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Charged Particle Microscope Provides Depth-Resolved Images

A technology of charged particle microscopy and electrons, applied in the direction of circuits, discharge tubes, electrical components, etc., can solve problems such as not providing means to solve beam/sample interaction

Active Publication Date: 2017-07-07
FEI CO
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Therefore, the non-automated methods in D1 and D2 do not provide means to resolve beam / sample interactions involving the full 3D PSF

Method used

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  • Charged Particle Microscope Provides Depth-Resolved Images
  • Charged Particle Microscope Provides Depth-Resolved Images
  • Charged Particle Microscope Provides Depth-Resolved Images

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0108] Figure 1A with 1B are interrelated flowcharts which show the general scheme for carrying out the method according to the invention. Referring to the nomenclature introduced in the discussion above, it is noted that:

[0109] - Figure 1A shows the iterative l For a given PSF kernel K at n algorithm. Apply sequentially for a given K n multiple iterations of the loop.

[0110] - can Figure 1A The iterative scheme in is applied sequentially to each PSF and spatial variable V. For any pair of K n , V, can perform one or more iterations in each loop. In the flow chart shown, the illustrated steps will now be explained in more detail. from Figure 1A start:

[0111] - 201: This step represents an iteration l K at n value (i.e. K n l ). exist l In the special case of =1, the previous initialization process will have been executed in order to "start" the iterative process.

[0112] -203: similarly, this step indicates an iteration l at the V value (i.e. V l ...

Embodiment 2

[0120] An intuitive way to think about the upcoming task of variable kernel deconvolution is to formulate it using so-called Bayesian statistics.

[0121] First define a number of probabilities that will be used throughout the description below:

[0122] - Pr(V|I n ) is the probability of extracting the spatial variable V, given the acquired input value I n case (see the Figure 1A Step 205 of the flow chart discussed above to obtain the value for "image" I n explanation of the concept). Similarly, Pr(I n |V) is the observed image value I given the sample structure described by V n The probability.

[0123] - Pr(V) is the so-called prior probability associated with V, representing one's knowledge about the structure to be reconstructed.

[0124] - Pr(I n ) is the probability associated with the acquired image; however, it is substantially constant, assuming the image I n is the actually observed / measured value.

[0125] Using Bayes' rule, we now get:

[0126] (1) ...

Embodiment 3

[0166] The prior distribution of the sample structure [Pr(V)] and the prior distribution of the PSF [Pr(K n )] in making the so-called posterior probability Pr(V, K n |I n ) can play an important regularization role in the process of maximization. Several well-known regularization methods are illustrated in the following publications.

[0167] [5] A. N. Tikhonov, On the Stability of Inverse Problems, Proceedings of Doklady Akademii Nauk SSSR, Russian Academy of Sciences, 1943, pp. 195-198.

[0168] [6] D. Strong, T. Chan, Edge-preserving and scale-dependent properties of total variation regularization, Inverse Problems, 2003, 19:S165–S187.

[0169] [7] P. O. Hoyer, Non-negative Matrix Factorization with Sparseness Constraints, Journal of Machine Learning Research 5, 2004, pp. 1457-1469.

[0170] [8] WD. Dong, H J. Feng, Z.H. Xu, Q. Li, A piecewise locally regularized Richardson-Lucy algorithm for remote sensing image deconvolution, Optics and Laser Technology 43, 2011, pp....

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Abstract

The present invention relates to charged particle microscopes that provide depth-resolved images. A method of examining a sample using a charged particle microscope, comprising the steps of: - positioning the sample on a sample holder; - directing at least one microparticle radiation beam onto the surface S of the sample using a particle-optical column, whereby an interaction occurs , the interaction results in emitted radiation from the sample; - detecting at least a portion of said emitted radiation using detector means, characterized by the steps of: - comprising said detector means to detect electrons in the emitted radiation; said detected The output On of the detector device is recorded as a function of the kinetic energy En of said electrons, thereby compiling a set of measurements M = {(On, En)} for multiple values ​​of En; deconvoluted and spatially decomposed into a resulting set R = {(Vk, Lk)}, where the spatial variable V exhibits values ​​Vk at associated discrete depth levels Lk referenced to the surface S, whereby n and k is a member of the sequence of integers, and the spatial variable V represents the physical properties of the sample as a function of position within the sample volume.

Description

technical field [0001] The present invention relates to the field of charged particle microscopy, and in particular to charged particle microscopes providing depth-resolved images. Background technique [0002] As used throughout this document, the following terms should be understood to be consistent with the following explanations: [0003] - The term "charged particle" contains electrons or ions (generally cations such as, for example, gallium ions or helium ions, although anions may also be possible; the ions in question may be charged atoms or molecules). The term can also refer to protons, for example. [0004] - The term "microscope" means a device used to produce magnified images of objects, features or parts that are generally too small to be seen in satisfactory detail with the naked human eye. In addition to its imaging capabilities, such a device could also have mechanical processing capabilities; for example, it could be used to Modify samples locally. The i...

Claims

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Application Information

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IPC IPC(8): H01J37/22H01J37/28
Inventor F.布格霍贝尔E.G.T.博施P.波托塞克X.朱格B.H.利希
Owner FEI CO
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