Aging analysis method and device for silicon oxide film

Abstract

The invention provides an aging analysis method and device for a silicon oxide film. The method comprises the following steps: configuring an aging factor set of the silicon oxide film; according to a preset environment type, performing frequency analysis on the aging factor set to obtain an aging factor single-cycle fluctuation curve; according to the aging factor single-period fluctuation curve, carrying out an accelerated aging experiment on the silicon oxide film sample to obtain a surface contact angle fluctuation curve, a light refractive index fluctuation curve and a light transmittance fluctuation curve; based on the expected period number, curve trend prediction is conducted on the surface contact angle fluctuation curve, the light refractive index fluctuation curve and the light transmittance fluctuation curve, and the maximum value of the surface contact angle, the maximum value of the light refractive index and the minimum value of the light transmittance are obtained; and marking the silicon oxide film sample based on the maximum value of the surface contact angle, the maximum value of the light refractive index and the minimum value of the light transmittance. According to the invention, the technical problems of low automation degree and low test efficiency in the existing silicon oxide film test are solved, and the test efficiency can be improved.

Description

Technical Field

[0001] The present application relates to the field of thin film testing, and in particular to an aging analysis method and device for silicon oxide thin films. Background Art

[0002] Silicon oxide thin films are widely used in solar photovoltaic power generation due to their excellent performance. To ensure the performance of solar photovoltaics, reliable testing and evaluation of silicon oxide thin films are required. Existing testing of silicon oxide thin films for aging resistance typically involves accelerated aging experiments and sample performance testing. This involves setting fixed aging conditions, manually recording experimental data, and judging the eligibility of the results. This testing method requires extensive manual calculations and judgments, resulting in low efficiency and a lack of automated auxiliary analysis solutions. Summary of the Invention

[0003] The present application aims to solve one of the technical problems in the related art at least to a certain extent.

[0004] Therefore, the first purpose of this application is to propose an aging analysis method for silicon oxide film to solve the problems of low automation and low test efficiency in existing silicon oxide film testing methods.

[0005] The second objective of this application is to provide an aging analysis device for silicon oxide thin films.

[0006] The third objective of this application is to provide an electronic device.

[0007] To achieve the above objectives, the first embodiment of the present application provides a method for analyzing aging of a silicon oxide film, comprising:

[0008] Configure the aging factor set for silicon oxide film;

[0009] Performing a frequency analysis on the aging factor set according to a preset environment type to obtain a single-cycle fluctuation curve of the aging factor;

[0010] According to the single-cycle fluctuation curve of the aging factor, an accelerated aging experiment is performed on the silicon oxide film sample to obtain a surface contact angle fluctuation curve, a light refractive index fluctuation curve, and a light transmittance fluctuation curve;

[0011] Based on the expected number of cycles, predicting the curve trends of the surface contact angle fluctuation curve, the light refractive index fluctuation curve, and the light transmittance fluctuation curve to obtain the maximum value of the surface contact angle, the maximum value of the light refractive index, and the minimum value of the light transmittance;

[0012] The silicon oxide thin film sample is labeled based on the maximum surface contact angle, the maximum light refractive index and the minimum light transmittance.

[0013] Optionally, configuring a target aging factor set for the silicon oxide film includes:

[0014] Based on literature search and expert experience, multiple environmental factors related to the aging of silicon oxide films were identified;

[0015] According to a preset quantity ratio and value range, a plurality of the environmental factors are randomly combined to generate a plurality of initial aging factor sets;

[0016] The plurality of initial aging factor sets are screened by using evaluation indicators to obtain the aging factor set with the best influence.

[0017] Optionally, performing frequency analysis on the aging factor set according to a preset environment type to obtain a single-cycle fluctuation curve of the aging factor includes:

[0018] According to the preset environment type, collecting a single-cycle initial fluctuation curve set of each aging factor in the aging factor set;

[0019] The single-period initial fluctuation curve set of each aging factor is traversed and frequent fitting is performed to obtain the single-period fluctuation curve of the aging factor.

[0020] Optionally, traversing the single-period initial fluctuation curve set of each aging factor and performing frequent fitting to obtain the single-period fluctuation curve of the aging factor includes:

[0021] Traverse the single-period initial fluctuation curve set of each aging factor and perform frequent cluster fitting to generate the single-period concentrated fluctuation curve of the aging factor;

[0022] The single-cycle concentrated fluctuation curve of the aging factor is traversed and neighborhood hierarchical clustering fitting is performed to obtain the single-cycle fluctuation curve of the aging factor.

[0023] Optionally, traversing the single-period initial fluctuation curve set of each aging factor and performing frequent cluster fitting to generate the single-period concentrated fluctuation curve of the aging factor includes:

[0024] Randomly extracting a first aging factor single-cycle initial fluctuation curve and a second aging factor single-cycle initial fluctuation curve from the single-cycle initial fluctuation curve set;

[0025] In a two-dimensional coordinate system, connecting the first position and the tail position of the first aging factor single-cycle initial fluctuation curve and the second aging factor single-cycle initial fluctuation curve respectively to obtain a characteristic image area;

[0026] When the area of ​​the characteristic image is less than or equal to an area threshold, the first aging factor single-cycle initial fluctuation curve and the second aging factor single-cycle initial fluctuation curve are set as the same type of curves; otherwise, they are set as different types of curves;

[0027] Repeatedly extracting and parsing from the single-cycle initial fluctuation curve set to obtain multiple clusters of single-cycle initial fluctuation curves of aging factors, wherein the multiple clusters of single-cycle initial fluctuation curves of aging factors have a number of curves within a cluster;

[0028] Based on the number of curves within the cluster, selecting the concentrated cluster aging factor single-cycle initial fluctuation curve with the maximum number of curves within the cluster;

[0029] The single-cycle initial fluctuation curve of the concentrated cluster aging factor is fitted with the mean value at the same time to generate the single-cycle concentrated fluctuation curve of the aging factor.

