Method and system for estimating service life of semiconductor laser

Abstract

The invention relates to a method for estimating the service life of a semiconductor laser, and the method comprises the following steps: carrying out the photoelectric characteristic measurement of the semiconductor laser, and obtaining 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 pattern; performing performance attenuation simulation on the semiconductor laser based on the defect expansion feature map to obtain a performance attenuation trajectory curve; and performing service life evaluation on the semiconductor laser based on the performance attenuation trajectory curve to obtain a service life estimation result, and solving the problems that a traditional service life evaluation method mainly depends on an accelerated aging experiment and a statistical model, although the aging trend of a device can be reflected to a certain extent, the period is long, the cost is high, and the reliability is poor. And the essential process of the internal failure mechanism of the device is difficult to reveal.

Description

Technical Field

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

[0002] As a core device in modern optoelectronic technology, semiconductor lasers are widely used in fields such as communication, medical treatment, industrial processing, and military. Their working stability and service life directly affect the reliability and maintenance cost of related equipment. Therefore, it is of great significance to predict the lifetime of semiconductor lasers. Traditional lifetime assessment methods mainly rely on accelerated aging experiments and statistical models. Although they can reflect the aging trend of devices to a certain extent, they often have a long cycle, high cost, and are difficult to reveal the essential process of internal failure mechanisms of devices.

[0003] In recent years, with the development of materials science and computational mechanics, lifetime prediction methods based on physical mechanisms have gradually attracted attention. By introducing means such as optoelectronic property measurement, stress analysis, and defect evolution simulation, researchers attempt to establish more accurate lifetime prediction models. However, there are still many challenges in practical applications. For example, problems such as significant multi-scale parameter coupling effects, difficult-to-accurately characterize interface stresses, and complex defect propagation paths lead to still large room for improvement in the prediction accuracy and universality of existing models.

[0004] In addition, most current research focuses on the influence of a single factor on the device lifetime, lacking a systematic analysis of the synergistic effects of multiple failure mechanisms. Especially under extreme working conditions such as high temperature and high current density, complex thermo-electro-mechanical coupling effects are prone to occur inside semiconductor lasers, thereby accelerating the formation and expansion of defects. How to establish a quantitative relationship between these microscopic behaviors and macroscopic performance degradation is still the 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-physical field analysis and failure evolution simulation to achieve efficient and accurate prediction of the lifetime of semiconductor lasers. Summary of the Invention

[0005] The main object 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 devices to a certain extent, they often have a long cycle, high cost, and are difficult to reveal the essential process of internal failure mechanisms of devices.

[0006] To achieve the above object, the present invention provides a method for predicting the lifetime of a semiconductor laser, including the following steps: Perform optoelectronic property measurement on the semiconductor laser to obtain an optoelectronic property parameter set; Perform stress analysis on the semiconductor laser based on the set of optoelectronic characteristic parameters to obtain the device stress characteristic spectrum; When the interfacial stress intensity in the device stress characteristic spectrum exceeds a preset threshold, perform defect evolution analysis on the semiconductor laser based on the device stress characteristic spectrum to obtain a defect propagation characteristic map; Perform performance degradation simulation on the semiconductor laser based on the defect propagation characteristic map to obtain a performance degradation trajectory curve; Perform lifetime assessment on the semiconductor laser based on the performance degradation trajectory curve to obtain a lifetime prediction result.

[0007] Further, the performing stress analysis on the semiconductor laser based on the set of optoelectronic characteristic parameters to obtain the device stress characteristic spectrum includes: Perform temperature field simulation on the semiconductor laser based on the set of optoelectronic characteristic parameters to obtain temperature distribution data, and through the finite element analysis method, perform thermal stress calculation on the semiconductor laser based on the temperature distribution data to obtain thermal stress distribution data; Perform dislocation density analysis on the semiconductor laser based on the thermal stress distribution data to obtain dislocation density distribution data, and based on the dislocation density distribution data, perform strain energy density analysis on the semiconductor laser to obtain strain energy density distribution data; Perform interfacial stress analysis on the semiconductor laser based on the strain energy density distribution data to obtain interfacial stress intensity data, and based on the interfacial stress intensity data and the thermal stress distribution data, construct a device stress characteristic spectrum for the semiconductor laser to obtain the device stress characteristic spectrum.

[0008] Further, the performing defect evolution analysis on the semiconductor laser based on the device stress characteristic spectrum to obtain a defect propagation characteristic map includes: Perform dislocation slip simulation on the semiconductor laser based on the transverse stress distribution and longitudinal stress distribution in the device stress characteristic spectrum to obtain dislocation slip path data, and based on the dislocation slip path data, perform dislocation density calculation on the semiconductor laser to obtain dislocation density distribution data; Perform defect diffusion simulation on the semiconductor laser based on the dislocation density distribution data to obtain defect concentration distribution data, and based on the defect concentration distribution data, perform defect reaction simulation on the semiconductor laser to obtain defect reaction rate data; Perform 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 based on the interface state energy level distribution data, perform carrier capture cross-section calculation on the semiconductor laser to obtain carrier capture cross-section data; Based on the Shockley-Read-Hall recombination model, carrier recombination rate calculation is performed on the semiconductor laser based on the carrier capture cross-section data to obtain carrier recombination rate data, and based on the carrier recombination rate data and the dislocation density distribution data, a defect expansion characteristic map of the semiconductor laser is constructed to obtain a defect expansion characteristic map.

[0009] Further, performing defect diffusion simulation on the semiconductor laser based on the dislocation density distribution data to obtain defect concentration distribution data includes: Calculating the stress field distribution of 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 through the non-equilibrium thermodynamics 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; Performing defect diffusion coefficient calculation on the semiconductor laser based on the defect concentration gradient distribution data to obtain defect diffusion coefficient tensor data, and performing defect diffusion flux calculation on the semiconductor laser based on the defect diffusion coefficient tensor data to obtain defect diffusion flux field data; Performing defect concentration spatio-temporal evolution calculation on the semiconductor laser based on the defect diffusion flux field data to obtain defect concentration spatio-temporal distribution data, and performing defect concentration conservation analysis on the defect concentration spatio-temporal distribution data through the continuity equation to obtain defect concentration distribution data.

[0010] Further, performing performance degradation simulation on the semiconductor laser based on the defect expansion characteristic map to obtain a performance degradation trajectory curve includes: Performing optical field distribution simulation on the semiconductor laser based on the defect expansion characteristic map to obtain optical field distribution data, and performing output power simulation on the semiconductor laser based on the optical 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 performing carrier concentration simulation on the semiconductor laser based on the interface state energy level in the defect expansion characteristic map to obtain carrier concentration data; Performing spectral simulation on the semiconductor laser based on the carrier concentration data to obtain spectral data, and performing wavelength drift amount calculation on the semiconductor laser based on the spectral data to obtain wavelength drift amount data; Perform mode stability analysis on the semiconductor laser based on the light field distribution data to obtain mode stability index data, and construct a performance degradation trajectory curve for the semiconductor laser based on the output power attenuation rate data, the wavelength drift amount data, and the mode stability index data to obtain a performance degradation trajectory curve.

