Abstract: A method and system for measuring overlay in a semiconductor manufacturing process comprise capturing an image of a feature in an article at a predetermined manufacturing stage, deriving a quantity of an image parameter from the image and converting the quantity into an overlay measurement. The conversion is by reference to an image parameter quantity derived from a reference image of a feature at the same predetermined manufacturing stage with known overlay (“OVL”). There is also disclosed a method of determining a device inspection recipe for use by an inspection tool comprising identifying device patterns as candidate device care areas that may be sensitive to OVL, deriving an OVL response for each identified pattern, correlating the OVL response with measured OVL, and selecting some or all of the device patterns as device care areas based on the correlation.

Abstract: Disclosed are apparatus and methods for determining process or structure parameters for semiconductor structures. A plurality of optical signals is acquired from one or more targets located in a plurality of fields on a semiconductor wafer. The fields are associated with different process parameters for fabricating the one or more targets, and the acquired optical signals contain information regarding a parameter of interest (POI) for a top structure and information regarding one or more underlayer parameters for one or more underlayers formed below such top structure. A feature extraction model is generated to extract a plurality of feature signals from such acquired optical signals so that the feature signals contain information for the POI and exclude information for the underlayer parameters. A POI value for each top structure of each field is determined based on the feature signals extracted by the feature extraction model.

Abstract: Embodiments herein include methods, systems, and apparatuses for die screening using inline defect information. Such embodiments may include receiving a plurality of defects, receiving wafersort electrical data for a plurality of dies, classifying each of the defects as a defect-of-interest or nuisance, determining a defect-of-interest confidence for each of the defects-of-interest, determining a die return index for each of the dies containing at least one of the defects-of-interest, determining a die return index cutline, and generating an inking map. Each of the defects may be associated with a die in the plurality of dies. Each of the dies may be tagged as passing a wafersort electrical test or failing the wafersort electrical test. Classifying each of the defects as a defect-of-interest or nuisance may be accomplished using a defect classification model, which may include machine learning. The inking map may be electronically communicated to an inking system.

Abstract: A method of measuring misregistration in the manufacture of semiconductor devices including providing a multilayered semiconductor device, using a scatterometry metrology tool to perform misregistration measurements at multiple sites on the multilayered semiconductor device, receiving raw misregistration data for each of the misregistration measurements, thereafter providing filtered misregistration data by removing outlying raw misregistration data points from the raw misregistration data for each of the misregistration measurements, using the filtered misregistration data to model misregistration for the multilayered semiconductor device, calculating correctables from the modeled misregistration for the multilayered semiconductor device, providing the correctables to the scatterometry metrology tool, thereafter recalibrating the scatterometry metrology tool based on the correctables and measuring misregistration using the scatterometry metrology tool following the recalibration.

Abstract: Disclosed are apparatus and methods for inspecting a sample. Locations corresponding to candidate defect events on a sample are provided from an inspector operable to acquire optical images from which such candidate defect events are detected at their corresponding locations across the sample. High-resolution images are acquired from a high-resolution inspector of the candidate defect events at their corresponding locations on the sample. Each of a set of modelled optical images, which have been modeled from a set of the acquired high-resolution images, is correlated with corresponding ones of a set of the acquired optical images, to identify surface noise events, as shown in the set of high-resolution images, as sources for the corresponding candidate events in the set of acquired optical images. Otherwise, a subsurface event is identified as a likely source for a corresponding candidate defect event.

Abstract: A method of focusing includes irradiating an object by directing radiation output by a radiating source through an objective lens, measuring a first intensity of reflected radiation that is reflected from the object, adjusting a distance between the objective lens and the object, measuring a second intensity of reflected radiation, and analyzing the first intensity of reflected radiation and the second intensity of reflected radiation to determine a focal distance between the objective lens and the object. The distance between the objective lens and the object is adjusted to the focal distance and the irradiating intensity is increased to mark the object. In another example, measuring the first intensity of reflected radiation is performed by directing reflected radiation from the object through the objective lens, a beam splitter, a focusing lens, and a pinhole and onto a sensor that outputs a signal indicative of sensed radiation intensity.

