Perovskite thin film quality management and control system integrating optical detection and data analysis

Abstract

The invention belongs to the technical field of semiconductor material detection, and relates to a perovskite film quality management and control system integrating optical detection and data analysis, which comprises a data acquisition and preprocessing module, a dynamic reference construction module, a defect index calculation module and a defect analysis and regulation module, the method comprises the following steps: acquiring photoluminescence intensity data and reflectivity data of the surface of a perovskite thin film, performing space pixel registration and preprocessing, constructing a two-dimensional feature space, and fitting a dynamic reference light intensity curve by using a dynamic sliding window algorithm; calculating a defect severity index of each pixel point by combining a nonlinear index mapping model based on the dynamic reference light intensity curve and the actually measured photoluminescence intensity; and rendering the defect severity index into a defect distribution thermodynamic diagram, and generating a production equipment regulation and control instruction according to the defect area proportion and the distribution form. According to the method, the interference of the film thickness on the photoluminescence signal is dynamically decoupled by using a data statistics rule, and the accuracy and the production yield of perovskite film defect detection are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of semiconductor material detection, and particularly relates to a perovskite film quality control system integrating optical detection and data analysis. BACKGROUND

[0002] As a new photovoltaic technology, perovskite solar cells have realized high-efficiency verification in laboratory small-area devices due to excellent photoelectric conversion performance, and have great potential to move towards GW-level mass production scale. However, the preparation process of large-area peroviskite films still faces core bottlenecks, which are key factors restricting its large-scale application. Currently, the mainstream industry adopts a slot coating method to prepare large-area peroviskite films. This process has the advantages of fast film formation speed and high production efficiency, and can meet the production capacity demand of mass production scenarios. However, in the actual production process, the film surface is prone to micron-level thickness fluctuations due to the influence of physical factors such as fluid viscosity, air knife drying rate, and substrate surface flatness. This thickness fluctuation is manifested as obvious fluid stripes in the macroscopic view, which not only destroys the appearance consistency of the film, but also directly negatively affects the photoelectric performance of the film.

[0003] In the quality detection link of peroviskite films, photoluminescence detection technology is widely used for crystal quality evaluation due to its core advantage of non-destructive detection. The detection principle is based on capturing the fluorescence intensity signal to judge the internal state of the crystal after laser excitation of the film. Non-radiative recombination centers in the crystal, as the main form of defects, will cause fluorescence quenching and form characteristic dark spot signals. However, the existing photoluminescence detection technology has a serious signal coupling problem: the fluorescence intensity signal is not only affected by the defect density of the crystal, but also has a significant positive correlation with the physical thickness of the film. In the area with thinner film, the total amount of material excited by the laser decreases, and the fluorescence signal naturally weakens. In the area with thicker film, the total amount of excited material increases, and the fluorescence signal correspondingly strengthens. This thickness-related signal change and the fluorescence quenching signal caused by defects are superimposed on each other, making it difficult for the detection system to distinguish the signal strength and restricting the accuracy of the detection result.

[0004] The existing detection algorithm mainly relies on two core logics of fixed threshold or whole image mean normalization. When using the fixed threshold algorithm, the natural weak fluorescence signal of the thin area of the film layer will be misjudged as the dark spot caused by the defect, resulting in a large number of false positive results. In the thick area of the film layer, the fluorescence quenching signal caused by the defect may be covered by the strong background fluorescence, and there is a serious missed detection problem, that is, false negative results. Although the whole image mean normalization algorithm attempts to calibrate the signal by the overall mean, it still cannot solve the signal deviation caused by the local thickness fluctuation, and it is also difficult to realize the accurate feedback of the real quality of the crystal. The distortion of the detection data directly leads to the fact that the production line cannot obtain accurate crystal quality information, cannot adjust the production process parameters in a targeted manner, and thus seriously restricts the production yield of the perovskite film, increases the cost of large-scale production, and hinders the industrialization process of the perovskite solar cell technology. SUMMARY

[0005] In view of this, the present application provides a perovskite film quality control system integrating optical detection and data analysis to solve the technical problem that the existing technology is prone to misjudgment and missed detection due to film thickness fluctuation in photoluminescence detection, and cannot accurately feedback the real crystal quality of the perovskite film.

