Semiconductor laser aging test system and method

Abstract

The present invention relates to the field of semiconductor laser technology, and in particular to a semiconductor laser aging test system and method. The system and method comprise the following steps: collecting the output light beam of the semiconductor laser in real time by a photodetector to obtain light intensity time series data; performing a thermal distribution scan on the semiconductor laser by a temperature sensor array to obtain a device temperature gradient diagram; predicting the performance degradation trend of the semiconductor laser based on the light intensity time series data and the device temperature gradient diagram to obtain life assessment data; and optimizing accelerated aging test parameters of the semiconductor laser based on the life assessment data to obtain a customized aging test solution, thereby solving the technical problem that traditional aging test methods are difficult to accurately reflect the performance evolution process of the device under actual working conditions.

Description

Technical Field

[0001] The present invention relates to the technical field of semiconductor lasers, and in particular to a semiconductor laser aging test system and method. Background Art

[0002] Semiconductor lasers, core components in optoelectronics, are widely used in communications, healthcare, industrial processing, and military applications. Their operational stability and service life directly impact the performance and reliability of related equipment, making aging testing and lifespan assessment of semiconductor lasers crucial. However, with increasing complexity in laser structures and increasing output power, traditional aging testing methods are no longer able to accurately reflect the performance evolution of devices under actual operating conditions.

[0003] Currently, conventional aging tests rely on constant current drive and long-term operation in a fixed temperature environment. These tests lack real-time device status monitoring and feedback mechanisms, resulting in long test cycles and low efficiency. Key degradation factors, such as local thermal effects and light output fluctuations, are often overlooked. Furthermore, existing testing methods typically employ a unified standard testing process, making it difficult to provide personalized, refined aging assessment solutions for lasers of different models or application scenarios, limiting the accuracy and practicality of test results.

[0004] To address these issues, there is an urgent need to develop an aging test method that integrates dynamic light intensity monitoring and thermal distribution analysis to achieve early identification and quantitative assessment of semiconductor laser performance degradation trends. This approach can then optimize accelerated aging parameters and develop more targeted aging test strategies. This will not only help improve test efficiency and prediction accuracy, but also provide reliable data support for laser design improvements and quality control. Summary of the Invention

[0005] The main purpose of the present invention is to provide a semiconductor laser aging test system and method, which solves the technical problem that traditional aging test methods are difficult to accurately reflect the performance evolution process of the device under actual working conditions.

[0006] To achieve the above objectives, the present invention provides a semiconductor laser aging test system, comprising:

[0007] The acquisition module is used to collect the output light beam of the semiconductor laser in real time through a photodetector to obtain light intensity time series data;

[0008] A scanning module is used to scan the thermal distribution of the semiconductor laser using a temperature sensor array to obtain a device temperature gradient map;

[0009] The prediction module is used to predict the performance degradation trend of the semiconductor laser based on the light intensity time series data and the device temperature gradient map to obtain the life assessment data;

[0010] The optimization module is used to optimize the accelerated aging test parameters of semiconductor lasers based on lifetime assessment data to obtain a customized aging test plan.

[0011] The present invention also provides a semiconductor laser aging test method, comprising the following steps:

[0012] The output beam of the semiconductor laser is collected in real time by a photodetector to obtain light intensity time series data;

[0013] Use the temperature sensor array to scan the thermal distribution of the semiconductor laser and obtain the device temperature gradient map;

[0014] Based on the light intensity time series data and device temperature gradient diagram, the performance degradation trend of the semiconductor laser is predicted to obtain life assessment data;

[0015] Based on the lifetime assessment data, the accelerated aging test parameters of the semiconductor laser are optimized to obtain a customized aging test plan.

[0016] Furthermore, a temperature sensor array is used to scan the thermal distribution of the semiconductor laser to obtain a device temperature gradient map, including:

[0017] Based on the temperature sensor array, the temperature characteristics of each functional module of the semiconductor laser are detected to obtain the temperature characteristic set of each functional module, and the heat diffusion pattern analysis of the temperature characteristic set of each functional module is performed to obtain the heat diffusion pattern characteristic set;

[0018] The heat energy accumulation area of ​​the semiconductor laser is located by using the heat diffusion pattern feature set to obtain a heat energy accumulation area location set. When an abnormal area exists in the heat energy accumulation area location set, a temperature abnormal point mark set in the heat energy accumulation area location set is marked.

[0019] Using the temperature anomaly point marker set, the temperature distribution profile of the semiconductor laser is outlined to obtain a temperature distribution profile map, and the gradient of the temperature distribution profile map is calculated to obtain a device temperature gradient map;

[0020] When there is no abnormal area in the thermal energy concentration area positioning set, the thermal energy concentration area positioning set is used to outline the temperature distribution contour map, and the temperature distribution contour map is gradient calculated to obtain the device temperature gradient map.

[0021] Furthermore, based on the light intensity time series data and device temperature gradient diagram, the performance degradation trend of the semiconductor laser is predicted to obtain life assessment data, including:

[0022] Based on the light intensity peaks and valleys in the light intensity time series data, the light output stability of the semiconductor laser is preliminarily evaluated to obtain a light output stability evaluation set. The light output stability evaluation set is then subjected to fluctuation pattern analysis to obtain a light output fluctuation pattern feature set.

[0023] Through the light output fluctuation pattern feature set, the hot spot position in the device temperature gradient map is correlated and analyzed to obtain the hot spot position correlation feature set. Based on the hot spot position correlation feature set, the thermal-optical coupling effect of the semiconductor laser is evaluated to obtain the thermal-optical coupling effect quantitative evaluation set.

[0024] Using the quantitative evaluation set of the thermal-optical coupling effect, the power transmission loss trend of the semiconductor laser is analyzed to obtain a power transmission loss trend set. Based on the power transmission loss trend set, the key factors of performance degradation of the semiconductor laser are located to obtain a performance degradation key factor location set.

[0025] Based on the key factors positioning set of performance attenuation, the remaining effective lifetime of the semiconductor laser is predicted to obtain a preliminary result set of lifetime prediction, and the preliminary result set of lifetime prediction is corrected by environmental factors to obtain lifetime assessment data.

[0026] Furthermore, the power transmission loss trend of semiconductor lasers is analyzed using the quantitative evaluation set of thermal-optical coupling effects, and a power transmission loss trend set is obtained, including:

[0027] Based on the coupling strength data of the thermal-optical coupling effect quantitative evaluation set, the energy transmission path of the semiconductor laser is segmented and identified to obtain an energy transmission path segment identification set, and the loss node analysis is performed on the energy transmission path segment identification set to obtain a loss node analysis set;

[0028] The power loss source of the semiconductor laser is located through the loss node analysis set to obtain a power loss source location set, and the loss distribution characteristics of the semiconductor laser are summarized based on the power loss source location set to obtain a loss distribution characteristic summary set;

[0029] By using the loss distribution characteristic summary set, the relationship between the power transmission of the semiconductor laser and time is established to obtain the power transmission time relationship set, and the key parameters of the power transmission time relationship set are extracted to obtain the key parameter extraction set;

[0030] Based on the key parameter extraction set, the power transmission loss trend of the semiconductor laser is analyzed to obtain the power transmission loss trend set.

[0031] Furthermore, based on the power loss source location set, the loss distribution characteristics of the semiconductor laser are summarized to obtain a loss distribution characteristic summary set, including:

[0032] Based on the power loss source location set, energy density detection is performed on each loss source area of ​​the semiconductor laser to obtain the energy density set of each loss source area, and spectrum analysis is performed on the energy density set of each loss source area to obtain a spectrum feature set;

[0033] The loss source spatial distribution of the semiconductor laser is located by using the spectrum feature set to obtain a loss source spatial distribution location set, and the loss source correlation relationship analysis of the semiconductor laser is performed based on the loss source spatial distribution location set to obtain a loss source correlation relationship analysis set;

[0034] Using the loss source correlation analysis set, the loss propagation path of the semiconductor laser is tracked to obtain a loss propagation path tracking set, and based on the loss propagation path tracking set, the loss accumulation effect of the semiconductor laser is evaluated to obtain a loss accumulation effect evaluation set;

[0035] Based on the loss accumulation effect evaluation set, the overall loss distribution characteristics of the semiconductor laser are summarized to obtain the loss distribution characteristic summary set, where the loss distribution characteristic summary set includes the loss source concentration area, the loss source dispersion degree and the loss source dominant type.

