Transmission imaging simulation method, device, equipment and medium for polar nanometer micro-area
By employing selective diffraction spot dark-field imaging and transmission electron microscopy, the problems of low imaging accuracy and dynamic simulation of PNRs have been solved, enabling high-contrast imaging and dynamic simulation of PNRs and supporting material property prediction.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-01-15
- Publication Date
- 2026-04-10
AI Technical Summary
Existing technologies for characterizing polar nanoregions (PNRs) suffer from insufficient imaging accuracy, making it difficult to identify the boundaries and dimensions of PNRs. They also lack dynamic simulation methods for the self-organization process of PNRs, resulting in high operational barriers and difficulties in optimizing material properties and designing devices.
By selectively diffractive spot dark-field imaging, the size distribution and areal density parameters of polar nanoregions are quantified. Combined with transmission electron microscopy imaging to obtain lattice distortion rate, a reaction-diffusion model is established to simulate the dynamic self-organization process of PNRs.
It improves the contrast and signal-to-noise ratio of PNRs imaging, provides a reliable data foundation, offers theoretical guidance for predicting the macroscopic properties of materials, and achieves direct comparability between simulation results and experimental observations.
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Figure CN121830746A_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of phase field simulation technology, specifically relating to a method, apparatus, equipment, and medium for simulating transmission imaging of polar nanoscale micro-regions. Background Technology
[0002] Relaxor ferroelectrics are widely used in electronic devices such as sensors, capacitors, and ultrasonic transducers due to their excellent dielectric, piezoelectric, and energy storage properties. Polar nanoregions (PNRs) are the core microstructural units of relaxor ferroelectrics, and their size, distribution, morphology, and self-organization behavior directly determine the macroscopic properties of the material. Therefore, accurate characterization and dynamic process analysis of PNRs are key to research in this field. Currently, characterization techniques for PNRs mainly rely on transmission electron microscopy (TEM), including bright-field imaging, dark-field imaging, and high-resolution transmission electron microscopy (HRTEM). However, existing technologies have the following significant problems and drawbacks: Insufficient imaging accuracy: Existing TEM imaging often focuses on a single crystal plane or local area, without optimizing imaging parameters (such as selected area diffraction range and electron beam acceleration voltage) for the superlattice diffraction characteristics of PNRs. This results in insignificant contrast differences between PNRs and the substrate, making it difficult to accurately identify the boundaries and size of PNRs. In particular, PNRs with a size of less than 5 nm are prone to missed or misidentification and incomplete identification. Lack of structure-performance correlation: Existing technologies can only obtain static morphological information of PNRs, lacking dynamic simulation methods for PNR self-organization processes (such as aggregation, growth, and phase transition), and cannot establish a quantitative correlation between the microscopic evolution of PNRs and the macroscopic properties of materials (such as changes in dielectric constant and piezoelectric response); Disconnect between simulation and experiment: Existing PNR simulations mostly use simplified thermodynamic models (such as the Ginzburg-Landau phase field model), without combining actual imaging observations of PNR size and distribution data, resulting in large deviations between simulation results and experimental phenomena, and making it impossible to accurately predict the dynamic evolution of PNRs; High operational threshold: Existing TEM imaging methods have not formed a standardized process, are highly dependent on the experience of operators, and the characterization results of PNRs obtained from different laboratories have poor repeatability, making it difficult to achieve cross-platform data comparison. The core reason for the above problems is that PNRs have the characteristics of small size (usually 2-4nm) and uneven distribution. Existing characterization techniques have not designed specific imaging and simulation schemes for their microscopic features. At the same time, existing studies often separate imaging and simulation, failing to form a closed-loop system of "experimental observation - data quantification - simulation verification". This results in insufficient analysis of the microscopic mechanism of PNRs, which restricts the performance optimization and device design of relaxor ferroelectric materials. Summary of the Invention
[0003] To address the shortcomings of existing technologies, the present invention aims to provide a method, apparatus, device, and storage medium for simulating transmission imaging of polar nanoscale microregions, thereby solving the technical problems of low accuracy in characterization and simulation imaging of PNRs in relaxor ferroelectrics, difficulty in analyzing dynamic processes, and disconnect between simulation and experiment.
[0004] According to one aspect of this application, a method for simulating transmission imaging of polar nanoscale regions is disclosed, the method comprising: Determine the target analytical region for relaxor ferroelectric samples; The target analysis area is imaged by transmission electron microscopy and dark-field imaging under diffraction conditions. The dark-field imaging enhances the imaging contrast of the polar nano-micro region by selecting superlattice diffraction spots of relaxor ferroelectrics. Based on the dark field image, the size distribution and areal density parameters of the polar nano-micro region are quantitatively extracted; Based on the transmission electron microscopy imaging, the lattice distortion rate parameter of the polar nano-micro region is quantitatively extracted; Based on the areal density parameter, the simulated concentration value of the reaction-diffusion model is determined; The reaction-diffusion model is controlled to run under set region size and boundary conditions to simulate the dynamic self-organization evolution process of the polar nanoregions, so as to obtain the polar nanoregion aggregation morphology of the polar nanoregions output by the model.