[0011] Furthermore, perform lifetime assessment on the semiconductor laser based on the performance degradation trajectory curve to obtain a lifetime prediction result, including: Through a double-exponential decay model, calculate the lifetime of the rapid decay stage of the semiconductor laser based on the performance degradation trajectory curve to obtain rapid decay stage lifetime data, and determine the starting point of the slow decay stage of the semiconductor laser based on the rapid decay stage lifetime data to obtain slow decay stage starting point data; Calculate the lifetime of the slow decay stage of the semiconductor laser based on the slow decay stage starting point data to obtain slow decay stage lifetime data, and extract the lifetime statistical distribution characteristics of the semiconductor laser based on the slow decay stage lifetime data to obtain lifetime statistical distribution characteristic data; Calculate the mean time to failure of the semiconductor laser based on the lifetime statistical distribution characteristic data to obtain mean time to failure data, and calculate the failure probability of the semiconductor laser based on the mean time to failure data to obtain failure probability data; Based on the failure probability data and the rapid decay stage lifetime data, perform total lifetime prediction on the semiconductor laser through a lifetime prediction model to obtain a lifetime prediction result.

[0012] Furthermore, calculate the lifetime of the slow decay stage of the semiconductor laser based on the slow decay stage starting point data to obtain slow decay stage lifetime data, including: Based on the slow decay stage starting point data, fit the characteristic lifetime of the slow decay stage of the semiconductor laser through a three-parameter Weibull distribution model to obtain slow decay stage characteristic lifetime parameters, and extract the shape parameter and scale parameter of the semiconductor laser based on the slow decay stage characteristic lifetime parameters to obtain Weibull shape parameter and scale parameter data; Through the maximum likelihood estimation method, estimate the parameters of the failure probability density function of the semiconductor laser based on the Weibull shape parameter and scale parameter data to obtain failure probability density function parameters, and calculate the cumulative failure probability function of the semiconductor laser based on the failure probability density function parameters to obtain cumulative failure probability function data; Based on the cumulative failure probability function data, calculate the confidence interval of the lifetime in the slow decay stage of the semiconductor laser to obtain the confidence interval data of the lifetime in the slow decay stage, and based on the confidence interval data of the lifetime in the slow decay stage, evaluate the lifetime reliability of the semiconductor laser to obtain the lifetime reliability data; Based on the lifetime reliability data and the lifetime data in the fast decay stage, calculate the lifetime weight in the slow decay stage of the semiconductor laser by the lifetime weighted average method to obtain the lifetime weight data in the slow decay stage, and based on the lifetime weight data in the slow decay stage and the lifetime data in the fast decay stage, calculate the total lifetime of the semiconductor laser to obtain the lifetime data in the slow decay stage.

[0013] The present invention also provides a semiconductor laser lifetime prediction system, including: A measurement module for measuring the optoelectronic characteristics of the semiconductor laser to obtain an optoelectronic characteristic parameter set; A first analysis module for performing stress analysis on the semiconductor laser based on the optoelectronic characteristic parameter set to obtain a device stress characteristic spectrum; A second analysis module for, when the interfacial 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 propagation characteristic map; A simulation module for performing performance degradation simulation on the semiconductor laser based on the defect propagation characteristic map to obtain a performance degradation trajectory curve; An evaluation module for evaluating the lifetime of the semiconductor laser based on the performance degradation trajectory curve to obtain a lifetime prediction result.

[0014] A method for predicting the lifetime of a semiconductor laser provided by the present invention includes the following steps: performing optoelectronic characteristic measurement on 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 interfacial 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 propagation characteristic map; performing performance degradation simulation on the semiconductor laser based on the defect propagation characteristic map 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 solves the technical problem that traditional lifetime assessment methods mainly rely on accelerated aging experiments and statistical models. Although they can reflect the aging trend of devices to a certain extent, they often have a long cycle, high cost, and are difficult to reveal the essential process of the internal failure mechanism of devices. It realizes performance degradation simulation using a defect propagation characteristic map and can obtain a specific performance degradation trajectory curve. This simulation takes into account various factors that may be encountered under actual working conditions, making the prediction result closer to the actual situation and improving the accuracy of lifetime prediction. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 is a schematic diagram of the steps of a method for predicting the lifetime of a semiconductor laser according to an embodiment of the present invention; Figure 2 is a structural block diagram of a system for predicting the lifetime of a semiconductor laser according to an embodiment of the present invention; Figure 3 is a schematic structural diagram of a computer device according to an embodiment of the present invention.

[0016] The implementation, functional features, and advantages of the present invention will be further described with reference to the embodiments and the accompanying drawings. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0017] In order to make the objectives, technical solutions, and advantages of the present invention clearer, 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 used to limit the present invention.

[0018] As Figure 1 shown, Figure 1 is a schematic diagram of the steps of a method for predicting the lifetime of a semiconductor laser according to an embodiment of the present invention; An embodiment of the present invention provides a method for predicting the lifetime of a semiconductor laser, including the following steps: Step S1, performing optoelectronic characteristic measurement on the semiconductor laser to obtain an optoelectronic characteristic parameter set.

[0019] Specifically, in the process of measuring the optoelectronic characteristics of the semiconductor laser to obtain the optoelectronic characteristic parameter set, key parameters such as the optical output power, current-voltage (I-V) characteristics, threshold current, and slope efficiency of the device are mainly obtained through standard test equipment and methods. 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 signal transmission quality. Therefore, it is necessary to repeatedly measure its optoelectronic performance at multiple time points to capture possible early degradation signs. By placing the laser in a temperature-controlled test environment and applying different drive currents, the relationship between its electrical response and optical output under different states can be recorded, thereby constructing a complete optoelectronic characteristic parameter set as the basic data source for evaluating its potential lifetime characteristics.

[0020] Step S2: Based on the optoelectronic characteristic parameter set, perform stress analysis on the semiconductor laser to obtain the device stress characteristic spectrum.

[0021] 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 to conduct physical modeling and mechanical simulation on the optoelectronic characteristic parameters obtained from the previous measurement, and then deduce the stress distribution of each layer of material inside the device under the working state. Specifically, using parameters such as the measured current-voltage characteristics, threshold current change, and output optical power, combined with physical property data such as the thermal expansion coefficient, elastic modulus, and lattice mismatch rate of the semiconductor material, input them into the finite element analysis model to simulate the thermal stress, mechanical stress, and interface stress endured by the laser under different drive conditions and temperature gradients, thereby generating a device stress characteristic spectrum that can reflect its internal stress state. For example, high-power semiconductor lasers applied 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.

[0022] Step S3: When the interface stress intensity in the device stress characteristic spectrum exceeds the preset threshold, perform defect evolution analysis on the semiconductor laser based on the device stress characteristic spectrum to obtain the defect propagation characteristic map.

[0023] Specifically, when the interfacial stress intensity in the device stress characteristic spectrum exceeds a preset threshold, the process of performing defect evolution analysis on the semiconductor laser based on the device stress characteristic spectrum to obtain a defect expansion characteristic map is based on the stress distribution data obtained in the previous step, and further introduces simulation methods for material failure mechanisms and microstructural evolution. Specifically, after identifying that the stress intensity in the key interface region 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 processes of defect nucleation, expansion, and aggregation that may occur in the high-stress region. This process can generate a defect expansion characteristic map reflecting the defect evolution path and expansion trend, which is used to reveal the evolution behavior of internal microdamage in the device. For example, in a high-power semiconductor laser used in an optical fiber communication system, significant thermal mismatch stress is generated at the heterojunction interface due to periodic thermal cycling, leading to microcracks or dislocation slip. Through the defect evolution analysis in this step, the expansion trajectories of these defects in the complex stress field and their potential impact on the device lifetime can be clearly captured, providing a physical basis for subsequent performance degradation simulation.

