Thermal stability multi-physical field coupling test method and system for micro-nano waveguide device

Abstract

The invention discloses a thermal stability multi-physical field coupling test method and system for a micro-nano waveguide device, and belongs to the technical field of micro-nano waveguide device detection.The method is characterized by comprising the steps that multiple sets of pulse data are obtained, and each set of pulse data corresponds to one pulse energy; for each group of pulse data, obtaining thermal relaxation time and peak temperature rise according to the reflected light intensity signal and the light spot position signal; according to the thermal relaxation time, the peak temperature rise and a thermal diffusion model, a thermal diffusion coefficient and local thermal resistance are obtained, and the thermal diffusion model comprises a hyperbolic heat conduction equation, a near-field surface effect formula and a far-field Sommerfeld radiation condition; according to the thermal diffusion coefficient and the local thermal resistance, thermal stability results of the micro-nano waveguide device under different pulse energies are obtained, nanosecond-level pulse laser is adopted as a heat source, the temperature is indirectly measured through reflectivity changes, it is ensured that local temperature signals are accurately obtained under the micrometer / nanometer scale, and the thermal stability of the micro-nano waveguide device under different pulse energies is obtained. And the measurement requirement of fast transient response of a small-size structure is met.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of micro-nano waveguide device detection, more particularly to a thermal stability multi-physical field coupling test method and system for micro-nano waveguide devices. BACKGROUND

[0002] In the fields of optical communication, optical sensing and integrated optoelectronics, micro-nano waveguides have become the core components for realizing efficient transmission and processing of optical signals due to their unique optical properties and extremely small size. However, the thermal stability of micro-nano waveguides directly affects the performance and reliability of the devices during operation. For example, in high-speed optical communication modules, temperature changes in micro-nano waveguides can cause changes in refractive index, resulting in increased signal transmission loss and wavelength drift, which severely affects communication quality.

[0003] Currently, traditional steady-state thermal test methods, such as contact temperature measurement based on thermocouples and transient thermal test techniques, cannot accurately measure the rapidly changing temperature field of micro-nano waveguides with small size and short thermal diffusion path, resulting in limited accuracy and reliability of test results, which cannot meet the growing research and production test demands of micro-nano waveguide devices. Therefore, the existing technology has deficiencies. SUMMARY

[0004] To address the deficiencies in the prior art, the present application aims to provide a thermal stability multi-physical field coupling test method and system for micro-nano waveguide devices, which captures the rapid thermal response of small-sized structures through nanosecond-level transient measurement technology, and combines non-Fourier heat conduction models and surface effect correction to break through the precision bottleneck in small-sized thermal stability analysis of the prior art.

[0005] To achieve the above-mentioned purpose, the present application provides the following technical solutions:

[0006] The present application provides a thermal stability multi-physical field coupling test method for micro-nano waveguide devices, comprising:

[0007] Obtaining multiple sets of pulse data, each set of pulse data corresponding to a pulse energy, and each set of pulse data including a reflected light intensity signal and a spot position signal;

[0008] For each set of pulse data, obtaining the thermal relaxation time and peak temperature rise according to the reflected light intensity signal and the spot position signal;

[0009] Obtaining the thermal diffusion coefficient and local thermal resistance according to the thermal relaxation time, the peak temperature rise and a thermal diffusion model, the thermal diffusion model including a hyperbolic heat conduction equation, a near-field surface effect formula and a far-field Sommerfeld radiation condition;

[0010] According to the thermal diffusivity and the local thermal resistance, a thermal stability result of the micro-nano waveguide device under different pulse energies is obtained.

[0011] As a further improvement of the application, according to the thermal relaxation time, the peak temperature rise and the thermal diffusion model, the thermal diffusivity and the local thermal resistance are obtained, comprising:

[0012] Obtaining material parameters and boundary conditions of the micro-nano waveguide, the material parameters including density and specific heat capacity, and the boundary conditions including near-field surface heat transfer coefficient, surface scattering coefficient and far-field wave number;

[0013] According to the material parameters, the boundary conditions and the thermal diffusion model, a predicted reflectivity change and a predicted thermal relaxation time are obtained;

[0014] According to the thermal relaxation time, the peak temperature rise, the predicted reflectivity change and the predicted thermal relaxation time, a target function is established;

[0015] According to the target function, the thermal diffusivity and the local thermal resistance are calculated.

[0016] As a further improvement of the application, according to the material parameters, the boundary conditions and the thermal diffusion model, a predicted reflectivity change and a predicted thermal relaxation time are obtained, comprising:

[0017] Setting an initial thermal diffusivity and an initial local thermal resistance;

[0018] According to the initial thermal diffusivity, the initial local thermal resistance, the material parameters, the boundary conditions and the thermal diffusion model, a temperature distribution is obtained;

[0019] According to the temperature distribution, the predicted reflectivity change and the predicted thermal relaxation time are obtained.

[0020] As a further improvement of the application, according to the temperature distribution, the predicted reflectivity change and the predicted thermal relaxation time are obtained, comprising:

[0021] According to the temperature distribution, a predicted peak temperature rise is obtained;

[0022] According to the predicted peak temperature rise, the predicted reflectivity change is obtained;

[0023] According to the predicted reflectivity change and a reference reflectivity, the predicted thermal relaxation time is obtained.

[0024] As a further improvement of the application, according to the thermal relaxation time, the peak temperature rise, the predicted reflectivity change and the predicted thermal relaxation time, a target function is established, comprising:

[0025] According to the peak temperature rise, a reflectivity change is obtained;

[0026] The target function is obtained according to the reflectivity change, the predicted reflectivity change, the thermal relaxation time, the predicted thermal relaxation time and a weight coefficient.

[0027] As a further improvement of the present application, the thermal diffusivity and the local thermal resistance are calculated according to the target function, comprising:

[0028] A first iteration operation is performed according to the target function, the first iteration operation comprising: calculating partial derivatives of the current thermal diffusivity and the current local thermal resistance according to the target function, constructing a Jacobian matrix according to the partial derivatives, obtaining a linear equation set according to the Jacobian matrix and a damping parameter, solving the linear equation set to obtain an update vector, updating the current thermal diffusivity and the current local thermal resistance according to the update vector, calculating a target function value according to the updated thermal diffusivity and the local thermal resistance, and outputting the updated thermal diffusivity and the local thermal resistance in the current iteration number as the thermal diffusivity and the local thermal resistance until the target function value meets a preset termination condition.

