A device for detecting a perovskite battery thin film and an evaluation method thereof

Abstract

The application discloses a kind of perovskite battery film detection device and its evaluation method, belong to film detection technical field, it is characterized by: confocal scanning microscopy system is launched to the film surface of perovskite battery to be measured laser and obtains the surface image of perovskite film, while fluorescent microscopy system obtains fluorescent image on substrate face, after surface image and fluorescent image are input image processing analysis system by photodiode simultaneously, superimposed, the photoelectric conversion efficiency and damage index of perovskite thin film battery are obtained by evaluating overlapping image, evaluation method is using image segmentation model to infer overlapping image, get damaged area cutout mask A and defect area cutout mask B, by the complement of the union of mask A and mask B, get normal area cutout mask C, using mask C obtains the segmentation image of perovskite film, by segmentation image, fluorescent image and surface homogenization index solve area fluorescent efficiency, obtain the evaluation of overall fluorescent efficiency.

Description

Technical Field

[0001] This invention belongs to the field of thin film detection technology, and relates to a thin film detection device, particularly to a detection device and evaluation method for perovskite solar cell thin films. Background Technology

[0002] Perovskite solar cells, as a rapidly developing type of photovoltaic cell, have gained attention due to their excellent photoelectric conversion efficiency, simple fabrication process, and tunable wavelength absorption range. As a light-harvesting layer, perovskite materials can absorb photons with energy higher than their band gap and transfer the energy to valence band electrons, causing them to jump to the conduction band and become excited electrons. The excited electrons then move directionally through the electron migration layer, forming a current when the external circuit is turned on, thus realizing the conversion of light energy into electrical energy. Therefore, the photoelectric conversion efficiency of perovskite solar energy materials directly affects the overall energy conversion efficiency of perovskite photovoltaic cells. Quality inspection of perovskite solar cell materials is the key to controlling the yield of perovskite photovoltaic cells.

[0003] Chinese Patent Publication No. CN111077165A, entitled "Online Inspection Device and Method for Perovskite Thin Film Quality Based on Machine Vision," discloses an inspection device for perovskite solar cells, comprising a shadowless light source with a lampshade, an industrial camera, an integrated bracket, a background plate, a driving device, a conveying device, and a control center. The device uses an industrial camera to photograph samples, and an image processing system compares the images captured by the industrial camera with standard images stored in a database at the control center. The quality of the perovskite thin film is determined by analyzing the deviation rate of indicators such as color difference, uniformity, and defect states. However, because this technical solution relies on comparing images taken with a camera, it cannot evaluate the fluorescence emitted by photons released when excited electrons in the conduction band transition back to the valence band in the thin film. Therefore, it lacks a means to simultaneously detect the photovoltaic characteristics of the thin film.

[0004] Chinese patent publication CN114914166A, entitled "Apparatus for Online Detection of Perovskite Solar Cells Using Photoluminescence," employs a semiconductor laser light source as the excitation source to acquire thin-film fluorescence signals. However, because this technical solution does not simultaneously acquire the physical properties of the thin film (e.g., CN111077165A mentioned above), it cannot assess whether changes in fluorescence intensity are due to poor sample performance or physical defects in that area. Therefore, the quality control analysis of the thin-film solar cell is not comprehensive. Furthermore, this technical solution still uses a camera to record fluorescence information, lacking a microscopic magnification system, making it impossible to assess small-scale fluorescence distribution. Summary of the Invention

[0005] The purpose of this invention is to solve the problem of incomplete analysis caused by the inability to simultaneously detect the surface condition and fluorescence emission of perovskite solar cell films in current perovskite solar cell film detection technologies. By setting up a fluorescence microscopy module to detect the excited electron concentration in each region of the perovskite film and a confocal microscopy module to detect whether the perovskite film is uniform or damaged, the photoelectric conversion capability of each region is detected and evaluated in order to improve the defects of perovskite solar cells and obtain higher conversion efficiency.

[0006] To achieve the above objectives, the specific technical solution adopted by the present invention is as follows:

[0007] A detection device and evaluation method for perovskite solar cell thin films are disclosed, characterized in that: the detection device includes a confocal scanning microscopy system, a fluorescence microscopy system, and an image processing and analysis system; the confocal scanning microscopy system emits a laser beam onto the surface of the perovskite solar cell under test to acquire a surface image of the perovskite thin film; simultaneously, the fluorescence microscopy system acquires a fluorescence image of the perovskite thin film on the substrate surface of the perovskite solar cell under test; the surface image and fluorescence image are simultaneously input into the image processing and analysis system via photodiodes for superposition; the superimposed image is then evaluated to obtain the fluorescence image of the perovskite thin film solar cell. Electroconversion efficiency and damage index; the evaluation method is to use an image segmentation model to reason about overlapping images to obtain a damaged area matting mask A and a defect area matting mask B, where the mask is a binary image of 0 and 1. By using the complement of the union of the damaged area matting mask A and the defect area matting mask B, a normal area matting mask C is obtained. Using the normal area matting mask C, a segmented image of the perovskite film is obtained. The regional fluorescence efficiency is solved by the segmented image of the perovskite film, the fluorescence image of the perovskite film, and the surface homogenization index to obtain an estimate of the overall fluorescence efficiency.

