Super-resolution defect detection system, defect identification method and device for nanomaterials

Abstract

This application discloses a super-resolution defect detection system, defect identification method, and apparatus for nanomaterials. The system includes: an excitation module for controlling an excitation laser to generate a fluorescence signal of the target; a probe module for filtering low spatial frequency fluorescence signals to obtain a super-resolution fluorescence signal of the target, and acquiring morphological information of the target in contact mode; and a scanning and fluorescence collection module for acquiring super-resolution fluorescence signals at each point in at least one region of the target, obtaining the distribution of super-resolution fluorescence spectral information of the target, so as to perform defect analysis on the target using morphological information and super-resolution fluorescence spectral information. This solves the technical problem in related technologies where the detection resolution is limited by the optical diffraction limit, making it impossible to achieve nanoscale defect detection and identification.

Description

Technical Field

[0001] This application relates to the fields of near-field nano-optics and semiconductor detection technology, and in particular to a super-resolution defect detection system, defect identification method and device for nanomaterials. Background Technology

[0002] Nanomaterials, such as monolayer transition metal sulfides (tungsten sulfide, molybdenum sulfide, tungsten telluride, molybdenum selenide, etc.), are typical low-dimensional nanomaterials. Exhibiting only nanoscale dimensions in the longitudinal direction, they possess excellent optoelectronic properties and exhibit a variety of novel physical phenomena, making them promising cornerstone materials for next-generation optoelectronic devices and integrated circuits, attracting widespread attention from academia and industry. However, due to the immaturity of low-dimensional material preparation methods, various local defects exist in the prepared low-dimensional materials. These defects often lead to spatial inhomogeneities in morphology, crystal structure, and electronic band structure, limiting the industrial application of low-dimensional materials. Therefore, detecting various nanoscale defects in low-dimensional materials plays a crucial role in their further application.

[0003] Optical characterization is a non-destructive, in-situ material characterization method that is often used for defect detection and characterization of various materials. Among them, fluorescence spectroscopy is a common characterization method for detecting materials with direct band gaps. It can directly reflect the band structure of the material and obtain its intrinsic properties, and is widely used in the detection and control of low-dimensional materials.

[0004] However, fluorescence spectroscopy scanning in related technologies is limited by the optical diffraction limit, often only achieving micrometer-level spatial resolution and obtaining only the global properties of materials, failing to effectively characterize local nanoscale defects, and thus needs improvement. Summary of the Invention

[0005] This application provides a super-resolution defect detection system, defect identification method, and device for nanomaterials to solve the technical problem in related technologies where the detection resolution is limited by the optical diffraction limit, making it impossible to achieve nano-area fluorescence detection and defect detection of nanomaterials.

[0006] The first aspect of this application provides a super-resolution defect detection system for nanomaterials, comprising: an excitation module for controlling an excitation laser to focus the excitation laser onto the surface of a nanomaterial detection target, generating a fluorescence signal of the detection target; a probe module for filtering low spatial frequency components in the fluorescence signal to obtain a super-resolution fluorescence signal of the detection target, and obtaining morphological information of the detection target when the probe module is in contact mode; and a scanning and fluorescence collection module for controlling the detection target to move and scan to obtain a super-resolution fluorescence signal of each point in at least one region of the detection target, obtaining super-resolution fluorescence spectral information of the detection target, and using the morphological information and the super-resolution fluorescence spectral information to perform defect analysis on the detection target.

[0007] Optionally, in one embodiment of this application, the excitation module includes: a laser and a polarizer, a half-wave plate, a beam expander, and a first preset aperture objective lens, which are sequentially arranged in the transmission optical path of the laser output laser, to control the excitation laser.

[0008] Optionally, in one embodiment of this application, the probe module includes: a probe, the bottom of which is coated with an aluminum film and has a through hole; and a probe feedback control unit, used to align the probe and the excitation laser, and control the probe to be in the contact mode during the scanning process to obtain the morphological information of the target being probed.

[0009] Optionally, in one embodiment of this application, the scanning and fluorescence collection module includes: a three-dimensional piezoelectric scanning stage for placing the detection target to perform nanometer-precision scanning on the detection target; a second preset aperture objective lens for collecting the super-resolution fluorescence signal of the detection target; a filter for filtering the excitation laser; and a spectrometer for converting the super-resolution fluorescence spectral information of the detection target into digital information.