[0005] In some embodiments, the superlattice diffraction spots are 1 / 2 (ooe) spots, where o is the odd exponent and e is the even exponent.
[0006] In some embodiments, the step of quantifying and extracting the size distribution and areal density parameters of the target polar nano-region based on the dark field image includes: Threshold segmentation is performed on the dark field image to extract the polar nanoscale region in the dark field imaging; Particle analysis was performed on the segmented polar nano-micro regions to obtain the size distribution and areal density of the polar nano-micro regions.
[0007] In some embodiments, the grayscale threshold range for threshold segmentation is 120 to 150.
[0008] In some embodiments, the quantitative extraction of the lattice distortion rate parameter of the target polar nanoscale region based on the transmission electron microscopy imaging includes: A two-dimensional Fourier transform is performed on the simulated image of the sample to obtain the reciprocal space power spectrum of the simulated image of the sample. In the power spectrum, at least one Bragg diffraction spot corresponding to a specific family of crystal planes of the relaxor ferroelectric crystal is identified; An inverse Fourier transform is performed on the Bragg diffraction spot region, and the lattice displacement field in the normal direction of the crystal plane family is calculated based on the correspondence between geometric phase and lattice displacement. Based on the lattice displacement field, the lattice strain field is calculated, and the strain values in the polar nano-micro region are statistically analyzed to obtain the lattice distortion rate parameter.
[0009] In some embodiments, during dark-field imaging, the objective aperture size of the transmission electron microscope is 2μm-3μm, and the electron beam dose of the transmission electron microscope is controlled to be ≤20e / Ų.
[0010] In some embodiments, determining the simulated concentration value of the reaction-diffusion model based on the areal density parameter includes: The areal density parameter is dimensionally normalized to convert it into a dimensionless relative density value. The relative density value is multiplied by a disorder structure factor related to the relaxor ferroelectric doping concentration to obtain the calibrated simulated concentration value; The dimensionless normalization process is determined based on the following formula: p_norm=p_exp*(A_pixel / A_ref), In the formula, p_norm is the normalized relative density value, p_exp is the experimentally measured areal density parameter, A_pixel is the real physical area represented by a single pixel in the simulated mesh, and A_ref is the selected reference area.
[0011] According to another aspect of this application, a transmission imaging simulation device for polar nanoscale regions is also disclosed, the device comprising: The target analysis region determination module is used to determine the target analysis region of the relaxor ferroelectric sample; The imaging module is used to perform transmission electron microscopy imaging and dark-field imaging under diffraction conditions on the target analysis area. The dark-field imaging enhances the imaging contrast of the polar nanoscale region by selecting the superlattice diffraction spots of the relaxor ferroelectric. The first parameter extraction module is used to quantitatively extract the size distribution and areal density parameters of the polar nano-micro region based on the dark field image. The second parameter extraction module is used to quantitatively extract the lattice distortion rate parameter of the polar nano-micro region based on the transmission electron microscope imaging. The simulated concentration value determination module is used to determine the simulated concentration value of the reaction-diffusion model based on the areal density parameter; The simulation control module is used to control the reaction-diffusion model to run and simulate the dynamic self-organization evolution process of the polar nanoregions under set region size and boundary conditions, so as to output the polar nanoregion aggregation morphology.
[0012] According to another aspect of this application, an electronic device is also disclosed, the electronic device including a memory and at least one processor, the memory storing instructions; the at least one processor invokes the instructions in the memory to cause the electronic device to perform various steps of the transmission imaging simulation method for polar nanoregions as described in any of the preceding claims.
