Isolated VCSEL detection method and related equipment

Abstract

The invention discloses an isolated VCSEL (Vertical Cavity Surface Emitting Laser) detection method and related equipment, and the method comprises the steps: carrying out the capacitance distribution and conductivity characteristic measurement of an isolation region of a to-be-detected laser under different frequencies, and obtaining an electrical characteristic data set of the isolation region; performing dynamic response parameter measurement on the thermoelectric coupling characteristic of the isolation area according to the electrical characteristic data set to obtain a multi-mode competitive electrothermal coupling characteristic spectrum; performing high-frequency electrical response characteristic measurement on the laser to be measured based on the data, and constructing a modal electrical fingerprint database; setting a multi-parameter triggering condition by using the fingerprint database, and performing electrical transient data capture and multi-time scale analysis to obtain a multi-scale abnormal characteristic spectrum; and determining electric field distribution regulation and control parameters according to the abnormal characteristic spectrum, constructing a risk distribution diagram, and realizing modal competition risk assessment. According to the technical scheme of the invention, the multi-modal competition behavior of the VCSEL is represented from the electrical perspective, and the accurate analysis of the electric parameter fluctuation and internal modal competition relationship of the device under the complex working condition is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of semiconductor laser electrical testing, and in particular to a detection method and related equipment for an isolated VCSEL. Background Art

[0002] A vertical cavity surface emitting laser (VCSEL) is a semiconductor laser whose laser beam is emitted perpendicular to the surface of the chip, which is significantly different from traditional edge-emitting lasers. In the structure of VCSEL, the laser resonant cavity is formed by two upper and lower distributed Bragg reflectors (DBRs), with an active area sandwiched in between. In order to improve the performance and reliability of VCSEL, an isolated structure is widely adopted. This structure forms effective electrical isolation between device units through processes such as oxide layers or ion implantation, accurately limits the area through which current passes, and prevents lateral diffusion of current. This isolated structure not only improves the current injection efficiency of VCSEL and reduces the threshold current, but also enhances the electro-optical conversion efficiency, making VCSEL widely used in optical communications, 3D sensing, biomedical sensing and other fields.

[0003] However, isolated VCSELs face performance instability caused by multimodal competition in practical applications. When the aperture size (i.e., the diameter of the active light-emitting area, typically defined by isolation structures such as oxide layers) or the drive current (the current used to stimulate lasing) of an isolated VCSEL is large, multiple transverse optical modes within the device can be simultaneously supported. These modes have different resonant frequencies, polarization states, and spatial distributions. They compete for the limited gain resources in the active region, causing fluctuations in electrical characteristics, ultimately manifesting as instabilities in output power, voltage response, and impedance characteristics. These abnormal variations in electrical characteristics can seriously affect the performance stability of VCSELs in practical applications. However, effective electrical testing methods are currently lacking to accurately detect and characterize this internal multimodal competition effect. Traditional electrical testing methods focus primarily on static characteristics or simple impedance parameters and are unable to capture the subtle electrical characteristic changes that occur during the modal competition process. Therefore, there is an urgent need to develop specialized electrical testing methods for multimodal competition to accurately analyze and predict the relationship between electrical parameter fluctuations and internal modal competition under complex device operating conditions. Summary of the Invention

[0004] The main purpose of the present invention is to solve the technical problem that the existing isolated VCSEL detection method cannot accurately characterize the instability of electrical characteristics caused by the multimodal competition effect.

[0005] A first aspect of the present invention provides a method for detecting an isolated VCSEL, the method comprising: The capacitance distribution and conductivity characteristics of the isolation region of the laser under test are measured at different frequencies, the electrical gradient characteristics at the edge of the isolation region are identified, and a data set of electrical characteristics of the isolation region is obtained; Based on the electrical characteristic data set, dynamic response parameter measurements are performed on the thermoelectric coupling characteristics of the isolation region, and the mutation characteristics of the voltage and impedance parameters under temperature changes are recorded to obtain a multi-modal competitive electrothermal coupling characteristic map; Based on the electrical characteristic data set and the electrothermal coupling characteristic map, the high-frequency electrical response characteristics of the laser to be tested are measured under different working conditions, the resonance characteristic parameters are extracted, and a modal electrical fingerprint library is constructed; The modal electrical fingerprint library is used to set multi-parameter trigger conditions, capture electrical transient data of modal jumps, perform multi-time scale analysis, and obtain a multi-scale abnormal feature map; According to the multi-scale abnormal characteristic map, the electric field distribution control parameters are determined, a risk distribution map within the working range is constructed, the monitoring strategy is dynamically adjusted, and the quantitative assessment and prediction of modal competition risk are achieved.

[0006] Preferably, the capacitance distribution and conductivity characteristics of the isolation region of the laser to be measured at different frequencies are measured, the electrical gradient characteristics of the isolation region edge are identified, and the electrical characteristic data set of the isolation region is obtained, including: Performing capacitance scanning measurement on the isolation area of the laser to be tested within a frequency range of 1 MHz to 1 GHz, recording capacitance frequency response curves at different positions, and obtaining a capacitance distribution map of the isolation area by analyzing the rate of change of the capacitance frequency response curves; Conducting electrical gradient measurement of the edge of the isolation region with nanometer-level resolution based on the isolation region capacitance distribution map, extracting capacitance mutation points and electrical gradient values at the edge of the isolation region, and forming electrical gradient characteristics of the isolation region edge; Perform multi-frequency conductivity measurement on the laser to be tested under different current bias conditions, record the curve of conductivity value changing with frequency, and obtain the conductivity frequency characteristic curve; Extracting inflection point frequency and slope parameters according to the conductivity-frequency characteristic curve to obtain current density distribution data; Analyzing the evolution of current density with bias change based on the current density distribution data to determine a critical point where the current distribution changes from uniform to non-uniform; The electrical gradient characteristics of the isolation region edge, the conductivity frequency characteristic curve and the current distribution critical point are correlated and analyzed to determine the electrical integrity index of the isolation structure and the current limiting effectiveness parameter, thereby obtaining an isolation region electrical characteristic data set.

[0007] Preferably, the method of measuring the dynamic response parameters of the thermoelectric coupling characteristics of the isolation region based on the electrical characteristic data set, recording the mutation characteristics of the voltage and impedance parameters under temperature changes, and obtaining a multi-modal competitive electrothermal coupling characteristic map includes: Based on the electrical integrity index of the isolation structure in the electrical characteristic data set, a temperature sweep test is performed on the laser to be tested within a temperature range of -40°C to 120°C, the temperature is changed at a rate of 0.1°C / minute, a resistance value versus temperature curve is recorded, a nonlinear parameter of the resistance temperature coefficient is calculated, and a mutation point of the resistance temperature coefficient during the temperature change process is extracted to obtain a nonlinear characteristic curve of the thermal resistor; Based on the current distribution critical point in the isolation region electrical characteristic data set, applying a current pulse with a duration of 1-10 microseconds to the laser to be tested, recording the transient change waveform of the voltage response, analyzing the asymmetry of the rise time and fall time of the voltage transient waveform, and obtaining thermal response time constant distribution data; Under preset current bias conditions, the temperature of the laser under test is step-scanned while recording changes in voltage, impedance parameters, and electrical noise. The critical temperature point where electrical parameter mutation occurs is identified, and feature extraction is performed on the electrical noise power spectrum. The noise frequency band enhancement phenomenon related to multimodal competition is identified, resulting in a temperature scan electrical mutation point dataset and a temperature-related noise characteristic spectrum. The nonlinear characteristic curve of the thermal resistor, the thermal response time constant distribution data, the temperature scanning electrical mutation point data set and the temperature-related noise characteristic spectrum are correlated and analyzed to calibrate the trigger temperature threshold and warning index of multimodal competition under thermoelectric coupling conditions, and obtain the multimodal competition electrothermal coupling characteristic map.

[0008] Preferably, based on the electrical characteristic data set and the electrothermal coupling characteristic map, high-frequency electrical response characteristics of the laser to be tested are measured under different working conditions, resonance characteristic parameters are extracted, and a modal electrical fingerprint library is constructed, including: According to the electrical integrity index of the isolation structure in the electrical characteristic data set and the trigger temperature threshold in the electrothermal coupling characteristic map, an operating condition matrix of the laser to be tested is set, and the complex impedance spectrum of the laser to be tested is measured in a frequency range of 0.1 to 40 GHz. The impedance amplitude and phase changes with frequency under different operating conditions are recorded to obtain high-frequency complex impedance spectrum data; Analyzing the high-frequency complex impedance spectrum data, extracting resonance peak and resonance valley features in the impedance spectrum, calculating the center frequency, bandwidth, and quality factor of the resonance peak, and forming a resonance characteristic parameter set; Based on the multimodal competition trigger point in the electrothermal coupling characteristic map, a parameter sweep is performed within a temperature range of ±2°C relative to the multimodal competition trigger point and a current range of ±5% relative to the nominal operating current, changes in the impedance spectrum are recorded, and the quantitative relationship between the resonance characteristics and changes in the operating conditions is analyzed to obtain boundary data of the modal competition region; According to the resonance characteristic parameter set and the boundary data of the modal competition area, feature extraction is performed on the high-frequency electrical response under different modal states, a correspondence between the resonance characteristics and the modal state is established, and a modal electrical fingerprint library is constructed.

