Test method, device and equipment of LED driving chip and storage medium

Abstract

The invention relates to the technical field of chip testing, and discloses an LED driving chip testing method, device and equipment and a storage medium, and the method comprises the steps: carrying out the multi-point temperature excitation and constant-current output synchronous collection of an LED driving chip, and obtaining electrothermal coupling test data; performing thermal resistance network decomposition on the temperature field in the chip to obtain temperature distribution characteristic parameters; performing nonlinear function fitting on the temperature-constant current coupling relation to obtain an electrothermal coupling transfer coefficient; performing four-dimensional coupling calculation on the temperature distribution characteristic parameter and the electrothermal coupling transfer coefficient to obtain four-dimensional coupling response characteristic data; and partial differential sensitivity calculation is carried out based on the four-dimensional coupling response characteristic data, and an electrothermal coupling sensitivity analysis result is obtained.According to the method, the problem of test errors caused by time desynchrony in a traditional separated temperature-current test is solved, quantitative sensitivity analysis of electrothermal coupling parameters is achieved, and the sensitivity of the electrothermal coupling parameters is improved. Therefore, the test accuracy of the LED driving chip is improved.

Description

Technical Field

[0001] The present invention relates to the field of chip testing technology, and in particular to a testing method, device, equipment and storage medium for an LED driver chip. Background Art

[0002] Electrothermally coupled LED driver chips are widely used in temperature-sensitive applications such as high-power lighting, automotive displays, and industrial control. Unlike conventional LED driver chips, these chips integrate temperature compensation circuitry and thermal feedback mechanisms, tightly coupling their constant-current output characteristics with the chip's internal temperature distribution. Temperature fluctuations can lead to complex, nonlinear constant-current drift. Traditional testing methods struggle to accurately distinguish between variations in constant-current accuracy caused by multiple factors, including thermally induced resistance changes, temperature compensation circuit response, and PWM modulation frequency drift. Furthermore, mismatches between the temperature excitation frequency and the current regulation response frequency can lead to distortion in coupling effect testing.

[0003] Currently, research on constant current drift testing under electrothermal synergy is limited. Most studies rely on static current testing methods based on a single temperature point, which ignores the dynamic interaction between the temperature and current fields in electrothermally coupled chips. Traditional testing methods often result in a pseudo-linear temperature-constant current response curve, which fails to accurately reflect the actual operating state of electrothermally coupled chips, thus affecting the accuracy of chip performance evaluation and optimization design. Summary of the Invention

[0004] The present invention provides a testing method, apparatus, device and storage medium for an LED driver chip. The present invention solves the problem of test errors caused by time asynchrony in traditional separate temperature-current testing, realizes quantitative sensitivity analysis of electrothermal coupling parameters, and thereby improves the accuracy of LED driver chip testing.

[0005] In a first aspect, the present invention provides a method for testing an LED driver chip, the method comprising: Perform multi-point temperature excitation and constant current output synchronous acquisition on LED driver chips to obtain electrothermal coupling test data; Performing thermal resistance network decomposition on the internal temperature field of the chip according to the electrothermal coupling test data to obtain temperature distribution characteristic parameters; Performing nonlinear function fitting on the temperature-constant current coupling relationship according to the electrothermal coupling test data and the temperature distribution characteristic parameters to obtain an electrothermal coupling transfer coefficient; Performing four-dimensional coupling calculation on the temperature distribution characteristic parameters and the electrothermal coupling transfer coefficient to obtain four-dimensional coupling response characteristic data; A partial differential sensitivity calculation is performed based on the four-dimensional coupling response characteristic data to obtain an electrothermal coupling sensitivity analysis result.

[0006] In combination with the first aspect, in a first implementation of the first aspect of the present invention, the step of performing multi-point temperature excitation and constant current output synchronous acquisition on the LED driver chip to obtain electrothermal coupling test data includes: Arrange a multi-point thermocouple sensor array and a high-precision current sensor on the surface of the LED driver chip to create an electrothermal coupling test system; Based on the electrothermal coupling test system, the LED driver chip is subjected to temperature excitation including linear temperature increase, step temperature increase, sinusoidal temperature change and random temperature fluctuation to obtain a multi-mode temperature excitation signal; Performing synchronous acquisition of constant current output of the electrothermal coupling test system according to the multi-mode temperature excitation signal to obtain temperature response data and constant current output response data; Electrothermal coupling feature recognition is performed on the temperature response data and the constant current output response data to obtain electrothermal coupling test data.

[0007] In combination with the first aspect, in a second implementation of the first aspect of the present invention, performing a thermal resistance network decomposition on the internal temperature field of the chip according to the electrothermal coupling test data to obtain temperature distribution characteristic parameters includes: According to the electrothermal coupling test data, the internal space of the LED driver chip is grid-divided to obtain a thermal resistance network unit matrix including a plurality of thermal resistance units; Calculating the instantaneous power consumption and heat diffusion Green's function of each thermal resistance unit according to the thermal resistance network unit matrix to obtain basic parameters of the thermal resistance network; Based on the basic parameters of the thermal resistance network, a coupled calculation is performed on the power consumption term of the temperature compensation circuit and the duty cycle change of the thermal feedback regulation mechanism to obtain an electrothermal coupling correction parameter; The temperature field distribution calculation is performed on the thermal resistance network basic parameters and the electrothermal coupling correction parameters to obtain temperature distribution characteristic parameters.

[0008] In combination with the first aspect, in a third implementation of the first aspect of the present invention, performing nonlinear function fitting on the temperature-constant current coupling relationship according to the electrothermal coupling test data and the temperature distribution characteristic parameters to obtain the electrothermal coupling transfer coefficient includes: Separately calculating the temperature compensation circuit response delay term, the resistance temperature coefficient term, and the PWM duty cycle change term based on the electrothermal coupling test data and the temperature distribution characteristic parameters to obtain a transfer function basic component; performing an integration operation on the time domain response relationship between the temperature change and the constant current output according to the basic component of the transfer function to obtain constant current drift time domain response data; The constant current drift time domain response data is input into a radial basis function for nonlinear fitting to obtain radial basis function fitting parameters, and the radial basis function fitting parameters are solved for weight coefficients using a least squares method to obtain an electrothermal coupling transfer coefficient.

[0009] In combination with the first aspect, in a fourth implementation of the first aspect of the present invention, performing four-dimensional coupling calculation on the temperature distribution characteristic parameter and the electrothermal coupling transfer coefficient to obtain four-dimensional coupling response characteristic data includes: Constructing a four-dimensional coupling response function based on the temperature distribution characteristic parameters and the electrothermal coupling transfer coefficient; Calculating the nonlinear temperature coefficient of the coupling relationship between the resistance field and the temperature field according to the four-dimensional coupling response function to obtain resistance-temperature coupling relationship data; Using a cross-correlation function to time-align the resistance-temperature coupling relationship data, and obtain four-dimensional response time alignment parameters including temperature field change delay, resistance field change delay, temperature compensation circuit response delay, and constant current output adjustment delay; A synchronous response analysis is performed on the four-dimensional response time alignment parameters to obtain four-dimensional coupled response characteristic data including transient temperature response, transient resistance response, compensation voltage response and constant current output response.

[0010] In combination with the first aspect, in a fifth implementation of the first aspect of the present invention, constructing a four-dimensional coupling response function based on the temperature distribution characteristic parameter and the electrothermal coupling transfer coefficient includes: Based on the temperature distribution characteristic parameters, the instantaneous temperature at each coordinate position inside the chip is spatially distributed to obtain an instantaneous temperature component function including an ambient temperature reference value, an instantaneous power consumption incentive term, and an external ambient temperature change term; Calculating the nonlinear temperature dependence of the instantaneous thermal resistance at each coordinate position according to the electrothermal coupling transfer coefficient to obtain an instantaneous thermal resistance component function including a nominal thermal resistance value, a first order temperature coefficient term, a second order temperature coefficient term, and a third order temperature coefficient term; Inputting the temperature distribution characteristic parameter and the electrothermal coupling transfer coefficient into the temperature compensation circuit transfer algorithm to adjust the reference voltage, thereby obtaining a compensation voltage component function including a temperature detection value, a reference temperature deviation, and a compensation gain coefficient; performing a coupled response analysis on the constant current output based on the temperature distribution characteristic parameter and the electrothermal coupling transfer coefficient to obtain a constant current output component function including a nominal constant current value, a temperature drift correction term, and a compensation circuit adjustment term; A four-dimensional coupling response function is constructed according to the instantaneous temperature component function, the instantaneous thermal resistance component function, the compensation voltage component function and the constant current output component function.

[0011] In combination with the first aspect, in a sixth implementation of the first aspect of the present invention, performing partial differential sensitivity calculation based on the four-dimensional coupling response characteristic data to obtain an electrothermal coupling sensitivity analysis result includes: Performing partial differential operations on the constant current output with respect to temperature, thermal resistance, power consumption, and PWM frequency according to the four-dimensional coupling response characteristic data to obtain an electrothermal coupling sensitivity matrix including a temperature sensitivity coefficient, a thermal resistance sensitivity coefficient, a power consumption sensitivity coefficient, and a frequency sensitivity coefficient; Constructing an objective function including a reciprocal term of a nominal constant current value, a temperature partial differential term, and a temperature variation product term based on the electrothermal coupling sensitivity matrix; Establishing electrothermal coupling optimization constraint conditions including maximum temperature gradient constraint and Jacobian matrix eigenvalue constraint according to the objective function; A constrained optimization solution is performed based on the objective function and the electrothermal coupling optimization constraint conditions to obtain an electrothermal coupling sensitivity analysis result including an optimal electrothermal coupling parameter combination and a minimum constant current drift value.