[0029] As a further improvement of the present application, the thermal relaxation time and the peak temperature rise are calculated according to the reflected light intensity signal and the spot position signal, comprising:

[0030] An error function is established according to the reflected light intensity signal and the spot position signal;

[0031] The thermal relaxation time is obtained according to the error function;

[0032] A maximum reflectivity change value is obtained according to the reflected light intensity signal;

[0033] The peak temperature rise is obtained according to the maximum reflectivity change value.

[0034] As a further improvement of the present application, the error function is established according to the reflected light intensity signal and the spot position signal, comprising:

[0035] A reflectivity change is obtained according to the reflected light intensity signal, the spot position signal and a proportionality coefficient;

[0036] A temperature change is obtained according to the reflectivity change and a reflectivity temperature coefficient;

[0037] A temperature fitting formula is obtained according to the temperature change;

[0038] The error function is obtained according to the temperature fitting formula.

[0039] As a further improvement of the present application, the thermal relaxation time is obtained according to the error function, comprising:

[0040] A second iterative operation is performed according to the error function, the second iterative operation including: calculating the gradient of the current thermal relaxation time according to the error function, updating the current thermal relaxation time according to the gradient and the learning rate, obtaining a fitting temperature value according to the updated thermal relaxation time and the temperature fitting formula, calculating the error function value according to the fitting temperature value, until the error function value meets a preset convergence condition, and outputting the updated thermal relaxation time in the current iteration number as the thermal relaxation time.

[0041] The present invention provides a multi-physics field coupling test system for thermal stability of micro-nano waveguide devices, comprising:

[0042] Acquisition module: used to obtain multiple sets of pulse data, each set of pulse data corresponds to a pulse energy, and each set of pulse data includes a reflected light intensity signal and a light spot position signal;

[0043] a calculation module for calculating, for each set of pulse data, a thermal relaxation time and a peak temperature rise based on the reflected light intensity signal and the light spot position signal; and obtaining a thermal diffusion coefficient and a local thermal resistance based on the thermal relaxation time, the peak temperature rise, and a thermal diffusion model, wherein the thermal diffusion model includes a hyperbolic heat conduction equation, a near-field surface effect formula, and a far-field Sommerfeld radiation condition;

[0044] An evaluation module is used to obtain thermal stability results of the micro-nano waveguide device under different pulse energies based on the thermal diffusion coefficient and the local thermal resistance.

[0045] The present invention uses nanosecond pulsed lasers and combines them with a high-speed data acquisition card to synchronously record transient changes in reflectivity, thereby capturing nanosecond temperature jumps, meeting the measurement requirements of small-scale structures with fast transient response, and ensuring accurate acquisition of local temperature signals at the micron / nanoscale. In addition, the heat conduction model in the present invention takes into account non-Fourier effects, and compared with the existing Fourier model, it can more accurately describe the time delay and fluctuation characteristics of heat transfer in small-scale structures. At the same time, the heat conduction model in the present invention takes into account surface effects, and compared with the existing technology, it can more realistically reflect the heat transfer characteristics of micro-nano structures, reduce the thermal resistance inversion error, and thus obtain more accurate thermal stability test results. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 Schematic diagram of the system structure of the present invention;

[0047] Figure 2 is a flow chart of the method steps of the present invention;

[0048] Figure 3 is a flowchart of the steps of the second iterative operation in the present invention;

[0049] Figure 4This is a flowchart of the steps of the first iterative operation in the present invention. DETAILED DESCRIPTION

[0050] The technical solution of the present invention is described in detail below through the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present invention and the specific features in the embodiments are detailed descriptions of the technical solution of the present invention, rather than limitations of the technical solution of the present invention.

[0051] Identical components are denoted by the same reference numerals. It should be noted that the terms "front," "rear," "left," "right," "upper," and "lower" used in the following description refer to directions in the accompanying drawings, and the terms "bottom," "top," "inner," and "outer" refer to directions toward or away from the geometric center of a particular component, respectively.

[0052] The term "and / or" in the following text simply describes an association between related objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A exists alone, A and B exist simultaneously, or B exists alone. Additionally, the character " / " generally indicates an "or" relationship between the related objects.

[0053] like Figure 1 As shown, the embodiment of the present application provides a multi-physics field coupling test system for thermal stability of micro-nano waveguide devices, comprising:

[0054] Acquisition module: used to obtain multiple sets of pulse data, each set of pulse data corresponds to a pulse energy, and each set of pulse data includes a reflected light intensity signal and a light spot position signal;

[0055] A calculation module is used to calculate the thermal relaxation time and peak temperature rise for each set of pulse data based on the reflected light intensity signal and the spot position signal; and to obtain the thermal diffusion coefficient and local thermal resistance based on the thermal relaxation time, peak temperature rise, and a thermal diffusion model. The thermal diffusion model includes a hyperbolic heat conduction equation, a near-field surface effect formula, and a far-field Sommerfeld radiation condition.

[0056] Evaluation module for determining the thermal stability of micro-nano waveguide devices at different pulse energies based on thermal diffusivity and local thermal resistance.

[0057] Among them, micro-nano waveguides (such as silicon photonic waveguides, silicon nitride waveguides, and photonic crystal waveguides) are miniature structures used to guide the transmission of light (or electromagnetic waves). Their sizes are in the micron and nanometer levels. Their core function is to confine the light field to an extremely small space through size limitations, thereby achieving efficient transmission, modulation, or coupling of optical signals. The sizes of traditional waveguides (such as metal waveguides and large-core optical fibers) are usually above the millimeter level.

[0058] like Figure 1As shown, the acquisition module, the calculation module and the evaluation module are located in the server, and the system provided by the embodiment further comprises a pulsed laser, an avalanche photodiode (APD), a four-quadrant photodetector (QPD), a lock-in amplifier, a high-speed data acquisition card and a temperature-controlled cavity.