[0013] According to another aspect of this application, a computer-readable storage medium is also disclosed, wherein instructions are stored on the computer-readable storage medium, characterized in that, when executed by a processor, the instructions implement the various steps of the transmission imaging simulation method for polar nanoscale micro-regions as described in any of the preceding claims. The present invention includes, but is not limited to, the following beneficial effects: (1) This scheme achieves selective enhancement of contrast of polar nanoregions (PNRs) by selecting superlattice diffraction spots for dark-field imaging, thereby improving the contrast and signal-to-noise ratio of PNR imaging; (2) This scheme achieves statistical description of morphology by obtaining size distribution and areal density from dark-field images, and achieves physical quantity description of structure by obtaining lattice distortion rate from high-resolution images, providing a reliable data basis for subsequent simulation and performance correlation; (3) This scheme transforms static experimental observation data (areal density) into the initial simulation conditions of dynamic simulation models, and the calibrated reaction-diffusion model has predictive capabilities, which can predict the possible morphology of PNRs when the composition is known but no time-consuming experiments are conducted. Since the morphology of PNRs determines the macroscopic properties such as dielectric and piezoelectric properties of relaxor ferroelectrics, this makes it possible to predict macroscopic properties from microstructure, providing theoretical guidance for the development of new materials; (4) This scheme clearly defines and selects specific superlattice diffraction spots. This scheme achieves specific extraction of polar nano-region (PNR) signals, ensuring that the contrast in dark field images can be derived from the contribution of PNRs, reducing the interference of background and other defect signals, improving the contrast and specificity of PNR imaging, and solving the problem of weak PNR contrast and easy submersion in conventional TEM imaging; (5) Starting from high-resolution transmission electron microscope images, this scheme outputs a quantitative lattice distortion rate parameter with physical units through Fourier transform → phase analysis → strain calculation, providing a precise and comparable digital measure for the micromechanical state of materials. This digital measurement statistics for polar nano-regions directly links the structural parameter of lattice distortion with the PNRs of materials, and can quantify the degree of lattice distortion in the core region of PNRs; (6) By establishing a precise scale mapping between the virtual space simulated by computer and the real physical space observed by transmission electron microscope, this scheme realizes that the simulation is carried out on the physical scale corresponding to the experiment, making the simulation results directly comparable to the experimental observations. Attached Figure Description
[0014] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below.
[0015] Figure 1 This is a flowchart of a transmission imaging simulation method for polar nanoscale regions according to an embodiment of this application; Figure 2 TEM image of the KBT-0.06BNZ sample according to an embodiment of this application; Figure 3 These are comparison diagrams of PNRs morphology under different doping concentrations according to embodiments of this application; Figure 4This is a comparison chart of simulation results and experimental results of the self-organizing process of PNRs in this application embodiment; Figure 5 This is a structural block diagram of the transmission imaging simulation device for polar nano-micro regions according to an embodiment of this application; Figure 6 This is a schematic diagram of the structure of the electronic device provided in the embodiments of this application. Detailed Implementation
[0016] The terms "first," "second," "third," "fourth," etc. (if present) in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" or "having" and any variations thereof are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.
[0017] For ease of understanding, the specific process of the embodiments of the present invention will be described below. Figure 1 This is a flowchart of the transmission imaging simulation method for polar nanoscale regions according to an embodiment of this application. (See attached document.) Figure 1 It includes the following steps: S100, Determine the target analytical region for the relaxor ferroelectric sample.
[0018] Specifically, firstly, transmission electron microscope (TEM) sections of relaxor ferroelectric samples (KBT-BNZ system) are prepared. The prepared TEM sections are 50-80 nm thick, ensuring the surface is free of stress damage and that the test area avoids defect-rich regions at the sample edges. Next, the sample sections are subjected to plasma cleaning (10-15 W power, 3-5 min) to remove surface contaminants and avoid affecting image quality. Finally, bright-field imaging is performed on the cleaned sections to determine the target analytical region of the relaxor ferroelectric sample based on the bright-field imaging.
[0019] S102. The target analysis area is imaged by transmission electron microscopy and dark-field imaging under diffraction conditions. Dark-field imaging enhances the imaging contrast of polar nano-micro regions by selecting superlattice diffraction spots of relaxor ferroelectrics.
[0020] The diffraction conditions include the diffraction spots, the position and size of the objective aperture, crystal orientation, and electron beam parameters. In this step, transmission electron microscopy imaging can specifically involve adjusting the transmission electron microscope to a high-resolution mode, for example, tilting the sample towards the low-index zone axis (e.g.,...). <001> The direction is adjusted so that the incident electron beam is parallel to the crystal axis to obtain an atomic lattice fringe image. Further, the underfocus of the objective lens is adjusted to obtain a phase contrast image. Further, the focus is adjusted and the image is taken under low electron beam dose conditions to obtain a transmission electron microscope image, in which the periodic arrangement of the lattice can be observed, while polar nanoscale regions usually show slight distortion, aberration or contrast variation of the local lattice.
[0021] Furthermore, keeping the sample position unchanged, switch from high-resolution mode to diffraction mode. In diffraction mode, the transmitted beam and diffracted beam will form a diffraction pattern on the back focal plane. Select a specific diffraction spot, such as a superlattice spot of the 1 / 2(00e) or 1 / 3(00e) class (where 0 is the odd index and e is the even index), and use the objective aperture to surround the selected superlattice diffraction spot, blocking the transmitted beam and all other diffracted beams. At this time, only electrons diffracted by this specific superlattice spot can participate in imaging. Switch back to imaging mode to obtain a dark-field image. It can be understood that only regions that meet the superlattice diffraction conditions (i.e., regions where PNR exists) can strongly diffract electrons onto the selected spot. Therefore, in the final dark-field image, the location of the PNR will appear very bright, while the matrix and other regions that do not meet the conditions will appear very dark, thus achieving high-contrast, specific imaging of the PNR.
