Dielectric high-resolution trap imaging method and system based on scanning probe microscopy

Abstract

The application discloses a dielectric high-space-resolution trap imaging method and system based on a scanning probe microscope, and the method comprises the following steps: recording a sample surface topography image sequence and a surface potential image sequence after removing a voltage; correcting image offset according to the surface topography image sequence, and extracting surface potential decay data of each pixel point from the corrected surface potential image sequence; determining trap distribution information of each pixel point by fitting and calculation according to the surface potential decay data of each pixel point; and forming deep and shallow trap energy level peak value images and / or deep and shallow trap density peak value images corresponding to the film surface topography according to the trap distribution information of all pixel points and the original positions of the pixel points. The method directly images the trap characteristics of a sample at a nanometer scale, and has the advantages of high spatial resolution and intuitive comparison.

Description

Technical Field

[0001] This invention relates to the field of thin film dielectric performance testing technology, and in particular to a high-resolution trap imaging method and system for dielectrics based on scanning probe microscopy. Background Technology

[0002] Polymer dielectrics are key materials for fabricating high-energy-density thin-film capacitors, but their low dielectric constant is a major factor hindering the improvement of energy storage density. Filling polymers with inorganic nanoparticles possessing high dielectric constants to create nanocomposite dielectrics promises to be an effective solution to this problem. However, due to the complexity of the micro-interfaces and multi-field coupling effects within the composite material system, current characterization techniques and methods cannot provide a clear and complete understanding of the mechanism of nanoparticle filling, which restricts the further development of energy storage dielectrics.

[0003] Studies have shown that one of the main effects of nanoparticle filling on dielectrics is its ability to alter the space charge behavior of polymers under high electric field stress. Based on the band structure theory of organic semiconductors, after space charges enter the sample surface, they undergo a process of being trapped by charge traps, thermally excited to detach and enter the conduction or valence bands, and then migrate along the conduction or valence bands. This process is accompanied by a potential decay, and the law of potential decay reflects the behavior of space charges under the influence of traps; that is, the potential decay law reflects the trap distribution characteristics. In existing technologies, researchers have studied the trap distribution and other characteristics of nanocomposites using macroscopic testing methods such as thermally stimulated current methods, pulsed electroacoustic methods, and isothermal surface potential decay methods. However, due to the limited spatial resolution of the equipment, direct characterization of the relevant properties of composite materials at the nanoscale is not possible.

[0004] Therefore, existing technologies still need to be improved and developed. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to provide a high spatial resolution trap imaging method and system for dielectrics based on scanning probe microscopy, which addresses the above-mentioned deficiencies of the prior art and aims to solve the problem that the existing technology cannot directly characterize the relevant properties of dielectric materials at the nanoscale.

[0006] The technical solution adopted by this invention to solve the technical problem is as follows:

[0007] A method for high spatial resolution trap imaging of dielectrics based on scanning probe microscopy, comprising the following steps:

[0008] Record the sequence of surface morphology images and surface potential images of the sample after the voltage is removed;

[0009] The image offset is corrected based on the surface morphology image sequence, and the surface potential attenuation data of each pixel is extracted from the corrected surface potential image sequence.

[0010] Based on the surface potential decay data of each pixel, the trap distribution information of each pixel is determined by fitting and calculation.

[0011] Based on the trap distribution information of all pixels, deep and shallow trap energy level peak images and / or deep and shallow trap density peak images corresponding to the original position of each pixel are formed.

[0012] The dielectric high spatial resolution trap imaging method based on scanning probe microscopy, wherein when recording the surface morphology image sequence and surface potential image sequence of the sample after voltage removal, dynamic monitoring of surface potential is adopted by charge injection and transport. Charge is injected through a nanoprobe and the change of material surface potential over time after charge injection is continuously recorded until the surface potential tends to stabilize.

[0013] The aforementioned high spatial resolution trap imaging method for dielectrics based on scanning probe microscopy includes, in which, image offset is corrected according to the surface morphology image sequence and the peak surface potential attenuation data of each pixel is extracted from the corrected surface potential image sequence, including:

[0014] Using the surface topography information of the surface topography image sequence as a reference, the image offset generated during the continuous scanning process is calibrated by the cropping of the image sequence and the correspondence of features, and the surface potential attenuation data of each pixel in the calibrated surface potential image sequence is extracted.