[0036] Furthermore, based on the lifetime assessment data, the accelerated aging test parameters of the semiconductor laser are optimized to obtain a customized aging test solution, including:

[0037] Based on the power attenuation rate in the lifetime assessment data, the energy conversion efficiency of the semiconductor laser is divided into levels to obtain an energy conversion efficiency level set, and a boundary value analysis is performed on the energy conversion efficiency level set to obtain an energy conversion efficiency boundary value set;

[0038] The heat dissipation demand of the semiconductor laser is evaluated by using the energy conversion efficiency boundary value set to obtain a heat dissipation demand evaluation set, and the optimal aging temperature range of the semiconductor laser is preliminarily determined based on the heat dissipation demand evaluation set to obtain a preliminary determination set of the optimal aging temperature range;

[0039] Using the preliminary determination set of the optimal aging temperature range, the current carrying capacity of the semiconductor laser is analyzed to obtain a current carrying capacity analysis set, and based on the current carrying capacity analysis set, the current stress level range of the semiconductor laser is defined to obtain a current stress level range definition set;

[0040] Based on the current stress level range definition set, the overall aging process of the semiconductor laser is time-planned to obtain the overall aging process time planning set. The rationality of the overall aging process time planning set is then verified to obtain a customized aging test plan.

[0041] Furthermore, the current carrying capacity of the semiconductor laser is analyzed using the preliminary determined set of optimal aging temperature ranges, and a current carrying capacity analysis set is obtained, including:

[0042] Based on the preliminary determination set of the optimal aging temperature range, the electrode material characteristics of the semiconductor laser are detected to obtain an electrode material characteristic set, and the conductivity analysis of the electrode material characteristic set is performed to obtain a conductivity analysis set;

[0043] Identifying a current conduction path of the semiconductor laser through the conductivity analysis set to obtain a current conduction path identification set, and performing a current distribution uniformity evaluation on the semiconductor laser based on the current conduction path identification set to obtain a current distribution uniformity evaluation set;

[0044] The current overload risk area of ​​the semiconductor laser is located using the current distribution uniformity assessment set to obtain the current overload risk area positioning set. Based on the current overload risk area positioning set, the current carrying limit of the semiconductor laser is measured to obtain the current carrying capacity analysis set, wherein the current carrying capacity analysis set includes the maximum safe current, current fluctuation tolerance and current carrying stability.

[0045] The semiconductor laser aging test method provided by the present invention includes the following steps: real-time acquisition of the output light beam of the semiconductor laser by a photodetector to obtain light intensity time series data; thermal distribution scanning of the semiconductor laser by a temperature sensor array to obtain a device temperature gradient diagram; performance degradation trend prediction of the semiconductor laser based on the light intensity time series data and the device temperature gradient diagram to obtain life assessment data; and optimization of accelerated aging test parameters of the semiconductor laser based on the life assessment data to obtain a customized aging test solution, which solves the technical problem that traditional aging test methods are difficult to accurately reflect the performance evolution process of the device under actual working conditions, realizes comprehensive analysis combining the light intensity time series data and the temperature gradient diagram, and constructs a more comprehensive performance degradation model, thereby achieving quantitative prediction of the semiconductor laser life and judgment of the degradation trend, and improving the scientific nature and engineering practicality of the life assessment. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 1 is a schematic diagram of the steps of a semiconductor laser aging test method according to an embodiment of the present invention;

[0047] Figure 2 is a structural block diagram of a semiconductor laser aging test system according to an embodiment of the present invention;

[0048] Figure 3 It is a schematic block diagram of the structure of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0049] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0050] like Figure 1 As shown, Figure 1 A semiconductor laser aging test method according to an embodiment of the present invention includes the following steps:

[0051] Step S1 : collecting the output light beam of the semiconductor laser in real time through a photodetector to obtain light intensity time series data.

[0052] Specifically, the output beam of the semiconductor laser is collected in real time by a photodetector to obtain light intensity time series data. The implementation of this step depends on placing the photodetector at an appropriate position in the laser output optical path during the aging test, so that it can continuously receive the light signal it emits and convert it into a corresponding electrical signal output. After the electrical signal is amplified, filtered and analog-to-digital converted by the signal processing module, it finally forms digital time series data with time as the independent variable and light intensity as the dependent variable, thereby reflecting the changing trend of the laser output power under continuous working conditions. For example, in the application scenario of high-power semiconductor lasers used in optical fiber communication systems, the photodetector can be integrated into the optical transmission module, close to the laser output end face, to monitor its light output stability under different current drive and temperature conditions in real time, and then capture the possible initial characteristics of light decay, such as periodic fluctuations, sudden drops or slow decreases and other abnormal modes, providing key data support for subsequent performance degradation analysis.

[0053] Step S2: Scanning the thermal distribution of the semiconductor laser using a temperature sensor array to obtain a device temperature gradient map.

[0054] Specifically, a temperature sensor array is used to scan the thermal distribution of a semiconductor laser to obtain a device temperature gradient map. This step is achieved by distributing multiple temperature sensors in an array on different areas of the surface of the semiconductor laser or on its adjacent heat dissipation structure during the aging test, so that the temperature information of each point of the device can be synchronously collected during continuous operation. The temperature data obtained by these sensors are spatially positioned and time-synchronized by the data acquisition system. Combined with the preset device geometric model, a temperature distribution image with spatial resolution is constructed, namely the device temperature gradient map, to reflect the non-uniformity of the heat distribution of the laser under different working conditions and its evolution law. For example, semiconductor lasers used in high-power optical fiber communication systems may cause local overheating and lead to material fatigue or interface failure due to long-term operation. At this time, the temperature sensor array can be arranged in different functional areas of the laser chip and key positions of its packaging shell to capture the hotspot generation and expansion process in real time, providing key thermal behavior basis for subsequent performance degradation trend prediction and life assessment, thereby supporting the dynamic optimization of accelerated aging test parameters and the formation of customized test plans.

[0055] Step S3: predicting the performance degradation trend of the semiconductor laser based on the light intensity time series data and the device temperature gradient diagram to obtain life assessment data.

[0056] Specifically, based on the light intensity time series data and the device temperature gradient map, the performance degradation trend of the semiconductor laser is predicted to obtain life assessment data. This step is achieved by performing multi-source information fusion analysis on the light intensity time series data collected by the aforementioned photodetector and the device temperature gradient map obtained by the temperature sensor array, and using a preset performance degradation model or machine learning algorithm to jointly model the output characteristic changes and thermal distribution evolution of the laser during the aging process, thereby identifying early degradation signals of the device performance and predicting its future evolution path. This process usually includes statistical analysis of the light intensity fluctuation trend, hot spot migration behavior, and the correlation between the two, and establishes a life decay function in combination with known aging mechanisms, and finally outputs life assessment data reflecting the remaining service life of the device. For example, during the long-term operation of a semiconductor laser used in a high-power optical fiber communication system, if the light intensity time series data shows periodic jitter in the output power, and the device temperature gradient map reveals a continuously rising local hot spot near the active region, the system can judge that the laser is experiencing a decrease in photon efficiency caused by heat accumulation, and combine historical data to predict its life margin, providing a basis for subsequent optimization of accelerated aging test parameters.

[0057] Step S4: Based on the lifetime assessment data, the accelerated aging test parameters of the semiconductor laser are optimized to obtain a customized aging test solution.