[0022] S104. Based on dark field images, quantitatively extract the size distribution and areal density parameters of polar nano-micro regions.
[0023] Specifically, threshold segmentation can be performed on the dark field image to extract the polar nanoscale regions in the dark field imaging; then particle analysis can be performed on the segmented polar nanoscale regions to obtain the size distribution and areal density of the polar nanoscale regions.
[0024] Before segmentation, digital filters (such as Gaussian filtering or median filtering) can be applied to the original dark-field image to smooth random noise, making the PNR boundary clearer and reducing errors during subsequent segmentation. During segmentation, a suitable grayscale threshold is selected, and all pixels with grayscale values higher than this threshold are identified as belonging to the PNR, while pixels with grayscale values lower than this threshold are identified as background. For example, the grayscale threshold range for threshold segmentation is 120 to 150.
[0025] Furthermore, each segmented PNR object is measured, and its overall statistical characteristics are calculated. For example, for each independent white connected region (i.e., a PNR) identified in the image, its equivalent circle diameter, area, circumference, aspect ratio, etc., are measured. The mean, median, and standard deviation of all PNR size parameters are calculated, and a size distribution histogram is plotted.
[0026] Furthermore, calculate the areal density: areal density (number / μm²) = total number of PNRs / actual physical area of the image (μm²).
[0027] S106. Based on transmission electron microscopy imaging, the lattice distortion rate parameter of polar nanoscale regions is quantitatively extracted.
[0028] Specifically, this step may include the following steps: Step 1: Perform a two-dimensional Fourier transform on the sample simulation image to obtain the reciprocal space power spectrum of the sample simulation image.
[0029] In this context, reciprocal space refers to the mathematical dual space of the real crystal space. The translation period (interplanar spacing *d*) in real space is represented as a point in reciprocal space, the distance of which to the origin being proportional to 1 / *d*. The power spectrum is the square of the modulus of the Fourier transform result. It retains only frequency and intensity information, discarding phase information. In the power spectrum, the position of a bright spot indicates the direction (spot direction) and period (distance of the spot from the center) of the corresponding periodic structure; its brightness reflects the saliency (or weight) of that periodic structure in the original image. For relaxor ferroelectrics, in addition to strong principal spots (from the matrix lattice), weaker superlattice spots (such as those at 1 / 2, 1 / 3, etc.) also appear in the power spectrum. These superlattice spots are generated by periodic lattice modulation caused by PNRs (polar nanoregions). Discovering and locating these superlattice spots is a prerequisite for proving the existence of PNRs and performing selective analysis (such as dark-field imaging).
[0030] Step 2: In the power spectrum, identify at least one Bragg diffraction spot corresponding to a specific family of crystal planes of the relaxor ferroelectric crystal.
[0031] Specifically, this step does not involve randomly or arbitrarily selecting spots, but rather having a clear physical purpose in identifying at least one spot that can be directly associated with the specific crystallographic information (crystal facet family) of the relaxor ferroelectric.
[0032] The specific operational steps include: a. In the power spectrum, each bright spot is a Bragg diffraction spot.
[0033] The position vector of the spot (the direction and distance from the center to the spot) corresponds to a set of crystal planes: Direction: Perpendicular to the crystal plane represented by the spot.
[0034] Distance: Inversely proportional to the interplanar spacing d of the corresponding crystal plane. The farther away from the center, the smaller the interplanar spacing.
[0035] The intensity of the spots reflects the strength of electron diffraction of the crystal planes and is related to the crystal structure factor, sample thickness, orientation, etc.
[0036] b. Identify the spots corresponding to a specific family of crystal faces: The crystal structure (e.g., perovskite structure) of the relaxor ferroelectric material under study and its orientation (e.g.) are determined in advance. <001> The standard diffraction pattern of the zone axis. It includes the theoretical positions of the main spots (such as {100}, {110}) and the superlattice spots (such as 1 / 2{ooo}, 1 / 3{ooo}, etc.) generated by crystal symmetry / modulation structure.
[0037] Identifying the master spot array: First, locate the regular quadrilateral or hexagonal grid composed of the strongest and brightest spots in the power spectrum. These are typically diffraction spots generated by low-index crystal planes of the matrix lattice (such as (100) and (010)). By measuring their distances to the center and the included angles, the reciprocal space scale of the power spectrum can be calibrated, and the precise orientation of the sample can be confirmed.