[0015] The aforementioned high spatial resolution trap imaging method for dielectrics based on scanning probe microscopy, wherein the fitting method is a bi-exponential fitting, which is based on the simultaneous presence of deep and shallow traps in the sample, specifically trap energy levels and trap density constructed based on organic semiconductor theory, including:

[0016] For the potential decay data of each pixel, the fitting parameters are adjusted in real time during the fitting process to obtain a smooth surface potential decay curve. The double peak relationship between the trap energy level and the trap density is established using the fitted surface potential decay curve to facilitate subsequent peak separation.

[0017] Based on the fitted surface potential decay curve, the trap density is expressed by the decay law of surface potential with time, and the trap energy level is expressed by the decay time.

[0018] The trap density is:

[0019]

[0020] Where, N t(E) represents the trap density, ε0 ​​represents the vacuum permittivity, and ε r Let q represent the relative permittivity, q represent the charge, L represent the thickness, and k represent the relative permittivity. B V represents the Boltzmann constant, T represents the absolute temperature, and V represents the absolute temperature. s (t) represents the surface potential at time t;

[0021] The trap energy level is:

[0022] E = k B T ln(υt)

[0023] Where E represents the trap energy level, k B denoted by Boltzmann constant, and υ by the escape frequency factor of the trapped charge.

[0024] The aforementioned high spatial resolution trap imaging method for dielectrics based on scanning probe microscopy includes the following: the deep and shallow trap energy level peak images include: deep and shallow trap energy level peak images under each sub-peak; the deep and shallow trap density images include: deep and shallow trap density peak images under each sub-peak; the step of forming deep and shallow trap energy level peak images and / or deep and shallow trap density peak images corresponding to the film surface morphology at the original positions of each pixel based on the trap distribution information of all pixels includes:

[0025] For each pixel, the curve formed by the trap energy level and trap density of that pixel is divided into peaks to obtain the deep and shallow trap energy level curves and deep and shallow trap density curves of that pixel under each peak.

[0026] For each peak, based on the peak values ​​of deep and shallow trap densities and the corresponding energy levels of all pixels at that peak value, and the original positions of each pixel, a peak image of deep and shallow trap energy levels and a peak image of deep and shallow trap density are formed for that peak.

[0027] The high spatial resolution trap imaging method for dielectrics based on scanning probe microscopy is described above, wherein the sample is a dielectric thin film, and the dielectric thin film includes: a pure phase dielectric thin film and a composite dielectric thin film.

[0028] The dielectric high spatial resolution trap imaging method based on scanning probe microscopy further includes:

[0029] Based on the surface morphology image sequence, the surface potential image sequence, the deep and shallow trap energy level peak images, and the deep and shallow trap density peak images, the potential decay curve and trap distribution information of the target location in the region to be characterized of the sample are determined.

[0030] The dielectric high spatial resolution trap imaging method based on scanning probe microscopy, wherein the region to be characterized contains locations with different trap information, the target location includes: any location on the surface morphology, the trap distribution information includes: trap energy level distribution information and / or trap density distribution information; the potential decay curve includes: potential decay time curve and / or trap energy level and trap density curve;

[0031] Based on the surface morphology image sequence, surface potential image sequence, deep and shallow trap energy level peak images, and deep and shallow trap density peak images, the potential decay curve and trap distribution information at the target location in the region to be characterized are determined, including:

[0032] Select the target location based on the surface topography image sequence;

[0033] Based on the surface potential image sequence and the surface morphology image sequence, the potential decay data at the target location is obtained;

[0034] Based on the target location and the peak images of the deep and shallow trap energy levels, the trap energy level distribution information at the target location is obtained;

[0035] Based on the target location and the peak density images of deep and shallow traps, the trap density distribution information at the target location is obtained;

[0036] Based on the potential decay data, the trap energy level distribution information, and the trap density distribution information, plot the potential decay time curve and the trap energy level / trap density curve at the target location.

[0037] The dielectric high spatial resolution trap imaging method based on scanning probe microscopy is described in which the surface morphology image sequence and the surface potential image sequence are both obtained under Kelvin probe force microscopy. A dual-electrode system of nanoprobe and ground electrode is constructed by Kelvin probe force microscopy to realize charge injection into the region to be characterized on the sample surface.

