A method for predicting reliability and screening aging of InP optical communication laser chip

Abstract

This invention provides a reliability prediction and aging screening method for InP optical communication laser chips, relating to the field of semiconductor laser manufacturing and reliability assessment technology. The method first induces a transient thermal lensing effect through asymmetric bipolar current perturbation, then uses reverse bias to clear charge interference, capturing the dynamic resistance and wavelength chirp sequence reflecting lattice dislocation climb. Subsequently, a physical failure mode database is constructed, and intrinsic thermal damping is stripped in the frequency domain, mapping degradation features to epitaxial stress relaxation and nonradiative recombination mechanisms to achieve in-depth defect fingerprint analysis. Finally, based on a physically embedded deep learning architecture, the carrier rate equation and entropy increase law are used as constraints for lifetime prediction, and the second derivative of the predicted trajectory is used to evaluate the remaining lattice structure margin. This invention solves the problems of insufficient latent defect identification and predictions violating physical laws, achieving high-precision performance grading and risk interception.

Description

Technical Field

[0001] This invention relates to the field of semiconductor laser manufacturing and optoelectronic device reliability assessment technology, specifically a method for reliability prediction and aging screening of InP optical communication laser chips. Background Technology

[0002] As global optical communication networks evolve towards ultra-high speeds and high integration, indium phosphide-based distributed feedback laser chips, as the core light source of optical modules, directly determine the stability of backbone network and data center communication systems through their long-term operational reliability. In existing laser chip manufacturing processes, reliability screening before shipment primarily relies on static accelerated aging tests.

[0003] Traditional accelerated aging tests typically place the chip in a constant high-temperature environment and apply a DC injection current several times that of the operating current. The screening logic is mainly based on the comparison of photoelectric parameters before and after aging. The chip's qualification is determined by monitoring the change in threshold current or the rate of decline in slope efficiency after a period of aging. When the parameter fluctuation exceeds a preset fixed threshold, the chip is considered to have a failure risk and is rejected.

[0004] In addition, existing data analysis methods mostly use statistical linear regression or simple black-box deep learning models to fit the chip's lifespan trend by collecting a large number of power degradation samples.

[0005] In applications involving high-density wavelength division multiplexing and high-capacity coherent optical communication, the aforementioned existing technologies have the following significant limitations:

[0006] First, traditional static aging tests cannot effectively excite and identify latent defects deep within indium phosphide materials in a short period of time. Many dislocation climbs caused by uneven epitaxial growth stress or the proliferation of non-radiative recombination centers at the interface often exhibit a false appearance of parameter stability in the early stages of aging, but catastrophic optical damage will occur after thousands of hours of actual service, leading to sudden failures in the backbone network.

[0007] Secondly, existing data analysis models lack constraints on the physical nature of semiconductors. Models based purely on data fitting are prone to overfitting when dealing with small samples and multi-parameter fluctuations in indium phosphide chip data, failing to accurately distinguish between normal process variations and potential physical degradation. The lifetime trends predicted by these models often violate the physical laws of carrier transport and thermodynamic entropy increase, resulting in prediction accuracy that cannot meet the requirements of telecommunications-grade equipment.

[0008] Finally, existing screening schemes lack refined performance grading strategies. Because it's impossible to quantitatively assess the remaining lattice structure margin of a chip throughout its entire lifecycle, manufacturers often adopt a one-size-fits-all screening standard. This not only prevents high-quality chips with high reliability potential from being accurately identified and applied to long-distance backbone networks, but also allows some chips that meet performance standards in the short term but pose extremely high long-term risks to infiltrate the supply chain, severely impacting the tiered deployment and cost optimization of optical communication systems.

[0009] Therefore, a screening scheme that can delve into the underlying physical failure mechanism of indium phosphide chips, combine physical law constraints, and achieve accurate lifetime prediction and multi-level classification is needed to solve the technical problems of insufficient screening accuracy and inability to predict the reliability of optical communication laser chips in a closed loop. Summary of the Invention

[0010] Technical problems to be solved

[0011] To address the shortcomings of existing technologies, this invention provides a method for reliability prediction and aging screening of InP optical communication laser chips, solving the following problems:

[0012] 1. Existing technologies using DC steady-state aging are insufficient to excite deep energy level defects within indium phosphide materials. This invention utilizes asymmetric bipolar pulse excitation and a strong reverse bias to effectively empty the trap charges in the quantum well, forcing dormant dislocation climb and lattice distortion to manifest earlier during the screening stage. By monitoring minute fluctuations in transient wavelength chirp and differential junction resistance, it achieves very early detection of deep physical damage such as dark line defect proliferation, preventing high-risk chips from entering the supply chain.

[0013] 2. Conventional data analysis models rely solely on mathematical fitting of parametric curves, often resulting in logical deviations from the physical laws of semiconductors. This invention directly embeds the carrier rate equation and the law of energy conservation into the loss function of the data analysis model, ensuring that the lifetime prediction curve always strictly follows the intrinsic law of luminous efficiency decay in indium phosphide. This physical constraint mechanism fundamentally improves the model's prediction accuracy and extrapolation reliability with small sample data.