[0006] To solve the above technical problems, the technical scheme of the perovskite film quality control system integrating optical detection and data analysis provided by the present application is as follows: The perovskite film quality control system integrating optical detection and data analysis comprises the following modules: A data acquisition and preprocessing module is configured to acquire photoluminescence intensity data and reflectivity data on the surface of the perovskite film, and to perform spatial pixel registration and preprocessing on the photoluminescence intensity data and the reflectivity data to obtain a light intensity matrix and a reflectivity matrix. A dynamic reference construction module is configured to construct a two-dimensional feature space with reflectivity values as the horizontal axis and photoluminescence intensity values as the vertical axis, to map the pixel points in the light intensity matrix and the reflectivity matrix to the two-dimensional feature space, and to fit a dynamic reference light intensity curve varying with reflectivity by using a dynamic sliding window algorithm. A defect index calculation module is configured to calculate the current theoretical reference light intensity of each pixel point based on the dynamic reference light intensity curve, and to calculate the defect severity index of each pixel point by using a nonlinear index mapping model in combination with the measured photoluminescence intensity of the pixel point. A defect analysis and control module is configured to render the calculated defect severity index as a defect distribution heat map, to generate corresponding production equipment control instructions to adjust the production process parameters according to the defect area ratio and defect distribution morphology in the defect distribution heat map.

[0007] Further, the construction formula of the dynamic reference light intensity curve is as follows:

[0008] In the formula, is the dynamic reference light intensity when the reflectivity is is the dynamic reference light intensity when the reflectivity is is a smoothing function, is a high quantile statistic function, is the photoluminescence intensity data, is the reflectivity data, is a statistical window radius, is a local sample set, is a compensation coefficient, is a robust local standard deviation.

[0009] By combining the high quantile statistics and the robust local standard deviation, the dynamic reference light intensity curve constructed represents the optimal quality at the thickness and has a certain statistical tolerance. This design cleverly eliminates the high-brightness outliers caused by dust or noise, and at the same time uses the compensation coefficient to ensure that the reference line covers most of the normal points, ensuring the robustness and reliability of the detection standard, and avoiding false judgments caused by statistical fluctuations.

[0010] Further, the high quantile statistic function takes the value of the 95th percentile in the local sample set; and the robust local standard deviation only calculates the standard deviation of the samples in the local sample set whose values are between the median and the maximum value.

[0011] Further, the statistical window radius is set to 1% to 2% of the reflectivity range; and the compensation coefficient has a value range of 2 to 3.

[0012] Further, constructing the dynamic reference light intensity curve further includes: if the number of samples in a certain reflectivity interval is less than a preset threshold, then the is generated by linear interpolation from adjacent effective window values.

[0013] Further, the calculation formula of the defect severity index is:

[0014] In the formula, is the defect severity index at the coordinate is the defect severity index at the coordinate is a sensitivity gain coefficient, is the theoretical reference light intensity of the current pixel point, is the measured photoluminescence intensity of the current pixel point, is a noise floor constant, is a one-way truncation function.

[0015] The formula design uses the measured value of photoluminescence intensity as the denominator, similar to the concept of signal-to-noise ratio, so that when the photoluminescence intensity is quenched to be very low, a small absolute difference can also be converted into a huge defect index. This nonlinear mapping greatly improves the detection sensitivity of serious defects, while introducing a noise floor constant to prevent calculation overflow under low light intensity, ensuring numerical stability of the algorithm under various extreme conditions.

[0016] Further, the noise floor constant is the dark noise mean value collected by the sensor under darkroom environment with the excitation light source turned off; the sensitivity gain coefficient is 5-10.