[0012] In a second aspect, the present invention provides a test device for an LED driver chip, the test device for the LED driver chip comprising: Synchronous acquisition module, used to perform multi-point temperature excitation and constant current output synchronous acquisition of LED driver chip to obtain electrothermal coupling test data; a thermal resistance network decomposition module, configured to perform thermal resistance network decomposition on the internal temperature field of the chip according to the electrothermal coupling test data to obtain temperature distribution characteristic parameters; a nonlinear function fitting module, configured to perform nonlinear function fitting on the temperature-constant current coupling relationship according to the electrothermal coupling test data and the temperature distribution characteristic parameters, to obtain an electrothermal coupling transfer coefficient; A four-dimensional coupling calculation module is used to perform four-dimensional coupling calculation on the temperature distribution characteristic parameters and the electrothermal coupling transfer coefficient to obtain four-dimensional coupling response characteristic data; The sensitivity calculation module is used to perform partial differential sensitivity calculation based on the four-dimensional coupling response characteristic data to obtain an electrothermal coupling sensitivity analysis result.

[0013] A third aspect of the present invention provides a computer device comprising: a memory and at least one processor, wherein the memory stores instructions; the at least one processor calls the instructions in the memory so that the computer device executes the above-mentioned LED driver chip testing method.

[0014] 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 above-mentioned method for testing the LED driver chip.

[0015] In the technical solution provided by the present invention, nanosecond-level time synchronization acquisition of temperature field and current field is achieved through the arrangement of multi-point thermocouple sensor arrays and the parallel configuration of high-precision current sensors, which solves the test error problem caused by time asynchrony in traditional separate temperature-current testing. Four excitation modes, namely linear temperature rise, step temperature rise, sinusoidal temperature change and random temperature fluctuation, are adopted to cover the multi-mode excitation of the entire temperature range, which can fully stimulate various response modes of the electrothermal coupling chip and avoid the limitations of a single excitation mode. The grid processing method of dividing the interior of the chip into N×M thermal resistance units, combined with the calculation of the heat diffusion Green's function, realizes the accurate modeling of the complex temperature field distribution inside the chip, breaking through the technical limitations of the traditional single heat capacity simplified model. An implicit electrothermal coupling transfer function is established that takes into account the response delay of the temperature compensation circuit, the resistance temperature coefficient and the change of the PWM duty cycle. The nonlinear fitting is performed by the radial basis function algorithm to accurately capture the nonlinear characteristics of the constant current drift under the electrothermal coupling condition. A four-dimensional dynamic response model of temperature field, resistance field, and constant current output was constructed. Time alignment was achieved through a cross-correlation function algorithm, resolving the technical challenge of traditional test methods' inability to simultaneously analyze the dynamic relationships of multiple coupling parameters. Through partial differential sensitivity calculation and Lagrange multiplier-constrained optimization, quantitative sensitivity analysis of electrothermal coupling parameters was achieved, thereby improving the accuracy of LED driver chip testing. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0017] Figure 1 Schematic diagram of the steps of a method for testing an LED driver chip according to an embodiment of the present invention; Figure 2 Schematic diagram of the structure of the test device of the LED driver chip in an embodiment of the present invention; Figure 3 It is a schematic block diagram of the structure of a computer device in an embodiment of the present invention. DETAILED DESCRIPTION

[0018] Embodiments of the present invention provide a method, apparatus, device, and storage medium for testing an LED driver chip. The terms "first," "second," "third," "fourth," and so on (if any) in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or precedence. It should be understood that the numbers used in this way are interchangeable where appropriate, so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "including" or "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units that are not explicitly listed or that are inherent to these processes, methods, products, or apparatus.

[0019] For ease of understanding, the specific process of the embodiment of the present invention is described below. Figure 1 An embodiment of a method for testing an LED driver chip according to an embodiment of the present invention includes: Step S1, performing multi-point temperature excitation and constant current output synchronous acquisition on the LED driver chip to obtain electrothermal coupling test data; It is understandable that the execution subject of the present invention can be a test device for an LED driver chip, or a terminal or a server, which is not limited here. The embodiment of the present invention is described by taking a server as the execution subject as an example.

[0020] Specifically, a multi-point thermocouple sensor array and a high-precision current sensor are arranged on the surface of the LED driver chip to establish an electrothermal coupling test system. At least 9 thermocouple sensors are evenly distributed on the chip surface to form a 3×3 array layout. The sensor has high-precision temperature measurement capabilities with an accuracy controlled within 0.1°C and a high-frequency sampling capability of 1kHz, ensuring that subtle and rapid temperature changes during chip operation can be captured. At the same time, a high-precision current sensor is connected in parallel to the constant current output end of the chip, with an accuracy of 1μA level, and the temperature and current sampling signals are synchronized at the nanosecond level in time to avoid additional errors due to sampling timing mismatch and ensure that the causal relationship between temperature changes and current responses is accurately reflected. Based on the established electrothermal coupling test system, multiple modes of temperature excitation signals are applied to the LED driver chip. A programmable temperature control device was used to apply temperature excitation patterns to the chip, including linear ramp, step ramp, sinusoidal temperature change, and random temperature fluctuation. The linear ramp process had a temperature ramp rate adjustable between 0.1°C / s and 10°C / s. The step ramp simulated a sudden thermal shock environment. The sinusoidal temperature change was used to test the chip's dynamic response to periodic temperature perturbations. The random temperature fluctuation simulated the complex and variable thermal environments encountered in actual use. The temperature excitation signals of various modes covered the full temperature range of -40°C to +125°C, ensuring the test results were widely applicable and representative of engineering applications. During the temperature excitation process, the test system simultaneously acquired data on the chip's temperature response and constant current output. Specifically, the temperature values of each sensor point on the chip and the corresponding constant current output data were collected at each sampling moment. This data was recorded using a high-speed data acquisition module, generating high-resolution temperature and constant current response data sequences. To ensure data reliability, the test system underwent rigorous calibration before acquisition, including zero-point calibration of the temperature sensor and sensitivity adjustment of the current sensor. After data acquisition is completed, the electrothermal coupling feature identification phase begins, where the temperature response data and constant current output response data are jointly analyzed. The temperature and current data are preprocessed, including noise removal, signal smoothing, and baseline correction. Based on the dynamic correlation between the data, key electrothermal coupling feature parameters are extracted, such as the response delay between the temperature change rate and the current drift rate, and the nonlinear relationship between the temperature and current change amplitudes. A cross-correlation function is used to analyze the time delay relationship between the two sets of data, or a frequency domain transform such as a discrete Fourier transform or a discrete electrothermal coupling transform is used to analyze the main frequency components of the temperature and current signals in the frequency domain. The main coupling mode in which temperature changes cause changes in the constant current output is identified, ultimately obtaining the electrothermal coupling test data.

[0021] Step S2: Decomposing the internal temperature field of the chip by a thermal resistance network according to the electrothermal coupling test data to obtain temperature distribution characteristic parameters; Specifically, the chip's internal spatial grid is divided based on electrothermal coupling test data. Considering the significant spatial non-uniformity of the temperature distribution within the LED driver chip, a regular gridding strategy is adopted, dividing the chip into N×M grid cells. Each grid cell is defined as an independent thermal resistance unit. A thermal resistance network unit matrix containing multiple thermal resistance units is constructed through discretization. Based on the thermal resistance network unit matrix, the instantaneous power consumption of each thermal resistance unit is calculated. This calculation is based on the product of the chip's current output and operating voltage at a specific moment, while also accounting for the impact of heat loss and local effects on the power consumption distribution. This calculation is then combined with the thermal diffusion Green's function, which describes the temperature response of a point heat source propagating across the two-dimensional surface of the chip. Taking into account the thermal diffusivity of the chip material and boundary conditions, an appropriate Green's function form is selected to accurately model the heat diffusion process and determine the temperature response contribution of each thermal resistance unit. This process generates a set of basic parameters that characterize the physical properties of the thermal resistance network, including the equivalent thermal resistance value, power consumption distribution characteristics, and thermal diffusion influence coefficient of each unit. In an electrothermally coupled LED driver chip, the temperature field distribution is determined not only by instantaneous power consumption and heat diffusion but also by power consumption variations in the temperature compensation circuit and the thermal feedback mechanism. The temperature compensation circuit dynamically adjusts the internal reference voltage based on local temperature fluctuations, causing internal power consumption to vary with temperature fluctuations. This variation exhibits nonlinear characteristics. To accurately describe this nonlinear effect, a temperature compensation circuit power consumption term is introduced based on the basic parameters of the thermal resistance network and modeled using the quadratic function characteristic of temperature deviation from the reference temperature. Furthermore, a thermal feedback mechanism is implemented within the chip to dynamically balance chip power consumption with heat dissipation load by adjusting the PWM modulation duty cycle. This mechanism calculates the PWM duty cycle variation based on the current temperature state and establishes a correction equation for the duty cycle variation with temperature. These two components form the electrothermal coupling correction parameter, which comprehensively accounts for the dual effects of temperature on internal circuit power consumption and PWM duty cycle regulation. The temperature field distribution is calculated using the basic parameters of the thermal resistance network and the electrothermal coupling correction parameter. By superimposing the local temperature rises calculated from the instantaneous power consumption of each thermal resistance unit using the heat diffusion Green's function, combined with the temperature compensation power consumption term and the PWM modulation change correction term, the temperature value of each grid cell within the chip at each moment is accumulated, thus constructing a three-dimensional time-space temperature distribution field. This temperature field reflects the temperature rise caused by the power consumption distribution within the chip and can capture the temperature variation characteristics caused by the dynamic response of the compensation circuit and PWM modulation. By extracting features from the temperature field data, temperature distribution characteristic parameters are obtained. These parameters include the local maximum temperature of the chip, the temperature uniformity index, the temperature gradient extreme value, and the spatiotemporal rate of change of the temperature field.

