Method, apparatus, device, medium and program for predicting service life of silicon-based light-emitting device

Abstract

The invention discloses a silicon-based light-emitting device service life prediction method, device, equipment, medium and program, and the method comprises the steps: obtaining the operation state data of a to-be-predicted device, and obtaining a preset service life prediction model configured for the to-be-predicted device; determining the device life of the to-be-predicted device based on the preset life prediction model and the operation state data; wherein the preset life prediction model at least comprises a corresponding relation between the acceleration factor and the temperature. The embodiment of the invention can predict the influence on the device life based on the temperature effect, and can improve the device life prediction precision.

Description

Technical Field

[0001] This invention relates to the field of computer application technology, and in particular to a method, apparatus, device, medium, and program for predicting the lifetime of silicon-based light-emitting devices. Background Technology

[0002] Silicon-based organic light-emitting diode (OLED) microdisplays have significant application value in fields such as augmented reality (AR) and virtual reality (VR) due to their high resolution, low power consumption, and miniaturization advantages. However, the brightness decay caused by material aging during long-term operation severely restricts their reliability and lifespan. Notably, intrinsic degradation of OLEDs leads to a non-linear decline in brightness over time, especially under high temperature and high duty cycle conditions, significantly shortening device lifespan. Therefore, constructing a high-precision lifetime prediction model to quantify the lifetime decay law under multi-stress coupling is a prerequisite for achieving effective compensation and reliability optimization. Meanwhile, traditional evaluation methods rely on accelerated aging experiments under extreme stress. Although brightness decay data can be obtained through thousands of hours of high-load testing, its high time and economic costs severely hinder the efficiency of technological iteration. Currently, although the nominal lifetime of silicon-based OLEDs has exceeded 10,000 hours, the actual service life is still far below the theoretical value, and the core bottleneck lies in the lack of accurate lifetime prediction methods. Currently, existing silicon-based OLED lifetime prediction methods are mostly based on accelerated aging tests using a single stress factor. It is difficult to quantify the dynamic adjustment process of the duty cycle, and the nonlinear effect of temperature on degradation leads to inconsistent device lifetime prediction accuracy, affecting the universality of subsequent compensation strategies. Summary of the Invention

[0003] This invention provides a method, apparatus, device, medium, and program for predicting the lifetime of silicon-based light-emitting devices. It can predict the lifetime of devices based on the influence of temperature effects, thereby improving the accuracy of device lifetime prediction.

[0004] According to one aspect of the present invention, a method for predicting the lifetime of a silicon-based light-emitting device is provided, wherein the method includes:

[0005] Obtain the operating status data of the device to be predicted, and obtain the preset lifetime prediction model configured for the device to be predicted;

[0006] The device lifetime of the device to be predicted is determined based on the preset lifetime prediction model and the operating status data.

[0007] The preset lifetime prediction model includes at least the correspondence between the acceleration factor and temperature.

[0008] According to another aspect of the present invention, a silicon-based light-emitting device lifetime prediction device is provided, comprising:

[0009] The information acquisition module is used to acquire the operating status data of the device to be predicted and to acquire the preset lifetime prediction model configured for the device to be predicted.

[0010] A lifetime prediction module is used to determine the device lifetime of the device to be predicted based on the preset lifetime prediction model and the operating status data.

[0011] The preset lifetime prediction model includes at least the correspondence between the acceleration factor and temperature.

[0012] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0013] At least one processor; and

[0014] A memory communicatively connected to the at least one processor; wherein,

[0015] The memory stores a computer program that can be executed by the at least one processor, which enables the at least one processor to perform the silicon-based light-emitting device lifetime prediction method according to any embodiment of the present invention.

[0016] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions for causing a processor to execute and implement the silicon-based light-emitting device lifetime prediction method according to any embodiment of the present invention.

[0017] The technical solution of this invention monitors the operating status data of the device to be predicted and obtains a preset lifetime prediction model according to the device to be predicted. The preset lifetime prediction model includes at least the correspondence between the acceleration factor and temperature. Based on the operating status data and the preset lifetime prediction model, the device lifetime of the device to be predicted is predicted. This invention considers the influence of temperature in the lifetime prediction process of silicon-based light-emitting devices, which can improve the lifetime prediction accuracy and help improve the lifetime management effect of silicon-based light-emitting devices.

[0018] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 This is a flowchart of a method for predicting the lifetime of a silicon-based light-emitting device according to Embodiment 1 of the present invention;

[0021] Figure 2 This is a flowchart of another method for predicting the lifetime of a silicon-based light-emitting device according to Embodiment 2 of the present invention;

[0022] Figure 3 This is an example diagram illustrating the effect of duty cycle on the brightness decay rate of an OLED device according to Embodiment 3 of the present invention;

[0023] Figure 4 This is an example diagram illustrating the effect of temperature on the brightness decay rate of an OLED device, provided in Embodiment 3 of the present invention.