[0017] Further, the generating corresponding production equipment control instruction comprises: Real-time statistics of the area ratio of defects with defect severity index exceeding the preset warning value; if the defects are point-like and dispersed, it is determined that the crystallization annealing temperature is abnormal, and an instruction for adjusting the annealing furnace temperature curve is output.

[0018] Further, the generating corresponding production equipment control instruction further comprises: if the defects are linear and continuous, it is determined that the slot coating head is blocked or scratched, and a stop alarm signal is output.

[0019] Further, the smoothing function uses a Savitzky-Golay filter to smoothly connect the calculated discrete points to form a continuous and derivable dynamic reference light intensity curve.

[0020] The beneficial effects of the present application are: the present application constructs a multi-dimensional feature space and an adaptive reference model, without needing to know the film thickness distribution model in advance, and dynamically decouples the film thickness interference by using data statistical rules. Compared with the prior art, the present application discards the traditional fixed threshold determination logic, constructs a reference line that dynamically changes with the film thickness by using reflectivity data, and solves the problem of large-area misjudgment caused by fluid stripes in the slot coating process. No matter how the film thickness is, as long as its luminous efficiency reaches the required level at that thickness, it is determined to be qualified. In addition, the nonlinear defect severity index constructed by the present application conforms to the semiconductor physical quenching mechanism, has extremely high sensitivity in the weak light region, and can effectively capture small defects. The noise floor constant and local robust standard deviation introduced in the present application give the system high robustness, enabling it to quickly adapt to different formulations and different processes in production scenarios, and significantly improving the quality control level of the perovskite film production line. BRIEF DESCRIPTION OF DRAWINGS

[0021] Figure 1 is a system block diagram of a perovskite film quality control system integrating optical detection and data analysis of the present application; Figure 2 Fitting diagram of two-dimensional feature space and dynamic reference; Figure 3 Comparison diagram of the present application and prior art in final imaging effect. DETAILED DESCRIPTION

[0022] The technical solutions in the embodiments of the present application will be clearly and completely described in combination with the drawings in the embodiments of the present application.

[0023] The specific embodiments of the perovskite thin film quality control system integrating optical detection and data analysis provided by the present application are as follows: As shown in the figure, the perovskite thin film quality control system integrating optical detection and data analysis includes a data acquisition and preprocessing module, a dynamic reference construction module, a defect index calculation module, and a defect analysis and regulation module. Figure 1 The data acquisition and preprocessing module is used to acquire photoluminescence intensity data and reflectivity data on the surface of the perovskite thin film, and to perform spatial pixel registration and preprocessing on the photoluminescence intensity data and the reflectivity data, to obtain a light intensity matrix and a reflectivity matrix.

[0024] Specifically, an integrated optical detection module is deployed above the conveying belt of the perovskite thin film production line, which includes a line scanning laser excitation unit, a PL fluorescence acquisition unit, and a reflection spectrum acquisition unit. During the uniform motion of the thin film along the conveying belt, the system synchronously acquires optical data in two dimensions in a line scanning manner.

[0025] The fluorescence signal emitted by the thin film under excitation is received by a high-sensitivity photodetector, and after analog-to-digital conversion, a light intensity matrix is formed, denoted as

[0026] The light intensity matrix represents the light-emitting capability of the thin film, which is jointly affected by the film thickness and defects. The reflectivity signal of the thin film surface to a specific waveband is acquired by a reflection spectrum probe, forming a reflectivity matrix, denoted as In one example, the specific waveband is the 550nm green light waveband, which is sensitive to thickness interference effects. Since thin films of different thicknesses will produce different degrees of interference reflection, the reflectivity data is directly used as a proxy variable representing the relative change in film thickness. The high-sensitivity photodetector and the reflection spectrum probe are spatially pixel-registered by an encoder trigger signal, ensuring that the same coordinate

[0027] in the light intensity matrix and the reflectivity matrix corresponds to the same physical microelement on the thin film; at the same time, dead pixels and saturated noise points in the data are removed.

