Automatic detection system and method for electrostatic protection performance of novel power device

Abstract

The invention belongs to the technical field of semiconductor testing, and discloses a novel automatic detection system and method for the electrostatic protection performance of a power device. A distributed parasitic parameter network model is constructed by acquiring packaging structure parameters of a to-be-tested power device and physical layout data of a test fixture, key parasitic parameter distribution is identified in combination with a time domain reflection measurement technology, a waveform distortion coefficient is calculated, and a spatio-temporal evolution model of electrostatic pulse propagation is constructed. Based on the model, a compensatory predistortion waveform is generated to counteract the parasitic effect of a transmission path, a device port response signal is collected in real time, an energy dissipation characteristic spectrum is extracted through wavelet packet decomposition, and quantitative evaluation of the electrostatic protection performance is achieved. According to the invention, the test precision and repeatability are improved, and the method is suitable for electrostatic protection tests of various novel power devices, especially high-frequency wide-band gap devices.

Description

Technical Field

[0001] This invention relates to the field of semiconductor testing technology, and more specifically, to an automated testing system and method for the electrostatic discharge (ESD) protection performance of novel power devices. Background Technology

[0002] Traditional electrostatic discharge (ESD) protection performance testing is primarily based on three standard methods: Human Body Model (HBM), Machine Model (MM), and Charged Device Model (CDM). HBM simulates the process of static electricity discharge from the human body, using an RC network of a 150pF capacitor and a 1.5kΩ resistor to generate a discharge waveform with a rise time of approximately 100ns. CDM simulates the discharge of a charged device through a ground pin, characterized by an extremely short rise time (typically <200ps) and a high peak current (on the order of several amperes). International standards such as JEDEC, ESDA, and AEC-Q100 have standardized the implementation procedures for these testing methods, providing a unified evaluation framework for the industry.

[0003] However, with the continuous increase in the operating frequency of power devices and the increasing complexity of packaging technologies, a prominent problem with traditional testing methods is the severe interference of parasitic parameters in the test environment on the test results. Parasitic parameters in the test fixture, especially PCB trace inductance and distributed capacitance, significantly alter the waveform characteristics of the electrostatic pulse actually applied to the device ports. In actual test environments, the distributed capacitance between high-frequency PCB traces (typically 20-30 nH / cm) and the ground plane forms a complex LC network, leading to distortion problems such as prolonged rise time, overshoot, ringing, and attenuation of high-frequency components in the test signal. This problem is particularly severe in CDM (Charged Device Model) testing because the rise time of the CDM pulse is typically less than 200 ps, ​​with abundant high-frequency components, making it more susceptible to parasitic parameters. The CDM withstand voltage values ​​obtained for the same device on different test boards vary, leading to serious inconsistencies between product specification definitions and design verification. As the operating frequency of WBG (Wide Bandgap) power devices such as SiC and GaN MOSFETs increases to the MHz or even GHz level, the limitations of traditional testing methods become even more pronounced, and the difference between test results and the electrostatic protection performance in actual application environments widens further. Furthermore, the testing conditions specified in current industry standards do not match the actual electrostatic threat characteristics of modern electronic systems. In addition, the lack of an effective compensation mechanism for parasitic parameters in the testing environment makes it impossible for design engineers to accurately assess and optimize the electrostatic protection capabilities of devices.

[0004] In view of this, the present invention proposes a novel automated testing system and method for the electrostatic discharge (ESD) protection performance of power devices to solve the above problems. Summary of the Invention

[0005] To overcome the aforementioned deficiencies of the prior art and to achieve the above objectives, on the one hand, the present invention provides a novel automated testing method for the electrostatic discharge (ESD) protection performance of power devices, comprising: Obtain the package structure parameters of the power device under test and the physical layout data of the test fixture; Based on the encapsulation structure parameters and physical layout data, a distributed parasitic parameter network model of the test loop is constructed. The impedance discontinuity distribution of each node of the test fixture is obtained by time-domain reflectometry, and the parasitic inductance concentration area and the distributed capacitance coupling node are identified. Based on the inductance density distribution in the parasitic inductance concentration region and the capacitance gradient change at the distributed capacitance coupling node, the waveform distortion coefficient of the electrostatic pulse on the transmission path is calculated. Based on the waveform distortion coefficient and distributed parasitic parameter network model, a spatiotemporal evolution model of electrostatic pulse propagation is constructed. Compensatory predistortion waveforms are generated based on a spatiotemporal evolution model, and waveform distortion caused by parasitic parameters is canceled by inverse modulation. During the application of the compensating predistortion waveform, the transient voltage and current response signals at the device ports are acquired in real time. By performing wavelet packet decomposition on the transient voltage and current response signal, the energy dissipation characteristic spectrum during electrostatic discharge is extracted. The electrostatic discharge (ESD) protection performance of power devices is evaluated based on the time-frequency distribution characteristics of the energy dissipation characteristic spectrum.

[0006] On the other hand, the present invention provides an automated testing system for the electrostatic discharge (ESD) protection performance of novel power devices, which is used to implement the automated testing method for the ESD protection performance of the novel power devices, including: The parameter acquisition module is used to acquire the package structure parameters of the power device under test and the physical layout data of the test fixture; The parasitic parameter modeling module is used to construct a distributed parasitic parameter network model of the test loop based on the package structure parameters and physical layout data. The time-domain reflectometry module is used to obtain the impedance discontinuity distribution of each node of the test fixture through time-domain reflectometry technology, and to identify the parasitic inductance concentration area and the distributed capacitance coupling node. The waveform distortion calculation module is used to calculate the waveform distortion coefficient of electrostatic pulses on the transmission path based on the inductance density distribution in the parasitic inductance concentration area and the capacitance gradient change of the distributed capacitance coupling node. The spatiotemporal evolution modeling module is used to construct a spatiotemporal evolution model of electrostatic pulse propagation based on waveform distortion coefficients and a distributed parasitic parameter network model. The predistortion waveform generation module is used to generate compensatory predistortion waveforms based on the spatiotemporal evolution model, and to cancel the waveform distortion caused by parasitic parameters through inverse modulation; The response signal acquisition module is used to acquire the transient voltage and current response signals of the device port in real time during the application of the compensating predistortion waveform; The energy feature extraction module is used to extract the energy dissipation feature spectrum during electrostatic discharge by performing wavelet packet decomposition on the transient voltage and current response signal. The performance evaluation module is used to evaluate the electrostatic protection performance of power devices based on the time-frequency distribution characteristics of the energy dissipation characteristic spectrum. The modules are connected via wired and / or wireless means to enable data transmission between them.