[0059] Specifically, when testing, the pulsed laser is connected with a single-mode optical fiber through a fiber coupler, and the position and angle of the fiber coupler are adjusted to make the laser be coupled into the optical fiber efficiently, the laser is guided to the micro-nano waveguide device clamp through the optical fiber, and the laser is collimated by using a fiber collimator, the focal length and position of the collimator are adjusted to make the laser spot diameter be less than 1 μm and be accurately focused on the surface of the micro-nano waveguide device, wherein the micro-nano waveguide device clamp is used to fix the micro-nano waveguide device, and the micro-nano waveguide device is located in the temperature-controlled cavity, the temperature-controlled cavity needs to be started before testing, and the pulsed laser, the lock-in amplifier and the data acquisition card can be started after the temperature of the temperature-controlled cavity is stable, and the testing is started. Then, a 50 / 50 beam splitter is installed in the direction of the reflected light of the micro-nano waveguide chip clamp, the reflected light is divided into two paths, one path of the reflected light is guided to the APD detector for capturing the rapid change of the reflected light intensity, and the other path of the reflected light is guided to the QPD detector for real-time monitoring of the change of the light spot position, the positions and angles of the beam splitter and the detectors are adjusted to ensure that the reflected light can accurately enter the detectors, and the photosensitive surfaces of the detectors are perpendicular to the reflected light. Then, the output signals of the APD detector and the QPD detector are connected to the input channels of the lock-in amplifier respectively, the output signal of the lock-in amplifier is connected to the analog input channel of the high-speed data acquisition card, the sampling frequency (such as 10 MHz or more) of the data acquisition card is set to ensure that the weak signal output by the lock-in amplifier can be accurately acquired, finally the high-speed data acquisition card is connected with the server through a specific interface (such as USB or PCIe), data analysis is performed through the server, and the thermal stability result of the micro-nano waveguide device is obtained. Preferably, the reference frequency of the lock-in amplifier is set to 1 kHz, and the time constant is set to 10 ms to balance the response speed and noise suppression ability of the signal.

[0060] To obtain the thermal stability results of the micro-nano waveguide device under different pulse energies, multiple sets of pulse tests with different energies need to be performed. For example, the laser pulse energy can be set to 10 nJ, 20 nJ, 30 nJ, 40 nJ, 60 nJ, 70 nJ, 80 nJ, 90 nJ, and 100 nJ in sequence, and 1000 frames of data are collected under each set of energy. After changing the laser energy, the sample temperature is allowed to recover to a stable state for 5-10 minutes before data collection. At the same time, the environmental temperature, laser power, and other parameters of each test are recorded for subsequent data analysis. Based on this, multiple sets of pulse data can be obtained, each set of pulse data corresponding to a pulse energy. Since the APD detector and QPD detector are provided in this embodiment, each set of pulse data includes a reflected light intensity signal and a spot position signal, wherein the reflected light intensity signal is obtained based on the APD detector, and the spot position signal is obtained based on the QPD detector.

[0061] Based on the above system settings, as shown in Figure 2 The embodiment of the present application provides a kind of thermal stability multi-physical field coupling test method of micro-nano waveguide device, comprising:

[0062] Obtain multiple sets of pulse data, each set of pulse data corresponds to a pulse energy, and each set of pulse data includes a reflected light intensity signal and a spot position signal;

[0063] For each set of pulse data, the thermal relaxation time and peak temperature rise are obtained according to the reflected light intensity signal and the spot position signal;

[0064] According to the thermal relaxation time, the peak temperature rise and the thermal diffusion model, the thermal diffusion coefficient and the local thermal resistance are obtained, and the thermal diffusion model includes hyperbolic heat conduction equation, near-field surface effect formula and far-field sommerfeld radiation condition;

[0065] According to the thermal diffusion coefficient and the local thermal resistance, the thermal stability results of the micro-nano waveguide device under different pulse energies are obtained.

[0066] Wherein, the thermal relaxation time refers to the time required for the temperature of the micro-nano waveguide device to drop from the peak value to 63% of the peak value after the action of laser pulse; the peak temperature rise refers to the difference between the maximum value of the temperature rise of the micro-nano waveguide device under the action of laser pulse and the initial temperature; the thermal diffusion coefficient reflects the ability of the micro-nano waveguide device to conduct heat, the larger the thermal diffusion coefficient, the faster the heat diffuses in the micro-nano waveguide device, which means that under the same heat source, the speed of the internal temperature of the device to tend to uniform is faster, and the thermal stability is better; the local thermal resistance represents the degree of hindering heat transfer in a specific region of the micro-nano waveguide device, the larger the local thermal resistance, the more difficult the heat to pass through the region, which will cause heat accumulation in the local area, causing temperature rise, and further affecting the optical performance and structural stability of the waveguide.

[0067] The above steps are repeated for each set of pulse data, i.e., the thermal diffusivity and local thermal resistance of the micro-nano waveguide device under different pulse energies are obtained, and then the values can be compared with the theoretical calculation values, the parameter values of the same micro-nano waveguide in the literature or the pre-set design standard. If the measured value is within a reasonable error range and consistent with the standard or expected value, it indicates that the thermal stability of the micro-nano waveguide device meets the design requirements; if the deviation is large, further analysis is needed to determine whether the thermal stability is not up to standard due to material performance, structure design or experimental error, etc.

[0068] In the embodiment, the thermal relaxation time and the peak temperature rise are obtained from the obtained pulse data, and the accurate thermal diffusivity and local thermal resistance are obtained by combining the thermal diffusion model, so as to accurately evaluate the thermal stability of the micro-nano waveguide device under different pulse energies. Moreover, the waveguide size used in the embodiment is micron and nanometer level, which belongs to small size type compared with traditional waveguide. The thermal diffusion path of the small size waveguide is short and the transient response is fast, and the characteristic size (such as width, thickness and height) of the waveguide structure is usually comparable to or smaller than the average free path of the thermal carriers (such as phonon and electron) in the micro-nano waveguide device. In this case, the thermal wave effect (i.e., the phonon transport has a relaxation time, resulting in thermal flow lagging behind the temperature gradient) is easy to occur. The traditional Fourier model cannot solve the problem that the test result is inaccurate due to the thermal wave effect. In the embodiment, the hyperbolic heat conduction equation is used, and the model can describe the wave characteristics of the thermal flow propagation by considering the thermal relaxation time, so as to avoid the underestimation of the thermal diffusivity by the Fourier model. Meanwhile, the surface effect is considered by the near-field surface effect formula and the far-field Sommerfeld radiation condition in the embodiment, so as to solve the problem that the collision probability of the phonon and the waveguide / substrate interface is significantly increased in the small size structure, resulting in the decrease of the effective thermal diffusivity, and ensure the accuracy of the calculation result.

[0069] Further, the embodiment provides a step of calculating the thermal relaxation time and the peak temperature rise according to the reflected light intensity signal and the light spot position signal, which comprises:

[0070] establishing an error function according to the reflected light intensity signal and the light spot position signal;

[0071] obtaining the thermal relaxation time according to the error function;

[0072] obtaining the maximum reflectivity change value according to the reflected light intensity signal;

[0073] obtaining the peak temperature rise according to the maximum reflectivity change value.

