KR-20260067315-A - METHODS OF DETERMINING A DEFORMATION CHARACTERISTIC AND ASSOCIATED SYSTEMS

Abstract

In one example, the method includes the step of determining a characteristic displacement gradient (DG) tensor that characterizes deformation characteristics. The method includes the step of, in an iterative routine, calculating an objective function value associated with the execution DG tensor based at least partially on pixel values within a zone of consideration (ZOC) that are automatically determined at least partially based on the execution DG tensor. In another example, the method includes the step of determining one or more rotation transformation components corresponding to a test image and the step of determining a characteristic DG tensor based at least partially on the rotation transformation component(s). In another example, the system includes an electro-optical assembly, a detector assembly, and a controller programmed to determine the characteristic DG tensor. In another example, a non-transient computer-readable medium stores instructions that, when executed by a computer, cause the computer to perform a method for determining deformation characteristics.

Inventors

• 카슈파르 얀
• 페트레크 마르틴
• 비스타벨 토마시
• 홀제르 야쿠브
• 크렌 미할

Assignees

• 에프이아이 컴파니

Dates

Publication Date

20260512

Application Date

20251020

Priority Date

20241105

Claims (20)

1. As a computer-implemented method for determining the deformation characteristics of a test image relative to a reference image, A processor, comprising the step of determining a characteristic displacement gradient (DG) tensor characterizing the deformation characteristic through an iterative routine, wherein the step of determining the characteristic DG tensor comprises, in one or more steps of the iterative routine, The objective function value is considered in the above test image and the above reference image. At least for pixel values at pixel locations within the zone of consideration (ZOC). Calculate the above objective function value associated with the execution DG tensor to be partially based step - the above ZOC is based at least partially on the above execution DG tensor Determined as copper - including, method.
2. A method according to claim 1, wherein the step of calculating the objective function value comprises, for each pixel position of the reference image within the ZOC, a step of calculating such that the objective function is calculated by a calculation that references the pixel value corresponding to the pixel position exactly once.
3. A method according to claim 1, further comprising, in each step of one or more steps of the iteration routine, determining the ZOC such that the ZOC represents a maximum set of overlaps of pixel locations between the pixel location of the reference image and the shifted pixel location of the test image based at least partially on the execution DG tensor.
4. A method according to claim 1, wherein the step of calculating the objective function value includes the step of applying a respective weight factor to the pixel value within the ZOC.
5. A method according to claim 4, further comprising a step of determining each of the weight factors, wherein the step of determining each of the weight factors comprises a step of analyzing one or both of the test image and the reference image.
6. A method according to claim 1, wherein the step of calculating the objective function value includes the step of calculating such that the objective function value is normalized with respect to the number of pixel locations within the ZOC.
7. A method according to claim 1, further comprising, prior to the step of determining the characteristic DG tensor, a step of generating an initial DG tensor based at least partially on one or both of the test image and the reference image, wherein the initial DG tensor is used as the execution DG tensor in at least one step of the iteration routine.
8. In claim 7, the step of generating the initial DG tensor is, Step of generating a set of trial rotation vectors - each trial rotation vector represents one or more corresponding rotation transformation components -; A step of calculating a rotation error factor associated with each trial rotation vector; and Based on the above rotation error factor, the method includes the step of selecting an initialization rotation vector from the set of the above-mentioned rotation vectors, A method comprising the step of generating the initial DG tensor such that the initial DG tensor represents a rotation corresponding to the initialization rotation vector.
9. A method according to claim 1, wherein the step of determining the characteristic DG tensor includes the step of determining that the characteristic DG tensor corresponds to an execution DG tensor associated with an objective function value that satisfies a convergence criterion.
10. A method according to claim 1, wherein the step of determining the characteristic DG tensor comprises the step of performing a Nelder-Mead minimization algorithm that operates to identify an execution DG tensor that yields a corresponding objective function value satisfying a convergence criterion.
11. As a computer-implemented method for determining the deformation characteristics of a test image relative to a reference image, A processor system for determining one or more rotation transformation components corresponding to the test image; A step of generating an initial DG tensor based at least partially on one or more of the above-mentioned rotational transformation components; and Using the initial DG tensor, the processor system includes the step of determining a characteristic DG tensor that characterizes the transformation characteristic, including calculating a scalar objective function value corresponding to each of one or more execution DG tensors in one or more steps of an iteration routine. A method in which, in the first step of the above iteration routine, one of the one or more execution DG tensors is the initial DG tensor.
12. In paragraph 11, the step of determining one or more rotational transformation components is, Step of generating a set of trial rotation vectors - each trial rotation vector represents one or more trial rotation transformation components -; A step of calculating one or more rotation error factors associated with each trial rotation vector; and Based on the above rotation error factor, the method includes the step of selecting an initialization rotation vector from the set of the above-mentioned rotation vectors, A method comprising the step of generating the initial DG tensor such that the initial DG tensor represents a rotation corresponding to the initialization rotation vector.
13. In paragraph 12, the step of generating the set of the above-mentioned rotation vectors is that the above-mentioned rotation vectors, (i) a vector interval based at least partially on the expected radius of convergence of the objective function value; and (ii) Maximum vector size based at least partially on the expected rotation size associated with the above test image It includes the step of generating to be distributed in a three-dimensional vector space having one or both of them, and The above method further comprises the step of receiving from a user one or both of the expected convergence radius and the expected rotation size.
14. In paragraph 11, the step of calculating the objective function value is, for each individual iteration DG tensor of the one or more iteration DG tensors in each step of the iteration routine, For each pixel position within the ZOC of the reference image, a step of determining the shifted pixel position of the test image based on the individual trial DG tensor; and A method comprising the step of calculating the objective function value such that the objective function value represents the weighted average difference between the pixel value at the pixel position of the reference image within the ZOC and the pixel value at the shifted pixel position of the test image.
15. A method according to claim 14, further comprising the step of determining the ZOC based partially on each individual execution DG tensor of each of the one or more execution DG tensors at each step of the iteration routine.
16. A method according to claim 15, wherein the step of determining the ZOC comprises determining that the ZOC represents the maximum set of overlaps of pixel locations between the pixel location of the reference image and the shifted pixel location of the test image.
17. A method according to claim 14, wherein at least one step of the iteration routine, the one or more execution DG tensors are composed of fewer DG tensors than the number of elements of each DG tensor that change at each step of the iteration routine.
18. An electron optical assembly configured to direct an electron beam to a selected location on a specimen; A detector assembly configured to record a diffraction pattern associated with the interaction between the electron beam and the specimen; and controller As a system including, the controller, When the electron beam is directed to a reference position on the specimen, the detector Receive a reference image from the assembly; When the electron beam is directed to a test location on the specimen, the detector Receive a test image from the assembly; Through the iteration routine, at each step of the above iteration routine, the objective function value pixels within the ZOC of each of the above test image and the above reference image The above objective function value corresponding to the execution DG tensor based on the pixel value of the chip The deformation characteristics of the test image relative to the reference image by calculating It is programmed to determine the characteristic DG tensor to be characterized, and the ZOC is the above time A system that is automatically determined based partially on the row DG tensor.
19. In paragraph 18, the controller is further programmed to determine one or more stress components corresponding to the characteristic DG tensor, and the system includes an output interface for delivering the one or more stress components to a user.
20. A system according to claim 18, further comprising a non-transient computer-readable medium storing instructions that, when executed by the controller, cause the controller to determine the characteristic DG tensor.