[0007] The technical effects and advantages of the novel automated testing system and method for electrostatic discharge (ESD) protection performance of power devices: This invention improves the accuracy and repeatability of test results by accurately characterizing and compensating for parasitic effects in the test environment. It enables engineers to gain a deeper understanding of the protection mechanisms and potential risks of different design schemes. Its adaptive nature makes it applicable to various power devices, from traditional silicon-based to emerging wide-bandgap devices, meeting the evolving needs of semiconductor technology. In practical applications, this invention effectively eliminates the dilemma of over-protection versus under-protection, reduces product development costs and shortens time-to-market, while providing a scientific basis for the reliability design of power modules. Particularly in high-reliability applications, this method can accurately predict the electrostatic discharge (ESD) protection performance of power devices in complex electromagnetic environments, significantly reducing the risk of system failure. Attached Figure Description

[0008] Figure 1 This is a schematic diagram of the automated testing method for the electrostatic protection performance of the novel power device of the present invention; Figure 2 This is a schematic diagram of an automated testing system for the electrostatic protection performance of a novel power device according to the present invention. Detailed Implementation

[0009] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0010] This application provides an automated testing system and method for the electrostatic discharge (ESD) protection performance of novel power devices. The system's execution entities include, but are not limited to, those mounted on the system: ESD protection testing equipment, semiconductor testing systems, electronic equipment reliability analysis platforms, power device evaluation workstations, etc., which can be considered general computing nodes in this application. The semiconductor testing systems include, but are not limited to, at least one of: a semiconductor parameter analyzer, a pulse testing system, and electrical characteristic evaluation equipment.

[0011] Please see Figure 1 In this embodiment of the invention, the detailed implementation steps of the automated testing method for the electrostatic discharge protection performance of novel power devices include: Obtain the package structure parameters of the power device under test (DUT) and the physical layout data of the test fixture. Package structure parameters include key parameters such as the number of pins, pin spacing, package size, and chip-lead connection methods, obtained through the device datasheet, 3D scanning, or precision measurement. Physical layout data of the test fixture includes information such as fixture dimensions, material properties, electrical connection paths, and contact point distribution, obtained through design documents or physical measurements. This data provides the foundation for subsequent test circuit model construction, ensuring consistency between the model and the actual test environment.

[0012] Based on the package structure parameters and physical layout data, a distributed parasitic parameter network model of the test circuit is constructed. This network model comprehensively describes the distribution of parasitic inductance, capacitance, and resistance in the test circuit, accurately reflecting the influence of the test environment on electrostatic pulse transmission. The construction process first analyzes the package structure parameters, extracting geometric features and material properties. Then, a spatial model is established based on the physical layout data, calculating the relative positional relationships between components. Finally, the distributed parasitic parameter values ​​are determined through electromagnetic field analysis, forming a complete network model.

[0013] Time-domain reflectometry (TD-RS) is used to obtain the impedance discontinuity distribution at each node of the test fixture, identifying regions of concentrated parasitic inductance and nodes coupled with distributed capacitance. TD-RS is a technique that identifies transmission path characteristics by analyzing reflected waveforms, providing a direct view of the location and nature of impedance change points. During the measurement process, a standard step pulse is injected into the test fixture, the reflected signal is acquired, and the impedance change rate curve is obtained through differentiation. Characteristic abrupt changes are identified and marked as candidate parasitic parameter concentration areas. Then, frequency-domain impedance scanning is used to determine the type of parasitic parameters in each region, providing a precise physical basis for subsequent waveform distortion analysis.

[0014] Based on the inductance density distribution in the parasitic inductance concentration region and the capacitance gradient change at the distributed capacitance coupling nodes, the waveform distortion coefficient of the electrostatic pulse along the transmission path is calculated. The waveform distortion coefficient quantifies the degree to which the electrostatic pulse is affected by parasitic parameters during transmission and is a key indicator for evaluating test accuracy. The calculation process first divides the parasitic inductance concentration region into multiple micro-segments to construct the inductance density distribution function. Simultaneously, a mesh model of the distributed capacitance coupling nodes is performed, and the capacitance change rate is calculated. Then, based on the product of inductance density and capacitance change rate, combined with the characteristics of the electrostatic pulse, the delay distortion coefficient is calculated, ultimately yielding the overall waveform distortion coefficient.

[0015] Based on the waveform distortion coefficient and distributed parasitic parameter network model, a spatiotemporal evolution model of electrostatic pulse propagation is constructed. This model dynamically describes the propagation process of the electrostatic pulse in the test circuit, including key characteristics such as energy distribution, waveform changes, and phase delay. The construction process involves establishing a transmission line equation set based on the distributed parasitic parameter network model, introducing the waveform distortion coefficient as a correction factor, and using numerical calculation methods to solve the corrected wave equation. This yields the time-domain waveforms of the electrostatic pulse at different spatial locations, forming a complete spatiotemporal evolution model and providing a theoretical basis for subsequent waveform compensation.

[0016] Compensating predistortion waveforms are generated based on a spatiotemporal evolution model, and inverse modulation is used to cancel waveform distortion caused by parasitic parameters. The compensating predistortion waveform is a specially modulated test signal that can cancel the effects of parasitic effects during transmission, ensuring that the waveform arriving at the device port meets standard requirements. The generation process first extracts the expected distortion features at the device port, obtains the inverse function of the distortion transfer function through inverse Fourier transform, and convolves it with a standard electrostatic pulse waveform to obtain the initial predistortion waveform. Then, an iterative optimization algorithm is used to adjust the waveform parameters so that the final output compensating predistortion waveform can accurately cancel the distortion effects of the transmission path.

[0017] During the application of a compensating predistortion waveform, transient voltage and current response signals at the device ports are acquired in real time. These transient response signals directly reflect the electrical behavior of the power device under electrostatic discharge (ESD) and serve as raw data for evaluating protection performance. The acquisition process employs a high-speed data acquisition system, simultaneously recording voltage and current waveforms. Sampling rates typically reach GHz levels, ensuring the capture of nanosecond-level transient changes. Data synchronization accuracy is better than 100 ps, ​​providing high-quality raw data for subsequent signal analysis.

[0018] By performing wavelet packet decomposition on the transient voltage and current response signal, the energy dissipation characteristic spectrum during electrostatic discharge is extracted. Wavelet packet decomposition is an efficient time-frequency analysis method that can simultaneously reflect the time and frequency domain characteristics of a signal, and is particularly suitable for analyzing non-stationary transient signals. The decomposition process first selects wavelet basis functions suitable for the characteristics of electrostatic pulses, performs multi-level decomposition of the response signal, calculates the wavelet coefficient energy of each decomposition level, constructs a time-frequency energy distribution matrix, identifies energy concentration regions and extracts characteristic parameters, and finally forms the energy dissipation characteristic spectrum, providing data basis for electrostatic protection performance evaluation.