[0074] Further, the embodiment provides a step of establishing an error function according to the reflected light intensity signal and the light spot position signal, which comprises:

[0075] obtaining the reflectivity change according to the reflected light intensity signal, the light spot position signal and the proportional coefficient.

[0076] obtaining the temperature change according to the reflectivity change and the reflectivity temperature coefficient;

[0077] obtaining a temperature fitting formula according to the temperature change;

[0078] obtaining an error function according to the temperature fitting formula.

[0079] Specifically, for the reflected light intensity signal and the light spot position signal, first, the filtering processing is performed on the signals to eliminate the noise in the signals, and then the signal calibration is performed to make the collected signals closer to the true values. Due to the influence of the device precision and the human operation error in the actual operation process, it is difficult to ensure that the light spot position is in a precise and stable state through the focal length and position of the collimator, therefore, the light spot position signal after calibration is used to accurately calculate the offset amount of the light spot compared with the center of the QPD detector, the position of the light spot is detected through the offset amount, and it is ensured that the same position of the waveguide is acted on by each laser pulse.

[0080] If it is found through calculation that the position of the light spot is different from the position of each laser pulse, the position of the light spot needs to be adjusted again, and the data is collected again, otherwise, if the position of each laser pulse is the same, the peak temperature rise is calculated according to the reflected light intensity signal, specifically, the calibrated reflected light intensity signal is first converted into the actual reflectivity change , the light intensity received by the detector is proportional to the reflectivity, assuming that the proportional coefficient is (which can be determined through the calibration experiment), then the reflectivity , wherein represents the calibrated reflected light intensity signal, can be represented as a vector, each element in the vector corresponds to a light intensity signal value, the actual reflectivity change , wherein represents the reference reflectivity when the laser does not irradiate the micro-nano waveguide device, the formula indicates that each element in is subtracted from .

[0081] Since 1000 frames of data are collected under each group of energy in the embodiment, both and the actual reflectivity change contain 1000 elements, each element corresponds to a data point, then the data located in a period of time after the end of the laser pulse (such as 0-1 after the end of the pulse) is intercepted from the actual reflectivity change , as the reflectivity change .

[0082] Then the reflectivity change is converted into the temperature change ,in The reflectivity temperature coefficient of materials used for micro-nano waveguides, such as silicon photonic waveguides, is the reflectivity temperature coefficient of silicon material, which is approximately −0.1% / K.

[0083] Then the reflectivity changes Find the element with the largest value as the maximum reflectivity change value and from Confirmed Corresponding maximum temperature change , and obtain the temperature of the micro-nano waveguide device before turning on the pulse laser, that is, the temperature set in the temperature control room (such as 25 degrees Celsius) , and finally get the peak temperature rise .

[0084] Then calculate the thermal relaxation time. Specifically, after obtaining the temperature change After that, combine You can get 0-1 after the pulse ends Time series of internal temperature changes , using the exponential fitting method, we can fit the temperature fitting formula according to the temperature change data over time: ,in is the peak temperature in the sequence, Indicates time, is the pulse end time, represents the thermal relaxation time. Then set The initial guess value is substituted into the above temperature fitting formula to obtain the 0-1 after the pulse ends. The fitting temperature value corresponding to each time point is then calculated according to the error function: The final thermal relaxation time is obtained, where Indicates 0-1 after the pulse ends The number of time points included in the pulse, that is, 0-1 after the pulse ends The number of data points collected in the The number of elements contained in express Middle The temperature value corresponding to the time point, According to the fitting formula The temperature value corresponding to a time point.

[0085] Further, such as Figure 3 As shown, this embodiment provides a step of obtaining thermal relaxation time according to the error function, including:

[0086] A second iterative operation is performed according to the error function. The second iterative operation includes calculating the gradient of the current thermal relaxation time according to the error function, updating the current thermal relaxation time according to the gradient and the learning rate, obtaining a fitting temperature value according to the updated thermal relaxation time and temperature fitting formula, calculating the error function value according to the fitting temperature value, until the error function value meets the preset convergence condition, and outputting the updated thermal relaxation time in the current iteration number as the thermal relaxation time.

[0087] Specifically, first calculate the gradient of the current thermal relaxation time according to the error function , since direct derivation may be complicated, numerical differentiation methods such as finite difference method can be used in actual calculations: ,in is a small positive number, such as Then according to the gradient and learning rate according to the formula Update the current thermal relaxation time, where Indicates the current iteration number, This is the initial guess value mentioned above, Indicates the learning rate corresponding to the current number of iterations. The updated thermal relaxation time Substitute the above temperature fitting formula to get the current fitting temperature value , and then the error function Replace with And recalculate the error function as , if the error function value Satisfy the preset convergence conditions and output the updated thermal relaxation time in the current iteration number , recorded as thermal relaxation time , used for subsequent calculations.

[0088] Among them, during the iteration process, the learning rate gradually decreases as the number of iterations increases. For example, ,in is the initial learning rate (such as 0.1), The attenuation factor (e.g., 0.99) is used. This embodiment uses a larger learning rate at the beginning of the iteration to speed up the convergence speed, and gradually reduces the learning rate as the iteration proceeds to avoid missing the optimal solution. The preset convergence condition can be determined based on the error function threshold, the parameter change threshold, and the maximum number of iterations. For example, if the error function threshold is set, the convergence condition is the error function value. Less than a pre-set threshold (e.g. ); If the parameter change threshold is set, the update amount of thermal relaxation time is calculated , the convergence condition is Less than a pre-set threshold (e.g. ) ; if according to the maximum iteration number setting, the convergence condition is that the current iteration number reaches the pre-set maximum iteration number.

[0089] The embodiment first sets two detectors, wherein the APD detector focuses on temperature-related information collection, and the QPD detector focuses on spot position monitoring, both of which work together to provide comprehensive and accurate data support for micro-nano waveguide device thermal stability testing, and then 0-1 temperature change data after the end of the pulse is selected, which can cover most of the key stages of the micro-nano waveguide from the temperature peak value, so as to ensure the accuracy of the calculation of the thermal relaxation time and the peak temperature rise.