[0038] A dielectric high spatial resolution trap imaging system based on scanning probe microscopy, comprising:

[0039] The image acquisition module constructs a dual-electrode system of nanoprobe and ground electrode using a scanning probe microscope to achieve charge injection into the area to be characterized on the sample surface and record the surface morphology image sequence and surface potential image sequence of the sample after the voltage is removed.

[0040] The calibration module corrects the image offset based on the surface morphology image sequence and extracts the surface potential attenuation data of each pixel from the corrected surface potential image sequence.

[0041] The data processing module determines the trap distribution information of each pixel by fitting and calculating the surface potential decay data of each pixel.

[0042] The trap image module generates deep and shallow trap energy level peak images and / or deep and shallow trap density peak images corresponding to the surface morphology of the thin film based on the trap distribution information of all pixels and the original position of each pixel.

[0043] Beneficial effects: This invention is a high spatial resolution trap distribution imaging method for dielectric materials. By constructing a dual-electrode system using a nanoprobe and a ground electrode, the surface potential image sequence of the sample surface after charge injection and voltage removal in the region to be characterized is recorded. Based on the surface potential image sequence, the spatial distribution of trap energy level images and / or trap density is obtained. This method can directly image the trap characteristics of the sample at the nanoscale and has the advantages of high spatial resolution and intuitive comparison. Attached Figure Description

[0044] Figure 1 This is a flowchart of a high-resolution trap distribution imaging method for nano-dielectric thin films according to an embodiment of the present invention.

[0045] Figure 2 This is a schematic diagram of a scanning probe microscope experimental apparatus constructed according to an embodiment of the present invention.

[0046] Figure 3 The micro-area surface morphology and surface potential variation over time of a polyvinylidene fluoride (BTO / PVDF) sample filled with barium titanate particles constructed according to an embodiment of the present invention are shown.

[0047] Figure 4a This is a trap density image of a shallow trap in a BTO / PVDF sample with high spatial resolution, constructed according to an embodiment of the present invention.

[0048] Figure 4b This is a trap energy level image of a shallow trap in a BTO / PVDF sample constructed according to an embodiment of the present invention with high spatial resolution.

[0049] Figure 4c This is a trap density image of a BTO / PVDF sample with high spatial resolution deep traps constructed according to an embodiment of the present invention.

[0050] Figure 4d This is a trap energy level image of a deep trap in a BTO / PVDF sample with high spatial resolution, constructed according to an embodiment of the present invention.

[0051] Figure 5a The surface potential decay curves at particle locations distinguished by a BTO / PVDF sample trap distribution image constructed according to an embodiment of the present invention.

[0052] Figure 5b The trap energy level and density map at the particle location distinguished by the trap distribution image of the BTO / PVDF sample constructed for one embodiment of the present invention.

[0053] Figure 5c The surface potential decay curves at matrix locations distinguished by a BTO / PVDF sample trap distribution image constructed according to an embodiment of the present invention.

[0054] Figure 5d Traps energy levels and density maps at matrix locations distinguished by a trap distribution image of a BTO / PVDF sample constructed according to an embodiment of the present invention. Detailed Implementation

[0055] To make the objectives, technical solutions, and advantages of this invention clearer and more explicit, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0056] Please also refer to Figures 1-5d This invention provides some embodiments of a dielectric high spatial resolution trap imaging method based on scanning probe microscopy.

[0057] like Figure 2 As shown, the characterization method of the present invention is applied to Kelvin probe force microscopy, by applying a DC bias voltage V to the nanoprobe. dc To compensate for the contact potential difference between the nanoprobe and the thin film sample, a frequency of ω is applied. ac AC voltage V ac Apply to the nanoprobe (or thin film sample) via V dc This method is used to characterize the surface potential information of the sample. The sample in this application can be a dielectric sample or a composite thin film sample, such as polyvinylidene fluoride (PVDF) filled with barium titanate (BTO) nanoparticles, which can also be written as BTO / PVDF. The thin film sample includes a thin film matrix and nanoparticles. During characterization, the nanoparticles can be exposed, thereby forming two-phase or multi-phase structures on the surface of the thin film sample. The thin film matrix is ​​polyvinylidene fluoride (PVDF), and the nanoparticles are barium titanate particles (BTO). High-resolution scanning of the composite dielectric surface morphology information is obtained through scanning probe microscopy, which provides height information during image calibration and can also be used to distinguish the matrix and particle positions, providing a reference for subsequent selection of specific locations.