[0019] Based on the time-frequency distribution characteristics of the energy dissipation characteristic spectrum, the electrostatic discharge (ESD) protection performance of power devices is evaluated. These performance indicators comprehensively reflect the power device's ability to resist ESD shocks, including multiple dimensions such as withstand voltage, response time, and energy absorption capacity. The evaluation process involves principal component analysis of the energy dissipation characteristic spectrum to extract key eigenvectors, calculate the matching degree with a standard ESD protection template, and assess the risk of local hotspots by combining the proportion of high-frequency energy. Finally, a comprehensive ESD protection performance index is generated, and the device's ESD protection level is determined by comparing it with a preset threshold, providing a reliable safety reference for device applications.

[0020] In this embodiment of the invention, the detailed implementation steps for constructing a distributed parasitic parameter network model of the test loop based on the packaging structure parameters and physical layout data include: The package structure parameters are analyzed to extract device geometry, material properties, and lead layout features. This step is fundamental to building the network model, obtaining necessary modeling parameters through accurate analysis of the device's physical structure. The analysis process includes quantization of pin length, width, and thickness; extraction of electrical parameters of the package material; and description of the chip-lead connection topology. For advanced packages such as flip chips or flat leadless packages, it is also necessary to identify microbump or pad distribution features to ensure that the model accurately reflects the physical characteristics of the actual device.

[0021] Based on physical layout data, a spatial coordinate system for the test fixture is established, and the relative positional relationships between the fixture components are calculated. The spatial coordinate system is a mathematical framework describing the test environment, providing a positional reference for subsequent parasitic parameter calculations. The establishment process first determines the origin and reference plane, typically chosen as the center of the test board or the device mounting location. Then, each component of the test fixture is mapped onto the coordinate system, the three-dimensional coordinates of key nodes are recorded, and the distances, angles, and relative orientations between components are calculated, forming a complete spatial topology that provides the geometric basis for electromagnetic field analysis.

[0022] By considering the geometric dimensions and relative positions of the devices, the distributed parasitic inductance and capacitance parameters in the test circuit are calculated. This step is the core of network model construction, transforming the physical structure into electrical parameters using electromagnetic field theory. The calculation method is based on microstrip line theory and the equivalent circuit method for some components, taking into account conductor geometry, material electromagnetic properties, and coupling effects between adjacent conductors. For parasitic inductance, the value per unit length is calculated using the following formula: ; in, Inductance per unit length The permeability of free space, The height of the conductor from the reference plane. Where is the conductor width. For distributed capacitance, a parallel plate capacitance model and edge effect correction are used for calculation to ensure the accuracy and completeness of parameter distribution.

[0023] A network topology analysis method is employed to map distributed parasitic inductance and capacitance parameters to an equivalent circuit model. Topology analysis transforms the parameter distribution in physical space into the connection relationships of a circuit network, establishing a correspondence between the electrical and physical domains. The analysis process first identifies key nodes in the test loop, including signal input points, device connection points, and grounding points. Then, based on the physical connection relationships, a network topology diagram is constructed, and the calculated distributed parameters are allocated to the corresponding network branches, forming a preliminary equivalent circuit model. The model considers the mutual inductance effect of inductors, the coupling effect of capacitors, and skin effects and dielectric losses at high frequencies to ensure the physical accuracy of the model.

[0024] Equivalent circuit models are integrated using numerical simulation tools to generate a complete distributed parasitic parameter network model, ensuring that the model covers all parasitic effects along the transmission path. Model integration is the final step in the construction process. Professional circuit simulation software is used to unify and verify the effectiveness of each model component. The integration process employs a layered modeling strategy, gradually expanding from the device level to the system level, addressing interface matching issues between levels, resolving parameter overlap and boundary effects, and ultimately forming a high-precision network model covering the entire transmission path. Model verification involves comparing simulation results with measured data, adjusting key parameters until the error is less than a preset threshold, ensuring that the model can accurately predict electrical characteristics in the actual test environment.

[0025] In this embodiment of the invention, the detailed implementation steps for obtaining the impedance discontinuity distribution of each node of the test fixture through time-domain reflectometry and identifying the parasitic inductance concentration region and the distributed capacitance coupling node include: A step pulse signal is injected into the test fixture, and reflection waveform data from each measurement node are acquired. The step pulse is the standard excitation signal for time-domain reflection measurement, effectively exciting characteristic reflections at impedance discontinuities in the transmission path. A high-performance signal generator is used to produce a standard step pulse with a rise time of less than 200 ps, ​​which is introduced into the test fixture through a precision connector. Simultaneously, a high-speed sampling oscilloscope is used to acquire reflection waveforms at each key node, with a sampling rate typically exceeding 20 GSa / s to ensure the capture of minute reflection characteristics. The acquired data undergoes noise filtering and baseline correction to form a high-quality reflection waveform dataset, providing a reliable data foundation for subsequent analysis.

[0026] The reflected waveform data is differentiated to obtain the impedance change rate curve. Differentiation is an effective method for extracting impedance change characteristics, converting slow changes in the reflected waveform into sharp characteristic peaks, facilitating the identification of impedance discontinuities. The processing employs a numerical differentiation algorithm to calculate the first derivative of the reflected waveform: ; in, The waveform represents the reflection coefficient. Its derivative is the impedance change rate. To reduce noise amplification during the differentiation process, a Savitzky-Golay filter is used for smoothing, preserving the high-frequency characteristics of the signal while suppressing random noise. This ensures that the impedance change rate curve maintains the clarity of its characteristic peaks and has a good signal-to-noise ratio, providing high-quality data for subsequent feature point identification.

[0027] In the impedance rate of change curve, abrupt slope changes are identified, and the segments between adjacent changes are marked as candidate parasitic parameter concentration areas. These slope changes correspond to impedance abrupt changes in the transmission path and are potential regions for parasitic parameter concentration. The identification process employs an adaptive thresholding method, calculating the second derivative of the impedance rate of change curve and setting a dynamic threshold to filter significant inflection points, which are then marked as slope changes. The segments between adjacent changes represent regions with relatively consistent impedance characteristics. Based on the polarity and amplitude characteristics of the changes, the types of parasitic parameters they may correspond to are preliminarily determined; positive polarity is usually associated with parasitic inductance, and negative polarity is usually associated with distributed capacitance, providing candidate regions for subsequent frequency domain verification.

[0028] Frequency domain impedance scanning is performed on each candidate parasitic parameter concentration region to obtain the complex impedance frequency response curve. Frequency domain impedance scanning is a reliable method for verifying parasitic parameter types and can provide more information on impedance characteristics. The scanning process generates a logarithmic sweep frequency signal sequence covering 1MHz to 1GHz, which is sequentially injected into the candidate regions. The voltage and current phase differences and amplitude ratios at each frequency point are measured, and the complex impedance value is calculated. ; in, For frequency Complex impedance at that point, and These are the amplitudes of voltage and current, respectively. and These represent the phases of voltage and current, respectively. The imaginary unit is represented. Polynomial fitting is performed on the measured data to obtain a continuous complex impedance frequency response curve. This curve comprehensively reflects the electrical characteristics of the candidate region, providing an accurate basis for determining the type of parasitic parameters.