[0024] Figure 5 This is an example diagram of a silicon-based OLED microdisplay provided according to an embodiment of the present invention;

[0025] Figure 6 This is an example diagram of a photoelectric characteristic monitoring system provided according to an embodiment of the present invention;

[0026] Figure 7 This is a fitting effect curve of an MSED model provided according to an embodiment of the present invention;

[0027] Figure 8 This is a fitting effect curve of an ALEM model provided according to an embodiment of the present invention;

[0028] Figure 9 This is a fitting effect curve of a brightness attenuation model provided according to an embodiment of the present invention;

[0029] Figure 10 This is a schematic diagram of the structure of a silicon-based light-emitting device lifetime prediction device according to Embodiment 4 of the present invention;

[0030] Figure 11 This is a schematic diagram of the structure of an electronic device that implements the silicon-based light-emitting device lifetime prediction method provided in Embodiment 5 of the present invention. Detailed Implementation

[0031] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0032] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0033] Example 1

[0034] Figure 1 This is a flowchart of a silicon-based light-emitting device lifetime prediction method according to Embodiment 1 of the present invention. This embodiment is applicable to the prediction of silicon-based light-emitting device lifetime. The method can be executed by a silicon-based light-emitting device device, which can be implemented in hardware and / or software. This device can be configured in the silicon-based light-emitting device or its host control system. Figure 1 As shown, the method includes:

[0035] Step 110: Obtain the operating status data of the device to be predicted, and obtain the preset lifetime prediction model configured for the device to be predicted.

[0036] The operating status data can be information representing the working state of the device to be predicted. The operating status data can include the duty cycle, temperature, current and brightness of the device to be predicted. The preset lifetime prediction model can be a parameter relationship model used to predict the lifetime of the device to be predicted. The preset lifetime prediction model can at least include the correspondence between the acceleration factor determined by temperature and the lifetime. This correspondence can be determined by the actual physical model of the silicon-based light-emitting device.

[0037] In this embodiment of the invention, various parameter information of the device to be predicted under its working state can be collected, including parameters such as the duty cycle, temperature and current of the device to be predicted. For example, the brightness of the device to be predicted can be collected by an optical characteristic measurement device, and the temperature data of the device to be predicted can be collected by a temperature monitoring device. The collected parameters such as the duty cycle, temperature and current of the device to be predicted can be used as its corresponding operating state data.

[0038] Specifically, a preset lifetime prediction model for the device to be predicted can be obtained. This preset lifetime prediction model can be pre-configured and associated with the device. The preset lifetime prediction model can be configured on the controller of the device or a remote server, or it can be configured within the device itself. It is understood that the same preset lifetime prediction model can be configured for devices from the same production batch.

[0039] Step 120: Determine the device lifetime of the device to be predicted based on the preset lifetime prediction model and operating status data; wherein, the preset lifetime prediction model includes at least the correspondence between the acceleration factor and temperature.

[0040] Specifically, the operating status data can be substituted into the preset lifetime prediction model, and the lifetime of the device to be predicted can be predicted through the preset lifetime prediction model. The lifetime of the device can be specifically defined as the lighting time when the brightness of the device to be predicted decays to half of its initial brightness.

[0041] In this embodiment of the invention, by monitoring the operating status data of the device to be predicted, a preset lifetime prediction model is obtained according to the device to be predicted. The preset lifetime prediction model includes at least the correspondence between the acceleration factor and temperature. Based on the operating status data and the preset lifetime prediction model, the device lifetime of the device to be predicted is predicted. This embodiment of the invention considers the influence of temperature in the lifetime prediction process of silicon-based light-emitting devices, which can improve the lifetime prediction accuracy and help improve the lifetime management effect of silicon-based light-emitting devices.

[0042] Example 2

[0043] Figure 2 This is a flowchart of another silicon-based light-emitting device lifetime prediction method provided by Embodiment 2 of the present invention. The embodiments of the present invention describe the generation process of the preset lifetime prediction model. See [link to documentation]. Figure 2 The method provided in this embodiment of the invention specifically includes the following steps:

[0044] Step 210: Obtain the brightness time-series decay dataset of the sample device under the accelerated aging environment of the system, wherein the sample device and the device to be predicted belong to the same production batch.

[0045] The sample devices can be silicon-based light-emitting devices belonging to the same production batch as the devices to be predicted. For example, one or more silicon-based light-emitting devices from each production batch can be selected as sample devices. The system accelerated aging environment can be the environment configuration used to conduct aging experiments on the sample devices. This environment configuration can include the current, duty cycle, and temperature used by the sample devices. The brightness time-series decay dataset can include the brightness values ​​of the sample devices as the lighting time changes. The brightness time-series decay dataset can include a set of brightness values ​​arranged in order of lighting time.