[0014] 3. Traditional screening methods rely solely on a binary judgment of whether power meets specifications, failing to assess a chip's resilience under extreme operating conditions. This invention introduces the concept of residual lattice structure margin. By performing a second derivative calculation on the threshold current drift, it quantitatively characterizes the physical distance of the chip from the critical point of catastrophic optical damage. This allows the screening process to not only focus on current performance but also predict the chip's boundary capacity to withstand power surges or thermal shocks during future service.

[0015] 4. To address the diverse reliability requirements of optical communication networks for light sources, this invention establishes multi-level reliability assessment criteria, enabling refined chip classification. Chips with extremely long predicted lifetimes and robust structural margins are precisely selected for backbone network level, while chips with mediocre performance but meeting basic specifications are classified as access network level. This classification strategy significantly improves the overall utilization rate and resource allocation accuracy of indium phosphide laser chip production lines while ensuring overall system security.

[0016] 5. By establishing a closed-loop feedback optimization mechanism, this invention utilizes a domain-adaptive transfer learning algorithm to continuously feed back degradation data from actual client-side operations to the failure mode database. This solves the mismatch between the accelerated aging environment in the laboratory and real-world complex operating conditions, enabling the prediction model to dynamically evolve with increasing production batches, ensuring the long-term stability of the optical communication system.

[0017] Technical solution

[0018] To achieve the above objectives, this invention provides a method for reliability prediction and aging screening of InP optical communication laser chips. This method involves constructing an accelerated aging test and failure mode database, predicting lifetime trends through data analysis models, and achieving reliability screening and performance grading before shipment. Specifically, it includes the following steps:

[0019] Sp1: Perform accelerated aging test, apply stepwise increasing injection current stress and high frequency pulse perturbation to the indium phosphide optical communication laser chip on a constant high temperature base, and collect the transient photoelectric response parameter sequence of the chip in real time during the accelerated aging test. The transient photoelectric response parameter sequence includes at least the threshold current drift, carrier recombination heat dissipation rate and emission wavelength redshift.

[0020] Sp2: Construct a failure mode database, extract nonlinear degradation feature components from the transient photoelectric response parameter sequence, and map the nonlinear degradation feature components to the underlying physical lattice defect mechanism of the indium gallium arsenide phosphide multi-quantum well active region. The underlying physical lattice defect mechanism includes epitaxial stress relaxation, dark line defect proliferation, and cleavage surface amorphization. Based on the mapping relationship, construct a failure mode database covering multiple physical degradation trajectories.

[0021] Sp3: Predict lifetime trends through data analysis models. Establish a data analysis model that integrates the physical laws of semiconductor carrier depletion with deep neural networks. Input the extracted nonlinear degradation feature components into the data analysis model. Combined with the reference benchmark in the failure mode database, deduce the intrinsic trajectory of optical power attenuation of the indium phosphide optical communication laser chip under standard operating conditions, thereby predicting the lifetime trend of the chip reaching the critical point of catastrophic optical damage.

[0022] SP4: Enables pre-shipment reliability screening and performance grading. Based on the lifetime trend prediction results and remaining lattice structure margin output by the data analysis model, it performs automated reliability screening and performance grading on chips that have completed accelerated aging tests, and outputs the high-reliability backbone network communication level, conventional access network communication level, and lattice defect rejection level.

[0023] Preferably, the step-increased injection current stress in Sp1 is superimposed with the high-frequency pulse perturbation. The high-frequency pulse perturbation is used to excite the bound charge of the deep trap level in the active region of the indium gallium arsenide phosphide multi-quantum well, and induce the transient spatial hole burning effect inside the chip to accelerate the exposure of epitaxial growth lattice dislocation defects that were in a dormant state in the early stage.

[0024] Preferably, the carrier recombination heat dissipation rate in Sp1 is obtained by separating the Joule thermal damping component and the Auger recombination damping component in the dynamic differential junction resistance sequence of the chip, and is used to separately characterize the abnormal local temperature rise gradient caused by the surge of nonradiative recombination centers in the active region.

[0025] Preferably, the process of mapping the nonlinear degradation characteristic components to the underlying physical lattice defect mechanism in Sp2 includes calculating the spectral frequency domain broadening coefficient caused by the microscopic mismatch of the epitaxial lattice constant, and storing the step change of the spectral frequency domain broadening coefficient as a judgment fingerprint for identifying epitaxial stress relaxation failure modes in the failure mode database.

[0026] Preferably, the data analysis model in Sp3 has a rigidly embedded physical energy conservation penalty term consisting of the spontaneous emission coefficient and the stimulated emission cross-sectional area in the cost function of the network weight backpropagation optimization, which constrains the lifetime trend output by the data analysis model to strictly follow the thermodynamic entropy increase law of semiconductors.

[0027] Preferably, the data analysis model in Sp3 also utilizes a Bayesian probabilistic inference network to perform uncertainty quantification analysis on the predicted lifespan trend, and outputs the mean of the predicted lifespan over a time span and the boundaries of continuous confidence intervals.

[0028] Preferably, the criteria for determining the high reliability backbone network communication level in Sp4 are that the predicted intrinsic trajectory of optical power attenuation exhibits a linear and gradual degradation characteristic within a specified period of time, and the attenuation rate of the remaining lattice structure margin is consistently lower than the preset safety baseline.