[0058] Specifically, based on the life assessment data, the accelerated aging test parameters of the semiconductor laser are optimized to obtain a customized aging test scheme. This step is achieved by combining the working conditions and failure mechanisms of the semiconductor laser in actual application scenarios with the aforementioned life assessment data, and adjusting key aging stress parameters such as current injection intensity, temperature cycle period, and working environment humidity to make the accelerated aging process closer to its actual degradation path. Specifically, the system will dynamically adjust the excitation intensity and duration in the aging test based on the device sensitive characteristics reflected by the life assessment data, such as the response rate to heat accumulation or the light output attenuation slope, thereby avoiding the problem of over-testing or under-testing caused by fixed parameters in traditional aging tests. For example, for semiconductor lasers used in high-power optical fiber communication systems, if the life assessment data shows that their performance degradation is mainly affected by local hot spots in the active area, the thermal cycle frequency of this area can be specifically increased or intermittent high current pulses can be introduced in the accelerated aging test to accelerate the defect evolution rate, while ensuring that the test results have engineering repeatability and predictive validity, and ultimately forming a customized aging test scheme for this type of laser structure and application mode.

[0059] In a specific embodiment, a temperature sensor array is used to perform a thermal distribution scan on a semiconductor laser to obtain a device temperature gradient map, including:

[0060] Based on the temperature sensor array, the temperature characteristics of each functional module of the semiconductor laser are detected to obtain the temperature characteristic set of each functional module, and the heat diffusion pattern analysis of the temperature characteristic set of each functional module is performed to obtain the heat diffusion pattern characteristic set;

[0061] The heat energy accumulation area of ​​the semiconductor laser is located by using the heat diffusion pattern feature set to obtain a heat energy accumulation area location set. When an abnormal area exists in the heat energy accumulation area location set, a temperature abnormal point mark set in the heat energy accumulation area location set is marked.

[0062] Using the temperature anomaly point marker set, the temperature distribution profile of the semiconductor laser is outlined to obtain a temperature distribution profile map, and the gradient of the temperature distribution profile map is calculated to obtain a device temperature gradient map;

[0063] When there is no abnormal area in the thermal energy concentration area positioning set, the thermal energy concentration area positioning set is used to outline the temperature distribution contour map, and the temperature distribution contour map is gradient calculated to obtain the device temperature gradient map.

[0064] Specifically, using a temperature sensor array to scan the semiconductor laser's thermal distribution and generate a device temperature gradient map is a key step in achieving high-precision lifetime assessment and performance degradation trend prediction within the entire aging test method. This step specifically involves detecting the temperature characteristics of each functional module of the semiconductor laser using the temperature sensor array to obtain a temperature feature set for each functional module. These temperature feature sets are then subjected to thermal diffusion pattern analysis to extract a thermal diffusion pattern feature set that reflects the heat propagation path and localized accumulation characteristics. Subsequently, this thermal diffusion pattern feature set is used to locate potential thermal energy accumulation areas within the semiconductor laser, forming a thermal energy accumulation area location set. If the system identifies an abnormal area within this location set, such as an area with a temperature exceeding 5°C above the surrounding average, it marks this location as a temperature anomaly point marker set, which is then used to more accurately outline the temperature distribution profile. If no obvious abnormal area is found, the system directly generates the temperature distribution profile based on the thermal energy accumulation area location set. In actual operation, the temperature sensor array is usually composed of multiple miniature, high-response speed sensors, which are evenly arranged on the surface of different functional modules of the semiconductor laser or in close proximity to its packaging structure, such as the active area of ​​the laser chip, the electrode connection area, the heat dissipation substrate, and the packaging shell. By collecting the temperature change data of these areas under continuous operation, a temperature feature set for each functional module can be constructed. Taking a typical high-power semiconductor laser as an example, when it is continuously operated in an optical fiber communication system, due to the uneven current density distribution and the difference in material thermal conductivity, local high temperature phenomena often occur near the active area. For example, when the laser operates at a driving current of 1.2A, the temperature of the active area is normally about 35°C. If defects such as microcracks or interface debonding appear in this area, the local temperature may rise to above 42°C, exceeding the set threshold. At this time, the system will mark this location as a key node in the temperature anomaly point mark set. Next, the system uses the obtained temperature anomaly point marker set to outline the temperature distribution profile of the entire device. This step actually uses a spatial interpolation algorithm to topologically connect the known temperature sampling points to generate a two-dimensional image that reflects the overall temperature change trend of the device, namely the temperature distribution profile map. This image can not only intuitively display the temperature distribution of different regions of the device, but also reveal whether there are obvious temperature jump zones or areas with steep thermal gradients. For example, in the application scenario of the above-mentioned optical fiber communication system, the temperature distribution profile map may show a trend of gradual cooling from the center of the laser to the edge, but in certain specific locations (such as the electrode contact area), local heating may occur, forming a "hotspot island" effect. After completing the construction of the temperature distribution profile map, the system further performs gradient calculation on it, that is, the temperature difference between adjacent pixels in the image is used as gradient information to generate the device temperature gradient map.This map not only reflects the spatial rate of temperature change but also reveals potential areas of thermal stress concentration, providing a key basis for predicting subsequent performance degradation trends. For example, in a certain semiconductor laser model, if the temperature gradient map shows a temperature gradient of 2.5°C per millimeter at the edge of the active region, significantly higher than the average gradient value of approximately 0.8°C per millimeter across the rest of the device, this indicates the presence of significant thermal stress in this region, which could easily lead to material fatigue or interface failure, thereby impacting the overall lifespan of the laser. Furthermore, this solution offers excellent adaptability and fault tolerance. If no abnormal areas are detected within the thermal energy concentration area localization set, the system automatically skips the abnormal point marking step and directly generates the temperature distribution profile and gradient calculation based on the thermal energy concentration area localization set, ensuring the integrity and stability of the entire process. For example, during burn-in testing of certain low-power lasers, due to low operating current and good heat dissipation, the temperature differences between the device's functional modules are minimal, resulting in a relatively uniform heat diffusion pattern. Consequently, no obvious localized high-temperature areas are observed. In this case, the system can directly generate the temperature distribution profile and gradient map without the need for additional anomaly detection mechanisms. In summary, this step forms a device temperature gradient map with spatial resolution by layer-by-layer processing and feature extraction of the raw temperature data collected by the temperature sensor array. This process not only enables a refined characterization of the internal thermal behavior of the semiconductor laser but also provides a reliable data foundation for subsequent prediction of comprehensive performance degradation trends using light intensity time series data. This is particularly applicable to the aging testing needs of high-power, highly integrated lasers under complex operating conditions.

[0065] In a specific embodiment, the performance degradation trend of the semiconductor laser is predicted based on the light intensity time series data and the device temperature gradient diagram to obtain the life assessment data, including:

[0066] Based on the light intensity peaks and valleys in the light intensity time series data, the light output stability of the semiconductor laser is preliminarily evaluated to obtain a light output stability evaluation set. The light output stability evaluation set is then subjected to fluctuation pattern analysis to obtain a light output fluctuation pattern feature set.

[0067] Through the light output fluctuation pattern feature set, the hot spot position in the device temperature gradient map is correlated and analyzed to obtain the hot spot position correlation feature set. Based on the hot spot position correlation feature set, the thermal-optical coupling effect of the semiconductor laser is evaluated to obtain the thermal-optical coupling effect quantitative evaluation set.

[0068] Using the quantitative evaluation set of the thermal-optical coupling effect, the power transmission loss trend of the semiconductor laser is analyzed to obtain a power transmission loss trend set. Based on the power transmission loss trend set, the key factors of performance degradation of the semiconductor laser are located to obtain a performance degradation key factor location set.

[0069] Based on the key factors positioning set of performance attenuation, the remaining effective lifetime of the semiconductor laser is predicted to obtain a preliminary result set of lifetime prediction, and the preliminary result set of lifetime prediction is corrected by environmental factors to obtain lifetime assessment data.