[0022] Step S3, performing nonlinear function fitting on the temperature-constant current coupling relationship according to the electrothermal coupling test data and the temperature distribution characteristic parameters to obtain the electrothermal coupling transfer coefficient; Specifically, the complex electrothermal response mechanism within the chip is thoroughly separated and modeled based on electrothermal coupling test data and temperature distribution characteristic parameters. Three core influencing terms are extracted from the overall electrothermal response: the temperature compensation circuit response delay term, the resistance temperature coefficient term, and the PWM duty cycle variation term. The temperature compensation circuit response delay term primarily reflects the dynamic hysteresis between temperature changes and the compensation circuit's regulated output. Frequency domain analysis or time domain convolution methods are required to extract the response delay time constant. The resistance temperature coefficient term reveals the nonlinear growth characteristics of the resistance of the chip's metal interconnects and active devices with temperature changes. First-order and second-order temperature coefficients are extracted by combining high-order fitting of the resistance-temperature relationship from actual testing. The PWM duty cycle variation term reflects the dynamic adjustment of the pulse-width modulation signal duty cycle by the chip's internal temperature feedback mechanism. Considering the direct modulation of the duty cycle on the constant current output, this term is modeled based on the sensitivity coefficient between temperature changes and duty cycle changes. After completing the separation and calculation of the basic components described above, the time domain response relationship between temperature changes and the constant current output is integrated based on the extracted basic components of the transfer function. Using temperature variation as the input stimulus signal, the dynamic response of the constant current output is calculated using each component in the transfer function. Taking into account the time lag and nonlinear coupling characteristics of each component, a convolution integral is performed to obtain complete constant current drift time-domain response data. This response data reflects the constant current output drift caused by temperature perturbations during actual chip operation and exhibits strong time dependence and nonlinear characteristics. This constant current drift time-domain response data is then input into a radial basis function network for nonlinear fitting. Radial basis functions, particularly Gaussian radial basis functions, capture local variations in the input space and are therefore well-suited for fitting the nonlinear drift patterns inherent in the temperature-constant current relationship. During the radial basis function fitting process, the response range and shape of each basis function are controlled by setting the center vector and width parameters, thereby ensuring that the entire function network covers the characteristic distribution of the constant current drift data. After the initial fitting is complete, the weight coefficients of the radial basis function fitting parameters are calculated using the least squares method. By constructing an objective function that minimizes the sum of squared errors between the predicted drift and the actual test data, the weight parameters corresponding to each basis function are optimized to ensure that the fitting result achieves the best approximation across the entire dataset. Through the fitting of the radial basis function and the least squares optimization, a set of electrothermal coupling transfer coefficients that accurately characterize the nonlinear coupling relationship between temperature change and constant current output is obtained.

[0023] Step S4, performing four-dimensional coupling calculation on the temperature distribution characteristic parameters and the electrothermal coupling transfer coefficient to obtain four-dimensional coupling response characteristic data; Specifically, a four-dimensional coupling response function is constructed based on temperature distribution characteristic parameters and electrothermal coupling transfer coefficients. This response function uses four physical quantities—temperature field T(x,y,t), resistance field R(x,y,t), temperature compensation voltage V(t), and constant current output I(t)—as core variables. By establishing a multivariable nonlinear coupling mapping relationship and taking the instantaneous power consumption stimulus and ambient temperature as inputs, it comprehensively describes the time-varying coupling dynamics of various physical quantities within the chip. The four-dimensional response function considers both the direct effect of temperature on resistance and the indirect effect of resistance change on voltage regulation. It also integrates the dynamic regulation characteristics of the temperature compensation circuit and the drift effect of the constant current output due to changes in internal temperature and resistance state within a single modeling framework, ensuring an accurate characterization of the chip's electrothermal response mechanism under real-world operating conditions. The nonlinear temperature coefficient of the coupling relationship between the resistance and temperature fields is calculated based on the four-dimensional response function. Because material resistance exhibits a nonlinear growth trend with increasing temperature, particularly exhibiting significant quadratic or even cubic nonlinearity in high-temperature regions, the first-, second-, and third-order temperature coefficients of resistance as it changes with temperature are calculated using the temperature distribution characteristic parameters to establish a data matrix for the resistance-temperature coupling relationship. This matrix not only contains the initial resistance value of each spatial cell and its dynamic evolution with temperature, but also accounts for practical factors such as material inhomogeneity and local resistance deviations introduced by the manufacturing process. This ensures that the resistance-temperature coupling relationship has greater physical realism and engineering applicability. After establishing the resistance-temperature coupling relationship data, a cross-correlation function is used to time-align the multidimensional data. Because the four processes—temperature change, resistance response, temperature compensation voltage adjustment, and constant current output adjustment—have different response speeds and delay characteristics, directly comparing their timing data would introduce significant errors and time domain offsets. Therefore, a cross-correlation function is used to calculate the optimal time delay between each pair of physical quantities. Specifically, the time offset that maximizes the cross-correlation function value between the two signal groups is found. Specifically, the temperature field change delay, resistance field change delay, temperature compensation circuit response delay, and constant current output adjustment delay are calculated separately to obtain a set of four-dimensional response time alignment parameters. Time alignment ensures that different physical quantities have a unified time base when analyzed, eliminating time domain misalignment caused by different response speeds, ensuring accurate extraction of coupling features and the effectiveness of subsequent analysis. A synchronous response analysis is performed on the four-dimensional response time alignment parameters. The aligned temperature field, resistance field, compensation voltage, and constant current output data are jointly processed to extract the transient response characteristics of each physical quantity at each moment. The transient temperature response reflects the dynamic process of local heat accumulation and heat diffusion in the chip, the transient resistance response reveals the real-time changes in the material's conductivity with thermal changes, the compensation voltage response captures the dynamic adjustment amplitude and speed of the temperature compensation circuit at different temperatures, and the constant current output response reflects the stability and drift behavior of the chip's constant current output capability in a complex electrothermal coupling environment.These four responses are analyzed synchronously and combined together to finally obtain four-dimensional coupled response characteristic data including transient temperature response, transient resistance response, compensation voltage response and constant current output response.

[0024] Based on the temperature distribution characteristic parameters, an instantaneous temperature component function is constructed for each discrete coordinate location (x, y) within the chip by solving a spatial temperature distribution model that includes an ambient temperature reference value, an instantaneous power consumption excitation term, and an external ambient temperature variation term. This function dynamically captures the temperature variations at each coordinate point on or within the chip. The ambient temperature reference value provides an initial reference, the instantaneous power consumption excitation term accounts for the local heating effects caused by the internal power supply, and the external ambient temperature variation term introduces the impact of external thermal disturbances on the overall temperature field, ensuring that the temperature distribution model accurately reflects the complex thermal dynamics of the actual operating environment. Based on the electrothermal coupling transfer coefficient, the nonlinear temperature dependence of the instantaneous thermal resistance at each coordinate location on the chip is calculated. Considering that the material resistance characteristics do not vary linearly under different temperature conditions, a nonlinear correction is introduced, including the nominal thermal resistance value, the first-order temperature coefficient term, the second-order temperature coefficient term, and the third-order temperature coefficient term, to form the instantaneous thermal resistance component function. The nominal thermal resistance value serves as a benchmark, reflecting the initial thermal resistance performance of the chip material under standard temperature conditions. The primary, secondary, and tertiary temperature coefficients, respectively, describe the linear, quadratic, and cubic nonlinear gain effects caused by increasing temperature. This makes the thermal resistance versus temperature curve more consistent with the material's actual thermal response characteristics, particularly under high temperatures or rapid temperature fluctuations, and more accurately reflects the thermal resistance variation pattern. The information based on the temperature distribution characteristic parameters and the electrothermal coupling transfer coefficient is input into the temperature compensation circuit transfer algorithm to adjust the reference voltage. The temperature value detected by the chip's internal temperature sensor module is compared with the set reference temperature to calculate the reference temperature deviation. This is then combined with the compensation gain coefficient to form a compensation voltage component function. The compensation voltage component function dynamically adjusts the internal reference voltage to counteract electrical characteristic drift caused by temperature changes, ensuring the stability of the chip's output current. This compensation mechanism enables the LED driver chip to maintain a constant current output close to the nominal value even under large temperature fluctuations or extreme thermal environments, effectively improving the system's robustness and reliability. The coupled response of the constant current output is analyzed based on the temperature distribution characteristic parameters and the electrothermal coupling transfer coefficient. By introducing the nominal constant current output value, superimposing the temperature drift correction term and the compensation circuit adjustment term, a constant current output component function is formed. The nominal constant current value defines the chip's target output under standard operating conditions, while the temperature drift correction term dynamically adjusts based on real-time temperature changes and the resulting thermal resistance drift. The compensation circuit adjustment term adjusts the output using the aforementioned compensation voltage component function, forming a multi-correction mechanism that works together to affect the constant current output. By integrating the instantaneous temperature component function, instantaneous thermal resistance component function, compensation voltage component function, and constant current output component function, a four-dimensional coupled response function is constructed through mathematical coupling relationships. This function outputs the chip's transient response characteristics under electrothermal coupling at given time and space coordinates, describing the complex dynamic relationship between the temperature field, resistance field, compensation voltage, and constant current output.