Abstract: Defects of interest can be captured by a classifier. Images of a semiconductor wafer can be received at a deep learning classification module. These images can be sorted into soft decisions with the deep learning classification module. A class of the defect of interest for an image can be determined from the soft decisions. The deep learning classification module can be in electronic communication with an optical inspection system or other types of semiconductor inspection systems.

Abstract: Methods are provided for designing metrology targets and estimating the uncertainty error of metrology metric values with respect to stochastic noise such as line properties (e.g., line edge roughness, LER). Minimal required dimensions of target elements may be derived from analysis of the line properties and uncertainty error of metrology measurements, by either CDSEM (critical dimension scanning electron microscopy) or optical systems, with corresponding targets. The importance of this analysis is emphasized in view of the finding that stochastic noise may have increased importance with when using more localized models such as CPE (correctables per exposure). The uncertainty error estimation may be used for target design, enhancement of overlay estimation and evaluation of measurement reliability in multiple contexts.

Abstract: Focusing methods and modules are provided for metrology tools and systems. Methods comprise capturing image(s) of at least two layers of a ROI in an imaging target, binning the captured image(s), deriving a focus shift from the binned captured image(s) by comparing the layers, and calculating a focus position from the derived focus shift. Disclosed methods are direct, may be carried out in parallel to a part of the overlay measurement process and provide fast and simple focus measurements that improve metrology performance.

Abstract: Methods and systems for measuring structural and material characteristics of semiconductor structures based on combined x-ray reflectometry (XRR) and x-ray photoelectron spectroscopy (XPS) are presented herein. A combined XRR and XPS system includes an x-ray illumination source and x-ray illumination optics shared by both the XRR and XPS measurement subsystems. This increases throughput and measurement accuracy by simultaneously collecting XRR and XPS measurement data from the same area of the wafer. A combined XRR and XPS system improves measurement accuracy by employing XRR measurement data to improve measurements performed by the XPS subsystem, and vice-versa. In addition, a combined XRR and XPS system enables simultaneous analysis of both XRR and XPS measurement data to more accurately estimate values of one of more parameters of interest. In a further aspect, any of measurement spot size, photon flux, beam shape, beam diameter, and illumination energy are independently controlled.

Abstract: A three-dimensional (3D) microscope includes various insertable components that facilitate multiple imaging and measurement capabilities. These capabilities include Nomarski imaging, polarized light imaging, quantitative differential interference contrast (q-DIC) imaging, motorized polarized light imaging, phase-shifting interferometry (PSI), and vertical-scanning interferometry (VSI).

Abstract: Methods and systems for improved detection and classification of defects of interest (DOI) on semiconductor wafers based on three-dimensional images are described herein. Three dimensional imaging of volumes of thick, layered structures enables accurate defect detection and estimation of defect location in three dimensions at high throughput. A series of images are acquired at a number of different wafer depths. A three dimensional image of a thick semiconductor structure is generated from the series of images. Defects are identified and classified based on an analysis of the three dimensional image of the thick semiconductor structure. In some examples, the three-dimensional image stack is visualized by contour plots or cross-sectional plots to identify a characteristic defect response. In some examples, the three-dimensional image is processed algorithmically to identify and classify defects. In another aspect, the location of a defect is estimated in three dimensions based on the three dimensional image.

Abstract: 3D measurements of features on a workpiece, such as ball height, co-planarity, component thickness, or warpage, are determined. The system includes a broadband light source, a microlens array, a tunable color filter, a lens system, and a detector. The microlens array can focus a light beam to points in a focal plane of the microlens array. The tunable color filter can narrow the light beam to a band at a central wavelength. The lens system can provide longitudinal chromatic aberration whereby different wavelengths are imaged at different distances from the lens system.

