The invention relates to a method 3D defect characterization of crystalline samples in a scanning type electron microscope. The method comprises Irradiating a sample provided on a stage, selecting one set of crystal lattice planes of the sample and orienting said set to a first Bragg condition with respect to a primary electron beam impinging on said sample, and obtaining Electron Channeling Contrast Image for an area of interest on the sample. The method is characterized by performing, at least once, the steps of orienting said selected set of crystal lattice planes to a further Bragg condition by at least tilting the sample stage with the sample by a user-selected angle about a first tilt axis, and obtaining by Electron Channeling Contrast Image for a further area of interest.

An apparatus and method for a large-scale high-throughput quantitative characterization and three-dimensional reconstruction of a material structure. The apparatus having a glow discharge sputtering unit, a sample transfer device, a scanning electron microscope unit and a GPU computer workstation. The glow discharge sputtering unit can achieve large size (cm order), nearly flat and fast sample preparation, and controllable achieve layer-by-layer ablation preparation along the depth direction of the sample surface; rapid scanning electron microscopy (SEM) can achieve large-scale and high-throughput acquisition of sample characteristic maps. The sample transfer device is responsible for transferring the sample between the glow discharge sputtering source and the scanning electron microscope in an accurately positioning manner. The GPU computer workstation performs splicing, processing, recognition and quantitative distribution characterization on the acquired sample characteristic maps, and carries out three-dimensional reconstruction of the structure of the sample prepared by layer-by-layer sputtering.

A multi-column assembly for a scanning electron microscopy (SEM) system is disclosed. The multi-column assembly includes a plurality of electron-optical columns arranged in an array defined by one or more spacings. Each electron-optical column includes one or more electron-optical elements. The plurality of electron-optical columns is configured to characterize one or more field areas on a surface of a sample secured on a stage. The number of electron-optical columns in the plurality of electron-optical columns equals an integer number of inspection areas in a field area of the one or more field areas. The one or more spacings of the plurality of electron-optical columns correspond to one or more dimensions of the inspection areas.

A charged particle beam apparatus acquires a scanned image by scanning a sample with a charged particle beam and detecting charged particles emitted from the sample. The charged particle beam apparatus includes: a plurality of detection units that detect charged particles emitted from the sample; and an image processing unit that generates the scanned image based on a plurality of detection signals outputted from the plurality of the detection units. The image processing unit performs a process of calculating a tilt direction of a sample surface and a tilt angle of the sample surface based on the plurality of the detection signals for an irradiation position of the charged particle beam; and a process of determining a color of a pixel of the scanned image according to the calculated tilt direction and the calculated tilt angle.

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.

The present invention provides apparatuses to inspect small particles on the surface of a sample such as wafer and mask. The apparatuses provide both high detection efficiency and high throughput by forming Dark-field BSE images. The apparatuses can additionally inspect physical and electrical defects on the sample surface by form SE images and Bright-field BSE images simultaneously. The apparatuses can be designed to do single-beam or even multiple single-beam inspection for achieving a high throughput.

An inspection method for a substrate, the inspection method including: providing an electron beam having a first polarization state to a sample of the semiconductor substrate; detecting a first response signal of the sample caused by interaction of the electron beam having the first polarization state with the sample; providing an electron beam having a second polarization state to the sample of the semiconductor substrate; detecting a second response signal of the sample caused by interaction of the electron beam having the second polarization state with the sample; and determining a geometric or material property of the sample, based on the first response signal and the second response signal.

A charged particle beam apparatus acquires a scanned image by scanning a sample with a charged particle beam and detecting charged particles emitted from the sample. The charged particle beam apparatus includes: a plurality of detection units that detect charged particles emitted from the sample, and an image processing unit that generates the scanned image based on a plurality of detection signals outputted from the plurality of the detection units. The image processing unit performs a process of acquiring the plurality of the detection signals at an irradiation position of the charged particle beam; a process of extracting a maximum value and a minimum value from among signal amounts of the plurality of the acquired detection signals, and calculating a difference between the maximum value and the minimum value; a process of calculating a sum total of the signal amounts of the plurality of the detection signals; and a process of determining a pixel value of a pixel of the scanned image corresponding to the irradiation position based on a sum of a first value that is obtained based on the sum total and a second value that is obtained based on the difference.

A system and method is provided for of characterizing nanostructured surfaces. A nanostructure sample is placed in an SEM chamber and imaged. The system and method locates one of the nanostructures using images from the SEM imaging, excises a top portion of the nanostructure, places said top portion on a substrate such that the nanostructures are perpendicular to the substrate and a base of the top portion contacts the substrate, performs high energy ion beam assisted deposition of metal at the base to attach the top portion to the substrate, SEM imaging the top portions in the SEM chamber, determining coordinates of the top portions relative to the substrate from the SEM imaging of the top portions, placing the substrate in an AFM chamber, and performing AFM imaging of the top portions using the coordinates previously determined.

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.