[0070] Specifically, the process of predicting the performance degradation trend of semiconductor lasers based on light intensity time series data and device temperature gradient maps to obtain lifetime assessment data is the core step in achieving intelligent, high-precision lifetime prediction and failure warning in the entire aging test method. This process first analyzes the peak and valley values ​​of light intensity in the light intensity time series data collected by the photodetector. For example, in a high-power semiconductor laser used in a fiber-optic communication system, its output beam is periodically sampled during continuous operation, collecting 1000 data points per second to form a light intensity variation curve with time resolution. By statistically analyzing the peak intensity values ​​(such as the maximum output power of 120mW) and valley intensity values ​​(such as the minimum output power of 95mW) in these data points, a preliminary assessment of the laser's light output stability under continuous operation can be made, thereby generating a light output stability assessment set. Further fluctuation pattern analysis is performed to identify the presence of typical fluctuation patterns such as periodic fluctuations, random noise, or trend-based attenuation, thereby extracting a characteristic set of light output fluctuation patterns that reflects the evolution of internal defects in the device. The system then correlates this light output fluctuation pattern feature set with the hotspot locations in the device temperature gradient map obtained in the previous step. Specifically, if the device temperature gradient map shows significant heat accumulation in a certain area (such as the edge of the active area), with a temperature gradient reaching 2.5°C per millimeter, and the light intensity time series data shows a phased decrease or jitter in the output power during this time period, it indicates that the hotspot may be affecting the photon emission efficiency. This hotspot location correlation feature set can be derived. This correlation analysis process not only reveals the dynamic interaction between local thermal effects and light output, but also provides basic data support for subsequent thermal-optical coupling effect assessment. Based on the hotspot location correlation feature set, the system evaluates the thermal-optical coupling effect of the semiconductor laser, quantitatively reflecting the specific impact of heat distribution on light output stability. For example, when the temperature of a certain area increases by 5°C, the corresponding output light intensity decreases by an average of approximately 3%, thus generating a quantitative evaluation set of the thermal-optical coupling effect. Next, the system uses this quantitative assessment set of thermal-optical coupling effects to analyze the power transmission loss trend of the semiconductor laser. This involves establishing a loss accumulation model over time to track the decay rate of the device's output power from the initial operation to the current moment. For example, assuming the laser's initial output power is 120mW, it drops to 114mW after 100 hours of operation, and then to 105mW at the 500th hour, showing a gradually accelerating decay trend. Combined with the thermal-optical coupling effect data, it can be inferred whether the primary mechanism causing this loss is due to factors such as material aging, interface degradation, or thermal stress accumulation.By classifying and weighting these factors, the system ultimately generates a power transmission loss trend set. Based on this set, it further locates the key factors that contribute to the performance degradation of semiconductor lasers, identifying key factors that dominate degradation paths, such as "active area material fatigue," "increased electrode contact resistance," or "thermal expansion mismatch in the packaging structure," forming a performance degradation key factor location set. Based on this, the system models and predicts device performance changes over the next period of time using the performance degradation key factor location set, and estimates the remaining useful life using algorithms such as exponential decay or linear regression. For example, based on existing data, a trend line is fitted, showing that for every 1mW decrease in output power, the lifespan decreases by approximately 50 hours, thereby generating a preliminary lifespan prediction result set. However, since environmental factors in actual application scenarios (such as ambient temperature fluctuations, unstable power supply voltage, and humidity changes) can also significantly affect the device aging rate, the system also needs to perform environmental factor corrections on this preliminary result set to improve the accuracy and practicality of the lifespan prediction. For example, if the laser operates in a high-temperature environment (such as an average ambient temperature of 45°C), its aging rate increases by about 20% compared to standard test conditions (25°C). In this case, the life prediction results need to be adjusted accordingly, and the originally expected service life of 1,000 hours should be revised to 800 hours, ultimately forming life assessment data that is closer to actual working conditions. In summary, this step builds a complete performance degradation trend prediction system by integrating light intensity time series data and device temperature gradient maps, realizing a closed-loop process from raw data collection to multi-dimensional feature extraction and then to comprehensive modeling and prediction. It is especially suitable for application scenarios such as high-power optical fiber communication systems that have extremely high requirements for laser reliability. It can effectively identify early degradation signals, quantify key failure mechanisms, and accurately predict remaining life, providing solid data support and technical guarantees for formulating customized aging test plans and optimizing device design.

[0071] In a specific embodiment, a power transmission loss trend set is obtained by performing a power transmission loss trend analysis on a semiconductor laser using a quantitative evaluation set of thermal-optical coupling effects, including:

[0072] Based on the coupling strength data of the thermal-optical coupling effect quantitative evaluation set, the energy transmission path of the semiconductor laser is segmented and identified to obtain an energy transmission path segment identification set, and the loss node analysis is performed on the energy transmission path segment identification set to obtain a loss node analysis set;

[0073] The power loss source of the semiconductor laser is located through the loss node analysis set to obtain a power loss source location set, and the loss distribution characteristics of the semiconductor laser are summarized based on the power loss source location set to obtain a loss distribution characteristic summary set;

[0074] By using the loss distribution characteristic summary set, the relationship between the power transmission of the semiconductor laser and time is established to obtain the power transmission time relationship set, and the key parameters of the power transmission time relationship set are extracted to obtain the key parameter extraction set;

[0075] Based on the key parameter extraction set, the power transmission loss trend of the semiconductor laser is analyzed to obtain the power transmission loss trend set.

[0076] Specifically, using a quantitative evaluation set of thermal-optical coupling effects to analyze the power transmission loss trends of semiconductor lasers and obtain a power transmission loss trend set is one of the key technical paths for achieving performance degradation trend prediction and life assessment in aging tests. This step first relies on the coupling strength data contained in the quantitative evaluation set of thermal-optical coupling effects, namely the response relationship between thermal changes and output light intensity. For example, during the operation of a high-power semiconductor laser, when the temperature of a certain area increases by 2°C, the corresponding output light intensity decreases by approximately 1.5%, thereby determining the presence of significant thermal-optical coupling effects in this area. Based on this data, the system segments the energy transmission path within the entire device, dividing the entire energy transmission chain from electrode injection, carrier recombination, photon emission to final output into multiple functional segments, such as the "electrode-substrate contact segment," the "active area-waveguide connection segment," and the "package interface output segment," forming a segmented energy transmission path identification set. The system then performs loss node analysis on each segment within these energy transmission path segment identification sets to identify key nodes that cause efficiency degradation during the energy transmission process. For example, in the semiconductor laser used in the aforementioned fiber-optic communication system, if the resistance value of the "electrode-substrate contact section" increases from an initial 0.3Ω to 0.45Ω due to material oxidation, this indicates a significant risk of energy loss. Simultaneously, combined with the device's temperature gradient map, it can be seen that the temperature at the corresponding location in this section has also locally increased, further confirming it as a potential loss node. By classifying and extracting features from these key nodes, the system generates a loss node analysis set, providing basic support for subsequently locating power loss sources. Based on this, the system uses the loss node analysis set to accurately locate the power loss sources of the semiconductor laser, forming a power loss source location set. For example, if a particular batch of lasers generally experiences a rapid drop in output power during aging testing, and the loss node analysis results show that this drop is primarily concentrated in the "active region-waveguide connection section," it can be preliminarily determined that the problem stems from interface defects or lattice mismatch between the waveguide structure and the active region during the manufacturing process, thus marking this area as a critical power loss source. The system then summarizes the spatial distribution, frequency, and impact of these loss sources, generating a summary set of loss distribution characteristics to reveal how different structural designs, material systems, or packaging methods affect power transmission stability. The system then uses this summary set of loss distribution characteristics to model how the power transmission of semiconductor lasers changes over time. Specifically, the system collects output light intensity data and corresponding thermal distribution information from multiple aging stages to construct a power transmission-time relationship set.For example, during a typical test cycle (e.g., 0-800 hours), the system records output power data every 100 hours: 120 mW at the initial moment, 117 mW at the 100th hour, 112 mW at the 300th hour, 105 mW at the 600th hour, and 98 mW at the 800th hour. Combining the corresponding thermal-optical coupling effect data with loss node information, the system can plot a time series curve reflecting the power decay trend and extract key parameters from it, such as the initial decay rate (a 2.5% decrease in the first 100 hours), the mid-term accelerated decay inflection point (approximately the 400th hour), and the long-term decay slope (an average decrease of 1.5 mW per 100 hours). These parameters are integrated into a key parameter extraction set for subsequent trend modeling and lifetime prediction. Finally, based on the key parameter extraction set, the system models and predicts the overall power transmission loss trend of the semiconductor laser, using methods such as exponential fitting or polynomial regression to extrapolate the power decay over a period of time. For example, based on existing data, a trend line was fitted, indicating that each 1mW drop in output power corresponds to a reduction in lifetime of approximately 40 hours. Combined with the current remaining output power (e.g., 98mW) and the set failure threshold (e.g., failure is considered below 80mW), the remaining useful lifetime of the laser can be estimated to be approximately 750 hours. This result was ultimately incorporated into the power transmission loss trend set, serving as an important input for subsequent performance degradation trend prediction and lifetime assessment. In summary, this step, through in-depth exploration of the quantitative evaluation set for thermal-optical coupling effects, combined with a multi-level data processing and analysis process involving energy transmission path segmentation identification, loss node analysis, power loss source location, loss distribution characterization, power transmission time relationship modeling, and key parameter extraction, enables systematic modeling and trend prediction of semiconductor laser power transmission loss trends. This approach is particularly suitable for the aging testing needs of high-power, highly integrated lasers in complex application scenarios. It can accurately identify weak links in the energy transmission process and quantify their impact on overall performance, providing solid technical support for the development of scientific aging test strategies and optimized device design.