[0038] Locating the spots of the target crystal plane family: If lattice distortion analysis is to be performed, two non-collinear principal spots are usually selected, such as g1=(100) and g2=(010). These two spots have clear orientations, which facilitates subsequent calculation of the two-dimensional displacement / strain field.
[0039] Step 3: Perform an inverse Fourier transform on the Bragg diffraction spot region, and calculate the lattice displacement field in the normal direction of the crystal plane family based on the correspondence between geometric phase and lattice displacement.
[0040] Specifically, an inverse Fourier transform is performed on the selected diffraction spot region in step 2. This operation generates a complex image, where the phase value of each pixel contains the local displacement information of the lattice relative to the ideal lattice at that point. The local phase (x,y) is proportional to the displacement component u_g(x,y) in the normal direction of the crystal plane family (diffraction vector g), i.e.: u_g(x,y) = -2m * g. By solving this relationship, the lattice displacement field uB(x,y) along the g direction can be obtained. If two non-collinear diffraction spots (such as g1 and g2) are selected, the two-dimensional displacement field u(x,y) can be calculated.
[0041] Step 4: Based on the lattice displacement field, calculate the lattice strain field and perform statistical analysis on the strain values in the polar nano-micro region to obtain the lattice distortion rate parameter.
[0042] Specifically, the lattice distortion rate (strain) is obtained by differentiating the displacement field u(x,y) in space. More specifically, the strain components exx, xy, y, etc., can be obtained by calculating the derivatives of the displacement field in each direction.
[0043] Polar nanoregion (PNR) quantization: First, the spatial distribution region of the PNR is determined within the same field of view of the dark-field image through lattice segmentation. Then, the strain values of the corresponding regions of the PNR are extracted from the calculated full-field strain distribution map (e.g., the exx distribution map). Finally, statistical analysis is performed on the strain values of all pixels within this region (e.g., calculating the average, maximum, or specific distribution). The resulting statistical value (e.g., an average strain value of 0.3%) is the lattice distortion rate of the PNR.
[0044] S108. Based on the areal density parameter, determine the simulated concentration value of the reaction-diffusion model.
[0045] Specifically, the surface density parameter is normalized to convert it into a dimensionless relative density value; The relative density value is multiplied by a disorder structure factor that is related to the relaxation ferroelectric doping concentration to obtain the calibrated simulated concentration value; The dimensionless normalization process is determined based on the following formula: p_norm=p_exp*(A_pixel / A_ref), In the formula, p_norm is the normalized relative density value, p_exp is the experimentally measured areal density parameter, A_pixel is the real physical area represented by a single pixel in the simulated mesh, and A_ref is the selected reference area.
[0046] Before running a reaction-diffusion model (such as the Gray-Scott model) for simulation, a two-dimensional discrete computational domain, i.e., the simulation grid, is first defined in the computer. Its settings include: Grid size: This refers to the number of grid points (pixels) in the X and Y directions, for example, set to 1000×1000.
[0047] The physical scale of the mesh: that is, how large a piece of real material the entire mesh represents. This is the premise for determining A_pixel in the claim.
[0048] In step S2, a dark-field image was captured using a transmission electron microscope (TEM). The physical field of view of this image is 2 μm × 2 μm. During simulation initialization, the physical scale of the simulation grid is set to be exactly the same as this experimental field of view. That is, if the experiment observes a 2 μm × 2 μm region, the simulation will be performed on a 2 μm × 2 μm region of the same size. Once the total physical size of the entire simulation region (e.g., 2 μm × 2 μm) and the number of pixels in the grid (e.g., 1000 × 1000) are determined, the physical area A_pixel represented by a single pixel can be calculated. A_pixel = (Total physical area of the simulated region) / (Total number of pixels in the grid) = (2μm * 2μm) / (1000 * 1000) = 4e-6μm² Specifically, dimensional normalization is used to establish the scale correspondence between simulation and reality: For example, a two-dimensional simulation mesh (e.g., 1000 pixels x 1000 pixels) is defined in the computer. This mesh represents a region of material to be simulated. Further, the size of a single pixel (Pxel) in the real physical area is determined. For example, through calibration, it can be set that 1 pixel (1 nm) corresponds to 1 nm in the real material. 2 =1m 2 The area.
[0049] Further calculations are performed: assuming measurements are taken from the experimental image (TEM), at 1 pm... 2 Within the actual material region, there are 150 PNRs. That is, the experimental surface density p_exp = 150 PNRs / µm. 2 That is, in the simulation results, 1m 2 The area corresponds to 1000 x 1000 = 1,000,000 pixels. These 150 PNRs are distributed across these 1,000,000 pixels, and the average probability or density of a PNR per pixel is calculated: relative density value = 150 / 1,000,000 = 0.00015. 0.00015 is a unitless number representing the initial concentration of PNRs at each grid point (pixel) in the simulation space. This value can be used as a baseline for the initial field u(x, y, t=0) of reactant u in the reaction-diffusion model.