[0028] ​​​The data acquisition and preprocessing module simultaneously acquires PL data (characterizing luminescence capability) and reflectivity data (characterizing film thickness variation), and performs precise spatial registration, providing a multi-dimensional data foundation for subsequent decoupling of film thickness interference. This dual-channel data acquisition method ensures that the optical properties on the same physical element can be accurately mapped, eliminating data errors caused by differences in sensor position or asynchronous acquisition time, and providing reliable raw data support for accurately evaluating film quality.

[0029] The dynamic reference construction module is used to construct a two-dimensional feature space with reflectance values ​​as the horizontal axis and photoluminescence intensity values ​​as the vertical axis. It maps the pixels in the light intensity matrix and the reflectance matrix to the two-dimensional feature space and uses a dynamic sliding window algorithm to fit a dynamic reference light intensity curve that varies with reflectance.

[0030] Specifically, the system iterates through all pixels in the light intensity matrix and the reflectivity matrix, mapping them to a two-dimensional feature space coordinate system. The horizontal axis represents the reflectivity value. The vertical axis represents the photoluminescence intensity value. This forms a data cloud whose shape varies with the thickness of the film.

[0031] The system utilizes a dynamic sliding window algorithm to move along the horizontal axis and fit the upper envelope of the data cloud. This dynamic reference light intensity curve represents the theoretically optimal luminescence intensity under different film thickness conditions, which is also the defect-free reference light intensity determined by the system. It forms the basis for calculating the defect severity index and provides a pass / fail standard that adapts to film thickness to eliminate interference from film thickness fluctuations in the detection process.

[0032] In this embodiment, the dynamic reference light intensity curve The construction formula is as follows:

[0033] In the formula, For reflectance Dynamic reference light intensity at that time. The statistical window radius is set to 1% to 2% of the reflectivity range, and is used to extract a local slice in the two-dimensional feature space. Let be a local sample set, representing all reflectances falling within the range of The set of photoluminescence intensities of pixels within the range. The high quantile statistical function sorts all photoluminescence intensity values ​​in the local sample set from smallest to largest, and then selects the value at the 95th percentile. This value represents the higher luminescence intensity that most normal points can achieve under this specific film thickness, which is close to the theoretical optimal value. The maximum value is not selected in order to eliminate high-brightness anomalies caused by dust or thermal noise. To achieve robust local standard deviation, and to prevent defective pixels from dragging down statistical fluctuations, this robust local standard deviation only calculates the standard deviation of samples whose values ​​in the photoluminescence intensity set are between the median and the maximum value. This is the compensation coefficient, typically ranging from 2 to 3; however, due to unavoidable random noise and statistical fluctuations in the measurement process, if only... If it's an absolute standard, then normal pixels that are slightly above or below that value due to normal statistical fluctuations might be misjudged. Therefore, adding... A robust local standard deviation of 10 times is used as a safety margin. To smooth the function, a Savitzky-Golay filter is used to smoothly connect the calculated discrete points. Furthermore, if the reflectivity interval... If the number of samples is less than a preset threshold, such as 50 points, then... Linear interpolation is used to generate adjacent valid window values.

[0034] To better understand this step, the following calculation example is provided: Assuming the current reflectivity range is 0 to 100%, set the statistical window radius. It is necessary to calculate the reflectivity. Dynamic reference light intensity at time The system selects all reflectivities within a certain range. arrive The pixels between, assuming the set of photoluminescence intensity values ​​of these pixels is The set contains 1000 data points.

[0035] First, calculate the higher quantile values. Assuming the 950th value after sorting is 100, then Then calculate the robust local standard deviation. Assuming the median is 90 and the maximum value after removing extreme outliers is 120, calculate the standard deviation of the sample between the median and the maximum value. Assume the result is... Set the compensation coefficient. Then the initial value for the baseline calculation under this reflectivity is: Finally, after Smoothing process yields the final result. This means that, theoretically, for an area with a reflectivity of 30%, its photoluminescence intensity should reach around 110 to be considered perfect and without defects.

