observation apparatus includes a storage unit configured to store defect information about defects detected by an external inspection apparatus; a first imaging unit configured to capture an image of a defect using a first imaging condition and a second imaging condition; a control unit configured to correct positional information on the defect using the image captured with the first imaging unit; and a second imaging unit configured to capture an image of the defect based on the corrected positional information.

An optical inspection system in accordance with the disclosure can be configured to simultaneously capture illumination reflected in multiple directions from the surface of a substrate, thereby overcoming inaccurate or incomplete characterization of substrate surface aspects as a result of reflected intensity variations that can arise when illumination is captured only from a single direction. Such a system includes a set of illuminators and an image capture device configured to simultaneously capture at least two beams of illumination that are reflected off the surface. The at least two beams of illumination that are simultaneously captured by the image capture device have different angular separations between their reflected paths of travel. The set of illuminators can include a set of thin line illuminators positioned and configured to supply one or more beams of thin line illumination incident to the surface. For instance, two beams of thin line illumination can be directed to the surface at different angles of incidence to a normal axis of the surface.

A method, a computer system and an apparatus are disclosed for inspection recipe generation for the automated inspection of semiconductor devices. In order to generate the inspection recipe a reference data set is used. Automatic inspection is carried out with an initial recipe on images of dies of the reference data set (reference wafermap). The detected inspection results from the automatic inspection are classified and the classified inspection results are compared with an expert classification of defects in dies. Overkill and underkill numbers are automatically generated. According to the overkill and underkill numbers the inspection recipe parameters are modified. Automatic inspection is repeated if the detection and/or the classification are below a predefined threshold.

Scatterometry overlay (SCOL) targets as well as design, production and measurement methods thereof are provided. The SCOL targets have several periodic structures at different measurement directions which share some of their structural target elements or parts thereof. An array of common elements may have symmetry directions which are parallel to the measurement directions and thus enable compacting the targets or alternatively increasing the area use efficiency of the targets. Various configurations enable high flexibility in arranging the number of layers in the target and measurement directions, and carrying out respective overlay measurements among the layers.