[0009] Preferably, the feature extraction of high-frequency electrical responses under different modal states is performed based on the resonance feature parameter set and the boundary data of the modal competition area, the corresponding relationship between the resonance feature and the modal state is established, and a modal electrical fingerprint library is constructed, including: Quantitatively analyzing the center frequency, bandwidth, and quality factor of the resonance peak in the high-frequency complex impedance spectrum, calculating the discrete degree and discrimination coefficient of each parameter, and screening out core resonance characteristic parameters that contribute more than 90% to the differentiation of modal states; Correlating the core resonance characteristic parameters with the electrical integrity index of the isolation region structure to determine key parameters for modal identification that reflect the unique electrical response characteristics of the laser to be tested; Based on the key parameters for modal identification, the high-frequency electrical response data under different temperature and current conditions are classified and summarized, a one-to-one correspondence between the core resonance characteristics and the specific modal states is established, and a modal electrical fingerprint library containing the electrical characteristics of the isolation structure is constructed.

[0010] Preferably, the method of setting multi-parameter trigger conditions by using the modal electrical fingerprint library, capturing electrical transient data of modal jumps, and performing multi-time scale analysis to obtain a multi-scale abnormal feature map includes: According to the correspondence between the resonance characteristics and modal states in the modal electrical fingerprint library, the combined change pattern of voltage, impedance and noise parameters is selected as the trigger condition, the trigger threshold and trigger logic are set, the electrical parameters of the laser to be tested are monitored in real time, and when the electrical parameters meet the trigger conditions, the complete transient electrical data is captured to obtain the modal jump transient electrical data set; Performing time-domain decomposition on the modal jump transient electrical data set, performing parameter change rate analysis in the 1-100 nanosecond, 1-100 microsecond, and 1-100 millisecond time windows, respectively, extracting characteristic parameters at each time scale, including voltage jump amplitude, impedance mutation rate, and noise burst pattern, to obtain a cross-time scale characteristic parameter set; According to the cross-time scale characteristic parameter set, the electrical anomaly characteristics are classified and statistically analyzed, the electrical anomaly patterns related to the isolation structure characteristics are extracted, the correlation between the anomaly patterns and the working conditions is calibrated, and a multi-scale anomaly feature map is obtained.

[0011] Preferably, the modal jump transient electrical data set is decomposed in the time domain, and parameter change rate analysis is performed in the 1-100 nanosecond, 1-100 microsecond and 1-100 millisecond time windows respectively, and characteristic parameters on each time scale are extracted, including voltage jump amplitude, impedance mutation rate and noise burst mode, to obtain a cross-time scale characteristic parameter set, including: Apply wavelet transform to electrical data in the 1-100 nanosecond time window to separate high-frequency transient components, extract mutation events with voltage jump amplitudes greater than 5mV and mutation features with impedance change rates exceeding 10% / ns, establish a correlation model between nanosecond-level electrical characteristics and changes in current distribution at the edge of the isolation region, and obtain nanosecond-level characteristic parameters; Spectral analysis of electrical data in the 1-100 microsecond time window was performed to identify the noise power enhancement phenomenon in the 1-10 MHz frequency band, quantify the duration and intensity distribution of noise bursts, determine the intermediate time scale electrical response characteristics related to the carrier redistribution process, and obtain microsecond-level characteristic parameters; By combining the nanosecond-level characteristic parameters, the microsecond-level characteristic parameters and the long-term evolution characteristics of the voltage, impedance and noise parameters calculated from the electrical data in the 1-100 millisecond time window, the correlation and transmission rules between the characteristic parameters of different time scales are analyzed to obtain a cross-time scale characteristic parameter set.

[0012] Preferably, the method of determining electric field distribution control parameters based on the multi-scale abnormal characteristic map, constructing a risk distribution map within the working range, dynamically adjusting the monitoring strategy, and achieving quantitative assessment and prediction of modal competition risk includes: Based on the electrical anomaly patterns in the multi-scale anomaly feature map, the correlation between uneven electric field distribution and multimodal competition is analyzed, and key parameters that can effectively regulate the electric field distribution in the isolation zone are extracted, including bias current distribution parameters and impedance matching parameters, to obtain an electric field distribution control parameter set; Adjusting the drive circuit parameters of the laser to be tested according to the electric field distribution control parameter set, measuring the changes in the electrical parameters before and after the adjustment, calculating the influence coefficient of the parameter adjustment on the modal stability, and obtaining electric field distribution control effect evaluation data; Performing statistical analysis on the time series data in the multi-scale anomaly feature map, calculating the probability and severity of modal competition under different working conditions, drawing risk contours on the temperature-current plane, and obtaining a risk distribution map within the working range; According to the risk distribution map, the working range is divided into a first risk area with a modal competition probability higher than 50%, a second risk area with a modal competition probability between 10% and 50%, and a third risk area with a modal competition probability lower than 10%. Monitoring parameters and sampling frequencies are assigned to the first risk area, the second risk area, and the third risk area, respectively, to form a zoning monitoring strategy. The partition monitoring strategy is combined with the electric field distribution control parameter set to establish a risk scoring system for the working condition change path, calculate the cumulative risk value of different operation sequences, predict the potential time and intensity of modal competition, and realize the quantitative assessment and prediction of modal competition risk.

[0013] A second aspect of the present invention provides an isolated VCSEL detection device, the isolated VCSEL detection device comprising: The electrical parameter measurement module is used to measure the capacitance distribution and conductivity characteristics of the isolation area of the laser to be tested at different frequencies, identify the electrical gradient characteristics at the edge of the isolation area, and obtain the electrical characteristic data set of the isolation area; A thermoelectric coupling analysis module is used to measure the dynamic response parameters of the thermoelectric coupling characteristics of the isolation area based on the electrical characteristic data set, record the mutation characteristics of the voltage and impedance parameters under temperature changes, and obtain a multi-modal competitive electrothermal coupling characteristic map; A high-frequency response measurement module is used to measure the high-frequency electrical response characteristics of the laser to be tested under different working conditions based on the electrical characteristic data set and the electrothermal coupling characteristic map, extract the resonance characteristic parameters, and construct a modal electrical fingerprint library; A transient capture and analysis module is used to set multi-parameter trigger conditions using the modal electrical fingerprint library, capture electrical transient data of modal jumps, perform multi-time scale analysis, and obtain a multi-scale abnormal feature map; The risk assessment and prediction module is used to determine the electric field distribution control parameters based on the multi-scale abnormal characteristic map, construct a risk distribution map within the working range, dynamically adjust the monitoring strategy, and realize quantitative assessment and prediction of modal competition risk.

[0014] A third aspect of the present invention provides an isolated VCSEL detection device, comprising: a memory and at least one processor, wherein the memory stores instructions, and the memory and the at least one processor are interconnected via a line; the at least one processor calls the instructions in the memory to cause the isolated VCSEL detection device to perform the steps of the isolated VCSEL detection method described above.

[0015] A fourth aspect of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores instructions, which, when executed on a computer, enable the computer to execute the steps of the above-mentioned isolated VCSEL detection method.

[0016] The technical solution provided in the embodiment of the present application first obtains electrical gradient information that is closely related to the lateral current distribution of the device by measuring the capacitance distribution and conductivity characteristics of the isolation area at different frequencies. In this way, if there are local defects or uneven current distribution in the edge area of the isolation structure on the chip surface, it can be reflected by the difference in capacitance and conductivity with frequency. Conventional electrical tests in the past usually only focused on a single frequency or a small range of frequency response, and it was difficult to capture the microscopic electrical discontinuities at the edge of the isolation area under high-frequency conditions. The approach here can more finely distinguish the effects of different lateral modes on the current distribution, allowing potential modal competition risks to be identified at an early stage. The electrical characteristic data set thus formed provides a key reference basis for subsequent further dynamic testing.

[0017] After obtaining the electrical characteristic data set, by measuring the dynamic response parameters of the thermoelectric coupling characteristics of the isolation area, the mutation behavior of voltage and impedance when the temperature changes can be observed, and based on this, the triggering characteristics of multimodal competition under heat accumulation or temperature fluctuations can be summarized. This method based on temperature scanning and pulse injection can approach the actual working state inside the device in a relatively harsh environment, thereby capturing transient or nonlinear changes caused by multimodal competition. It is difficult to discover these mutation points by relying solely on conventional steady-state measurements, because multimodal competition is often accompanied by local accumulation and release of heat in small areas, and only manifests itself in a narrow temperature range or during rapid current changes. By recording these mutation characteristics, an electrothermal coupling map for different temperatures and injection conditions can be established, thereby providing guidance for modal identification in higher frequency bands and more complex working conditions.

[0018] Next, by collecting the complex impedance spectrum in the high-frequency range and extracting the resonant characteristic parameters, the changes in the resonant peaks and quality factors corresponding to different modes can be further distinguished. These resonant characteristic parameters are not only related to the device structure and material properties, but are also affected by the combined effects of heat and carrier distribution. When multiple transverse optical modes compete simultaneously, several interacting resonant peaks or bandwidth changes will appear in the complex impedance spectrum. Without combining the previous electrical characteristic data of the isolation region and the electrothermal coupling spectrum, simply performing impedance testing at high frequencies may make it difficult to accurately locate which mode is dominant, or to determine the triggering conditions for switching between different modes. In this method, by performing correlation analysis with previously obtained data, the resonant characteristic parameters can be clearly mapped to the specific mode competition state, thereby establishing a fingerprint library that can identify mode hopping.

[0019] With this fingerprint library, multi-parameter trigger conditions can be set during testing to monitor voltage, impedance, and possible electrical noise changes. Once a combination of features related to mode hopping appears, transient electrical data can be captured in real time. By performing multi-time scale analysis on these transient data, the voltage or impedance change patterns at nanosecond, microsecond, or even longer time scales can be pieced together to discover the evolution characteristics of multimodal competition in different time windows. Precisely because multimodal competition often exhibits suddenness and nonlinearity in the time domain, this cross-time scale observation method is required. Most previous conventional testing methods only collected data at a single time scale and were unable to distinguish the coupling process between fast mode hopping and slow thermal effects. This step can take both fast and slow electrical responses into consideration.