[0036] The dynamic reference modeling module sets a qualified standard for each pixel point according to its own thickness attribute by dynamically fitting the reference line. This method can effectively identify the photoluminescence intensity that is theoretically optimal for the crystal quality under different thicknesses, thereby eliminating the interference caused by film thickness fluctuations in principle, avoiding misjudgment caused by film thickness thinning in the traditional fixed threshold method, and realizing dynamic tracking of the detection standard for film thickness changes.

[0037] The defect index calculation module is configured to calculate a current theoretical reference light intensity of each pixel point based on the dynamic reference light intensity curve, and calculate a defect severity index of each pixel point by using a nonlinear index mapping model in combination with a measured photoluminescence intensity of the pixel point.

[0038] Specifically, after obtaining the reference line that self-adaptively floats with the film thickness, the system calculates the light intensity loss rate of each pixel point. In order to meet the exponential quenching rule of non-radiative recombination centers on carrier lifetime in semiconductor physics, a nonlinear defect severity index is constructed. The calculation formula of the defect severity index is as follows:

[0039] In the formula, I is the defect severity index of the current pixel point , and the value range is between 0 and 1; I is the theoretical reference light intensity of the current pixel point, the reflectivity measured by the pixel point is substituted into the calculation formula of the dynamic reference light intensity curve to obtain the theoretical light intensity that should be had at this thickness; I is the measured photoluminescence intensity of the current pixel point; is a one-way truncation function for processing the case that the measured value is occasionally higher than the reference value due to measurement noise; is a noise floor constant, which is the average dark noise collected in a darkroom environment with the excitation light source turned off; is a sensitivity gain coefficient, and the value is usually between 5 and 10.

[0040] In order to more clearly illustrate the calculation process and effect of the formula, a calculation example is provided as follows: Suppose the system sets the parameters: sensitivity gain , noise floor constant .

[0041] Scenario one: a normal point located in the thick film area is detected, the measured reflectivity corresponds to the reference value , and the measured photoluminescence intensity value is . Substituting these data into the above calculation formula obtains: = 0.18, which is close to 0, and is determined to be qualified.

[0042] Scenario two: a serious defect point is detected in the thin film area. Since the film is thin, the reference value is low. Assuming that at this time Due to the defect, the fluorescence is quenched, and the measured photoluminescence intensity value Substitute these data into the above calculation formula to obtain: The calculation result is close to 1, and is determined to be a serious defect.

[0043] It should be noted that if it is a simple linear difference, the difference (40) of scenario two is much smaller than the reference value (200) of scenario one. However, in the present formula, since the measured photoluminescence intensity value is used, the difference is extremely sensitive in the low light intensity area, so that the defect in the thin film area can be accurately identified.

[0044] By introducing the defect severity index, the exponential quenching rule of non-radiative recombination centers on carrier lifetime in semiconductor materials is highly consistent. In particular, in the weak light area, the sensitivity of the index increases exponentially, so that it can sharply capture the tiny defects with very low contrast in the traditional grayscale map, and accurately evaluate the severity of the defects, greatly reducing the missed detection rate.

[0045] The defect analysis and regulation module is used to render the calculated defect severity index into a defect distribution thermogram, and according to the defect area ratio and defect distribution morphology in the defect distribution thermogram, corresponding production equipment regulation instructions are generated to adjust the production process parameters.

[0046] Specifically, since the originally collected data is processed into a light intensity matrix and a reflectivity matrix, and the calculation of the defect severity index is performed for each pixel point in the two matrices, the calculated is a two-dimensional matrix consistent with the length and width dimensions of the input image, and the defect analysis and regulation module renders the two-dimensional matrix into a pseudo-color thermogram. The low light intensity area caused by the thinning of the film thickness has a measured photoluminescence intensity matching the dynamic reference light intensity at the thickness, and the calculation result is 0, which is displayed as a white background. The real crystal defect area, whether in the thick film area or the thin film area, will show high values and be displayed as red / yellow patches.