[0029] Preferably, the method further includes a closed-loop feedback optimization step, which involves collecting actual service degradation data of the manufactured chips in the client's long-distance optical fiber transmission equipment, and using a domain adaptive transfer learning algorithm to dynamically calibrate the failure mode database in Sp2 and the data analysis model in Sp3 and iteratively update the network weights.

[0030] This invention provides a method for reliability prediction and aging screening of InP optical communication laser chips. It has the following beneficial effects:

[0031] 1. This invention overcomes the steady-state limitations of traditional DC aging tests by applying asymmetric bipolar perturbation excitation. This excitation method effectively removes bound charges within the quantum well, allowing latent defects such as epitaxial layer stress mismatch and lattice dislocations, which are difficult to detect in conventional tests, to be excited and manifested earlier during the screening stage. By capturing the microscopic fluctuations in transient wavelength chirp and differential junction resistance, this invention achieves highly sensitive detection of the risk of very early failure in indium phosphide chips, greatly reducing the probability of chips with potential physical damage entering the backbone network market.

[0032] 2. Unlike traditional black-box deep learning models, this invention deeply embeds the carrier rate equation and thermodynamic conservation law into the data analysis model. This physical constraint mechanism ensures that the lifetime trend prediction curve output by the model not only has the accuracy of data fitting but also possesses logical rigor at the physical level. The luminous efficiency decay trajectory derived by the model strictly follows the intrinsic degradation law of indium phosphide material, fundamentally solving the prediction bias and overfitting problems that easily occur in pure data-driven models under small sample conditions, and providing highly reliable data support for telecommunications-grade equipment.

[0033] 3. This invention abandons the binary judgment logic of traditional screening, which is either black or white, and introduces the remaining lattice structure margin as a core quantitative indicator. By performing high-order derivative analysis on the threshold current degradation rate, this invention can accurately assess how much room there is left for the chip to reach the physical critical point of catastrophic damage. This not only enables quantitative ranking of chip reliability, but also supports precise hierarchical deployment for different application scenarios such as backbone networks and access networks, maximizing the overall utilization rate of semiconductor epitaxial wafer production lines while ensuring the overall security of the system.

[0034] 4. This invention utilizes a domain-adaptive transfer learning algorithm to dynamically feed long-term degradation data of chips in actual customer service back to the failure mode database. This mechanism solves the mismatch problem between accelerated aging environments in the laboratory and real-world complex operating conditions, enabling the predictive model to automatically optimize weights as production scales up and service life increases. This continuously evolving closed-loop capability ensures that aging screening criteria keep pace with changes in process iteration and market application needs, achieving reliability control throughout the product lifecycle.

[0035] 5. Through the powerful extrapolation capability of the physical embedded model, this invention can accurately predict the performance evolution trend of the chip under tens of thousands of hours of normal operation within a relatively short accelerated aging time. Compared with traditional long-term sampling aging lifetime tests, this invention significantly shortens the R&D and verification cycle of new products, reduces the time and energy consumption of expensive aging equipment, and provides an efficient technical solution for the large-scale rapid mass production and quality consistency monitoring of indium phosphide laser chips. Attached Figure Description

[0036] Figure 1 The method composition cloud diagram of the present invention;

[0037] Figure 2 This is a system architecture diagram of the present invention;

[0038] Figure 3 This is a flowchart of the process of the present invention;

[0039] Figure 4 This is a timing diagram of the excitation signal and physical response of the present invention;

[0040] Figure 5 This is the constraint logic diagram of the physical embedded model loss function of the present invention;

[0041] Figure 6 This is a diagram showing the second derivative and lattice structure margin analysis of the present invention. Detailed Implementation

[0042] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention. Specific Implementation Example 1:

[0044] like Figures 1 to 6 As shown, a reliability prediction and aging screening method for InP optical communication laser chips is proposed. This method constructs an accelerated aging test and failure mode database, predicts lifetime trends through data analysis models, and achieves reliability screening and performance grading before shipment. Specifically, it includes the following steps:

[0045] Sp1: Perform accelerated aging test, apply stepwise increasing injection current stress and high frequency pulse perturbation to the indium phosphide optical communication laser chip on a constant high temperature base, and collect the transient photoelectric response parameter sequence of the chip in real time during the accelerated aging test. The transient photoelectric response parameter sequence includes at least the threshold current drift, carrier recombination heat dissipation rate and emission wavelength redshift.

[0046] Sp2: Construct a failure mode database, extract nonlinear degradation feature components from the transient photoelectric response parameter sequence, and map the nonlinear degradation feature components to the underlying physical lattice defect mechanism of the indium gallium arsenide phosphide multi-quantum well active region. The underlying physical lattice defect mechanism includes epitaxial stress relaxation, dark line defect proliferation, and cleavage surface amorphization. Based on the mapping relationship, construct a failure mode database covering multiple physical degradation trajectories.

[0047] Sp3: Predict lifetime trends through data analysis models. Establish a data analysis model that integrates the physical laws of semiconductor carrier depletion with deep neural networks. Input the extracted nonlinear degradation feature components into the data analysis model. Combined with the reference benchmark in the failure mode database, deduce the intrinsic trajectory of optical power attenuation of the indium phosphide optical communication laser chip under standard operating conditions, thereby predicting the lifetime trend of the chip reaching the critical point of catastrophic optical damage.