[0010] A second aspect of this application provides a method for defect identification of nanomaterials, comprising the following steps: acquiring super-resolution fluorescence signals of a detection target and morphological information of the detection target; controlling the detection target to move and scan to obtain super-resolution fluorescence signals of each point in at least one region of the detection target, thereby obtaining super-resolution fluorescence spectral information of the detection target; cross-validating the morphological information and the super-resolution fluorescence spectral information, and matching corresponding defect identification results based on the analysis results.

[0011] Optionally, in one embodiment of this application, the cross-validation analysis of the morphological information and the super-resolution fluorescence spectral information, and the matching of corresponding defect identification results based on the analysis results, includes: if the analysis result shows that the defect in morphology is an integer multiple of the height of a single-layer region and is generally weakened in the super-resolution fluorescence spectrum, then the matched defect identification result is a stacking defect; if the analysis result shows that the defect in morphology is a hemispherical protrusion and is weakened in the super-resolution fluorescence spectrum with enhanced neutral exciton emission peaks and enhanced charged exciton emission peaks, then the matched defect identification result is a bubble defect; if the analysis result shows that the defect in morphology is a long strip-shaped protrusion and is enhanced and redshifted in the super-resolution fluorescence spectrum, then the matched defect identification result is a wrinkled defect.

[0012] Optionally, in one embodiment of this application, after matching the corresponding defect identification result based on the analysis result, the method further includes: matching the corresponding defect effect based on the defect identification result; if the defect identification result is a stacking defect, then the corresponding defect effect is a transition from a direct bandgap to an indirect bandgap at the defect location; if the defect identification result is a bubble defect, then the corresponding defect effect is a charge doping effect at the defect location; if the defect identification result is a wrinkled defect, then the corresponding defect effect is stress-induced band broadening and exciton funnel effect at the defect location.

[0013] A third aspect of this application provides a defect identification device for nanomaterials, comprising: an acquisition module for acquiring super-resolution fluorescence signals of a detection target and morphological information of the detection target; a scanning module for controlling the detection target to move and scan, thereby acquiring super-resolution fluorescence signals of each point in at least one region of the detection target, and obtaining super-resolution fluorescence spectral information of the detection target; and an analysis module for cross-validating the morphological information and the super-resolution fluorescence spectral information, and matching corresponding defect identification results based on the analysis results.

[0014] Optionally, in one embodiment of this application, the analysis module includes: a first matching unit, configured to identify a stacked defect when the analysis result shows that the defect is morphologically a multiple of the height of a single-layer region and is generally weakened in the super-resolution fluorescence spectrum; a second matching unit, configured to identify a bubble defect when the analysis result shows that the defect is morphologically a hemispherical protrusion and is characterized by a weakened neutral exciton emission peak and an enhanced charged exciton emission peak in the super-resolution fluorescence spectrum; and a third matching unit, configured to identify a wrinkled defect when the analysis result shows that the defect is morphologically a long strip-shaped protrusion and is characterized by an enhanced fluorescence peak and a redshift in the super-resolution fluorescence spectrum.

[0015] Optionally, in one embodiment of this application, it further includes: a matching module, used to match the corresponding defect effect based on the defect identification result, wherein when the defect identification result is a stacking defect, the corresponding defect effect is a transition from a direct bandgap to an indirect bandgap at the defect location; when the defect identification result is a bubble defect, the corresponding defect effect is a charge doping effect at the defect location; and when the defect identification result is a wrinkle defect, the corresponding defect effect is stress-induced band broadening and exciton funnel effect at the defect location.

[0016] A fourth aspect of this application provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the defect identification method for nanomaterials as described in the above embodiments.

[0017] A fifth aspect of this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for identifying defects in nanomaterials.

[0018] This application embodiment utilizes an excitation module to control the excitation laser to generate a fluorescence signal of the target. A probe module filters low spatial frequency fluorescence signals to obtain a super-resolution fluorescence signal of the target, avoiding the influence of probe enhancement on the detection results. In contact mode, morphological information of the target is acquired. Finally, a scanning and fluorescence collection module controls the target to move and scan, obtaining super-resolution fluorescence signals for each point in at least one region of the target, thus obtaining super-resolution fluorescence spectral information. This facilitates defect analysis of the target using morphological and super-resolution fluorescence spectral information, leading to more accurate detection results. Therefore, this solves the technical problem in related technologies where the detection resolution is limited by the optical diffraction limit, preventing the realization of nano-area fluorescence detection and defect detection of nanomaterials.