[0046] In this embodiment of the invention, the sample device can be subjected to aging experiments according to the system accelerated aging environment. The brightness values ​​of the sample device at different lighting durations during the aging experiment can be obtained. The collected brightness values ​​can be used as a brightness time-series decay dataset. In some embodiments, the brightness time-series decay dataset also includes the lighting duration corresponding to each brightness value. It is understood that multiple sets of system accelerated aging environment configurations can exist; that is, the same sample device can be subjected to different aging experiments under different system accelerated aging environments, thereby improving the accuracy of sample device testing.

[0047] Step 220: Solve the model parameters in the preset lifetime prediction model template based on the environmental configuration of the accelerated aging environment of the system, the device attribute parameters of the sample devices, and the brightness time-series decay dataset.

[0048] Among them, the environment configuration can be information on configuring the accelerated aging environment of the system. The environment configuration can include the temperature, current and duty cycle of the sample device for aging experiments. The device attribute parameters can be information reflecting the physical characteristics of the sample device. The device attribute parameters can include the device material characteristic coefficient and initial brightness, etc. The model parameters can be relevant parameters that affect the lifetime acceleration factor. The model parameters can reflect the correspondence between the acceleration factor and temperature.

[0049] In this embodiment of the invention, the environmental configuration, device attribute parameters, and each set of brightness data and illumination duration in the brightness time-series decay dataset can be substituted into a preset lifetime prediction model template. The model parameters in each preset lifetime prediction model template after parameter filling can be solved to obtain the model parameter value for each model parameter. The preset lifetime prediction model template can specifically be:

[0050] lnt 1 / 2 =lnC-n(T)lnL0

[0051] Where n(T) = aexp(b*T) c )+d,n(T) represents the acceleration factor, T represents the temperature, C represents the material property coefficient of the sample device, L0 represents the initial brightness of the sample device, and t 1 / 2The lifetime of the sample device is represented by a, b, c, and d, which represent the model parameters in the preset lifetime prediction model template.

[0052] Step 230: Use the preset lifetime prediction model template with the obtained model parameters as the preset lifetime prediction model.

[0053] Specifically, the obtained model parameters can be used to fill the preset lifespan prediction model template, and the filled preset lifespan prediction model template can be used as the preset lifespan prediction model.

[0054] Step 240: Obtain the operating status data of the device to be predicted, and obtain the preset lifetime prediction model configured for the device to be predicted.

[0055] Step 250: Determine the device lifetime of the device to be predicted based on the preset lifetime prediction model and operating status data; wherein, the preset lifetime prediction model includes at least the correspondence between the acceleration factor and temperature.

[0056] In this embodiment of the invention, by collecting a dataset of brightness time-series decay of sample devices under accelerated aging conditions, the model parameters in a preset lifetime prediction model template are solved according to the environmental configuration of the accelerated aging environment, the device attribute parameters of the sample devices, and the brightness time-series decay dataset. The preset lifetime prediction model template with the obtained model parameters can be used as a preset lifetime prediction model for devices to be predicted in the same production batch as the sample devices. The operating status data of the devices to be predicted is obtained, and the operating status data is substituted into the preset lifetime prediction model to determine the device lifetime. This embodiment of the invention constructs a preset lifetime prediction model based on the relationship between the acceleration factor and temperature. Based on the preset lifetime prediction model, the lifetime of silicon-based light-emitting devices is predicted. The influence of temperature can be considered in the device lifetime prediction process, which can improve the lifetime prediction accuracy and help improve the lifetime management effect of silicon-based light-emitting devices.

[0057] Based on the above embodiments of the invention, the model parameters within the preset lifetime prediction model template are solved according to the environmental configuration of the accelerated aging environment of the system, the device attribute parameters of the sample devices, and the brightness time-series decay dataset, including:

[0058] Substitute the brightness decay data of each group in the brightness time-series decay dataset, the duty cycle of the environment configuration corresponding to each group of brightness decay data, and the initial brightness of the device attribute parameters into the first relational formula, and fit each first relational formula to obtain the device structure coefficient and initial brightness coefficient of the sample device.

[0059] Substitute the device structure coefficients and brightness attenuation data of each group into the second relational formula, and fit each second relational formula to obtain the material property coefficients and model parameters of the sample device.

[0060] The first relational formula includes at least the following: L0 represents the initial brightness, L(t) represents the brightness at time t, D represents the duty cycle, and τ full β represents the initial luminance coefficient when the duty cycle is 1, and β represents the device structure coefficient.