[0024] Step S4: Perform performance degradation simulation on the semiconductor laser based on the defect expansion characteristic map to obtain a performance degradation trajectory curve.

[0025] Specifically, the process of performing performance degradation simulation on the semiconductor laser based on the defect expansion characteristic map to obtain a performance degradation trajectory curve is based on the previously obtained defect expansion characteristic map, and further deeply analyzes the relationship between internal microstructural damage and macroscopic performance changes in the device. 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 working principle and material characteristics of the laser, numerical simulation methods such as finite element analysis or Monte Carlo simulation are used to predict the performance degradation of the device caused by the existence and development of these defects. In this process, considering the influence of factors such as temperature fluctuations and current density changes in the actual working environment, the simulation will take these external conditions as variables into consideration, so as to more accurately reflect the performance changes of the device under different working conditions. For example, in a high-power semiconductor laser used in an optical fiber communication system, the thermal accumulation caused by long-term operation may cause the gradual expansion of internal microcracks, thereby affecting the optical output power and wavelength stability. Through the performance degradation simulation in this step, the change trends of parameters such as the output characteristics of the laser, such as efficiency and wavelength offset, over time can be estimated and presented in the form of a performance degradation trajectory curve, providing a basis for equipment maintenance to ensure the stability and reliability of the system. This simulation not only helps to understand how defects gradually affect the performance of the laser, but also provides important reference data for optimizing the design and extending the service life.

[0026] Step S5: Based on the performance degradation trajectory curve, perform life assessment on the semiconductor laser to obtain a life prediction result.

[0027] Specifically, the process of performing life assessment on the semiconductor laser based on the performance degradation trajectory curve to obtain a life prediction result is to use the performance degradation trajectory curve obtained in the previous step as the input basis, and combine the device failure criterion and the reliability model to quantitatively analyze the time evolution process from the initial state to functional failure under specific working conditions. Specifically, the performance degradation trajectory curve reflects the change trends of the key performance parameters of the laser (such as output optical power, threshold current, wavelength stability, etc.) over time or usage cycles. By identifying the performance degradation inflection points, slope change rates, and time nodes approaching the failure threshold in the curve, a mapping relationship between performance degradation and life can be established. For example, for a semiconductor laser applied in an optical fiber communication system, its service life is usually judged 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 degradation trajectory curve in this step, the time required for the device to reach the failure criterion can be predicted, thereby obtaining the life prediction result of the laser, providing a scientific basis for system design, maintenance strategy formulation, and device selection.

[0028] In a specific embodiment, the stress analysis of the semiconductor laser based on the optoelectronic characteristic parameter set to obtain the device stress characteristic spectrum includes: Perform temperature field simulation on the semiconductor laser based on the optoelectronic characteristic parameter set to obtain temperature distribution data, and through the finite element analysis method, perform thermal stress calculation on the semiconductor laser based on the temperature distribution data to obtain thermal stress distribution data; Perform dislocation density analysis on the semiconductor laser based on the thermal stress distribution data to obtain dislocation density distribution data, and based on the dislocation density distribution data, perform strain energy density analysis on the semiconductor laser to obtain strain energy density distribution data; Perform interface stress analysis on the semiconductor laser based on the strain energy density distribution data to obtain interface stress intensity data, and based on the interface stress intensity data and the thermal stress distribution data, construct the device stress characteristic spectrum for the semiconductor laser to obtain the device stress characteristic spectrum.

[0029] 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 a system engineering of multi-physical field coupling analysis. It closely depends on the set of optoelectronic characteristic parameters obtained in the previous step. These parameters include, but are not limited to, the output optical power, threshold current, slope efficiency, and current-voltage characteristic curve of the laser under different drive currents. By deeply analyzing 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 set of optoelectronic characteristic parameters, researchers will input the measured current-voltage characteristics and output optical power data into the heat conduction model. Combining the thermal conductivity parameters of the laser chip structure (such as the active region thickness, layered material composition) and the packaging structure, a three-dimensional temperature field model is established using the finite element method. This model can calculate the temperature distribution data of each region of the laser in continuous operation or pulse mode. For example, in a 1.55 μm band distributed feedback (DFB) laser used in a fiber optic communication system, when its typical operating current is 20 mA, the temperature of the active region may reach above 80°C, while the surrounding cladding region remains at about 60°C. This temperature gradient will directly cause differences in thermal expansion, thereby inducing the generation of thermal stress. Then, through the finite element analysis method, thermal stress calculation is performed on the semiconductor laser based on the temperature distribution data to obtain thermal stress distribution data. In this process, the model will consider the mechanical properties such as the thermal expansion coefficient and elastic modulus of each layer of material, and assume that the material is within the range of linear elastic deformation. For example, for an InP-based laser structure on a GaAs substrate, due to the lattice mismatch and difference in thermal expansion coefficient between InP and GaAs, large tensile or compressive stresses will be generated at the interface during the heating process. The simulation results show that in the above DFB laser, the peak thermal stress near the active region can reach above 300 MPa, far higher than the material yield strength. Therefore, it is very likely to induce plastic deformation or defect generation. Subsequently, 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 relies on the dislocation dynamics model or empirical formula to convert the local stress level into the possibility of dislocation multiplication inside the material. Usually, when the thermal stress in a certain region exceeds the critical slip stress (about 100 MPa), dislocations start to move and multiply, 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 this region has entered a significant crystal damage stage. Further, based on the dislocation density distribution data, strain energy density analysis is performed on the semiconductor laser to obtain strain energy density distribution data.Strain energy density is an important indicator to measure the energy stored inside materials due to deformation, and it is usually closely related to the tendency of defect propagation. In actual calculations, the strain energy density can be approximately estimated by the integral of the product of thermal stress and strain. For example, in the quantum well region of a DFB laser, due to the increase in dislocation density, the strain energy density in this region can reach more than 10 J / m³, significantly higher than other regions, indicating that this place is more likely to become the starting point of defect propagation. Next, based on the strain energy density distribution data, interface stress analysis is carried out on the semiconductor laser to obtain interface stress intensity data. This process focuses on the interface region between different material layers and evaluates its stability under the action of a complex stress field. For example, in the above DFB laser, the interface stress intensity between the p-type InP cladding layer and the n-type InP substrate can reach 400 MPa·√m, far exceeding the fracture toughness threshold of typical materials (about 200 MPa·√m), indicating that there is a high risk of cracking at this interface. Finally, based on the interface stress intensity data and the thermal stress distribution data, a device stress characteristic spectrum is constructed for the semiconductor laser to obtain the device stress characteristic spectrum. 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 a DFB laser, this characteristic spectrum clearly reveals the high-stress concentration band in the interface region and its consistency with the dislocation propagation direction, providing accurate 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 lifetime prediction.