[0008] The confocal scanning microscopy system includes a laser, a light source aperture, a mirror, a scanning galvanometer, an objective lens, a bandpass filter I, a detector aperture, and a photodiode I. The laser emits a beam that passes through the circular aperture of the light source aperture and is reflected twice by two mutually perpendicular mirrors before reaching the scanning galvanometer. The scanning galvanometer then splits the reflected light into two laser beams through a beam splitter. One of the laser beams is focused onto the thin film surface of the perovskite solar cell by the objective lens. The reflected light from the thin film surface is then split by the beam splitter and, together with the other laser beam, passes through the bandpass filter I, the detector aperture, and the photodiode I to the computer of the image processing and analysis system.

[0009] The fluorescence microscopy system includes a mirror, a focusing lens, a bandpass filter II, and a photodiode II. The excited fluorescence is reflected by the mirror, passes through the focusing lens, the bandpass filter II, and the photodiode II, and is then connected to the computer of the image processing and analysis system.

[0010] The laser is a single-wavelength laser with a power of 24mW and a wavelength of 600nm. The laser photon energy is higher than the band gap of the perovskite material.

[0011] The beam splitter has a reflectance-to-transmittance ratio (T:R) of 50:50, the wavelength of bandpass filter I is 590~610nm, and the wavelength of bandpass filter II is 750~770nm.

[0012] The light source aperture limits the focused illumination area of ​​the light source on the perovskite thin film, and the scanning galvanometer controls the laser to scan on the XY plane of the perovskite thin film.

[0013] The surface image is the image of the uppermost layer of the thin film. The uncoated substrate is first focused using a confocal microscope, and then the focal plane is adjusted to the upper surface of the film thickness for imaging.

[0014] The method for calculating the homogenization index is as follows:

[0015] Step 1: Calculate the gradient G(x,y) at each pixel of the surface image. Define I(x,y) as the gradient value at each pixel, where x and y are the pixel coordinates.

[0016] ;

[0017] Step 2: Using a sliding window approach, calculate the mean V(x,y) of the squared gradients within an n * n window (where n is an even number), and use this as the gradient energy value at (x,y):

[0018] ;

[0019] Step 3: Simultaneously perform normalization using the following methods and calculate the normalization index:

[0020] ;

[0021] Where min(V) refers to the minimum gradient value, and max(V) refers to the maximum gradient value;

[0022] The method for calculating the regional fluorescence efficiency F(x,y) is as follows:

[0023] ;

[0024] Among them, I f (x,y) is the setting value of the fluorescence image, with a setting value range of 0~255, representing fluorescence brightness from small to large; M(x,y) is the setting value of the segmented image, with a setting value of 0 and 1, where 1 is a complete image and 0 is a defective image.

[0025] The estimated overall fluorescence efficiency F is the average of the regional fluorescence efficiencies F(x,y).

[0026] The image processing and analysis system analyzes the regional fluorescence efficiency F(x,y), and then analyzes the obtained regional fluorescence efficiency F(x,y), overall fluorescence efficiency F, and the mask A of the damaged area using a perovskite quality classification model. The classification model divides the perovskite thin-film battery into three quality levels (Good, Fair, Bad) and feeds the classification results back to the sorting equipment for sorting.

[0027] The positive effects of this invention are as follows: by setting up a confocal scanning microscopy system and a fluorescence microscopy system, surface images and fluorescence images are obtained. By superimposing and analyzing the images through an image processing and analysis system, more accurate quality analysis data is obtained, thereby improving the defects of perovskite solar cells and achieving higher conversion efficiency. Attached Figure Description

[0028] Figure 1 : Schematic diagram of the optical structure of the present invention.

[0029] Figure 2 : Schematic diagram of the evaluation system of the present invention.

[0030] Figure 3 : A superimposed image structure diagram of surface image and fluorescence image in an embodiment of the present invention.

[0031] Figure 4 The superimposed image of the surface image and the fluorescence image of the present invention, and the analysis diagram of the fluorescence intensity of the grains.

[0032] In the diagram, 1. Laser, 2. Light source aperture, 3. Mirror, 4. Scanning galvanometer, 5. Beam splitter, 6. Bandpass filter I, 7. Detector aperture, 8. Photodiode I, 9. Objective lens, 10. Perovskite solar cell, 11. Bandpass filter II, 12. Photodiode II, 13. Computer, 14. Detection device, 15. Fluorescent image, 16. Surface image, 17. Overlapping image, 18. Segmented image, 19. Perovskite quality classification model, 20. Focusing lens. Detailed Implementation

[0033] This embodiment is based on quality testing and evaluation of perovskite thin films (CH3NH3PbI3).