[0061] The second relational formula includes at least: ln[-ln(α)]=β(lnDt-lnτ) full )τ full =CD / (L0) n(T) (ln2) 1 / β ), n(T)=aexp(b*T) c )+d,n(T) represents the acceleration factor, T represents the temperature, C represents the material property coefficient, L0 represents the initial brightness, and t 1 / 2 The lifetime of the sample device is represented by , and a, b, c, and d represent model parameters.

[0062] Among them, the brightness decay data can be the correlation data between the lighting duration and brightness of a single sample device, while the brightness time-series decay dataset can include the brightness decay data of multiple sample devices.

[0063] In this embodiment of the invention, each set of brightness attenuation data, duty cycle, and initial brightness can be filled into a first relational formula. The obtained first relational formulas can then be fitted to obtain the device structure coefficients and initial brightness coefficients. Specifically, the acquired...

[0064] Specifically, the determined device structure coefficients and each set of brightness attenuation data can be filled into a second relational formula. By fitting each second relational formula, the device structure coefficients and initial brightness coefficients of the sample device can be obtained.

[0065] Based on the above embodiments of the invention, the first relational formula includes at least: L0 represents the initial brightness, L(t) represents the brightness at time t, D represents the duty cycle, and τ full β represents the initial luminance coefficient when the duty cycle is 1, and β represents the device structure coefficient.

[0066] The second relational formula includes at least: ln[-ln(α)]=β(lnDt-lnτ) full )τ full =CD / (L0) n(T) (ln2) 1 / β ), n(T)=aexp(b*T) c )+d,n(T) represents the acceleration factor, T represents the temperature, C represents the material property coefficient, L0 represents the initial brightness, and t 1 / 2 The lifetime of the sample device is represented by , and a, b, c, and d represent model parameters.

[0067] Furthermore, based on the above embodiments of the invention, the operating status data of the device to be predicted is obtained, and a preset lifetime prediction model configured for the device to be predicted is obtained, including:

[0068] The initial brightness and current operating temperature of the device to be predicted are collected as operating status data; the preset lifetime prediction model is read from the storage element of the device to be predicted.

[0069] Specifically, during the operation of the device to be predicted, initial two points and the current stable operation are collected as the operating status data of the device to be predicted, and a pre-configured preset lifetime prediction model is read from the storage element of the device to be predicted.

[0070] Based on the above embodiments of the invention, the method further includes: performing a performance evaluation on a preset lifetime prediction model based on a brightness time-series decay dataset, wherein the performance evaluation includes at least one of an average error evaluation and a coefficient of determination evaluation.

[0071] In this embodiment of the invention, after obtaining the preset lifetime prediction model, the average error and coefficient of determination of the preset lifetime prediction model can be evaluated according to the brightness time-series decay dataset. The average error evaluation can be determined in the following way:

[0072] Where, α actual α represents the true brightness of the brightness temporal decay dataset, while α estimate This represents the predicted brightness generated by the preset lifetime prediction model. The closer r is to 0, the higher the prediction accuracy of the preset lifetime prediction model.

[0073] The coefficient of determination can be determined in the following ways:

[0074]

[0075] Where N is the number of sampling points, y i These are the measured values ​​at the sampling points. This is the average value of the measured values. R represents the predicted value of the sampled points. 2 The closer the value is to 1, the better the prediction performance of the preset lifetime prediction model.

[0076] Example 3

[0077] The brightness decay of OLED devices is directly related to their initial brightness. The existing SED model can reflect the brightness decay curve of OLEDs, and the mathematical expression of this brightness decay curve is as follows:

[0078]

[0079] L0 n t 1 / 2 =C

[0080] Where L(t) is the brightness value at time t, L0 is the initial brightness, τ is the initial brightness coefficient related to the initial brightness, n is the acceleration factor related to the decay rate, and t 1 / 2 β represents the time it takes for the brightness of an OLED device to decay to half of its initial brightness, i.e., the lifetime of the OLED device. β and C are coefficients related to the device structure and material properties, and their values ​​remain constant within the same production batch. Experimental studies show that, under the same initial brightness conditions, the lifetime characteristics of silicon-based OLED devices in the same production batch are affected by the driving duty cycle. Figure 3 As shown, the brightness decay rate exhibits a non-linear accelerating trend as the duty cycle increases, such as... Figure 4 As shown, ambient temperature also significantly accelerates the brightness decay rate. Currently, the correlation between pulse width modulation (PWM) driving and device degradation is discussed. PWM controls brightness based on time-domain integration, adjusting the effective emission time through duty cycle; its pulse characteristics directly affect carrier injection stability. However, existing SED models treat the decay factor as a static constant, failing to consider the impact of temperature and duty cycle on OLED brightness decay. To address these shortcomings, the brightness decay model provided in this embodiment is as follows:

[0081]