[0077] In a specific embodiment, loss distribution characteristics of a semiconductor laser are summarized based on the power loss source location set to obtain a loss distribution characteristic summary set, including:

[0078] Based on the power loss source location set, energy density detection is performed on each loss source area of ​​the semiconductor laser to obtain the energy density set of each loss source area, and spectrum analysis is performed on the energy density set of each loss source area to obtain a spectrum feature set;

[0079] The loss source spatial distribution of the semiconductor laser is located by using the spectrum feature set to obtain a loss source spatial distribution location set, and the loss source correlation relationship analysis of the semiconductor laser is performed based on the loss source spatial distribution location set to obtain a loss source correlation relationship analysis set;

[0080] Using the loss source correlation analysis set, the loss propagation path of the semiconductor laser is tracked to obtain a loss propagation path tracking set, and based on the loss propagation path tracking set, the loss accumulation effect of the semiconductor laser is evaluated to obtain a loss accumulation effect evaluation set;

[0081] Based on the loss accumulation effect evaluation set, the overall loss distribution characteristics of the semiconductor laser are summarized to obtain the loss distribution characteristic summary set, where the loss distribution characteristic summary set includes the loss source concentration area, the loss source dispersion degree and the loss source dominant type.

[0082] Specifically, the process of summarizing the loss distribution characteristics of semiconductor lasers based on the power loss source location set and obtaining the loss distribution characteristic summary set is a key link in achieving systematic cognition of device aging behavior and construction of a life prediction model. This step is first based on the information of each key loss area contained in the power loss source location set. For example, in semiconductor lasers used in high-power optical fiber communication systems, their main power loss sources may be distributed in specific locations such as the "active area-waveguide connection section", "electrode contact interface" and "package structure edge". The system uses these positioning results to detect the energy density of each loss source area, that is, using high-resolution thermal imaging or local current density measurement methods to obtain the energy dissipation intensity per unit area of ​​each loss area. For example, in a certain test, the energy density of the "active area-waveguide connection section" was 2.3W per square millimeter, and the energy density of the "electrode contact interface" was 1.8W per square millimeter, thereby forming an energy density set for each loss source area. The system then performs spectral analysis on this energy density data to identify energy distribution patterns at different frequency components. For example, converting the time-varying energy density data into frequency-domain signals reveals that energy fluctuations in some loss regions are concentrated in low-frequency bands (e.g., 0.1-1 Hz), indicating that they are significantly affected by long-term aging mechanisms. In contrast, other regions exhibit significant peaks in high-frequency bands (e.g., 10-100 Hz), indicating that they are more susceptible to transient changes in operating conditions. This generates a spectral feature set, which further reveals the spatial distribution characteristics of the loss sources. The system then uses this spectral feature set to precisely locate the spatial distribution of loss sources within the semiconductor laser. Loss regions with similar spectral characteristics are categorized and mapped onto a three-dimensional device geometry model. For example, in one laser model, the system identified two primary loss concentration regions: a hotspot approximately 1.2 mm to the left of the chip center and a diffuse loss region located at the right edge of the package housing, forming a spatial distribution location set for the loss sources. On this basis, the system further analyzes the correlation between the loss sources of the semiconductor laser based on the spatial distribution positioning set, that is, to determine whether there is a causal relationship, a co-evolution trend or an energy transfer path between the loss sources. For example, during continuous operation, if the energy density of the "electrode contact interface" increases before the change of the "active area-waveguide connection section", and there is a significant time delay and amplitude correlation between the two, it can be inferred that the former may be one of the inducements of the degradation process of the latter. By modeling and statistics of such interaction relationships, the system generates a loss source correlation analysis set, which provides a basis for further tracking the loss propagation path. Then, the system uses the loss source correlation analysis set to track the loss propagation path inside the semiconductor laser, that is, to simulate the process of expansion from the initial loss source to the surrounding area through dynamic modeling.For example, in the aforementioned fiber-optic communication system application scenario, the system observed that when a small amount of oxidation occurred in the "electrode contact interface" region, causing an increase in resistance, heat gradually transferred to the adjacent active region, causing the temperature in this region to rise by approximately 3°C. This in turn led to a decrease in photon emission efficiency and a reduction in output power of approximately 2%. This process was recorded as one of the typical paths in the loss propagation path tracking set. The system also summarized the loss propagation over multiple aging cycles, analyzing whether it exhibited a fixed propagation order, speed, or range, thereby revealing the inherent connection between the evolution of material defects and energy migration within the device. Furthermore, based on the loss propagation path tracking set, the system evaluated the cumulative loss effect of semiconductor lasers, calculating the total energy loss caused by each loss source over the entire device lifecycle and its contribution to overall performance degradation. For example, during aging testing of a batch of lasers, the system statistically found that the "electrode contact interface" region caused a cumulative output power drop of 6% over the entire test cycle, while the "active region-waveguide connection" region caused a cumulative drop of 4%, and the remaining regions decreased by 3% in total, for a total power attenuation of 13%. Based on these data, the system generates a loss cumulative effect evaluation set to quantify the overall impact of different loss sources on device life. Ultimately, based on the loss cumulative effect evaluation set, the system summarizes the overall loss distribution characteristics of the semiconductor laser, including three dimensions: the concentrated area of ​​the loss source, the degree of dispersion of the loss source, and the dominant type of the loss source, thereby forming a loss distribution characteristic summary set. For example, in a certain type of high-power laser, the system concludes that its loss sources are mainly concentrated in the central area of ​​the chip (accounting for 55% of the total loss), and the remaining losses are discretely distributed at the edge of the packaging structure (accounting for 25%) and the electrode interface (accounting for 20%), and thermally induced loss is the dominant type (accounting for more than 70%). This summary not only helps to understand the degradation mechanism within the device, but also provides clear direction support for the subsequent formulation of customized aging test plans. In summary, this step starts from the power loss source location set and combines multiple technical levels such as energy density detection, spectrum analysis, spatial distribution positioning, correlation modeling, propagation path tracking, and cumulative effect evaluation to achieve a systematic summary and modeling of the internal loss behavior of semiconductor lasers. It is particularly suitable for the aging test needs of highly integrated, multi-physical field coupled lasers under complex working conditions, and can effectively support the construction of life prediction models and the optimization of test strategies.