[0020] Finally, by integrating the abnormal characteristic maps measured at each stage, the electric field distribution in the isolation zone can be further regulated, and based on this, a risk distribution map under different operating conditions can be constructed, and then combined with the partition monitoring strategy to achieve the prediction of multimodal competition risks. This means that in real applications, if the device operates under a specific temperature, current or frequency condition, it can judge the probability and severity of potential modal competition based on the previously constructed map, and intervene in time by dynamically adjusting the monitoring parameters. This capability is particularly important in scenarios such as optical communications and 3D sensing, because once modal competition causes output power instability or current overshoot, it may cause performance degradation or device failure. Through full tracking and correlation analysis of electrical parameters such as the isolation zone edge and thermoelectric coupling characteristics, the above method can clearly present and quantitatively evaluate the modal competition risk inside the device under complex working conditions, thereby overcoming the problem that traditional electrical testing can only provide a single or static parameter and cannot accurately characterize the dynamic process of multimodal competition. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the structures shown in these drawings without paying any creative work.

[0022] Figure 1 Schematic diagram of an embodiment of a method for detecting an isolated VCSEL according to an embodiment of the present invention; Figure 2 Schematic diagram of an embodiment of a detection device for an isolated VCSEL according to an embodiment of the present invention; Figure 3 FIG. 1 is a schematic diagram of an embodiment of a detection device for an isolated VCSEL according to an embodiment of the present invention.

[0023] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0024] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0025] It should be noted that if the embodiments of the present invention involve directional indications (such as up, down, left, right, front, back, etc.), such directional indications are only used to explain the relative position relationship, movement status, etc. between the various components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication will also change accordingly.

[0026] In addition, the descriptions of "first", "second", etc. in the present invention are only for descriptive purposes and cannot be understood as indicating or implying their relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In addition, "and / or" in the full text includes three solutions. Taking A and / or B as an example, it includes technical solution A, technical solution B, and technical solution that satisfies both A and B. In addition, the technical solutions between the various embodiments can be combined with each other, and must be based on the ability of ordinary technicians in this field to implement. When the combination of technical solutions is mutually contradictory or cannot be implemented, it should be deemed that such a combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0027] An embodiment of the present application provides a method for detecting an isolated VCSEL. Figure 1 A flow chart of a method for detecting an isolated VCSEL provided in one embodiment of the present application. In this embodiment, the method includes: See also Figure 1 , measure the capacitance distribution and conductivity characteristics of the isolation area of the laser to be tested at different frequencies, identify the electrical gradient characteristics of the isolation area edge, and obtain the isolation area electrical characteristic data set; In one embodiment of the present invention, the capacitance distribution and conductivity characteristics of the isolation region of the laser to be tested are measured at different frequencies, the electrical gradient characteristics of the isolation region edge are identified, and the electrical characteristic data set of the isolation region is obtained, including: Performing capacitance scanning measurement on the isolation area of the laser to be tested within a frequency range of 1 MHz to 1 GHz, recording capacitance frequency response curves at different positions, and obtaining a capacitance distribution map of the isolation area by analyzing the rate of change of the capacitance frequency response curves; Conducting electrical gradient measurement of the edge of the isolation region with nanometer-level resolution based on the isolation region capacitance distribution map, extracting capacitance mutation points and electrical gradient values at the edge of the isolation region, and forming electrical gradient characteristics of the isolation region edge; Perform multi-frequency conductivity measurement on the laser to be tested under different current bias conditions, record the curve of conductivity value changing with frequency, and obtain the conductivity frequency characteristic curve; Extracting inflection point frequency and slope parameters according to the conductivity-frequency characteristic curve to obtain current density distribution data; Analyzing the evolution of current density with bias change based on the current density distribution data to determine a critical point where the current distribution changes from uniform to non-uniform; The electrical gradient characteristics of the isolation region edge, the conductivity frequency characteristic curve and the current distribution critical point are correlated and analyzed to determine the electrical integrity index of the isolation structure and the current limiting effectiveness parameter, thereby obtaining an isolation region electrical characteristic data set.

[0028] The following is a detailed description of the steps involved in the above embodiment: In the first step, a common impedance analyzer or network analyzer can be used to apply a sweeping frequency signal to the isolation region of the laser under test, measuring the capacitance value at different locations as a function of frequency. To spatially distinguish the electrical response at different locations, a high-precision motion device is required on the test platform to collect the capacitance frequency response curve of the measured area point by point. When the frequency increases from 1MHz to hundreds of MHz or even close to 1GHz, parasitic or coupling effects may occur, requiring standard calibration of the measurement probe and fixture, and appropriate de-embedding techniques to eliminate the influence of additional circuits. After measurement, by comparing the rate of change of the capacitance curve at each point with frequency, a capacitance distribution map reflecting the overall distribution characteristics of the isolation region can be plotted and output. The 1MHz to 1GHz range was chosen because this frequency band is sufficient to reveal the space charge accumulation phenomenon in the isolation region at low frequencies and the edge parasitics and coupling effects at higher frequencies, thereby obtaining a more comprehensive characterization of the isolation structure details.

[0029] When implementing the second step, it is necessary to use a local probe device or a local scanning measurement platform to move the probe to the edge of the isolation area, repeat the capacitance frequency response test with a smaller step resolution, and record the change amplitude and change rate of the capacitance value within this small range. By comparing these data with the overall capacitance distribution map, the points where the capacitance changes rapidly can be marked in the corresponding edge area, which are so-called capacitance mutation points. The electrical gradient value can be calculated by dividing the capacitance change between adjacent points by their spatial step distance, and through comprehensive comparison at different points on the edge, the capacitance mutation point and electrical gradient parameters that can reflect the electrical characteristics of the interface at the edge of the isolation area are finally formed. Doing so can reveal the extreme electric field distribution phenomenon at the edge of the isolation area, which helps to determine whether the device will experience local overheating or gain saturation when operating at high injection or high frequency.

[0030] During the specific implementation of the third step, different drive current biases can be set based on the aforementioned impedance analyzer or network analyzer, such as gradually increasing the injection current from a low level to the normal operating range or even slightly exceeding its nominal value, so that the laser can collect conductivity data under different operating conditions. During measurement, the value corresponding to the conductance is obtained by analyzing the real part of the complex impedance, and its curve that changes with frequency is recorded. As the injection current changes, the distribution of carriers inside the device and the structures such as the depletion region and charge accumulation region will also evolve. The conductance curve may have an inflection point or a change in slope in a specific frequency band. This process can reflect the changes in the gain and conduction path of the laser under different biases, providing a quantifiable basis for the subsequent judgment of the current density distribution.

[0031] During the fourth step, numerical fitting or piecewise linear analysis of the conductivity curve is performed to identify the point where a clear inflection point occurs within a specific frequency band. The frequency value at this point and the difference in the conductivity slope on both sides are recorded. The inflection point frequency often represents the boundary where the current transitions from one transport mechanism to another within the device, while the slope parameter is related to the transport characteristics of majority carriers in a specific region. Combining this information with known geometric structure and material parameters, the carrier concentration and flow direction within the isolation region under various injection currents can be calculated. From this, the distribution of current density across the device cross-section can be derived, forming quantitative data that characterizes the spatial current distribution.

[0032] During the fifth step, by comparing the current density distributions calculated at different injection currents, we can observe whether the current is concentrated in the center of the isolation region at low current biases and whether there is a tendency for the current to shift toward the edges at higher current biases. By quantifying the changes in carrier concentration and distribution slope during this lateral migration process, we can determine the critical point of the current distribution when the current density concentration area corresponding to a certain bias value deviates significantly from the center. This critical point is often closely related to the laser threshold current, thermal effects, and the interface properties of the edge oxide layer, and is therefore of great reference value.

[0033] During the sixth step, the capacitance discontinuity point at the edge of the isolation region is compared with the location of the critical point of current distribution to determine whether the electrical gradient at the edge significantly overlaps with the current inhomogeneity region. The inflection point in the conductance-frequency characteristic curve is then examined to determine whether the inflection point coincides with the critical point of current distribution or the capacitance discontinuity point. If these characteristic points show a consistent or partially overlapping trend, the isolation structure's ability to effectively limit lateral current diffusion under high-frequency and high-injection conditions can be deduced, thereby quantifying an electrical integrity metric reflecting the overall structural reliability. Based on this, a current limiting effectiveness parameter can be defined to measure the device's ability to suppress peripheral leakage and maintain stability in the central gain region under complex operating conditions. This comprehensive analysis generates a complete dataset describing the isolation region's structural state, encompassing multiple key parameters and their interplay, providing a quantitative basis for subsequent assessments of modal competition and performance reliability.