[0082] L0 n(T) t 1 / 2 =C

[0083] In the above formula, τfull is the value of τ when D = 1, and its relationship with L0 can be expressed by the following formula: τ full =CD / (L0) n(T) (ln2) 1 / β This indicates that, based on this, we can further derive the following formula:

[0084] L(t) = L0exp{-[tL0]} n(T) (ln2) 1 / β / CD] β}

[0085] To determine the brightness decay model of OLED devices, a systematic aging experiment can be conducted under specific driving stress conditions. This systematic aging process can cover m combinations of driving variables (S i (i = 1, 2, ..., m), each group includes n i Sample devices were prepared in the same batch. The brightness decay process of each sample device was monitored using a high-precision optical spot detection system, and L was defined as... ik(t) represents the brightness value of the k-th sample in the i-th group (k = 1, 2, ..., m) at time t, and records the corresponding lifetime t for each group. 1 / 2ik .

[0086] Based on statistical analysis, the average brightness decay data [t,Li(t)] and average lifetime data [T,t] of OLED samples under each driving variable can be obtained. 1 / 2i [(T)], where t represents the illumination duration of the OLED device, Li(t) represents the brightness value of the OLED device at time t, and T represents the brightness value of the OLED device. 1 / 2i (T) represents the lifetime at brightness level T. This can be expressed as follows for each driving quantity:

[0087]

[0088] Normalizing the above equation: α = L(t) / L0, and then taking the logarithm twice on both sides, we get:

[0089] ln[-ln(α)]=β(lnDt-lnτ full )

[0090] Where, τ full =CD / (L0) n(T) (ln2) 1 / β Substitute [t, Li(t)] into the above equation, and determine the parameters β and τ using the least squares method. full .

[0091] Similarly, this can be applied to L0. n(T) t 1 / 2 Taking the logarithm of both sides of C gives:

[0092] lnt 1 / 2 =lnC-n(T)lnL0

[0093] [T,t] 1 / 2i Substituting (T) into the above formula, and determining the parameter C and the parameter values ​​in the expression of n(T) using the least squares method, the specific expression of n(T) can be n(T) = aexp(b*T) c Based on the determined parameters C and the expression for n(T), the lifetime prediction model lnt is determined. 1 / 2 =lnC-n(T)lnL0.

[0094] Based on the above lifetime prediction model, a systematic aging experiment can be conducted under multi-stress coupling scenarios to address the brightness decay mechanism of OLED microdisplays. By using a photoelectric characteristic monitoring system to obtain discrete time-series sampling measurements of the luminous brightness evolution data of the OLED microdisplay during the decay period, and by performing nonlinear fitting on the measured luminous brightness evolution data based on the least squares method, a specific lifetime prediction model for the corresponding OLED microdisplay can be obtained. This specific lifetime prediction model can be called a brightness decay model, and the brightness decay of the OLED microdisplay under different conditions can be accurately predicted based on the specific lifetime prediction model.

[0095] In this embodiment of the invention, 11 sets of accelerated aging experiments can be executed, and their parameter configurations are detailed in Table 1. The fixed parameter F covers the key driving variables, while the monitored variable M is used to track the device state evolution in real time. Accelerated aging experiments 1-3 are conducted in constant current driving mode, aiming to construct a basic model parameter fitting dataset through standardized decay data acquisition, for... and L0 n(T) t 1 / 2 The accurate fitting of the coefficients in C provides support; accelerated aging experiments (groups 4-11) constitute the core module of the validation system, specifically used to evaluate the model's predictive accuracy and the engineering applicability of the compensation strategy. A systematic data comparison and analysis method is employed to achieve a dynamic matching and quantitative characterization of the model's predicted trajectory and the measured degradation curve, and the prediction effect is analyzed based on the difference in lifespan between the predicted and actual groups. This validation framework, through the quantitative analysis of prediction accuracy indicators, provides an evaluation basis for assessing the model's technical feasibility.

[0096] Table 1 Experimental parameter settings

[0097]

[0098]

[0099] Based on the degradation trajectory analysis of the preliminary experiments, the brightness decay of OLED devices exhibits nonlinear time-varying characteristics, with the decay rate decreasing significantly over time. To accurately capture the dynamic characteristics of different degradation stages, an adaptive sampling strategy was adopted: a dense sampling interval of 2 hours was set in the initial high decay rate range; a medium sampling frequency of 6 hours was adjusted in the mid-term stable decay stage; and a sparse sampling scheme of 12 hours was used in the later near-steady-state degradation range, effectively reducing the amount of redundant data. The test was terminated when the brightness decayed to 50% of the initial value. The number of OLED samples in each experiment was 20 to ensure the reliability of the statistical analysis.