[0030] Optionally, the step of performing curve trend prediction on the surface contact angle fluctuation curve, the light refractive index fluctuation curve, and the light transmittance fluctuation curve based on the expected number of cycles to obtain the maximum value of the surface contact angle, the maximum value of the light refractive index, and the minimum value of the light transmittance includes:

[0031] Traversing the surface contact angle fluctuation curve, the light refractive index fluctuation curve, and the light transmittance fluctuation curve to obtain a single-cycle attenuation step length of the surface contact angle, a single-cycle attenuation step length of the light refractive index, and a single-cycle attenuation step length of the light transmittance;

[0032] In response to the user interaction, the user terminal configures the attenuation gain coefficient;

[0033] Determine a surface contact angle prediction attenuation step length, a light refractive index prediction attenuation step length, and a light transmittance prediction attenuation step length according to the expected number of cycles and the attenuation gain coefficient, in combination with the surface contact angle single-cycle attenuation step length, the light refractive index single-cycle attenuation step length, and the light transmittance single-cycle attenuation step length;

[0034] The surface contact angle fluctuation curve, the light refractive index fluctuation curve and the light transmittance fluctuation curve are extended according to the surface contact angle predicted attenuation step, the light refractive index predicted attenuation step and the light transmittance predicted attenuation step to generate the surface contact angle maximum value, the light refractive index maximum value and the light transmittance minimum value.

[0035] Optionally, the marking of the silicon oxide thin film sample based on the maximum surface contact angle, the maximum light refractive index, and the minimum light transmittance includes:

[0036] When the maximum value of the surface contact angle is greater than or equal to the surface contact angle failure threshold, or / and the maximum value of the light refractive index is greater than or equal to the light refractive index failure threshold, or / and the minimum value of the light transmittance is less than or equal to the light transmittance failure threshold, the silicon oxide film sample is marked as unqualified;

[0037] When the maximum value of the surface contact angle is less than the surface contact angle failure threshold, the maximum value of the light refractive index is less than the light refractive index failure threshold, and the minimum value of the light transmittance is greater than the light transmittance failure threshold, the silicon oxide film sample is marked as qualified.

[0038] Optionally, when the maximum surface contact angle is less than the surface contact angle failure threshold, the maximum light refractive index is less than the light refractive index failure threshold, and the minimum light transmittance is greater than the light transmittance failure threshold, before marking the silicon oxide thin film sample as qualified, the method further includes:

[0039] When the maximum value of the surface contact angle is less than the surface contact angle failure threshold, the maximum value of the light refractive index is less than the light refractive index failure threshold, and the minimum value of the light transmittance is greater than the light transmittance failure threshold, performing a secondary prediction of the curve trends of the surface contact angle fluctuation curve, the light refractive index fluctuation curve, and the light transmittance fluctuation curve to generate a predicted failure cycle number;

[0040] The silicon oxide film sample is marked as qualified according to the predicted number of failure cycles.

[0041] To achieve the above-mentioned purpose, a second embodiment of the present application provides an aging analysis device for a silicon oxide film, comprising:

[0042] An aging factor configuration module is used to configure the aging factor set of the silicon oxide film;

[0043] A frequency analysis module, configured to perform frequency analysis on the aging factor set according to a preset environment type to obtain a single-cycle fluctuation curve of the aging factor;

[0044] An accelerated aging experiment module is used to perform an accelerated aging experiment on a silicon oxide film sample according to the aging factor single-cycle fluctuation curve to obtain a surface contact angle fluctuation curve, a light refractive index fluctuation curve, and a light transmittance fluctuation curve;

[0045] a curve trend prediction module, configured to perform curve trend prediction on the surface contact angle fluctuation curve, the light refractive index fluctuation curve, and the light transmittance fluctuation curve based on the expected number of cycles, to obtain a maximum value of the surface contact angle, a maximum value of the light refractive index, and a minimum value of the light transmittance;

[0046] An identification module identifies the silicon oxide film sample based on the maximum surface contact angle, the maximum light refractive index and the minimum light transmittance.

[0047] To achieve the above-mentioned purpose, a third embodiment of the present application provides an electronic device, comprising: a processor, and a memory communicatively connected to the processor;

[0048] The memory stores computer-executable instructions;

[0049] The processor executes the computer-executable instructions stored in the memory to implement the method as described in any one of the first aspects above.

[0050] The technical solutions provided by the embodiments of this application bring at least the following beneficial effects:

[0051] By configuring the aging factor set, data on factors affecting the aging of silicon oxide films are prepared to provide a basis for the aging analysis of silicon oxide films; a frequency analysis of the aging factor set is performed to obtain a single-cycle fluctuation curve of the aging factor, and the aging of the silicon oxide film under the use environment is simulated in a targeted manner; based on the single-cycle fluctuation curve of the aging factor, accelerated aging experiments are performed on silicon oxide film samples to carry out scientific aging treatment; surface contact angle fluctuation curves, refractive index fluctuation curves, and transmittance fluctuation curves are obtained through testing to comprehensively reflect the performance degradation of silicon oxide films under aging conditions; trend prediction is performed on the test curves to obtain extreme values ​​of performance parameters, shorten the test cycle, and quickly judge the aging trend of the sample; when the performance parameters exceed the failure threshold, the silicon oxide film is judged to be unqualified, which solves the technical problems of low automation and low test efficiency in existing silicon oxide film testing, and achieves the technical effect of improving the automation level of silicon oxide film testing and improving test efficiency.

