Methods and systems for detecting and classifying defects based on the phase of dark field scattering from a sample are described herein. In some embodiments, throughput is increased by detecting and classifying defects with the same optical system. In one aspect, a defect is classified based on the measured relative phase of scattered light collected from at least two spatially distinct locations in the collection pupil. The phase difference, if any, between the light transmitted through any two spatially distinct locations at the pupil plane is determined from the positions of the interference fringes in the imaging plane. The measured phase difference is indicative of the material composition of the measured sample. In another aspect, an inspection system includes a programmable pupil aperture device configured to sample the pupil at different, programmable locations in the collection pupil.

Various embodiments of systems and methods for calibrating wafer inspection system modules are disclosed herein. More specifically, the present disclosure provides various embodiments of systems and methods to calibrate the multiple spectral band values obtained from a substrate by a camera system included within a WIS module. In one embodiment, multiple spectral band values are red, green, and blue (RGB) values. As described in more detail below, the calibration methods disclosed herein may use a test wafer having a predetermined pattern of thickness changes or color changes to generate multiple spectral band offset values. The multiple spectral band offset values can be applied to the multiple spectral band values obtained from the substrate to generate calibrated RGB values, which compensate for spectral responsivity differences between camera systems included within a plurality of WIS modules.

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.

The purpose of the present invention is to provide a substrate inspection device that increases the flatness of a substrate during inspection, and improves the detection sensitivity of foreign matter. Therefore, the present invention is a substrate inspection device provided with a turntable on which a substrate to be inspected is mounted, and a clamp mechanism that holds the substrate on the turntable. The substrate inspection device is characterized in that the clamp mechanism has an abutting part that moves in an in-plane direction of the substrate and presses the substrate. Preferably, the abutting part contacts or separates from an outer peripheral side surface of the substrate by rotating centered on a rotational axis in an out-of-plane direction of the substrate.

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.

Modulation-encoded light, using different spectral bin coded light components, can illuminate a stationary or moving (relative) target object or scene. Response signal processing can use information about the respective different time-varying modulation functions, to decode to recover information about a respective response parameter affected by the target object or scene. Electrical or optical modulation encoding can be used. LED-based spectroscopic analysis of a composition of a target (e.g., SpO2, glucose, etc.) can be performed; such can optionally include decoding of encoded optical modulation functions. Baffles or apertures or optics can be used, such as to constrain light provided by particular LEDs. Coded light illumination can be used with a focal plane array light imager receiving response light for inspecting a moving semiconductor or other target. Encoding can use orthogonal functions, such as an RGB illumination sequence, or a sequence of combinations of spectrally contiguous or non-contiguous colors.

An assembly for collimating broadband radiation, the assembly including: a convex refractive singlet lens having a first spherical surface for coupling the broadband radiation into the lens and a second spherical surface for coupling the broadband radiation out of the lens, wherein the first and second spherical surfaces have a common center; and a mount for holding the convex refractive singlet lens at a plurality of contact points having a centroid coinciding with the common center.

A substrate inspection apparatus configured to inspect a substrate by using an image of a surface of the substrate includes a holder configured to hold the substrate; a first light source unit configured to emit visible light to the substrate; a second light source unit configured to emit ultraviolet light to the substrate; a first imaging sensor configured to perform capturing of a visible light image of the substrate by receiving reflected light from the substrate; a second imaging sensor configured to perform capturing of an ultraviolet light image of the substrate by receiving reflected light or scattered light from the substrate; and a controller configured to acquire the visible light image from the first imaging sensor and the ultraviolet light image from the second imaging sensor. The visible light image and the ultraviolet light image are images obtained by imaging a common region of the substrate.

Multilayered product structures are formed on substrates by a combination of patterning steps, physical processing steps and chemical processing steps. An inspection apparatus illuminates a plurality of target structures and captures pupil images representing the angular distribution of radiation scattered by each target structure. The target structures have the same design but are formed at different locations on a substrate and/or on different substrates. Based on a comparison of the images the inspection apparatus infers the presence of process-induced stack variations between the different locations. In one application, the inspection apparatus separately measures overlay performance of the manufacturing process based on dark-field images, combined with previously determined calibration information. The calibration is adjusted for each target, depending on the stack variations inferred from the pupil images.