[0025] Step S5: performing partial differential sensitivity calculation based on the four-dimensional coupling response characteristic data to obtain an electrothermal coupling sensitivity analysis result.

[0026] Specifically, mathematical processing is performed on the four-dimensional coupled response characteristic data. Taking the constant current output as the research object, partial differential operations are performed with respect to four key physical variables: temperature, thermal resistance, power consumption, and PWM frequency. By taking the derivatives of the local rates of change of these variables, the sensitivity of the constant current output to each influencing factor is quantified. This results in the construction of an electrothermal coupling sensitivity matrix, which includes the temperature sensitivity coefficient, thermal resistance sensitivity coefficient, power consumption sensitivity coefficient, and frequency sensitivity coefficient. Based on this electrothermal coupling sensitivity matrix, an objective function is constructed to quantify the overall system's response to temperature changes, which serves as the basis for optimization. The objective function consists of three components: the reciprocal of the nominal constant current value, which reflects the baseline output current; the temperature partial differential term, which directly reflects the sensitivity of the constant current output to temperature perturbations; and the temperature variation product term, which measures the magnitude of the impact of temperature changes in actual applications. By combining these three components, the degree of constant current output deviation caused by temperature changes under different operating conditions is effectively characterized, providing a mathematically quantified evaluation metric for sensitivity optimization. Based on the objective function, electrothermal coupling optimization constraints are established, including maximum temperature gradient constraints and Jacobian matrix eigenvalue constraints. The maximum temperature gradient constraint is set to prevent reliability issues caused by local overheating within the chip and ensure that the maximum temperature gradient along any direction in the temperature field distribution does not exceed the set safety threshold, thereby ensuring thermal management performance. Based on the Jacobian matrix eigenvalue constraints, the overall stability of the system is restricted. The Jacobian matrix eigenvalues reflect the convergence of the system's local dynamic response. The maximum eigenvalue is required to not exceed the preset stability threshold to avoid divergent and unstable behavior under electrothermal disturbances. Constrained optimization is performed based on the objective function and electrothermal coupling optimization constraints. The Lagrangian multiplier method or a more efficient convex optimization technique is selected to introduce the constraints into the objective function through the Lagrangian factor, construct the Lagrangian function, and then solve the KKT condition. Through this process, a set of optimal electrothermal coupling parameter combinations are obtained, including the optimal temperature compensation coefficient, the optimal thermal resistance control coefficient, the optimal power consumption modulation strategy, and the PWM frequency adjustment parameters, so that the constant current drift reaches the minimum value while satisfying the thermal gradient and stability constraints. Finally, the electrothermal coupling sensitivity analysis results including the optimal electrothermal coupling parameter combination and the minimum constant current drift value are obtained.

[0027] In an embodiment of the present invention, nanosecond-level time synchronization acquisition of the temperature field and the current field is achieved by arranging a multi-point thermocouple sensor array and configuring high-precision current sensors in parallel, thus solving the problem of test errors caused by time asynchrony in traditional separate temperature-current testing. Four excitation modes, namely linear temperature rise, step temperature rise, sinusoidal temperature change and random temperature fluctuation, are adopted to cover the multi-mode excitation of the entire temperature range, which can fully stimulate the various response modes of the electrothermal coupling chip and avoid the limitations of a single excitation mode. The grid processing method of dividing the chip into N×M thermal resistance units, combined with the calculation of the heat diffusion Green's function, realizes the accurate modeling of the complex temperature field distribution inside the chip, breaking through the technical limitations of the traditional single heat capacity simplified model. An implicit electrothermal coupling transfer function is established that takes into account the response delay of the temperature compensation circuit, the resistance temperature coefficient and the change of the PWM duty cycle. The nonlinear fitting is performed by the radial basis function algorithm to accurately capture the nonlinear characteristics of the constant current drift under the electrothermal coupling condition. A four-dimensional dynamic response model of temperature field, resistance field, and constant current output was constructed. Time alignment was achieved through a cross-correlation function algorithm, resolving the technical challenge of traditional test methods' inability to simultaneously analyze the dynamic relationships of multiple coupling parameters. Through partial differential sensitivity calculation and Lagrange multiplier-constrained optimization, quantitative sensitivity analysis of electrothermal coupling parameters was achieved, thereby improving the accuracy of LED driver chip testing.

[0028] In a specific embodiment, the process of executing step S1 may specifically include the following steps: Arrange a multi-point thermocouple sensor array and a high-precision current sensor on the surface of the LED driver chip to create an electrothermal coupling test system; Based on the electrothermal coupling test system, the LED driver chip is subjected to temperature excitations including linear temperature increase, step temperature increase, sinusoidal temperature change, and random temperature fluctuation to obtain a multi-mode temperature excitation signal. According to the multi-mode temperature excitation signal, the constant current output of the electrothermal coupling test system is synchronously collected to obtain temperature response data and constant current output response data; The temperature response data and the constant current output response data are subjected to electrothermal coupling feature recognition to obtain electrothermal coupling test data.

[0029] Specifically, a multi-point thermocouple sensor array and high-precision current sensors are placed on the surface of the LED driver chip to establish a complete, dynamically responsive electrothermal coupling test system. At least nine thermocouple sensors are arranged in a regular grid pattern on the chip surface, forming a 3×3 array. Each sensor monitors temperature changes in a localized area of the chip surface. To ensure accurate and real-time temperature monitoring, the thermocouples must have a high resolution of less than 0.1°C and a sampling frequency of at least 1kHz, capable of capturing subtle local temperature changes during rapid heating or cooling. A high-precision current sensor is connected in parallel to the chip's constant current output port. The current sensor must have a resolution of 1μA and a high sampling frequency. It must be time-synchronized with the temperature sensor, achieving nanosecond-level sampling accuracy. This ensures accurate alignment of temperature changes and constant current output responses on the timeline, thus avoiding measurement errors caused by timing misalignment. Once the sensors are deployed, a high-speed, multi-channel data acquisition card is used to centrally manage and synchronously acquire all sensor signals, establishing the foundation for the test hardware system. This electrothermal coupling test system applies multi-mode temperature excitation to LED driver chips, including linear temperature ramp, step temperature ramp, sinusoidal temperature change, and random temperature fluctuation. Linear temperature ramp excitation uses a constant temperature ramp rate, such as 0.1°C / s to 10°C / s, to linearly increase the chip temperature. This is used to evaluate the chip's constant current drift characteristics during a uniform temperature ramp. Step temperature ramp excitation simulates a chip experiencing a sudden temperature change within a short period of time, such as a temperature jump from 25°C to 85°C within a few seconds. This examines the chip's electrothermal response to sudden thermal shock. Sinusoidal temperature change excitation simulates real-world conditions of periodic ambient temperature fluctuations by setting periodic temperature changes of varying frequencies and amplitudes. This allows analysis of the chip's dynamic adaptability to periodic thermal perturbations. Random temperature fluctuation excitation generates statistically random temperature sequences, such as Gaussian white noise or colored noise, to simulate chip behavior under non-deterministic temperature fluctuations in natural environments, comprehensively testing the chip's stability and disturbance tolerance. To achieve these complex temperature excitations, high-precision programmable temperature control equipment is used to ensure that the temperature change accuracy is controlled within ±0.5°C, and it can quickly respond to control instructions to achieve temperature excitation changes within a high dynamic range. While applying multi-mode temperature excitation, the electrothermal coupling test system is started to synchronously collect the temperature response and constant current output of the chip. According to the set sampling frequency, the temperature change curve of each thermocouple sensor is recorded in real time, and the constant current output value changes fed back by the current sensor are synchronously recorded. The temperature response data and constant current output response data are pre-processed by the data acquisition system, including signal filtering, zero point calibration and outlier removal to ensure the cleanliness and reliability of the data. Data storage uses timestamp marking to ensure that each temperature data point and the corresponding current data point have a strict time correspondence, which is convenient for subsequent data correlation analysis.The entire acquisition process covers the entire temperature excitation cycle, from the onset of the excitation signal to its steady state or termination, ensuring data continuity and integrity. After synchronous acquisition, the temperature response data and the constant current output response data are used to identify electrothermal coupling characteristics. Time-domain analysis of the temperature data extracts basic parameters such as the heating rate, temperature change amplitude, and temperature fluctuation frequency characteristics at each sensing point. Synchronous analysis of the constant current output data extracts the constant current drift, drift rate, drift delay time, and its interaction with temperature changes. To capture the causal relationship between temperature changes and current drift, cross-correlation function analysis is used to calculate the correlation coefficient and delay time between the temperature change sequence and the constant current drift sequence, identifying the key dynamic characteristics of the electrothermal response. Fourier transform or discrete electrothermal coupling transform is used to map the time domain signals to the frequency domain. The main frequency components and energy distribution are analyzed to reveal the dominant frequency modes and possible harmonic distortion in the electrothermal coupling response. To extract deeper electrothermal coupling characteristics and perform nonlinear feature analysis, machine learning methods such as radial basis function networks or support vector machines are used to fit and classify the nonlinear relationship between temperature change and constant current drift, revealing the complex nonlinear mechanisms of the chip's electrothermal response. During feature identification, key parameters such as the temperature drift coefficient, electrothermal coupling delay time constant, and thermal resistance change rate are extracted. Through these testing and analysis processes, an electrothermal coupling test dataset is generated.