[0029] The type of parasitic parameter is determined by the sign of the imaginary part of the complex impedance frequency response curve. Regions with a positive imaginary part are marked as areas of concentrated parasitic inductance, and regions with a negative imaginary part are marked as nodes of distributed capacitance coupling. The sign of the imaginary part of the complex impedance directly reflects the nature of the circuit element; a positive value corresponds to inductance, and a negative value corresponds to capacitance. The determination process analyzes the characteristics of the imaginary part of the complex impedance curve in each frequency band, identifying regions with a consistently positive imaginary part in the main frequency band as areas of concentrated parasitic inductance, and regions with a consistently negative imaginary part as nodes of distributed capacitance coupling. For regions where the sign of the imaginary part changes with frequency, the dominant parasitic parameter type is determined by analyzing the resonant frequency and quality factor. This step completes the accurate classification of the parasitic characteristics of each node of the test fixture, providing an accurate physical basis for subsequent waveform distortion analysis.

[0030] In this embodiment of the invention, the detailed implementation steps for calculating the waveform distortion coefficient of an electrostatic pulse on the transmission path based on the inductance density distribution in the parasitic inductance concentration region and the capacitance gradient change at the distributed capacitance coupling node include: The parasitic inductance concentration region is divided into multiple micro-segments, and the inductance per unit length of each micro-segment is calculated. Micro-segmentation is fundamental for high-precision analysis, accurately reflecting the spatial distribution characteristics of parasitic inductance. The segmentation process divides the concentrated parasitic inductance region into several equal micro-segments based on its length and variation characteristics. Each segment is typically 1 / 20 to 1 / 50 of the total length to ensure sufficient spatial resolution. For each micro-segment, based on its geometric parameters and material properties, the inductance per unit length is calculated using a quasi-static electromagnetic field analysis method, forming a discrete inductance distribution dataset, providing the foundational data for subsequent spatial distribution function construction.

[0031] A spatial variation function of inductance density distribution is constructed to obtain the local peak locations of the inductance density. The inductance density distribution function describes the spatial variation of parasitic inductance and is a key basis for analyzing waveform distortion. The construction process employs a piecewise polynomial fitting method, interpolating the inductance values ​​of the infinitesimal segments to form a continuous inductance density distribution function. ; in, For position Inductance density at that point The coefficients of the polynomial, For reference position, The exponent is the power of the polynomial. After the function is constructed, the extreme points of the function are identified through numerical analysis, and the local peak positions of the inductance density are determined. These positions are the regions where waveform distortion is most significant, and their impact on electrostatic pulse propagation needs to be considered.

[0032] Mesh modeling is performed on the distributed capacitance coupling nodes, and the rate of capacitance change between adjacent grid cells is calculated. Mesh modeling is an effective method for analyzing the spatial characteristics of distributed capacitance, accurately capturing the non-uniformity of capacitance distribution. The modeling process divides the distributed capacitance coupling node region into two-dimensional or three-dimensional grids, typically with millimeter-level grid sizes, dynamically adjusted according to the region's complexity. For each grid cell, its equivalent capacitance value is calculated, and then the capacitance difference between adjacent grid cells is analyzed, calculating the rate of capacitance change per unit distance to form discrete capacitance gradient data. These data reflect the spatial non-uniformity of capacitance distribution and are important parameters for evaluating waveform distortion.

[0033] Based on the product of the local peak value of inductance density and the rate of change of capacitance, and combined with the rise time of the electrostatic pulse, the delay distortion coefficient of the pulse leading edge is calculated. The delay distortion coefficient quantifies the degree of time distortion of the electrostatic pulse leading edge during transmission and is a major manifestation of waveform distortion. The calculation formula is as follows: ; in, The delay distortion coefficient, As a correction factor, For the first Local peak values ​​of inductance density This represents the rate of change of capacitance at the corresponding location. Let be the rise time of the electrostatic pulse. This formula takes into account the coupling effect of the peak inductance density and the rate of change of capacitance, as well as the influence of pulse characteristics on distortion sensitivity, and can accurately predict the delayed distortion behavior of the electrostatic pulse leading edge.

[0034] The waveform distortion coefficient is calculated based on the delay distortion coefficient and the total length of the transmission path. The waveform distortion coefficient is a comprehensive assessment of the overall transmission path distortion effect, reflecting the degree of overall waveform distortion before the electrostatic pulse reaches the device port. The calculation formula is: ; in, The waveform distortion coefficient is... The delay distortion coefficient, The path length influencing factor, The total transmission path length is denoted by . This formula considers the impact of path length on the cumulative distortion effect; the longer the path, the more severe the distortion, which aligns with actual physical phenomena. The waveform distortion coefficient is a dimensionless parameter, typically ranging from [0,1]. A larger value indicates more severe distortion, providing a quantitative basis for subsequent waveform compensation and model construction.

[0035] In this embodiment of the invention, the detailed implementation steps for constructing a spatiotemporal evolution model of electrostatic pulse propagation based on waveform distortion coefficients and a distributed parasitic parameter network model include: Based on a distributed parasitic parameter network model, a set of transmission line equations is established, which includes the spatial distribution functions of parasitic inductance and distributed capacitance. Transmission line equations are fundamental equations describing the propagation behavior of electromagnetic waves on transmission lines, accurately reflecting waveform changes during transmission. The establishment process transforms the distributed parasitic parameter network model into partial differential equations, yielding a set of transmission line equations for voltage V and current I with respect to time t and spatial location x. These equations accurately describe the propagation behavior of electrostatic pulses along the transmission path, considering the spatially non-uniform distribution of parasitic parameters, and providing a mathematical foundation for spatiotemporal evolution models.

[0036] The waveform distortion coefficient is introduced as a correction factor into the transmission line equations to obtain the corrected wave equation. The correction step is crucial for considering non-ideal effects; by introducing the waveform distortion coefficient, the model can accurately reflect waveform changes in the actual transmission environment. The correction process introduces the distortion coefficient Kw into the transmission line equations, forming the corrected wave equation: ; in, The spatial-temporal function describing the distortion distribution is usually related to the signal frequency and location; It is a voltage function, representing the position in space. and time The voltage value; For position The inductance distribution function per unit length at a given point describes the spatial distribution of parasitic inductance along the transmission path; For position The capacitance distribution function per unit length describes the spatial distribution of the distributed capacitance along the transmission path. This modified equation considers the influence of waveform distortion on wave propagation speed and characteristic impedance, accurately simulating the propagation behavior of electrostatic pulses in actual test environments, and providing a complete mathematical model for subsequent numerical solutions.