[0058] The inventors discovered that Kelvin probe force microscopy can construct a dual-electrode system using nanoparticle tips and a ground electrode to record a sequence of surface potential images of the sample surface after charge injection and voltage removal in the region to be characterized. By combining Kelvin probe force microscopy with the band structure theory of organic semiconductors, the decay law of the potential was characterized. Through image calibration, data extraction, fitting, and calculation, trap distribution images were obtained at the nanoscale. Distinguishing the trap characteristics of different phases in composite materials is a very significant contribution, helping to understand the microscopic mechanism of space charge behavior in dielectrics under high fields. This provides more intuitive data support for explaining the mechanism of nanoparticle filling, and has significant scientific and commercial value for promoting research on the energy storage performance of dielectrics.

[0059] like Figure 1 As shown, the dielectric high spatial resolution trap imaging method based on scanning probe microscopy of this invention includes the following steps:

[0060] Step S100: Record the surface morphology image sequence and surface potential image sequence of the sample after the voltage is removed.

[0061] Step S200: Correct the image offset according to the surface morphology image sequence and extract the surface potential attenuation data of each pixel from the corrected surface potential image sequence.

[0062] Step S300: Based on the surface potential decay data of each pixel, determine the trap distribution information of each pixel through fitting and calculation.

[0063] Step S400: Based on the trap distribution information of all pixels, form deep and shallow trap energy level peak images and / or deep and shallow trap density peak images corresponding to the original position of each pixel and the surface morphology of the thin film.

[0064] Specifically, a dual-electrode system is constructed using a nanoprobe and a ground electrode. A pressurization process is achieved at the center of the region to be characterized via the nanoprobe tip, with a tip voltage ranging from -150V to 150V (e.g., 100V), and the tip is held for 2 minutes. During pressurization, the nanoprobe contacts the BTO / PVDF sample surface, injecting charge through high-voltage contact and ensuring sufficient charge capture on the sample surface. The region to be characterized for the thin film sample can be determined as needed, typically based on the target to be characterized, with the region containing the target being designated as the characterization region. For composite dielectric samples, the target can be a micro-region containing different phases simultaneously. Specifically, the region on the surface of the composite dielectric sample containing both particles and matrix is ​​identified as the characterization region under a scanning probe microscope.

[0065] The dual-electrode system is a dynamic monitoring system for surface potential, enabling charge injection and transport. It injects charge using a nanoprobe and continuously records the change in surface potential over time until the surface potential stabilizes. Specifically, after pressurization, the nanoprobe is detached from the thin-film sample. The charges trapped on the sample surface gradually escape the trap energy level through thermal excitation and reach the ground electrode via the conduction band, resulting in surface potential decay on the composite dielectric sample surface. After moving the nanoprobe to a preset position, a sequence of surface morphology images and a sequence of surface potential images of the region to be characterized are acquired using the nanoprobe. The surface morphology image sequence is used to extract height information to calibrate image offset, and the surface potential image sequence is used to extract information on the change in surface potential over time to calculate trap distribution. The surface morphology image sequence includes several surface morphology images arranged chronologically, and the surface potential image sequence includes several surface potential images arranged chronologically (e.g., ...). Figure 3 As shown, the number of surface potential images can be configured as needed, for example, from 30 to 150 images. Surface potential images can be continuously acquired until the surface potential of the thin film sample stabilizes. This stability is reflected in the images as continuous images with no visible differences, and in the curve as a surface potential decay curve gradually approaching horizontal. To quickly obtain a sufficient number of surface potential images, the resolution can be reduced. A lower resolution, for example, using 64*64 pixels, allows for the acquisition of one surface potential image every 63 seconds. Reducing the resolution increases the number of surface potential images acquired, thus obtaining more detailed information on the surface potential change over time, facilitating subsequent calculation of trap information based on trap energy level and density formulas. After obtaining the surface potential image sequence, the spatial distribution of the trap energy level peaks and / or trap density peaks in the region to be characterized is obtained based on the surface morphology image sequence and the surface potential image sequence.

[0066] In this application, a dual-electrode system is constructed using a nanoprobe and a ground electrode to record the sample surface morphology image sequence and sample surface potential image sequence after charge injection and voltage removal in the region to be characterized on the sample surface. Based on the surface morphology image sequence and surface potential image sequence, the spatial distribution of the trap energy level peak and / or trap density peak is obtained, thereby realizing direct imaging of the trap characteristics of composite dielectric samples at the nanoscale.

