A semiconductor laser life prediction method and system

Abstract

The present invention relates to a method for estimating the life of a semiconductor laser, comprising the following steps: measuring the photoelectric characteristics of the semiconductor laser to obtain a photoelectric characteristic parameter set; performing stress analysis on the semiconductor laser based on the photoelectric characteristic parameter set to obtain a device stress characteristic spectrum; performing defect evolution analysis on the semiconductor laser based on the device stress characteristic spectrum to obtain a defect expansion characteristic diagram; performing performance attenuation simulation on the semiconductor laser based on the defect expansion characteristic diagram to obtain a performance attenuation trajectory curve; and performing life assessment on the semiconductor laser based on the performance attenuation trajectory curve to obtain a life estimation result. The method solves the technical problem that traditional life assessment methods mainly rely on accelerated aging experiments and statistical models, which, although can reflect the aging trend of devices to a certain extent, often have a long cycle, high cost, and are difficult to reveal the essential process of the internal failure mechanism of the device.

Description

Technical Field

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

[0002] Semiconductor lasers, core components in modern optoelectronics, are widely used in communications, healthcare, industrial processing, and military applications. Their operational stability and service life directly impact the reliability and maintenance costs of related equipment, making lifespan estimation crucial. Traditional lifespan assessment methods rely primarily on accelerated aging experiments and statistical models. While these methods can reflect device aging trends to a certain extent, they often require long cycles, are costly, and struggle to reveal the underlying failure mechanisms.

[0003] In recent years, with the advancement of materials science and computational mechanics, lifetime prediction methods based on physical mechanisms have gradually attracted attention. By introducing methods such as photoelectric property measurements, stress analysis, and defect evolution simulation, researchers have attempted to establish more accurate lifetime prediction models. However, many challenges remain in practical application, such as significant multi-scale parameter coupling effects, difficulty in accurately characterizing interface stresses, and complex defect propagation paths. As a result, existing models still have considerable room for improvement in terms of prediction accuracy and universality.

[0004] In addition, most current research focuses on the impact of a single factor on device lifetime, lacking a systematic analysis of the synergistic effects of multiple failure mechanisms. Especially under extreme operating conditions such as high temperature and high current density, complex thermal-electrical-mechanical coupling effects are easily generated inside semiconductor lasers, thereby accelerating the formation and expansion of defects. How to establish a quantitative connection between these microscopic behaviors and macroscopic performance degradation remains a key bottleneck restricting the transition of lifetime prediction from theory to engineering application. Therefore, there is an urgent need to develop a comprehensive evaluation method that integrates multi-physics field analysis and failure evolution simulation to achieve efficient and accurate prediction of semiconductor laser lifetime. Summary of the Invention

[0005] The main purpose of the present invention is to provide a method for predicting the lifetime of a semiconductor laser, which solves the technical problems that traditional lifetime assessment methods mainly rely on accelerated aging experiments and statistical models. Although they can reflect the aging trend of the device to a certain extent, they often have long cycles, high costs, and are difficult to reveal the essential process of the internal failure mechanism of the device.

[0006] To achieve the above object, the present invention provides a method for estimating the lifetime of a semiconductor laser, comprising the following steps:

[0007] measuring the photoelectric characteristics of the semiconductor laser to obtain a set of photoelectric characteristic parameters;

[0008] Performing stress analysis on the semiconductor laser based on the optoelectronic characteristic parameter set to obtain a device stress characteristic spectrum;

[0009] When the interface stress intensity in the device stress characteristic spectrum exceeds a preset threshold, defect evolution analysis is performed on the semiconductor laser based on the device stress characteristic spectrum to obtain a defect expansion characteristic map;

[0010] Performing a performance degradation simulation on the semiconductor laser based on the defect expansion characteristic diagram to obtain a performance degradation trajectory curve;

[0011] The life of the semiconductor laser is evaluated based on the performance attenuation trajectory curve to obtain a life estimation result.

[0012] Furthermore, the performing stress analysis on the semiconductor laser based on the optoelectronic characteristic parameter set to obtain a device stress characteristic spectrum includes:

[0013] Performing a temperature field simulation on the semiconductor laser based on the optoelectronic characteristic parameter set to obtain temperature distribution data, and performing a thermal stress calculation on the semiconductor laser based on the temperature distribution data using a finite element analysis method to obtain thermal stress distribution data;

[0014] performing a dislocation density analysis on the semiconductor laser based on the thermal stress distribution data to obtain dislocation density distribution data, and performing a strain energy density analysis on the semiconductor laser based on the dislocation density distribution data to obtain strain energy density distribution data;

[0015] An interface stress analysis is performed on the semiconductor laser based on the strain energy density distribution data to obtain interface stress intensity data, and a device stress characteristic spectrum is constructed for the semiconductor laser based on the interface stress intensity data and the thermal stress distribution data to obtain a device stress characteristic spectrum.

[0016] Furthermore, the defect evolution analysis of the semiconductor laser is performed based on the device stress characteristic spectrum to obtain a defect expansion characteristic map, including:

[0017] performing a dislocation slip simulation on the semiconductor laser based on the transverse stress distribution and the longitudinal stress distribution in the stress characteristic spectrum of the device to obtain dislocation slip path data, and performing a dislocation density calculation on the semiconductor laser based on the dislocation slip path data to obtain dislocation density distribution data;

[0018] performing a defect diffusion simulation on the semiconductor laser based on the dislocation density distribution data to obtain defect concentration distribution data, and performing a defect reaction simulation on the semiconductor laser based on the defect concentration distribution data to obtain defect reaction rate data;

[0019] performing interface state energy level calculation on the semiconductor laser based on the defect reaction rate data to obtain interface state energy level distribution data, and performing carrier capture cross section calculation on the semiconductor laser based on the interface state energy level distribution data to obtain carrier capture cross section data;

[0020] The carrier recombination rate of the semiconductor laser is calculated based on the carrier capture cross-section data using a Shockley-Read-Hall recombination model to obtain carrier recombination rate data, and a defect extension characteristic map is constructed for the semiconductor laser based on the carrier recombination rate data and the dislocation density distribution data to obtain a defect extension characteristic map.

[0021] Furthermore, the performing defect diffusion simulation on the semiconductor laser based on the dislocation density distribution data to obtain defect concentration distribution data includes:

[0022] performing stress field distribution calculation on the dislocation density distribution data to obtain defect diffusion driving force distribution data, and performing defect migration path analysis on the semiconductor laser based on the defect diffusion driving force distribution data to obtain defect migration channel distribution data;

[0023] performing defect diffusion kinetics analysis on the defect migration channel distribution data using a non-equilibrium thermodynamic equation to obtain defect diffusion flux density data, and performing defect concentration gradient calculation on the semiconductor laser based on the defect diffusion flux density data to obtain defect concentration gradient distribution data;

[0024] Calculating a defect diffusion coefficient of the semiconductor laser based on the defect concentration gradient distribution data to obtain defect diffusion coefficient tensor data, and calculating a defect diffusion flux of the semiconductor laser based on the defect diffusion coefficient tensor data to obtain defect diffusion flux field data;

[0025] The defect concentration spatiotemporal evolution of the semiconductor laser is calculated based on the defect diffusion flux field data to obtain the defect concentration spatiotemporal distribution data, and the defect concentration conservation analysis is performed on the defect concentration spatiotemporal distribution data through the continuity equation to obtain the defect concentration distribution data.

[0026] Furthermore, the semiconductor laser is subjected to a performance degradation simulation based on the defect expansion characteristic diagram to obtain a performance degradation trajectory curve, including:

[0027] Performing a light field distribution simulation on the semiconductor laser based on the defect expansion characteristic diagram to obtain light field distribution data, and performing an output power simulation on the semiconductor laser based on the light field distribution data to obtain output power data;

[0028] Calculating the output power attenuation rate of the semiconductor laser based on the output power data to obtain output power attenuation rate data, and simulating the carrier concentration of the semiconductor laser based on the interface state energy level in the defect expansion characteristic diagram to obtain carrier concentration data;

[0029] Performing spectrum simulation on the semiconductor laser based on the carrier concentration data to obtain spectrum data, and calculating wavelength drift of the semiconductor laser based on the spectrum data to obtain wavelength drift data;

[0030] A mode stability analysis is performed on the semiconductor laser based on the light field distribution data to obtain mode stability index data, and a performance attenuation trajectory curve is constructed for the semiconductor laser based on the output power attenuation rate data, the wavelength drift data, and the mode stability index data to obtain a performance attenuation trajectory curve.