[0037] The finite-difference time-domain (FDT) method is used to solve the modified wave equation, obtaining the time-domain waveforms of electrostatic pulses at different spatial locations. The FDT method is an efficient numerical technique for solving wave equations, capable of handling complex boundary conditions and inhomogeneous media problems. The solution process first discretizes time and space, constructing a difference scheme, typically using central difference or higher-order differences to improve accuracy. Then, appropriate time steps and spatial grid sizes are set to ensure numerical stability. Finally, iterative calculations are used to obtain the voltage and current values ​​at each grid point over time. Virtual probes are placed at key locations to record complete time-domain waveform data. This data visually demonstrates the waveform changes of the electrostatic pulse during its propagation from the generator to the device port, providing concrete content for the spatiotemporal evolution model.

[0038] A three-dimensional visualization process is performed on time-domain waveform data to construct a spatiotemporal evolution model of electrostatic pulse propagation. This model reflects the dynamic changes of the pulse waveform along its transmission path. Visualization is the final step in building an intuitive model, transforming abstract data into a comprehensible visual representation. The process uses time, spatial location, and signal amplitude as three-dimensional coordinates to generate a spatiotemporal evolution surface of the waveform, clearly demonstrating the wavefront changes, amplitude attenuation, and shape distortion of the electrostatic pulse during transmission. The model also includes two-dimensional cross-sections at key time points and time-domain waveforms at key locations, providing multi-angle analytical views. This spatiotemporal evolution model is a comprehensive description that fully reflects the propagation characteristics of electrostatic pulses in the test system, providing an intuitive basis for pre-distortion waveform design and serving as an important tool for evaluating the performance of the test system.

[0039] In this embodiment of the invention, the detailed implementation steps for generating a compensatory predistortion waveform based on a spatiotemporal evolution model and canceling waveform distortion caused by parasitic parameters through inverse modulation include: Based on the spatiotemporal evolution model, the expected distortion waveform characteristics at the device port are extracted. These characteristics form the basis for designing the compensation waveform, reflecting the actual waveform state of the electrostatic pulse arriving at the device port without compensation. The extraction process selects the time-domain waveform at the device port location from the spatiotemporal evolution model and analyzes its differences from the standard electrostatic pulse waveform, including features such as rise time extension, peak attenuation, ringing effect, and tail distortion. These features are quantitatively described to obtain a set of distortion characteristic parameters, including rise delay coefficient, peak attenuation rate, ringing frequency, and ringing amplitude. These parameters comprehensively characterize the impact of parasitic responses on the waveform, providing clear targets for subsequent compensation design.

[0040] The inverse Fourier transform of the expected distorted waveform characteristics is performed to obtain the inverse function of the distortion transfer function. The distortion transfer function describes the frequency domain transformation relationship of parasitic parameters on the signal, and its inverse function is crucial for designing predistortion. The processing first involves performing a Fourier transform on the expected distorted waveform and the standard waveform, calculating the ratio of their spectra, and obtaining the distortion transfer function. : ; in, This represents the spectrum of the distorted waveform. The spectrum of the standard waveform is given. Then the inverse function of the transfer function is calculated. This inverse function serves as the frequency domain characteristic of pre-distortion, used to compensate for the distortion effects of the transmission path. For frequency points that may lead to numerical instability, regularization is employed to ensure the effectiveness and stability of the inverse function.

[0041] The initial predistorted waveform is generated by convolving the standard electrostatic pulse waveform with its inverse function. Convolution is an effective method for frequency-domain filtering in the time domain, applying the characteristics of the inverse function to the standard waveform. The operation can be performed in the frequency domain by multiplying the spectrum of the standard electrostatic pulse waveform by the inverse function and then transforming it back to the time domain using an inverse Fourier transform to obtain the initial predistorted waveform. Alternatively, the convolution operation can be performed directly in the time domain, calculating the convolution integral of the time-domain response of the standard waveform and the corresponding inverse function according to the convolution theorem. The initial predistorted waveform exhibits characteristics significantly different from the standard waveform, typically including overshoot, pre-emphasis, and phase pre-adjustment. These characteristics are designed to counteract parasitic effects during transmission, ensuring that the waveform arriving at the device port closely approximates standard requirements.

[0042] The amplitude envelope and phase distribution of the initial predistortion waveform are adjusted by an iterative optimization algorithm to minimize the error between the actual waveform at the device port and the standard electrostatic pulse waveform, resulting in an optimized waveform. Iterative optimization is a key step in improving the compensation effect. Through multiple simulations and parameter adjustments, the design of the predistortion waveform is continuously improved. The optimization process first sets an objective function, typically the mean square error between the actual waveform and the standard waveform at the device port. Then, techniques such as gradient descent, genetic algorithms, or particle swarm optimization are used to adjust the key parameters of the predistortion waveform, including the amplitude envelope, phase distribution, and frequency components. Simulation evaluations are repeated until the error reaches a preset threshold or no longer shows significant improvement. The optimized result is a predistortion waveform that can maximally offset the effects of parasitic effects and, after actual transmission, can generate a near-standard electrostatic pulse waveform at the device port.

[0043] The optimized waveform is output as a compensating predistortion waveform to the electrostatic pulse generator. The output stage is the final step in the predistortion design, transforming the theoretical design into a practically executable control signal. The output process first converts the optimized waveform into a digital control signal or analog drive waveform acceptable to the electrostatic pulse generator, then transmits it to the generator via a standard interface, sets the corresponding trigger conditions and output parameters, and prepares for actual testing. Before output, the system performs a waveform validity check to ensure that the predistortion waveform will not cause generator overload or generate unsafe electrical outputs. The compensating predistortion waveform achieves active compensation for parasitic effects in the test system, significantly improving the accuracy and reliability of electrostatic protection testing, and providing technical support for the accurate evaluation of the protection performance of power devices.

[0044] In this embodiment of the invention, the detailed implementation steps for extracting the energy dissipation characteristic spectrum during electrostatic discharge by performing wavelet packet decomposition on the transient voltage and current response signal include: Multi-level wavelet packet decomposition is performed on transient voltage and current response signals by selecting wavelet basis functions that match the spectral characteristics of the electrostatic pulse. The choice of wavelet basis functions directly affects the decomposition effect and needs to match the characteristics of the analysis object. The selection process considers the time-frequency characteristics of the electrostatic pulse, and orthogonal wavelet bases with good time-frequency localization characteristics, such as Daubechies wavelet, Symlet wavelet, or Meyer wavelet, are usually selected. Basis functions that can effectively express transient changes and high-frequency oscillations are preferred. Wavelet packet decomposition is an extension of standard wavelet decomposition, decomposing not only the low-frequency part but also the high-frequency part, providing more uniform frequency band division and more refined time-frequency analysis capabilities. The decomposition process uses the fast wavelet transform algorithm to decompose the signal layer by layer into approximate coefficients and detail coefficients, usually performing 4-6 layers of decomposition to form a complete wavelet packet decomposition tree, covering the entire frequency band from low frequency to high frequency, providing comprehensive data for energy distribution analysis.