[0067] Step S200 specifically includes:

[0068] Step S210: Using the surface topography information of the surface topography image sequence as a reference, the image offset generated during the continuous scanning process is calibrated by the cropping of the image sequence and the correspondence of the height information features. During the surface topography image offset calibration process, the corresponding surface potential image sequence is also calibrated, and the surface potential attenuation data of each pixel in the calibrated surface potential image sequence is extracted.

[0069] The fitting method is a double exponential fitting, which is based on the assumption that the composite dielectric sample contains both deep and shallow traps. Specifically, it is based on the trap energy level and density formula constructed from organic semiconductor theory. Step S300 specifically includes:

[0070] Step S310: For the potential decay data of each pixel, the fitting parameters are adjusted in real time during the fitting process to obtain a smooth surface potential decay curve. The double exponential fitting is based on the assumption that deep traps and shallow traps exist simultaneously in the composite dielectric sample. The double peak relationship between the trap energy level and the trap density is established using the fitted surface potential decay curve to facilitate subsequent peak separation.

[0071] Step S320: Based on the fitted surface potential decay curve and the formulas for trap energy levels and trap density, the trap density is expressed by the decay law of surface potential over time, and the trap energy level is expressed by the decay time. The length of time the surface potential decays over time reflects the depth of the energy level, and the rate of decay of surface potential over time reflects the density of each energy level.

[0072] Specifically, surface potential information is extracted pixel by pixel from all surface potential images in the surface potential image sequence. Then, the surface potential information of each pixel is arranged according to time and fitted to form surface potential decay data V. s (t), specifically as follows Figure 5a and 5c As shown. For each pixel, based on the surface potential decay data V of that pixel. s (t) and the formulas for trap energy levels and density, where t represents the trap energy level and V represents the density. s The relationship between (t) and t expresses the trap density and determines the trap energy level and trap density of the pixel.

[0073] Specifically, the trap density is:

[0074]

[0075] Where, N t (E) represents the trap density, ε0 ​​represents the vacuum permittivity, and ε r Let q represent the relative permittivity, q represent the charge, L represent the thickness, and k represent the relative permittivity. B V represents the Boltzmann constant, T represents the absolute temperature, and V represents the absolute temperature. s (t) represents the surface potential at time t.

[0076] The trap energy level is:

[0077] E = k B T ln(υt)

[0078] Where E represents the trap energy level, k B denoted by Boltzmann constant, and υ by the escape frequency factor of the trapped charge.

[0079] Obtain surface potential decay data V s After (t), the trap density N can be established. t The relationship between (E) and the trap energy level E is as follows: Figure 5b and Figure 5d As shown, the trap energy level and trap density can be obtained.

[0080] The peak values ​​of deep and shallow trap energy levels and densities are extracted based on the relationship between trap energy levels and trap density for each pixel. Then, peak images of deep and shallow trap energy levels and trap density are obtained according to the position of each pixel. Specifically, the peak values ​​of deep and shallow trap energy levels of all pixels are arranged according to their positions to form peak images of deep and shallow trap energy levels, and the peak values ​​of deep and shallow trap density of all pixels are arranged according to their positions to form peak images of deep and shallow trap density. The peak values ​​of trap energy levels and densities reflect the magnitude of the charge trapping ability of the composite dielectric; the larger the trap energy level and the greater the trap density, the stronger the charge trapping ability.

[0081] The surface potential decay data were fitted using a double exponential function, and trap densities N were established accordingly. t The relationship between (E) and the trap energy level E is achieved by double exponential fitting based on the simultaneous presence of deep and shallow traps in the sample, thus realizing peak separation.

[0082] The deep and shallow trap energy level peak images include: deep and shallow trap energy level peak images under each sub-peak; the deep and shallow trap density images include: deep and shallow trap density peak images under each sub-peak; step S400 includes:

[0083] Step S410: For each pixel, perform peak division on the curve formed by the trap energy level and trap density of that pixel to obtain the deep and shallow trap energy level curves and deep and shallow trap density curves of that pixel under each peak division.

[0084] Step S420: For each peak, based on the peak values ​​of deep and shallow trap densities and the corresponding energy levels of all pixels at the peak values ​​of deep and shallow trap densities under that peak, and the original positions of each pixel, form the peak image of deep and shallow trap energy levels and the peak image of deep and shallow trap density under that peak.