[0052] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0054] Figure 1 This is a flow chart of a method for analyzing aging of a silicon oxide film according to an embodiment of the present application;

[0055] Figure 2 is a flow chart showing a method for obtaining a single-cycle fluctuation curve of an aging factor according to an embodiment of the present application;

[0056] Figure 3 This is a flow chart of obtaining a single-cycle concentrated fluctuation curve of an aging factor according to an embodiment of the present application;

[0057] Figure 4 This is a flow chart showing how to obtain the maximum surface contact angle, the maximum light refractive index, and the minimum light transmittance according to an embodiment of the present application;

[0058] Figure 5This is a block diagram of an aging analysis device for a silicon oxide film according to an embodiment of the present application;

[0059] Figure 6 It is a block diagram of an electronic device. DETAILED DESCRIPTION

[0060] The following describes in detail embodiments of the present application. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0061] The following describes an aging analysis method and apparatus for a silicon oxide film according to an embodiment of the present application with reference to the accompanying drawings.

[0062] Figure 1 FIG. 1 is a flow chart of an aging analysis method for a silicon oxide film according to an embodiment of the present application. Figure 1 As shown, the method includes the following steps:

[0063] Step 101: configuring an aging factor set for a silicon oxide film.

[0064] In the embodiment of the present application, in order to implement aging analysis of the silicon oxide film, it is necessary to first configure a set of various environmental factors that affect the aging of the silicon oxide film to establish basic conditions for the aging experiment.

[0065] First, based on literature search and expert experience, multiple environmental factors related to the aging of silicon oxide films were identified. Then, according to the preset quantity ratio and value range, multiple environmental factors were randomly combined to generate multiple initial aging factor sets. Finally, multiple initial aging factor sets were screened using evaluation indicators to obtain the aging factor set with the best influence.

[0066] It is understandable that the aging factor set can be configured with factors such as temperature, humidity, pH and oxygen content, and other types such as light, electric field, magnetic field, etc. can also be selected as needed.

[0067] Among them, the temperature and humidity factors determine the thermal conditions when the silicon oxide film works; the pH factor reflects the corrosiveness of the environment; and the oxygen content factor is related to the oxidation reaction.

[0068] Step 102: Perform frequency analysis on the aging factor set according to the preset environment type to obtain a single-cycle fluctuation curve of the aging factor.

[0069] In the embodiment of the present application, a frequency analysis is performed on the aging factor set according to a preset environment type.

[0070] like Figure 2As shown, step 102 further includes:

[0071] Step 201 : According to a preset environment type, a single-cycle initial fluctuation curve set of each aging factor in an aging factor set is collected.

[0072] In one possible embodiment, the aging factor set includes a temperature factor, a humidity factor, a pH factor, and an oxygen content factor. The temperature factor reflects the changing trend of the ambient temperature. An increase in temperature will accelerate the structural degradation of the silicon oxide film. The humidity factor reflects the moisture content in the environment. The higher the humidity, the more moisture the silicon oxide film absorbs and the greater the possibility of oxidation. The pH factor reflects the corrosive properties of the environment. Changes in pH will erode the surface and internal structure of the silicon oxide film. The oxygen content factor is the oxygen content in the environment, which will intensify the occurrence of oxidation reactions and accelerate the performance degradation of the silicon oxide film.

[0073] Then, based on the determined preset environment type, a set of single-cycle initial fluctuation curves for each aging factor in that environment is collected. The preset environment type is the climate environment of a specific geographical location, such as a city on the southeast coast. The single cycle can be set to a periodic time period such as one year. Subsequently, the single-cycle variation trend of the temperature in that region is collected as the single-cycle initial fluctuation curve set of the temperature factor; the single-cycle variation trend of the humidity in that region is collected as the single-cycle initial fluctuation curve set of the humidity factor; the single-cycle variation trend of the pH in that region is collected as the single-cycle initial fluctuation curve set of the pH factor; and the single-cycle variation trend of the oxygen content in that region is collected as the single-cycle initial fluctuation curve set of the oxygen content factor.

[0074] It is understandable that the single-period initial fluctuation curve set of the above factors can be obtained by querying historical meteorological data or collecting actual data through environmental monitoring equipment, and this application does not make specific limitations here.

[0075] Step 202 : traverse the single-period initial fluctuation curve set of each aging factor and perform frequent cluster fitting to generate the single-period concentrated fluctuation curve of the aging factor.

[0076] Further, such as Figure 3 As shown, step 202 further includes:

[0077] Step 301 : Randomly extract a first aging factor single-cycle initial fluctuation curve and a second aging factor single-cycle initial fluctuation curve from a single-cycle initial fluctuation curve set.

[0078] In the embodiment of the present application, the temperature factor single-cycle initial fluctuation curve set is taken as an example for illustration, and the calculation method of the humidity factor single-cycle concentrated fluctuation curve, the pH factor single-cycle concentrated fluctuation curve and the oxygen content factor single-cycle concentrated fluctuation curve is the same as the calculation method of the temperature factor single-cycle concentrated fluctuation curve.