[0100] In this embodiment of the invention, the experimental samples can be as follows: Figure 5 The silicon-based OLED microdisplay shown can be used as a photoelectric property monitoring system. Figure 6As shown, the minimum measurement range of this system is 3mm × 3mm, and the brightness range is 0.003~500000cd / m2 (±2%). By performing nonlinear fitting on the measured data from aging experiments 1 to 3, the fitting parameters β=0.9808 and C=68722.8296 were determined. The functional relationship between the acceleration factor n and temperature T is shown below:

[0101] n = 0.2633exp(0.0025)×T 0.2407 -0.2386

[0102] To verify the reliability of the brightness attenuation model, a model was constructed that includes the average error r and the coefficient of determination (R²). 2 The dual-indicator evaluation system (R) is used. 2 The closer r is to 1 and the closer r is to 0, the higher the model's prediction accuracy and the more concentrated the error distribution. Through a dual-index joint verification mechanism, the theoretical completeness and engineering application suitability of the model can be comprehensively evaluated.

[0103] Under the conditions of aging experiments 4 to 11, the effectiveness of the model prediction results was verified based on the above evaluation system. Specific data are shown in Table 2. Experimental data show that under the same temperature test conditions, the duty cycle and fitting accuracy are significantly negatively correlated; however, when the duty cycle remains the same, the fitting accuracy generally increases with temperature, indicating that the model has higher fitting accuracy and more ideal performance in the medium-high temperature region and under higher duty cycles. By comparing statistical indicators under different temperature conditions, R0 at 30℃, 40℃, 50℃, and 60℃... 2 The average values ​​were 0.987, 0.993, 0.992, and 0.994, respectively, with R at 60℃. 2 The value is highest at 30℃ and slightly lower at 30℃, indicating that the model can achieve reliable fitting in the 30℃-60℃ range. The model shows the best fit consistency in the mid-to-high temperature range and maintains good performance in the low temperature range. The average values ​​of r are 0.52%, 0.30%, 1.26%, and 2.60%, respectively, showing an increasing trend with increasing temperature. This indicates that although the fitting accuracy is higher in the mid-to-high temperature range, the brightness jump is smaller in the mid-to-low temperature range. These data characteristics show that, under constant driving current conditions, the DTSED model can accurately estimate the brightness degradation process, with R values ​​for each temperature range. 2 The values ​​are all above 0.98, demonstrating the excellent consistency of the model's fitted curves. Furthermore, the r-value in the medium-low temperature region is below 2%, significantly better than the 2.60% in the high-temperature region, highlighting the model's accuracy advantage under different temperature gradients. The experimental results quantitatively validate the reliability of the model.

[0104] Table 2 Prediction results of brightness attenuation model

[0105]

[0106] To systematically verify the rationality and prediction accuracy of the proposed brightness decay model, the classical model ALEM (Accelerated Life Extrapolation Model) considering temperature and the classical model MSED (Modified Stretched Exponential Decay) considering duty cycle were selected as benchmarks. The prediction comparison data between the proposed model and the benchmark models are shown in Table 3. It can be seen that R 2 There is a clear pattern: R 2 MSED <R 2 ALEM <R 2 此模型 The proposed model significantly outperforms the DDDM, MSED, and ALEM models in terms of fitting accuracy, and r also exhibits a pattern: r MSED >r ALED >r 此模型 The proposed model shows a significant reduction in the error rate (r) compared to the baseline model, indicating higher spatial consistency in its predictions. Compared to the baseline model, the proposed model not only demonstrates a significant advantage in fitting accuracy but also exhibits a more concentrated error distribution and a higher degree of agreement between its predictions and measured data, fully demonstrating its ability to accurately characterize the brightness decay process under different driving conditions. Experimental results confirm the superiority of the proposed model in prediction accuracy and stability compared to existing methods.

[0107] Table 3 Comparison of prediction results for brightness attenuation model

[0108]

[0109] For each model, four sets of curves were selected: (40℃, 100%), (40℃, 50%), (50℃, 100%), and (50℃, 50%). The fitting effect curves of the three models are shown below. Figure 7 , Figure 8 as well as Figure 9 As shown, the experimental data exhibit typical time-dependent nonlinear decay characteristics, with the decay amplitude significantly increasing with increasing driving duty cycle and ambient temperature. This closely matches the actual degradation behavior of OLED devices, directly verifying the reliability of the test data and experimental scheme. The model prediction curve and the measured data show good consistency across the entire temperature range, especially under medium-low temperature conditions and extreme high-temperature conditions, accurately capturing the dynamic trend of brightness decay. This fully demonstrates the engineering applicability and predictive effectiveness of the proposed model under wide temperature range and different duty cycle driving conditions.