[0034] Please continue reading Figure 1 , based on the electrical characteristic data set, the dynamic response parameter measurement of the thermoelectric coupling characteristics of the isolation area is performed, the mutation characteristics of the voltage and impedance parameters under temperature changes are recorded, and the multi-modal competitive electrothermal coupling characteristic map is obtained; In one embodiment of the present invention, the dynamic response parameter measurement of the thermoelectric coupling characteristics of the isolation region is performed based on the electrical characteristic data set, and the mutation characteristics of the voltage and impedance parameters under temperature changes are recorded to obtain a multi-modal competitive electrothermal coupling characteristic map, including: Based on the electrical integrity index of the isolation structure in the electrical characteristic data set, a temperature sweep test is performed on the laser to be tested within a temperature range of -40°C to 120°C, the temperature is changed at a rate of 0.1°C / minute, a resistance value versus temperature curve is recorded, a nonlinear parameter of the resistance temperature coefficient is calculated, and a mutation point of the resistance temperature coefficient during the temperature change process is extracted to obtain a nonlinear characteristic curve of the thermal resistor; Based on the current distribution critical point in the isolation region electrical characteristic data set, applying a current pulse with a duration of 1-10 microseconds to the laser to be tested, recording the transient change waveform of the voltage response, analyzing the asymmetry of the rise time and fall time of the voltage transient waveform, and obtaining thermal response time constant distribution data; Under preset current bias conditions, the temperature of the laser under test is step-scanned while recording changes in voltage, impedance parameters, and electrical noise. The critical temperature point where electrical parameter mutation occurs is identified, and feature extraction is performed on the electrical noise power spectrum. The noise frequency band enhancement phenomenon related to multimodal competition is identified, resulting in a temperature scan electrical mutation point dataset and a temperature-related noise characteristic spectrum. The nonlinear characteristic curve of the thermal resistor, the thermal response time constant distribution data, the temperature scanning electrical mutation point data set and the temperature-related noise characteristic spectrum are correlated and analyzed to calibrate the trigger temperature threshold and warning index of multimodal competition under thermoelectric coupling conditions, and obtain the multimodal competition electrothermal coupling characteristic map.

[0035] The following is a detailed description of the steps involved in the above embodiment: During the first step, based on the previously acquired electrical integrity indicators of the isolation structure, a temperature-controlled test platform can be selected to place the laser under test within a temperature change range of -40°C to 120°C for testing. During the measurement process, the temperature adjustment rate can be set to 0.1°C / minute, and closed-loop control is implemented using a high-precision temperature sensor and an automatic temperature control module to ensure uniform and smooth temperature changes. A commonly used four-terminal method or impedance analyzer is used to record the temperature variation curve of the laser under test in real time, and the increment of the resistance temperature coefficient in each temperature range is obtained through numerical calculation methods. After the resistance temperature coefficient at consecutive temperature points is represented by a curve fitting method, the slope and curvature of the curve are examined in different temperature ranges, and finally the location where the resistance temperature coefficient shows a significant jump is extracted. To ensure that the thermal differences of the material and microstructure are accurately reflected over a wide temperature range, a range of -40°C to 120°C is set, and a scanning rate of 0.1°C / minute is selected to balance test time and measurement accuracy. In this way, the nonlinear change of the resistance temperature coefficient in the high and low temperature intersection area can be obtained and the mutation point can be recorded, thereby forming a nonlinear characteristic curve of the thermal resistor for subsequent analysis.

[0036] During the second step, laser drive conditions are set based on the critical point information of the current distribution. A pulse current with a duration range of 1-10 microseconds is applied to the laser under test, and the transient voltage waveform is recorded over time using an oscilloscope or high-speed data acquisition system. When applying the pulse, the test platform must ensure that the amplitude and pulse width of the output pulse are consistent and free of noticeable jitter, allowing for accurate comparison of the voltage rise and fall times under different pulses. The transient voltage waveform corresponding to each pulse is segmented and statistically analyzed, and the time difference between the rising and falling edges is compared to identify differences in the rate of heat dissipation within the laser structure. Pulse testing is performed in the 1-10 microsecond range because this timescale reflects the primary coupling process between carriers and thermal effects within the laser and effectively distinguishes transient optical effects from the relatively slow thermal diffusion process. By calculating the ratio of the rise and fall times at different locations or biases, a distribution of the thermal response time constant is generated, which can be used to evaluate the thermal transport characteristics within the device during pulse injection.

[0037] During the third step, a temperature step scan can be performed on the laser under test under preset current bias conditions, while simultaneously recording changes in voltage, impedance parameters, and electrical noise. To observe subtle differences in the temperature dimension of the multimodal competition effect, the temperature can be increased stepwise from low to high in certain steps, and the steady-state time at each temperature point can be maintained long enough to complete the voltage and impedance test. Noise acquisition can be achieved using a noise analyzer or an instrument with spectrum analysis capabilities. After converting the recorded time-domain signal to the frequency domain, the noise power spectrum is observed and the energy distribution changes in specific frequency bands at each temperature point are compared. When a significant discontinuous change in voltage or impedance occurs in a certain temperature region, it can be identified as the critical temperature point where a sudden change in electrical parameters occurs. If there is a noise enhancement phenomenon in a certain frequency band in the power spectrum, this frequency band can be associated with the multimodal competition state to generate a temperature-scanned electrical sudden change point dataset and a temperature-dependent noise characteristic spectrum. The reason for focusing on the noise power spectrum is that multimodal competition often leaves specific fluctuation characteristics in the electrical signal, and this characteristic tends to intensify near the critical temperature point, showing signs of coupling with thermal effects and optical mode switching.

[0038] The fourth step, when implemented, requires a comprehensive comparison of the nonlinear characteristic curves of the thermal resistor, the distribution of thermal response time constants, the data set of electrical transition points during temperature scanning, and the temperature-dependent noise signature. Data visualization or cluster analysis can be used to identify the correlation between the resistivity transition and the pulsed thermal response at different operating temperatures. The enhanced range of the noise power spectrum can be matched to the temperature transition points to identify the temperature conditions under which multimodal competition occurs and the most significant patterns in its manifestation. This process can infer the degree of coupling between carrier concentration, thermal resistance characteristics, and mode switching. Multiple test results can be used to determine the temperature threshold that triggers multimodal competition and early warning indicators for assessing its risk level. Ultimately, these data are integrated into a single graph, forming an electrothermal coupling analysis diagram for multimodal competition, providing direct guidance for subsequent operating status monitoring and risk assessment. The reason for correlating these multiple test results is that multimodal competition is a complex process that is simultaneously governed by factors such as temperature, carrier distribution, and structural defects. Only by combining observational data from different angles can its triggering mechanism and potential failure trends be accurately identified.

[0039] Please continue reading Figure 1 Based on the electrical characteristic data set and the electrothermal coupling characteristic map, the high-frequency electrical response characteristics of the laser to be tested are measured under different working conditions, the resonance characteristic parameters are extracted, and a modal electrical fingerprint library is constructed; In one embodiment of the present invention, based on the electrical characteristic data set and the electrothermal coupling characteristic map, high-frequency electrical response characteristics of the laser to be tested are measured under different working conditions, resonance characteristic parameters are extracted, and a modal electrical fingerprint library is constructed, including: According to the electrical integrity index of the isolation structure in the electrical characteristic data set and the trigger temperature threshold in the electrothermal coupling characteristic map, an operating condition matrix of the laser to be tested is set, and the complex impedance spectrum of the laser to be tested is measured in a frequency range of 0.1 to 40 GHz. The impedance amplitude and phase changes with frequency under different operating conditions are recorded to obtain high-frequency complex impedance spectrum data; Analyzing the high-frequency complex impedance spectrum data, extracting resonance peak and resonance valley features in the impedance spectrum, calculating the center frequency, bandwidth, and quality factor of the resonance peak, and forming a resonance characteristic parameter set; Based on the multimodal competition trigger point in the electrothermal coupling characteristic map, a parameter sweep is performed within a temperature range of ±2°C relative to the multimodal competition trigger point and a current range of ±5% relative to the nominal operating current, changes in the impedance spectrum are recorded, and the quantitative relationship between the resonance characteristics and changes in the operating conditions is analyzed to obtain boundary data of the modal competition region; According to the resonance characteristic parameter set and the boundary data of the modal competition area, feature extraction is performed on the high-frequency electrical response under different modal states, a correspondence between the resonance characteristics and the modal state is established, and a modal electrical fingerprint library is constructed.

[0040] The following is a detailed description of the steps involved in the above embodiment: The first step involves setting a specific operating condition combination for the laser under test based on the already determined electrical integrity of the isolation structure and the trigger temperature threshold. A common approach is to start with a step-by-step setup from low to high bias current, while simultaneously sampling multiple points within the temperature range above and below the trigger temperature threshold. This creates a matrix of operating conditions covering a variety of bias and temperature configurations. Subsequently, a high-frequency measurement platform (such as a network analyzer with S-parameter measurement capabilities) applies a swept frequency signal to the laser under test, collecting complex impedance information from 0.1 to 40 GHz. This frequency range spans from low-end RF to microwave and millimeter-wave frequencies, helping to identify resonances or impedance fluctuations within different modes within the device at high frequencies. To minimize parasitic effects during high-frequency testing, specialized high-frequency fixtures and appropriate calibration methods are required to ensure that the measured complex impedance value is the true complex impedance of the laser under test at the specified temperature and current. The goal of this step is to obtain high-frequency complex impedance spectroscopy data, providing comprehensive frequency-domain information for subsequent analysis.

[0041] When the second step is implemented, it is necessary to conduct a detailed analysis of the complex impedance spectrum data obtained above to extract the key information reflecting the internal modal characteristics of the laser. In the specific implementation, the peaks and valleys appearing in the complex impedance curve can be located by numerical fitting methods or peak-valley identification algorithms, and characteristic quantities such as resonance peaks and resonance valleys can be obtained based on the center frequency, bandwidth and quality factor distribution of these extreme values. The center frequency can refer to the frequency point where the response increases or decreases most significantly. The bandwidth can be measured by dropping the amplitude by 3dB or at a certain agreed threshold, and the quality factor is often directly related to the sharpness of the resonance and the energy loss. By summarizing these characteristic parameters into a unified data structure, the response changes of the device in the high-frequency region under different working conditions can be more intuitively reflected.