[0031] Furthermore, the lifetime evaluation of the semiconductor laser based on the performance attenuation trajectory curve to obtain a lifetime prediction result includes:

[0032] By using a double exponential decay model, based on the performance decay trajectory curve, the fast decay phase lifetime of the semiconductor laser is calculated to obtain fast decay phase lifetime data; and based on the fast decay phase lifetime data, a slow decay phase starting point of the semiconductor laser is determined to obtain slow decay phase starting point data;

[0033] performing a slow decay phase lifetime calculation on the semiconductor laser based on the slow decay phase starting point data to obtain slow decay phase lifetime data, and performing a lifetime statistical distribution feature extraction on the semiconductor laser based on the slow decay phase lifetime data to obtain lifetime statistical distribution feature data;

[0034] Calculating the mean failure time of the semiconductor laser based on the lifetime statistical distribution characteristic data to obtain mean failure time data, and calculating the failure probability of the semiconductor laser based on the mean failure time data to obtain failure probability data;

[0035] Based on the failure probability data and the rapid decay stage life data, a total life prediction is performed on the semiconductor laser using a life prediction model to obtain a life prediction result.

[0036] Furthermore, the calculating of the lifetime of the semiconductor laser in the slow decay phase based on the starting point data of the slow decay phase to obtain the lifetime data in the slow decay phase includes:

[0037] Based on the slow decay stage starting point data, performing slow decay stage characteristic lifetime fitting on the semiconductor laser using a three-parameter Weibull distribution model to obtain slow decay stage characteristic lifetime parameters, and extracting shape parameters and scale parameters of the semiconductor laser based on the slow decay stage characteristic lifetime parameters to obtain Weibull shape parameter and scale parameter data;

[0038] Estimating the failure probability density function parameters of the semiconductor laser based on the Weibull shape parameter and scale parameter data using a maximum likelihood estimation method to obtain failure probability density function parameters, and calculating the cumulative failure probability function of the semiconductor laser based on the failure probability density function parameters to obtain cumulative failure probability function data;

[0039] performing a lifetime confidence interval calculation on the semiconductor laser in a slow decay phase based on the cumulative failure probability function data to obtain lifetime confidence interval data for the slow decay phase, and performing a lifetime reliability assessment on the semiconductor laser based on the lifetime confidence interval data for the slow decay phase to obtain lifetime reliability data;

[0040] Based on the lifetime reliability data and the fast decay stage lifetime data, the lifetime weight of the semiconductor laser in the slow decay stage is calculated by the lifetime weighted average method to obtain the slow decay stage lifetime weight data, and based on the slow decay stage lifetime weight data and the fast decay stage lifetime data, the total lifetime of the semiconductor laser is calculated to obtain the slow decay stage lifetime data.

[0041] The present invention also provides a semiconductor laser lifetime prediction system, comprising:

[0042] A measurement module, configured to measure the photoelectric characteristics of the semiconductor laser to obtain a set of photoelectric characteristic parameters;

[0043] A first analysis module is configured to perform stress analysis on the semiconductor laser based on the optoelectronic characteristic parameter set to obtain a device stress characteristic spectrum;

[0044] a second analysis module, configured to perform defect evolution analysis on the semiconductor laser based on the device stress characteristic spectrum to obtain a defect expansion characteristic diagram when the interface stress intensity in the device stress characteristic spectrum exceeds a preset threshold;

[0045] A simulation module, configured to perform a performance degradation simulation on the semiconductor laser based on the defect expansion characteristic diagram to obtain a performance degradation trajectory curve;

[0046] The evaluation module is used to evaluate the life of the semiconductor laser based on the performance attenuation trajectory curve to obtain a life estimation result.

[0047] The present invention provides a method for predicting the life of a semiconductor laser, comprising the following steps: measuring the optoelectronic characteristics of the semiconductor laser to obtain an optoelectronic characteristic parameter set; performing stress analysis on the semiconductor laser based on the optoelectronic characteristic parameter set to obtain a device stress characteristic spectrum; when the interface stress intensity in the device stress characteristic spectrum exceeds a preset threshold, performing defect evolution analysis on the semiconductor laser based on the device stress characteristic spectrum to obtain a defect expansion characteristic diagram; performing performance degradation simulation on the semiconductor laser based on the defect expansion characteristic diagram to obtain a performance degradation trajectory curve; and performing lifetime assessment on the semiconductor laser based on the performance degradation trajectory curve to obtain a lifetime prediction result. This method solves the technical problem that traditional lifetime assessment methods mainly rely on accelerated aging experiments and statistical models, which, although they can reflect the aging trend of devices to a certain extent, are often long-term, costly, and difficult to reveal the essential process of the device's internal failure mechanism. By using the defect expansion characteristic diagram to perform performance degradation simulation, a specific performance degradation trajectory curve can be obtained. This simulation takes into account various factors that may be encountered under actual working conditions, making the prediction results closer to the actual situation and improving the accuracy of lifetime prediction. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 1 is a schematic diagram of the steps of a method for estimating the lifetime of a semiconductor laser according to an embodiment of the present invention;

[0049] Figure 2 is a structural block diagram of a semiconductor laser lifetime prediction system according to one embodiment of the present invention;

[0050] Figure 3 It is a schematic block diagram of the structure of a computer device according to an embodiment of the present invention.

[0051] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0052] 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.

[0053] like Figure 1 As shown, Figure 1 This is a schematic diagram of the steps of a method for estimating the lifetime of a semiconductor laser according to one embodiment of the present invention;

[0054] In one embodiment of the present invention, a method for estimating the lifetime of a semiconductor laser is provided, comprising the following steps:

[0055] Step S1 : measuring the photoelectric characteristics of the semiconductor laser to obtain a set of photoelectric characteristic parameters.

[0056] Specifically, in the process of measuring the optoelectronic characteristics of the semiconductor laser and obtaining the optoelectronic characteristic parameter set, standard test equipment and methods are mainly used to obtain key parameters such as the optical output power, current-voltage (IV) characteristics, threshold current, slope efficiency, etc. of the device under different operating conditions. These parameters together constitute the optoelectronic characteristic parameter set for subsequent analysis. For example, in practical applications, when a semiconductor laser is used in an optical fiber communication system, the stability of its output optical power directly affects the quality of signal transmission. Therefore, it is necessary to repeatedly measure its optoelectronic performance at multiple time points to capture possible early signs of degradation. By placing the laser in a constant temperature controlled test environment and applying different drive currents, the relationship between its electrical response and optical output in different states can be recorded, thereby constructing a complete optoelectronic characteristic parameter set as a basic data source for evaluating its potential lifetime characteristics.

[0057] Step S2: performing stress analysis on the semiconductor laser based on the optoelectronic characteristic parameter set to obtain a device stress characteristic spectrum.

[0058] Specifically, the process of performing stress analysis on the semiconductor laser based on the set of optoelectronic characteristic parameters to obtain the device stress characteristic spectrum is to perform physical modeling and mechanical simulation on the optoelectronic characteristic parameters obtained by the aforementioned measurements, thereby deducing the stress distribution of each layer of material within the device under operating conditions. Specifically, the measured current-voltage characteristics, threshold current changes, and output optical power parameters are combined with physical properties such as the thermal expansion coefficient, elastic modulus, and lattice mismatch rate of the semiconductor material and input into a finite element analysis model to simulate the thermal stress, mechanical stress, and interface stress experienced by the laser under different driving conditions and temperature gradients, thereby generating a device stress characteristic spectrum that can reflect its internal stress state. For example, high-power semiconductor lasers used in optical fiber communication systems are prone to generating large residual stresses at the heterojunction interface due to long-term exposure to high current density and periodic thermal cycling, which in turn induces defect propagation. Through this stress analysis step, it is possible to effectively identify whether the stress intensity in these high-risk areas exceeds the safety threshold, providing a key basis for subsequent defect evolution analysis.