[0079] In this step, the temperature factor single-cycle initial fluctuation curve set is traversed, the total number of curves N is counted, and two different random integers between 1 and N are randomly generated as the serial numbers of the two initial fluctuation curves. The corresponding two curves are extracted from the temperature factor single-cycle initial fluctuation curve set according to the selected serial numbers and are respectively identified as the first temperature factor single-cycle initial fluctuation curve and the second temperature factor single-cycle initial fluctuation curve.

[0080] Step 302 : In a two-dimensional coordinate system, the first position and the tail position of the first aging factor single-cycle initial fluctuation curve and the second aging factor single-cycle initial fluctuation curve are connected respectively to obtain a characteristic image area.

[0081] In an embodiment of the present application, a two-dimensional rectangular coordinate system is established, with the horizontal axis representing time and the vertical axis representing temperature value. A single-cycle initial fluctuation curve of the first temperature factor and a single-cycle initial fluctuation curve of the second temperature factor are drawn in the two-dimensional coordinate system, the initial point positions of the two curves on the time axis are connected, and the last point positions of the two curves on the time axis are connected. The area value of the closed characteristic image region formed by the above-mentioned connecting lines is calculated as the characteristic image area.

[0082] Step 303 : when the area of ​​the characteristic image is less than or equal to the area threshold, the first aging factor single-cycle initial fluctuation curve and the second aging factor single-cycle initial fluctuation curve are set as the same type of curves; otherwise, they are set as different type of curves.

[0083] It can be understood that the area threshold is set according to the actual scenario. By pre-setting the area threshold as the demarcation standard for judging whether the two curves are of the same type, the obtained characteristic image area is compared with the set area threshold. If the characteristic image area is less than or equal to the area threshold, the first temperature factor single-cycle initial fluctuation curve and the second temperature factor single-cycle initial fluctuation curve are judged to belong to the same type and are classified into one cluster; if the characteristic image area is greater than the area threshold, the two curves are judged to belong to different types and are respectively classified into different clusters.

[0084] Step 304 : repeatedly extracting and parsing from the single-cycle initial fluctuation curve set to obtain multiple clusters of single-cycle initial fluctuation curves of aging factors, wherein the multiple clusters of single-cycle initial fluctuation curves of aging factors have the number of curves within the cluster.

[0085] In this example, two different combinations of initial temperature fluctuation curves were randomly extracted. Each set of curves was then labeled with its cluster affiliation using the aforementioned process. This process was repeated until all curves were assigned a cluster affiliation. The curves within each cluster were counted to obtain multiple clusters of single-cycle initial temperature fluctuation curves. Each cluster of single-cycle initial temperature fluctuation curves had a corresponding number of curves within the cluster.

[0086] Step 305 : Based on the number of curves within the cluster, select the concentrated cluster aging factor single-period initial fluctuation curve with the maximum number of curves within the cluster.

[0087] In an embodiment of the present application, multiple clusters of temperature factor single-cycle initial fluctuation curves obtained by statistics are traversed, the number of curves contained in each cluster of temperature factor single-cycle initial fluctuation curves is analyzed, the number of curves within each cluster is compared, the maximum number of curves is selected, and the cluster of temperature factor single-cycle initial fluctuation curves corresponding to the maximum number of curves is identified as the concentrated cluster temperature factor single-cycle initial fluctuation curve.

[0088] Step 306 , performing simultaneous mean fitting on the initial fluctuation curve of the concentrated cluster aging factor single cycle to generate a concentrated fluctuation curve of the aging factor single cycle.

[0089] In an embodiment of the present application, each single-cycle initial fluctuation curve of the temperature factor in the concentrated cluster is traversed, the sampling moments and corresponding temperature values ​​therein are analyzed, and at the same sampling moment, the arithmetic mean of the temperature values ​​of each curve is calculated to obtain the concentrated cluster mean temperature at that moment, and then the concentrated cluster mean temperatures of each sampling moment are connected in time series order to generate a single-cycle concentrated fluctuation curve of the temperature factor.

[0090] It can be understood that the calculation method of the single-cycle concentrated fluctuation curve of the humidity factor, the single-cycle concentrated fluctuation curve of the pH factor and the single-cycle concentrated fluctuation curve of the oxygen content factor is the same as the calculation method of the single-cycle concentrated fluctuation curve of the temperature factor. Therefore, according to the method of obtaining the single-cycle concentrated fluctuation curve of the temperature factor, the single-cycle concentrated fluctuation curve of the humidity factor, the single-cycle concentrated fluctuation curve of the pH factor and the single-cycle concentrated fluctuation curve of the oxygen content factor are obtained in turn.

[0091] Step 203 , traverse the single-cycle concentrated fluctuation curve of the aging factor and perform neighborhood hierarchical clustering fitting to obtain the single-cycle fluctuation curve of the aging factor.

[0092] Still taking the embodiment in the above steps as an example, if the single-cycle concentrated fluctuation curve of the aging factor includes the single-cycle concentrated fluctuation curve of the temperature factor, the single-cycle concentrated fluctuation curve of the humidity factor, the single-cycle concentrated fluctuation curve of the pH factor and the single-cycle concentrated fluctuation curve of the oxygen content factor, then the single-cycle concentrated fluctuation curve of the temperature factor, the single-cycle concentrated fluctuation curve of the humidity factor, the single-cycle concentrated fluctuation curve of the pH factor and the single-cycle concentrated fluctuation curve of the oxygen content factor are traversed to determine whether the difference between adjacent time series sampling points is less than a preset threshold set based on experience. If it is less than or equal to the preset threshold, these sampling points are merged into a characteristic interval segment, and the average value of the sampling points in the interval is calculated as the representative fluctuation value of the segment.