[0030] In a specific embodiment, the process of executing step S2 may specifically include the following steps: Based on the electrothermal coupling test data, the internal space of the LED driver chip is grid-divided to obtain a thermal resistance network unit matrix containing multiple thermal resistance units; According to the thermal resistance network unit matrix, the instantaneous power consumption and heat diffusion Green's function of each thermal resistance unit are calculated to obtain the basic parameters of the thermal resistance network; Based on the basic parameters of the thermal resistance network, the power consumption term of the temperature compensation circuit and the duty cycle change of the thermal feedback regulation mechanism are coupled and calculated to obtain the electrothermal coupling correction parameters; The temperature field distribution is calculated based on the basic parameters of the thermal resistance network and the electrothermal coupling correction parameters to obtain the temperature distribution characteristic parameters.

[0031] Specifically, a gridding scheme is established based on the chip's actual physical dimensions and functional unit layout. The chip's two-dimensional surface is divided into N×M cells using a regular grid. Each cell's coverage area must be small enough to capture local temperature gradients, while balancing computational complexity and avoiding data redundancy and increased computational load caused by excessive divisions. Each cell is defined as an independent thermal resistance unit, and thermal coupling is established with its neighboring cells through equivalent thermal conductivity. After gridding is completed, the temperature data is spatially interpolated to the center of each grid based on the temperature change curves of each sensing point recorded in the electrothermal coupling test data. This creates a preliminary thermal distribution state and forms a thermal resistance network unit matrix containing multiple thermal resistance units. After obtaining the thermal resistance network unit matrix, the instantaneous power consumption and heat diffusion Green's function are calculated for each thermal resistance unit. The instantaneous power consumption is determined based on the current density and operating voltage distribution of each functional module under the chip's operating state. Combined with the local power density model, the instantaneous power consumption value of each unit is calculated. The power consumption distribution not only reflects the Joule heating generated by current flowing through different circuit units but also accounts for secondary effects such as internal leakage and switching losses, making the power consumption calculation more accurate to actual operating conditions. The thermal diffusion Green's function is introduced to model the diffusion behavior of local heat within the chip. The Green's function G(x-xi,y-yj) describes the temperature rise contribution of a unit heat source propagating to any location in a two-dimensional plane. It considers the thermal conductivity, specific heat capacity, and boundary conditions of the chip material to ensure that the diffusion process conforms to actual physical laws. By convolving the power consumption of each thermal resistance unit with the corresponding Green's function, the thermal diffusion contribution of that unit to the overall chip temperature field is obtained. The calculated results of all units are combined to form the basic parameter set of the thermal resistance network, including the equivalent thermal resistance value, instantaneous power consumption value, and corresponding thermal diffusion influence coefficient for each unit. Based on these basic parameters of the thermal resistance network, the unique dynamic effects of electrothermal coupling are considered, namely the power consumption term of the temperature compensation circuit and the duty cycle variation of the thermal feedback regulation mechanism. The LED driver chip has an integrated temperature compensation module that adjusts the reference voltage or bias current in real time based on detected temperature changes, resulting in nonlinear fluctuations in the chip's internal power consumption with temperature. Specifically, the power consumption term of the temperature compensation circuit is modeled as a quadratic function of the temperature deviation, with the coefficient representing the sensitivity of the temperature compensation circuit to temperature changes. At the same time, the thermal feedback regulation mechanism in the chip indirectly changes the constant current output amplitude and average power consumption by dynamically adjusting the PWM modulation duty cycle, ensuring that the chip maintains a stable constant current output at different temperatures. The duty cycle variation term is modeled as a linear function of the duty cycle with the temperature offset (T-T0), with the coefficient being the thermal feedback sensitivity. By modeling the instantaneous temperature field and thermal feedback characteristics, the temperature compensation power consumption term and the PWM duty cycle variation term are coupled and calculated, and then superimposed on the original instantaneous power consumption model to obtain the corrected thermal power consumption distribution, forming a set of electrothermal coupling correction parameters. The temperature field distribution is calculated for the basic parameters of the thermal resistance network and the electrothermal coupling correction parameters.The calculation process uses a discretized heat conduction equation, discretizing the heat diffusion equation onto the grid cells. Finite difference or finite element methods are then used to solve the temperature evolution of each cell at different time steps. Within each time step, the temperature update of each cell depends on the combined effects of its own power consumption, the heat diffusion input from neighboring cells, the power consumption correction of the temperature compensation circuit, and the influence of PWM modulation. Through step-by-step iterative calculations, a complete temperature field distribution map of the chip under steady-state or dynamic excitation is obtained. This temperature field distribution reflects the absolute temperature values of each region within the chip and also reveals the spatial distribution of temperature gradients, including key information such as hotspot formation, heat diffusion paths, and boundary heat flow. Based on the temperature field distribution data, characteristic parameters of the temperature distribution are extracted. These parameters include the local maximum temperature value, local temperature gradient extremes, average temperature rise, temperature uniformity indicators (such as standard deviation and maximum and minimum differences), hotspot area ratio, and the spatiotemporal evolution rate of temperature change.

[0032] In a specific embodiment, the process of executing step S3 may specifically include the following steps: Based on the electrothermal coupling test data and temperature distribution characteristic parameters, the temperature compensation circuit response delay term, resistance temperature coefficient term and PWM duty cycle change term are calculated separately to obtain the basic components of the transfer function. The time domain response relationship between temperature change and constant current output is integrated according to the basic component of the transfer function to obtain the constant current drift time domain response data; The constant current drift time domain response data is input into the radial basis function for nonlinear fitting to obtain the radial basis function fitting parameters. The radial basis function fitting parameters are then solved for the weight coefficients using the least squares method to obtain the electrothermal coupling transfer coefficient.

[0033] Specifically, the electrothermal coupling test data is preprocessed and feature extracted. Based on data collected simultaneously from multi-point temperature excitation and constant current, the chip's instantaneous temperature, constant current output, voltage response, and their changing trends at different time points are extracted. Using this basic data, the temperature compensation circuit response delay term is analyzed. Because the temperature compensation circuit has a certain response lag, the reference voltage adjustment lags behind the temperature change. Therefore, the delay between the temperature change signal and the compensation voltage change signal is extracted through cross-correlation analysis. The time delay corresponding to the cross-correlation peak is the temperature compensation response delay, which is recorded to form the basic parameter of the temperature compensation circuit response delay. The resistance temperature coefficient term is separately calculated based on the electrothermal coupling data. The resistance temperature characteristic exhibits a nonlinear growth, especially in the high temperature range, where the resistance value exhibits a polynomial growth trend with temperature. By fitting a polynomial to the resistance value changes corresponding to each temperature point and using least squares regression, the first-order temperature coefficient (linear term), second-order temperature coefficient (nonlinear term), and any higher-order terms are extracted to form a nonlinear response model for temperature-resistance changes. The PWM duty cycle change term is also separately calculated. Because LED driver chips use PWM modulation for constant current control, temperature changes indirectly cause fine-tuning of the PWM duty cycle by affecting the chip's internal reference voltage and control logic, thereby affecting the constant current output. To isolate the duty cycle variation term, a regression model is constructed between the duty cycle and the temperature offset, extracting the linear variation coefficient. This creates a direct mapping between temperature variation and PWM duty cycle variation. This effectively separates the temperature compensation delay term, the resistor temperature coefficient term, and the PWM duty cycle variation term, forming a set of basic components of the transfer function. Based on these extracted basic components of the transfer function, the time-domain response relationship between temperature variation and constant current output is derived. Taking the temperature variation signal as input, the dynamic response hysteresis model introduced by the delay term, combined with the nonlinear resistance variation characteristics modeled by the resistor temperature coefficient term and the dynamic modulation effect modeled by the PWM duty cycle variation term, yields the response function of the constant current output to temperature variation. This response function is expressed in a convolutional form. By performing a time-domain convolution of the temperature variation signal with the transfer function, the time-domain response data of the constant current drift are obtained. In the actual calculation process, discrete convolution or numerical integration methods are used to discretize the continuous signal, discretizing the temperature change signal into a sequence of sampling points with equal time steps. The transfer function is expressed as a discrete time response. Ultimately, by solving the convolution and implementing time-domain integration, the drift behavior of the constant current output under different temperature excitations is accurately described. The constant current drift time-domain response data is input into a radial basis function for nonlinear fitting. As a family of functions with extremely strong local approximation capabilities, radial basis functions can effectively capture nonlinear pattern changes in the data and are suitable for describing the nonlinear drift phenomenon in the electrothermal coupling process.During the fitting process, the appropriate radial basis function type is selected, and the Gaussian kernel function is used. Its center position and width parameters are determined by clustering algorithm or empirical setting. The constant current drift response data is input into the radial basis function network as a training sample. By minimizing the fitting error, the network weights and basis function parameters are trained to obtain preliminary fitting results. In order to improve the fitting accuracy and optimize the generalization ability of the model, the weight coefficients of the radial basis function fitting parameters are solved by the least squares method. Let the radial basis function output matrix be X and the target constant current drift vector be Y. Then, by solving the normal equation, the optimal weight vector w is obtained to minimize the mean square error. This process ensures the high approximation ability of the fitting model to the existing data, and suppresses the overfitting phenomenon through the regularization term, thereby enhancing the prediction performance of the model on new temperature change data. The radial basis function network optimized by the least squares method forms a nonlinear mapping model, and its weight parameters and basis function parameters constitute the mathematical expression of the electrothermal coupling transfer coefficient.