[0047] The system real-time statistics the defect area ratio whose defect severity index exceeds a preset warning value, such as 0.5. If the defects are point-like and dispersedly distributed, it is determined that the crystallization annealing temperature is abnormal, and an output signal adjusts the annealing furnace temperature curve. If the defects are linearly and continuously distributed, it is determined that the slit coating head is blocked or scratched, and an output stop alarm signal is output.

[0048] By converting the detection results into intuitive images and specific control instructions, a closed-loop management from detection to control is realized. This intelligent management and control mode can trigger corresponding processing strategies according to different defect characteristics, correct production deviations in time, prevent the generation of batch waste, and thus guarantee the efficiency and stability of the production process.

[0049] The technical solutions and technical effects of the present application are further described below. Figure 2 and Figure 3 The technical solutions and technical effects of the present application are further described below.

[0050] Figure 2 The two-dimensional feature space and dynamic reference fitting diagram constructed by the present application are shown in the figure, where the horizontal axis is reflectivity (representing film thickness), and the larger the value, the thicker the film layer; the vertical axis is photoluminescence (PL) signal intensity, representing the light-emitting ability of the material; each gray point represents a measured pixel point on the thin film. As can be seen from the figure, the data points show a diagonal distribution trend, which is consistent with the physical law that the thicker the film layer, the more the total amount of excited material, and the stronger the PL signal. The horizontal dashed line is the fixed threshold line of the prior art, that is, a fixed photoluminescence intensity value is set as the passing line. In the low film thickness area, a large number of normal data points, although belonging to normal distribution, are incorrectly judged as unqualified due to their absolute position being lower than the fixed threshold line, which directly shows why the traditional method produces a large number of false positives in the thin film area. Figure 2 The diagonal curve in the figure is the dynamic reference light intensity curve constructed by the present application, which is generated by using a dynamic sliding window algorithm to closely follow the upper envelope of the data cloud, and is self-adaptive and floating according to the change of reflectivity. In the low film thickness area, the dynamic reference line automatically lowers the standard; in the high film thickness area, the dynamic reference line automatically increases. Only those points significantly lower than the dynamic reference line, i.e. points far below the cloud, are calculated as high values. This realizes the decoupling of film thickness and defects, and no matter how the film thickness changes, as long as the light-emitting efficiency reaches the required level at that thickness, it is judged as qualified.

[0051] Figure 3 The comparison diagram of the present application and the prior art in the final imaging effect is shown in the figure, where the detection result graph of the prior art is a black and white binary image with a very dirty background; the large black area in the figure is actually a film thickness area caused by fluid stripes. Since these areas are thin, the photoluminescence (PL) signal intensity is weak and lower than the fixed threshold (refer to Figure 2The true defects, the areas marked with a frame in the figure, are submerged in these huge false defects, causing the system to fail to identify the real process problems, or even fail to produce normally. In contrast, the detection result map of the present application is a color pseudo-color heat map, the background is pure, and the color bar value on the right represents the defect severity index. The vertical wavy lines, i.e. the physical thickness fluctuation, can still be seen in the background, but they are all rendered as qualified, indicating that the system confirms through formula calculation that these areas are normal in terms of light intensity relative to their thickness, eliminating misjudgment, and the several square-shaped spots clearly shown in the figure are real defects. As can be seen, the present application eliminates the misjudgment interference caused by film thickness fluctuation, and realizes high signal-to-noise ratio crystal quality imaging.

[0052] Although the present application has been shown and described with respect to several embodiments thereof, it will be apparent to those skilled in the art that many modifications, changes and substitutions can be made without departing from the spirit and scope of the present application.