[0048] SP4: Enables pre-shipment reliability screening and performance grading. Based on the lifetime trend prediction results and remaining lattice structure margin output by the data analysis model, it performs automated reliability screening and performance grading on chips that have completed accelerated aging tests, and outputs the high-reliability backbone network communication level, conventional access network communication level, and lattice defect rejection level.

[0049] Sp1 Asymmetric Bipolar Perturbation and Thermodynamic Response Data Acquisition:

[0050] The core hardware of this step relies on the collaborative operation of an ultra-high frequency probe station and a thermoelectric cooler with milliKelvin-level precision. During the initialization phase, the thermoelectric cooler anchors the indium phosphide optical communication laser chip at a designated high-temperature stress reference point with extremely low temperature fluctuations. Subsequently, a programmable arbitrary waveform generator begins outputting a carefully designed alternating perturbation signal.

[0051] The forward overload pulse of this perturbation signal is strictly limited to the microsecond level. Its physical purpose is to instantaneously pump a massive number of charge carriers into the active region of the indium gallium arsenide phosphide multi-quantum well, filling all conventional recombination levels and forcing excess charge carriers to overflow into deep lattice defects, generating a severe local thermal lensing effect. Immediately afterwards, the system seamlessly switches to a strong reverse bias stage at the nanosecond level. This extremely short reverse electric field generates a strong electric field extraction effect, forcibly pulling out the charge carriers that have just been trapped in deep defects.

[0052] During this dramatic alternation of forward and reverse signals, the dynamic signal analyzer operates synchronously. Because the charging and discharging effects of parasitic capacitance are effectively suppressed by reverse bias, the dynamic junction voltage sequence acquired by the analyzer can purely reflect the microscopic creep of lattice dislocations within the active region under thermal stress. Simultaneously, the nanosecond-level spectrometer captures the redshift of the center wavelength during the forward pulse and the blueshift during the reverse pulse, forming a complete transient wavelength chirp trajectory, providing a raw slice of the physical field response for subsequent analysis.

[0053] Sp2 epitaxial stress relaxation analysis and lattice failure mode database construction:

[0054] After acquiring the raw response data, the system enters the physical feature stripping and reconstruction stage. First, a Fast Fourier Transform (FFT) is performed on the transient signal to convert the junction voltage fluctuations in the time domain to the frequency domain. In the frequency domain spectrum, an adaptive bandpass filter is used to precisely filter out the low-frequency damping background caused by pure Joule heating and the mid-frequency damping component caused by normal Auger recombination. The remaining high-frequency anomalous noise substrate is then extracted as the core fingerprint characterizing the non-radiative recombination heat dissipation rate.

[0055] Subsequently, the system performs phase space reconstruction on the transient wavelength chirped trajectory. The system extracts wavelength drift data points within multiple consecutive perturbation cycles, sets the delay time and embedding dimension, and unfolds the one-dimensional time series into a high-dimensional topological phase diagram. Within this high-dimensional phase diagram, the system continuously calculates the divergence index of local trajectories. When the indium phosphide epitaxial layer maintains perfect lattice matching with the substrate, the phase diagram trajectory will exhibit a compact limiting cycle; once stress relaxation or microcracks begin to occur in the underlying layer, the divergence index of the phase diagram trajectory will experience a step-like surge. The system captures this surge node, packages it with the synchronously extracted heat dissipation rate fingerprint, and maps it to a solidified dark-line defect proliferation kinetic model, thereby establishing a unique electronic failure mode profile for this chip in the database.

[0056] Sp3 physical laws embedded data analysis model predicts lifespan trends:

[0057] This step is the intelligent hub of the entire system, and its innovation lies in the use of a physically embedded neural network. This network includes an input processing layer, multiple long short-term memory hidden layers, and an output prediction layer with an attention mechanism.

[0058] In conventional deep learning's weight backpropagation process, the system relies solely on prediction errors to update network parameters. However, in the physically embedded architecture of this invention, the system additionally weaves a penalty network based on semiconductor physics laws when calculating the cost function of gradient descent. Specifically, in each training cycle, the system substitutes the network's current output predicted carrier concentration and luminous efficiency decay into the built-in semiconductor carrier continuity conservation rule and thermodynamic energy diffusion rule for verification.

[0059] When a neural network attempts to adapt to abnormally fluctuating training data, it might output a predictive trend that violates the physical law of conservation of matter and energy—for example, predicting a long-term, unfounded increase in optical power under sustained high-temperature aging. In this case, the built-in penalty network immediately generates a very large numerical penalty term. This penalty term dominates the cost function, forcibly cutting off this erroneous weight update path and compelling the neural network to learn along a path consistent with the underlying thermodynamic entropy increase law of indium phosphide. This rigid physical boundary constraint allows the model to extrapolate a lifetime decay trajectory of tens of thousands of hours with extremely high physical reliability within a very short testing time window.