[0030] In a specific embodiment, the defect evolution analysis of the semiconductor laser based on the device stress characteristic spectrum to obtain a defect propagation characteristic map includes: Based on the transverse stress distribution and longitudinal stress distribution in the device stress characteristic spectrum, dislocation slip simulation is carried out on the semiconductor laser to obtain dislocation slip path data, and based on the dislocation slip path data, dislocation density calculation is carried out on the semiconductor laser to obtain dislocation density distribution data; Based on the dislocation density distribution data, defect diffusion simulation is carried out on the semiconductor laser to obtain defect concentration distribution data, and based on the defect concentration distribution data, defect reaction simulation is carried out on the semiconductor laser to obtain defect reaction rate data; Based on the defect reaction rate data, interface state energy level calculation is carried out on the semiconductor laser to obtain interface state energy level distribution data, and based on the interface state energy level distribution data, carrier capture cross-section calculation is carried out on the semiconductor laser to obtain carrier capture cross-section data; Through the Shockley-Read-Hall recombination model, the carrier recombination rate of the semiconductor laser is calculated based on the carrier capture cross-section data to obtain carrier recombination rate data, and based on the carrier recombination rate data and the dislocation density distribution data, the defect expansion characteristic map of the semiconductor laser is constructed to obtain the defect expansion characteristic map.

[0031] Specifically, the process of defect evolution analysis of a semiconductor laser based on the device stress characteristic spectrum to obtain a defect expansion characteristic map is based on the device stress characteristic spectrum constructed in the previous step, and deeply explores the internal microstructure damage mechanism and defect dynamic evolution behavior. This process first uses the transverse stress distribution and longitudinal stress distribution in the device stress characteristic spectrum as input conditions to carry out dislocation slip simulation to predict the possible movement paths of crystal defects inside the material. Specifically, in high stress concentration regions (such as heterojunction interfaces or near quantum wells), dislocations are more likely to slip along specific crystal directions, and these slip paths can be traced through the finite element-molecular dynamics coupling method to form dislocation slip path data. For example, in a 1.55 μm DFB laser used in an optical fiber communication system, due to the thermal expansion mismatch of the InP / GaAs heterojunction, during long-term operation, the dislocation slip length in some regions can reach hundreds of nanometers, indicating that significant crystal structure damage has occurred in this region. 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 usually adopts a discrete dislocation dynamics model or a continuum dislocation density evolution equation, combined with the slip system parameters and local stress state of the material, to deduce the dislocation multiplication rate in different regions. For example, near the active region of a DFB laser, the initial dislocation density is about 1×10^6 cm / cm², while after high-temperature accelerated aging, the dislocation density in this region can rise to more than 3×10^9 cm / cm², showing an obvious crystal defect accumulation effect. On this basis, based on the dislocation density distribution data, defect diffusion simulation is carried out on the semiconductor laser to obtain defect concentration distribution data. This simulation takes into account the mobility, diffusion coefficient of point defects (such as vacancies, interstitial atoms) in the lattice and the interaction mechanism with dislocations. For example, under high-temperature working conditions, the diffusion rate of vacancy-like 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 a DFB laser, the defect concentration can rise from the initial 1×10^16 cm¯³ to 1×10^18 cm¯³, indicating an obvious material degradation trend in this region. Next, based on the defect concentration distribution data, defect reaction simulation is carried out on the semiconductor laser to obtain defect reaction rate data. This step mainly involves the chemical reaction kinetics modeling of defects with impurities, dopants or other defects, including processes such as recombination, aggregation, precipitation, etc. For example, in the above DFB laser, the binding rate of vacancies and zinc-doped atoms can reach 1×10^-25 cm³ / s, indicating that a stable complex has been formed between defects and doping elements, which will affect the effective concentration and transport characteristics of carriers.Further, based on the defect reaction rate data, the interface state energy levels of the semiconductor laser are calculated to obtain the interface state energy level distribution data. The interface state energy level is an important indicator for measuring the electron states introduced by defects at the interface, and usually affects the capture and emission behaviors of carriers. For example, at the oxide / semiconductor interface of a DFB laser, the interface state density can be as high as 1×10^11 eV¯¹cm¯², and its energy level positions are concentrated in the middle region of the bandgap, significantly affecting the electrical performance stability 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 the carrier capture cross-section data. This parameter describes the capture efficiency of defects for free carriers and is one of the key factors affecting the light emission efficiency and threshold current variation of the device. For example, in the above DFB laser, the cross-section for electrons to be captured by the interface state can reach 1×10^-14 cm², indicating that the defects have a strong confinement effect on carriers, thereby leading to a decrease in the output optical power. Next, through the Shockley-Read-Hall recombination model, based on the carrier capture cross-section data, the carrier recombination rate of the semiconductor laser is calculated to obtain the carrier recombination rate data. This model comprehensively considers factors such as the defect state density, capture cross-section, and carrier concentration, and can effectively predict the efficiency decrease caused by non-radiative recombination. For example, in the later stage of aging of a DFB laser, the non-radiative recombination rate of carriers can reach 1×10^7 s¯¹, which is much higher than 1×10^5 s¯¹ in the initial stage, indicating that the defect density inside the device has increased significantly, resulting in a sharp decrease in the light emission efficiency. Finally, based on the carrier recombination rate data and the dislocation density distribution data, the defect expansion characteristic map of the semiconductor laser is constructed to obtain the defect expansion characteristic map. This characteristic map not only contains multi-dimensional information such as dislocation paths, defect concentrations, and interface state distributions, but also integrates the variation trend of the carrier recombination behavior, forming a visual map reflecting the whole process of defect evolution inside the device. For example, in the failure analysis of a DFB laser, this characteristic map clearly reveals the complete evolution path of defects starting from dislocation slip, gradually developing into high-concentration defect aggregation, and finally leading to enhanced carrier recombination and decreased light emission efficiency, providing a key physical basis for subsequent performance degradation simulation. In summary, this defect evolution analysis process realizes the step-by-step mapping from the macroscopic stress field to the microscopic defect behavior, laying a solid theoretical foundation for accurately evaluating the reliability and lifespan of semiconductor lasers under complex working conditions.

[0032] In a specific embodiment, the defect diffusion simulation of the semiconductor laser based on the dislocation density distribution data to obtain the defect concentration distribution data includes: Perform stress field distribution calculations on the dislocation density distribution data to obtain defect diffusion driving force distribution data, and perform defect migration path analysis on the semiconductor laser based on the defect diffusion driving force distribution data to obtain defect migration channel distribution data; Perform defect diffusion kinetics analysis on the defect migration channel distribution data through non-equilibrium thermodynamics equations to obtain defect diffusion flux density data, and perform defect concentration gradient calculations on the semiconductor laser based on the defect diffusion flux density data to obtain defect concentration gradient distribution data; Perform defect diffusion coefficient calculations on the semiconductor laser based on the defect concentration gradient distribution data to obtain defect diffusion coefficient tensor data, and perform defect diffusion flux calculations on the semiconductor laser based on the defect diffusion coefficient tensor data to obtain defect diffusion flux field data; Perform defect concentration spatio-temporal evolution calculations on the semiconductor laser based on the defect diffusion flux field data to obtain defect concentration spatio-temporal distribution data, and perform defect concentration conservation analysis on the defect concentration spatio-temporal distribution data through the continuity equation to obtain defect concentration distribution data.