[0042] When the third step is implemented, a further scanning range can be set in combination with the multi-modal competition trigger point given by the electrothermal coupling characteristic map, such as the ±2°C range above and below the trigger temperature point and the ±5% injection range above and below the nominal operating current, to measure multiple sets of complex impedance spectra of the laser under test again. After the data acquisition is completed, this series of measurement results can be differentially or comparatively calculated to observe the slight drift of the center frequency, bandwidth and quality factor of the resonance peak when the temperature and current are fine-tuned, and to determine under which combination of conditions obvious mode switching or competition between modes will occur. In this way, the boundary information of the multi-modal competition area on the temperature and current plane can be obtained. The above-mentioned temperature and current scanning range is to match the deviation range of the multi-modal competition trigger point, so that the measurement is more focused on the key area where the mode jump is about to or has occurred, and the stable working range of different modes can be located more accurately.

[0043] The fourth step, when implemented, requires integrating the aforementioned resonant characteristic parameters and the boundary data of the multimodal competition region to distinguish the characteristic performance of the laser's high-frequency response in each mode and construct a fingerprint library that can be used to identify different modal states. During this operation, each identified mode is associated with a corresponding center frequency segment, quality factor range, and bandwidth variation trend, recording the characteristic vectors or scalar sets of different modes under different test conditions. If new peak positions or peak-valley shapes that differ from existing mode characteristics are observed during the scanning process, they can also be added to the fingerprint library for expansion. Through this comparison and classification approach, a set of modal identification rules covering various operating conditions can be formed, allowing for rapid location and identification of the laser's multimodal competition state in any subsequent similar high-frequency tests or dynamic changes in operating conditions. This method combines information on electrical integrity, thermal coupling, and high-frequency impedance to achieve differentiation between different transverse modes and proactively identify potential competition risks.

[0044] In one embodiment of the present invention, the method of extracting features of high-frequency electrical responses under different modal states based on the resonance feature parameter set and the boundary data of the modal competition region, establishing a correspondence between resonance features and modal states, and constructing a modal electrical fingerprint library includes: Quantitatively analyzing the center frequency, bandwidth, and quality factor of the resonance peak in the high-frequency complex impedance spectrum, calculating the discrete degree and discrimination coefficient of each parameter, and screening out core resonance characteristic parameters that contribute more than 90% to the differentiation of modal states; Correlating the core resonance characteristic parameters with the electrical integrity index of the isolation region structure to determine key parameters for modal identification that reflect the unique electrical response characteristics of the laser to be tested; Based on the key parameters for modal identification, the high-frequency electrical response data under different temperature and current conditions are classified and summarized, a one-to-one correspondence between the core resonance characteristics and the specific modal states is established, and a modal electrical fingerprint library containing the electrical characteristics of the isolation structure is constructed.

[0045] The following is a detailed description of the steps involved in the above embodiment: The first step, when implemented, involves identifying the location of each resonant peak in the high-frequency complex impedance spectrum and extracting the three parameters of center frequency, bandwidth, and quality factor for each peak. For measurement, a network analyzer or impedance analyzer with S-parameter measurement capabilities can be used. Preliminary testing can be performed to obtain high-frequency complex impedance curves under different temperature and current conditions. Then, in post-processing, a peak identification algorithm and numerical fitting methods are combined to extract the peak shape characteristics of each curve. Once these three parameters are obtained, all curves under different conditions can be compared horizontally, the degree of dispersion of each parameter can be calculated, and an indicator of whether the modal states can be effectively distinguished can be calculated. A common approach is to observe the cluster distribution of these parameters under different modes in three-dimensional or multi-dimensional space. If the distribution areas of different modes on a particular parameter axis have little overlap, it indicates that the parameter has high discriminatory power. Selecting a contribution of 90% or more ensures that the selected features maintain high recognition accuracy while minimizing dimensionality, thereby effectively filtering out redundant parameters that are not helpful for modal differentiation.

[0046] The second step, during implementation, leverages previously acquired electrical integrity metrics of the isolation region structure to correlate the aforementioned core resonant characteristic parameters with the specific properties of the isolation structure. Specifically, based on the isolation region's electrical integrity metrics, such as edge current distribution characteristics or edge capacitance mutation parameters, high-risk regions that could potentially cause modal jumps or fluctuations are identified. Next, core characteristics such as the resonant peak center frequency and bandwidth are observed to indicate significant shifts or widening within these high-risk regions. By comparing the matching degree between the core parameters corresponding to different test points and data such as the isolation region edge gradient and local gain distribution, the most sensitive sets of parameters for the laser, known as key parameters for modal identification, are identified. This correlation analysis is performed because edge defects or carrier stacking in isolated structures can easily trigger competition between different transverse modes. Strong correlations between core characteristic parameters and these structural metrics can directly reflect the device's actual operating status and potential failure modes.

[0047] During the third step, the previously selected key parameters for modal identification are used to classify and organize the collected high-frequency electrical response data under different temperature and current conditions. Based on the numerical range and combined characteristics of each set of key parameters, the high-frequency characteristics of the laser can be labeled according to the actual measured modal state, thereby gradually summarizing the typical response patterns under each mode in the data set, and ultimately establishing a correspondence between the core resonance characteristics and the specific modal state. This correspondence further integrates the electrical characteristic information of the isolation structure, such as the presence of obvious edge defects and sudden changes in the temperature coefficient of resistance, to form a fingerprint information library covering temperature, current, and structural properties. In subsequent applications, this fingerprint library can quickly identify whether the laser is in a multimodal competition stage and determine whether the current mode is stable, thereby providing a quantitative evaluation basis for real-time monitoring or online diagnosis. This method can combine structural characteristics with high-frequency response characteristics to more accurately reflect the evolution of different transverse modes in complex working environments.

[0048] Please continue reading Figure 1 , using the modal electrical fingerprint library to set multi-parameter trigger conditions, capture electrical transient data of modal jumps, perform multi-time scale analysis, and obtain a multi-scale abnormal feature map; In one embodiment of the present invention, the method of setting a multi-parameter trigger condition using the modal electrical fingerprint library, capturing electrical transient data of the modal jump, and performing multi-time scale analysis to obtain a multi-scale abnormal feature map includes: According to the correspondence between the resonance characteristics and modal states in the modal electrical fingerprint library, the combined change pattern of voltage, impedance and noise parameters is selected as the trigger condition, the trigger threshold and trigger logic are set, the electrical parameters of the laser to be tested are monitored in real time, and when the electrical parameters meet the trigger conditions, the complete transient electrical data is captured to obtain the modal jump transient electrical data set; Performing time-domain decomposition on the modal jump transient electrical data set, performing parameter change rate analysis in the 1-100 nanosecond, 1-100 microsecond, and 1-100 millisecond time windows, respectively, extracting characteristic parameters at each time scale, including voltage jump amplitude, impedance mutation rate, and noise burst pattern, to obtain a cross-time scale characteristic parameter set; According to the cross-time scale characteristic parameter set, the electrical anomaly characteristics are classified and statistically analyzed, the electrical anomaly patterns related to the isolation structure characteristics are extracted, the correlation between the anomaly patterns and the working conditions is calibrated, and a multi-scale anomaly feature map is obtained.

[0049] The following is a detailed description of the steps involved in the above embodiment: When the first step is implemented, the combination change pattern of voltage, impedance and noise parameters is selected according to the correspondence between the existing resonant characteristics and the modal state, and a specific trigger threshold and trigger logic are set for each combination pattern. In order to achieve real-time monitoring, the laser test platform can be equipped with a high-speed data acquisition device and a high-bandwidth probe to continuously record the dynamic fluctuations of voltage, impedance and noise over time. Once any parameter or combination of multiple parameters in the monitoring data triggers a pre-defined critical condition, the system will automatically capture the raw data from before to after the trigger, and generate a complete transient electrical data set. The reason why voltage, impedance and noise are included in the trigger conditions here is that multimodal competition is often accompanied by sudden changes in multiple electrical manifestations. Judging by combining multiple parameters can reduce the possibility of missing or misjudging a single parameter, and ensure that the captured jump moment is more accurate.

[0050] The second step, during implementation, involves segmenting the captured transient electrical data set into timescales for detailed analysis. The rate of voltage or impedance changes can be examined first within a nanosecond to microsecond time window, followed by longer microsecond to millisecond intervals to observe slower changes caused by carrier distribution rebalancing or heat diffusion. To achieve this, a high-speed digital oscilloscope or multi-channel data acquisition system can be used to segment the signal into timescales of 1-100 nanoseconds, 1-100 microseconds, and 1-100 milliseconds, respectively. This time-domain decomposition allows for intuitive visualization of parameter transition characteristics at different timescales. For example, within a short time window of 1-100 nanoseconds, the voltage jump amplitude can be used to measure the strength of the electrical transient, the impedance jump rate can characterize rapid switching between local modes, and the high-frequency components of the noise power spectrum can reveal possible resonances or carrier annihilation processes. Over longer time windows, statistically analyzing the rate of voltage or impedance changes can provide insights into the impact of thermal distribution or material stress on modal competition. In this way, independent key characteristic parameters at different time scales can be obtained. By classifying and summarizing these parameters, a set of cross-time scale characteristic parameters can be formed.