[0085] Specifically, trap energy levels and trap density can be segmented according to deep traps and shallow traps, such as... Figure 5b and Figure 5d As shown by the dashed line, the data is divided into two peaks. The traps with larger trap energy levels at the peak are deep traps, while those with smaller trap energy levels are shallow traps. A trap density peak image of shallow traps can be generated based on their trap energy levels and trap densities (e.g., ...). Figure 4a (as shown) and trap level peak images (e.g.) Figure 4b As shown), the trap energy level and trap density of deep traps can form a trap density peak image of deep traps (e.g., Figure 4c (as shown) and trap level peak images (e.g.) Figure 4d (As shown).

[0086] Step S100 specifically includes:

[0087] Step S101: Obtain the initial surface morphology image and initial surface potential image of the region to be characterized using a nanoprobe.

[0088] Step S102: Charge injection and subsequent recording of surface morphology image sequence and surface potential image sequence are achieved in the region to be characterized using a nanoprobe.

[0089] The surface morphology image sequence and surface potential image sequence were both obtained in Kelvin probe force microscopy mode. Under Kelvin probe force microscopy, the distance between the nanoprobe and the sample is greater than 50 nm to obtain the electrostatic force between the nanoprobe and the sample surface. This electrostatic force is a long-range force; for example, the distance is 300 nm. After determining the region to be characterized in the sample, an initial surface morphology image of the region to be characterized is obtained under Kelvin probe force microscopy using the nanoprobe. The initial surface morphology image has a relatively high resolution, for example, using 256*256 pixels. Of course, the initial surface potential of the region to be characterized can also be obtained to help determine the applied voltage.

[0090] The imaging method further includes:

[0091] Step S500: Based on the surface morphology image sequence, surface potential image sequence, deep and shallow trap energy level peak images, and deep and shallow trap density peak images, determine the potential decay curve and trap distribution information of the target location in the region to be characterized of the sample.

[0092] Specifically, sometimes it is necessary to determine the potential decay curve and trap distribution information at a certain point within the region to be characterized. The target location within the region can be determined using surface topography images. Based on the target location and a calibrated sequence of surface potential images, the surface potential versus time curve is obtained. Finally, based on the target location and trap energy level and density images, trap distribution information is obtained. Here, the trap energy level and trap density images refer to those at peak subdivisions.

[0093] The region to be characterized contains locations with different trap information. The target locations include: arbitrary locations on the surface morphology. The trap distribution information includes: trap energy level distribution information and / or trap density distribution information. The potential decay curve includes: potential decay time curve and / or trap energy level / trap density curve. Step S500 includes:

[0094] Step S510: Select the target location based on the surface morphology image sequence;

[0095] Step S520: Obtain the potential decay data at the target location based on the surface potential image sequence and the surface morphology image sequence.

[0096] Step S530: Based on the target location and the deep and shallow trap energy level peak images, obtain the trap energy level distribution information of the target.

[0097] Step S540: Obtain the trap density distribution information at the target location based on the target location and the deep and shallow trap density images.

[0098] Step S550: Based on the potential decay data, the trap energy level distribution information, and the trap density distribution information, plot the potential decay time curve and the trap energy level / trap density curve at the target location.

[0099] Specifically, for composite thin films, there are at least two phases. Due to the different materials, these two phases inevitably possess different trapping characteristics. For example, in the characterization region of a BTO / PVDF dielectric thin film, there is a film matrix and nanoparticles. The film matrix is ​​PVDF, and the nanoparticles are BTO. The purpose of filling with nanoparticles is to increase the dielectric constant. Trapping information can explain one reason for the increase in dielectric constant; generally, the particle location has a higher trapping energy level and density than the matrix location. The matrix location of the thin film matrix can be determined through surface morphology images (e.g., ...). Figure 4c Position 2 shown), and the particle position of the nanoparticles (e.g. Figure 4c Position 1 shown, thus obtaining the surface potential change data over time based on the surface potential decay image sequence (e.g., Figure 5c(As shown); the trap energy level distribution information of the thin film matrix is ​​obtained from the matrix location and trap energy level image, and the trap density distribution information of the thin film matrix is ​​obtained from the matrix location and trap density image; the trap energy level distribution information of the nanoparticles is obtained from the particle location and trap energy level image, and the trap density distribution information of the nanoparticles is obtained from the particle location and trap density image.