[0045] The wavelet coefficient energies at each decomposition level are calculated to construct a time-frequency energy distribution matrix. Wavelet coefficient energies reflect the energy distribution of the signal in different time-frequency regions and are core data for analyzing energy dissipation characteristics. The calculation process involves summing the squares of the wavelet coefficients at each node (corresponding to a specific time-frequency region) in the wavelet packet decomposition tree to obtain the energy value of that node. ; in, For the first Layer The energy of each node For the corresponding node's first Each wavelet coefficient is used. The energy values ​​of all nodes are arranged according to time and frequency coordinates to construct a two-dimensional time-frequency energy distribution matrix. Each element in the matrix represents the signal energy in a specific time and frequency range, which intuitively reflects the energy distribution law in the time and frequency domain during electrostatic discharge.

[0046] Energy concentration regions are identified in the time-frequency energy distribution matrix, and the time nodes and frequency components corresponding to the energy peaks are extracted. These energy concentration regions represent key time-frequency characteristics in the electrostatic discharge process and are crucial for judging device response characteristics. The identification process employs cluster analysis and peak detection algorithms to locate local areas in the time-frequency energy distribution matrix with significantly higher energy values ​​than the surrounding areas, marking them as energy concentration regions. For each concentration region, the time node and frequency component corresponding to its center position, as well as the region's total energy value, area, and shape characteristics, are recorded. These characteristic parameters reflect the time-frequency characteristics of energy distribution during electrostatic discharge. Different devices or different protection structures will exhibit significantly different characteristic patterns, providing a basis for differentiation in subsequent protection performance evaluation.

[0047] The energy dissipation rate of the electrostatic discharge process is calculated based on the distribution density of time nodes and the bandwidth of frequency components. The energy dissipation rate describes the dynamic process of energy absorption and dissipation by a device under electrostatic shock and is a key indicator for evaluating protection response speed and efficiency. The calculation method is based on the temporal distribution characteristics of the energy concentration region. First, the time span of the main energy concentration region is determined, and then the rate of energy change per unit time is calculated. ; in, For time The rate of energy dissipation at that location, For time window The energy changes within the device are analyzed. Simultaneously, the bandwidth characteristics of the frequency components are analyzed; a more dispersed frequency indicates more diverse energy dissipation paths, which helps prevent the formation of local hot spots. Combining these two factors, a complete energy dissipation rate curve is obtained, reflecting the change in the device's energy handling capability throughout the entire electrostatic shock process.

[0048] The energy dissipation rate over different time periods is combined to form an energy dissipation characteristic spectrum. This spectrum provides a comprehensive description of the energy behavior throughout the electrostatic discharge process, encompassing complete information across three dimensions: time, frequency, and energy. The combination process integrates the energy dissipation rate curve with frequency distribution characteristics to create a multidimensional characteristic spectrum, typically including characteristic parameters for three key stages: the initial response stage, the main discharge stage, and the recovery stage. The characteristic spectrum undergoes normalization to eliminate the influence of differences in test conditions, ensuring comparability of test results from different devices. The energy dissipation characteristic spectrum is core data for evaluating electrostatic protection performance; devices with different protection structures and performance levels will exhibit significantly different characteristic spectrum patterns, providing a comprehensive quantitative basis for subsequent protection performance evaluation.

[0049] In this embodiment of the invention, the detailed implementation steps for evaluating the electrostatic discharge (ESD) protection performance of power devices based on the time-frequency distribution characteristics of the energy dissipation characteristic spectrum include: Principal component analysis (PCA) is performed on the energy dissipation characteristic spectrum to extract key eigenvectors characterizing electrostatic protection performance. PCA is an effective technique for dimensionality reduction and feature extraction, capable of identifying the most representative feature combinations from complex characteristic spectra. The analysis process first standardizes the multidimensional data of the energy dissipation characteristic spectrum to eliminate dimensional and scale differences. Then, the covariance matrix between features is calculated, and its eigenvalues ​​and eigenvectors are solved. Principal components are selected by sorting the eigenvalues. Typically, the top few principal components with a cumulative contribution rate of over 85% are selected as key eigenvectors. These vectors contain the main information of the energy dissipation characteristic spectrum, simplifying the complexity of subsequent analysis while retaining the key features required for evaluation, providing a concise and effective feature representation for protection performance assessment.

[0050] The correlation coefficient between the key feature vector and the standard protection performance template is calculated to obtain the protection performance matching degree. The standard protection performance template is a reference standard established based on a large amount of test data and theoretical analysis, representing ideal protection performance characteristics. The matching degree is calculated using the cosine similarity method, which calculates the cosine value of the angle between the key feature vector and the standard template vector: ; in, For matching degree, As the key feature vector, For standard template vectors, and These represent the magnitudes of the two vectors. The matching degree ranges from -1 to 1; a value closer to 1 indicates that the protection characteristics of the device under test are closer to the ideal standard, and it has better protection performance. For different application scenarios or different types of electrostatic threats, different standard templates can be used to calculate the matching degree to comprehensively evaluate the device's protection capabilities.

[0051] The risk of localized hotspot formation within a device is assessed based on the proportion of high-frequency components in the energy dissipation characteristic spectrum. The proportion of high-frequency components reflects the energy distribution in high-frequency channels and is directly related to the risk of localized hotspot formation. The assessment method first identifies the high-frequency region in the characteristic spectrum, typically defined as a frequency band exceeding twice the center frequency of the characteristic spectrum. Then, the proportion of energy in this region to the total energy is calculated as the high-frequency component proportion indicator. A higher proportion indicates excessive energy concentration in high-frequency channels, potentially leading to localized hotspot formation within the device and increasing the risk of thermal damage; a lower proportion indicates a more uniform energy distribution and lower thermal risk. By setting reasonable thresholds, the high-frequency component proportion is converted into a quantitative hotspot risk rating, providing a reference for the thermal stability dimension of the comprehensive assessment.

[0052] A comprehensive electrostatic discharge (ESD) protection performance index is generated based on a weighted combination of protection performance matching degree and the risk of local hotspot formation. This comprehensive index provides an overall evaluation of the device's ESD protection performance, considering the combined effects of multiple evaluation dimensions. The index is generated using a weighted calculation method. ; in, To comprehensively assess electrostatic protection performance indicators, To ensure the matching of protection performance, Risks may arise from localized hotspots. and These are weighting coefficients, typically This reflects the dominant role of protection effectiveness in the overall assessment. The specific value of the weighting coefficient can be dynamically adjusted according to the application scenario and reliability requirements. For applications requiring long-term reliable operation, the weighting coefficient can be appropriately increased. Greater emphasis is placed on thermal stability. Comprehensive indicators are usually standardized to the range of [0,100] for easy intuitive understanding and classification.