[0059] Step S3: When the interface stress intensity in the device stress characteristic spectrum exceeds a preset threshold, defect evolution analysis is performed on the semiconductor laser based on the device stress characteristic spectrum to obtain a defect expansion characteristic diagram.

[0060] Specifically, when the interface stress intensity in the device stress characteristic spectrum exceeds a preset threshold, the semiconductor laser is subjected to defect evolution analysis based on the device stress characteristic spectrum. The process of obtaining the defect expansion characteristic diagram is based on the stress distribution data obtained in the previous step, and further introduces a simulation method of material failure mechanism and microstructure evolution. Specifically, after identifying that the stress intensity in the critical interface area has exceeded the safety threshold, a dislocation dynamics model or a phase field simulation method is used, combined with parameters such as the lattice structure, defect density, and slip system of the material, to dynamically simulate the defect nucleation, expansion, and aggregation processes that may occur in the high stress area. This process can generate a defect expansion characteristic diagram that reflects the defect evolution path and expansion trend, which is used to reveal the evolution behavior of microscopic damage inside the device. For example, in high-power semiconductor lasers used in optical fiber communication systems, the heterojunction interface generates significant thermal mismatch stress due to periodic thermal cycling, thereby inducing microcracks or dislocation slip. Through the defect evolution analysis of this step, the expansion trajectory of these defects in the complex stress field and their potential impact on the device life can be clearly captured, providing a physical basis for subsequent performance degradation simulation.

[0061] Step S4: performing a performance degradation simulation on the semiconductor laser based on the defect expansion characteristic diagram to obtain a performance degradation trajectory curve.

[0062] Specifically, the semiconductor laser is simulated for performance degradation based on the defect expansion characteristic map to obtain a performance degradation trajectory curve. This process is based on the previously obtained defect expansion characteristic map and further analyzes the relationship between the microstructural damage inside the device and the macroscopic performance changes. First, using the key information such as the defect evolution path, expansion rate, and distribution density provided in the defect expansion characteristic map, combined with the operating principle and material properties of the laser, numerical simulation methods such as finite element analysis or Monte Carlo simulation are used to predict the device performance degradation caused by the presence and development of these defects. In this process, the influence of factors such as temperature fluctuations and current density changes in the actual working environment is taken into account. The simulation will take these external conditions into account as variables, thereby more accurately reflecting the changes in device performance under different operating conditions. For example, in high-power semiconductor lasers used in optical fiber communication systems, heat accumulation caused by long-term operation may cause internal microcracks to gradually expand, thereby affecting the optical output power and wavelength stability. Through the performance degradation simulation in this step, the changing trends of laser output characteristics such as efficiency and wavelength offset over time can be estimated and displayed in the form of a performance degradation trajectory curve, providing a basis for equipment maintenance and ensuring the stability and reliability of the system. This simulation not only helps understand how defects gradually affect the performance of lasers, but also provides important reference data for optimizing design and extending service life.

[0063] Step S5: performing a life evaluation on the semiconductor laser based on the performance attenuation trajectory curve to obtain a life estimation result.

[0064] Specifically, the process of evaluating the lifespan of a semiconductor laser based on the performance decay trajectory curve and obtaining a lifespan prediction result is to use the performance decay trajectory curve obtained in the previous step as input, combine the device failure criteria and reliability model, and quantitatively analyze the time evolution of the laser from the initial state to functional failure under specific operating conditions. Specifically, the performance decay trajectory curve reflects the changing trend of the laser's key performance parameters (such as output optical power, threshold current, wavelength stability, etc.) over time or usage cycle. By identifying the performance degradation inflection point, slope change rate, and time points approaching the failure threshold in the curve, a mapping relationship between performance degradation and lifespan can be established. For example, the service life of semiconductor lasers used in optical fiber communication systems is generally determined by the output optical power dropping to 90% of the initial value or the threshold current rising to a certain critical value. By fitting and extrapolating the performance decay trajectory curve in this step, the time required for the device to reach the failure criteria can be predicted, thereby obtaining a lifespan prediction result for the laser, providing a scientific basis for system design, maintenance strategy formulation, and device selection.

[0065] In a specific embodiment, performing stress analysis on the semiconductor laser based on the optoelectronic characteristic parameter set to obtain a device stress characteristic spectrum includes:

[0066] Performing a temperature field simulation on the semiconductor laser based on the optoelectronic characteristic parameter set to obtain temperature distribution data, and performing a thermal stress calculation on the semiconductor laser based on the temperature distribution data using a finite element analysis method to obtain thermal stress distribution data;

[0067] performing a dislocation density analysis on the semiconductor laser based on the thermal stress distribution data to obtain dislocation density distribution data, and performing a strain energy density analysis on the semiconductor laser based on the dislocation density distribution data to obtain strain energy density distribution data;

[0068] An interface stress analysis is performed on the semiconductor laser based on the strain energy density distribution data to obtain interface stress intensity data, and a device stress characteristic spectrum is constructed for the semiconductor laser based on the interface stress intensity data and the thermal stress distribution data to obtain a device stress characteristic spectrum.

[0069] Specifically, the process of performing stress analysis on the semiconductor laser based on the optoelectronic characteristic parameter set to obtain the device stress characteristic spectrum is a systematic process involving multi-physics field coupling analysis. It is closely dependent on the optoelectronic characteristic parameter set obtained in the previous step. These parameters include, but are not limited to, the laser's output optical power, threshold current, slope efficiency, and current-voltage characteristic curve at different drive currents. Through in-depth analysis of these parameters, initial boundary conditions and heat source distribution information can be provided for subsequent temperature field simulations, thereby achieving an effective mapping from electro-optical behavior to thermodynamic response. First, in the process of performing temperature field simulation on the semiconductor laser based on the optoelectronic characteristic parameter set, the researchers input the measured current-voltage characteristics and output optical power data into a thermal conduction model. Combined with the laser chip structure (such as active region thickness, layered material composition) and the thermal conductivity parameters of the packaging structure, a three-dimensional temperature field model is established using the finite element method. This model can calculate the temperature distribution data for each region of the laser under continuous operation or pulsed mode. For example, in a 1.55 μm distributed feedback (DFB) laser used in fiber-optic communication systems, the active region temperature can reach over 80°C at a typical operating current of 20 mA, while the surrounding cladding region remains around 60°C. This temperature gradient directly leads to differential thermal expansion, which in turn triggers thermal stress. Next, a finite element analysis method is used to calculate the thermal stress distribution of the semiconductor laser based on the temperature distribution data. This model considers mechanical properties such as the thermal expansion coefficient and elastic modulus of each layer of material and assumes that the material is within the linear elastic deformation range. For example, in an InP-based laser structure on a GaAs substrate, due to the lattice mismatch and thermal expansion coefficient difference between InP and GaAs, large tensile or compressive stresses are generated at the interface during heating. Simulation results show that in this DFB laser, the peak thermal stress near the active region can reach over 300 MPa, far exceeding the material yield strength, and therefore highly likely to induce plastic deformation or defect formation. Subsequently, a dislocation density analysis is performed on the semiconductor laser based on the thermal stress distribution data to obtain dislocation density distribution data. This step mainly uses a dislocation dynamics model or an empirical formula to convert the local stress level into the possibility of dislocation proliferation within the material. Generally, when the thermal stress in a certain area exceeds the critical slip stress (such as about 100 MPa), dislocations begin to move and proliferate, forming a high-density dislocation network. For example, near the heterojunction interface of the aforementioned DFB laser, the dislocation density can rise from the initial 1×10^6 cm / cm² to 1×10^9 cm / cm², indicating that the area has entered a significant crystal damage stage. Further, based on the dislocation density distribution data, a strain energy density analysis is performed on the semiconductor laser to obtain strain energy density distribution data.Strain energy density is an important indicator of the energy stored within a material due to deformation and is often closely related to the tendency of defect propagation. In practical calculations, strain energy density can be approximately estimated by multiplying thermal stress and strain. For example, in the quantum well region of a DFB laser, due to the increased dislocation density, the strain energy density in this region can reach over 10 J / m³, significantly higher than in other regions, indicating that this region is more likely to become a starting point for defect propagation. Next, based on the strain energy density distribution data, an interface stress analysis is performed on the semiconductor laser to obtain interface stress intensity data. This process focuses on the interface region between different material layers and assesses its stability under complex stress fields. For example, in the aforementioned DFB laser, the interface stress intensity between the p-type InP cladding and the n-type InP substrate can reach 400 MPa·√m, far exceeding the fracture toughness threshold of typical materials (e.g., approximately 200 MPa·√m), indicating a high risk of cracking at this interface. Finally, a device stress signature spectrum is constructed for the semiconductor laser based on the interface stress intensity data and the thermal stress distribution data. This characteristic spectrum not only includes the stress distribution in each region in space, but also integrates key parameters such as interface strength, strain energy density, and dislocation evolution path, forming a visual map that comprehensively reflects the internal mechanical state of the device. For example, in the failure analysis of DFB lasers, this characteristic spectrum clearly reveals the high stress concentration zone in the interface region and its consistency with the dislocation extension direction, providing precise initial conditions and key input parameters for subsequent defect evolution analysis. In summary, this series of steps realizes an effective transition from macroscopic optoelectronic performance measurement to microscopic mechanical behavior modeling, laying a solid physical foundation for subsequent defect evolution analysis and life prediction.