[0019] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0020] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein:

[0021] Figure 1 This is a schematic diagram of the structure of a super-resolution defect detection system for nanomaterials according to an embodiment of this application;

[0022] Figure 2 This is a schematic diagram of a super-resolution defect detection system for nanomaterials according to an embodiment of this application;

[0023] Figure 3 This is a flowchart of a defect identification method for nanomaterials according to an embodiment of this application;

[0024] Figure 4 This is a schematic diagram of morphological scanning results and a schematic diagram of super-resolution fluorescence scanning results according to an embodiment of this application;

[0025] Figure 5 This is a schematic diagram of super-resolution spectral identification of stacking defects according to an embodiment of this application;

[0026] Figure 6 This is a schematic diagram of super-resolution spectral identification of bubble defects according to an embodiment of this application;

[0027] Figure 7 This is a schematic diagram of super-resolution spectral identification of a wrinkle defect according to an embodiment of this application;

[0028] Figure 8 This is a schematic diagram of the structure of a defect identification device for nanomaterials according to an embodiment of this application;

[0029] Figure 9 This is a schematic diagram of the structure of an electronic device provided according to an embodiment of this application. Detailed Implementation

[0030] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0031] The following description, with reference to the accompanying drawings, outlines a super-resolution defect detection system, defect identification method, and apparatus for nanomaterials according to embodiments of this application. Addressing the technical problem mentioned in the background section where detection resolution is limited by the optical diffraction limit, hindering the realization of nano-area fluorescence detection and defect detection in nanomaterials, this application provides a super-resolution defect detection system for nanomaterials. In this system, an excitation module can control an excitation laser to generate a fluorescence signal of the target. A probe module filters low spatial frequency fluorescence signals to obtain the super-resolution fluorescence signal of the target, avoiding the influence of probe enhancement on the detection results. In contact mode, the morphological information of the target is obtained. Finally, a scanning and fluorescence collection module controls the target to move and scan, obtaining the super-resolution fluorescence signal of each point in at least one region of the target, thus acquiring the super-resolution fluorescence spectrum information of the target. This facilitates defect analysis of the target using the morphological and super-resolution fluorescence spectrum information, resulting in more accurate detection results. Therefore, this solves the technical problem in related technologies where detection resolution is limited by the optical diffraction limit, hindering the realization of nano-area fluorescence detection and defect detection in nanomaterials.

[0032] Specifically, Figure 1 This is a schematic diagram of the structure of a super-resolution defect detection system for nanomaterials provided in an embodiment of this application.

[0033] like Figure 1 As shown, the super-resolution defect detection system 10 for this nanomaterial includes: an excitation module 100, a probe module 200, and a scanning and fluorescence collection module 300.

[0034] Specifically, the excitation module 100 is used to regulate the excitation laser to control the excitation laser to focus on the surface of the nanomaterial detection target and generate a fluorescence signal of the detection target.

[0035] In actual implementation, the excitation module 100 of this application embodiment can regulate the emitted high-energy excitation laser and focus it on the upper surface of the detection target, thereby generating a low-energy fluorescence signal. The excitation module 100 can realize incident light with different polarizations according to different detection targets to excite the detection target to generate a fluorescence signal, which serves as the basis for subsequent defect detection, so as to achieve non-destructive fluorescence spectroscopy detection that breaks through the diffraction limit.

[0036] Optionally, in one embodiment of this application, the excitation module 100 includes: a laser, a polarizer, a half-wave plate, a beam expander and collimator, and a first preset aperture objective lens.

[0037] The laser and a polarizer, a half-wave plate, a beam expander, a collimator, and a first preset aperture objective lens are sequentially arranged in the transmission optical path of the laser output to control the excitation laser.

[0038] Specifically, the excitation module 100 may include a laser. A polarizer, a half-wave plate, a beam expander and collimator, and a first preset aperture objective lens may be sequentially arranged on the transmission optical path of the laser output. The first preset aperture objective lens is a low numerical aperture objective lens. Those skilled in the art can select the best low numerical aperture objective lens based on common knowledge in the art, and no specific restrictions are made here.

[0039] The probe module 200 is used to filter low spatial frequency components in the fluorescence signal to obtain the super-resolution fluorescence signal of the target, and to obtain the morphological information of the target when the probe module 200 is in contact mode.

[0040] As one possible implementation, the probe module 200 of this application embodiment can generate an evanescent field spot exceeding the diffraction limit in the test area of ​​the target surface, converting the far-field fluorescence signal into a near-field fluorescence signal. This is used to filter out the low spatial frequency components in the fluorescence signal generated by the excitation module 100, thereby obtaining a super-resolution fluorescence signal of the test area on the target surface. This allows the present application embodiment to detect defects that cannot be identified by conventional resolution fluorescence spectroscopy. Through the probe module 200, the present application embodiment can achieve a resolution of less than 100 nm, realizing super-resolution fluorescence detection of materials. This effectively solves the problem in related technologies where fluorescence detection is limited by the diffraction limit, providing only micrometer-scale resolution and unable to detect nanoscale defects.