Claims

1. A perovskite thin film quality management system integrating optical detection and data analysis, characterized in that, The method comprises the following modules: A data acquisition and preprocessing module is configured to acquire photoluminescence intensity data and reflectivity data of a perovskite film surface, and to perform spatial pixel registration and preprocessing on the photoluminescence intensity data and the reflectivity data to obtain a light intensity matrix and a reflectivity matrix; A dynamic reference construction module is configured to construct a two-dimensional feature space with reflectivity values as the horizontal axis and photoluminescence intensity values as the vertical axis, to map pixel points in the light intensity matrix and the reflectivity matrix to the two-dimensional feature space, and to fit a dynamic reference light intensity curve varying with reflectivity by using a dynamic sliding window algorithm; A defect index calculation module is configured to calculate a current theoretical reference light intensity of each pixel point based on the dynamic reference light intensity curve, and to calculate a defect severity index of each pixel point by using a nonlinear index mapping model in combination with a measured photoluminescence intensity of the pixel point; A defect analysis and regulation module is configured to render the calculated defect severity index into a defect distribution heat map, to generate corresponding production equipment regulation instructions to adjust production process parameters according to a defect area ratio and a defect distribution morphology in the defect distribution heat map.

2. The integrated optical detection and data analysis perovskite thin film quality management system of claim 1, wherein, The construction formula of the dynamic reference light intensity curve is: wherein is a dynamic reference light intensity for reflectance is a dynamic reference light intensity for reflectance is a smoothing function, is a high quantile statistical function, is photoluminescence intensity data, is reflectance data, is a statistical window radius, is a local sample set, is a compensation coefficient, is a robust local standard deviation.

3. The integrated optical detection and data analysis perovskite thin film quality management system of claim 2, wherein, the high quantile statistical function taking the value at the 95th percentile of the local sample set; the robust local standard deviation only calculating the standard deviation of samples in the local sample set with values between the median and the maximum.

4. The integrated optical detection and data analysis perovskite thin film quality management system of claim 2, wherein, The statistical window radius Is set to 1%~2% of the reflectivity range; the compensation coefficient The value range is 2~3.

5. The integrated optical detection and data analysis perovskite thin film quality management system of claim 2, wherein, The constructing the dynamic reference light intensity curve further comprises: if the number of samples in a reflectivity interval is less than a preset threshold, the interval corresponding to the reflectivity interval is not included in the dynamic reference light intensity curve. are generated from adjacent valid window values using linear interpolation.

6. The integrated optical detection and data analysis perovskite thin film quality management system of claim 1, wherein, The calculation formula of the defect severity index is: In the formula, is the coordinate is the defect severity index at the coordinate is the sensitivity gain coefficient is the theoretical reference light intensity of the current pixel point is the measured photoluminescence intensity of the current pixel point is the noise floor constant is the one-way truncation function.

7. The integrated optical detection and data analysis perovskite thin film quality management system of claim 6, wherein, The noise floor constant is the dark noise mean value collected by the sensor under darkroom environment with the excitation light source turned off; the sensitivity gain coefficient is 5-10.

8. The integrated optical detection and data analysis perovskite thin film quality management system of claim 1, wherein, The generation of the corresponding production equipment regulation instructions comprises: Real-time statistics of a defect area ratio with a defect severity index exceeding a preset warning value; if the defects present a point-like diffuse distribution, it is determined that the crystallization annealing temperature is abnormal, and an instruction to adjust the annealing furnace temperature curve is output.

9. The integrated optical detection and data analysis perovskite thin film quality management system of claim 8, wherein, The generation of the corresponding production equipment regulation instructions further comprises: If the defects present a linear continuous distribution, it is determined that the slot coating head is blocked or scratched, and a stop alarm signal is output.

10. The integrated optical detection and data analysis perovskite thin film quality management system of claim 2, wherein, The smoothing function uses a Savitzky-Golay filter to smoothly connect the calculated discrete points to form a continuously derivable dynamic reference light intensity curve.

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