[0110] This invention addresses the significant impact of duty cycle and ambient temperature on OLED lifetime. Traditional prediction models suffer from accuracy limitations due to the lack of coupling between these two key stress parameters. Based on measured brightness degradation data from seven sets of silicon-based OLED microdisplays under various operating conditions, this study proposes a brightness degradation model based on the SED model. This model incorporates duty cycle and ambient temperature as core variables, achieving accurate modeling of the OLED brightness degradation process under different driving conditions. Experimental results show that within the temperature range of 30℃-60℃, the model's coefficient of determination is consistently higher than 0.98, the relative error is controlled within 2%, and the error distribution is highly concentrated, significantly outperforming traditional prediction models.

[0111] Example 4

[0112] Figure 10 This is a schematic diagram of a silicon-based light-emitting device lifetime prediction device according to Embodiment 4 of the present invention. Figure 10 As shown, the device includes:

[0113] The information acquisition module 310 is used to acquire the operating status data of the device to be predicted and to acquire the preset lifetime prediction model configured for the device to be predicted.

[0114] The lifetime prediction module 320 is used to determine the device lifetime of the device to be predicted based on the preset lifetime prediction model and the operating status data; wherein, the preset lifetime prediction model includes at least the correspondence between the acceleration factor and temperature.

[0115] In this embodiment of the invention, the operating status data of the device to be predicted is monitored by the information acquisition module, and the lifetime prediction module obtains a preset lifetime prediction model according to the device to be predicted. The preset lifetime prediction model includes at least the correspondence between the acceleration factor and temperature. Based on the operating status data and the preset lifetime prediction model, the device lifetime of the device to be predicted is predicted. This embodiment of the invention considers the influence of temperature in the lifetime prediction process of silicon-based light-emitting devices, which can improve the lifetime prediction accuracy and help improve the lifetime management effect of silicon-based light-emitting devices.

[0116] In this embodiment of the invention, the silicon-based light-emitting device lifetime prediction device further includes: a model building module, which includes:

[0117] The data acquisition unit is used to acquire the brightness time-series decay dataset of the sample device under the accelerated aging environment of the system, wherein the sample device and the device to be predicted belong to the same production batch.

[0118] The model parameter unit is used to solve the model parameters in the preset lifetime prediction model template based on the environmental configuration of the accelerated aging environment of the system, the device attribute parameters of the sample device, and the brightness time-series decay dataset.

[0119] The model storage unit is used to use the preset life prediction model template with the obtained model parameters as the preset life prediction model.

[0120] Based on the above embodiments of the invention, the data acquisition unit is specifically used to: substitute each set of brightness decay data in the brightness time-series decay dataset, the duty cycle of the environment configuration corresponding to each set of brightness decay data, and the initial brightness of the device attribute parameters into the first relational formula, and fit each of the first relational formulas to obtain the device structure coefficient and initial brightness coefficient of the sample device.

[0121] Substitute the device structure coefficients and the brightness attenuation data of each group into the second relational formula, and fit each of the second relational formulas to obtain the material property coefficients and model parameters of the sample device;

[0122] Wherein, the first relational formula includes at least: L0 represents the initial brightness, L(t) represents the brightness at time t, D represents the duty cycle, and τ full β represents the initial luminance coefficient when the duty cycle is 1, and β represents the device structure coefficient.

[0123] The second relational formula includes at least: ln[-ln(α)]=β(lnDt-lnτ) full )τ full =CD / (L0) n (T) (ln2) 1 / β ), n(T)=aexp(b*T) c )+d,n(T) represents the acceleration factor, T represents the temperature, C represents the material property coefficient, L0 represents the initial brightness, and t 1 / 2 The lifetime of the sample device is represented by , and a, b, c, and d represent the model parameters.

[0124] Based on the above embodiments of the invention, the preset lifetime prediction model template includes at least:

[0125] lnt 1 / 2 =lnC-n(T)lnL0

[0126] Where n(T) = aexp(b*T) c )+d,n(T) represents the acceleration factor, T represents the temperature, C represents the material property coefficient of the sample device, L0 represents the initial brightness of the sample device, and t 1 / 2 The lifetime of the sample device is represented by a, b, c, and d, which represent the model parameters within the preset lifetime prediction model template.

[0127] Based on the above embodiments of the invention, the information acquisition module 310 is specifically used to: collect the initial brightness and current operating temperature of the device to be predicted as the operating status data; and read the preset lifetime prediction model pre-configured in the storage element of the device to be predicted.

[0128] Based on the above embodiments of the invention, the model building module further includes a performance module unit, specifically used to perform performance evaluation on the preset lifetime prediction model based on the brightness time-series decay dataset, wherein the performance evaluation includes at least one of average error evaluation and coefficient of determination evaluation.

[0129] The silicon-based light-emitting device lifetime prediction device provided in the embodiments of the present invention can execute the silicon-based light-emitting device lifetime prediction method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of executing the method.