[0034] In a specific embodiment, the process of executing step S4 may specifically include the following steps: A four-dimensional coupling response function is constructed based on temperature distribution characteristic parameters and electrothermal coupling transfer coefficients; The nonlinear temperature coefficient of the coupling relationship between the resistance field and the temperature field is calculated based on the four-dimensional coupling response function to obtain the resistance-temperature coupling relationship data; The resistance-temperature coupling relationship data is time-aligned using a cross-correlation function to obtain four-dimensional response time alignment parameters, including temperature field change delay, resistance field change delay, temperature compensation circuit response delay, and constant current output adjustment delay. A synchronous response analysis is performed on the four-dimensional response time alignment parameters to obtain four-dimensional coupled response characteristic data including transient temperature response, transient resistance response, compensation voltage response and constant current output response.

[0035] Specifically, a temperature field distribution function T(x, y, t) is established based on temperature distribution characteristic parameters. Using spatial gridding, the chip surface or internal area is discretized into multiple small cells, each corresponding to a temperature sampling point. Combining the thermal resistance network cell matrix and power consumption distribution information, the finite difference method is used to solve the instantaneous temperature evolution process, obtaining fine-grained temperature distribution data. Using the electrothermal coupling transfer coefficient, the temperature field data is mapped to the resistance change data to construct the instantaneous resistance field R(x, y, t). Considering the significant nonlinear characteristics of resistance variation with temperature, multi-order temperature coefficients, such as the first-order temperature coefficient, the second-order temperature coefficient, and the third-order temperature coefficient, are introduced to establish a nonlinear relationship between resistance and temperature, reflecting the rate of change and curve trend of the resistance response at different temperatures. Based on the constructed temperature-resistance mapping, a response model for the temperature compensation circuit is introduced. The compensation circuit's reference voltage is dynamically adjusted with temperature deviation. Therefore, the temperature field data is input into the compensation voltage calculation module, and the compensation voltage response is calculated by combining the compensation voltage gain coefficient and the delay time constant. Changes in the compensation voltage directly affect the PWM duty cycle adjustment, which indirectly affects the constant current output. Therefore, the temperature change, the compensation voltage change, and the constant current output change are coupled through the electrothermal coupling transfer coefficient to obtain the constant current output response I(t). Through the above multi-level modeling, a four-dimensional coupling response function is formed. Once the four-dimensional coupling response function is constructed, the nonlinear temperature coefficient of the coupling relationship between the resistance field and the temperature field is calculated based on this function. By extracting data from the resistance field R(x, y, t) and the temperature field T(x, y, t) at different time points, a nonlinear regression method is used to fit the resistance-temperature curve to obtain the resistance-temperature coupling relationship data. The fitting process not only considers the linear relationship but also introduces higher-order terms to capture the nonlinear characteristics of the resistance variation with temperature. This results in a set of nonlinear temperature coefficient parameters, including first-order, second-order, and even third-order temperature coefficients, which characterize the resistance's sensitivity to temperature and its variation trends across different temperature ranges. To ensure the correspondence between the response data of various physical quantities on the time axis, the resistance-temperature coupling relationship data is time-aligned using a cross-correlation function. By calculating the cross-correlation function between the temperature field change sequence and the resistance field change sequence, the time delay corresponding to the maximum correlation coefficient is found. Similarly, the cross-correlation function between the temperature field change and the compensation voltage change is calculated to obtain the compensation voltage response delay. The cross-correlation function between the temperature field change and the constant current output change is calculated to obtain the constant current output adjustment delay. By performing cross-correlation analysis on each pair of physical quantity signals, the time offset between each signal is identified, thereby obtaining a four-dimensional response time alignment parameter set, including the temperature field change delay, the resistance field change delay, the temperature compensation circuit response delay, and the constant current output adjustment delay. These time alignment parameters ensure that physical quantities with inconsistent response speeds at different time scales can be analyzed synchronously on a unified time base, avoiding data offset or distortion introduced by response lags.Synchronous response analysis is performed on the time-aligned data. At each synchronized time point, the corresponding instantaneous temperature response T(x,y,t), instantaneous resistance response R(x,y,t), compensation voltage response V(t), and constant current output response I(t) are extracted and jointly analyzed as a complete response vector. Through statistical analysis and curve fitting, the dynamic characteristics of each physical quantity are extracted, such as the rise rate and maximum gradient point of the temperature response, the amplitude and nonlinear inflection point of the resistance response, the dynamic adjustment amplitude and delay characteristics of the compensation voltage response, and the drift amplitude, recovery time, and stability indicators of the constant current output response. Synchronous analysis reveals the dynamic coupling relationship between the various physical quantities, such as temperature changes before resistance changes, resistance changes triggering voltage compensation, which in turn adjusts the PWM duty cycle, ultimately affecting the complete electrothermal dynamic chain of the constant current output. Combining these analysis results yields a four-dimensional coupled response feature dataset consisting of the instantaneous temperature response, instantaneous resistance response, compensation voltage response, and constant current output response.

[0036] In a specific embodiment, the process of executing the step of constructing a four-dimensional coupling response function based on the temperature distribution characteristic parameters and the electrothermal coupling transfer coefficient may specifically include the following steps: Based on the temperature distribution characteristic parameters, the spatial distribution of the instantaneous temperature at each coordinate position inside the chip is calculated to obtain an instantaneous temperature component function including the ambient temperature reference value, the instantaneous power consumption incentive term, and the external ambient temperature change term; The nonlinear temperature dependence of the instantaneous thermal resistance at each coordinate position is calculated based on the electrothermal coupling transfer coefficient, and the instantaneous thermal resistance component function including the nominal thermal resistance value, the first temperature coefficient term, the second temperature coefficient term, and the third temperature coefficient term is obtained; Inputting the temperature distribution characteristic parameters and the electrothermal coupling transfer coefficient into the temperature compensation circuit transfer algorithm to adjust the reference voltage, thereby obtaining a compensation voltage component function including the temperature detection value, the reference temperature deviation and the compensation gain coefficient; Based on the temperature distribution characteristic parameters and the electrothermal coupling transfer coefficient, the coupled response analysis of the constant current output is carried out to obtain the constant current output component function including the nominal constant current value, the temperature drift correction term and the compensation circuit adjustment term; A four-dimensional coupled response function is constructed based on the instantaneous temperature component function, the instantaneous thermal resistance component function, the compensation voltage component function and the constant current output component function.

[0037] Specifically, based on the temperature distribution characteristic parameters obtained from the electrothermal coupling test, a spatial grid is created according to the chip's actual geometric dimensions and functional module distribution. The chip's internal area is divided into several small cells, each corresponding to a discrete coordinate, and temperature calculations are performed at each coordinate. The instantaneous temperature is composed of three components: the ambient temperature baseline value, the local temperature rise caused by the instantaneous power consumption stimulus, and the dynamic perturbation of the local temperature caused by the external ambient temperature change. By superimposing these three components, the temperature distribution state at each coordinate position within the chip is restored at each moment, constructing an instantaneous temperature field with joint spatial and temporal resolution. After the instantaneous temperature field calculation is completed, the nonlinear temperature dependence of the instantaneous thermal resistance at each coordinate position is calculated based on the electrothermal coupling transfer coefficient. Because the thermal resistance of actual materials does not remain constant with temperature, especially exhibiting significant nonlinear growth in high-temperature regions, first-, second-, and third-order temperature coefficients of temperature variation are introduced for high-order modeling. For each grid cell, the instantaneous thermal resistance is composed of the nominal thermal resistance value and a multi-step temperature variation response. This comprehensively considers the material's thermal conductivity variations, accurately reflecting the chip's heat transfer characteristics under different thermal environments and improving the overall thermal resistance model's physical realism and computational accuracy. After the temperature and thermal resistance fields are modeled, the temperature distribution characteristic parameters and the electrothermal coupling transfer coefficient are input into the dynamic adjustment module of the temperature compensation circuit to adjust the reference voltage. The compensation circuit continuously monitors the actual temperature within the chip and dynamically adjusts the reference voltage to offset the adverse effects of temperature changes on chip performance. During operation, the temperature measurement values of key nodes within the chip are extracted and compared with the set reference temperature to calculate the reference temperature deviation. Based on the gain design of the temperature compensation circuit, this deviation is amplified or reduced to form a compensation voltage adjustment variable for adjustment. The reference voltage is dynamically adjusted by adding this adjustment variable, thereby achieving real-time updating of the temperature compensation voltage. Through this modeling, the compensation voltage component function accurately simulates the temperature variation that affects the chip's electrical operating point, ensuring the stability of the constant current output. The coupled response of the constant current output is analyzed based on the temperature distribution characteristic parameters and the electrothermal coupling transfer coefficient. The constant current output of an LED driver chip is not only directly modulated by the PWM control strategy but also indirectly influenced by multiple factors, including resistance changes caused by temperature variations through electrothermal coupling, compensation voltage adjustments, and other factors. The constant current output is broken down into three components: the nominal constant current value, a temperature drift correction term, and a compensation circuit adjustment term. The temperature drift correction term, based on changes in the temperature and resistance fields, reflects the drift in the constant current output due to material resistance changes as the chip's temperature rises or falls. The compensation circuit adjustment term dynamically adjusts the temperature compensation voltage, affecting the duty cycle of the PWM modulation signal and thereby correcting for constant current output offset. By integrating these three components, a comprehensive model is developed to accurately capture the dynamic evolution of the chip's output current by comprehensively modeling the response of the constant current output under different temperature conditions.After all the above component functions are established, a four-dimensional coupled response function is constructed based on the instantaneous temperature component function, the instantaneous thermal resistance component function, the compensation voltage component function, and the constant current output component function. This function can reflect the temperature changes and thermal resistance evolution at various locations within the chip in real time, and simultaneously track the reference voltage adjustment and constant current output drift, forming a panoramic view of the electrothermal dynamic response with high temporal and spatial resolution.