[0050] Furthermore, a disorder structure factor can be introduced to optimize this baseline value. Specifically, in the simulation, the value of the introduced disorder structure factor S.config is adjusted, and the model is run. For example, when S.config = 0.8, the simulated PNRs are scattered points; when S.config = 1.2, the simulated PNRs are connected into a network. Further, the simulation results under different S.config values are compared with the experimentally obtained morphology. The S.config value that best matches the simulated morphology with the experimental morphology is selected (e.g., S_config = 1.04 when the matching degree is >90%). This 1.04 is the calibrated S_config value for this specific component (x = 0.25), which can be used as the simulated concentration value.
[0051] S110. The controlled reaction-diffusion model is run under set region size and boundary conditions to simulate the dynamic self-organization evolution process of polar nanoregions, so as to obtain the polar nanoregion aggregation morphology of the output polar nanoregions.
[0052] Furthermore, for ease of understanding, this application combines... Figures 2 to 4 For example, the following is an example, where, Figure 2 This is a TEM image of the KBT-0.06BNZ sample from an embodiment of this application. Figure 2 In the diagram, a is a schematic diagram of the origin of white contrast in HRTEM from lattice distortion, b is an HRTEM image of the KBT-0.06BNZ sample and its lattice distance measurement, c is the GPA analysis result corresponding to the HRTEM image, with the lattice distortion rate distribution marked in pseudo-color, d is a 6BNZ dark field image obtained using superlattice satellite point 1 / 2 (ooe), the marked area is the PNRs region which is basically consistent with the GPA result, and e is a schematic diagram of polar nanoregions (PNRs). Figure 3 This is a comparison diagram of the morphology of PNRs under different doping concentrations according to embodiments of this application. Figure 3 In the image, ad represents the TEM image and dark field image obtained using the superlattice diffraction points 1 / 2 (ooe) of KBT, 3BNZ and 6BNZ, respectively, and ef represents the schematic diagram of the Gray-Scott process of PNR self-organization. Figure 4 This is a comparison chart of simulation results and experimental results of the self-organizing process of PNRs in this application. Figure 3 In the figure, a is a schematic diagram of the Gray-Scott model, b is the point clustering pattern of PNRs in the dark field image of the 3BNZ experiment, c is the strip clustering pattern of PNRs in the dark field image of the 6BNZ experiment, d is the point clustering pattern of PNRs simulated by the Gray-Scott model, and e is the strip clustering pattern of PNRs simulated by the Gray-Scott model.
[0053] Example 1: Imaging and simulation of PNRs in KBT-0.06BNZ relaxor ferroelectrics Sample preparation KBT-0.06BNZ (K0.5Bi0.5TiO3-0.06Bi(Ni1 / 2Zr1 / 2)O3) ceramic samples were selected, and TEM samples were prepared using FIB technology: The samples were fixed on a copper grid, cut into 10×10μm blocks using a Ga ion beam (accelerating voltage 30kV), then thinned to a thickness of 60nm, and finally polished with a 5kV ion beam to remove the damaged layer; the samples were cleaned using a plasma cleaner (model: GatanSolarus955) at a power of 12W for 4min. TEM imaging A JEOL JEMF200 thermal field emission TEM (accelerating voltage 200kV) was used, and half (ooe) of the superlattice diffraction spot was selected as the dark-field imaging region. The objective aperture size was 2.5μm, and dark-field images (field of view 2×2μm) were captured. HRTEM images (resolution 0.19nm) of the same region were also captured. GPA analysis was performed using Gatan Digital Micrograph software, and the lattice distortion rate of the PNRs region was calculated to be 0.2%. Data Quantification The dark field image was processed using ImageJ software, with the threshold set to a grayscale value of 135. The PNRs region was extracted, and the average size of the PNRs was found to be 2-4 nm. Simulation process Based on the above experimental data, the Gray-Scott model parameters were set as follows: initial reactant concentration of 0.018, conversion rate of 0.056, diffusion coefficients of 0.21 and 0.105, all of which are dimensionless. Running the simulation program in MATLAB with a time step of 0.01s, after 100s of simulation, the output PNRs clustering pattern is "mainly dotted, with local short strips", which is highly consistent with the experimental dark field image. Example 2: Imaging of PNRs in KBT-0.03BNZ Relaxor Ferroelectrics Sample preparation: KBT-0.03BNZ (K0.5Bi0.5TiO3-0.03Bi(Ni1 / 2Zr1 / 2)O3) samples were prepared using ion thinning technology. The thinning voltage was 6kV and the time was 45min. The thickness of the thinned sample was 70nm, and the thickness deviation was confirmed to be ≤8nm by bright-field imaging. TEM imaging: Half (ooe) of the superlattice diffraction spot was selected as the dark-field imaging area. The objective aperture size was 3μm. Dark-field images were captured, and the average size of PNRs was found to be 2-4nm.