[0033] Specifically, the process of performing defect diffusion simulation on the semiconductor laser based on the dislocation density distribution data to obtain the defect concentration distribution data is a multi-step and multi-level complex analysis process, which reveals the diffusion behavior of defects in the material and its impact on device performance through a series of calculations and simulations. First, this process begins with the calculation of the stress field distribution of the dislocation density distribution data to obtain the defect diffusion driving force distribution data, and based on this, the defect migration path analysis of the semiconductor laser is carried out to obtain the defect migration channel distribution data. For example, in a DFB laser applied in an optical fiber communication system, due to the significant difference in the coefficient of thermal expansion at the heterojunction interface, the dislocation density in this region is relatively high. Under high-temperature working conditions, these dislocations serve as defect sources, and the stress field around them will drive point defects such as vacancies or interstitial atoms to move to low-energy positions, forming specific defect migration paths. Research shows that in this case, the defect migration path length in some high-stress regions can reach several micrometers, indicating the active diffusion of defects in the local area. Next, the defect diffusion kinetics analysis is carried out on the defect migration channel distribution data through the non-equilibrium thermodynamics equation to obtain the defect diffusion flux density data, and based on this, the defect concentration gradient calculation of the semiconductor laser is carried out to obtain the 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 DFB laser, when the working temperature rises to 100 °C, the diffusion flux density of vacancy-type 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 the high-density region to the low-density region, an obvious concentration gradient distribution is formed. For example, in the region near the p-type doping layer, the initial defect concentration is 1×10^16 cm¯³, and after a period of time, this value may decrease to 5×10^15 cm¯³, showing the trend of defect diffusion to other regions. Subsequently, based on the defect concentration gradient distribution data, the defect diffusion coefficient calculation of the semiconductor laser is carried out to obtain the defect diffusion coefficient tensor data, and these data are further used to perform the defect diffusion flux calculation of the semiconductor laser to obtain the defect diffusion flux field data. This step needs to consider the influence of different crystal orientations and grain boundaries on the diffusion process. For example, near the active region of a DFB laser, due to the presence of multiple slip systems and grain boundaries, the diffusion coefficients of defects in different directions may vary significantly. 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 lead to complex diffusion patterns of defects in the material, thereby affecting the microstructure stability of the entire device.Then, based on the defect diffusion flux field data, the spatio-temporal evolution of the defect concentration in the semiconductor laser is calculated to obtain the spatio-temporal distribution data of the defect concentration. The defect concentration conservation analysis is carried out on the spatio-temporal distribution data of the defect concentration through the continuity equation, and finally the defect concentration distribution data is obtained. This process not only focuses on the change in the number of defects, but also examines the dynamic change law in the time and space dimensions. For example, in a long-term operating DFB laser, after thousands of hours of operation, it can be observed that the defect concentration gradually evolves from the initial uniform distribution to a phenomenon of concentration in certain specific regions. Especially in the region near the electrode contact point, due to the current crowding effect, the local temperature increases, and the defect concentration may increase to more than 1×10^18 cm¯³, while the region far from the electrode relatively maintains a low defect level (about 1×10^16 cm¯³). This non-uniform defect distribution will directly affect the light-emitting efficiency, threshold current, and reliability of the device. In summary, through a series of in-depth analyses and calculations of the dislocation density distribution data, a comprehensive understanding of the defect diffusion behavior inside the semiconductor laser is achieved. This process not only reveals how defects migrate under the action of the stress field and form specific diffusion paths, but also provides accurate distribution information about defects in time and space by quantifying the kinetic parameters of defect diffusion, such as diffusion flux density, diffusion coefficient tensor, etc. These information are of crucial significance for predicting the lifetime of the laser, optimizing design parameters, and formulating effective maintenance strategies. For example, in practical engineering applications, by accurately grasping the diffusion characteristics of defects inside the DFB laser, it can help engineers select more suitable material combinations or improve packaging technologies, thereby extending the device lifetime and improving the overall reliability of the system.

[0034] In a specific embodiment, the performance degradation simulation of the semiconductor laser based on the defect expansion feature map to obtain the performance degradation trajectory curve includes: Performing a light field distribution simulation on the semiconductor laser based on the defect expansion feature map 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 performing a carrier concentration simulation on the semiconductor laser based on the interface state energy level in the defect expansion feature map to obtain carrier concentration data; Performing a spectrum simulation on the semiconductor laser based on the carrier concentration data to obtain spectrum data, and calculating the wavelength drift amount of the semiconductor laser based on the spectrum data to obtain wavelength drift amount data; Perform mode stability analysis on the semiconductor laser based on the light field distribution data to obtain mode stability index data, and construct a performance decay trajectory curve for the semiconductor laser based on the output power decay rate data, the wavelength drift amount data, and the mode stability index data to obtain a performance decay trajectory curve.

[0035] Specifically, the process of performing performance degradation simulation on the semiconductor laser based on the defect expansion feature map to obtain the performance degradation trajectory curve is a key step in establishing a quantitative relationship between the microscopic defect evolution behavior and the macroscopic optoelectronic characteristic changes. Through the collaborative analysis of multiple sub-modules, this process gradually reveals the complete evolution path from defect expansion to device performance degradation. First, perform optical field distribution simulation on the semiconductor laser based on the defect expansion feature map to obtain optical field distribution data, and further carry out output power simulation on this basis to obtain output power data. In this stage, mainly use finite element optical simulation tools (such as COMSOL or Lumerical) to establish a laser waveguide structure model, and combine the material damage region information (such as dislocation density, interface state concentration, etc.) provided in the defect expansion feature map to model the propagation behavior of light in the active region and waveguide layer. For example, in a DFB laser used in a fiber optic communication system, as defects continuously accumulate in the quantum well region, its local refractive index changes, resulting in distortion of the optical field distribution, partial light being scattered or absorbed, and the output power decreasing accordingly. The simulation results show that at the initial state, the output power of the laser can reach 20 mW, while after long-term operation, due to the increase in non-radiative recombination and optical loss caused by defects, the output power may drop below 15 mW. Subsequently, calculate the output power attenuation rate of the semiconductor laser based on the output power data to obtain output power attenuation rate data. This step extracts the power attenuation rate per unit time by fitting the curve of the output power changing with time. For example, in an accelerated aging experiment, the output power of a certain DFB laser drops from 20 mW to 17 mW after working for 1000 hours, then its average attenuation rate is 3 mW / 1000 h. This data provides a direct basis for subsequent life prediction. At the same time, perform carrier concentration simulation on the semiconductor laser based on the interface state energy levels in the defect expansion feature map to obtain carrier concentration data. Since the existence of defects, especially interface states, will significantly affect the injection efficiency and recombination behavior of carriers, it is necessary to consider the influence of these defects on the distribution of electrons and holes. For example, if the interface state density near the active region in a DFB laser rises from 1×10^10 eV¯¹cm¯² to 5×10^11 eV¯¹cm¯², it will cause the effective carrier concentration to drop from 1×10^18 cm¯³ to 6×10^17 cm¯³, thereby reducing the gain coefficient and affecting the luminescence efficiency. Further, perform spectral simulation on the semiconductor laser based on the carrier concentration data to obtain spectral data, and calculate the wavelength drift amount data based on this. The change in spectral characteristics is an important indicator reflecting the internal material degradation and mode instability of the device. For example, in the above DFB laser, the initial emission wavelength is 1550 nm, and after the defect causes distortion of the quantum well energy band structure, the wavelength may undergo red shift or blue shift.After 1000 hours of aging, the measured wavelength drift can reach +0.4 nm, indicating that irreversible changes have occurred in the material structure. In addition, based on the optical field distribution data, mode stability analysis is performed on the semiconductor laser 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, the mode stability index is 0.95 (close to the ideal value of 1) in the initial state, while after waveguide distortion caused by defects, this index may drop below 0.7, indicating that the device begins to exhibit multimode oscillation or a decrease in the side mode suppression ratio. Finally, based on the output power decay rate data, the wavelength drift amount data, and the mode stability index data, a performance decay trajectory curve is constructed for the semiconductor laser to obtain a performance decay trajectory curve. This curve has 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 a DFB laser, it can be clearly seen that within 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 region, the output power decreases rapidly, the wavelength drift increases significantly, and at the same time, the mode stability index drops rapidly, indicating that the device performance enters the 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 characteristic map. It can not only accurately capture the dynamic evolution laws 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 the life prediction and failure warning of lasers in high-reliability application scenarios.