[0051] The third step, during implementation, involves classifying and statistically analyzing electrical anomalies during multimodal transitions based on a set of cross-timescale characteristic parameters. By comparing the parameter combinations characteristic of each transition event, anomaly patterns associated with factors such as isolation region edge defects, uneven current distribution, or sudden changes in thermal resistance can be identified. These patterns are then mapped to different temperature or bias current conditions to form a multiscale anomaly feature map. This map identifies recurring characteristic patterns and provides their probability or severity under specific operating conditions. In this way, once similar voltage, impedance, and noise triggering characteristics are detected in a new test environment or online monitoring scenario, they can be quickly compared with known multiscale anomaly patterns to assess whether the laser is in, or is about to enter, an unfavorable modal competition state. The parameter range and time window are set to span nanoseconds to milliseconds to cover multiple critical processes, from rapid carrier recombination to thermal diffusion equilibrium, ensuring that transient or slow-changing phenomena caused by multimodal competition effects are captured on a more comprehensive timescale. This allows the true dynamics of internal mode switching to be reflected from multiple dimensions, enabling more effective identification and classification of multimodal competition.

[0052] In one embodiment of the present invention, the modal jump transient electrical data set is decomposed in the time domain, and parameter change rate analysis is performed in the 1-100 nanosecond, 1-100 microsecond, and 1-100 millisecond time windows, respectively, to extract characteristic parameters at each time scale, including voltage jump amplitude, impedance mutation rate, and noise burst pattern, to obtain a cross-time scale characteristic parameter set, including: Apply wavelet transform to electrical data in the 1-100 nanosecond time window to separate high-frequency transient components, extract mutation events with voltage jump amplitudes greater than 5mV and mutation features with impedance change rates exceeding 10% / ns, establish a correlation model between nanosecond-level electrical characteristics and changes in current distribution at the edge of the isolation region, and obtain nanosecond-level characteristic parameters; Spectral analysis of electrical data in the 1-100 microsecond time window was performed to identify the noise power enhancement phenomenon in the 1-10 MHz frequency band, quantify the duration and intensity distribution of noise bursts, determine the intermediate time scale electrical response characteristics related to the carrier redistribution process, and obtain microsecond-level characteristic parameters; By combining the nanosecond-level characteristic parameters, the microsecond-level characteristic parameters and the long-term evolution characteristics of the voltage, impedance and noise parameters calculated from the electrical data in the 1-100 millisecond time window, the correlation and transmission rules between the characteristic parameters of different time scales are analyzed to obtain a cross-time scale characteristic parameter set.

[0053] The following is a detailed description of the steps involved in the above embodiment: The first step involves performing a wavelet transform on the electrical data collected within the 1-100 nanosecond timeframe. This requires obtaining nanosecond-scale waveforms of voltage and impedance variations over time using a high-speed digital storage oscilloscope or similar high-bandwidth data acquisition system. These waveforms are then imported into a wavelet analysis module, either offline or online. The wavelet transform performs a multi-scale decomposition of the signal, separating high-frequency transient components from low-frequency or DC components. By searching for regions with voltage jumps greater than 5mV within the decomposed high-frequency portion, it is possible to identify small but rapid electrical transitions. Furthermore, if an impedance change rate exceeding 10% / ns per unit time is detected within the same data, this is considered a sudden change. After locating these sudden changes and their occurrence times, they are compared with previously acquired current distribution characteristics at the isolation zone edge to determine whether transient electrical anomalies caused by multimodal competition occur at this nanosecond level. The thresholds are set at 5mV and 10% / ns because signal noise and parasitic interference account for a relatively high proportion in nanosecond measurements. It is necessary to select a numerical range that can distinguish between true mutations and interference, thereby avoiding over-triggering and ensuring effective capture of significant modal jumps.

[0054] The second step, when implemented, involves performing a spectral analysis of the electrical signal within a 1-100 microsecond time window. This requires first converting each time-domain waveform to the frequency domain using a fast Fourier transform or other spectral transformation method, and observing whether there is significant noise power accumulation within the 1-10 MHz range. The calculation of the noise power spectrum can be achieved with the aid of a spectrum analyzer or digital signal processing software, and characterized by a curve showing the distribution of noise energy versus frequency. If a sharp increase in energy occurs within a specific frequency band, the duration and intensity of this phenomenon must be further calculated to determine the impact of carrier redistribution or other intermediate rate processes on modal competition. The 1-10 MHz range encompasses mid-frequency characteristics caused by typical processes such as carrier lifetime and gain recovery, which helps identify changes in electrical properties exhibited by multimodal competition on slower time scales. The quantification of frequency band and duration in this step is intended to better distinguish between ordinary background noise and noise burst characteristics truly associated with transverse mode switching.

[0055] The third step, when implemented, requires integrating the long-term evolution trends of voltage, impedance, and noise changes collected within the 1-100 millisecond time window with the nanosecond and microsecond analysis results to form a comprehensive set of cross-timescale characteristic parameters. To achieve this correlation, the transient jump characteristics obtained at the nanosecond level and the noise enhancement information at the microsecond level can be uniformly numbered. Then, they can be time-aligned and feature vectors can be concatenated with the slow drift curves of impedance or voltage at the millisecond level. Visualization or data clustering methods can be used to identify the correlations between the various timescales. For example, if a high-intensity noise burst occurs within a certain microsecond interval after a nanosecond jump, accompanied by a gradual shift in impedance or voltage over the subsequent millisecond timeframe, this series of events can be summarized as a complete cross-timescale electrical anomaly pattern. The 1-100 millisecond range is often associated with heat diffusion effects within the chip or slow structural stress changes. Combining these phenomena with carrier transient processes at the nanosecond to microsecond scale can more accurately reveal the triggering and evolution of multimodal competition within the laser and its impact on overall performance. The cross-time scale characteristic parameter set obtained in this way can not only provide a reference for subsequent diagnosis and prediction, but also help identify the key influencing factors leading to modal instability in production testing and failure analysis.

[0056] Please continue reading Figure 1 According to the multi-scale abnormal characteristic map, the electric field distribution control parameters are determined, the risk distribution map within the working range is constructed, the monitoring strategy is dynamically adjusted, and the quantitative evaluation and prediction of modal competition risk are realized.

[0057] In one embodiment of the present invention, determining the electric field distribution control parameters based on the multi-scale abnormal characteristic map, constructing a risk distribution map within the working range, dynamically adjusting the monitoring strategy, and achieving quantitative assessment and prediction of modal competition risk include: Based on the electrical anomaly patterns in the multi-scale anomaly feature map, the correlation between uneven electric field distribution and multimodal competition is analyzed, and key parameters that can effectively regulate the electric field distribution in the isolation zone are extracted, including bias current distribution parameters and impedance matching parameters, to obtain an electric field distribution control parameter set; Adjusting the drive circuit parameters of the laser to be tested according to the electric field distribution control parameter set, measuring the changes in the electrical parameters before and after the adjustment, calculating the influence coefficient of the parameter adjustment on the modal stability, and obtaining electric field distribution control effect evaluation data; Performing statistical analysis on the time series data in the multi-scale anomaly feature map, calculating the probability and severity of modal competition under different working conditions, drawing risk contours on the temperature-current plane, and obtaining a risk distribution map within the working range; According to the risk distribution map, the working range is divided into a first risk area with a modal competition probability higher than 50%, a second risk area with a modal competition probability between 10% and 50%, and a third risk area with a modal competition probability lower than 10%. Monitoring parameters and sampling frequencies are assigned to the first risk area, the second risk area, and the third risk area, respectively, to form a zoning monitoring strategy. The partition monitoring strategy is combined with the electric field distribution control parameter set to establish a risk scoring system for the working condition change path, calculate the cumulative risk value of different operation sequences, predict the potential time and intensity of modal competition, and realize the quantitative assessment and prediction of modal competition risk.

[0058] The following is a detailed description of the steps involved in the above embodiment: During the implementation of the first step, the degree of unevenness in the electric field distribution in the lateral and longitudinal directions needs to be analyzed based on the electrical anomaly patterns identified in the multi-scale anomaly feature map, and compared with the jump or abnormal noise bands generated by multimodal competition. During the execution process, the most significant abnormal phenomena under high temperature or high current density conditions can be determined by using the transient data collected earlier and the cross-time scale characteristics. Then, based on the electrical integrity indicators of the isolation region structure and the edge current distribution information, the location causing the local excessive electric field enhancement can be located. These local excessive enhancement points are mapped with the observed multimodal competition triggering events to form a specific mapping relationship between the uneven electric field distribution and multimodal competition. From this, two key control quantities, bias current distribution parameters and impedance matching parameters, are extracted. The bias current distribution parameters are used to measure the diffusion range of carriers in the lateral direction under different injection conditions, and the impedance matching parameters are used to determine the energy coupling efficiency between the external drive circuit and the internal gain region. These two parameters were chosen because multimodal competition is often related to excessive lateral current concentration and external loop impedance mismatch. By quantifying the adjustable range of these two parts, a reference can be provided for subsequent electric field suppression at the edge of the isolation zone or local hotspots.

[0059] During the specific implementation of the second step, the driving circuit or peripheral matching circuit of the laser under test is appropriately adjusted according to the electric field distribution control parameter set obtained above, and the corresponding voltage, impedance and optical power changes before and after the adjustment are measured in real time. A common implementation method is to change the distribution ratio of the bias current in the driving circuit, or to add a controllable impedance element to correct the impedance mismatch in the high-frequency band; at the same time, a network analyzer or oscilloscope is used to observe whether the electrical response curve of the laser after adjustment shows a decrease in threshold current, a decrease in noise peak, etc. After comparing multiple sets of data before and after adjustment, the influence coefficient can be calculated based on the relationship between the parameter adjustment amplitude and the multimodal competition suppression effect, which is usually quantified in the form of a percentage or amplitude ratio. For example, if the noise power is reduced by more than 50% under a certain matching condition, it can be considered that the control method has a good suppression effect under the corresponding working conditions. This comparison method is selected to eliminate the influence of random fluctuations in measurement and provide comparable quantitative indicators for different types of lasers or different working scenarios.