[0041] Meanwhile, the probe module 200 in this embodiment can also allow the user to obtain the morphological information of the target being probed. Specifically, the probe module 200 can obtain the morphological information of the target being probed by using an atomic force microscope when it is in contact mode.

[0042] Optionally, in one embodiment of this application, the probe module 200 includes a probe and a probe feedback control unit.

[0043] The probe has an aluminum film plated on its bottom and has through holes.

[0044] The probe feedback control unit is used to align the probe and excite the laser, and to control the probe to be in contact mode during scanning in order to obtain the topographic information of the target.

[0045] Furthermore, the probe module 200 may include a probe and a probe feedback control unit. The probe is a pyramid-shaped hollow probe, and the main material can be silicon, which does not depend on precious metals and avoids fluorescence quenching. The bottom of the probe is coated with an aluminum film and has a through hole. The through hole can be a through hole with a diameter of 70-150 nanometers to convert the incident focused laser output by the excitation module 100 into a super-resolution spot with a scale comparable to the through hole.

[0046] The probe feedback control unit enables the probe to be aligned with the excitation light, facilitating the passage of light, and controls the probe to operate in contact mode during subsequent scanning to obtain the morphological information of the target.

[0047] The scanning and fluorescence collection module 300 is used to control the movement and scanning of the detection target to obtain the super-resolution fluorescence signal of each point in at least one region of the detection target, thereby obtaining the super-resolution fluorescence spectrum information of the detection target, and using the morphological information and super-resolution fluorescence spectrum information to perform defect analysis on the detection target.

[0048] In some embodiments, the scanning and fluorescence collection module 300 can control the detection target to move and scan with nanometer precision, so that based on the movement of the detection target, the probe module 200 can obtain the super-resolution fluorescence signal of each point in different regions of the detection target, thereby collecting the super-resolution fluorescence signal of each point in at least one region of the detection target, and obtaining the super-resolution fluorescence spectrum information of the detection target, which facilitates the use of morphological information and super-resolution fluorescence spectrum information to perform defect analysis on the detection target.

[0049] Optionally, in one embodiment of this application, the scanning and fluorescence collection module 300 includes: a three-dimensional piezoelectric scanning stage, a second preset aperture objective lens, a filter, and a spectrometer.

[0050] Among them, the three-dimensional piezoelectric scanning stage is used to place the detection target in order to perform nanometer-precision scanning on the detection target.

[0051] The second preset aperture objective lens is used to collect super-resolution fluorescence signals from the target being detected.

[0052] A filter used to filter the laser excitation.

[0053] A spectrometer is used to convert the super-resolution fluorescence spectral information of a target into digital information.

[0054] In actual implementation, the scanning and fluorescence collection module 300 may include a three-dimensional piezoelectric scanning stage, a second preset aperture objective lens, a filter, and a spectrometer.

[0055] Among them, the three-dimensional piezoelectric scanning stage can be used to place the test target, which facilitates nanometer-precision scanning of the target.

[0056] The second preset aperture objective lens can be used to collect super-resolution fluorescence signals of the target being detected. The second preset aperture objective lens can be a long working distance high data aperture objective lens to ensure that the objective lens will not collide with the sample stage. The specific aperture objective lens can be selected by those skilled in the art based on common knowledge in the field, and no specific restrictions are made here.

[0057] Filters can be used to filter excitation lasers. The specific selection and setting of filters can be made according to the wavelength of the actual excitation laser, so as to avoid interference from the excitation laser affecting the acquisition of super-resolution fluorescence signals.

[0058] A spectrometer can be used to convert the super-resolution fluorescence spectral information of a target into digital information to obtain the super-resolution fluorescence spectrum of the target being tested.

[0059] Combination Figure 2 As shown, the working principle of the super-resolution defect detection system for nanomaterials of this application is explained in detail with reference to one embodiment.

[0060] like Figure 2 As shown, the system 10 in this embodiment may include: an excitation module 100, a laser 110, a polarizer 120, a half-wave plate 130, a beam expander and collimator 140, a low numerical aperture objective lens 150, a probe module 200, a probe 210, a probe feedback control unit 220, a scanning and fluorescence collection module 300, a three-dimensional piezoelectric scanning stage 310, a long working distance high data aperture objective lens 320, a filter 330, and a spectrometer 340.