[0060] SP4 pre-shipment reliability screening and performance grading based on structural margin:

[0061] After obtaining the lifespan degradation trajectory throughout its entire lifecycle, the automated sorting system intervenes to perform physical decisions. The system is not satisfied with simply reading a single final lifespan point; instead, it performs continuous time-based quadratic derivative calculations on the threshold current drift along the entire degradation trajectory. The physical significance of this advanced mathematical processing lies in extracting the acceleration of the chip's aging rate.

[0062] The system defines this acceleration as the decay rate of the remaining lattice structure margin. For backbone benchmark-level judgments, the system is extremely stringent: it requires not only a long absolute prediction lifetime but also that the decay rate of the structure margin remain close to zero and absolutely smooth throughout the entire prediction period. This means there is no potential risk of microstructural collapse within the chip. If the quadratic derivative curve shows a sharp spike at some future prediction point, even if the chip's current output power is still strong, the system will determine that its internal lattice has begun irreversible avalanche-like defect proliferation, thus triggering the robotic arm's rejection action without hesitation and placing it in the rejection bin. Specific Implementation Example 2:

[0064] like Figures 1 to 6 As shown in the above specific embodiments, the following content is further disclosed:

[0065] The core concept of this invention lies in changing the traditional observation mode of static DC accelerated aging, and instead employing dynamic perturbation techniques to directly target the lattice dynamics evolution process of the indium phosphide multi-quantum-well layer. The technical solution of this invention will be elaborated in detail below with specific operational procedures.

[0066] Sp1. Specific implementation of asymmetric bipolar perturbation and thermodynamic response data acquisition:

[0067] In this step, the precision of hardware operation and data acquisition directly determines the detection rate of latent defects. The specific execution process is as follows:

[0068] First, the indium phosphide optical communication laser chip to be screened is fixed to a high-precision semiconductor cooling and temperature control platform equipped with a high-frequency radio frequency transmission microstrip line using gold wire bonding. The control terminal of the semiconductor cooling and temperature control platform is adjusted to rapidly raise the chip base temperature to 120 degrees Celsius and maintain it at a constant temperature, thus entering a state of thermodynamic stress equilibrium.

[0069] Secondly, an asymmetric bipolar alternating current excitation is applied to the chip via a programmable arbitrary waveform generator. A complete cycle of this excitation signal comprises two stages: the first stage is a forward overload injection stage, applying a narrow pulse with an amplitude three times the rated operating current and a duration of two microseconds. The purpose is to induce transient high-density carrier crowding and spatial hole burning effects in the active region, artificially accelerating the formation of thermal lenses in localized areas. The second stage is a strong reverse bias clearing stage, immediately following the forward pulse, applying an extremely narrow pulse with an amplitude of negative two volts and a duration of five hundred nanoseconds. The physical effect of this reverse bias is to instantaneously widen the depletion layer, using a strong electric field to forcibly extract the bound charges previously trapped in deep lattice dislocation traps. This evacuation mechanism eliminates the tailing interference of the substrate junction capacitance charging and discharging effect on subsequent transient spectral measurements.

[0070] During the aforementioned alternating excitation cycle, a dynamic signal analyzer with a sampling rate of tens of megahertz is simultaneously activated to capture the dynamic differential junction resistance sequence at both ends of the chip in real time. Simultaneously, a dynamic spectrometer with nanosecond-level response resolution is used to continuously record the transient wavelength chirp evolution trajectory of the active region's emission wavelength within the perturbation period.

[0071] Detailed implementation of Sp2. Epitaxial stress relaxation analysis and lattice failure mode database construction:

[0072] This step aims to extract a purely physical fingerprint representing microscopic lattice damage from macroscopic electro-optic signals.

[0073] The first step is the precise extraction of the nonradiative recombination heat dissipation rate. After obtaining the dynamic differential junction resistance sequence in Sp1, it is transformed to the frequency domain using discrete sequence transformation. In the frequency domain, the conventional Joule thermal damping component, which exhibits a linear relationship with the injected current, is removed, and the intrinsic Auger recombination resistance damping component, which is related to the cubic power of the carrier concentration, is further removed. After this double removal, the remaining anomalous resistance damping fluctuation represents the local temperature rise gradient caused purely by the multiplication of nonradiative recombination centers at the multi-quantum-well interface. This gradient is defined as the lattice heat dissipation rate.

[0074] Secondly, the microscopic mismatch characteristics of the lattice constant are extracted. The phase space of the transient wavelength chirp evolution trajectory is reconstructed, and its strange attractor divergence in multidimensional space is calculated. When the divergence exhibits a discontinuous step increase, it indicates that the lattice stress between the indium phosphide epitaxial layer and the substrate is undergoing irreversible physical relaxation.

[0075] Finally, the extracted abrupt change nodes of lattice heat dissipation rate and stress relaxation divergence are used as multidimensional input vectors and matched with the theoretically derived dark line defect proliferation dynamics model. The multidimensional vector groups under different degradation rates are classified and stored to construct a failure mode database covering the entire process from very early damage, linear degradation to catastrophic optical damage.

[0076] Detailed implementation of SP3. Data analysis model embedded in physical laws to predict lifespan trends:

[0077] This step abandons the black-box nature of conventional data fitting and gives the artificial intelligence network boundary constraints from semiconductor physics.

[0078] A deep hybrid neural network incorporating multilayer perceptrons and time-series long short-term memory units is constructed. During the training phase of this network, a novel composite loss gradient function is defined. The first part of this function is the mean squared error between the conventional predicted output and the actual monitored data; the second part is a hard-implemented physical law penalty term.