[0100] In summary, this invention, based on the band structure theory of organic semiconductors, utilizes scanning probe microscopy to develop a high spatial resolution imaging method for trap distribution characteristics, which includes charge injection, potential decay, potential extraction, data fitting, formula calculation, and data mapping. This method offers advantages such as high spatial resolution, intuitive results, and the ability to provide new technologies for characterizing traps in complex micro-regions of dielectrics.

[0101] This invention is a high spatial resolution trap distribution imaging method for dielectric materials. This method advances the spatial resolution of trap distribution to the nanoscale, enabling the differentiation of trap characteristics between filling particles and polymer matrix at the nanoscale. It features high spatial resolution and intuitive comparison. This method is applicable to dielectric material systems, including pure-phase dielectric films, modified polymer dielectric films, and nanocomposite dielectric films, with a wide range of applications. It helps to understand the microscopic mechanism of space charge behavior inside dielectric materials and provides more intuitive data support for explaining the mechanism of nanoparticle filling. It is of great significance for promoting the research on the energy storage performance of composite dielectric films.

[0102] Based on any of the above embodiments of the dielectric high spatial resolution trap imaging method based on scanning probe microscopy, the present invention also provides a dielectric high spatial resolution trap imaging system based on scanning probe microscopy, comprising:

[0103] The image acquisition module constructs a dual-electrode system of nanoprobe and ground electrode using a scanning probe microscope to achieve charge injection into the area to be characterized on the sample surface and record the surface morphology image sequence and surface potential image sequence of the sample after the voltage is removed.

[0104] The calibration module corrects the image offset based on the surface morphology image sequence and extracts the surface potential attenuation data of each pixel from the corrected surface potential image sequence.

[0105] The data processing module determines the trap distribution information of each pixel by fitting and calculating the surface potential decay data of each pixel.

[0106] The trap image module, based on the trap distribution information of all pixels, forms deep and shallow trap energy level peak images and / or deep and shallow trap density peak images corresponding to the original position of each pixel and the surface morphology of the thin film.

[0107] It should be understood that the application of the present invention is not limited to the examples above. Those skilled in the art can make improvements or modifications based on the above description, and all such improvements and modifications should fall within the protection scope of the appended claims.

Claims

1. A method of dielectric high spatial resolution trap imaging based on a scanning probe microscope, characterized in that, The method comprises the steps of: recording a sequence of surface topography images and a sequence of surface potential images of the sample after the removal of the voltage; during the recording of the sequence of surface topography images and the sequence of surface potential images of the sample after the removal of the voltage, the surface potential is dynamically monitored by injection and transport of charges, the charges are injected by the nano-probe, and the surface potential of the material after the injection of the charges is recorded continuously until the surface potential tends to be stable; image offset is corrected according to the sequence of surface topography images, and surface potential decay data of each pixel point is extracted from the corrected sequence of surface potential images; trap distribution information of each pixel point is determined by fitting and calculation according to the surface potential decay data of each pixel point; deep and shallow trap energy level peak value images and / or deep and shallow trap density peak value images corresponding to the surface topography of the thin film are formed according to the trap distribution information of all pixel points and the original positions of the pixel points; image offset is corrected according to the sequence of surface topography images, and surface potential decay data of each pixel point is extracted from the corrected sequence of surface potential images, which comprises: the surface topography information of the sequence of surface topography images is taken as a reference, the image sequence is intercepted and the features are corresponded to correct the image offset generated in the continuous scanning process, and the surface potential decay data of each pixel point of the corrected sequence of surface potential images is extracted.

2. The scanning probe microscope based dielectric high spatial resolution trap imaging method according to claim 1, wherein, The fitting method is double exponential fitting, which is based on the existence of deep traps and shallow traps in the sample, and specifically based on the trap energy level and trap density constructed according to the theory of organic semiconductors, which comprises: for the potential decay data of each pixel point, the fitting parameters are adjusted in real time during the fitting process to obtain a smooth surface potential decay curve, the trap energy level and trap density double-peak relationship is established by using the surface potential decay curve after fitting, so as to facilitate subsequent peak separation; according to the surface potential decay curve after fitting, the trap density is expressed by the decay law of the surface potential with time, and the trap energy level is expressed by the decay time; the trap density is: wherein, represents a trap density, represents a vacuum permittivity, represents a relative permittivity, represents a charge amount, represents a thickness, represents a Boltzmann constant, represents an absolute temperature, represents a surface potential value at the time the trap energy level is: wherein represents a trap energy level, represents the Boltzmann constant, represents an escape frequency factor of the trap charge.