[0061] The excitation module 100 is used to regulate and focus the emitted high-energy excitation laser onto the upper surface of the target, thereby generating a low-energy fluorescence signal. The excitation module 100 includes a laser 110, a polarizer 120, a half-wave plate 130, a beam expander and collimator 140, and a low numerical aperture objective lens 150. The polarizer 120, half-wave plate 130, beam expander and collimator 140, and low numerical aperture objective lens 150 are sequentially arranged in the transmission optical path of the laser output from the laser 110.

[0062] The probe module 200 is used to generate an evanescent field spot exceeding the diffraction limit in the desired area of ​​the target surface, thereby generating a super-resolution fluorescence signal of the target. The probe module 200 includes a probe 210 and a probe feedback control unit 220. The probe 210 is a hollow pyramid probe. The main material of the probe 210 is silicon, and the bottom is coated with an aluminum film with a through hole with a diameter of 70-150 nanometers. This through hole can convert the incident focused laser output from the excitation module 100 into a super-resolution spot with a size comparable to the through hole. The probe feedback control unit 220 can align the probe 210 with the incident focused laser output from the excitation module 100 and control the probe 210 to operate in contact mode during scanning, while obtaining the morphological information of the target.

[0063] The scanning and fluorescence collection module 300 is used to move and scan the target with nanometer precision, while simultaneously collecting the super-resolution fluorescence spectral information of the target. The scanning and fluorescence collection module 300 includes a three-dimensional piezoelectric scanning stage 310, a long working distance high data aperture objective lens 320, a filter 330, and a spectrometer 340. The three-dimensional piezoelectric scanning stage 310 is used to place the target, enabling nanometer-precision scanning; the long working distance high data aperture objective lens 320 is used to collect the super-resolution fluorescence signal of the target, and the long working distance ensures that the objective lens will not collide with the sample stage; the filter 330 is used to filter out the excitation laser; and the spectrometer 340 is used to convert the super-resolution fluorescence spectral information of the target into digital information, thereby obtaining the measured super-resolution fluorescence spectrum.

[0064] In actual implementation, the embodiments of this application can excite the sample to generate fluorescence signals by using incident light with different polarizations through the excitation module 100, depending on the different detection targets. Then, the probe module 200 filters out the super-resolution fluorescence signals, and the scanning and fluorescence collection module 300 obtains the super-resolution fluorescence spectrum information of each point in the measured area, thereby realizing the scanning of the detection target and the detection of defects in the detection target.

[0065] The super-resolution defect detection system for nanomaterials proposed in this application utilizes an excitation module to control an excitation laser to generate a fluorescence signal of the target. A probe module filters low spatial frequency fluorescence signals to obtain the super-resolution fluorescence signal of the target, avoiding the influence of probe enhancement on the detection results. In contact mode, morphological information of the target is acquired. Finally, a scanning and fluorescence collection module controls the target to move and scan, obtaining the super-resolution fluorescence signal of each point in at least one region of the target, thus obtaining the super-resolution fluorescence spectrum information of the target. This facilitates defect analysis of the target using the morphological and super-resolution fluorescence spectral information, leading to more accurate detection results. Therefore, this solves the technical problem in related technologies where the detection resolution is limited by the optical diffraction limit, preventing the realization of nano-area fluorescence detection and defect detection of nanomaterials.

[0066] Figure 3 This is a schematic flowchart illustrating a method for identifying defects in nanomaterials provided in an embodiment of this application.

[0067] like Figure 3 As shown, the defect identification method for this nanomaterial includes the following steps:

[0068] In step S301, the super-resolution fluorescence signal of the target and the morphological information of the target are acquired.

[0069] In practical implementation, the defect identification method for nanomaterials in this application embodiment can be based on, for example... Figure 1 or Figure 2 The super-resolution defect detection system for nanomaterials shown in this application can obtain the fluorescence signal of the target based on the excitation module in the super-resolution defect detection system for nanomaterials, and obtain the morphological information of the target by the super-resolution fluorescence signal of the target through the probe module.

[0070] In step S302, the detection target is controlled to move and scan in order to obtain the super-resolution fluorescence signal of each point in at least one region of the detection target, thereby obtaining the super-resolution fluorescence spectral information of the detection target.

[0071] As one possible approach, embodiments of this application can be based on the scanning and fluorescence collection module in a super-resolution defect detection system for nanomaterials, controlling the detection target to move and scan with nanometer precision, while simultaneously collecting the super-resolution fluorescence spectral information of the detection target.

[0072] In step S303, cross-validation analysis is performed on morphological information and super-resolution fluorescence spectral information, and the corresponding defect identification results are matched based on the analysis results.