[0036] In a specific embodiment, the life assessment of the semiconductor laser based on the performance decay trajectory curve to obtain a life prediction result includes: Using a double-exponential decay model, based on the performance decay trajectory curve, the life of the semiconductor laser in the rapid decay stage is calculated to obtain rapid decay stage life data, and based on the rapid decay stage life data, the starting point of the slow decay stage of the semiconductor laser is determined to obtain slow decay stage starting point data; Based on the slow decay stage starting point data, the life of the semiconductor laser in the slow decay stage is calculated to obtain slow decay stage life data, and based on the slow decay stage life data, the life statistical distribution characteristics of the semiconductor laser are extracted to obtain life statistical distribution characteristic data; Calculate the mean time to failure of the semiconductor laser based on the life statistical distribution characteristic data to obtain mean time to failure data, and calculate the failure probability of the semiconductor laser based on the mean time to failure data to obtain failure probability data; Based on the failure probability data and the life data in the rapid decay stage, perform a total life prediction on the semiconductor laser through a life prediction model to obtain a life prediction result.

[0037] Specifically, the process of evaluating the lifespan of a semiconductor laser based on the performance degradation trajectory curve to obtain a lifespan prediction result is a systematic and hierarchical analysis process. It combines the performance degradation behaviors of the device at different aging stages with a statistical reliability model to achieve a scientific prediction of its entire life cycle. First, through the double-exponential decay model, the lifespan of the semiconductor laser in the rapid decay stage is calculated based on the performance degradation trajectory curve to obtain the lifespan data in the rapid decay stage. This model assumes that due to factors such as the release of process defects or the relaxation of interface stress in the initial stage of the laser operation, the performance drops rapidly, and then enters a relatively slow degradation stage. For example, in a DFB laser used in an optical fiber communication system, its output power drops rapidly from an initial 20 mW to 18.5 mW within the first 200 hours, and the corresponding lifespan data in the rapid decay stage is about 300 hours. Subsequently, based on the lifespan data in the rapid decay stage, the starting point of the slow decay stage of the semiconductor laser is determined to obtain the starting point data of the slow decay stage. Generally, the slow decay stage starts at the position where the slope of the performance curve significantly becomes gentler. For example, in the above DFB laser, the slow decay stage starts at about 300 hours, at which time the downward trend of the output power tends to be flat but still continues to decrease slowly. Next, based on the starting point data of the slow decay stage, the lifespan of the semiconductor laser in the slow decay stage is calculated to obtain the lifespan data in the slow decay stage. This stage 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 stage may last for more than 8000 hours, during which the output power gradually drops from 18.5 mW to 12 mW (i.e., reaching the failure criterion: the output power drops to 60% of the initial value). On this basis, further based on the lifespan data in the slow decay stage, the lifespan statistical distribution characteristics of the semiconductor laser are extracted to obtain the lifespan statistical distribution characteristic data. These characteristics include, but are not limited to, the mean lifespan, standard deviation, skewness, and kurtosis, etc., which are used to describe the lifespan fluctuations of a batch of lasers under the same working conditions. For example, through the statistical analysis of the lifespan data of 100 DFB lasers in a certain batch, it is found that their lifespan follows a Weibull distribution, with a shape parameter of 1.8 and a scale parameter of 9000 hours, indicating that this batch of devices has a certain tendency of early failure but also has a relatively long main service life. Subsequently, based on the lifespan statistical distribution characteristic data, the mean time to failure of the semiconductor laser is calculated to obtain the mean time to failure data, and based on this data, the failure probability of the semiconductor laser is calculated to obtain the failure probability data. The mean time to failure is one of the key indicators to measure the reliability of the device. For example, in the above DFB laser, its mean time to failure is about 8500 hours.The failure probability represents the likelihood of a device experiencing functional failure at a specific operating time. For example, at 5000 hours, the failure probability of this laser is approximately 5%, and it rises to 40% at 10,000 hours. These data not only help to understand the reliability level of the device but also provide a statistical basis for subsequent life prediction. Finally, based on the failure probability data and the life data of the rapid decay stage, the total life of the semiconductor laser is predicted through a life prediction model to obtain a life estimation result. This model comprehensively considers the behavioral characteristics of the device in two main aging stages and makes a determination in combination with the failure threshold in engineering applications (such as the output power being lower than 12 mW, the wavelength drift exceeding ±1 nm, or the mode stability index being lower than 0.7). For example, in the above DFB laser, the life of its rapid decay stage is 300 hours, and the life of its slow decay stage is 8500 hours. After combining these two parts, the total life estimation result is 8800 hours. In addition, considering the uncertainty of the actual operating environment (such as temperature fluctuations, current noise, etc.), a safety factor (such as 0.9) can be introduced for correction, and finally, the conservatively estimated life is 7920 hours to ensure the highly reliable operation of the system. In summary, this life evaluation process fully integrates the dynamic evolution information reflected by the performance decay trajectory curve and the statistical reliability theory, realizing the whole-process modeling from micro-defect expansion to macro life prediction. This not only improves the accuracy of life estimation but also provides solid technical support for the design optimization, maintenance strategy formulation, and failure warning mechanism construction of semiconductor lasers in high-reliability application scenarios such as optical fiber communication.

[0038] In a specific embodiment, the calculation of the life of the semiconductor laser in the slow decay stage based on the starting point data of the slow decay stage to obtain the life data of the slow decay stage includes: Based on the starting point data of the slow decay stage, the characteristic life of the semiconductor laser in the slow decay stage is fitted through a three-parameter Weibull distribution model to obtain the characteristic life parameters of the slow decay stage, and based on the characteristic life parameters of the slow decay stage, the shape parameter and scale parameter of the semiconductor laser are extracted to obtain the Weibull shape parameter and scale parameter data; Through the maximum likelihood estimation method, the parameter estimation of the failure probability density function of the semiconductor laser is carried out based on the Weibull shape parameter and scale parameter data to obtain the failure probability density function parameters, and based on the failure probability density function parameters, the cumulative failure probability function of the semiconductor laser is calculated to obtain the cumulative failure probability function data; Based on the cumulative failure probability function data, calculate the life confidence interval of the semiconductor laser in the slow decay stage to obtain the life confidence interval data of the slow decay stage, and based on the life confidence interval data of the slow decay stage, evaluate the life reliability of the semiconductor laser to obtain the life reliability data; Based on the life reliability data and the life data in the fast decay stage, calculate the life weight of the semiconductor laser in the slow decay stage by the life weighted average method to obtain the life weight data of the slow decay stage, and based on the life weight data of the slow decay stage and the life data in the fast decay stage, calculate the total life of the semiconductor laser to obtain the life data of the slow decay stage.