[0060] During implementation, the third step involves incorporating the different time series data recorded in the multi-scale anomaly feature map into a statistical model. The frequency and intensity of modal competition under various temperature and current combinations are analyzed, and the probability of multi-modal competition and the resulting output fluctuations are calculated. This statistical process can be implemented using a database or data visualization software. The number and severity of electrical anomaly events corresponding to each operating condition are mapped onto a temperature-current plane. The risk level is represented using color gradients or contour lines, resulting in a risk distribution map containing multiple contour lines. This type of diagram can often help differentiate the modal instability risk faced by a device under different operating temperatures and current biases. The temperature-current plane was chosen because, for isolated structures, temperature gradients and current density are the most important factors affecting the internal lateral modal distribution. It can intuitively reflect the device's operating limits and potential thermal-electrical coupling effects.

[0061] When implementing the fourth step, it is necessary to partition the working range according to the risk distribution map, and mark the area where the probability of modal competition exceeds 50% as the first risk area, the area between 10% and 50% as the second risk area, and the area below 10% as the third risk area. Each partition corresponds to a different monitoring strategy and sampling frequency, so that more intensive online measurements can be arranged in high-risk areas, routine inspections can be maintained in medium-risk areas, and the monitoring intensity can be moderately reduced in safer areas. Taking the application in the field of optical communications as an example, if the laser operates in the first risk area during high-speed data transmission, it is necessary to call for higher-bandwidth monitoring methods and perform more precise real-time control of the driving current. The consideration of this partitioned monitoring strategy is to make full use of limited detection resources and allocate the most critical measurement resources to the areas with the highest risk, thereby improving the overall detection efficiency and accuracy.

[0062] The fifth step, during implementation, combines the partition monitoring strategy with a set of electric field distribution control parameters to construct a risk scoring system based on the path of operating condition changes. In simulations or real-world experiments across multiple operating sequences, the temperature or current bias can be gradually varied, and the cumulative risk value is calculated in real time based on the previously defined risk distribution map. If the current path approaches the boundary of a high-risk partition, the electric field distribution control parameters are used to attempt to reduce the peak drive current or improve impedance matching, and the dynamic trend of the risk value is observed. If the risk value remains high after multiple adjustments, it indicates that the device is prone to multimodal competition or instability under this operating sequence, and consideration should be given to switching to a more stable operating strategy or adopting an improved heat dissipation design. This scoring system allows for the cumulative risk effects of different operating steps to be tracked. It also allows for the prediction of potential excitation intensities and multimodal transition timings before the laser is deployed, enabling quantitative assessment and proactive management of the modal competition risk in isolated lasers.

[0063] The above describes the detection method of the isolated VCSEL in the embodiment of the present invention. The following describes the detection device of the isolated VCSEL in the embodiment of the present invention. Figure 2 An embodiment of a detection device for an isolated VCSEL according to an embodiment of the present invention includes: The electrical parameter measurement module 101 is used to measure the capacitance distribution and conductivity characteristics of the isolation region of the laser to be tested at different frequencies, identify the electrical gradient characteristics of the isolation region edge, and obtain an isolation region electrical characteristic data set; Thermoelectric coupling analysis module 102 is used to measure the dynamic response parameters of the thermoelectric coupling characteristics of the isolation region based on the electrical characteristic data set, record the mutation characteristics of the voltage and impedance parameters under temperature changes, and obtain a multi-modal competitive electrothermal coupling characteristic map; The high-frequency response measurement module 103 is used to measure the high-frequency electrical response characteristics of the laser to be tested under different working conditions based on the electrical characteristic data set and the electrothermal coupling characteristic map, extract the resonance characteristic parameters, and construct a modal electrical fingerprint library; The transient capture and analysis module 104 is used to set multi-parameter trigger conditions using the modal electrical fingerprint library, capture electrical transient data of the modal jump, perform multi-time scale analysis, and obtain a multi-scale abnormal feature map; The risk assessment and prediction module 105 is used to determine the electric field distribution control parameters based on the multi-scale abnormal characteristic map, construct a risk distribution map within the working range, dynamically adjust the monitoring strategy, and realize quantitative assessment and prediction of modal competition risk.

[0064] above Figure 2The detection device of the isolated VCSEL in the embodiment of the present invention is described in detail from the perspective of modular functional entities. The detection device of the isolated VCSEL in the embodiment of the present invention is described in detail from the perspective of hardware processing.

[0065] Figure 3 Figure 2 is a schematic diagram of the structure of an isolated VCSEL testing device provided by an embodiment of the present invention. The isolated VCSEL testing device 200 may vary significantly depending on configuration or performance. It may include one or more central processing units (CPUs) 210 (e.g., one or more processors), a memory 220, and one or more storage media 230 (e.g., one or more mass storage devices) storing application programs 233 or data 232. The memory 220 and storage medium 230 may be either transient or persistent storage. The program stored in the storage medium 230 may include one or more modules (not shown), each of which may include a series of instructions for operating on the isolated VCSEL testing device 200. Furthermore, the processor 210 may be configured to communicate with the storage medium 230, executing the series of instructions stored in the storage medium 230 on the isolated VCSEL testing device 200 to implement the steps of the isolated VCSEL testing method described above.

[0066] The isolated VCSEL detection device 200 may further include one or more power supplies 240, one or more wired or wireless network interfaces 250, one or more input / output interfaces 260, and / or one or more operating systems 231, such as Windows Server, Mac OS X, Unix, Linux, FreeBSD, etc. It will be understood by those skilled in the art that Figure 3 The structure of the isolated VCSEL detection device shown does not limit the isolated VCSEL detection device provided by the present invention, and may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently.

[0067] The present invention also provides a computer-readable storage medium, which may be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium. The computer-readable storage medium stores instructions. When the instructions are executed on a computer, the computer executes the steps of the isolated VCSEL detection method.

[0068] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described systems, devices, and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0069] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes various media that can store program code, such as a USB flash drive, a mobile hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0070] The above description is only a preferred embodiment of the present invention and does not limit the patent scope of the present invention. All equivalent structural transformations made by using the contents of the present invention description and drawings under the inventive concept of the present invention, or direct / indirect application in other related technical fields are included in the patent protection scope of the present invention.

Claims

1. A method for detecting an isolated VCSEL, characterized in that: include: The capacitance distribution and conductivity characteristics of the isolation region of the laser under test are measured at different frequencies, the electrical gradient characteristics at the edge of the isolation region are identified, and a data set of electrical characteristics of the isolation region is obtained; Based on the electrical characteristic data set, dynamic response parameter measurements are performed on the thermoelectric coupling characteristics of the isolation region, and the mutation characteristics of the voltage and impedance parameters under temperature changes are recorded to obtain a multi-modal competitive electrothermal coupling characteristic map; Based on the electrical characteristic data set and the electrothermal coupling characteristic map, the high-frequency electrical response characteristics of the laser to be tested are measured under different working conditions, the resonance characteristic parameters are extracted, and a modal electrical fingerprint library is constructed; The modal electrical fingerprint library is used to set multi-parameter trigger conditions, capture electrical transient data of modal jumps, perform multi-time scale analysis, and obtain a multi-scale abnormal feature map; According to the multi-scale abnormal characteristic map, the electric field distribution control parameters are determined, a risk distribution map within the working range is constructed, the monitoring strategy is dynamically adjusted, and the quantitative assessment and prediction of modal competition risk are achieved.

2. The detection method of isolated VCSEL according to claim 1, characterized in that: The capacitance distribution and conductivity characteristics of the isolation region of the laser to be tested are measured at different frequencies, the electrical gradient characteristics at the edge of the isolation region are identified, and the electrical characteristic data set of the isolation region is obtained, including: Performing capacitance scanning measurement on the isolation area of the laser to be tested within a frequency range of 1 MHz to 1 GHz, recording capacitance frequency response curves at different positions, and obtaining a capacitance distribution map of the isolation area by analyzing the rate of change of the capacitance frequency response curves; Conducting electrical gradient measurement of the edge of the isolation region with nanometer-level resolution based on the isolation region capacitance distribution map, extracting capacitance mutation points and electrical gradient values at the edge of the isolation region, and forming electrical gradient characteristics of the isolation region edge; Perform multi-frequency conductivity measurement on the laser to be tested under different current bias conditions, record the curve of conductivity value changing with frequency, and obtain the conductivity frequency characteristic curve; Extracting inflection point frequency and slope parameters according to the conductivity-frequency characteristic curve to obtain current density distribution data; Analyzing the evolution of current density with bias change based on the current density distribution data to determine a critical point where the current distribution changes from uniform to non-uniform; The electrical gradient characteristics of the isolation region edge, the conductivity frequency characteristic curve and the current distribution critical point are correlated and analyzed to determine the electrical integrity index of the isolation structure and the current limiting effectiveness parameter, thereby obtaining an isolation region electrical characteristic data set.