[0038] In a specific embodiment, the process of executing step S5 may specifically include the following steps: Based on the four-dimensional coupling response characteristic data, partial differential operation is performed on the constant current output with respect to temperature, thermal resistance, power consumption and PWM frequency to obtain the electrothermal coupling sensitivity matrix including temperature sensitivity coefficient, thermal resistance sensitivity coefficient, power consumption sensitivity coefficient and frequency sensitivity coefficient; Based on the electrothermal coupling sensitivity matrix, an objective function is constructed, which includes the reciprocal term of the nominal constant current value, the temperature partial differential term, and the product term of the temperature variation. According to the objective function, the electrothermal coupling optimization constraint conditions including the maximum temperature gradient constraint and the Jacobian matrix eigenvalue constraint are established; Based on the objective function and electrothermal coupling optimization constraints, a constrained optimization solution is performed to obtain the electrothermal coupling sensitivity analysis results including the optimal electrothermal coupling parameter combination and the minimum constant current drift.

[0039] Specifically, local rate of change analysis is performed based on four-dimensional coupled response data. The four-dimensional coupled response data provides the chip's instantaneous temperature distribution, thermal resistance changes, dynamic compensation voltage regulation, and constant current output characteristics under various temperature excitations and operating conditions. Based on this data set, local perturbation analysis is performed for temperature changes, thermal resistance changes, power consumption changes, and PWM frequency changes. While keeping other parameters fixed, small perturbations are introduced one by one, and the magnitude of the constant current output response is recorded. Through local perturbation and response observation, the sensitivity of the constant current output to different parameter changes is quantified, and the temperature sensitivity coefficient, thermal resistance sensitivity coefficient, power consumption sensitivity coefficient, and frequency sensitivity coefficient are calculated. To ensure the stability and accuracy of the partial differential operation, the small perturbation amplitude is controlled to ensure that the perturbation is small enough to approximate the local linear response, but not too small to cause numerical instability or degrade the signal-to-noise ratio. Each sensitivity coefficient reflects the relative response amplitude of the constant current output to a unit change in the corresponding parameter at the current operating point. The temperature sensitivity coefficient describes the response sensitivity of the constant current output to small temperature changes. The thermal resistance sensitivity coefficient reveals the impact of variations in the thermal conductivity of the chip's internal materials on constant current stability. The power consumption sensitivity coefficient reflects the transmission effect of fluctuations in the power consumption of internal heat sources on the constant current output. The PWM frequency sensitivity coefficient describes the impact of variations in the modulation signal frequency on the control accuracy of the constant current output. These sensitivity coefficients are arranged in a matrix to form an electrothermal coupling sensitivity matrix. Based on this electrothermal coupling sensitivity matrix, an objective function is constructed to optimize the temperature stability of the constant current output. This objective function aims to comprehensively quantify the overall sensitivity of the constant current output to temperature changes and use this as a basis for optimization. The objective function consists of three components: the reciprocal term of the nominal constant current value is introduced to normalize the sensitivity to avoid incomparable sensitivity values caused by different constant current values across different chip designs; the temperature partial differential term is introduced to directly reflect the local sensitivity of the constant current output to temperature perturbations; and the term is multiplied by the temperature variation to comprehensively consider the actual impact of the amplitude and frequency of temperature changes on the constant current output in actual applications. Through the combination of these three components, the objective function can uniformly evaluate the temperature stability of the constant current output under different operating environments. Electrothermal coupling optimization constraints are introduced to ensure the physical rationality and engineering feasibility of the optimization results. A maximum temperature gradient constraint is set, requiring that the temperature gradient between any adjacent cells within the chip does not exceed a set safety threshold to avoid material failure or functional abnormalities caused by local overheating. Specifically, the maximum value of the temperature field gradient is extracted and compared with the preset allowable maximum value as part of the optimization constraint. A system stability constraint based on the eigenvalues of the Jacobian matrix is introduced, requiring that the maximum eigenvalue of the Jacobian matrix of the electrothermal dynamic system be within the stability domain.The eigenvalues of the Jacobian matrix reflect the system's response speed and stability. Excessively large eigenvalues indicate oversensitivity to disturbances, leading to oscillation or divergence. Therefore, the eigenvalues are constrained within a reasonable range to ensure that the optimized system maintains good dynamic response characteristics and sufficient robustness. A constrained optimization solution is performed based on the objective function and electrothermal coupling optimization constraints. The Lagrange multiplier method is used to introduce the constraints into the objective function via multipliers, constructing a Lagrangian function. The optimal solution is obtained by solving the optimality condition that the first-order derivative is zero. To handle complex constraints and non-convex optimization problems, second-order optimization techniques such as Newton's method are introduced, or modern convex optimization methods are employed to improve solution efficiency and convergence accuracy by introducing slack variables and dual optimization strategies. During the solution process, multiple iterations are performed to address the nonlinear characteristics of the objective function. Each iteration adjusts the optimization path based on feedback from the current sensitivity matrix and the constraints, gradually approaching the optimal solution. After the constrained optimization solution, electrothermal coupling sensitivity analysis results are obtained, including the optimal electrothermal coupling parameter combination and the minimum constant current drift. The optimal parameter combination includes the best temperature compensation gain coefficient, the most appropriate thermal resistance control parameters, a reasonable power consumption distribution control strategy and a PWM frequency adjustment range. These parameters work together to minimize the temperature drift of the constant current output while ensuring the thermal stability and electrical reliability of the chip.

[0040] The above describes the testing method of the LED driver chip in the embodiment of the present invention. The following describes the testing device of the LED driver chip in the embodiment of the present invention. Figure 2 In one embodiment of the present invention, a test device for an LED driver chip includes: Synchronous acquisition module, used to perform multi-point temperature excitation and constant current output synchronous acquisition of LED driver chip to obtain electrothermal coupling test data; Thermal resistance network decomposition module, used to perform thermal resistance network decomposition of the chip internal temperature field based on electrothermal coupling test data to obtain temperature distribution characteristic parameters; A nonlinear function fitting module is used to perform nonlinear function fitting on the temperature-constant current coupling relationship based on the electrothermal coupling test data and the temperature distribution characteristic parameters to obtain the electrothermal coupling transfer coefficient; Four-dimensional coupling calculation module, used to perform four-dimensional coupling calculation on temperature distribution characteristic parameters and electrothermal coupling transfer coefficient to obtain four-dimensional coupling response characteristic data; The sensitivity calculation module is used to perform partial differential sensitivity calculation based on the four-dimensional coupling response characteristic data to obtain the electrothermal coupling sensitivity analysis results.

[0041] Through the collaborative efforts of these components, a multi-point thermocouple sensor array and a parallel configuration of high-precision current sensors enable nanosecond-level time-synchronized acquisition of the temperature and current fields, resolving the test errors caused by time asynchrony in traditional separate temperature-current testing. Four excitation modes, including linear temperature ramp, step temperature ramp, sinusoidal temperature change, and random temperature fluctuation, are employed to achieve multi-mode excitation across the entire temperature range, fully stimulating the various response modes of the electrothermal coupling chip and avoiding the limitations of a single excitation mode. A gridding method, which divides the chip interior into N×M thermal resistance units and combines it with the calculation of thermal diffusion Green's functions, enables accurate modeling of the complex internal temperature field distribution, overcoming the technical limitations of the traditional single heat capacitance simplified model. An implicit electrothermal coupling transfer function is established, accounting for the response delay of the temperature compensation circuit, the resistance temperature coefficient, and the PWM duty cycle. Nonlinear fitting using a radial basis function algorithm accurately captures the nonlinear characteristics of constant current drift under electrothermal coupling conditions. A four-dimensional dynamic response model of temperature field, resistance field, and constant current output was constructed. Time alignment was achieved through a cross-correlation function algorithm, resolving the technical challenge of traditional test methods' inability to simultaneously analyze the dynamic relationships of multiple coupling parameters. Through partial differential sensitivity calculation and Lagrange multiplier-constrained optimization, quantitative sensitivity analysis of electrothermal coupling parameters was achieved, thereby improving the accuracy of LED driver chip testing.