[0073] Furthermore, embodiments of this application can cross-validate the morphological information and super-resolution fluorescence spectral information of the target to obtain the defect analysis results of the target, thereby matching the corresponding defect identification results and realizing the defect identification of the target.

[0074] Optionally, in one embodiment of this application, cross-validation analysis is performed on morphological information and super-resolution fluorescence spectral information, and corresponding defect identification results are matched based on the analysis results. This includes: if the analysis results show that the defect in morphology is an integer multiple of the height of a single-layer region and is weakened overall in the super-resolution fluorescence spectrum, then the matched defect identification result is a stacked defect; if the analysis results show that the defect in morphology is a hemispherical protrusion and is weakened in the neutral exciton emission peak and enhanced in the charged exciton emission peak in the super-resolution fluorescence spectrum, then the matched defect identification result is a bubble defect; if the analysis results show that the defect in morphology is a long strip protrusion and is enhanced and redshifted in the super-resolution fluorescence spectrum, then the matched defect identification result is a wrinkled defect.

[0075] In some embodiments, the present application can analyze morphological information and super-resolution fluorescence spectral information separately, and combine the analysis results to match the corresponding defects.

[0076] If the morphological information shows that the height is an integer multiple of the single-layer region, and the super-resolution fluorescence spectral information shows that the overall intensity is reduced, then the matched defect identification result is a stacked defect.

[0077] If the morphological information shows a hemispherical protrusion, and the super-resolution fluorescence spectral information shows a decrease in the neutral exciton emission peak and an increase in the charged exciton emission peak, then the matched defect identification result is a bubble defect.

[0078] If the morphological information shows elongated protrusions, and the super-resolution fluorescence spectral information shows enhanced fluorescence peaks and redshifts, then the matched defect identification result is a wrinkled defect.

[0079] Optionally, in one embodiment of this application, after matching the corresponding defect identification result based on the analysis result, the method further includes: matching the corresponding defect effect based on the defect identification result; if the defect identification result is a stacking defect, the corresponding defect effect is a transition from a direct bandgap to an indirect bandgap at the defect location; if the defect identification result is a bubble defect, the corresponding defect effect is a charge doping effect at the defect location; if the defect identification result is a wrinkled defect, the corresponding defect effect is stress-induced band broadening and exciton funnel effect at the defect location.

[0080] Furthermore, embodiments of this application can also match the corresponding defect generation effect based on the defect identification result.

[0081] If the defect identification result is a stacking defect, the corresponding defect effect is a transition from a direct bandgap to an indirect bandgap at the defect location;

[0082] If the defect identification result is a bubble defect, the corresponding defect effect is that a charge doping effect occurs at the defect location;

[0083] If the defect identification result is a wrinkle defect, the corresponding defect effect is stress-induced band broadening and exciton funnel effect at the defect location.

[0084] Combination Figures 4 to 7 As shown, the working principle of the defect identification method for nanomaterials in this application is explained in detail with reference to one embodiment.

[0085] Taking a single-layer tungsten sulfide sample as an example, it has longitudinal nanoscale characteristics, fluorescence emission characteristics, and nanoscale defect characteristics. Nanoscale defects are widely present in its growth, preparation, and processing, exhibiting nanoscale spatial anisotropy in morphology and electronic structure, including but not limited to: stacking, bubbles, wrinkles, which cannot be identified by conventional resolution fluorescence spectra and require super-resolution methods for identification.

[0086] Utilize Figure 2 The super-resolution defect detection system for nanomaterials shown in this application embodiment can achieve the following: Figure 4 The image shows the morphological information and super-resolution fluorescence spectrum information of the target being detected.

[0087] Furthermore, through cross-validation analysis, the embodiments of this application can identify four regions of the detection target and divide them into a flat region 410 and three types of defects 420-440 according to the state of the four regions.

[0088] like Figure 5 As shown, defect 420 is characterized by a height that is an integer multiple of that of flat region 410. In contrast, defect 420 is characterized by an overall weakening in super-resolution fluorescence spectrum compared to flat region 410, indicating that it is a stacked defect, which undergoes a transition from direct bandgap to indirect bandgap.

[0089] like Figure 6 As shown, defect 430 appears as a hemispherical protrusion in morphology, and in super-resolution fluorescence spectroscopy, it appears as a weakening of the neutral exciton emission peak and an enhancement of the charged exciton emission peak, which indicates that it is a bubble defect and that it has a charge doping effect.