Abstract: Metrology methods are provided for deriving metrology measurement parameter value(s) by identifying the value(s) in which the corresponding metrology measurement signal(s) have minimal amplitude asymmetry. Selecting the measurement parameter values as disclosed reduces significantly the measurement inaccuracy. For example, wavelength values and/or focus values may be detected to indicate minimal amplitude asymmetry and/or minimal phase asymmetry. In certain embodiments, wavelength values which provide minimal amplitude asymmetry also provide minimal signal sensitivity to focus. Developed metrics may be further used to indicate process robustness across wafers and lots. In some embodiments, imaging accuracy may be enhanced by through-focus landscaping of the amplitude asymmetry and detection of parameters values with minimal amplitude asymmetry.

Abstract: Methods and systems for controlling illumination beam spot size for Transmission, Small-Angle X-ray Scatterometry (T-SAXS) measurements of different sized metrology targets are described herein. An X-ray illumination optics subsystem includes one or more focusing optical elements with object and image planes at fixed locations and one or more illumination apertures or slits that independently control magnification and beam divergence. In a further aspect, the illumination source size and shape is controlled, along with magnification and beam divergence. In this manner, beam divergence and illumination spot size on a specimen are independently controlled, while maintaining constant illumination flux.

Abstract: A method of monitoring overlay is used in a manufacturing process in which successive layers are deposited one over another to form a stack. Each layer may include a periodic structure such as a diffraction grating to be aligned with a periodic structure in another layer. The stacked periodic structures may be illuminated to form + and ? first order diffraction patterns from the periodic structures. An image of the stacked periodic structures may be captured including + and ? diffraction patterns. The + and ? diffraction patterns may be compared to calculate the overlay between successive layers.

Abstract: Metrology targets, target design methods and menology measurement methods are provided, which estimate the effects of asymmetric aberrations, independently or in conjunction with metrology overlay estimations. Targets comprise one or more pairs of segmented periodic structures having a same coarse pitch, a same 1:1 line to space ratio and segmented into fine elements at a same fine pitch, wherein the segmented periodic structures differ from each other in that one thereof lacks at least one of its corresponding fine elements and/or in that one thereof comprises two groups of the fine elements which are separated from each other by a multiple of the fine pitch. The missing element(s) and/or central gap enable deriving the estimation of aberration effects from measurements of the corresponding segmented periodic structures. The fine pitches may be selected to correspond to the device fine pitches in the corresponding layer.

Abstract: Multilayer targets enabling fast and accurate, absolute calibration and alignment of X-ray based measurement systems are described herein. The multilayer calibration targets have very high diffraction efficiency and are manufactured using fast, low cost production techniques. Each target includes a multilayer structure built up with pairs of X-ray transparent and X-ray absorbing materials. The layers of the multilayer target structure is oriented parallel to an incident X-ray beam. Measured diffraction patterns indicate misalignment in position and orientation between the incident X-Ray beam and the multilayer target. In another aspect, a composite multilayer target includes at least two multilayer structures arranged adjacent one another along a direction aligned with the incident X-ray beam, adjacent one another along a direction perpendicular to the incident X-ray beam, or a combination thereof. In some embodiments, the multilayer structures are spatially separated from one another by a gap distance.

Abstract: A stochastic calculation engine receives inputs from a semiconductor inspection tool or semiconductor review tool. The stochastic calculation engine determines abnormal locations and pattern variation from the inputs and determines stochastic failures from the inputs. An electronic data storage unit connected with the stochastic calculation engine can include a database with known stochastic behavior and known process metrology variations. The stochastic calculation engine can flag stochastic features, determine a failure rate, or determine fail probability.

Abstract: Disclosed is apparatus for inspecting a sample. The apparatus includes illumination optics for simultaneously directing a plurality of incident beams at a plurality of azimuth angles towards a sample and collection optics for directing a plurality of field portions of output light from two or more of the plurality of angles towards two or more corresponding sensors. The two or more sensors are arranged for receiving the field portions corresponding to two or more angles and generating two or more corresponding images.