[0079] The physical law penalty term is composed of the semiconductor carrier continuity equation and the partial differential equation of lattice heat conduction. During forward propagation of the model to predict future power degradation trajectories, the system calculates the first-order slope and second-order curvature of the prediction curve in real time. When the neural network attempts to output a prediction of an anomalous increase in optical power, or when the rate of luminous efficiency decay falls below the lower limit of thermodynamic entropy increase, the value of the physical law penalty term will be exponentially amplified. This massive gradient penalty forces the neural network's weight matrix to immediately correct towards a direction consistent with the intrinsic physical degradation trajectory of indium phosphide. Under this physical constraint mechanism, even using only tens of hours of accelerated aging perturbation data, the model can robustly and accurately deduce the chip's lifetime trend prediction curve for over 100,000 hours under standard operating conditions.

[0080] Sp4. Detailed Implementation of Pre-shipment Reliability Screening and Performance Classification Based on Structural Margin:

[0081] The final step is to transform the complex forecast data into physical sorting actions on the production line.

[0082] The system extracts the threshold current drift curve of the Sp3 output over its entire lifecycle and calculates its second derivative over time to obtain the residual lattice structure margin index, which characterizes the chip's resistance to physical shocks. This index reflects the chip's remaining physical capacity in its current aging state, before cleavage plane meltdown or active region amorphization catastrophe.

[0083] The automated sorting system receives this quantitative indicator and the absolute value of the predicted lifespan, and drives the robotic arm to perform three levels of physical isolation:

[0084] For excellent chips with a predicted lifespan far exceeding 100,000 hours and a remaining lattice structure margin decay rate that continues to approach zero, the system determines that their internal lattice structure is intact and sorts them to the backbone network benchmark-level silo.

[0085] For chips whose predicted lifetime reaches the 50,000-hour standard line, but whose structural margin fluctuates slightly within the perturbation period and then converges, the system determines that there is stress relaxation within an acceptable range and sorts them into the access network reference level silo.

[0086] For chips whose heat dissipation rate triggers the disaster warning threshold in the early stage of aging, or whose remaining lattice structure margin shows accelerated collapse characteristics, regardless of whether the current test optical power is normal, the system determines that there is a potential risk of latent dark line defect proliferation and directly moves them into the lattice defect rejection stage material bin for interception and physical destruction. Specific Implementation Example 3:

[0088] like Figures 1 to 6 As shown, based on the content of the above specific embodiments, the working principle of the present invention is further disclosed:

[0089] Traditional static DC aging tests passively wait for defects to surface, which often only eliminates inferior chips that have already undergone severe degradation. This solution, however, is based on the active excitation of asymmetric bipolar perturbations.

[0090] In Sp1, the system applies a specific waveform to the chip, which is in a high-temperature reference state. During the positive strong pulse phase, a microsecond-level overload injection, several times the operating current, is used to instantaneously create high-density carrier crowding in the multi-quantum-well active region. This transient high-energy injection creates a local thermal lensing effect, forcing microscopic deformation of the epitaxial layer stress mismatch points and lattice dislocations that were originally in a dormant state.

[0091] The subsequent reverse strong bias stage applies a nanosecond-level negative voltage to instantly widen the depletion layer. The strong reverse electric field generates a strong electric field extraction effect, forcibly evacuating the bound charges trapped in the deep energy level traps. This alternating positive and negative action eliminates signal tailing interference caused by the charging and discharging of parasitic junction capacitance, allowing the subsequently acquired high-frequency dynamic differential junction resistance to purely and accurately reflect the microscopic abnormal creep caused by stress changes within the crystal lattice.

[0092] After obtaining a pure electro-optic perturbation response, the system needs to extract the physical failure fingerprint that determines lifetime from massive amounts of data in Sp2. The working principle of this process is based on the nonradiative recombination mechanism of semiconductor light-emitting devices.

[0093] First, the system performs a frequency domain transformation on the acquired dynamic differential junction resistance sequence to accurately filter out low-frequency damping caused by conventional ohmic heating and mid-frequency damping caused by intrinsic Auger recombination. The remaining high-frequency anomalous noise component represents the abnormal local temperature rise gradient caused by the multiplication of nonradiative recombination centers, which the system extracts as a nonradiative recombination heat dissipation rate characteristic.

[0094] Secondly, the system performs topological-level phase space reconstruction on the synchronously acquired transient wavelength chirp evolution trajectory. In the multidimensional phase diagram, the system continuously monitors the divergence of its strange attractors. When microscopic physical cracking or stress release begins to occur within the indium phosphide epitaxial layer, the divergence of the phase diagram trajectory exhibits a discontinuous step increase. The system integrates this physical relaxation fingerprint with the heat dissipation rate characteristics to jointly establish the early underlying damage state of the chip.

[0095] After mastering the early failure fingerprint, the system enters the lifespan prediction stage of SP3. This scheme abandons the black-box fitting logic of traditional pure data-driven models. Its working principle is to allow physical laws to directly supervise the learning and deduction process of artificial intelligence.