[0039] Specifically, the process of calculating the lifetime of the semiconductor laser in the slow decay stage based on the starting point data of the slow decay stage is a crucial part of the entire lifetime assessment process. It combines physical aging behavior with a statistical reliability model to achieve quantitative analysis of the long-term degradation process of the device. This process first uses the starting point data of the slow decay stage obtained in the previous step as the input basis and conducts characteristic lifetime fitting in combination with the three-parameter Weibull distribution model to extract the key lifetime parameters of the slow decay stage. Specifically, after a DFB laser used in an optical fiber communication system experiences a rapid decay stage, it usually enters a stage where its performance tends to be stable but continues to degrade slowly at around 300 hours. At this time, by collecting the performance decay trajectory curves of multiple lasers of the same batch and identifying the starting time point of the slow decay stage based on the trend of the output power decline. Subsequently, the three-parameter Weibull distribution model is used to fit the data of this stage, and a location parameter (i.e., the starting point), a shape parameter, and a scale parameter are introduced to describe the distribution characteristics of the device lifetime. For example, the characteristic lifetime fitting results of a batch of DFB lasers in the slow decay stage show that the Weibull shape parameter is 1.5, indicating that the lifetime distribution in this stage shows a certain concentration trend; the scale parameter is 8000 hours, representing the typical lifetime level of this batch of lasers in the slow decay stage; and the location parameter is approximately 300 hours, corresponding to the start time of the slow decay stage. Further, based on the above Weibull shape parameter and scale parameter data, the failure probability density function parameters of the semiconductor laser are estimated by the maximum likelihood estimation method to obtain the failure probability density function parameters, and on this basis, the cumulative failure probability function data is calculated. This step is used to establish a failure probability model of the device changing with time in the slow decay stage. For example, when running to 6000 hours, the cumulative failure probability of this DFB laser is about 20%, and it rises to 70% when running to 10000 hours. This indicates that as the defects expand and the interface states accumulate, the device gradually approaches the functional failure threshold. Next, based on the cumulative failure probability function data, the lifetime confidence interval of the semiconductor laser in the slow decay stage is calculated to obtain the lifetime confidence interval data of the slow decay stage, and further lifetime reliability assessment is carried out to obtain the lifetime reliability data. For example, through the statistical analysis of 100 DFB laser samples, it is obtained that the 95% confidence interval of its slow decay stage is [7500, 8500] hours, which means that there is a 95% probability that the lifetime of this stage falls within this interval. At the same time, if the lifetime reliability is defined as the probability that the device can still maintain normal operation within 10000 hours, the lifetime reliability of this 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 lifetime data in the rapid decay stage, the lifetime weight of the semiconductor laser in the slow decay stage is calculated by the lifetime weighted average method to obtain the lifetime weight data in the slow decay stage. Combining the aforementioned lifetime data in the rapid decay stage, the total lifetime is calculated to obtain the lifetime data in the slow decay stage. For example, assume that the lifetime in the rapid decay stage is 300 hours and the weight is 5%; the lifetime in the slow decay stage is 8000 hours and the weight is 95%. Then, through weighted average, the estimated total lifetime result is 7600 hours. This calculation method based on the lifetime weights in different stages not only considers the different degradation rates of the device in the early and late stages but also improves the accuracy and engineering practicability of lifetime prediction. In summary, the lifetime calculation process in the slow decay stage constructs a scientific and systematic lifetime modeling framework by integrating statistical reliability theory and experimental data analysis. From the extraction of Weibull distribution parameters to the estimation of failure probability density, then to the confidence interval and reliability assessment, and finally combined with the rapid decay stage for weighted lifetime prediction, it completely reflects the hierarchical mapping relationship from micro-defect evolution to macro-lifetime assessment. This method provides a solid theoretical support and technical guarantee for the lifetime management of semiconductor lasers in practical applications, especially suitable for key fields such as optical fiber communication with high-precision and long-lifetime requirements.

[0040] The lifetime prediction method of the semiconductor laser in the embodiment of the present invention is described above. Next, the lifetime prediction system of the semiconductor laser in the embodiment of the present invention will be described. Please refer to Figure 2 , an embodiment of the lifetime prediction system of the semiconductor laser in the embodiment of the present invention includes: A measurement module 21, configured to measure the optoelectronic characteristics of the semiconductor laser to obtain an optoelectronic characteristic parameter set; A first analysis module 22, 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 23, configured to, when the interface stress intensity in the device stress characteristic spectrum exceeds a preset threshold, perform defect evolution analysis on the semiconductor laser based on the device stress characteristic spectrum to obtain a defect expansion characteristic map; A simulation module 24, configured to perform performance degradation simulation on the semiconductor laser based on the defect expansion characteristic map to obtain a performance degradation trajectory curve; An evaluation module 25, configured to perform lifetime evaluation on the semiconductor laser based on the performance degradation trajectory curve to obtain a lifetime prediction result.

[0041] In this embodiment, for the specific implementation of each unit in the above system embodiment, please refer to that described in the above method embodiment, and details will not be repeated here.

[0042] Refer to Figure 3, an embodiment of the present invention further provides a computer device, and its internal structure may be as shown in Figure 3 . The computer device includes a processor, a memory, a display screen, an input device, a network interface, and a database connected through a system bus. Among them, 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 the 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 through a network connection. When the computer program is executed by the processor, the above method is implemented.

[0043] Those skilled in the art can understand that Figure 3 the structure shown in is only a block diagram of a part 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.

[0044] An embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the above method is implemented. It can be understood that the computer-readable storage medium in this embodiment may be a volatile readable storage medium or a non-volatile readable storage medium.

[0045] Those of ordinary skill in the art can understand that all or part of the processes in the above embodiment methods can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it may include the processes of the above method embodiments. Among them, any reference to a memory, storage, database, or other medium provided by the present invention and used in the embodiments may include non-volatile and / or volatile memories. 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 an external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM, etc.

[0046] It should be noted that in this text, the terms "comprising", "including" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, apparatus, article or method comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or further includes elements inherent to such process, apparatus, article or method. Without further limitation, an element defined by the statement "comprising an..." does not exclude the presence of additional identical elements in the process, apparatus, article or method comprising such element.

[0047] The above are only the preferred embodiments of the present invention, and do not limit the patent scope of the present invention accordingly. Any equivalent structural or equivalent process transformations made by using the content of the specification and drawings of the present invention, or directly or indirectly applied in other related technical fields, are equally included in the patent protection scope of the present invention.

Claims

1. A method for predicting the lifetime of a semiconductor laser, characterized in that, Including the following steps: Perform optoelectronic characteristic measurement on the semiconductor laser to obtain an optoelectronic characteristic parameter set; Perform 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, perform defect evolution analysis on the semiconductor laser based on the device stress characteristic spectrum to obtain a defect expansion characteristic map; Perform performance degradation simulation on the semiconductor laser based on the defect expansion characteristic map to obtain a performance degradation trajectory curve; Perform lifetime assessment on the semiconductor laser based on the performance degradation trajectory curve to obtain a lifetime prediction result.

