Abstract:
A method of performing tomographic imaging of a sample in a charged-particle microscope, comprising the following steps: - Providing a beam of charged particles; - Providing the sample on a sample holder that can be tilted relative to said beam; - Directing the beam through the sample and so as to form an image of the sample at an image detector; - Repeating this procedure at each of a first series of sample tilts so as to acquire a corresponding set of images; - Mathematically combining images from said set so as to construct a composite image,
which method comprises the following steps: - Selecting a second series of sample tilts; - At each of said second series of sample tilts, using a spectral detector to accrue a spectral map of said sample, thus acquiring a collection of spectral maps; - Analyzing said spectral maps to derive compositional data pertaining to the sample; - Employing said compositional data in constructing said composite image Said spectral maps may, for example, be acquired using a technique selected from the group comprising EDX, EELS, EFTEM, and combinations hereof.
Abstract:
A method of performing tomographic imaging of a sample in a charged-particle microscope, comprising the following steps: - Providing a beam of charged particles; - Providing the sample on a sample holder that can be tilted relative to said beam; - Directing the beam through the sample and so as to form an image of the sample at an image detector; - Repeating this procedure at each of a first series of sample tilts so as to acquire a corresponding set of images; - Mathematically combining images from said set so as to construct a composite image,
which method comprises the following steps: - Selecting a second series of sample tilts; - At each of said second series of sample tilts, using a spectral detector to accrue a spectral map of said sample, thus acquiring a collection of spectral maps; - Analyzing said spectral maps to derive compositional data pertaining to the sample; - Employing said compositional data in constructing said composite image Said spectral maps may, for example, be acquired using a technique selected from the group comprising EDX, EELS, EFTEM, and combinations hereof.
Abstract:
Disclosed is a charged particle radiation apparatus capable of capturing a change in a sample due to gaseous atmosphere, light irradiation, heating or the like without exposing the sample to atmosphere. The present invention relates to a sample holder provided with a sample stage that is rotatable around a rotation axis perpendicular to an electron beam irradiation direction, the sample holder being capable of forming an airtight chamber around the sample stage. A sample is allowed to chemically react in any atmosphere, and three-dimensional analysis on the reaction is enabled. A sample liable to change in atmosphere can be three-dimensionally analyzed without exposing the sample to the atmosphere.
Abstract:
A method of investigating a sample using Scanning Electron Microscopy (SEM), comprising the following steps: - Irradiating a surface (S) of the sample using a probing electron beam in a plurality (N) of measurement sessions, each measurement session having an associated beam parameter (P) value that is chosen from a range of such values and that differs between measurement sessions; - Detecting stimulated radiation emitted by the sample during each measurement session, associating a measurand (M) therewith and noting the value of this measurand for each measurement session, thus allowing compilation of a data set (D) of data pairs (P i , M i ), where 1 ≤ i ≤N,
wherein: - A statistical Blind Source Separation (BSS) technique is employed to automatically process the data set (D) and spatially resolve it into a result set (R) of imaging pairs (Q k , L k ), in which an imaging quantity (Q) having value Q k is associated with a discrete depth level L k referenced to the surface S.
A suitable example of such a BSS technique is Principal Component Analysis (PCA), e.g. employing a Karhunen-Loeve transform operation. This technique allows high-resolution 3D volume reconstruction from a sequence of backscattered images acquired by a SEM. The method differs from known techniques in that it can be used on complex samples with unknown structure. With this method, one can compute compensation factors between high- and low-energy images using second-order (or higher-order) multivariate statistics, which allows for the effective separation of different depth layers in a sample without using a priori knowledge of sample structure. The method has a wide range of applications in life-science and material science imaging.
Abstract:
A method of investigating a sample using Scanning Electron Microscopy (SEM), comprising the following steps: - Irradiating a surface (S) of the sample using a probing electron beam in a plurality (N) of measurement sessions, each measurement session having an associated beam parameter (P) value that is chosen from a range of such values and that differs between measurement sessions; - Detecting stimulated radiation emitted by the sample during each measurement session, associating a measurand (M) therewith and noting the value of this measurand for each measurement session, thus allowing compilation of a data set (D) of data pairs (P i , M i ), where 1 ≤ i ≤ N,
wherein: - A statistical Blind Source Separation (BSS) technique is employed to automatically process the data set (D) and spatially resolve it into a result set (R) of imaging pairs (Q k , L k ), in which an imaging quantity (Q) having value Q k is associated with a discrete depth level L k referenced to the surface S.
A suitable example of such a BSS technique is Principal Component Analysis (PCA), e.g. employing a Karhunen-Loeve transform operation. This technique allows high-resolution 3D volume reconstruction from a sequence of backscattered images acquired by a SEM. The method differs from known techniques in that it can be used on complex samples with unknown structure. With this method, one can compute compensation factors between high- and low-energy images using second-order (or higher-order) multivariate statistics, which allows for the effective separation of different depth layers in a sample without using a priori knowledge of sample structure. The method has a wide range of applications in life-science and material science imaging.
Abstract:
A method and apparatus for performing a slice and view technique with a charged particle beam system. The feature of interest in an image of a sample is located by machine vision, and the area to be milled and imaged in a subsequent slice and view iteration is determined through analysis of data gathered by the machine vision at least in part. A determined milling area may be represented as a bounding box around a feature, which dimensions can be changed in accordance with the analysis step. In a dual beam system, the FIB is then adjusted accordingly to slice and mill a new face in the subsequent slice and view iteration, and the SEM images the new face. Because the present invention accurately locates the feature and determines an appropriate size of area to mill and image, efficiency is increased by preventing the unnecessary milling of substrate that does not contain the feature of interest.
Abstract:
A method and system are described for reconstructing a coherent radiation tomographic image of at least one object. The method comprises performing back-projection, whereby performing back-projection comprises taking into account propagation waves from the point of interaction to a plane of detection. Furthermore a method is described for studying objects, wherein a plurality of objects are imaged on a substrate using coherent radiation tomography and whereby the method as described above is used for characterizing the objects.