[0093] Repeat the above determination and merging process to eventually form a temperature factor single-cycle fluctuation curve with multiple characteristic intervals, a humidity factor single-cycle fluctuation curve, a pH factor single-cycle fluctuation curve, and an oxygen content factor single-cycle fluctuation curve. Subsequently, the obtained temperature factor single-cycle fluctuation curve, humidity factor single-cycle fluctuation curve, pH factor single-cycle fluctuation curve, and oxygen content factor single-cycle fluctuation curve are summarized to form an aging factor single-cycle fluctuation curve.

[0094] Step 103 : performing an accelerated aging test on the silicon oxide film sample according to the aging factor single-cycle fluctuation curve to obtain a surface contact angle fluctuation curve, a light refractive index fluctuation curve, and a light transmittance fluctuation curve.

[0095] In the embodiment of the present application, after obtaining the single-cycle fluctuation curve of the aging factor, the single-cycle fluctuation curve of the aging factor is used to drive an accelerated aging test of the silicon oxide film sample to obtain a performance attenuation curve of the silicon oxide film under the use environment.

[0096] Specifically, a silicon oxide film accelerated aging test platform is configured, a silicon oxide film sample is placed on the test platform, and a control system is set up to match the single-cycle fluctuation curve of the aging factor. The single-cycle fluctuation curve of the aging factor is then input into the control system, and the silicon oxide film sample is cyclically exposed according to the fluctuation pattern. Simultaneously, data on the changes in the surface contact angle, refractive index, and light transmittance of the silicon oxide film sample during the accelerated aging process are extracted and recorded as corresponding performance fluctuation curves: surface contact angle fluctuation curve, refractive index fluctuation curve, and light transmittance fluctuation curve.

[0097] In a possible embodiment, the aging factor single-cycle fluctuation curve includes a temperature factor single-cycle fluctuation curve, a humidity factor single-cycle fluctuation curve, a pH factor single-cycle fluctuation curve, and an oxygen content factor single-cycle fluctuation curve.

[0098] It can be understood that by applying the fluctuation law of the single-cycle fluctuation curve of the aging factor to actual silicon oxide film samples, the material aging and performance degradation law consistent with the actual use environment is obtained, providing reliable data support for the performance prediction of silicon oxide films.

[0099] Step 104 : Based on the expected number of cycles, the surface contact angle fluctuation curve, the light refractive index fluctuation curve, and the light transmittance fluctuation curve are subjected to curve trend prediction to obtain the maximum value of the surface contact angle, the maximum value of the light refractive index, and the minimum value of the light transmittance.

[0100] like Figure 4 As shown, step 104 also includes:

[0101] Step 401 , traverse the surface contact angle fluctuation curve, the light refractive index fluctuation curve and the light transmittance fluctuation curve to obtain the surface contact angle single cycle attenuation step length, the light refractive index single cycle attenuation step length and the light transmittance single cycle attenuation step length.

[0102] It can be understood that after obtaining the surface contact angle fluctuation curve, the light refractive index fluctuation curve and the light transmittance fluctuation curve, by respectively detecting the peak-to-valley difference of the surface contact angle fluctuation curve, the light refractive index fluctuation curve and the light transmittance fluctuation curve in one cycle, the decreasing step length corresponding to the peak-to-valley difference in one cycle is calculated, and the single-cycle attenuation step length value of each parameter is obtained, that is, the single-cycle attenuation step length of the surface contact angle, the single-cycle attenuation step length of the light refractive index and the single-cycle attenuation step length of the light transmittance, which can reflect the degree of decay of the performance of the silicon oxide film in each cycle under the aging environment.

[0103] Step 402: In response to the user interaction, the user terminal configures the attenuation gain coefficient.

[0104] In the embodiment of the present application, an input box prompt for the attenuation gain coefficient is added to the user interaction interface, requiring the user to input the attenuation gain coefficient based on experience.

[0105] It should be noted that the attenuation gain coefficient takes into account the gradual breakdown of the internal structure of the silicon oxide film during the aging process, leading to a further increase in the subsequent aging rate. For example, in the first cycle, the surface contact angle decays from 80° to 79° in 0.1 steps, with a step size of 0.9. In the second cycle, it will begin to decay based on the end point of the previous cycle, 79°. However, since the film structure has begun to deteriorate, the attenuation step size will increase at this time. The calculated attenuation step size is 0.9*(1+attenuation gain coefficient).

[0106] In other words, the attenuation gain coefficient reflects the subsequent aging aggravation caused by the aging of the silicon oxide film.

[0107] Step 403, based on the expected number of cycles and the attenuation gain coefficient, combined with the surface contact angle single-cycle attenuation step, the light refractive index single-cycle attenuation step and the light transmittance single-cycle attenuation step, determines the surface contact angle predicted attenuation step, the light refractive index predicted attenuation step and the light transmittance predicted attenuation step.

[0108] In this embodiment of the present application, the number of service life cycles of the silicon oxide film predetermined by the user is extracted as the expected number of cycles, and the configured attenuation gain coefficient is extracted. Then, the attenuation step length per cycle of the surface contact angle, the attenuation step length per cycle of the light refractive index, and the attenuation step length per cycle of the light transmittance are calculated according to the attenuation gain coefficient to obtain the predicted attenuation step length of the surface contact angle, the predicted attenuation step length of the light refractive index, and the predicted attenuation step length of the light transmittance.

[0109] Step 404, extending the surface contact angle fluctuation curve, the light refractive index fluctuation curve and the light transmittance fluctuation curve according to the surface contact angle prediction attenuation step length, the light refractive index prediction attenuation step length and the light transmittance prediction attenuation step length to generate the surface contact angle maximum value, the light refractive index maximum value and the light transmittance minimum value.