[0090] Specifically, during the action of the laser pulse, the micro-nano waveguide not only absorbs laser energy to rise in temperature, but also dissipates heat to the surrounding environment, at this time, the temperature change is the result of the joint action of heat absorption and heat dissipation. After the end of the laser pulse, the waveguide mainly enters the heat dissipation stage, at this time, the collected temperature change data can more purely reflect the process of the heat absorbed by the waveguide being dissipated, that is, the heat relaxation process. The thermal relaxation time describes the time required for the temperature to drop from the peak value to a specific proportion (63%), and the heat dissipation data after the end of the pulse is selected, so as to accurately calculate this key parameter reflecting the heat dissipation ability of the waveguide. And the micro-nano waveguide has small size, short heat diffusion path and fast transient response, so the heat relaxation process is usually completed in a very short time.0-1 The time range of 0-1 The data in this time period is selected. In the heat relaxation process, the temperature change with time follows the exponential decay law, and according to the above temperature fitting formula, the amplitude of the temperature drop relative to the peak temperature at this time is 63%.

[0091] Further, the embodiment of the present application provides a step of obtaining the heat diffusion coefficient and the local thermal resistance according to the thermal relaxation time, the peak temperature rise and the heat diffusion model, comprising:

[0092] obtaining the material parameters and the boundary conditions of the micro-nano waveguide, the material parameters including density and specific heat capacity, and the boundary conditions including near-field surface heat transfer coefficient, surface scattering coefficient and far-field wave number;

[0093] obtaining the predicted reflectivity change and the predicted thermal relaxation time according to the material parameters, the boundary conditions and the heat diffusion model;

[0094] ​According to the thermal relaxation time, the peak temperature rise, the predicted reflectivity change, and the predicted thermal relaxation time, a target function is established;

[0095] According to the target function, the thermal diffusivity and the local thermal resistance are calculated.

[0096] Specifically, first, the microstructure of the micro-nano waveguide device is detected by a scanning electron microscope (SEM) to obtain the actual width, height, bending radius, surface roughness, and other size parameters of the waveguide. Then, the ellipsometer is used to measure the absorption rate of the micro-nano waveguide device to laser. Next, by consulting relevant literature or through experimental measurement, the basic physical parameters of the micro-nano waveguide material are obtained, such as density , specific heat capacity , thermal conductivity , etc. For example, for a silicon photon waveguide, , , .

[0097] Next, the thermal relaxation time of the standard sample is measured. The standard sample is determined for the micro-nano waveguide material, for example, for a silicon photon waveguide, the standard sample is a standard silicon wafer. The standard sample is placed in the same environmental conditions as the micro-nano waveguide device in this embodiment, and a pulse heating is applied. The time required for the temperature of the standard sample to rise from the initial value to 63% of the peak value is recorded and substituted into the formula to obtain the surface heat transfer coefficient , where is the thermal diffusivity of the standard sample, represents the perimeter of the sample, represents the surface area of the sample. For example, for a standard silicon wafer, the thermal diffusivity , is the perimeter of the silicon wafer, is the surface area of the silicon wafer. represents the characteristic length, which is a representative length parameter introduced to simplify the analysis of complex heat conduction problems. It can be selected according to the typical size characteristics of the micro-nano waveguide, for example, for a rectangular cross-section micro-nano waveguide, the characteristic length can be the width or height of the waveguide, or according to the specific heat conduction problem, a certain length size that can reflect the main influencing factors of heat conduction is selected, represents the time required for the entire heating process.

[0098] Then, according to the surface roughness and the specific waveguide material, the surface scattering coefficient of the micro-nano waveguide device is estimated, and the far-field wave number is obtained according to the pulse laser wavelength , where the surface scattering coefficient can be estimated based on the phonon scattering model or the diffuse reflection model.

[0099] The embodiment predicts the reflectivity change and the thermal relaxation time according to the material parameters and the boundary conditions through a thermal diffusion model, wherein the thermal diffusion model integrates a hyperbolic heat conduction equation, a near-field surface effect formula and a far-field Sommerfeld radiation condition, not only considers non-Fourier effect and surface effect, but also can simulate the transient thermal response process of the micro-nano waveguide under the action of a laser pulse, and through the steps provided by the embodiment, the thermal diffusion coefficient and the local thermal resistance of the micro-nano waveguide device can be accurately obtained, so as to deeply analyze the heat conduction mechanism inside the micro-nano waveguide, and finally obtain the thermal stability result of the micro-nano waveguide device.

[0100] Further, the embodiment provides a step for obtaining the predicted reflectivity change and the predicted thermal relaxation time according to the material parameters, the boundary conditions and the thermal diffusion model, comprising:

[0101] setting an initial thermal diffusion coefficient and an initial local thermal resistance;

[0102] obtaining a temperature distribution according to the initial thermal diffusion coefficient, the initial local thermal resistance, the material parameters, the boundary conditions and the thermal diffusion model;

[0103] obtaining the predicted reflectivity change and the predicted thermal relaxation time according to the temperature distribution.

[0104] Specifically, the thermal diffusion model in the embodiment comprises:

[0105] a hyperbolic heat conduction equation: , ;

[0106] a near-field surface effect formula: ;

[0107] a far-field Sommerfeld radiation condition: .

[0108] wherein the initial thermal diffusion coefficient and the initial local thermal resistance can be estimated based on the waveguide material, for example, if the material is metal (gold, aluminum), the initial thermal diffusion coefficient should be in the range of , and the initial local thermal resistance should be in the range of , if the material is a semiconductor (such as silicon, silicon carbide), the initial thermal diffusion coefficient should be in the range of , and the initial local thermal resistance should be in the range of .

[0109] In the hyperbolic heat conduction equation, represents the thermal diffusion coefficient of the micro-nano waveguide device, represents the temperature, represents the spatial step, which refers to the distance between adjacent grid nodes after the continuous micro-nano waveguide structure is discretized into a finite number of grid units, and the distance should be smaller than or equal to the order of magnitude of the average free path of phonons, represents the local thermal resistance of the micro-nano waveguide device, Thermal conductivity is related to heat flow and reflects the difficulty of heat conduction along a specific path. It can be determined by the thermal conductivity of the waveguide material. It represents the effective heat transferred to the micro-nano waveguide. When there is local thermal resistance, the volume heat source The generated heat will be lost due to thermal resistance when it is transferred to the waveguide. This loss relationship is reflected in Indicates the thermal resistance's effect on heat transfer, where the volume heat source , represents the absorption rate of the micro-nano waveguide device to the laser, represents the pulsed laser power, represents the spot area, Indicates the length of the discrete unit in the axial direction of the waveguide. The length of the discrete unit in the axial direction of the waveguide refers to the length of each unit after the waveguide axial direction (generally the length direction of the waveguide) is divided into discrete grid units when performing numerical calculations on micro-nano waveguides. In the near-field surface effect formula, Indicates the outer normal direction of the micro-nano waveguide surface. When performing the following discretization, the micro-nano waveguide surface needs to be divided into multiple boundary nodes along the outer normal direction. In the far-field Sommerfeld radiation condition, is the imaginary unit, It represents the distance from the center of the micro-nano waveguide heat source to a point in the far field area, for example, it can be the distance from the center of the micro-nano waveguide heat source to the boundary of the far field area.