[0070] In a specific embodiment, the defect evolution analysis of the semiconductor laser is performed based on the device stress characteristic spectrum to obtain a defect expansion characteristic map, including:

[0071] performing a dislocation slip simulation on the semiconductor laser based on the transverse stress distribution and the longitudinal stress distribution in the stress characteristic spectrum of the device to obtain dislocation slip path data, and performing a dislocation density calculation on the semiconductor laser based on the dislocation slip path data to obtain dislocation density distribution data;

[0072] performing a defect diffusion simulation on the semiconductor laser based on the dislocation density distribution data to obtain defect concentration distribution data, and performing a defect reaction simulation on the semiconductor laser based on the defect concentration distribution data to obtain defect reaction rate data;

[0073] performing interface state energy level calculation on the semiconductor laser based on the defect reaction rate data to obtain interface state energy level distribution data, and performing carrier capture cross section calculation on the semiconductor laser based on the interface state energy level distribution data to obtain carrier capture cross section data;

[0074] The carrier recombination rate of the semiconductor laser is calculated based on the carrier capture cross-section data using a Shockley-Read-Hall recombination model to obtain carrier recombination rate data, and a defect extension characteristic map is constructed for the semiconductor laser based on the carrier recombination rate data and the dislocation density distribution data to obtain a defect extension characteristic map.

[0075] Specifically, the defect evolution analysis of a semiconductor laser based on the device stress signature spectrum and the resulting defect expansion feature map builds on the device stress signature spectrum constructed in the previous step to further explore the internal microstructural damage mechanism and dynamic defect evolution behavior. This process first uses the transverse and longitudinal stress distributions in the device stress signature spectrum as input conditions to perform dislocation slip simulations to predict the possible motion paths of crystal defects within the material. Specifically, in areas of high stress concentration (such as heterojunction interfaces or near quantum wells), dislocations are more likely to slip along specific crystal directions. Finite element-molecular dynamics coupling methods can track these slip paths and generate dislocation slip path data. For example, in 1.55 μm DFB lasers used in fiber-optic communication systems, due to the thermal expansion mismatch of the InP / GaAs heterojunction, dislocation slip lengths in certain regions can reach hundreds of nanometers during long-term operation, indicating significant crystal structural damage in these areas. Subsequently, based on the dislocation slip path data, the dislocation density of the semiconductor laser is further calculated to obtain dislocation density distribution data. This calculation process typically employs a discrete dislocation dynamics model or a continuous medium dislocation density evolution equation, combined with the material's slip system parameters and local stress state, to derive the dislocation proliferation rates in different regions. For example, near the active region of a DFB laser, the initial dislocation density is approximately 1×10^6 cm / cm². After high-temperature accelerated aging, the dislocation density in this region can rise to over 3×10^9 cm / cm², demonstrating a significant crystal defect accumulation effect. Based on this dislocation density distribution data, a defect diffusion simulation is performed on the semiconductor laser to obtain defect concentration distribution data. This simulation considers the mobility and diffusion coefficient of point defects (such as vacancies and interstitial atoms) in the crystal lattice, as well as their interaction mechanisms with dislocations. For example, under high-temperature operating conditions, the diffusion rate of vacancy defects can reach 1×10^-12 m² / s, causing defects to gradually accumulate in the dislocation core region, resulting in an increase in local defect concentration. The simulation results show that in the p-type doped layer of the DFB laser, the defect concentration can increase from the initial 1×10^16cm¯³ to 1×10^18 cm¯³, indicating that a significant material degradation trend has appeared in this area. Next, based on the defect concentration distribution data, the defect reaction of the semiconductor laser is simulated to obtain defect reaction rate data. This step mainly involves modeling the chemical reaction kinetics between defects and impurities, dopants or other defects, including processes such as recombination, aggregation, and precipitation. For example, in the above-mentioned DFB laser, the binding rate of vacancies and zinc doping atoms can reach 1×10^-25cm³ / s, indicating that a stable complex is formed between the defect and the doping element, which will affect the effective concentration and transport characteristics of the carriers.Furthermore, the interface state energy level of the semiconductor laser is calculated based on the defect reaction rate data to obtain interface state energy level distribution data. The interface state energy level is an important indicator to measure the electronic state introduced by the defect at the interface, which usually affects the capture and emission behavior of carriers. For example, at the oxide / semiconductor interface of the DFB laser, the interface state density can be as high as 1×10^11 eV¯¹cm¯², and its energy level position is concentrated in the middle region of the bandgap, which significantly affects the stability of the electrical performance of the device. Then, based on the interface state energy level distribution data, the carrier capture cross section of the semiconductor laser is calculated to obtain carrier capture cross section data. This parameter describes the capture efficiency of the defect on free carriers and is one of the key factors affecting the device's luminous efficiency and threshold current changes. For example, in the above-mentioned DFB laser, the cross section of electrons captured by the interface state can reach 1×10^-14 cm², indicating that the defect has a strong confinement effect on carriers, which in turn leads to a decrease in the output light power. Next, the carrier recombination rate of the semiconductor laser is calculated based on the carrier capture cross-section data using the Shockley-Read-Hall recombination model to obtain carrier recombination rate data. This model comprehensively considers factors such as defect state density, capture cross-section, and carrier concentration, and can effectively predict the efficiency drop caused by non-radiative recombination. For example, in the late aging stage of the DFB laser, the non-radiative recombination rate of carriers can reach 1×10^7 s¯¹, which is much higher than the initial 1×10^5 s¯¹, indicating that the defect density inside the device has increased significantly, resulting in a sharp drop in luminous efficiency. Finally, based on the carrier recombination rate data and the dislocation density distribution data, a defect expansion feature map is constructed for the semiconductor laser to obtain a defect expansion feature map. This feature map not only contains multi-dimensional information such as dislocation path, defect concentration, and interface state distribution, but also integrates the changing trend of carrier recombination behavior to form a visual map reflecting the entire process of defect evolution inside the device. For example, in the failure analysis of DFB lasers, this characteristic map clearly reveals the complete evolutionary path of defects, starting from dislocation slip, gradually developing into high-concentration defect aggregation, and ultimately leading to enhanced carrier recombination and decreased luminous efficiency, providing a key physical basis for subsequent performance degradation simulations. In summary, this defect evolution analysis process achieves a step-by-step mapping from macroscopic stress fields to microscopic defect behavior, laying a solid theoretical foundation for accurately evaluating the reliability and lifespan of semiconductor lasers under complex operating conditions.