[0034] like Figure 1The diagram shows the optical structure. The perovskite solar cell 10 is constructed by depositing a perovskite thin film on a glass substrate, with the film facing upwards. The glass substrate is placed on a detection stage. A confocal scanning microscopy system is located above the film, and a fluorescence microscopy system is located below the substrate. Both systems are connected to an image processing and analysis system. The laser 1 in the confocal scanning microscopy system is a 24mW single-wavelength laser with a wavelength of 600nm. The emitted laser light passes through the aperture 2 of the light source and is reflected twice by the mirror 3 to the scanning galvanometer 4. The scanning galvanometer 4 controls the scanning of the laser light in the XY plane of the perovskite solar cell 10. The scanning galvanometer 4 reflects the laser light to the detector stage. The laser beam is split into two paths on the 0:50 beam splitter 5. One path is focused onto the thin film by the objective lens 9, and the reflected light from the thin film is returned and together with the other laser beam, it passes through the 600nm bandpass filter I6 and is directed to the photodiode I8. The photodiode I8 is connected to the computer 13 of the image processing and analysis system to form a surface image 16. The fluorescence emitted by the thin film after being excited by the laser is reflected by the mirror 3 of the fluorescence microscopy system and then passes through the focusing lens 20 and the 760nm bandpass filter II11 to the photodiode II12. The photodiode II12 is connected to the computer 13 of the image processing and analysis system to form a fluorescence image 15.

[0035] like Figure 2 The schematic diagram of the evaluation system shown shows that the detection device 14 overlays the detected fluorescence image 15 and surface image into an overlapping image 17 in the computer 13, uses an image segmentation model to infer the overlapping image 17 into a segmented image 18, and then uses a perovskite quality classification model 19 to analyze the measured film layer and classify it into three quality levels: good, average, and poor.

[0036] During operation, the testing steps are as follows:

[0037] (1) Turn on laser 1;

[0038] (2) Turn on the scanning mirror 4 and synchronize it with photodiode I8 and photodiode II12;

[0039] (3) Adjust the distance between the objective lens 9 and the perovskite film so that the reflected light scanning signal collected by the photodiode I8 becomes the surface image 16 of the film. Use the autofocus algorithm to analyze the surface image 16 of the perovskite film. Adjust the distance between the objective lens 9 and the platform through software to obtain a clear surface image 16.

[0040] (4) Adjust the distance of the focusing lens 20 so that the fluorescence scanning signal collected by the photodiode II12 becomes a thin film fluorescence image. Use the autofocus algorithm to analyze the fluorescence image 15 of the perovskite thin film. Adjust the distance of the focusing lens 20 and the gain of the photodiode II12 through software to make the collected fluorescence scanning signal into a clear, high-contrast fluorescence image 15.

[0041] (5) By overlaying the thin film surface image 16 and the fluorescence image 15 on the computer 13 to form an overlay image 17, the local sharpness analysis is used on the perovskite sample thin film surface image 16 to obtain the local uniformity index image of the thin film surface. The image segmentation model is used to segment the overlay image 17 to obtain the defect area matting mask and the damaged area matting mask.

[0042] (6) Combine the normal area matting mask C, the defect area matting mask B, the damaged area matting mask A and the fluorescence image 15 to calculate the overall photoelectric conversion efficiency of the thin film;

[0043] (7) Perovskite quality classification model 18 Based on the photoelectric conversion efficiency and damage index of the samples, the samples are divided into three quality levels.

[0044] By analyzing the fluorescence of defect regions in surface images, the photoelectric conversion efficiency of the defect regions can be determined, such as... Figure 3 As shown, the light white shapes enclose the grains on the film. The color intensity of the grains represents their fluorescence intensity, as shown in the color intensity diagram on the right. The bright white shapes enclose spatial defects, indicating that the perovskite film is damaged at this point. Taking the white and black dashed circles as examples, the white dashed circle shows two connected grains with strong fluorescence intensity, while the black dashed circle shows a spatial defect with weak fluorescence intensity.

[0045] By analyzing the fluorescence at the center and edge of grains in surface images, the photoelectric conversion efficiency of grain edges and centers can be determined. Figure 4 As shown, the left side is Figure 3 The magnified image of the two connected grains within the white dashed circle (overlapping image 17 of thin film surface image 16 and fluorescence image 15) shows that the brightness at the grain center is higher than that at the grain edge, indicating that the fluorescence intensity at the center is higher. The right image shows the fluorescence intensity measured at each pixel position along the dashed line in the left image. It can be seen that the fluorescence intensity has two peaks, corresponding to the two grain centers, while the valley in the middle represents the boundary between the two grains. From this, we can conclude that the photoelectric conversion ability at the grain center is stronger than that at the grain edge.