[0110] Specifically, the derivative term in the thermal diffusion model ( 、 、 、 、 ) In actual calculation, it is necessary to combine the specific grid division (such as dividing the grid along the length of the waveguide and using the finite difference method to discretize), and calculate according to the temperature value of each grid node at different times. For example, the spatial area of ​​the micro-nano waveguide is divided along Direction is divided into After the grid is evenly spaced, the position of each grid node is recorded as , at the time point , the derivative term It can be expressed as the backward difference formula ,in Indicates the Time point, The discrete temperature value of space steps, denotes the time interval between two adjacent time points, based on which the discretized representation of each derivative term at each time point can be obtained, where the temperature values corresponding to the 0th time point are all the initial temperature, i.e. the temperature set by the temperature control chamber.

[0111] For each time point, the discretized representation of each derivative term at the time point and the material parameters and boundary conditions involved in the heat diffusion model are substituted into the heat diffusion model, so that a nonlinear equation group including the discretized temperature value, the heat diffusion coefficient and the local thermal resistance at the time point can be obtained. After the current heat diffusion coefficient and the current local thermal resistance are substituted, a nonlinear equation group about the discretized temperature value can be obtained. Then, by solving the nonlinear equation group, the specific numerical value of each discretized temperature value at the time point can be obtained. In the process of solving the discretized temperature value, a coefficient matrix can be obtained based on the nonlinear equation group first, and then an iterative algorithm is used to solve based on the coefficient matrix. Specifically, taking the Jacobi iteration method as an example, the coefficient matrix can be decomposed into a diagonal matrix, an upper triangular matrix and a lower triangular matrix, and an iteration formula is constructed. Through continuous iteration, when the difference between the solution vectors obtained by two adjacent iterations meets the preset convergence condition (such as the norm being less than a certain threshold), the iteration is stopped, and the approximate solution of each discretized temperature value is obtained as the specific numerical value of each discretized temperature value. By repeating the above steps at each time point, the specific numerical value of each discretized temperature value at each time point can be obtained, and thus the temperature distribution at the current heat diffusion coefficient and the current local thermal resistance .

[0112] The heat diffusion model is set in this embodiment to accurately describe the heat transfer process in the micro-nano waveguide. First, a hyperbolic heat conduction equation is set, where the second-order time derivative term on the left side of the equation represents the acceleration of heat flow, describes the second-order derivative of temperature with respect to time, and reflects the inertial characteristics of heat flow, solving the problem that the traditional Fourier model (first-order derivative) is applied to this embodiment and the delay of heat flow propagation is ignored, represents the rate of change of temperature with respect to time, reflecting the conventional heat diffusion effect, is the second-order derivative of the temperature gradient along the length direction (x-axis) of the waveguide, reflecting the diffusion of heat flow from the high-temperature region to the low-temperature region, and through this equation, the dynamic change process of temperature with respect to time and space when the micro-nano waveguide is subjected to transient thermal excitation such as pulsed laser can be simulated more accurately. Then, a near-field surface effect formula is set to describe the surface effect caused by the significant increase in the proportion of surface atoms in the micro-nano waveguide, where represents the gradient of temperature along the surface normal, and the negative sign indicates that the direction of heat flow is opposite to that of the temperature gradient, i.e. the heat flow flows from the high-temperature region to the low-temperature region, The formula represents the convective heat transfer between the waveguide surface and the surrounding environment. This formula links the heat conduction inside the waveguide with the convective heat transfer in the external environment, allowing the heat diffusion model to accurately consider the influence of surface effects, thereby more realistically simulating the thermal transfer characteristics of the micro-nano waveguide. The near field refers to the area immediately adjacent to the surface of the micro-nano waveguide structure. The electromagnetic field or thermal field distribution in this area is significantly affected by the geometric shape and material properties of the waveguide structure. The far-field Sommerfeld radiation condition is then used to describe the propagation law of the far-field thermal wave. The far field refers to the area far away from the micro-nano waveguide structure. In this area, the electromagnetic field or thermal field distribution gradually breaks away from the influence of the waveguide structure details and tends to be regularized. Based on this regularization characteristic, the Sommerfeld radiation condition is selected in this embodiment. It assumes that the heat flow propagates only outward without reflection, which conforms to the actual physical phenomenon of far-field thermal wave propagation. It can accurately describe the propagation law of far-field thermal waves while reducing the difficulty of mathematical operations.

[0113] This embodiment establishes a thermal diffusion model by considering non-Fourier effects (referring to a series of special heat transfer phenomena that occur when the heat conduction process no longer satisfies the instantaneous transfer assumption of Fourier's law) and surface effects. By combining three equations, the model completely covers the entire spatial scale of micro-nano waveguide heat transfer, from the waveguide interior to the surface to the far field. This model comprehensively and meticulously describes the entire heat transfer process of the micro-nano waveguide under transient thermal excitation, avoiding model deviations caused by ignoring the characteristics of a certain region, improving the accuracy of the model calculation results, and thus improving the calculation accuracy of the thermal diffusion coefficient and local thermal resistance, thereby accurately evaluating the thermal stability of micro-nano waveguide devices.

[0114] Furthermore, this embodiment provides a step of obtaining a predicted reflectivity change and a predicted thermal relaxation time based on the temperature distribution, including:

[0115] The predicted peak temperature rise is obtained based on the temperature distribution;

[0116] The predicted reflectivity change is obtained based on the predicted peak temperature rise;

[0117] The predicted thermal relaxation time is obtained based on the predicted reflectivity change and the reference reflectivity.