[0076] In a specific embodiment, performing defect diffusion simulation on the semiconductor laser based on the dislocation density distribution data to obtain defect concentration distribution data includes:

[0077] performing stress field distribution calculation on the dislocation density distribution data to obtain defect diffusion driving force distribution data, and performing defect migration path analysis on the semiconductor laser based on the defect diffusion driving force distribution data to obtain defect migration channel distribution data;

[0078] performing defect diffusion kinetics analysis on the defect migration channel distribution data using a non-equilibrium thermodynamic equation to obtain defect diffusion flux density data, and performing defect concentration gradient calculation on the semiconductor laser based on the defect diffusion flux density data to obtain defect concentration gradient distribution data;

[0079] Calculating a defect diffusion coefficient of the semiconductor laser based on the defect concentration gradient distribution data to obtain defect diffusion coefficient tensor data, and calculating a defect diffusion flux of the semiconductor laser based on the defect diffusion coefficient tensor data to obtain defect diffusion flux field data;

[0080] The defect concentration spatiotemporal evolution of the semiconductor laser is calculated based on the defect diffusion flux field data to obtain the defect concentration spatiotemporal distribution data, and the defect concentration conservation analysis is performed on the defect concentration spatiotemporal distribution data through the continuity equation to obtain the defect concentration distribution data.

[0081] Specifically, the process of simulating defect diffusion in the semiconductor laser based on the dislocation density distribution data to obtain defect concentration distribution data is a complex multi-step, multi-level analysis process that reveals the diffusion behavior of defects within the material and its impact on device performance through a series of calculations and simulations. First, the process begins by calculating the stress field distribution of the dislocation density distribution data to obtain defect diffusion driving force distribution data. Based on this, the defect migration path analysis of the semiconductor laser is performed to obtain defect migration channel distribution data. For example, DFB lasers used in optical fiber communication systems have a high dislocation density in this area due to the significant difference in thermal expansion coefficient at the heterojunction interface. Under high-temperature operating conditions, these dislocations act as defect sources, and the stress field around them drives point defects such as vacancies or interstitial atoms to move to low-energy states, forming specific defect migration paths. Studies have shown that in this case, the defect migration path length in some high-stress areas can reach several microns, indicating active diffusion of defects in local areas. Next, the defect diffusion kinetics of the defect migration channel distribution data are analyzed using non-equilibrium thermodynamic equations to obtain defect diffusion flux density data. Based on this, the defect concentration gradient of the semiconductor laser is calculated to obtain defect concentration gradient distribution data. In this process, the effects of factors such as temperature, stress, and chemical potential on the defect diffusion rate are considered. For example, in the above-mentioned DFB laser, when the operating temperature is increased to 100°C, the diffusion flux density of vacancy defects can reach 1×10^12 m¯²s¯¹, while that of interstitial atoms may be lower, which mainly depends on their respective activation energies and diffusion mechanisms. As defects diffuse from high-density areas to low-density areas, a clear concentration gradient distribution is formed. For example, in the area near the p-type doped layer, the initial defect concentration is 1×10^16 cm¯³, but after a period of time, this value may decrease to 5×10^15 cm¯³, indicating a tendency for defects to diffuse to other areas. Subsequently, the defect diffusion coefficient of the semiconductor laser is calculated based on the defect concentration gradient distribution data to obtain defect diffusion coefficient tensor data, and these data are used to further calculate the defect diffusion flux of the semiconductor laser to obtain defect diffusion flux field data. This link needs to take into account the influence of different crystal orientations and grain boundaries on the diffusion process. For example, near the active area of ​​the DFB laser, due to the presence of multiple slip systems and grain boundaries, the diffusion coefficients of defects along different directions may vary greatly. The diffusion coefficient in some directions may be as high as 1×10^-14 m² / s, while in other directions it may be only 1×10^-16 m² / s. These differences will cause defects to form complex diffusion patterns within the material, which in turn affects the microstructural stability of the entire device.Then, based on the defect diffusion flux field data, the temporal and spatial evolution of the defect concentration in the semiconductor laser is calculated to obtain the temporal and spatial distribution data of the defect concentration. This data is then subjected to defect concentration conservation analysis using the continuity equation, ultimately yielding the defect concentration distribution data. This process not only focuses on the change in the number of defects but also examines their dynamic variations in both time and space. For example, in a long-term DFB laser, after thousands of hours of operation, it can be observed that the defect concentration gradually evolves from an initially uniform distribution to a concentration in certain specific areas. In particular, near the electrode contact point, the defect concentration can increase to over 1×10^18 cm¯³ due to the local temperature increase caused by current crowding, while areas farther from the electrode maintain relatively low defect levels (approximately 1×10^16 cm¯³). This uneven defect distribution directly affects the device's luminous efficiency, threshold current, and reliability. In summary, through a series of in-depth analyses and calculations of dislocation density distribution data, a comprehensive understanding of the internal defect diffusion behavior of semiconductor lasers is achieved. This process not only reveals how defects migrate under stress fields and form specific diffusion paths, but also provides precise information about the temporal and spatial distribution of defects by quantifying the kinetic parameters of defect diffusion, such as diffusion flux density and diffusion coefficient tensor. This information is crucial for predicting laser lifetime, optimizing design parameters, and formulating effective maintenance strategies. For example, in practical engineering applications, accurately understanding the diffusion characteristics of defects within DFB lasers can help engineers select more appropriate material combinations or improve packaging technology, thereby extending device life and improving overall system reliability.

[0082] In a specific embodiment, the performing of a performance degradation simulation on the semiconductor laser based on the defect expansion characteristic diagram to obtain a performance degradation trajectory curve includes:

[0083] Performing a light field distribution simulation on the semiconductor laser based on the defect expansion characteristic diagram to obtain light field distribution data, and performing an output power simulation on the semiconductor laser based on the light field distribution data to obtain output power data;

[0084] Calculating the output power attenuation rate of the semiconductor laser based on the output power data to obtain output power attenuation rate data, and simulating the carrier concentration of the semiconductor laser based on the interface state energy level in the defect expansion characteristic diagram to obtain carrier concentration data;

[0085] Performing spectrum simulation on the semiconductor laser based on the carrier concentration data to obtain spectrum data, and calculating wavelength drift of the semiconductor laser based on the spectrum data to obtain wavelength drift data;

[0086] A mode stability analysis is performed on the semiconductor laser based on the light field distribution data to obtain mode stability index data, and a performance attenuation trajectory curve is constructed for the semiconductor laser based on the output power attenuation rate data, the wavelength drift data, and the mode stability index data to obtain a performance attenuation trajectory curve.