[0083] In a specific embodiment, based on the lifetime assessment data, accelerated aging test parameters are optimized for the semiconductor laser to obtain a customized aging test solution, including:

[0084] Based on the power attenuation rate in the lifetime assessment data, the energy conversion efficiency of the semiconductor laser is divided into levels to obtain an energy conversion efficiency level set, and a boundary value analysis is performed on the energy conversion efficiency level set to obtain an energy conversion efficiency boundary value set;

[0085] The heat dissipation demand of the semiconductor laser is evaluated by using the energy conversion efficiency boundary value set to obtain a heat dissipation demand evaluation set, and the optimal aging temperature range of the semiconductor laser is preliminarily determined based on the heat dissipation demand evaluation set to obtain a preliminary determination set of the optimal aging temperature range;

[0086] Using the preliminary determination set of the optimal aging temperature range, the current carrying capacity of the semiconductor laser is analyzed to obtain a current carrying capacity analysis set, and based on the current carrying capacity analysis set, the current stress level range of the semiconductor laser is defined to obtain a current stress level range definition set;

[0087] Based on the current stress level range definition set, the overall aging process of the semiconductor laser is time-planned to obtain the overall aging process time planning set. The rationality of the overall aging process time planning set is then verified to obtain a customized aging test plan.

[0088] Specifically, based on the lifetime assessment data, the process of optimizing the parameters of the accelerated aging test of the semiconductor laser and obtaining a customized aging test plan is one of the key technical paths to achieve efficient and accurate aging test strategy formulation and optimal configuration of test resources. This step first divides the energy conversion efficiency of the semiconductor laser into levels based on the core indicator of the power attenuation rate in the lifetime assessment data, that is, the electro-optical conversion efficiency of the device under different working conditions is divided into several levels. For example, the energy conversion efficiency above 70% is defined as the "high-efficiency operation layer", the energy conversion efficiency between 60% and 70% is defined as the "medium-efficiency operation layer", and the energy conversion efficiency below 60% is defined as the "low-efficiency operation layer", thus forming an energy conversion efficiency level set. Subsequently, the system performs boundary value analysis on the energy conversion efficiency level set to identify the critical points between each level. For example, in a semiconductor laser used in a high-power optical fiber communication system, the boundary value between the "high-efficiency operation layer" and the "medium-efficiency operation layer" is 70%, while the boundary value between the "medium-efficiency operation layer" and the "low-efficiency operation layer" is 60%. These boundary values ​​not only reflect the stage characteristics of device performance degradation, but also provide a basic basis for subsequent heat dissipation demand assessment, thereby generating an energy conversion efficiency boundary value set. Next, the system uses this energy conversion efficiency boundary value set to evaluate the heat dissipation requirements of the semiconductor laser at different operating stages, that is, combining the power attenuation rate and the quantitative evaluation results of the thermal-optical coupling effect to determine the level of thermal load borne by the device at a specific energy conversion efficiency. For example, in the "high-efficiency operation layer", the heat flux density per unit area is approximately 1.2W / cm². After entering the "medium-efficiency operation layer", due to the decrease in partial carrier recombination efficiency, heat accumulation increases, and the heat flux density rises to 1.8W / cm². If it further enters the "low-efficiency operation layer", the heat flux density may reach more than 2.5W / cm². Based on these data, the system can generate a heat dissipation demand assessment set and, based on this, preliminarily determine the optimal aging temperature range required for the semiconductor laser during the accelerated aging test. For example, considering that the rated operating temperature of this type of laser under standard operating conditions is 25°C, but in order to accelerate the aging process and avoid the introduction of non-real failure modes due to excessively high temperatures, the system preliminarily sets the optimal aging temperature range to 45~60°C and marks it as the preliminary determination set of the optimal aging temperature range. On this basis, the system uses this preliminary determination set of the optimal aging temperature range to analyze the current carrying capacity of the semiconductor laser, that is, to evaluate the maximum continuous operating current that the device can withstand within this temperature range and its corresponding reliability performance. For example, in a 45°C environment, the maximum safe operating current of a certain laser is 1.3A, while in a 60°C environment, its maximum safe current drops to 1.1A, indicating that high temperature will significantly affect the current carrying capacity of the device.By performing statistical analysis and trend fitting on data from multiple aging stages, the system extracts safe thresholds for current stress under different temperature conditions, thereby defining a current stress level range suitable for the laser structure and application scenario, such as between 0.9A and 1.2A, and forming a current stress level range definition set. The system then schedules the overall aging process for the semiconductor laser based on this current stress level range definition set, comprehensively considering multiple factors such as temperature gradient, power decay rate, and current stress level to develop a reasonable aging test schedule. For example, for the laser in the aforementioned optical fiber communication system, if the initial test current is set to 1.1A and the aging temperature is set to 55°C, it is estimated that approximately 600 hours of operation under these conditions will simulate the aging effects of 1000 hours under actual operating conditions. Therefore, the system schedules the entire aging test cycle to 600 hours and sets multiple key monitoring nodes in segments (such as collecting light intensity time series data and temperature gradient maps every 100 hours) to ensure the controllability of the aging process and the integrity of the data, ultimately generating a time plan for the overall aging process. Finally, the system verifies the rationality of the overall aging process time plan set. That is, by comparing historical data, simulation predictions, and small sample test results, it verifies whether the planned aging test parameters can effectively shorten the test cycle while ensuring that no atypical failures are caused. For example, in a set of comparative experiments, the traditional constant temperature and constant current aging method requires 1000 hours to reach the target performance degradation level, while the test parameters optimized by this scheme only take 620 hours to achieve the same effect, and no abnormal failure mode occurs, verifying the feasibility and superiority of the scheme, and finally generating a customized aging test scheme. In summary, this step starts from the life assessment data and completes the energy conversion efficiency level division, boundary value analysis, heat dissipation demand assessment, optimal aging temperature range determination, current carrying capacity analysis, current stress level definition, aging time planning and rationality verification and other multiple levels of technical processing processes, thus achieving systematic optimization of semiconductor laser accelerated aging test parameters and personalized customization of test schemes. It is particularly suitable for the aging test needs of high-power, high-integration lasers in complex application scenarios. It can effectively improve test efficiency, reduce test costs, and provide a scientific basis and technical support for laser design improvement and quality control.

[0089] In a specific embodiment, the current carrying capacity of the semiconductor laser is analyzed using the preliminary determination set of the optimal aging temperature range to obtain a current carrying capacity analysis set, including:

[0090] Based on the preliminary determination set of the optimal aging temperature range, the electrode material characteristics of the semiconductor laser are detected to obtain an electrode material characteristic set, and the conductivity analysis of the electrode material characteristic set is performed to obtain a conductivity analysis set;

[0091] Identifying a current conduction path of the semiconductor laser through the conductivity analysis set to obtain a current conduction path identification set, and performing a current distribution uniformity evaluation on the semiconductor laser based on the current conduction path identification set to obtain a current distribution uniformity evaluation set;

[0092] The current overload risk area of ​​the semiconductor laser is located using the current distribution uniformity assessment set to obtain the current overload risk area positioning set. Based on the current overload risk area positioning set, the current carrying limit of the semiconductor laser is measured to obtain the current carrying capacity analysis set, wherein the current carrying capacity analysis set includes the maximum safe current, current fluctuation tolerance and current carrying stability.