[0096] The system employs a physically embedded neural network architecture for analysis. When the network forward extrapolates the optical power degradation trajectory over the next tens of thousands of hours, the system rigidly incorporates the semiconductor carrier continuity equation and the thermodynamic energy conservation law as penalty kernels into its computational cost function.

[0097] When a deep learning algorithm attempts to output an abnormal increase in luminous efficiency or predict a decay rate below the theoretical lower limit of thermodynamic entropy increase in order to accommodate certain initial abnormal noise points, the built-in physical law kernel will immediately generate a huge gradient penalty. This mandatory physical boundary constraint cuts off all prediction paths that violate the logic of natural science, forcing the neural network to output a highly reliable full-lifecycle degradation curve only along the direction that conforms to the intrinsic decay law of indium phosphide material.

[0098] Finally, this scheme transforms the predicted lifetime curve into executable physical grading actions in Sp4 through extremely rigorous mathematical derivative calculations.

[0099] The system extracts the threshold current drift curve over the entire lifecycle and calculates its continuous quadratic derivatives along the time axis. In physics, the first derivative represents the current degradation rate, while the second derivative represents the acceleration of degradation. The system defines this acceleration as the decay rate of the chip's remaining lattice structure margin.

[0100] The sorting system's operating logic is no longer limited to a single absolute value of predicted lifetime, but focuses on the evolution of secondary micro-defects. Only when the secondary micro-defect consistently approaches zero and the predicted lifetime is extremely long, does the system determine that the chip's lattice structure is intact and possesses the physical capacity to resist long-term high-frequency surges, thus classifying it as a backbone network benchmark. When the secondary micro-defect exhibits a sharp spike at a future projected time point, it indicates an avalanche-like proliferation of dark line defects within the chip. Even if the chip's current luminous power is normal, the system will immediately output a physical interception command to move it into the rejection bin.

[0101] In summary, the working principle of this scheme is to actively tap the crystal lattice with asymmetric electrical stress, monitor the response characteristics of the microstructure using thermodynamic and topological analysis, and finally deduce the intrinsic decay trajectory of the chip with the help of intelligent algorithms that are subject to hard constraints of physical laws, thereby achieving comprehensive closed-loop management from early detection of hidden dangers to precise resource allocation. Specific Implementation Example 4:

[0103] like Figures 1 to 6 As shown in the above specific embodiments, the following content is further disclosed, and specific use cases are provided below:

[0104] 1. Case Background:

[0105] Optoelectronic companies are producing a batch of indium phosphide-based distributed feedback laser chips with a wavelength of 1550 nanometers for next-generation data centers. Because backbone networks have extremely high requirements for chip lifespan, needing continuous stable operation for more than 15 years, traditional accelerated aging screening methods can only eliminate chips that have already failed, and cannot identify chips with hidden physical damage.

[0106] 2. Detailed implementation process of the technical solution:

[0107] Sp1. Data capture under asymmetric perturbation excitation:

[0108] An automated screening system randomly selects a chip to be tested from the chip array after wafer cleaving.

[0109] Environment settings: Place the chip on a heat sink base at 120 degrees Celsius.

[0110] Excitation application: The system applies an excitation waveform, including:

[0111] Positive overload: A 300 mA pulse lasting 2 microseconds (4 times the chip’s rated current) is designed to induce transient expansion of potential lattice stress points within the quantum well.

[0112] Reverse extraction: Immediately afterwards, a negative 2-volt bias voltage of 500 nanoseconds is applied to instantly clear the residual charge at the quantum well interface using a strong electric field.

[0113] Data Acquisition: During this oscillation process, the high-frequency signal analyzer captured the microsecond-level oscillation sequence of the junction voltage at a sampling rate of 10G and recorded the 0.05-nanometer-level transient shift in wavelength during this period.

[0114] Sp2. Extraction of physical fingerprints of underlying defects:

[0115] The system performs mode decomposition on the acquired raw voltage sequence.

[0116] Feature separation: Normal ohmic heating interference was eliminated through algorithms.

[0117] Anomaly detection: The system detected that after the 500th perturbation cycle, the non-radiative composite heat dissipation rate of the chip showed a nonlinear step of 12%.

[0118] Topological analysis: Through phase space reconstruction, a strange attractor with outward diffusion was found in the trajectory phase diagram of wavelength chirp.

[0119] Diagnostic conclusion: By comparing with the failure mode database, the system determined that there is lattice dislocation climb at the interface of indium gallium arsenide phosphide multiple quantum wells inside the chip, which is a very early-stage latent damage.

[0120] Sp3. Lifetime trajectory extrapolation based on physical constraints:

[0121] The system inputs the above features into a physical embedded neural network that incorporates the carrier continuity equation.

[0122] Trend projection: The neural network has begun to simulate the chip's operating status over the next 100,000 hours.

[0123] Physical correction: At the 50,000th hour of the simulation, the model predicted that the light power would rebound. However, the built-in physical law constraint module immediately recognized that this violated the principle of thermodynamic entropy increase and forcibly corrected the predicted trajectory to an accelerated degradation curve that conforms to physical facts.

[0124] Prediction results: The chip is predicted to reach 20% power degradation at 62,000 hours, which is far below the 150,000-hour standard required by the backbone network.