[0110] In the present embodiment, the endpoint values ​​of the surface contact angle fluctuation curve, the refractive index fluctuation curve, and the light transmittance fluctuation curve on the time axis are taken, and each fluctuation curve is extrapolated and extended according to the respective predicted attenuation step lengths, namely, the surface contact angle predicted attenuation step length, the light refractive index predicted attenuation step length, and the light transmittance predicted attenuation step length. The step-by-step periodic attenuation calculation is repeated until a predetermined number of desired cycles is reached. All sampling points are combined, and the maximum predicted value of the performance parameter is output, namely, the maximum surface contact angle, the maximum refractive index, and the minimum light transmittance value.

[0111] Step 105 : marking the silicon oxide thin film sample based on the maximum surface contact angle, the maximum light refractive index, and the minimum light transmittance.

[0112] In the embodiment of the present application, after predicting the end-of-life limit values ​​of multiple performance parameters of the silicon oxide film, these limit indicators are compared with the performance failure judgment threshold to complete the screening of unqualified silicon oxide film samples.

[0113] As a possible implementation, a surface contact angle failure threshold, a light refractive index failure threshold, and a light transmittance failure threshold are configured and pre-set in the determination unit. The predicted limit values ​​of the silicon oxide film performance parameters, namely, the maximum surface contact angle, the maximum light refractive index, and the minimum light transmittance, are then called. Subsequently, the limit indicators are compared one by one with their respective failure thresholds, namely, the maximum surface contact angle is compared with the surface contact angle failure threshold, the maximum light refractive index is compared with the light refractive index failure threshold, and the minimum light transmittance is compared with the light transmittance failure threshold.

[0114] It is understandable that when any threshold condition is reached or exceeded, the sample is judged as unqualified, that is, when the maximum value of the surface contact angle is greater than or equal to the surface contact angle failure threshold, or / and the maximum value of the light refractive index is greater than or equal to the light refractive index failure threshold, or / and the minimum value of the light transmittance is less than or equal to the light transmittance failure threshold, the silicon oxide film sample is marked as unqualified;

[0115] Conversely, when the maximum value of the surface contact angle is less than the surface contact angle failure threshold, the maximum value of the light refractive index is less than the light refractive index failure threshold, and the minimum value of the light transmittance is greater than the light transmittance failure threshold, the silicon oxide film sample is marked as qualified.

[0116] That is, when the maximum value of the surface contact angle is less than the surface contact angle failure threshold, the maximum value of the light refractive index is less than the light refractive index failure threshold, and the minimum value of the light transmittance is greater than the light transmittance failure threshold, the silicon oxide film sample is judged as a qualified sample and marked as qualified to confirm that the silicon oxide film sample meets the standard requirements and complete the qualified product screening.

[0117] It should also be noted that when the maximum value of the surface contact angle is less than the surface contact angle failure threshold, and the maximum value of the light refractive index is less than the light refractive index failure threshold, and the minimum value of the light transmittance is greater than the light transmittance failure threshold, a secondary prediction is performed on the surface contact angle fluctuation curve, the light refractive index fluctuation curve, and the light transmittance fluctuation curve extension curve trend to obtain a secondary prediction result. During the curve trend extension process, it is repeatedly judged whether the secondary prediction result meets the performance failure threshold requirement, and the number of use cycles extending from the current time point to the secondary prediction failure is counted and recorded as the predicted failure cycle number of the silicon oxide film sample. Subsequently, a standard threshold value for the predicted failure cycle number is preset, and the predicted failure cycle number of the silicon oxide film sample is extracted to determine whether the predicted failure cycle number meets the standard threshold requirement. For silicon oxide film samples whose predicted failure cycle number is greater than or equal to the standard threshold, it is confirmed to be a qualified silicon oxide film and the qualified identification is completed.

[0118] The embodiment of the present application configures an aging factor set to prepare data on factors affecting the aging of silicon oxide films, providing a basis for aging analysis of silicon oxide films; performs frequency analysis on the aging factor set to obtain a single-cycle fluctuation curve of the aging factor, and simulates the aging of the silicon oxide film under the use environment in a targeted manner; based on the single-cycle fluctuation curve of the aging factor, an accelerated aging experiment is performed on the silicon oxide film sample to carry out scientific aging treatment; the surface contact angle fluctuation curve, the refractive index fluctuation curve and the transmittance fluctuation curve are obtained by testing, which comprehensively reflect the performance degradation of the silicon oxide film under aging conditions; the test curve is trend predicted to obtain the extreme value of the performance parameter, shorten the test cycle, and quickly judge the aging trend of the sample; when the performance parameter exceeds the failure threshold, the silicon oxide film is judged to be unqualified, which solves the technical problems of low automation and low test efficiency in the existing silicon oxide film testing, and achieves the technical effect of improving the automation level of silicon oxide film testing and improving test efficiency.

[0119] Figure 5 1 is a block diagram of a silicon oxide thin film aging analysis device 10 according to an embodiment of the present application, comprising:

[0120] An aging factor configuration module 100 is used to configure an aging factor set for a silicon oxide film;

[0121] The frequency analysis module 200 is used to perform frequency analysis on the aging factor set according to the preset environment type to obtain a single-cycle fluctuation curve of the aging factor;

[0122] The accelerated aging experiment module 300 is used to perform an accelerated aging experiment on the silicon oxide film sample according to the single-cycle fluctuation curve of the aging factor to obtain the surface contact angle fluctuation curve, the light refractive index fluctuation curve and the light transmittance fluctuation curve;

[0123] A curve trend prediction module 400 is used to predict the curve trends of the surface contact angle fluctuation curve, the light refractive index fluctuation curve, and the light transmittance fluctuation curve based on the expected number of cycles, and obtain the maximum value of the surface contact angle, the maximum value of the light refractive index, and the minimum value of the light transmittance;

[0124] The identification module 500 identifies the silicon oxide film sample based on the maximum surface contact angle, the maximum light refractive index and the minimum light transmittance.