[0093] Specifically, using the preliminary set of optimal aging temperature ranges to analyze the current-carrying capacity of semiconductor lasers and obtain the current-carrying capacity analysis set is a key step in optimizing accelerated aging test parameters and developing customized aging test plans. This step first builds on the aforementioned preliminary set of optimal aging temperature ranges. That is, based on the knowledge that this type of semiconductor laser has the best acceleration effect under specific aging conditions (such as 45-60°C), the electrode material properties are further investigated to evaluate the device's conductivity performance changes in high-temperature environments. For example, a certain type of semiconductor laser used in high-power fiber-optic communication systems has a gold-titanium alloy composite electrode material. Its resistivity was measured at room temperature (25°C) to be 3.5×10¯. 8 Ω·m, and at 55℃ the value rises to 4.2×10¯ 8Ω·m, indicating that increasing temperature degrades its electrical conductivity. By collecting parameters such as the thermal stability, carrier mobility, and contact resistance of the electrode material at multiple aging temperature points (e.g., 45°C, 50°C, 55°C, and 60°C), the system constructs a set of electrode material characteristics. Based on this, it conducts a conductivity analysis, identifying how the electrode material's electrical properties change under different temperature conditions, thereby generating a conductivity analysis set. Based on this conductivity analysis set, the system further identifies the current conduction path within the semiconductor laser. Specifically, by combining the device's physical structure model with current distribution simulation data, the system determines how current flows between the various functional layers within the chip after an external power supply is input, for example, whether it is concentrated at the edges of the active area or in the waveguide region. For example, in one test sample, the system found that when the operating temperature rose to 55°C, the previously uniformly distributed current began to shift toward the center of the electrode, causing the local current density to increase from 1.8 kA per square millimeter under standard conditions to 2.4 kA per square millimeter, resulting in a significant current concentration phenomenon. By modeling and analyzing the spatial distribution characteristics of these current paths, the system generates a current conduction path identification set, and further evaluates its current distribution uniformity to obtain a current distribution uniformity evaluation set. Subsequently, the system locates the current overload risk areas that may exist inside the semiconductor laser based on the current distribution uniformity evaluation set, that is, by setting a threshold to determine whether there are problems such as excessive local current density, hot spot formation, or uneven carrier injection. For example, in the application scenario of the above-mentioned optical fiber communication system, the system detected that during the 55°C aging test of a certain laser, the current density in the "electrode-substrate contact section" area reached 2.7kA per square millimeter, which is much higher than the average value of 1.9kA per square millimeter in the rest of the area, and the temperature gradient map of the area showed that its local temperature had exceeded the safety threshold (set to 85°C), thereby marking the area as a current overload risk area. Based on this, the system generates a set of current overload risk area locations and uses this as a basis to determine the laser's current carrying capacity. This involves monitoring the stability of the output light intensity, heat accumulation, and critical failure point of the device while gradually increasing the drive current. Ultimately, the system determines the maximum safe current, current fluctuation tolerance, and current carrying capacity stability under the current aging temperature conditions, forming a current carrying capacity analysis set. Specifically, the maximum safe current refers to the maximum continuous operating current that a device can withstand without causing permanent damage.For example, experimental data shows that under 55°C aging conditions, the maximum safe current of this laser model is 1.1A. Above this value, the output light intensity begins to decline irreversibly. Current fluctuation tolerance reflects the device's ability to operate stably under small current fluctuations. For example, within a current disturbance range of ±5%, the output light intensity fluctuation is controlled within ±1.5%, indicating good anti-interference capabilities. Current carrying stability describes the device's ability to maintain current transmission characteristics during long-term operation. For example, after 600 hours of continuous power supply, the slope of its current-voltage curve changes by only 0.8%, indicating high long-term stability. In summary, this step, through the application of the preliminary determination of the optimal aging temperature range, deeply explores the electrode material properties, identifies the current conduction path, and evaluates the uniformity of the current distribution. This allows the device to locate the current overload risk area and determine the current carrying limit, ultimately forming a current carrying capacity analysis set that includes the maximum safe current, current fluctuation tolerance, and current carrying stability. This process not only realizes the systematic evaluation of the electrical performance of semiconductor lasers under high-temperature aging conditions, but also provides a scientific basis for the precise setting of subsequent aging test parameters. It is particularly suitable for the aging test needs of high-power lasers under complex working conditions. It can effectively avoid atypical failures caused by excessive current stress and improve the reliability and engineering practicality of test results.

[0094] The semiconductor laser aging test method according to the embodiment of the present invention is described above. The semiconductor laser aging test system according to the embodiment of the present invention is described below. Figure 2 In one embodiment of the present invention, a semiconductor laser aging test system includes:

[0095] The acquisition module 21 is used to acquire the output light beam of the semiconductor laser in real time through a photodetector to obtain light intensity time series data;

[0096] Scanning module 22, used to scan the thermal distribution of the semiconductor laser using a temperature sensor array to obtain a device temperature gradient map;

[0097] Prediction module 23, used to predict the performance degradation trend of the semiconductor laser based on the light intensity time series data and the device temperature gradient map to obtain life assessment data;

[0098] The optimization module 24 is used to optimize the accelerated aging test parameters of the semiconductor laser based on the lifetime assessment data to obtain a customized aging test plan.

[0099] In this embodiment, for the specific implementation of each unit in the above system embodiment, please refer to the above method embodiment, which will not be repeated here.

[0100] Reference Figure 3In an embodiment of the present invention, a computer device is also provided, wherein the internal structure of the computer device can be as follows: Figure 3 As shown. The computer device includes a processor, memory, display screen, input device, network interface and database connected via a system bus. The processor of the computer design is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The database of the computer device is used to store the corresponding data in this embodiment. The network interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, the above method is implemented.

[0101] Those skilled in the art will understand that Figure 3 The structure shown in the figure is merely a block diagram of a portion of the structure related to the solution of the present invention and does not constitute a limitation on the computer device to which the solution of the present invention is applied.

[0102] An embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon, which implements the above-described method when executed by a processor. It is understood that the computer-readable storage medium in this embodiment can be a volatile readable storage medium or a non-volatile readable storage medium.

[0103] Those skilled in the art will appreciate that all or part of the processes in the above-described method embodiments can be implemented by instructing the relevant hardware using a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the above-described method embodiments. Any reference to memory, storage, database, or other media provided herein and used in the embodiments may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double-speed SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct RAMbus dynamic RAM (DRDRAM), and RAMbus dynamic RAM.

[0104] It should be noted that, in this document, the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, apparatus, article, or method comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, apparatus, article, or method. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, apparatus, article, or method comprising the element.

[0105] The above description is only a preferred embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made by using the contents of the present invention description and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. Semiconductor laser aging test system, characterized in that, include: The acquisition module is used to collect the output light beam of the semiconductor laser in real time through a photodetector to obtain light intensity time series data; A scanning module is used to scan the thermal distribution of the semiconductor laser using a temperature sensor array to obtain a device temperature gradient map; The prediction module is used to predict the performance degradation trend of semiconductor lasers based on the light intensity time series data and the device temperature gradient map to obtain life assessment data. Specifically, it includes: Based on the light intensity peaks and valleys in the light intensity time series data, the light output stability of the semiconductor laser is preliminarily evaluated to obtain a light output stability evaluation set. The light output stability evaluation set is then subjected to fluctuation pattern analysis to obtain a light output fluctuation pattern feature set. Through the light output fluctuation pattern feature set, the hot spot position in the device temperature gradient map is correlated and analyzed to obtain the hot spot position correlation feature set. Based on the hot spot position correlation feature set, the thermal-optical coupling effect of the semiconductor laser is evaluated to obtain the thermal-optical coupling effect quantitative evaluation set. Using the quantitative evaluation set of the thermal-optical coupling effect, the power transmission loss trend of the semiconductor laser is analyzed to obtain a power transmission loss trend set. Based on the power transmission loss trend set, the key factors of performance degradation of the semiconductor laser are located to obtain a performance degradation key factor location set. Based on the key factors of performance degradation, the remaining useful life of the semiconductor laser is predicted to obtain a preliminary result set of life prediction, and the preliminary result set of life prediction is corrected by environmental factors to obtain life assessment data; The optimization module is used to optimize the accelerated aging test parameters of semiconductor lasers based on lifetime assessment data to obtain a customized aging test plan, including: Based on the power attenuation rate in the lifetime assessment data, the energy conversion efficiency of the semiconductor laser is divided into levels to obtain an energy conversion efficiency level set, and a boundary value analysis is performed on the energy conversion efficiency level set to obtain an energy conversion efficiency boundary value set; The heat dissipation demand of the semiconductor laser is evaluated by using the energy conversion efficiency boundary value set to obtain a heat dissipation demand evaluation set, and the optimal aging temperature range of the semiconductor laser is preliminarily determined based on the heat dissipation demand evaluation set to obtain a preliminary determination set of the optimal aging temperature range; Using the preliminary determination set of the optimal aging temperature range, the current carrying capacity of the semiconductor laser is analyzed to obtain a current carrying capacity analysis set, and based on the current carrying capacity analysis set, the current stress level range of the semiconductor laser is defined to obtain a current stress level range definition set; Based on the current stress level range definition set, the overall aging process of the semiconductor laser is time-planned to obtain the overall aging process time planning set. The rationality of the overall aging process time planning set is then verified to obtain a customized aging test plan.