[0125] Sp4. Residual Structure Margin Assessment and Final Classification:

[0126] The system executes the final logical decision.

[0127] Acceleration calculation: The second derivative of the predicted curve is obtained. The system found that after three years of service, the rate of increase in the chip's threshold current will show an accelerating trend, which means that the lattice structure margin has collapsed and it is no longer able to resist surge impacts.

[0128] Graded action: Upon receiving the instruction, the automated robotic arm abandons the process of transferring the chip to the backbone network baseline level, and instead, based on its predicted lifespan of 62,000 hours, precisely places it into the access network conventional communication level hopper.

[0129] 3. Comparison table with existing technologies:

[0130] ;

[0131] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising a reference structure" does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes the element.

[0132] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for reliability prediction and aging screening of InP optical communication laser chips, characterized in that, Specifically, the following steps are included: Sp1: Perform accelerated aging test, apply stepwise increasing injection current stress and high frequency pulse perturbation to the indium phosphide optical communication laser chip on a constant high temperature base, and collect the transient photoelectric response parameter sequence of the chip in real time during the accelerated aging test. The transient photoelectric response parameter sequence includes at least the threshold current drift, carrier recombination heat dissipation rate and emission wavelength redshift. Sp2: Construct a failure mode database, extract nonlinear degradation feature components from the transient photoelectric response parameter sequence, and map the nonlinear degradation feature components to the underlying physical lattice defect mechanism of the indium gallium arsenide phosphide multi-quantum well active region. The underlying physical lattice defect mechanism includes epitaxial stress relaxation, dark line defect proliferation, and cleavage surface amorphization. Based on the mapping relationship, construct a failure mode database covering multiple physical degradation trajectories. Sp3: Predict lifetime trends through data analysis models. Establish a data analysis model that integrates the physical laws of semiconductor carrier depletion with deep neural networks. Input the extracted nonlinear degradation feature components into the data analysis model. Combined with the reference benchmark in the failure mode database, deduce the intrinsic trajectory of optical power attenuation of the indium phosphide optical communication laser chip under standard operating conditions, thereby predicting the lifetime trend of the chip reaching the critical point of catastrophic optical damage. SP4: Implements pre-shipment reliability screening and performance grading. Based on the lifetime trend prediction results and remaining lattice structure margin output by the data analysis model, it performs automated reliability screening and performance grading on chips that have completed accelerated aging tests, and outputs high reliability backbone network communication level, conventional access network communication level and lattice defect elimination level. The stepped-incremental injection current stress in Sp1 is superimposed with the high-frequency pulse perturbation. The high-frequency pulse perturbation is used to excite the bound charge of the deep trap level in the active region of the indium gallium arsenide phosphide multi-quantum well, and induce the transient spatial hole burning effect inside the chip to accelerate the exposure of epitaxial growth lattice dislocation defects that were in a dormant state in the early stage.

2. The method for reliability prediction and aging screening of InP optical communication laser chips according to claim 1, characterized in that: The carrier recombination heat dissipation rate in Sp1 is obtained by separating the Joule thermal damping component and Auger recombination damping component in the dynamic differential junction resistance sequence of the chip, and is used to characterize the abnormal local temperature rise gradient caused by the surge of nonradiative recombination centers in the active region.

3. The method for reliability prediction and aging screening of InP optical communication laser chips according to claim 1, characterized in that: The process of mapping nonlinear degradation characteristic components to underlying physical lattice defect mechanisms in Sp2 includes calculating the spectral frequency domain broadening coefficient caused by microscopic mismatch of epitaxial lattice constants, and storing the step change of the spectral frequency domain broadening coefficient as a judgment fingerprint for identifying epitaxial stress relaxation failure modes in the failure mode database.

4. The method for reliability prediction and aging screening of InP optical communication laser chips according to claim 1, characterized in that: The data analysis model in Sp3 has a hard-embedded physical energy conservation penalty term consisting of the spontaneous emission coefficient and the stimulated emission cross-sectional area in the cost function of the backpropagation optimization of network weights. This constrains the lifetime trend output by the data analysis model to strictly follow the thermodynamic entropy increase law of semiconductors.

5. The method for reliability prediction and aging screening of InP optical communication laser chips according to claim 1, characterized in that: The data analysis model in Sp3 also utilizes a Bayesian probabilistic inference network to perform uncertainty quantification analysis on the predicted lifetime trend, outputting the mean of the predicted lifetime over the time span and the boundaries of continuous confidence intervals.

6. The method for reliability prediction and aging screening of InP optical communication laser chips according to claim 1, characterized in that: The criteria for determining the high reliability backbone network communication level in Sp4 are that the predicted intrinsic trajectory of optical power attenuation exhibits a linear and gradual degradation characteristic within a specified period of time, and the attenuation rate of the remaining lattice structure margin is consistently lower than the preset safety baseline.

7. The method for reliability prediction and aging screening of InP optical communication laser chips according to claim 1, characterized in that: The method also includes a closed-loop feedback optimization step, which involves collecting actual service degradation data of the manufactured chips in the client's long-distance fiber optic transmission equipment, and using a domain adaptive transfer learning algorithm to dynamically calibrate the failure mode database in Sp2 and the data analysis model in Sp3 and iteratively update the network weights.

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