3. The scanning probe microscope based dielectric high spatial resolution trap imaging method according to claim 2, wherein, The deep and shallow trap energy level peak value images comprise deep and shallow trap energy level peak value images under each peak, and the deep and shallow trap density images comprise deep and shallow trap density peak value images under each peak; the deep and shallow trap energy level peak value images and / or the deep and shallow trap density peak value images corresponding to the surface topography of the thin film are formed according to the trap distribution information of all pixel points and the original positions of the pixel points, which comprises: for each pixel point, the curve formed by the trap energy level and the trap density of the pixel point is separated into peaks to obtain the deep and shallow trap energy level curves and the deep and shallow trap density curves of the pixel point under each peak; for each peak, according to the deep and shallow trap density peak values and the deep and shallow trap energy levels corresponding to the deep and shallow trap density peak values of all pixel points under the peak, the deep and shallow trap energy level peak value images under the peak and the deep and shallow trap density peak value images under the peak are formed corresponding to the original positions of the pixel points.

4. The scanning probe microscope based dielectric high spatial resolution trap imaging method according to claim 1, wherein, The sample is a dielectric thin film, and the dielectric thin film comprises a pure-phase dielectric thin film and a composite dielectric thin film.

5. The scanning probe microscope based high spatially resolved dielectric trap imaging method according to claim 3, wherein, The imaging method further comprises: According to the surface topography image sequence, the surface potential image sequence, the deep and shallow trap energy level peak value image and the deep and shallow trap density peak value image, the potential decay curve and the trap distribution information of the target position in the sample region to be characterized are determined.

6. The scanning probe microscope based dielectric high spatial resolution trap imaging method according to claim 5, wherein, The target position includes an arbitrary position of the surface topography, and the trap distribution information includes trap energy level distribution information and / or trap density distribution information; the potential decay curve includes a potential decay time curve and / or a trap energy level trap density curve. According to the surface topography image sequence, the surface potential image sequence, the deep and shallow trap energy level peak value image and the deep and shallow trap density peak value image, the potential decay curve and the trap distribution information of the target position in the sample region to be characterized are determined, comprising: According to the surface topography image sequence, a target position is selected; According to the surface potential image sequence and the surface topography image sequence, potential decay data of the target position are obtained; According to the target position and the deep and shallow trap energy level peak value image, trap energy level distribution information of the target position is obtained; According to the target position and the deep and shallow trap density peak value image, trap density distribution information of the target position is obtained; According to the potential decay data, the trap energy level distribution information and the trap density distribution information, a potential decay time curve and a trap energy level trap density curve of the target position are drawn.

7. The scanning probe microscope based high spatially resolved dielectric trap imaging method according to any one of claims 1 to 6, characterized in that The surface topography image sequence and the surface potential image sequence are obtained under a Kelvin probe force microscope, a double electrode system of a nano probe and a ground electrode is constructed by the Kelvin probe force microscope, and charge injection of a sample surface region to be characterized is realized.

8. A scanning probe microscope based high spatial resolution dielectric trap imaging system, characterized in that, Comprising: An image acquisition module realizes charge injection of a sample surface region to be characterized by constructing a double electrode system of a nano probe and a ground electrode through a scanning probe microscope, and records a surface topography image sequence and a surface potential image sequence of the sample after the voltage is removed; during the recording of the surface topography image sequence and the surface potential image sequence of the sample after the voltage is removed, surface potential dynamic monitoring of charge injection and transport is adopted, charge is injected through the nano probe, and uninterrupted recording of the change of the material surface potential with time after the charge injection is performed until the surface potential tends to be stable; A calibration module corrects image offset according to the surface topography image sequence, and extracts surface potential decay data of each pixel point from the corrected surface potential image sequence; A data processing module determines trap distribution information of each pixel point by fitting and calculation according to the surface potential decay data of each pixel point; A trap image module forms deep and shallow trap energy level peak value images and / or deep and shallow trap density peak value images corresponding to the surface topography of the film according to the original positions of each pixel point according to the trap distribution information of all pixel points; The calibration module specifically takes the surface topography information of the surface topography image sequence as a reference, corrects the image offset generated in the uninterrupted scanning process by image sequence interception and feature correspondence, and extracts surface potential decay data of each pixel point of the corrected surface potential image sequence.

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