[0087] Specifically, simulating the performance degradation of the semiconductor laser based on the defect expansion feature map to obtain a performance degradation trajectory curve is a key step in establishing a quantitative connection between microscopic defect evolution behavior and macroscopic optoelectronic property changes. This process, through the collaborative analysis of multiple submodules, gradually reveals the complete evolutionary path from defect expansion to device performance degradation. First, based on the defect expansion feature map, the semiconductor laser's light field distribution is simulated to obtain light field distribution data. Based on this data, output power simulation is further performed to obtain output power data. This stage primarily utilizes finite element optical simulation tools (such as COMSOL or Lumerical) to establish a laser waveguide structure model. Combined with the material damage region information (such as dislocation density and interface state concentration) provided by the defect expansion feature map, the light propagation behavior in the active region and waveguide layer is modeled. For example, in DFB lasers used in optical fiber communication systems, as defects accumulate in the quantum well region, the local refractive index changes, resulting in distortion of the light field distribution, scattering or absorption of some light, and a subsequent decrease in output power. Simulation results show that in the initial state, the laser's output power can reach 20 mW. However, after long-term operation, the output power may drop below 15 mW due to non-radiative recombination and increased optical loss caused by defects. Subsequently, the output power decay rate of the semiconductor laser is calculated based on the output power data to obtain output power decay rate data. This step extracts the power decay rate per unit time by fitting the output power variation curve over time. For example, in an accelerated aging experiment, the output power of a DFB laser dropped from 20 mW to 17 mW after 1000 hours of operation, with an average decay rate of 3 mW / 1000 h. This data provides a direct basis for subsequent lifetime prediction. At the same time, carrier concentration simulation of the semiconductor laser is performed based on the interface state energy levels in the defect expansion characteristic diagram to obtain carrier concentration data. Because the presence of defects, especially interface states, can significantly affect carrier injection efficiency and recombination behavior, it is necessary to consider the impact of these defects on electron and hole distribution. For example, if the interface state density near the active region of a DFB laser increases from 1×10^10 eV¯¹cm¯² to 5×10^11 eV¯¹cm¯², the effective carrier concentration will decrease from 1×10^18 cm¯³ to 6×10^17 cm¯³, thereby reducing the gain coefficient and affecting the luminous efficiency. Furthermore, based on the carrier concentration data, a spectral simulation of the semiconductor laser is performed to obtain spectral data, and the wavelength drift data is calculated based on this. Changes in spectral characteristics are important indicators reflecting material degradation and mode instability within the device. For example, in the above-mentioned DFB laser, the initial emission wavelength is 1550 nm, and after defects cause distortion of the quantum well band structure, the wavelength may undergo a redshift or blueshift.After 1000 hours of aging, the measured wavelength drift can reach +0.4 nm, indicating that the material structure has undergone irreversible changes. In addition, based on the light field distribution data, the mode stability of the semiconductor laser is analyzed to obtain mode stability index data. Mode stability reflects the ability of the laser to maintain single-mode output during long-term operation. For example, in the initial state, the mode stability index is 0.95 (close to the ideal value of 1), but after defects cause waveguide distortion, the index may drop below 0.7, indicating that the device begins to experience multi-mode oscillation or a decrease in the side mode suppression ratio. Finally, based on the output power attenuation rate data, the wavelength drift data, and the mode stability index data, a performance attenuation trajectory curve is constructed for the semiconductor laser to obtain a performance attenuation trajectory curve. This curve uses time as the horizontal axis and various performance parameters as the vertical axis, comprehensively presenting the degradation trend of the laser at different aging stages. For example, in the performance decay trajectory curve of the DFB laser, it can be clearly seen that: in the first 500 hours, the output power slowly decreases by about 1 mW, and the wavelength drift is less than 0.1 nm; after 500 hours, due to the diffusion of defects into the critical interface area, the output power decreases rapidly, the wavelength drift increases significantly, and the mode stability index decreases rapidly, indicating that the device performance has entered a rapid degradation stage. In summary, this performance decay simulation process realizes the full process simulation of the macroscopic performance changes of semiconductor lasers by integrating the microscopic information in the defect expansion feature map. It can not only accurately capture the dynamic evolution of output power, wavelength, and mode stability, but also provide solid data support for subsequent life assessment. This simulation method based on physical mechanisms has higher prediction accuracy than traditional empirical models, and is particularly suitable for laser life estimation and failure warning in high-reliability application scenarios.

[0088] In a specific embodiment, the lifetime evaluation of the semiconductor laser based on the performance attenuation trajectory curve to obtain a lifetime prediction result includes:

[0089] By using a double exponential decay model, based on the performance decay trajectory curve, the fast decay phase lifetime of the semiconductor laser is calculated to obtain fast decay phase lifetime data; and based on the fast decay phase lifetime data, a slow decay phase starting point of the semiconductor laser is determined to obtain slow decay phase starting point data;

[0090] performing a slow decay phase lifetime calculation on the semiconductor laser based on the slow decay phase starting point data to obtain slow decay phase lifetime data, and performing a lifetime statistical distribution feature extraction on the semiconductor laser based on the slow decay phase lifetime data to obtain lifetime statistical distribution feature data;

[0091] Calculating the mean failure time of the semiconductor laser based on the lifetime statistical distribution characteristic data to obtain mean failure time data, and calculating the failure probability of the semiconductor laser based on the mean failure time data to obtain failure probability data;

[0092] Based on the failure probability data and the rapid decay stage life data, a total life prediction is performed on the semiconductor laser using a life prediction model to obtain a life prediction result.

[0093] Specifically, the process of evaluating the life of a semiconductor laser based on the performance decay trajectory curve and obtaining a life prediction result is a systematic and hierarchical analysis process. It combines the performance degradation behavior of the device at different aging stages with the statistical reliability model, thereby achieving a scientific prediction of its entire life cycle. First, the rapid decay phase lifetime of the semiconductor laser is calculated based on the performance decay trajectory curve using a double exponential decay model to obtain rapid decay phase lifetime data. This model assumes that the laser's performance rapidly declines in the early stages of operation due to factors such as process defect release or interface stress relaxation, and then enters a relatively slow degradation phase. For example, in a DFB laser used in an optical fiber communication system, its output power rapidly decreases from an initial 20 mW to 18.5 mW within the first 200 hours, and the corresponding rapid decay phase lifetime data is approximately 300 hours. Subsequently, the starting point of the slow decay phase of the semiconductor laser is determined based on the rapid decay phase lifetime data to obtain the slow decay phase starting point data. Typically, the slow decay phase begins at the point where the slope of the performance curve significantly slows. For example, in the aforementioned DFB laser, the slow decay phase begins at approximately 300 hours, at which point the output power decline trend flattens but continues to slowly decrease. Next, the slow decay phase lifetime of the semiconductor laser is calculated based on the data at the start of the slow decay phase to obtain slow decay phase lifetime data. This phase reflects the stable but continuous performance degradation of the device during long-term operation due to mechanisms such as defect diffusion, dislocation proliferation, and interface state accumulation. For example, in a DFB laser, the slow decay phase may last for more than 8000 hours, during which the output power gradually decreases from 18.5 mW to 12 mW (i.e., reaching the failure criterion: the output power drops to 60% of the initial value). Based on this, the lifetime statistical distribution characteristics of the semiconductor laser are further extracted based on the slow decay phase lifetime data to obtain lifetime statistical distribution characteristics data. These characteristics include, but are not limited to, mean lifetime, standard deviation, skewness, and kurtosis, and are used to describe the lifetime fluctuations of a batch of lasers under the same operating conditions. For example, by statistically analyzing the lifespan data of a batch of 100 DFB lasers, it was found that their lifespans followed a Weibull distribution with a shape parameter of 1.8 and a scale parameter of 9,000 hours, indicating that this batch of devices had a certain tendency to fail early, but also had a relatively long main service life. Subsequently, the mean time to failure of the semiconductor lasers was calculated based on the lifetime statistical distribution characteristic data to obtain mean time to failure data, and the failure probability of the semiconductor lasers was calculated based on this data to obtain failure probability data. Mean time to failure is one of the key indicators for measuring device reliability. For example, in the above-mentioned DFB laser, its mean time to failure was approximately 8,500 hours.The failure probability indicates the likelihood of a device experiencing functional failure within a specific operating time. For example, at 5,000 hours, the failure probability of this laser is approximately 5%, rising to 40% at 10,000 hours. This data not only helps understand the device's reliability level but also provides a statistical basis for subsequent lifetime prediction. Finally, based on the failure probability data and the rapid decay phase lifetime data, a lifetime prediction model is used to predict the total lifetime of the semiconductor laser, yielding a lifetime estimate. This model comprehensively considers the device's behavioral characteristics in the two main aging phases and incorporates failure thresholds used in engineering applications (such as output power below 12 mW, wavelength drift exceeding ±1 nm, or a mode stability index below 0.7). For example, in the aforementioned DFB laser, its rapid decay phase lifetime is 300 hours, and its slow decay phase lifetime is 8,500 hours. Combining these two components yields a total lifetime estimate of 8,800 hours. Furthermore, considering the uncertainties of the actual operating environment (such as temperature fluctuations and current noise), a safety factor (such as 0.9) can be introduced for correction, ultimately resulting in a conservatively estimated lifetime of 7920 hours to ensure high-reliability operation of the system. In summary, this lifetime assessment process fully integrates the dynamic evolution information reflected by the performance decay trajectory curve with statistical reliability theory, achieving full-process modeling from microscopic defects to macroscopic lifetime prediction. This not only improves the accuracy of lifetime prediction, but also provides solid technical support for design optimization, maintenance strategy formulation, and failure warning mechanism construction of semiconductor lasers in high-reliability application scenarios such as optical fiber communications.