[0125] Regarding the apparatus in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0126] Figure 6 A schematic block diagram of an example electronic device 700 that can be used to implement an embodiment of the present application is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present application described and / or claimed herein.

[0127] like Figure 6 As shown, the device 700 includes a computing unit 701, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 702 or a computer program loaded from a storage unit 708 into a random access memory (RAM) 703. Various programs and data required for the operation of the device 700 can also be stored in the RAM 703. The computing unit 701, the ROM 702, and the RAM 703 are connected to each other via a bus 704. An input / output (I / O) interface 705 is also connected to the bus 704.

[0128] Various components in device 700 are connected to I / O interface 705, including an input unit 706, such as a keyboard, mouse, etc.; an output unit 707, such as various types of displays, speakers, etc.; a storage unit 708, such as a magnetic disk, optical disk, etc.; and a communication unit 709, such as a network card, modem, wireless communication transceiver, etc. The communication unit 709 allows device 700 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0129] The computing unit 701 can be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 701 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units that run machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 701 performs the various methods and processes described above, such as the voice command response method. For example, in some embodiments, the voice command response method can be implemented as a computer software program that is tangibly contained in a machine-readable medium, such as a storage unit 708. In some embodiments, part or all of the computer program can be loaded and / or installed on the device 700 via the ROM 702 and / or the communication unit 709. When the computer program is loaded into the RAM 703 and executed by the computing unit 701, one or more steps of the voice command response method described above can be performed. Alternatively, in other embodiments, the computing unit 701 can be configured to perform the voice command response method in any other appropriate manner (e.g., by means of firmware).

[0130] Various embodiments of the systems and techniques described herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), system-on-chip systems (SOCs), programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include being implemented in one or more computer programs that are executable and / or interpreted on a programmable system that includes at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0131] The program code for implementing the methods of the present application can be written in any combination of one or more programming languages. Such program code can be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the program code is executed by the processor or controller, the functions / operations specified in the flow charts and / or block diagrams are implemented. The program code can be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0132] In the context of the present application, a machine-readable medium can be a tangible medium that can contain or store a program for use by an instruction execution system, device or equipment or used in combination with an instruction execution system, device or equipment. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared or semiconductor system, device or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium can include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0133] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0134] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), the Internet, and a blockchain network.

[0135] A computer system may include a client and a server. The client and server are generally remote from each other and typically interact via a communication network. This client-server relationship is established by computer programs running on the respective computers, establishing a client-server relationship. The server may be a cloud server, also known as a cloud computing server or cloud host, a host product within the cloud computing service ecosystem that addresses the management difficulties and limited scalability of traditional physical hosts and VPS services ("Virtual Private Servers" or simply "VPS"). The server may also be a server in a distributed system or a server integrated with blockchain.

[0136] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in this application can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution of this application can be achieved. This is not limited herein.

[0137] The above specific embodiments do not constitute a limitation on the scope of protection of this application. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application shall be included within the scope of protection of this application.

Claims

1. A method for analyzing aging of a silicon oxide film, characterized in that: include: Configure the aging factor set for silicon oxide film; Performing a frequency analysis on the aging factor set according to a preset environment type to obtain a single-cycle fluctuation curve of the aging factor; According to the single-cycle fluctuation curve of the aging factor, an accelerated aging experiment is performed on the silicon oxide film sample to obtain a surface contact angle fluctuation curve, a light refractive index fluctuation curve, and a light transmittance fluctuation curve; Based on the expected number of cycles, predicting the curve trends of the surface contact angle fluctuation curve, the light refractive index fluctuation curve, and the light transmittance fluctuation curve to obtain the maximum value of the surface contact angle, the maximum value of the light refractive index, and the minimum value of the light transmittance; The silicon oxide thin film sample is labeled based on the maximum surface contact angle, the maximum light refractive index and the minimum light transmittance.

2. The method according to claim 1, characterized in that The target aging factor set for configuring the silicon oxide film includes: Based on literature search and expert experience, multiple environmental factors related to the aging of silicon oxide films were identified; According to a preset quantity ratio and value range, a plurality of the environmental factors are randomly combined to generate a plurality of initial aging factor sets; The plurality of initial aging factor sets are screened by using evaluation indicators to obtain the aging factor set with the best influence.

3. The method according to claim 1, characterized in that The frequency analysis of the aging factor set is performed according to the preset environment type to obtain a single-cycle fluctuation curve of the aging factor, including: According to the preset environment type, collecting a single-cycle initial fluctuation curve set of each aging factor in the aging factor set; The single-period initial fluctuation curve set of each aging factor is traversed and frequent fitting is performed to obtain the single-period fluctuation curve of the aging factor.

4. The method according to claim 3, characterized in that The step of traversing the single-cycle initial fluctuation curve set of each aging factor and performing frequent fitting to obtain the single-cycle fluctuation curve of the aging factor includes: Traverse the single-period initial fluctuation curve set of each aging factor and perform frequent cluster fitting to generate the single-period concentrated fluctuation curve of the aging factor; The single-cycle concentrated fluctuation curve of the aging factor is traversed and neighborhood hierarchical clustering fitting is performed to obtain the single-cycle fluctuation curve of the aging factor.