Claims

1. A detection device for perovskite solar cell thin films, characterized in that: The detection device includes a confocal scanning microscopy system, a fluorescence microscopy system, and an image processing and analysis system; The confocal scanning microscopy system includes a laser, a light source aperture, a mirror, a scanning galvanometer, an objective lens, a bandpass filter I, a detector aperture, and a photodiode I. The laser emits a laser beam that passes through the circular aperture of the light source aperture and is reflected twice by two mutually perpendicular mirrors before reaching the scanning galvanometer. The scanning galvanometer then splits the reflected light into two laser beams through a beam splitter. One of the laser beams is focused onto the thin film surface of the perovskite solar cell by the objective lens. The reflected light from the thin film surface is then split by the beam splitter and the other laser beam passes through the bandpass filter I, the detector aperture, and the photodiode I to the computer of the image processing and analysis system. The fluorescence microscopy system includes a mirror, a focusing lens, a bandpass filter II, and a photodiode II. The excited fluorescence is reflected by the mirror, passes through the focusing lens, the bandpass filter II, and the photodiode II, and is then connected to the computer of the image processing and analysis system. The confocal scanning microscopy system emits a laser onto the thin film surface of the perovskite solar cell under test to obtain a surface image of the perovskite thin film. The surface image is the image of the uppermost layer of the film. The confocal microscope first focuses on the uncoated substrate, and then adjusts the focal plane to the upper surface of the film thickness for imaging. At the same time, the fluorescence microscopy system obtains a fluorescence image of the perovskite thin film on the substrate surface of the perovskite solar cell under test. The surface image and fluorescence image are simultaneously input into the image processing and analysis system via photodiodes for superposition. The superimposed image is then evaluated to obtain the photoelectric conversion efficiency of the perovskite thin film solar cell.

2. The detection device for a perovskite solar cell thin film as described in claim 1, characterized in that: The laser is a single-wavelength laser with a power of 24mW and a wavelength of 600nm. The laser photon energy is higher than the band gap of the perovskite material.

3. The detection device for a perovskite solar cell thin film as described in claim 1, characterized in that: The beam splitter has a reflection-to-transmission ratio of 50:50, the bandpass filter I has a wavelength of 590~610nm, and the bandpass filter II has a wavelength of 750~770nm.

4. The detection device for a perovskite solar cell thin film as described in claim 1, characterized in that: The light source aperture limits the focused illumination area of ​​the light source on the perovskite thin film, and the scanning galvanometer controls the laser to scan on the XY plane of the perovskite thin film.

5. The evaluation method for a perovskite solar cell thin film as described in any one of claims 1 to 4, characterized in that: The evaluation method described above uses an image segmentation model to reason about overlapping images to obtain a broken region matting mask A and a defect region matting mask B, where the mask is a binary image of 0 and 1. The normal region matting mask C is obtained by taking the complement of the union of the broken region matting mask A and the defect region matting mask B. The segmented image of the perovskite film is obtained using the normal region matting mask C. The regional fluorescence efficiency is calculated by using the segmented image of the perovskite film, the fluorescence image of the perovskite film, and the surface homogenization index to obtain an estimate of the overall fluorescence efficiency. The method for calculating the homogenization index is as follows: Step 1: Calculate the gradient G(x,y) at each pixel of the surface image. Define I(x,y) as the gradient value at each pixel, where x and y are the pixel coordinates. ； Step 2: Using a sliding window approach, calculate the mean V(x,y) of the squared gradients within an n*n window (where n is an even number), and use this as the gradient energy value at (x,y). ； Step 3: Simultaneously perform normalization using the following methods and calculate the normalization index: ； Where min(V) refers to the minimum gradient value, and max(V) refers to the maximum gradient value; The method for calculating the regional fluorescence efficiency F(x,y) is as follows: ； Among them, I f (x,y) is the setting value of the fluorescence image, with a setting value range of 0~255, representing fluorescence brightness from small to large; M(x,y) is the setting value of the segmented image, with a setting value of 0 and 1, where 1 is a complete image and 0 is a defective image.

6. The evaluation method for a perovskite solar cell thin film as described in claim 5, characterized in that: The estimated overall fluorescence efficiency is the average of the regional fluorescence efficiencies.

7. The evaluation method for a perovskite solar cell thin film as described in claim 5, characterized in that: The image processing and analysis system analyzes the regional fluorescence efficiency F(x,y), and then analyzes the obtained regional fluorescence efficiency F(x,y), overall fluorescence efficiency, and the mask A for the damaged area using a perovskite quality classification model. The classification model divides the perovskite thin-film battery into three quality levels and feeds the classification results back to the sorting equipment for sorting.

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