[0053] The overall electrostatic discharge (ESD) protection performance index is compared with preset thresholds to determine the ESD protection level of power devices. The protection level is the final evaluation result, directly guiding the selection of device applications and the formulation of protective measures. The determination process sets a series of thresholds based on industry standards and actual application requirements, mapping the overall index to specific protection levels, typically categorized into four levels: excellent, good, average, and insufficient. Different application areas may use different threshold standards; for example, automotive electronics and medical devices usually have higher protection level requirements than consumer electronics. The determination result includes both the overall protection level and detailed scores for each evaluation dimension, providing comprehensive reference information for device application and design improvement, ensuring that electronic systems have sufficient ESD protection capabilities in real-world application environments.

[0054] The above describes the automated testing method for the electrostatic discharge (ESD) protection performance of novel power devices in the embodiments of this application. The following describes the automated testing system for the ESD protection performance of novel power devices in the embodiments of this application. Please refer to [link / reference]. Figure 2 One embodiment of the automated testing system for the electrostatic discharge protection performance of novel power devices in this application includes: The parameter acquisition module is used to acquire the package structure parameters of the power device under test and the physical layout data of the test fixture; The parasitic parameter modeling module is used to construct a distributed parasitic parameter network model of the test loop based on the package structure parameters and physical layout data. The time-domain reflectometry module is used to obtain the impedance discontinuity distribution of each node of the test fixture through time-domain reflectometry technology, and to identify the parasitic inductance concentration area and the distributed capacitance coupling node. The waveform distortion calculation module is used to calculate the waveform distortion coefficient of electrostatic pulses on the transmission path based on the inductance density distribution in the parasitic inductance concentration area and the capacitance gradient change of the distributed capacitance coupling node. The spatiotemporal evolution modeling module is used to construct a spatiotemporal evolution model of electrostatic pulse propagation based on waveform distortion coefficients and a distributed parasitic parameter network model. The predistortion waveform generation module is used to generate compensatory predistortion waveforms based on the spatiotemporal evolution model, and to cancel the waveform distortion caused by parasitic parameters through inverse modulation; The response signal acquisition module is used to acquire the transient voltage and current response signals of the device port in real time during the application of the compensating predistortion waveform; The energy feature extraction module is used to extract the energy dissipation feature spectrum during electrostatic discharge by performing wavelet packet decomposition on the transient voltage and current response signal. The performance evaluation module is used to evaluate the electrostatic protection performance of power devices based on the time-frequency distribution characteristics of the energy dissipation characteristic spectrum. The modules are connected via wired and / or wireless means to enable data transmission between them.

[0055] This invention acquires the packaging structure parameters of the power device under test and the physical layout data of the test fixture, constructs a distributed parasitic parameter network model of the test circuit, and combines time-domain reflectometry and waveform distortion analysis to generate a compensating pre-distortion waveform to offset the waveform distortion caused by parasitic parameters, thereby achieving accurate evaluation of the electrostatic discharge (ESD) protection performance of power devices. It features high precision, high reliability, and high testing efficiency, and can meet the ESD protection performance testing needs of various new power devices.

[0056] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

[0057] It should be noted that all formulas in this manual are calculated by removing dimensions and taking their numerical values. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters and thresholds in the formulas are set by those skilled in the art according to the actual situation.

[0058] Although embodiments of the invention have been shown and described, those skilled in the art will understand that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.

Claims

1. An automated testing method for the electrostatic discharge (ESD) protection performance of novel power devices, characterized in that, include: Obtain the package structure parameters of the power device under test and the physical layout data of the test fixture; Based on the encapsulation structure parameters and the physical layout data, a distributed parasitic parameter network model of the test loop is constructed. The impedance discontinuity distribution of each node of the test fixture is obtained by time-domain reflectometry, and the parasitic inductance concentration area and the distributed capacitance coupling node are identified. Based on the inductance density distribution in the parasitic inductance concentration region and the capacitance gradient change of the distributed capacitance coupling node, the waveform distortion coefficient of the electrostatic pulse on the transmission path is calculated. Based on the waveform distortion coefficient and the distributed parasitic parameter network model, a spatiotemporal evolution model for electrostatic pulse propagation is constructed. Based on the spatiotemporal evolution model, a compensatory predistortion waveform is generated, and the waveform distortion caused by parasitic parameters is canceled by inverse modulation; During the application of the compensating predistortion waveform, the transient voltage and current response signals at the device ports are acquired in real time. By performing wavelet packet decomposition on the transient voltage and current response signal, the energy dissipation characteristic spectrum during electrostatic discharge is extracted. The electrostatic discharge (ESD) protection performance of power devices is evaluated based on the time-frequency distribution characteristics of the energy dissipation characteristic spectrum.

2. The automated testing method for the electrostatic discharge (ESD) protection performance of novel power devices according to claim 1, characterized in that, The method of obtaining the impedance discontinuity distribution of each node of the test fixture through time-domain reflectometry and identifying parasitic inductance concentration areas and distributed capacitance coupling nodes includes: A step pulse signal is injected into the test fixture, and the reflected waveform data of each measurement node is collected; The reflected waveform data is differentiated to obtain the impedance change rate curve; Identify abrupt slope changes in the impedance rate curve and mark the segments between adjacent abrupt changes as candidate parasitic parameter concentration areas. Frequency domain impedance scanning is performed on each of the candidate parasitic parameter concentration regions to obtain the complex impedance frequency response curve; The parasitic parameter type is determined based on the sign of the imaginary part of the complex impedance frequency response curve. Regions with a positive imaginary part are marked as the parasitic inductance concentration region, and regions with a negative imaginary part are marked as the distributed capacitance coupling node.

3. The automated testing method for the electrostatic discharge protection performance of novel power devices according to claim 1, characterized in that, The calculation of the waveform distortion coefficient of the electrostatic pulse on the transmission path based on the inductance density distribution of the parasitic inductance concentration region and the capacitance gradient change of the distributed capacitance coupling node includes: The parasitic inductance concentration region is divided into multiple micro-segments, and the inductance value per unit length of each micro-segment is calculated. Construct a spatial variation function of the inductance density distribution to obtain the local peak positions of the inductance density; The distributed capacitance coupling nodes are modeled in a grid, and the capacitance change rate between adjacent grids is calculated. Based on the product of the local peak value of the inductance density and the rate of change of capacitance, and combined with the rise time of the electrostatic pulse, the delay distortion coefficient of the pulse leading edge is calculated. The waveform distortion coefficient is calculated based on the delay distortion coefficient and the total length of the transmission path.