2. A semiconductor laser aging test method, using the system of claim 1, characterized in that: The following steps are involved: The output beam of the semiconductor laser is collected in real time by a photodetector to obtain light intensity time series data; Use the temperature sensor array to scan the thermal distribution of the semiconductor laser and obtain the device temperature gradient map; Based on the light intensity time series data and device temperature gradient diagram, the performance degradation trend of the semiconductor laser is predicted to obtain the life assessment data, including: Based on the light intensity peaks and valleys in the light intensity time series data, the light output stability of the semiconductor laser is preliminarily evaluated to obtain a light output stability evaluation set. The light output stability evaluation set is then subjected to fluctuation pattern analysis to obtain a light output fluctuation pattern feature set. Through the light output fluctuation pattern feature set, the hot spot position in the device temperature gradient map is correlated and analyzed to obtain the hot spot position correlation feature set. Based on the hot spot position correlation feature set, the thermal-optical coupling effect of the semiconductor laser is evaluated to obtain the thermal-optical coupling effect quantitative evaluation set. Using the quantitative evaluation set of the thermal-optical coupling effect, the power transmission loss trend of the semiconductor laser is analyzed to obtain a power transmission loss trend set. Based on the power transmission loss trend set, the key factors of performance degradation of the semiconductor laser are located to obtain a performance degradation key factor location set. Based on the key factors of performance degradation, the remaining useful life of the semiconductor laser is predicted to obtain a preliminary result set of life prediction, and the preliminary result set of life prediction is corrected by environmental factors to obtain life assessment data; Based on the lifetime assessment data, the accelerated aging test parameters of the semiconductor laser are optimized to obtain a customized aging test solution, including: Based on the power attenuation rate in the lifetime assessment data, the energy conversion efficiency of the semiconductor laser is divided into levels to obtain an energy conversion efficiency level set, and a boundary value analysis is performed on the energy conversion efficiency level set to obtain an energy conversion efficiency boundary value set; The heat dissipation demand of the semiconductor laser is evaluated by using the energy conversion efficiency boundary value set to obtain a heat dissipation demand evaluation set, and the optimal aging temperature range of the semiconductor laser is preliminarily determined based on the heat dissipation demand evaluation set to obtain a preliminary determination set of the optimal aging temperature range; Using the preliminary determination set of the optimal aging temperature range, the current carrying capacity of the semiconductor laser is analyzed to obtain a current carrying capacity analysis set, and based on the current carrying capacity analysis set, the current stress level range of the semiconductor laser is defined to obtain a current stress level range definition set; Based on the current stress level range definition set, the overall aging process of the semiconductor laser is time-planned to obtain the overall aging process time planning set. The rationality of the overall aging process time planning set is then verified to obtain a customized aging test plan.

3. The semiconductor laser aging test method according to claim 2, characterized in that: Use the temperature sensor array to scan the thermal distribution of the semiconductor laser and obtain the device temperature gradient map, including: Based on the temperature sensor array, the temperature characteristics of each functional module of the semiconductor laser are detected to obtain the temperature characteristic set of each functional module, and the heat diffusion pattern analysis of the temperature characteristic set of each functional module is performed to obtain the heat diffusion pattern characteristic set; The heat energy accumulation area of ​​the semiconductor laser is located by using the heat diffusion pattern feature set to obtain a heat energy accumulation area location set. When an abnormal area exists in the heat energy accumulation area location set, a temperature abnormal point mark set in the heat energy accumulation area location set is marked. Using the temperature anomaly point marker set, the temperature distribution profile of the semiconductor laser is outlined to obtain a temperature distribution profile map, and the gradient of the temperature distribution profile map is calculated to obtain a device temperature gradient map; When there is no abnormal area in the thermal energy concentration area positioning set, the thermal energy concentration area positioning set is used to outline the temperature distribution contour map, and the temperature distribution contour map is gradient calculated to obtain the device temperature gradient map.

4. The semiconductor laser aging test method according to claim 2, characterized in that: Using the quantitative evaluation set of thermal-optical coupling effects, the power transmission loss trend of semiconductor lasers is analyzed, and a power transmission loss trend set is obtained, including: Based on the coupling strength data of the thermal-optical coupling effect quantitative evaluation set, the energy transmission path of the semiconductor laser is segmented and identified to obtain an energy transmission path segment identification set, and the loss node analysis is performed on the energy transmission path segment identification set to obtain a loss node analysis set; The power loss source of the semiconductor laser is located through the loss node analysis set to obtain a power loss source location set, and the loss distribution characteristics of the semiconductor laser are summarized based on the power loss source location set to obtain a loss distribution characteristic summary set; By using the loss distribution characteristic summary set, the relationship between the power transmission of the semiconductor laser and time is established to obtain the power transmission time relationship set, and the key parameters of the power transmission time relationship set are extracted to obtain the key parameter extraction set; Based on the key parameter extraction set, the power transmission loss trend of the semiconductor laser is analyzed to obtain the power transmission loss trend set.

5. The semiconductor laser aging test method according to claim 4, characterized in that: Based on the power loss source location set, the loss distribution characteristics of the semiconductor laser are summarized to obtain the loss distribution characteristic summary set, including: Based on the power loss source location set, energy density detection is performed on each loss source area of ​​the semiconductor laser to obtain the energy density set of each loss source area, and spectrum analysis is performed on the energy density set of each loss source area to obtain a spectrum feature set; The loss source spatial distribution of the semiconductor laser is located by using the spectrum feature set to obtain a loss source spatial distribution location set, and the loss source correlation relationship analysis of the semiconductor laser is performed based on the loss source spatial distribution location set to obtain a loss source correlation relationship analysis set; Using the loss source correlation analysis set, the loss propagation path of the semiconductor laser is tracked to obtain a loss propagation path tracking set, and based on the loss propagation path tracking set, the loss accumulation effect of the semiconductor laser is evaluated to obtain a loss accumulation effect evaluation set; Based on the loss accumulation effect evaluation set, the overall loss distribution characteristics of the semiconductor laser are summarized to obtain the loss distribution characteristic summary set, where the loss distribution characteristic summary set includes the loss source concentration area, the loss source dispersion degree and the loss source dominant type.

6. The semiconductor laser aging test method according to claim 2, characterized in that: Using the optimal aging temperature range preliminarily determined set, the current carrying capacity of the semiconductor laser is analyzed to obtain the current carrying capacity analysis set, including: Based on the preliminary determination set of the optimal aging temperature range, the electrode material characteristics of the semiconductor laser are detected to obtain an electrode material characteristic set, and the conductivity analysis of the electrode material characteristic set is performed to obtain a conductivity analysis set; Identifying a current conduction path of the semiconductor laser through the conductivity analysis set to obtain a current conduction path identification set, and performing a current distribution uniformity evaluation on the semiconductor laser based on the current conduction path identification set to obtain a current distribution uniformity evaluation set; The current overload risk area of ​​the semiconductor laser is located using the current distribution uniformity assessment set to obtain the current overload risk area positioning set. Based on the current overload risk area positioning set, the current carrying limit of the semiconductor laser is measured to obtain the current carrying capacity analysis set, wherein the current carrying capacity analysis set includes the maximum safe current, current fluctuation tolerance and current carrying stability.

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