[0118] Specifically, the temperature distribution when the current thermal diffusion coefficient and the current local thermal resistance are obtained Then, select the maximum value from the temperature distribution , based on the maximum value and initial temperature, the predicted peak temperature rise can be obtained According to the relationship between reflectivity and temperature, the change of reflectivity can be predicted. According to the definition of thermal relaxation time, it is the time it takes for the temperature to drop from peak to peak. The time required, so first calculate The value of , then in the temperature distribution, find corresponding time point , and searches backward from the time point to find the first temperature value less than or equal to , and determines the time corresponding to the temperature value , and finally obtains the predicted thermal relaxation time

[0119] Further, the embodiment provides a step of establishing a target function according to the thermal relaxation time, the peak temperature rise, the predicted reflectivity change, and the predicted thermal relaxation time, including:

[0120] obtaining the reflectivity change according to the peak temperature rise;

[0121] obtaining the target function according to the reflectivity change, the predicted reflectivity change, the thermal relaxation time, the predicted thermal relaxation time, and a weight coefficient.

[0122] Specifically, only the maximum reflectivity change value can be obtained according to the peak temperature rise, and the reflectivity change values at other time points need to be calculated according to the actually collected data, and finally the reflectivity change is obtained, or the reflectivity change may be directly calculated based on the actually collected data, and the specific steps are described in detail in the above scheme, and the embodiment will not be repeated here.

[0123] Then, the target function is obtained according to the reflectivity change , the predicted reflectivity change , the thermal relaxation time , the predicted thermal relaxation time , and the weight coefficient, and is:

[0124]

[0125] wherein, and are weight coefficients, and preferably, in order to balance the influence of the reflectivity change and the thermal relaxation time, the weight coefficients are both set to 0.5.

[0126] The embodiment selects two parameters of the thermal relaxation time and the reflectivity change, wherein the thermal relaxation time reflects the heat dissipation speed of the waveguide, the reflectivity change is directly related to the temperature change of the waveguide, and further reflects the thermal response characteristics, and the target function set by the embodiment considers the two key parameters at the same time, and comprehensively evaluates the consistency of the model calculation result and the experimental measurement value.

[0127] Further, as shown in Figure 4 , the embodiment provides a step of calculating the thermal diffusion coefficient and the local thermal resistance according to the target function, including:

[0128] A first iterative operation is performed according to the objective function. The first iterative operation includes calculating partial derivatives of the current thermal diffusion coefficient and the current local thermal resistance according to the objective function, constructing a Jacobian matrix according to the partial derivatives, obtaining a linear equation system according to the Jacobian matrix and a damping parameter, solving the linear equation system to obtain an update vector, updating the current thermal diffusion coefficient and the current local thermal resistance according to the update vector, calculating the objective function value according to the updated thermal diffusion coefficient and the local thermal resistance, until the objective function value meets a preset termination condition, and outputting the updated thermal diffusion coefficient and the local thermal resistance in the current iteration as the thermal diffusion coefficient and the local thermal resistance.

[0129] For example, in the first iteration, the current thermal diffusion coefficient and the current local thermal resistance are the initial thermal diffusion coefficient and the initial local thermal resistance. According to the above analysis, for each time point, the initial thermal diffusion coefficient and the initial local thermal resistance are substituted into the thermal diffusion model, and a nonlinear equation group about discrete temperature values ​​can be obtained. By solving the equation group, the temperature distribution corresponding to the initial thermal diffusion coefficient and the initial local thermal resistance can be obtained. According to the temperature distribution, the predicted reflectivity change and the predicted thermal relaxation time can be obtained. According to the predicted reflectivity change and the predicted thermal relaxation time, the objective function at this time can be obtained. Then the objective function calculates the initial thermal diffusion coefficient and initial local thermal resistance The partial derivative of and In actual calculation, the finite difference method can be used to calculate and obtain , we can also get . Then construct the Jacobian matrix based on the two partial derivatives , and based on the Jacobian matrix and damping parameters Get the matrix ,in is the identity matrix, and then based on the matrix Constructing a system of linear equations , the update vector can be obtained by solving the linear equations , and are the updated amounts of thermal diffusion coefficient and local thermal resistance respectively. According to the updated amount, initial thermal diffusion coefficient and initial local thermal resistance, the updated thermal diffusion coefficient can be obtained as , the updated local thermal resistance is Repeat the above steps to calculate the objective function value based on the updated thermal diffusion coefficient and local thermal resistance. If the objective function value meets the preset termination condition, the updated thermal diffusion coefficient in the current iteration is output. and local thermal resistance , the current thermal diffusion coefficient and the current local thermal resistance are the updated thermal diffusion coefficient and the updated local thermal resistance in the first iteration, and so on. The preset termination condition can be that the objective function value is less than a certain threshold (such as ), or the number of iterations reaches a preset maximum number of iterations.

[0130] The embodiment describes the heat conduction process of the micro-nano waveguide device through the thermal diffusion model. Although the material parameters and the boundary conditions are fully considered in the model, unknown or difficult-to-accurately-describe factors (such as environmental noise and instrument error) still exist. Based on this, the embodiment further sets an objective function, compares the experimental values (reflectivity change, thermal relaxation time) with the calculated values (predicted reflectivity change, predicted thermal relaxation time) based on the objective function, and performs iteration operation based on the comparison (objective function value) to adjust the output result. To some extent, the influence of these factors can be compensated for, the final output result is combined with the theoretical basis and verified by actual data, and the accuracy of the output result is enhanced. Finally, the thermal diffusion coefficient and the local thermal resistance obtained through the embodiment are used to evaluate the thermal stability of the micro-nano waveguide device, so that researchers can understand the thermal performance of the micro-nano waveguide under different working conditions, guide the material selection, structure design and process optimization of the micro-nano waveguide, and effectively solve the performance degradation problem of the micro-nano waveguide caused by thermal effects.

[0131] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer usable program code.

[0132] The present application is described with reference to flowcharts and / or block diagrams according to the method, device (system) and computer program product of the embodiments of the present application. It should be understood that each flow and / or block in the flowchart and / or block diagram, and the combination of the flows and / or blocks in the flowchart and / or block diagram can be realized by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device produce a device that implements the functions specified in the flowchart and / or block diagram. Figure 1 The device that implements the functions specified in one or more flows and / or blocks. Figure 1 The device that implements the functions specified in one or more flows and / or blocks.

[0133] These computer program instructions can also be stored in a computer readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer readable memory produce an article of manufacture including instructions which implement the flow Figure 1 The flow or flows and / or blocks Figure 1 The flow or flows and / or blocks

[0134] The above description is only preferred embodiments of the present application, the protection scope of the present application is not limited to the above-mentioned embodiments, any technical solutions falling within the concept of the present application shall be considered as falling within the protection scope of the present application. It should be noted that, for ordinary skilled in the art, some improvements and refinements without departing from the principles of the present application, these improvements and refinements should also be considered as the protection scope of the present application.