4. The automated testing method for the electrostatic discharge protection performance of novel power devices according to claim 1, characterized in that, The step of constructing a spatiotemporal evolution model for electrostatic pulse propagation based on the waveform distortion coefficient and the distributed parasitic parameter network model includes: Based on the distributed parasitic parameter network model, a set of transmission line equations is established, which includes the spatial distribution functions of parasitic inductance and distributed capacitance. The waveform distortion coefficient is introduced as a correction factor into the transmission line equations to obtain the corrected wave equation. The modified wave equation is solved using the finite difference time-domain method to obtain the time-domain waveforms of the electrostatic pulse at different spatial locations; The time-domain waveform is subjected to three-dimensional visualization processing to construct a spatiotemporal evolution model of the electrostatic pulse propagation.

5. The automated testing method for the electrostatic discharge protection performance of novel power devices according to claim 1, characterized in that, The generation of a compensatory predistortion waveform based on the spatiotemporal evolution model, and the cancellation of waveform distortion caused by parasitic parameters through inverse modulation, includes: Based on the spatiotemporal evolution model, extract the expected distortion waveform features at the device port; Perform an inverse Fourier transform on the expected distorted waveform characteristics to obtain the inverse function of the distortion transfer function; The standard electrostatic pulse waveform is convolved with the inverse function to generate the initial pre-distortion waveform. The amplitude envelope and phase distribution of the initial predistortion waveform are adjusted by an iterative optimization algorithm to minimize the error between the actual waveform at the device port and the standard electrostatic pulse waveform, thereby obtaining the optimized waveform. The optimized waveform is output to the electrostatic pulse generator as the compensating predistortion waveform.

6. The automated testing method for the electrostatic discharge (ESD) protection performance of a novel power device according to claim 1, characterized in that, The step of extracting the energy dissipation feature spectrum during electrostatic discharge by performing wavelet packet decomposition on the transient voltage and current response signal includes: Select a wavelet basis function that matches the spectral characteristics of the electrostatic pulse, and perform multi-layer wavelet packet decomposition on the transient voltage and current response signal; Calculate the wavelet coefficient energy of each decomposition level and construct the time-frequency energy distribution matrix; Identify energy concentration regions in the time-frequency energy distribution matrix and extract the time nodes and frequency components corresponding to the energy peaks; The energy dissipation rate of the electrostatic discharge process is calculated based on the distribution density of the time nodes and the bandwidth of the frequency components. The energy dissipation rates at different time periods are combined to form the energy dissipation characteristic spectrum.

7. The automated testing method for the electrostatic discharge protection performance of novel power devices according to claim 1, characterized in that, The evaluation of the electrostatic discharge (ESD) protection performance of power devices based on the time-frequency distribution characteristics of the energy dissipation characteristic spectrum includes: Principal component analysis was performed on the energy dissipation characteristic spectrum to extract key feature vectors characterizing electrostatic protection performance; Calculate the correlation coefficient between the key feature vector and the standard protection performance template to obtain the protection performance matching degree; The risk of local hotspot formation inside the device is assessed based on the proportion of high-frequency components in the energy dissipation characteristic spectrum. A comprehensive electrostatic discharge (ESD) protection performance index is generated based on a weighted combination of the protection performance matching degree and the risk of local hot spots forming. The electrostatic discharge (ESD) protection performance index is compared with a preset threshold to determine the ESD protection level of the power device.

8. The automated testing method for the electrostatic discharge protection performance of novel power devices according to claim 2, characterized in that, The step of performing a frequency domain impedance scan on each of the candidate parasitic parameter concentration regions to obtain a complex impedance frequency response curve includes: Generate an equally spaced sweep frequency signal sequence within a preset frequency range; The frequency sweep signal sequence is injected sequentially into the candidate parasitic parameter concentration region, and the voltage and current phase difference at each frequency point is measured. Calculate the complex impedance value at each frequency point based on the voltage-current phase difference and amplitude ratio. Polynomial fitting is performed on the complex impedance value to obtain a continuous complex impedance frequency response curve; The quality factor analysis of the complex impedance frequency response curve is performed to identify the resonant frequency point and the anti-resonant frequency point.

9. The automated testing method for the electrostatic discharge protection performance of a novel power device according to claim 4, characterized in that, The step of solving the modified wave equation using the finite difference time-domain method to obtain the time-domain waveforms of the electrostatic pulse at different spatial locations includes: The transmission path is discretized into a spatial grid, and the time step is set to satisfy the numerical stability condition. An update equation for voltage and current is established at each grid node, and the update equation includes local correction terms for parasitic parameters; The update equation is solved using an alternating direction implicit algorithm to obtain the voltage and current distribution at each time step; Virtual probes are set at key spatial locations to record time-domain waveform data at the virtual probes. The time-domain waveform data is interpolated and reconstructed to generate a continuous electrostatic pulse spatial propagation waveform, i.e., a time-domain waveform.

10. An automated testing system for the electrostatic discharge (ESD) protection performance of novel power devices, used to implement the automated testing method for the ESD protection performance of novel power devices as described in any one of claims 1 to 9, characterized in that, include: The parameter acquisition module is used to acquire the package structure parameters of the power device under test and the physical layout data of the test fixture; The parasitic parameter modeling module is used to construct a distributed parasitic parameter network model of the test loop based on the encapsulation structure parameters and the physical layout data. The time-domain reflectometry module is used to obtain the impedance discontinuity distribution of each node of the test fixture through time-domain reflectometry technology, and to identify the parasitic inductance concentration area and the distributed capacitance coupling node. The waveform distortion calculation module is used to calculate the waveform distortion coefficient of the electrostatic pulse on the transmission path based on the inductance density distribution of the parasitic inductance concentration region and the capacitance gradient change of the distributed capacitance coupling node. The spatiotemporal evolution modeling module is used to construct a spatiotemporal evolution model of electrostatic pulse propagation based on the waveform distortion coefficients and the distributed parasitic parameter network model. The predistortion waveform generation module is used to generate a compensating predistortion waveform based on the spatiotemporal evolution model, and to cancel the waveform distortion caused by parasitic parameters through inverse modulation; The response signal acquisition module is used to acquire the transient voltage and current response signals of the device port in real time during the application of the compensating predistortion waveform; The energy feature extraction module is used to extract the energy dissipation feature spectrum during electrostatic discharge by performing wavelet packet decomposition on the transient voltage and current response signal. The performance evaluation module is used to evaluate the electrostatic protection performance index of the power device based on the time-frequency distribution characteristics of the energy dissipation characteristic spectrum. The modules are connected via wired and / or wireless means to enable data transmission between them.

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