[0090] like Figure 7 As shown, defect 440 is characterized by elongated protrusions in morphology and by enhanced fluorescence peaks and redshift in super-resolution fluorescence spectroscopy, indicating a wrinkled defect that exhibits stress-induced band broadening and exciton funnel effect.

[0091] The defect identification method for nanomaterials proposed in this application utilizes an excitation module to control an excitation laser, generating a fluorescence signal of the target. A probe module filters low spatial frequency fluorescence signals to obtain a super-resolution fluorescence signal of the target, avoiding the influence of probe enhancement on the detection results. In contact mode, morphological information of the target is acquired. Finally, a scanning and fluorescence collection module controls the target to move and scan, obtaining super-resolution fluorescence signals for each point in at least one region of the target, thus acquiring super-resolution fluorescence spectral information. This facilitates defect analysis of the target using morphological and super-resolution fluorescence spectral information, leading to more accurate detection results. This solves the technical problem in related technologies where detection resolution is limited by the optical diffraction limit, preventing the realization of nano-area fluorescence detection and defect detection in nanomaterials.

[0092] Next, with reference to the accompanying drawings, a defect identification device for nanomaterials proposed according to an embodiment of this application is described.

[0093] Figure 8 This is a block diagram of a defect identification device for nanomaterials according to an embodiment of this application.

[0094] like Figure 8 As shown, the defect identification device 80 for the nanomaterial includes: an acquisition module 801, a scanning module 802, and an analysis module 803.

[0095] Specifically, the acquisition module 801 is used to acquire the super-resolution fluorescence signal of the target and the morphological information of the target.

[0096] The scanning module 802 is used to control the movement and scanning of the detection target to obtain the super-resolution fluorescence signal of each point in at least one region of the detection target, thereby obtaining the super-resolution fluorescence spectral information of the detection target.

[0097] Analysis module 803 is used to cross-validate morphological information and super-resolution fluorescence spectral information, and to match the corresponding defect identification results based on the analysis results.

[0098] Optionally, in one embodiment of this application, the analysis module 803 includes: a first matching unit, a second matching unit, and a third matching unit.

[0099] The first matching unit is used to identify stacked defects when the analysis results show that the defects are morphologically represented as having a height that is an integer multiple of the height of a single-layer region and are generally weakened in the super-resolution fluorescence spectrum.

[0100] The second matching unit is used to identify bubble defects when the analysis results show that the defects are hemispherical protrusions in morphology and weakened neutral exciton emission peaks and enhanced charged exciton emission peaks in super-resolution fluorescence spectroscopy.

[0101] The third matching unit is used to identify wrinkled defects when the analysis results show that the defects are elongated protrusions in morphology and enhanced fluorescence peaks and redshifts in super-resolution fluorescence spectra.

[0102] Optionally, in one embodiment of this application, the defect identification device 80 for nanomaterials further includes a matching module.

[0103] The matching module is used to match the corresponding defect effect based on the defect identification result. When the defect identification result is a stacking defect, the corresponding defect effect is a transition from a direct bandgap to an indirect bandgap at the defect location. When the defect identification result is a bubble defect, the corresponding defect effect is a charge doping effect at the defect location. When the defect identification result is a wrinkle defect, the corresponding defect effect is stress-induced band broadening and exciton funnel effect at the defect location.

[0104] It should be noted that the foregoing explanation of the defect identification method for nanomaterials also applies to the defect identification device for nanomaterials in this embodiment, and will not be repeated here.

[0105] The defect identification device for nanomaterials proposed in this application can utilize an excitation module to control an excitation laser to generate a fluorescence signal of the target. A probe module filters low spatial frequency fluorescence signals to obtain a super-resolution fluorescence signal of the target, avoiding the influence of probe enhancement on the detection results. In contact mode, morphological information of the target is obtained. Finally, a scanning and fluorescence collection module controls the target to move and scan, obtaining super-resolution fluorescence signals for each point in at least one region of the target, thus acquiring super-resolution fluorescence spectral information. This facilitates defect analysis of the target using morphological and super-resolution fluorescence spectral information, leading to more accurate detection results. Therefore, this solves the technical problem in related technologies where the detection resolution is limited by the optical diffraction limit, preventing the realization of nano-area fluorescence detection and defect detection of nanomaterials.

[0106] Figure 9 A schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device may include:

[0107] The memory 901, the processor 902, and the computer program stored on the memory 901 and capable of running on the processor 902.

[0108] When processor 902 executes the program, it implements the defect identification method for nanomaterials provided in the above embodiments.

[0109] Furthermore, electronic devices also include:

[0110] Communication interface 903 is used for communication between memory 901 and processor 902.