Claims

1. A multi-physics field coupling test method for thermal stability of micro-nano waveguide devices, characterized in that: include: Acquire multiple sets of pulse data, each set of pulse data corresponds to a pulse energy, and each set of pulse data includes a reflected light intensity signal and a light spot position signal; For each set of pulse data, obtaining the thermal relaxation time and the peak temperature rise according to the reflected light intensity signal and the light spot position signal; Obtaining a thermal diffusion coefficient and a local thermal resistance according to the thermal relaxation time, the peak temperature rise, and a thermal diffusion model, wherein the thermal diffusion model includes a hyperbolic heat conduction equation, a near-field surface effect formula, and a far-field Sommerfeld radiation condition; According to the thermal diffusion coefficient and the local thermal resistance, thermal stability results of the micro-nano waveguide device under different pulse energies are obtained.

2. The multi-physics field coupling test method for thermal stability of micro-nano waveguide devices according to claim 1, characterized in that: According to the thermal relaxation time, the peak temperature rise and the thermal diffusion model, the thermal diffusion coefficient and the local thermal resistance are obtained, including: Obtaining material parameters and boundary conditions of the micro-nano waveguide, wherein the material parameters include density and specific heat capacity, and the boundary conditions include near-field surface heat transfer coefficient, surface scattering coefficient, and far-field wave number; Obtaining a predicted reflectivity change and a predicted thermal relaxation time according to the material parameters, the boundary conditions, and the thermal diffusion model; Establishing an objective function based on the thermal relaxation time, the peak temperature rise, the predicted reflectivity change, and the predicted thermal relaxation time; The thermal diffusion coefficient and the local thermal resistance are calculated according to the objective function.

3. The multi-physics field coupling test method for thermal stability of micro-nano waveguide devices according to claim 2, characterized in that: Obtaining a predicted reflectivity change and a predicted thermal relaxation time according to the material parameters, the boundary conditions, and the thermal diffusion model, including: Set the initial thermal diffusivity and initial local thermal resistance; Obtaining a temperature distribution according to the initial thermal diffusivity, the initial local thermal resistance, the material parameters, the boundary conditions, and the thermal diffusion model; The predicted reflectivity change and the predicted thermal relaxation time are obtained according to the temperature distribution.

4. The multi-physics field coupling test method for thermal stability of micro-nano waveguide devices according to claim 3, characterized in that: Obtaining the predicted reflectivity change and the predicted thermal relaxation time according to the temperature distribution, comprising: Obtaining a predicted peak temperature rise based on the temperature distribution; Obtaining the predicted reflectivity change according to the predicted peak temperature rise; The predicted thermal relaxation time is obtained according to the predicted reflectivity change and the reference reflectivity.

5. The multi-physics field coupling test method for thermal stability of micro-nano waveguide devices according to claim 2, characterized in that: Establishing an objective function according to the thermal relaxation time, the peak temperature rise, the predicted reflectivity change, and the predicted thermal relaxation time, including: Obtaining a reflectivity change according to the peak temperature rise; The objective function is obtained according to the reflectivity change, the predicted reflectivity change, the thermal relaxation time, the predicted thermal relaxation time and a weight coefficient.

6. The multi-physics field coupling test method for thermal stability of micro-nano waveguide devices according to claim 2, characterized in that: Calculating the thermal diffusion coefficient and the local thermal resistance according to the objective function includes: A first iterative operation is performed according to the objective function, the first iterative operation including: calculating partial derivatives of a current thermal diffusion coefficient and a current local thermal resistance according to the objective function, constructing a Jacobian matrix according to the partial derivatives, obtaining a linear equation system according to the Jacobian matrix and a damping parameter, solving the linear equation system to obtain an update vector, updating the current thermal diffusion coefficient and the current local thermal resistance according to the update vector, calculating an objective function value according to the updated thermal diffusion coefficient and the local thermal resistance, until the objective function value satisfies a preset termination condition, and outputting the updated thermal diffusion coefficient and the local thermal resistance in the current iteration as the thermal diffusion coefficient and the local thermal resistance.

7. The multi-physics field coupling test method for thermal stability of micro-nano waveguide devices according to claim 1, characterized in that: Calculating the thermal relaxation time and the peak temperature rise according to the reflected light intensity signal and the light spot position signal includes: Establishing an error function according to the reflected light intensity signal and the light spot position signal; Obtaining the thermal relaxation time according to the error function; The maximum reflectivity change value is obtained according to the reflected light intensity signal; The peak temperature rise is obtained according to the maximum reflectivity change value.

8. The multi-physics field coupling test method for thermal stability of micro-nano waveguide devices according to claim 7, characterized in that: Establishing an error function according to the reflected light intensity signal and the light spot position signal, comprising: Obtaining a reflectivity change according to the reflected light intensity signal, the light spot position signal and a proportional coefficient; Obtaining a temperature change according to the reflectivity change and the reflectivity temperature coefficient; Obtaining a temperature fitting formula according to the temperature change; The error function is obtained according to the temperature fitting formula.

9. The multi-physics field coupling test method for thermal stability of micro-nano waveguide devices according to claim 8, characterized in that: Obtaining the thermal relaxation time according to the error function includes: A second iterative operation is performed according to the error function, the second iterative operation including: calculating the gradient of the current thermal relaxation time according to the error function, updating the current thermal relaxation time according to the gradient and the learning rate, obtaining a fitting temperature value according to the updated thermal relaxation time and the temperature fitting formula, calculating the error function value according to the fitting temperature value, until the error function value meets a preset convergence condition, and outputting the updated thermal relaxation time in the current iteration number as the thermal relaxation time.

10. A multi-physics field coupling test system for thermal stability of micro-nano waveguide devices, characterized in that: include: Acquisition module: used to obtain multiple sets of pulse data, each set of pulse data corresponds to a pulse energy, and each set of pulse data includes a reflected light intensity signal and a light spot position signal; a calculation module for calculating, for each set of pulse data, a thermal relaxation time and a peak temperature rise based on the reflected light intensity signal and the light spot position signal; and obtaining a thermal diffusion coefficient and a local thermal resistance based on the thermal relaxation time, the peak temperature rise, and a thermal diffusion model, wherein the thermal diffusion model includes a hyperbolic heat conduction equation, a near-field surface effect formula, and a far-field Sommerfeld radiation condition; An evaluation module is used to obtain thermal stability results of the micro-nano waveguide device under different pulse energies based on the thermal diffusion coefficient and the local thermal resistance.

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