2. The semiconductor laser life prediction method according to claim 1, characterized in that The performing stress analysis on the semiconductor laser based on the optoelectronic characteristic parameter set to obtain a device stress characteristic spectrum includes: Perform temperature field simulation on the semiconductor laser based on the optoelectronic characteristic parameter set to obtain temperature distribution data, and perform thermal stress calculation on the semiconductor laser based on the temperature distribution data by means of finite element analysis method to obtain thermal stress distribution data; Perform dislocation density analysis on the semiconductor laser based on the thermal stress distribution data to obtain dislocation density distribution data, and perform strain energy density analysis on the semiconductor laser based on the dislocation density distribution data to obtain strain energy density distribution data; Perform interface stress analysis on the semiconductor laser based on the strain energy density distribution data to obtain interface stress intensity data, and perform device stress characteristic spectrum construction on the semiconductor laser based on the interface stress intensity data and the thermal stress distribution data to obtain a device stress characteristic spectrum.

3. The semiconductor laser lifetime prediction method according to claim 1, wherein The performing defect evolution analysis on the semiconductor laser based on the device stress characteristic spectrum to obtain a defect expansion characteristic map includes: Perform dislocation slip simulation on the semiconductor laser based on the transverse stress distribution and longitudinal stress distribution in the device stress characteristic spectrum to obtain dislocation slip path data, and perform dislocation density calculation on the semiconductor laser based on the dislocation slip path data to obtain dislocation density distribution data; Perform defect diffusion simulation on the semiconductor laser based on the dislocation density distribution data to obtain defect concentration distribution data, and perform defect reaction simulation on the semiconductor laser based on the defect concentration distribution data to obtain defect reaction rate data; Perform 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 perform 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; Perform carrier recombination rate calculation on the semiconductor laser based on the carrier capture cross-section data through the Shockley-Read-Hall recombination model to obtain carrier recombination rate data, and perform defect expansion characteristic map construction on the semiconductor laser based on the carrier recombination rate data and the dislocation density distribution data to obtain a defect expansion characteristic map.

4. The semiconductor laser lifetime prediction method according to claim 3, characterized in that Performing defect diffusion simulation on the semiconductor laser based on the dislocation density distribution data to obtain defect concentration distribution data, including: Calculating the stress field distribution of the dislocation density distribution data to obtain defect diffusion driving force distribution data, and analyzing the defect migration path of 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 through the non-equilibrium thermodynamics equation to obtain defect diffusion flux density data, and calculating the defect concentration gradient of the semiconductor laser based on the defect diffusion flux density data to obtain defect concentration gradient distribution data; Calculating the defect diffusion coefficient of the semiconductor laser based on the defect concentration gradient distribution data to obtain defect diffusion coefficient tensor data, and calculating the defect diffusion flux of the semiconductor laser based on the defect diffusion coefficient tensor data to obtain defect diffusion flux field data; Calculating the spatio-temporal evolution of the defect concentration of the semiconductor laser based on the defect diffusion flux field data to obtain defect concentration spatio-temporal distribution data, and performing defect concentration conservation analysis on the defect concentration spatio-temporal distribution data through the continuity equation to obtain defect concentration distribution data.

5. The method for predicting the lifetime of a semiconductor laser according to claim 1, characterized in that Performing performance degradation simulation on the semiconductor laser based on the defect expansion feature map to obtain a performance degradation trajectory curve, including: Performing optical field distribution simulation on the semiconductor laser based on the defect expansion feature map to obtain optical field distribution data, and performing output power simulation on the semiconductor laser based on the optical 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 performing carrier concentration simulation on the semiconductor laser based on the interface state energy level in the defect expansion feature map to obtain carrier concentration data; Performing spectral simulation on the semiconductor laser based on the carrier concentration data to obtain spectral data, and calculating the wavelength drift amount of the semiconductor laser based on the spectral data to obtain wavelength drift amount data; Performing mode stability analysis on the semiconductor laser based on the optical field distribution data to obtain mode stability index data, and constructing a performance degradation trajectory curve for the semiconductor laser based on the output power attenuation rate data, the wavelength drift amount data, and the mode stability index data to obtain a performance degradation trajectory curve.

6. The semiconductor laser lifetime prediction method according to claim 1, characterized in that Performing lifetime assessment on the semiconductor laser based on the performance degradation trajectory curve to obtain a lifetime prediction result, including: Calculating the lifetime of the fast decay stage of the semiconductor laser based on the performance degradation trajectory curve through a double exponential decay model to obtain fast decay stage lifetime data, and determining the starting point of the slow decay stage of the semiconductor laser based on the fast decay stage lifetime data to obtain slow decay stage starting point data; Based on the starting point data of the slow decay stage, calculate the lifetime of the semiconductor laser in the slow decay stage to obtain the lifetime data of the slow decay stage, and based on the lifetime data of the slow decay stage, extract the lifetime statistical distribution characteristics of the semiconductor laser to obtain the lifetime statistical distribution characteristic data; Based on the lifetime statistical distribution characteristic data, calculate the mean time to failure of the semiconductor laser to obtain the mean time to failure data, and based on the mean time to failure data, calculate the failure probability of the semiconductor laser to obtain the failure probability data; Based on the failure probability data and the lifetime data of the fast decay stage, predict the total lifetime of the semiconductor laser through a lifetime prediction model to obtain the lifetime prediction result.

7. The semiconductor laser lifetime prediction method according to claim 6, wherein The calculation of the lifetime of the semiconductor laser in the slow decay stage based on the starting point data of the slow decay stage to obtain the lifetime data of the slow decay stage includes: Through the three-parameter Weibull distribution model, fit the characteristic lifetime of the semiconductor laser in the slow decay stage based on the starting point data of the slow decay stage to obtain the characteristic lifetime parameters of the slow decay stage, and based on the characteristic lifetime parameters of the slow decay stage, extract the shape parameter and scale parameter of the semiconductor laser to obtain the Weibull shape parameter and scale parameter data; Through the maximum likelihood estimation method, estimate the parameters of the failure probability density function of the semiconductor laser based on the Weibull shape parameter and scale parameter data to obtain the parameters of the failure probability density function, and based on the parameters of the failure probability density function, calculate the cumulative failure probability function of the semiconductor laser to obtain the cumulative failure probability function data; Based on the cumulative failure probability function data, calculate the lifetime confidence interval of the semiconductor laser in the slow decay stage to obtain the lifetime confidence interval data of the slow decay stage, and based on the lifetime confidence interval data of the slow decay stage, evaluate the lifetime reliability of the semiconductor laser to obtain the lifetime reliability data; Based on the lifetime reliability data and the lifetime data of the fast decay stage, calculate the lifetime weight of the semiconductor laser in the slow decay stage through the lifetime weighted average method to obtain the lifetime weight data of the slow decay stage, and based on the lifetime weight data of the slow decay stage and the lifetime data of the fast decay stage, calculate the total lifetime of the semiconductor laser to obtain the lifetime data of the slow decay stage.

8. A semiconductor laser lifetime prediction system, characterized in that It includes: A measurement module for measuring the optoelectronic characteristics of the semiconductor laser to obtain an optoelectronic characteristic parameter set; A first analysis module for performing stress analysis on the semiconductor laser based on the optoelectronic characteristic parameter set to obtain a device stress characteristic spectrum; A second analysis module for, 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 propagation characteristic map; A simulation module for simulating the performance decay of the semiconductor laser based on the defect propagation characteristic map to obtain a performance decay trajectory curve; An evaluation module, configured to perform lifetime evaluation on the semiconductor laser based on the performance degradation trajectory curve to obtain a lifetime prediction result.

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