[0111] The memory 901 is used to store computer programs that can run on the processor 902.

[0112] The memory 901 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0113] If the memory 901, processor 902, and communication interface 903 are implemented independently, then the communication interface 903, memory 901, and processor 902 can be interconnected via a bus to complete communication between them. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. The bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 9 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0114] Optionally, in a specific implementation, if the memory 901, processor 902, and communication interface 903 are integrated on a single chip, then the memory 901, processor 902, and communication interface 903 can communicate with each other through an internal interface.

[0115] The processor 902 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application.

[0116] This embodiment also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for identifying defects in nanomaterials.

[0117] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0118] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0119] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or N executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0120] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0121] It should be understood that the various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0122] Those skilled in the art will understand that all or part of the steps of the methods described in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it includes one or a combination of the steps of the method embodiments.

[0123] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0124] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of this application.

Claims

1. A super-resolution defect detection system for nanomaterials, characterized in that, include: An excitation module is used to regulate the excitation laser to control the excitation laser to be focused on the surface of the nanomaterial detection target, thereby generating a fluorescence signal of the detection target; The probe module is used to filter the low spatial frequency components in the fluorescence signal to obtain the super-resolution fluorescence signal of the target, and to obtain the morphological information of the target when the probe module is in contact mode. The scanning and fluorescence collection module is used to control the movement and scanning of the detection target to obtain the super-resolution fluorescence signal of each point in at least one region of the detection target, and to obtain the super-resolution fluorescence spectral information of the detection target, so as to perform defect analysis on the detection target using the morphology information and the super-resolution fluorescence spectral information. The excitation module includes a laser and a polarizer, a half-wave plate, a beam expander, and a first preset aperture objective lens, which are sequentially arranged in the transmission optical path of the laser output laser, to control the excitation laser. The probe module includes: a probe, the bottom of which is coated with an aluminum film and has a through hole; and a probe feedback control unit, used to align the probe and the excitation laser, and control the probe to be in the contact mode during the scanning process to obtain the morphological information of the target being detected. The scanning and fluorescence collection module includes: a three-dimensional piezoelectric scanning stage for placing the detection target to perform nanometer-precision scanning of the detection target; a second preset aperture objective lens for collecting the super-resolution fluorescence signal of the detection target; a filter for filtering the excitation laser; and a spectrometer for converting the super-resolution fluorescence spectral information of the detection target into digital information.

2. A method for defect identification in nanomaterials, characterized in that, The super-resolution defect detection system for nanomaterials as described in claim 1, wherein the method includes the following steps: Acquire super-resolution fluorescence signals of the target and morphological information of the target; The detection target is controlled to move and scan in order to obtain the super-resolution fluorescence signal of each point in at least one region of the detection target, thereby obtaining the super-resolution fluorescence spectral information of the detection target; Cross-validation analysis was performed on the morphological information and the super-resolution fluorescence spectral information, and the corresponding defect identification results were matched based on the analysis results; The cross-validation analysis of the morphological information and the super-resolution fluorescence spectral information, and the matching of corresponding defect identification results based on the analysis results, includes: if the analysis result shows that the defect in morphology is an integer multiple of the height of a single-layer region and shows an overall weakening in the super-resolution fluorescence spectrum, then the matched defect identification result is a stacking defect; if the analysis result shows that the defect in morphology is a hemispherical protrusion and shows a weakening of the neutral exciton emission peak and an enhancement of the charged exciton emission peak in the super-resolution fluorescence spectrum, then the matched defect identification result is a bubble defect; if the analysis result shows that the defect in morphology is a long strip-shaped protrusion and shows an enhancement and redshift of the fluorescence peak in the super-resolution fluorescence spectrum, then the matched defect identification result is a wrinkled defect. The process includes, after matching the corresponding defect identification results based on the analysis results, matching the corresponding defect effects based on the defect identification results; if the defect identification result is a stacking defect, the corresponding defect effect is a transition from a direct bandgap to an indirect bandgap at the defect location; if the defect identification result is a bubble defect, the corresponding defect effect is a charge doping effect at the defect location; if the defect identification result is a wrinkled defect, the corresponding defect effect is stress-induced band broadening and exciton funnel effect at the defect location.

3. An electronic device, characterized in that, include: A memory, a processor, and a computer program stored in the memory and executable on the processor, the processor executing the program to implement the defect identification method for nanomaterials as described in claim 2.

4. A computer-readable storage medium having a computer program stored thereon, characterized in that, The program is executed by the processor to implement the defect identification method for nanomaterials as described in claim 2.

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