Description

Methods of Determining a Deformation Characteristic and Associated Systems The present disclosure generally relates to a method for determining the deformation characteristics of a test image relative to a reference image, and an associated system. In the field of charged particle microscopy, electron backscatter diffraction (EBSD) represents a technique for obtaining information regarding the crystallographic structure of a specimen. Specifically, in EBSD, an electron beam is directed toward a tilted specimen, and backscattered electrons form a diffraction pattern that is recorded by a detector. The generated electron backscatter diffraction pattern (EBSP) can be analyzed to provide information regarding crystal grain structure, grain orientation, deformation, and other such features. In some cases, material deformation of a specimen can be characterized by comparing the EBSP recorded on an undeformed "reference" portion of the specimen with the undeformed "test" portion of the specimen. However, many prior techniques for performing such analyses suffer from limitations in computational efficiency and/or universality. In a representative example, a computer-implemented method for determining the deformation characteristics of a test image relative to a reference image includes the step of determining a characteristic displacement gradient tensor that characterizes the deformation characteristics through an iterative routine with a processor. The step of determining the characteristic displacement gradient tensor includes, in one or more steps of the iterative routine, the step of calculating an objective function value associated with the trial displacement gradient tensor. The objective function value is based at least partially on pixel values at pixel locations within a consideration region of the test image and the reference image. The consideration region is automatically determined at least partially based on the trial displacement gradient tensor. In another representative example, a computer-implemented method for determining deformation characteristics of a test image relative to a reference image comprises, by a processor system, the step of determining one or more rotational transformation components corresponding to the test image and the step of generating an initial displacement gradient tensor based at least partially on the one or more rotational transformation components. The method further comprises, by using the initial displacement gradient tensor, the step of determining a characteristic displacement gradient tensor that characterizes deformation characteristics by a processor system. The step of determining the characteristic displacement gradient tensor comprises determining it by calculating a scalar objective function value corresponding to each of one or more trial displacement gradient tensors in one or more steps of an iterative routine. In the first step of the iterative routine, one of the one or more trial displacement gradient tensors is an initial displacement gradient tensor. In another representative example, the system includes an electron optical assembly configured to direct an electron beam to a selected location on a specimen, a detector assembly configured to record a diffraction pattern associated with the interaction between the electron beam and the specimen, and a controller. The controller is programmed to receive a reference image from the detector assembly when the electron beam is directed to a reference location on the specimen, and to receive a test image from the detector assembly when the electron beam is directed to a test location on the specimen. The controller is further programmed, through an iterative routine, to determine a characteristic displacement gradient tensor that characterizes the deformation characteristics of the test image relative to the reference image. At each step of the iterative routine, the controller is programmed to calculate an objective function value corresponding to the trial displacement gradient tensor such that the objective function value is based on the pixel values of pixel locations within the consideration region of each of the test image and the reference image. The consideration region is automatically determined in part based on the trial displacement gradient tensor. In another representative example, a non-transient computer-readable medium stores instructions that, when executed by a computer, cause the computer to perform a method for determining the deformation characteristics of a test image relative to a reference image. The method includes the step of determining a characteristic displacement gradient tensor that characterizes the deformation characteristics through an iterative routine. The instructions include, at each step of the iterative routine, instructions for determining the characteristic displacement gradient tensor by calculating an objective function value corresponding to the trial displace