5. The method according to claim 4, characterized in that The traversing of the single-period initial fluctuation curve set of each aging factor and performing frequent cluster fitting to generate the single-period concentrated fluctuation curve of the aging factor includes: Randomly extracting a first aging factor single-cycle initial fluctuation curve and a second aging factor single-cycle initial fluctuation curve from the single-cycle initial fluctuation curve set; In a two-dimensional coordinate system, connecting the first position and the tail position of the first aging factor single-cycle initial fluctuation curve and the second aging factor single-cycle initial fluctuation curve respectively to obtain a characteristic image area; When the area of ​​the characteristic image is less than or equal to an area threshold, the first aging factor single-cycle initial fluctuation curve and the second aging factor single-cycle initial fluctuation curve are set as the same type of curves; otherwise, they are set as different types of curves; Repeatedly extracting and parsing from the single-cycle initial fluctuation curve set to obtain multiple clusters of single-cycle initial fluctuation curves of aging factors, wherein the multiple clusters of single-cycle initial fluctuation curves of aging factors have a number of curves within a cluster; Based on the number of curves within the cluster, selecting the concentrated cluster aging factor single-cycle initial fluctuation curve with the maximum number of curves within the cluster; The single-cycle initial fluctuation curve of the concentrated cluster aging factor is fitted with the mean value at the same time to generate the single-cycle concentrated fluctuation curve of the aging factor.

6. The method according to claim 1, wherein The method of performing curve trend prediction on the surface contact angle fluctuation curve, the light refractive index fluctuation curve, and the light transmittance fluctuation curve based on the expected number of cycles to obtain the maximum value of the surface contact angle, the maximum value of the light refractive index, and the minimum value of the light transmittance includes: Traversing the surface contact angle fluctuation curve, the light refractive index fluctuation curve, and the light transmittance fluctuation curve to obtain a single-cycle attenuation step length of the surface contact angle, a single-cycle attenuation step length of the light refractive index, and a single-cycle attenuation step length of the light transmittance; In response to the user interaction, the user terminal configures the attenuation gain coefficient; Determine a surface contact angle prediction attenuation step length, a light refractive index prediction attenuation step length, and a light transmittance prediction attenuation step length according to the expected number of cycles and the attenuation gain coefficient, in combination with the surface contact angle single-cycle attenuation step length, the light refractive index single-cycle attenuation step length, and the light transmittance single-cycle attenuation step length; The surface contact angle fluctuation curve, the light refractive index fluctuation curve and the light transmittance fluctuation curve are extended according to the surface contact angle predicted attenuation step, the light refractive index predicted attenuation step and the light transmittance predicted attenuation step to generate the surface contact angle maximum value, the light refractive index maximum value and the light transmittance minimum value.

7. The method according to claim 1, characterized in that The marking of the silicon oxide thin film sample based on the maximum surface contact angle, the maximum light refractive index and the minimum light transmittance includes: When the maximum value of the surface contact angle is greater than or equal to the surface contact angle failure threshold, or / and the maximum value of the light refractive index is greater than or equal to the light refractive index failure threshold, or / and the minimum value of the light transmittance is less than or equal to the light transmittance failure threshold, the silicon oxide film sample is marked as unqualified; When the maximum value of the surface contact angle is less than the surface contact angle failure threshold, the maximum value of the light refractive index is less than the light refractive index failure threshold, and the minimum value of the light transmittance is greater than the light transmittance failure threshold, the silicon oxide film sample is marked as qualified.

8. The method according to claim 7, characterized in that When the maximum value of the surface contact angle is less than the surface contact angle failure threshold, the maximum value of the light refractive index is less than the light refractive index failure threshold, and the minimum value of the light transmittance is greater than the light transmittance failure threshold, before marking the silicon oxide thin film sample as qualified, the method further includes: When the maximum value of the surface contact angle is less than the surface contact angle failure threshold, the maximum value of the light refractive index is less than the light refractive index failure threshold, and the minimum value of the light transmittance is greater than the light transmittance failure threshold, performing a secondary prediction of the curve trends of the surface contact angle fluctuation curve, the light refractive index fluctuation curve, and the light transmittance fluctuation curve to generate a predicted failure cycle number; The silicon oxide film sample is marked as qualified according to the predicted number of failure cycles.

9. An aging analysis device for silicon oxide thin films, characterized in that: include: An aging factor configuration module is used to configure the aging factor set of the silicon oxide film; A frequency analysis module, configured to perform frequency analysis on the aging factor set according to a preset environment type to obtain a single-cycle fluctuation curve of the aging factor; An accelerated aging experiment module is used to perform an accelerated aging experiment on a silicon oxide film sample according to the aging factor single-cycle fluctuation curve to obtain a surface contact angle fluctuation curve, a light refractive index fluctuation curve, and a light transmittance fluctuation curve; a curve trend prediction module, configured to perform curve trend prediction on the surface contact angle fluctuation curve, the light refractive index fluctuation curve, and the light transmittance fluctuation curve based on the expected number of cycles, to obtain a maximum value of the surface contact angle, a maximum value of the light refractive index, and a minimum value of the light transmittance; An identification module identifies the silicon oxide film sample based on the maximum surface contact angle, the maximum light refractive index and the minimum light transmittance.

10. An electronic device, characterized in that: include: a processor, and a memory communicatively connected to the processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory to implement the method according to any one of claims 1 to 8.

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