[0094] In a specific embodiment, the calculating of the lifetime of the semiconductor laser in the slow decay phase based on the slow decay phase starting point data to obtain the lifetime data in the slow decay phase includes:

[0095] Based on the slow decay stage starting point data, performing slow decay stage characteristic lifetime fitting on the semiconductor laser using a three-parameter Weibull distribution model to obtain slow decay stage characteristic lifetime parameters, and extracting shape parameters and scale parameters of the semiconductor laser based on the slow decay stage characteristic lifetime parameters to obtain Weibull shape parameter and scale parameter data;

[0096] Estimating the failure probability density function parameters of the semiconductor laser based on the Weibull shape parameter and scale parameter data using a maximum likelihood estimation method to obtain failure probability density function parameters, and calculating the cumulative failure probability function of the semiconductor laser based on the failure probability density function parameters to obtain cumulative failure probability function data;

[0097] performing a lifetime confidence interval calculation on the semiconductor laser in a slow decay phase based on the cumulative failure probability function data to obtain lifetime confidence interval data for the slow decay phase, and performing a lifetime reliability assessment on the semiconductor laser based on the lifetime confidence interval data for the slow decay phase to obtain lifetime reliability data;

[0098] Based on the lifetime reliability data and the fast decay stage lifetime data, the lifetime weight of the semiconductor laser in the slow decay stage is calculated by the lifetime weighted average method to obtain the slow decay stage lifetime weight data, and based on the slow decay stage lifetime weight data and the fast decay stage lifetime data, the total lifetime of the semiconductor laser is calculated to obtain the slow decay stage lifetime data.

[0099] Specifically, calculating the slow decay phase lifetime of the semiconductor laser based on the slow decay phase starting point data is a critical step in the overall lifetime assessment process. This process combines physical aging behavior with statistical reliability models to quantitatively analyze the long-term degradation of the device. This process first uses the slow decay phase starting point data obtained in the previous step as input and performs characteristic lifetime fitting in conjunction with a three-parameter Weibull distribution model to extract key lifetime parameters of the slow decay phase. Specifically, after experiencing a rapid decay phase, DFB lasers used in optical fiber communication systems typically enter a phase of performance stabilization but continued slow degradation around 300 hours. At this point, the slow decay phase start time is identified by collecting performance decay trajectory curves from multiple groups of lasers from the same batch and based on their output power decline trends. Subsequently, the data from this phase is fitted using a three-parameter Weibull distribution model, which introduces a position parameter (i.e., starting point), a shape parameter, and a scale parameter to describe the distribution characteristics of the device lifetime. For example, the characteristic lifetime fitting results for a batch of DFB lasers during the slow decay phase showed a Weibull shape parameter of 1.5, indicating a certain concentration trend in the lifetime distribution during this phase; a scale parameter of 8,000 hours, representing the typical lifetime level for this batch of lasers during the slow decay phase; and a position parameter of approximately 300 hours, corresponding to the onset of the slow decay phase. Furthermore, based on the above Weibull shape parameter and scale parameter data, the failure probability density function parameters of the semiconductor lasers were estimated using the maximum likelihood estimation method, thereby obtaining the failure probability density function parameters, and based on this, the cumulative failure probability function data was calculated. This step is used to establish a model for the time-varying failure probability of the device during the slow decay phase. For example, after 6,000 hours of operation, the cumulative failure probability of this DFB laser was approximately 20%, rising to 70% after 10,000 hours of operation. This indicates that as defects expand and interface states accumulate, the device gradually approaches the functional failure threshold. Next, based on the cumulative failure probability function data, the semiconductor laser's lifetime confidence interval in the slow decay phase is calculated to obtain the lifetime confidence interval data in the slow decay phase. Furthermore, a lifetime reliability assessment is performed to obtain lifetime reliability data. For example, through statistical analysis of 100 DFB laser samples, it is found that the 95% confidence interval in the slow decay phase is [7500, 8500] hours, which means that there is a 95% probability that the lifetime in this phase falls within this interval. At the same time, if lifetime reliability is defined as the probability that the device can still maintain normal operation within 10,000 hours, the lifetime reliability of the DFB laser can reach 85%, indicating that it has strong stability in application scenarios with high reliability requirements.Finally, based on the lifetime reliability data and the rapid decay phase lifetime data, the semiconductor laser's slow decay phase lifetime weight is calculated using a lifetime weighted average method. This weighted data is then combined with the rapid decay phase lifetime data to complete the total lifetime calculation, yielding the slow decay phase lifetime data. For example, assuming a rapid decay phase lifetime of 300 hours with a weight of 5%, and a slow decay phase lifetime of 8000 hours with a weight of 95%, the weighted average yields a total lifetime estimate of 7600 hours. This calculation method, based on weights for different phases of lifetime, not only accounts for the different degradation rates of the device in its early and later stages but also improves the accuracy and engineering practicality of lifetime prediction. In summary, this slow decay phase lifetime calculation process integrates statistical reliability theory with experimental data analysis to construct a scientific and systematic lifetime modeling framework. From Weibull distribution parameter extraction to failure probability density estimation, to confidence intervals and reliability assessment, and finally, weighted lifetime prediction incorporating the rapid decay phase, this fully demonstrates the step-by-step mapping from microscopic defect evolution to macroscopic lifetime assessment. This method provides solid theoretical support and technical guarantee for the life management of semiconductor lasers in practical applications, and is particularly suitable for key fields such as optical fiber communications with high precision and long life requirements.

[0100] The above describes the semiconductor laser life prediction method in the embodiment of the present invention. The following describes the semiconductor laser life prediction system in the embodiment of the present invention. Figure 2 In one embodiment of the present invention, a semiconductor laser lifetime prediction system includes:

[0101] A measurement module 21 is used to measure the photoelectric characteristics of the semiconductor laser to obtain a set of photoelectric characteristic parameters;

[0102] A first analysis module 22 is configured to perform stress analysis on the semiconductor laser based on the optoelectronic characteristic parameter set to obtain a device stress characteristic spectrum;

[0103] A second analysis module 23 is configured to perform defect evolution analysis on the semiconductor laser based on the device stress characteristic spectrum to obtain a defect expansion characteristic diagram when the interface stress intensity in the device stress characteristic spectrum exceeds a preset threshold;

[0104] A simulation module 24 is configured to perform a performance degradation simulation on the semiconductor laser based on the defect expansion characteristic diagram to obtain a performance degradation trajectory curve;

[0105] The evaluation module 25 is configured to evaluate the life of the semiconductor laser based on the performance attenuation trajectory curve to obtain a life estimation result.

[0106] 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.

