In one embodiment, a method includes generating a model trained to predict a low-probability stochastic defect, calibrating, using unbiased measurement data, the model to a specific lithography process, patterning process, or both to generate a calibrated model, using the calibrated model to predict the low-probability stochastic defect; and modifying, based on the low-probability stochastic defect, a variable, parameter, setting, or some combination of a manufacturing process of a device.

Methods, systems, and devices for electron beam probing techniques and related structures are described to enable inline testing of memory device structures. Conductive loops may be formed, some of which may be grounded and others of which may be electrically floating in accordance with a predetermined pattern. The loops may be scanned with an electron beam and image analysis techniques may be used to generate an optical pattern. The generated optical pattern may be compared to an expected optical pattern, which may be based on the predetermined pattern of grounded and floating loops. An electrical defect may be determined based on any difference between the generated optical pattern and the expected optical pattern. For example, if a second loop appears as having a brightness corresponding to a grounded loop, this may indicate that an unintended short exists. Fabrication techniques may be adjusted for subsequent devices to correct identified defects.

An image forming device is provided that is capable of forming a proper integrated signal even when an image or a signal waveform is acquired from a pattern having the possibility of preventing proper matching, such as a repetition pattern, a shrinking pattern, and the like. In particular, the image forming device forms an integrated image by integrating a plurality of image signals and is provided with: a matching processing section that performs a matching process between the plurality of image signals; an image integration section that integrates the plurality of image signals for which positioning has been performed by the matching processing section; and a periodicity determination section that determines a periodicity of a pattern contained in the image signals. The matching processing section varies a size of an image signal area for the matching in accordance with a determination by the periodicity determination section.

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 discloses topography information extracted from scanning electron microscope (SEM) images to determine the atomic force microscope (AFM) image scanning speed at each sampling point or in each region on a sample. The method includes the processing of SEM images to extract possible topography features and create a feature metric map (step 1), the conversion of the feature metric map into AFM scan speed map (step 2), and performing AFM scan according to the scan speed map (step 3). The method enables AFM scan with higher scan speeds in areas with less topography feature, and lower scan speeds in areas that are rich in topography features.

To implement a calibration sample by which an incident angle can be measured with high accuracy, an electron beam adjustment method, and an electron beam apparatus using the calibration sample. To adjust an electron beam using a calibration sample, the calibration sample includes a silicon single crystal substrate 201 whose upper surface is a {110} plane, a first recess structure 202 opening in the upper surface and extending in a first direction, and a second recess structure 203 opening in the upper surface and extending in a second direction intersecting the first direction, in which the first recess structure and the second recess structure each include a first side surface and a first bottom surface that intersects the first side surface, and a second side surface and a second bottom surface that intersects the second side surface, the first side surface and the second side surface are {111} planes, and the first bottom surface and the second bottom surface are crystal planes different from the {110} planes.

An edge detection system is provided that generates a scanning electron microscope (SEM) linescan image of a pattern structure including a feature with edges that require detection. The edge detection system includes an inverse linescan model tool that receives measured linescan information for the feature from the SEM. In response, the inverse linescan model tool provides feature geometry information that includes the position of the detected edges of the feature.

Systems and methods are disclosed that remove noise from roughness measurements to determine roughness of a feature in a pattern structure. In one embodiment, a method for determining roughness of a feature in a pattern structure includes generating, using an imaging device, a set of one or more images, each including measured linescan information that includes noise. The method also includes detecting edges of the features within the pattern structure of each image without filtering the images, generating a biased power spectral density (PSD) dataset representing feature geometry information corresponding to the edge detection measurements, evaluating a high-frequency portion of the biased PSD dataset to determine a noise model for predicting noise over all frequencies of the biased PSD dataset, and subtracting the noise predicted by the determined noise model from a biased roughness measure to obtain an unbiased roughness measure.

An edge detection system is disclosed. The edge detection system includes an imaging device configured for imaging a pattern structure to form a first image, wherein the pattern structure includes a predetermined feature, and the imaging device images the pattern structure to generate measured linescan information that includes image noise. The edge detection system includes a processor, coupled to the imaging device, configured to receive the measured linescan information including image noise from the imaging device, wherein the processor is configured to: apply the measured linescan information to an inverse linescan model that relates the measured linescan information to feature geometry information, determine, from the inverse linescan model, feature geometry information that describes feature edge positions of the predetermined feature corresponding to the measured linescan information, determine from the feature geometry information at least one metric that describes a property of the edge detection system.

The purpose of the present invention is to provide a charged-particle beam device capable of stable performance of processes such as a measurement or test, independent of fluctuations in sample electric electric potential or the like. To this end, this charged-particle beam device comprises an energy filter for filtering the energy of charged particles released from the sample and a deflector for deflecting the charged particles released from the sample toward the energy filter. A control device generates a first image on the basis of the output of a detector, adjusts the voltage applied to the energy filter so that the first image reaches a prescribed state, and calculates deflection conditions for the deflector on the basis of the post-adjustment voltage applied to the energy filter.