[0054] Simulation process: The phase-field model is used to replace the Gray-Scott model. The polarization intensity vector (P, containing x, y, and z components) of PNRs is used as the order parameter to construct a free energy density function that includes gradient energy, elastic energy, and electric dipole interaction energy. The polarization gradient coefficient κ = 3 × 10⁻¹ is set. 0 Using J・m⁻¹ (the dielectric constant of the sample being matched) and the electric dipole interaction parameter ω=1.2eV・nm³, the simulated PNRs aggregated in a dotted pattern, which is consistent with the experimental results.
[0055] Understandably, in other feasible solutions, a phase-field model can be used to replace the Gray-Scott model: using the polarization intensity vector (P, containing x, y, and z components) of the PNRs as the order parameter, and adjusting the polarization gradient coefficient (κ=1×10⁻¹) by constructing a free energy density function that includes gradient energy, elastic energy, and electric dipole interaction energy. 0 ~5×10⁻¹ 0 J・m⁻¹, which is positively correlated with the dielectric constant of the relaxor ferroelectric and the electric dipole interaction parameters (ω=0.5~2eV・nm³, reflecting the polarization coupling strength between PNRs), to achieve qualitative and semi-quantitative simulation of the aggregation morphology (point-like / strip-like) and evolution process of PNRs, and can be directly correlated with the polarization direction distribution characteristics of PNRs observed in the experiment.
[0056] Furthermore, Figure 5 This is a structural block diagram of the transmission imaging simulation device for polar nano-micro regions according to the application embodiment, such as... Figure 5 As shown, the device includes: The target analysis region determination module is used to determine the target analysis region of the relaxor ferroelectric sample; The imaging module is used to perform transmission electron microscopy imaging and dark-field imaging under diffraction conditions on the target analysis area. The dark-field imaging enhances the imaging contrast of the polar nanoscale region by selecting the superlattice diffraction spots of the relaxor ferroelectric. The first parameter extraction module is used to quantitatively extract the size distribution and areal density parameters of the polar nano-micro region based on the dark field image. The second parameter extraction module is used to quantitatively extract the lattice distortion rate parameter of the polar nano-micro region based on the transmission electron microscope imaging. The simulated concentration value determination module is used to determine the simulated concentration value of the reaction-diffusion model based on the areal density parameter; The simulation control module is used to control the reaction-diffusion model to run and simulate the dynamic self-organization evolution process of the polar nanoregions under set region size and boundary conditions, so as to output the polar nanoregion aggregation morphology.
[0057] The application of the relevant modules of the device in this example can be referred to the relevant introduction of the method principle above, and will not be repeated here.
[0058] above Figure 5 The apparatus in the embodiments of the present invention will be described in detail from the perspective of modular functional entities. The electronic device in the embodiments of the present invention will be described in detail from the perspective of hardware processing.
[0059] Figure 5 This is a schematic diagram of the structure of an electronic device 600 provided in an embodiment of the present invention. The electronic device 600 can vary significantly due to different configurations or performance characteristics. It may include one or more central processing units (CPUs) 610 (e.g., one or more processors) and a memory 620, and one or more storage media 630 (e.g., one or more mass storage devices) for storing application programs 633 or data 632. The memory 620 and storage media 630 can be temporary or persistent storage. The program stored in the storage media 630 may include one or more modules (not shown in the diagram), each module including a series of instruction operations on the electronic device 600. Furthermore, the processor 610 may be configured to communicate with the storage media 630 and execute the series of instruction operations in the storage media 630 on the electronic device 600.
[0060] Electronic device 600 may also include one or more power supplies 640, one or more wired or wireless network interfaces 650, one or more input / output interfaces 660, and / or one or more operating systems 631, such as Windows Server, MacOSX, Unix, Linux, FreeBSD, etc. Those skilled in the art will understand that... Figure 6 The illustrated electronic device structure does not constitute a limitation on electronic devices and may include more or fewer components than illustrated, or combine certain components, or have different component arrangements.
[0061] The present invention also provides a computer-readable storage medium, which can be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium, wherein the computer-readable storage medium stores instructions that, when executed on a computer, cause the computer to perform the steps of any of the above-described polar nano-micro-region transmission imaging simulation methods.
[0062] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the system, device, or unit described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.
[0063] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.