[0042] Reference Figure 3 In an embodiment of the present invention, a computer device is also provided. The computer device may be a server, and its internal structure may be as follows: Figure 3 As shown. The computer device includes a processor, memory, display screen, input device, network interface and database connected via a system bus. The processor of the computer design is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The database of the computer device is used to store the corresponding data in this embodiment. The network interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, the above method is implemented.

[0043] Those skilled in the art will understand that Figure 3 The structure shown in the figure is merely a block diagram of a portion of the structure related to the solution of the present invention and does not constitute a limitation on the computer device to which the solution of the present invention is applied.

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

[0045] Those skilled in the art will appreciate that all or part of the processes in the above-described method embodiments can be implemented by instructing the relevant hardware using a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the above-described method embodiments. Any reference to memory, storage, database, or other media provided herein and used in the embodiments may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double-speed SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct RAMbus dynamic RAM (DRDRAM), and RAMbus dynamic RAM.

[0046] 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, systems and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0047] 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.

[0048] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that the technical solutions described in the above embodiments can still be modified, or some of the technical features thereof can be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for testing an LED driver chip, characterized in that: include: Perform multi-point temperature excitation and constant current output synchronous acquisition on LED driver chips to obtain electrothermal coupling test data; Performing thermal resistance network decomposition on the internal temperature field of the chip according to the electrothermal coupling test data to obtain temperature distribution characteristic parameters; Performing nonlinear function fitting on the temperature-constant current coupling relationship according to the electrothermal coupling test data and the temperature distribution characteristic parameters to obtain an electrothermal coupling transfer coefficient; Performing four-dimensional coupling calculation on the temperature distribution characteristic parameters and the electrothermal coupling transfer coefficient to obtain four-dimensional coupling response characteristic data; A partial differential sensitivity calculation is performed based on the four-dimensional coupling response characteristic data to obtain an electrothermal coupling sensitivity analysis result.

2. The method for testing an LED driver chip according to claim 1, wherein: The method of performing multi-point temperature excitation and constant current output synchronous acquisition on the LED driver chip to obtain electrothermal coupling test data includes: Arrange a multi-point thermocouple sensor array and a high-precision current sensor on the surface of the LED driver chip to create an electrothermal coupling test system; Based on the electrothermal coupling test system, the LED driver chip is subjected to temperature excitation including linear temperature increase, step temperature increase, sinusoidal temperature change and random temperature fluctuation to obtain a multi-mode temperature excitation signal; Performing synchronous acquisition of constant current output of the electrothermal coupling test system according to the multi-mode temperature excitation signal to obtain temperature response data and constant current output response data; Electrothermal coupling feature recognition is performed on the temperature response data and the constant current output response data to obtain electrothermal coupling test data.

3. The method for testing an LED driver chip according to claim 1, wherein: The step of performing thermal resistance network decomposition on the internal temperature field of the chip according to the electrothermal coupling test data to obtain temperature distribution characteristic parameters includes: According to the electrothermal coupling test data, the internal space of the LED driver chip is grid-divided to obtain a thermal resistance network unit matrix including a plurality of thermal resistance units; Calculating the instantaneous power consumption and heat diffusion Green's function of each thermal resistance unit according to the thermal resistance network unit matrix to obtain basic parameters of the thermal resistance network; Based on the basic parameters of the thermal resistance network, a coupled calculation is performed on the power consumption term of the temperature compensation circuit and the duty cycle change of the thermal feedback regulation mechanism to obtain an electrothermal coupling correction parameter; The temperature field distribution calculation is performed on the thermal resistance network basic parameters and the electrothermal coupling correction parameters to obtain temperature distribution characteristic parameters.

4. The method for testing an LED driver chip according to claim 1, wherein: The performing of nonlinear function fitting on the temperature-constant current coupling relationship according to the electrothermal coupling test data and the temperature distribution characteristic parameters to obtain the electrothermal coupling transfer coefficient includes: Separately calculating the temperature compensation circuit response delay term, the resistance temperature coefficient term, and the PWM duty cycle change term based on the electrothermal coupling test data and the temperature distribution characteristic parameters to obtain a transfer function basic component; performing an integration operation on the time domain response relationship between the temperature change and the constant current output according to the basic component of the transfer function to obtain constant current drift time domain response data; The constant current drift time domain response data is input into a radial basis function for nonlinear fitting to obtain radial basis function fitting parameters, and the radial basis function fitting parameters are solved for weight coefficients using a least squares method to obtain an electrothermal coupling transfer coefficient.

5. The method for testing an LED driver chip according to claim 1, wherein: The performing four-dimensional coupling calculation on the temperature distribution characteristic parameter and the electrothermal coupling transfer coefficient to obtain four-dimensional coupling response characteristic data includes: Constructing a four-dimensional coupling response function based on the temperature distribution characteristic parameters and the electrothermal coupling transfer coefficient; Calculating the nonlinear temperature coefficient of the coupling relationship between the resistance field and the temperature field according to the four-dimensional coupling response function to obtain resistance-temperature coupling relationship data; Using a cross-correlation function to time-align the resistance-temperature coupling relationship data, and obtain four-dimensional response time alignment parameters including temperature field change delay, resistance field change delay, temperature compensation circuit response delay, and constant current output adjustment delay; A synchronous response analysis is performed on the four-dimensional response time alignment parameters to obtain four-dimensional coupled response characteristic data including transient temperature response, transient resistance response, compensation voltage response and constant current output response.

6. The method for testing an LED driver chip according to claim 5, wherein: The constructing of a four-dimensional coupling response function based on the temperature distribution characteristic parameter and the electrothermal coupling transfer coefficient includes: Based on the temperature distribution characteristic parameters, the instantaneous temperature at each coordinate position inside the chip is spatially distributed to obtain an instantaneous temperature component function including an ambient temperature reference value, an instantaneous power consumption incentive term, and an external ambient temperature change term; Calculating the nonlinear temperature dependence of the instantaneous thermal resistance at each coordinate position according to the electrothermal coupling transfer coefficient to obtain an instantaneous thermal resistance component function including a nominal thermal resistance value, a first order temperature coefficient term, a second order temperature coefficient term, and a third order temperature coefficient term; Inputting the temperature distribution characteristic parameter and the electrothermal coupling transfer coefficient into the temperature compensation circuit transfer algorithm to adjust the reference voltage, thereby obtaining a compensation voltage component function including a temperature detection value, a reference temperature deviation, and a compensation gain coefficient; performing a coupled response analysis on the constant current output based on the temperature distribution characteristic parameter and the electrothermal coupling transfer coefficient to obtain a constant current output component function including a nominal constant current value, a temperature drift correction term, and a compensation circuit adjustment term; A four-dimensional coupling response function is constructed according to the instantaneous temperature component function, the instantaneous thermal resistance component function, the compensation voltage component function and the constant current output component function.

7. The method for testing an LED driver chip according to claim 1, wherein: The partial differential sensitivity calculation is performed based on the four-dimensional coupling response characteristic data to obtain the electrothermal coupling sensitivity analysis result, including: Performing partial differential operations on the constant current output with respect to temperature, thermal resistance, power consumption, and PWM frequency according to the four-dimensional coupling response characteristic data to obtain an electrothermal coupling sensitivity matrix including a temperature sensitivity coefficient, a thermal resistance sensitivity coefficient, a power consumption sensitivity coefficient, and a frequency sensitivity coefficient; Constructing an objective function including a reciprocal term of a nominal constant current value, a temperature partial differential term, and a temperature variation product term based on the electrothermal coupling sensitivity matrix; Establishing electrothermal coupling optimization constraint conditions including maximum temperature gradient constraint and Jacobian matrix eigenvalue constraint according to the objective function; A constrained optimization solution is performed based on the objective function and the electrothermal coupling optimization constraint conditions to obtain an electrothermal coupling sensitivity analysis result including an optimal electrothermal coupling parameter combination and a minimum constant current drift value.

8. A testing device for an LED driver chip, characterized in that: A method for testing an LED driver chip according to any one of claims 1 to 7, wherein the testing device comprises: Synchronous acquisition module, used to perform multi-point temperature excitation and constant current output synchronous acquisition of LED driver chip to obtain electrothermal coupling test data; A thermal resistance network decomposition module is used to perform thermal resistance network decomposition on the internal temperature field of the chip according to the electrothermal coupling test data to obtain temperature distribution characteristic parameters; a nonlinear function fitting module, configured to perform nonlinear function fitting on the temperature-constant current coupling relationship according to the electrothermal coupling test data and the temperature distribution characteristic parameters, to obtain an electrothermal coupling transfer coefficient; A four-dimensional coupling calculation module is used to perform four-dimensional coupling calculation on the temperature distribution characteristic parameters and the electrothermal coupling transfer coefficient to obtain four-dimensional coupling response characteristic data; The sensitivity calculation module is used to perform partial differential sensitivity calculation based on the four-dimensional coupling response characteristic data to obtain an electrothermal coupling sensitivity analysis result.

9. A computer device, characterized in that: The method comprises a memory and a processor, wherein the memory stores a computer program that can be run on the processor, and the processor implements the test method of the LED driver chip according to any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium, characterized in that A computer program is stored thereon, and when the computer program is executed by a processor, the processor is enabled to execute the testing method for the LED driver chip according to any one of claims 1 to 7.

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