[0130] Example 5

[0131] Figure 11 This is a schematic diagram of an electronic device that implements the silicon-based light-emitting device lifetime prediction method provided in Embodiment 5 of the present invention. 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 processors, cellular phones, smartphones, wearable devices (such as helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0132] like Figure 11 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 may also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0133] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0134] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as silicon-based light-emitting device lifetime prediction methods.

[0135] In some embodiments, the silicon-based light-emitting device lifetime prediction method can be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the silicon-based light-emitting device lifetime prediction method described above can be performed. Alternatively, in other embodiments, processor 11 can be configured to perform the silicon-based light-emitting device lifetime prediction method by any other suitable means (e.g., by means of firmware).

[0136] Various embodiments of the systems and techniques described above 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), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0137] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0138] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0139] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device 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 provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, 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 sound input, voice input, or tactile input).

[0140] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0141] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through a communication network. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0142] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0143] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for predicting the lifetime of a silicon-based light-emitting device, characterized in that, The method includes: Obtain the operating status data of the device to be predicted, and obtain the preset lifetime prediction model configured for the device to be predicted; The device lifetime of the device to be predicted is determined based on the preset lifetime prediction model and the operating status data. The preset lifetime prediction model includes at least the correspondence between the acceleration factor and temperature.

2. The method according to claim 1, characterized in that, Also includes: Obtain the brightness time-series decay dataset of sample devices under accelerated aging environment of the system, wherein the sample devices and the device to be predicted belong to the same production batch; The model parameters in the preset lifetime prediction model template are solved based on the environmental configuration of the accelerated aging environment of the system, the device attribute parameters of the sample devices, and the brightness time-series decay dataset. The preset life prediction model template with the obtained model parameters is used as the preset life prediction model.

3. The method according to claim 2, characterized in that, The step of solving the model parameters within the preset lifetime prediction model template based on the environmental configuration of the accelerated aging environment of the system, the device attribute parameters of the sample devices, and the brightness time-series decay dataset includes: Substitute each set of brightness decay data in the brightness time-series decay dataset, the duty cycle of the environmental configuration corresponding to each set of brightness decay data, and the initial brightness of the device attribute parameters into the first relational formula, and fit each of the first relational formulas to obtain the device structure coefficient and initial brightness coefficient of the sample device. Substitute the device structure coefficients and the brightness attenuation data of each group into the second relational formula, and fit each of the second relational formulas to obtain the material property coefficients and model parameters of the sample device; Wherein, the first relational formula includes at least: L0 represents the initial brightness, L(t) represents the brightness at time t, D represents the duty cycle, and τ full β represents the initial luminance coefficient when the duty cycle is 1, and β represents the device structure coefficient. The second relational formula includes at least: ln[-ln(α)]=β(lnDt-lnτ) full )τ full =CD / (L0) n(T) (ln2) 1 / β ), n(T)=aexp(b*T) c )+d,n(T) represents the acceleration factor, T represents the temperature, C represents the material property coefficient, L0 represents the initial brightness, and t 1 / 2 The lifetime of the sample device is represented by , and a, b, c, and d represent the model parameters.

4. The method according to claim 2, characterized in that, The preset lifetime prediction model template includes at least: lnt 1 / 2 =lnC-n(T)lnL0 Where n(T) = aexp(b*T) c )+d,n(T) represents the acceleration factor, T represents the temperature, C represents the material property coefficient of the sample device, L0 represents the initial brightness of the sample device, and t 1 / 2 The lifetime of the sample device is represented by a, b, c, and d, which represent the model parameters within the preset lifetime prediction model template.

5. The method according to claim 1, characterized in that, The step of acquiring the operating status data of the device to be predicted and acquiring the preset lifetime prediction model configured for the device to be predicted includes: The initial brightness and current operating temperature of the device to be predicted are collected as the operating status data; The preset lifetime prediction model is read from the storage element of the device to be predicted.

6. The method according to claim 2, characterized in that, Also includes: The performance of the preset lifetime prediction model is evaluated based on the brightness time-series decay dataset. The performance evaluation includes at least one of the average error evaluation and the coefficient of determination evaluation.

7. A silicon-based light-emitting device lifetime prediction device, characterized in that, include: The information acquisition module is used to acquire the operating status data of the device to be predicted and to acquire the preset lifetime prediction model configured for the device to be predicted. A lifetime prediction module is used to determine the device lifetime of the device to be predicted based on the preset lifetime prediction model and the operating status data. The preset lifetime prediction model includes at least the correspondence between the acceleration factor and temperature.

8. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the silicon-based light-emitting device lifetime prediction method according to any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that, when executed by a processor, implement the silicon-based light-emitting device lifetime prediction method according to any one of claims 1-6.

10. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the silicon-based light-emitting device lifetime prediction method according to any one of claims 1-6.

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