3. The detection method of isolated VCSEL according to claim 1, characterized in that: The method measures the dynamic response parameters of the thermoelectric coupling characteristics of the isolation region based on the electrical characteristic data set, records the mutation characteristics of the voltage and impedance parameters under temperature changes, and obtains a multi-modal competitive electrothermal coupling characteristic map, including: Based on the electrical integrity index of the isolation structure in the electrical characteristic data set, a temperature sweep test is performed on the laser to be tested within a temperature range of -40°C to 120°C, the temperature is changed at a rate of 0.1°C / minute, a resistance value versus temperature curve is recorded, a nonlinear parameter of the resistance temperature coefficient is calculated, and a mutation point of the resistance temperature coefficient during the temperature change process is extracted to obtain a nonlinear characteristic curve of the thermal resistor; Based on the current distribution critical point in the isolation region electrical characteristic data set, applying a current pulse with a duration of 1-10 microseconds to the laser to be tested, recording the transient change waveform of the voltage response, analyzing the asymmetry of the rise time and fall time of the voltage transient waveform, and obtaining thermal response time constant distribution data; Under preset current bias conditions, the temperature of the laser under test is step-scanned while recording changes in voltage, impedance parameters, and electrical noise. The critical temperature point where electrical parameter mutation occurs is identified, and feature extraction is performed on the electrical noise power spectrum. The noise frequency band enhancement phenomenon related to multimodal competition is identified, resulting in a temperature scan electrical mutation point dataset and a temperature-related noise characteristic spectrum. The nonlinear characteristic curve of the thermal resistor, the thermal response time constant distribution data, the temperature scanning electrical mutation point data set and the temperature-related noise characteristic spectrum are correlated and analyzed to calibrate the trigger temperature threshold and warning index of multimodal competition under thermoelectric coupling conditions, and obtain the multimodal competition electrothermal coupling characteristic map.

4. The detection method of isolated VCSEL according to claim 1, characterized in that: Based on the electrical characteristic data set and the electrothermal coupling characteristic map, high-frequency electrical response characteristics of the laser to be tested are measured under different working conditions, resonance characteristic parameters are extracted, and a modal electrical fingerprint library is constructed, including: According to the electrical integrity index of the isolation structure in the electrical characteristic data set and the trigger temperature threshold in the electrothermal coupling characteristic map, an operating condition matrix of the laser to be tested is set, and the complex impedance spectrum of the laser to be tested is measured in a frequency range of 0.1 to 40 GHz. The impedance amplitude and phase changes with frequency under different operating conditions are recorded to obtain high-frequency complex impedance spectrum data; Analyzing the high-frequency complex impedance spectrum data, extracting resonance peak and resonance valley features in the impedance spectrum, calculating the center frequency, bandwidth, and quality factor of the resonance peak, and forming a resonance characteristic parameter set; Based on the multimodal competition trigger point in the electrothermal coupling characteristic map, a parameter sweep is performed within a temperature range of ±2°C relative to the multimodal competition trigger point and a current range of ±5% relative to the nominal operating current, changes in the impedance spectrum are recorded, and the quantitative relationship between the resonance characteristics and changes in the operating conditions is analyzed to obtain boundary data of the modal competition region; According to the resonance characteristic parameter set and the boundary data of the modal competition area, feature extraction is performed on the high-frequency electrical response under different modal states, a correspondence between the resonance characteristics and the modal state is established, and a modal electrical fingerprint library is constructed.

5. The detection method of isolated VCSEL according to claim 4, characterized in that: The method of extracting features of high-frequency electrical responses under different modal states based on the resonance feature parameter set and the boundary data of the modal competition region, establishing a corresponding relationship between resonance features and modal states, and constructing a modal electrical fingerprint library includes: Quantitatively analyzing the center frequency, bandwidth, and quality factor of the resonance peak in the high-frequency complex impedance spectrum, calculating the discrete degree and discrimination coefficient of each parameter, and screening out core resonance characteristic parameters that contribute more than 90% to the differentiation of modal states; Correlating the core resonance characteristic parameters with the electrical integrity index of the isolation region structure to determine key parameters for modal identification that reflect the unique electrical response characteristics of the laser to be tested; Based on the key parameters for modal identification, the high-frequency electrical response data under different temperature and current conditions are classified and summarized, a one-to-one correspondence between the core resonance characteristics and the specific modal states is established, and a modal electrical fingerprint library containing the electrical characteristics of the isolation structure is constructed.

6. The detection method of isolated VCSEL according to claim 1, characterized in that: The method of using the modal electrical fingerprint library to set multi-parameter trigger conditions, capturing electrical transient data of modal jumps, and performing multi-time scale analysis to obtain a multi-scale abnormal feature map includes: According to the correspondence between the resonance characteristics and modal states in the modal electrical fingerprint library, the combined change pattern of voltage, impedance and noise parameters is selected as the trigger condition, the trigger threshold and trigger logic are set, the electrical parameters of the laser to be tested are monitored in real time, and when the electrical parameters meet the trigger conditions, the complete transient electrical data is captured to obtain the modal jump transient electrical data set; Performing time-domain decomposition on the modal jump transient electrical data set, performing parameter change rate analysis in the 1-100 nanosecond, 1-100 microsecond, and 1-100 millisecond time windows, respectively, extracting characteristic parameters at each time scale, including voltage jump amplitude, impedance mutation rate, and noise burst pattern, to obtain a cross-time scale characteristic parameter set; According to the cross-time scale characteristic parameter set, the electrical anomaly characteristics are classified and statistically analyzed, the electrical anomaly patterns related to the isolation structure characteristics are extracted, the correlation between the anomaly patterns and the working conditions is calibrated, and a multi-scale anomaly feature map is obtained.

7. The method for detecting an isolated VCSEL according to claim 6, wherein: The modal jump transient electrical data set is decomposed in the time domain, and parameter change rate analysis is performed in the 1-100 nanosecond, 1-100 microsecond and 1-100 millisecond time windows respectively. The characteristic parameters at each time scale are extracted, including the voltage jump amplitude, impedance mutation rate and noise burst mode, to obtain a cross-time scale characteristic parameter set, including: Apply wavelet transform to electrical data in the 1-100 nanosecond time window to separate high-frequency transient components, extract mutation events with voltage jump amplitudes greater than 5mV and mutation features with impedance change rates exceeding 10% / ns, establish a correlation model between nanosecond-level electrical characteristics and changes in current distribution at the edge of the isolation region, and obtain nanosecond-level characteristic parameters; Spectral analysis of electrical data in the 1-100 microsecond time window was performed to identify the noise power enhancement phenomenon in the 1-10 MHz frequency band, quantify the duration and intensity distribution of noise bursts, determine the intermediate time scale electrical response characteristics related to the carrier redistribution process, and obtain microsecond-level characteristic parameters; By combining the nanosecond-level characteristic parameters, the microsecond-level characteristic parameters and the long-term evolution characteristics of the voltage, impedance and noise parameters calculated from the electrical data in the 1-100 millisecond time window, the correlation and transmission rules between the characteristic parameters of different time scales are analyzed to obtain a cross-time scale characteristic parameter set.

8. The method for detecting an isolated VCSEL according to claim 1, wherein: The method of determining electric field distribution control parameters based on the multi-scale abnormal characteristic map, constructing a risk distribution map within the working range, dynamically adjusting the monitoring strategy, and achieving quantitative assessment and prediction of modal competition risk includes: Based on the electrical anomaly patterns in the multi-scale anomaly feature map, the correlation between uneven electric field distribution and multimodal competition is analyzed, and key parameters that can effectively regulate the electric field distribution in the isolation zone are extracted, including bias current distribution parameters and impedance matching parameters, to obtain an electric field distribution control parameter set; Adjusting the drive circuit parameters of the laser to be tested according to the electric field distribution control parameter set, measuring the changes in the electrical parameters before and after the adjustment, calculating the influence coefficient of the parameter adjustment on the modal stability, and obtaining electric field distribution control effect evaluation data; Performing statistical analysis on the time series data in the multi-scale anomaly feature map, calculating the probability and severity of modal competition under different working conditions, drawing risk contours on the temperature-current plane, and obtaining a risk distribution map within the working range; According to the risk distribution map, the working range is divided into a first risk area with a modal competition probability higher than 50%, a second risk area with a modal competition probability between 10% and 50%, and a third risk area with a modal competition probability lower than 10%. Monitoring parameters and sampling frequencies are assigned to the first risk area, the second risk area, and the third risk area, respectively, to form a zoning monitoring strategy. The partition monitoring strategy is combined with the electric field distribution control parameter set to establish a risk scoring system for the working condition change path, calculate the cumulative risk value of different operation sequences, predict the potential time and intensity of modal competition, and realize the quantitative assessment and prediction of modal competition risk.

9. A detection device for an isolated VCSEL, characterized in that: The isolated VCSEL detection device adopts the isolated VCSEL detection method according to any one of claims 1 to 8, and the isolated VCSEL detection device includes: The electrical parameter measurement module is used to measure the capacitance distribution and conductivity characteristics of the isolation area of the laser to be tested at different frequencies, identify the electrical gradient characteristics at the edge of the isolation area, and obtain the electrical characteristic data set of the isolation area; A thermoelectric coupling analysis module is used to measure the dynamic response parameters of the thermoelectric coupling characteristics of the isolation area based on the electrical characteristic data set, record the mutation characteristics of the voltage and impedance parameters under temperature changes, and obtain a multi-modal competitive electrothermal coupling characteristic map; A high-frequency response measurement module is used to measure the high-frequency electrical response characteristics of the laser to be tested under different working conditions based on the electrical characteristic data set and the electrothermal coupling characteristic map, extract the resonance characteristic parameters, and construct a modal electrical fingerprint library; A transient capture and analysis module is used to set multi-parameter trigger conditions using the modal electrical fingerprint library, capture electrical transient data of modal jumps, perform multi-time scale analysis, and obtain a multi-scale abnormal feature map; The risk assessment and prediction module is used to determine the electric field distribution control parameters based on the multi-scale abnormal characteristic map, construct a risk distribution map within the working range, dynamically adjust the monitoring strategy, and realize quantitative assessment and prediction of modal competition risk.

10. An isolated VCSEL detection device, characterized in that: The isolated VCSEL detection device includes: a memory and at least one processor, wherein the memory stores instructions; The at least one processor calls the instructions in the memory to enable the isolated VCSEL detection device to perform the steps of the isolated VCSEL detection method according to any one of claims 1 to 8.

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