[0107] Reference Figure 3 In 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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. A method for estimating the lifetime of a semiconductor laser, characterized in that: The following steps are involved: measuring the photoelectric characteristics of the semiconductor laser to obtain a set of photoelectric characteristic parameters; Performing stress analysis on the semiconductor laser based on the optoelectronic characteristic parameter set to obtain a device stress characteristic spectrum specifically includes: Performing a temperature field simulation on the semiconductor laser based on the optoelectronic characteristic parameter set to obtain temperature distribution data, and performing a thermal stress calculation on the semiconductor laser based on the temperature distribution data using a finite element analysis method to obtain thermal stress distribution data; performing a dislocation density analysis on the semiconductor laser based on the thermal stress distribution data to obtain dislocation density distribution data, and performing a strain energy density analysis on the semiconductor laser based on the dislocation density distribution data to obtain strain energy density distribution data; performing an interface stress analysis on the semiconductor laser based on the strain energy density distribution data to obtain interface stress intensity data, and constructing a device stress characteristic spectrum for the semiconductor laser based on the interface stress intensity data and the thermal stress distribution data to obtain a device stress characteristic spectrum; When the interface stress intensity in the device stress characteristic spectrum exceeds a preset threshold, defect evolution analysis is performed on the semiconductor laser based on the device stress characteristic spectrum to obtain a defect expansion characteristic map; The semiconductor laser is subjected to a performance degradation simulation based on the defect expansion characteristic diagram to obtain a performance degradation trajectory curve, specifically comprising: Performing a light field distribution simulation on the semiconductor laser based on the defect expansion characteristic diagram to obtain light field distribution data, and performing an output power simulation on the semiconductor laser based on the light field distribution data to obtain output power data; Calculating the output power attenuation rate of the semiconductor laser based on the output power data to obtain output power attenuation rate data, and simulating the carrier concentration of the semiconductor laser based on the interface state energy level in the defect expansion characteristic diagram to obtain carrier concentration data; Performing spectrum simulation on the semiconductor laser based on the carrier concentration data to obtain spectrum data, and calculating wavelength drift of the semiconductor laser based on the spectrum data to obtain wavelength drift data; performing a mode stability analysis on the semiconductor laser based on the light field distribution data to obtain mode stability index data, and constructing a performance attenuation trajectory curve for the semiconductor laser based on the output power attenuation rate data, the wavelength drift data, and the mode stability index data to obtain a performance attenuation trajectory curve; The life of the semiconductor laser is evaluated based on the performance attenuation trajectory curve to obtain a life estimation result.

2. The semiconductor laser lifetime estimation method according to claim 1, wherein: The defect evolution analysis of the semiconductor laser is performed based on the device stress characteristic spectrum to obtain a defect expansion characteristic map, including: performing a dislocation slip simulation on the semiconductor laser based on the transverse stress distribution and the longitudinal stress distribution in the stress characteristic spectrum of the device to obtain dislocation slip path data, and performing a dislocation density calculation on the semiconductor laser based on the dislocation slip path data to obtain dislocation density distribution data; performing a defect diffusion simulation on the semiconductor laser based on the dislocation density distribution data to obtain defect concentration distribution data, and performing a defect reaction simulation on the semiconductor laser based on the defect concentration distribution data to obtain defect reaction rate data; performing interface state energy level calculation on the semiconductor laser based on the defect reaction rate data to obtain interface state energy level distribution data, and performing carrier capture cross section calculation on the semiconductor laser based on the interface state energy level distribution data to obtain carrier capture cross section data; The carrier recombination rate of the semiconductor laser is calculated based on the carrier capture cross-section data using a Shockley-Read-Hall recombination model to obtain carrier recombination rate data, and a defect extension characteristic map is constructed for the semiconductor laser based on the carrier recombination rate data and the dislocation density distribution data to obtain a defect extension characteristic map.

3. The semiconductor laser lifetime estimation method according to claim 2, wherein: The performing defect diffusion simulation on the semiconductor laser based on the dislocation density distribution data to obtain defect concentration distribution data includes: performing stress field distribution calculation on the dislocation density distribution data to obtain defect diffusion driving force distribution data, and performing defect migration path analysis on the semiconductor laser based on the defect diffusion driving force distribution data to obtain defect migration channel distribution data; performing defect diffusion kinetics analysis on the defect migration channel distribution data using a non-equilibrium thermodynamic equation to obtain defect diffusion flux density data, and performing defect concentration gradient calculation on the semiconductor laser based on the defect diffusion flux density data to obtain defect concentration gradient distribution data; Calculating a defect diffusion coefficient of the semiconductor laser based on the defect concentration gradient distribution data to obtain defect diffusion coefficient tensor data, and calculating a defect diffusion flux of the semiconductor laser based on the defect diffusion coefficient tensor data to obtain defect diffusion flux field data; The defect concentration spatiotemporal evolution of the semiconductor laser is calculated based on the defect diffusion flux field data to obtain the defect concentration spatiotemporal distribution data, and the defect concentration conservation analysis is performed on the defect concentration spatiotemporal distribution data through the continuity equation to obtain the defect concentration distribution data.

4. The semiconductor laser lifetime estimation method according to claim 1, wherein: The lifetime evaluation of the semiconductor laser based on the performance attenuation trajectory curve to obtain a lifetime estimation result includes: By using a double exponential decay model, based on the performance decay trajectory curve, the fast decay phase lifetime of the semiconductor laser is calculated to obtain fast decay phase lifetime data; and based on the fast decay phase lifetime data, a slow decay phase starting point of the semiconductor laser is determined to obtain slow decay phase starting point data; performing a slow decay phase lifetime calculation on the semiconductor laser based on the slow decay phase starting point data to obtain slow decay phase lifetime data, and performing a lifetime statistical distribution feature extraction on the semiconductor laser based on the slow decay phase lifetime data to obtain lifetime statistical distribution feature data; Calculating the mean failure time of the semiconductor laser based on the lifetime statistical distribution characteristic data to obtain mean failure time data, and calculating the failure probability of the semiconductor laser based on the mean failure time data to obtain failure probability data; Based on the failure probability data and the rapid decay stage life data, a total life prediction is performed on the semiconductor laser using a life prediction model to obtain a life prediction result.

5. The semiconductor laser lifetime estimation method according to claim 4, characterized in that: The calculating the lifetime of the semiconductor laser in the slow decay phase based on the slow decay phase starting point data to obtain the lifetime data in the slow decay phase includes: Using a three-parameter Weibull distribution model, a slow decay phase characteristic lifetime of the semiconductor laser is fitted based on the slow decay phase starting point data to obtain slow decay phase characteristic lifetime parameters, and shape parameters and scale parameters of the semiconductor laser are extracted based on the slow decay phase characteristic lifetime parameters to obtain Weibull shape parameter and scale parameter data; Estimating the failure probability density function parameters of the semiconductor laser based on the Weibull shape parameter and scale parameter data using a maximum likelihood estimation method to obtain failure probability density function parameters, and calculating the cumulative failure probability function of the semiconductor laser based on the failure probability density function parameters to obtain cumulative failure probability function data; performing a lifetime confidence interval calculation on the semiconductor laser in a slow decay phase based on the cumulative failure probability function data to obtain lifetime confidence interval data for the slow decay phase, and performing a lifetime reliability assessment on the semiconductor laser based on the lifetime confidence interval data for the slow decay phase to obtain lifetime reliability data; Based on the lifetime reliability data and the fast decay stage lifetime data, the lifetime weight of the semiconductor laser in the slow decay stage is calculated by the lifetime weighted average method to obtain the slow decay stage lifetime weight data, and based on the slow decay stage lifetime weight data and the fast decay stage lifetime data, the total lifetime of the semiconductor laser is calculated to obtain the slow decay stage lifetime data.

6. A semiconductor laser lifetime prediction system, applied to the semiconductor laser lifetime prediction method according to any one of claims 1 to 5, characterized in that: include: A measurement module, configured to measure the photoelectric characteristics of the semiconductor laser to obtain a set of photoelectric characteristic parameters; A first analysis module is configured to perform stress analysis on the semiconductor laser based on the optoelectronic characteristic parameter set to obtain a device stress characteristic spectrum; a second analysis module, configured to perform defect evolution analysis on the semiconductor laser based on the device stress characteristic spectrum to obtain a defect expansion characteristic diagram when the interface stress intensity in the device stress characteristic spectrum exceeds a preset threshold; A simulation module, configured to perform a performance degradation simulation on the semiconductor laser based on the defect expansion characteristic diagram to obtain a performance degradation trajectory curve; The evaluation module is used to evaluate the life of the semiconductor laser based on the performance attenuation trajectory curve to obtain a life estimation result.

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