[0064] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims
1. A method for simulating transmission imaging of polar nanoscale regions, characterized in that, The method includes: Determine the target analytical region for relaxor ferroelectric samples; The target analysis area is imaged by transmission electron microscopy and dark-field imaging under diffraction conditions. The dark-field imaging enhances the imaging contrast of the polar nano-micro region by selecting superlattice diffraction spots of relaxor ferroelectrics. Based on the dark field image, the size distribution and areal density parameters of the polar nano-micro region are quantitatively extracted; Based on the transmission electron microscopy imaging, the lattice distortion rate parameter of the polar nano-micro region is quantitatively extracted; Based on the areal density parameter, the simulated concentration value of the reaction-diffusion model is determined; The reaction-diffusion model is controlled to run under set region size and boundary conditions to simulate the dynamic self-organization evolution process of the polar nanoregions, so as to obtain the polar nanoregion aggregation morphology of the polar nanoregions output by the model.
2. The method for simulating transmission imaging of polar nano-micro regions according to claim 1, characterized in that, The superlattice diffraction spots are 1 / 2 (ooe) spots, where o is the odd exponent and e is the even exponent.
3. The method for simulating transmission imaging of polar nano-micro regions according to claim 2, characterized in that, The step of quantifying and extracting the size distribution and areal density parameters of the target polar nano-region based on the dark field image includes: Threshold segmentation is performed on the dark field image to extract the polar nanoscale region in the dark field imaging; Particle analysis was performed on the segmented polar nano-micro regions to obtain the size distribution and areal density of the polar nano-micro regions.
4. The method for simulating transmission imaging of polar nano-micro regions according to claim 3, characterized in that, The grayscale threshold range for the threshold segmentation is 120 to 150.
5. The method for simulating transmission imaging of polar nano-micro regions according to claim 1, characterized in that, The quantitative extraction of the lattice distortion rate parameter of the target polar nanoscale region based on the transmission electron microscopy imaging includes: A two-dimensional Fourier transform is performed on the simulated image of the sample to obtain the reciprocal space power spectrum of the simulated image of the sample. In the power spectrum, at least one Bragg diffraction spot corresponding to a specific family of crystal planes of the relaxor ferroelectric crystal is identified; An inverse Fourier transform is performed on the Bragg diffraction spot region, and the lattice displacement field in the normal direction of the crystal plane family is calculated based on the correspondence between geometric phase and lattice displacement. Based on the lattice displacement field, the lattice strain field is calculated, and the strain values in the polar nano-micro region are statistically analyzed to obtain the lattice distortion rate parameter.
6. The method for simulating transmission imaging of polar nano-micro regions according to claim 1, characterized in that, In dark-field imaging, the objective aperture size of the transmission electron microscope is 2μm-3μm, and the electron beam dose of the transmission electron microscope is ≤20e / Ų.
7. The method for simulating transmission imaging of polar nano-micro regions according to claim 1, characterized in that, The determination of the simulated concentration value of the reaction-diffusion model based on the areal density parameter includes: The areal density parameter is dimensionally normalized to convert it into a dimensionless relative density value. The relative density value is multiplied by a disorder structure factor related to the relaxor ferroelectric doping concentration to obtain the calibrated simulated concentration value; The dimensionless normalization process is determined based on the following formula: p_norm=p_exp*(A_pixel / A_ref), In the formula, p_norm is the normalized relative density value, p_exp is the experimentally measured areal density parameter, A_pixel is the real physical area represented by a single pixel in the simulated mesh, and A_ref is the selected reference area.
8. A transmission imaging simulation device for polar nanoscale regions, characterized in that, The device includes: The target analysis region determination module is used to determine the target analysis region of the relaxor ferroelectric sample; The imaging module is used to perform transmission electron microscopy imaging and dark-field imaging under diffraction conditions on the target analysis area. The dark-field imaging enhances the imaging contrast of the polar nanoscale region by selecting the superlattice diffraction spots of the relaxor ferroelectric. The first parameter extraction module is used to quantitatively extract the size distribution and areal density parameters of the polar nano-micro region based on the dark field image. The second parameter extraction module is used to quantitatively extract the lattice distortion rate parameter of the polar nano-micro region based on the transmission electron microscope imaging. The simulated concentration value determination module is used to determine the simulated concentration value of the reaction-diffusion model based on the areal density parameter; The simulation control module is used to control the reaction-diffusion model to run and simulate the dynamic self-organization evolution process of the polar nanoregions under set region size and boundary conditions, so as to output the polar nanoregion aggregation morphology.
9. An electronic device, characterized in that, The electronic device includes a memory and at least one processor, the memory storing instructions; the at least one processor invokes the instructions in the memory to cause the electronic device to perform the steps of the transmission imaging simulation method for polar nanoscale regions as described in any one of claims 1-7.
10. A computer-readable storage medium storing instructions thereon, characterized in that, When the instructions are executed by the processor, they implement the various steps of the transmission imaging simulation method for polar nanoscale regions as described in any one of claims 1-7.