Method, device and equipment for analyzing electrostatic protection characteristics of TVS tube

Abstract

The present application discloses a method, device and equipment for analyzing the electrostatic protection characteristics of a TVS tube, and relates to the field of electrostatic discharge protection analysis. The method includes: obtaining information data of the TVS tube; inputting the information data into a combination model to obtain electrostatic protection characteristic data; the electrostatic protection characteristic data is used to characterize the electrostatic protection characteristics of the TVS tube; the combination model includes: an RLC circuit model and a dual-capacitor model; the RLC circuit model is a physical simulation model obtained by optimizing and fitting based on parasitic effects using a particle swarm optimization algorithm; the dual-capacitor model is a simulation model that uses simulation software and electrostatic voltage to achieve the reproduction of the current waveform corresponding to electrostatic excitation; the present application can improve the accuracy and universality of electrostatic protection characteristic analysis.

Description

Technical Field

[0001] The present application relates to the field of electrostatic discharge protection analysis, and in particular to a method, device and equipment for analyzing the electrostatic protection characteristics of a TVS tube. Background Art

[0002] Static electricity is a ubiquitous phenomenon in which positive and negative charges are locally unbalanced on the surface of an object. Electrostatic discharge (ESD) is one of the main causes of circuit failure in various stages of chip manufacturing, assembly, transportation, and application. If protection is not in place, the instantaneous charge release is likely to cause complete failure of the chip, and may also cause system-level failures, and even endanger the safety of life and property. Transient Voltage Suppressor (TVS) is a voltage-limiting overvoltage protection device. Due to its extremely fast response time and high surge absorption capacity, as an off-chip ESD protection device, it is usually connected in parallel at both ends of the protection circuit to protect the internal circuit from the risk of excessive voltage breakdown.

[0003] In order to evaluate the protection effect of TVS tubes and their application circuits in different occasions, different electrostatic excitation models such as human body models, machine models, and charged device charging models are often used to test the protection ability of TVS tubes against electrostatic discharge. In fact, different electrostatic generators have different waveforms and different test methods. If the effectiveness of different electrostatic discharge protection circuits is to be fully evaluated, a lot of time and manpower are required. By establishing an electrostatic excitation model through simulation methods and analyzing the electrostatic protection ability of TVS tubes and their application circuits, the cost and time of electrostatic protection tests can be saved, and the protection effect of TVS tube circuits can be predicted. The existing standards define the waveforms and circuit schematics of different electrostatic models, but the characteristic parameters of the electrostatic model built according to the schematic cannot fully meet the waveforms specified in the standards. At the same time, the TVS tube diode model also has the problem of insufficient consideration of parasitic parameters in the actual protection circuit, and the sensitivity to the electrical characteristic parameters of TVS tubes of different specifications is different, so the universality is not strong. Therefore, how to improve the accuracy and universality of modeling has become a technical problem that needs to be solved in this field. Summary of the invention

[0004] The purpose of this application is to provide a method, device and equipment for analyzing the electrostatic protection characteristics of a TVS tube, which can improve the accuracy and universality of the electrostatic protection characteristics analysis.

[0005] To achieve the above objectives, this application provides the following solutions:

[0006] In a first aspect, the present application provides a method for analyzing the electrostatic protection characteristics of a TVS tube, comprising:

[0007] Acquire information data of the TVS tube; the information data includes: setting parameters and operating frequency range; the setting parameters include: breakdown voltage and clamping voltage;

[0008] The information data is input into the combined model to obtain electrostatic protection characteristic data; the electrostatic protection characteristic data includes: an impedance waveform curve and an electrostatic excitation waveform curve; the electrostatic protection characteristic data is used to characterize the electrostatic protection characteristics of the TVS tube; the combined model includes: an RLC circuit model and a dual capacitor model; the RLC circuit model is a physical simulation model obtained by optimizing and fitting based on parasitic effects using a particle swarm optimization algorithm; the dual capacitor model is a simulation model that uses simulation software to reproduce the current waveform corresponding to electrostatic excitation based on electrostatic voltage; the impedance waveform curve is a curve of impedance changes with operating frequency; the electrostatic excitation waveform curve is a curve based on the change of the set parameters with time.

[0009] Optionally, the method for determining the RLC circuit model specifically includes:

[0010] Get the operating frequency range of the tested TVS tube;

[0011] Using a network analyzer to perform an impedance test on the test TVS tube within a corresponding operating frequency range to obtain a test impedance waveform curve;

[0012] Constructing a TVS tube parasitic parameter RLC model; the TVS tube parasitic parameter RLC model is a physical model constructed based on the parasitic parameters of the TVS tube; the parasitic parameters include: parasitic inductance, breakdown resistance, radio frequency resistance, junction capacitance and parasitic capacitance;

[0013] The particle swarm optimization algorithm is used to perform impedance analysis and iterative optimization of parasitic parameters on the RLC model of TVS tube parasitic parameters based on the tested impedance waveform curve.

[0014] The TVS tube parasitic parameter RLC model corresponding to the optimized parasitic parameters is determined as the RLC circuit model.

[0015] Optionally, a particle swarm optimization algorithm is used to perform impedance analysis and iterative optimization of parasitic parameters on the RLC model of the TVS tube parasitic parameters based on the tested impedance waveform curve, specifically including:

[0016] Input the operating frequency range of the tested TVS tube into the TVS tube parasitic parameter RLC model to obtain the output impedance waveform curve;

[0017] The objective function is determined by the least square method; the objective function is the accumulation of the square sum of the errors of the real part and the imaginary part of the impedance corresponding to the impedance curve; the impedance curve includes: a tested impedance waveform curve and an output impedance waveform curve;

[0018] The particle swarm optimization algorithm is adopted to iteratively optimize the parasitic parameters with the minimum value of the objective function as the goal.

[0019] Optionally, the objective function is expressed as:

[0020] ;

[0021] ;

[0022] ;

[0023] in, is the objective function; is the real part of the impedance corresponding to the output impedance waveform curve; The real part of the impedance corresponding to the impedance waveform curve tested; is the imaginary part of impedance corresponding to the output impedance waveform curve; The imaginary part of the impedance corresponding to the impedance waveform curve tested; is the breakdown resistance; is the RF resistance; is the junction capacitance; is the frequency; is the first real coefficient corresponding to the output impedance waveform curve; is the real second coefficient corresponding to the output impedance waveform curve; is the parasitic capacitance; is the imaginary coefficient corresponding to the output impedance waveform curve; is the serial number; The total number of information data.

[0024] Optionally, a particle swarm optimization algorithm is used to iteratively optimize the parasitic parameters with the minimum value of the objective function as the target, specifically including:

[0025] Determine initial parameters and initial positions of individual particles; the initial parameters include: spatial dimension, learning factor, flight speed and set number of iterations; the individual particles are information data in the combined model;

[0026] Determine a particle swarm population according to the spatial dimension; the particle swarm population is composed of a plurality of the particle individuals;

[0027] According to the learning factor at the current iteration number, the real-time information of each individual particle in the particle swarm at the current iteration number is determined based on the boundary constraint; the real-time information includes: real-time position and real-time flight speed;

[0028] Calculating the relative position and relative speed of each individual particle according to the real-time information;

[0029] Determine the objective function as a fitness function;

[0030] Based on the fitness function, determining a fitness value at a current number of iterations according to the relative position and the relative speed;

[0031] Determine whether the iteration stop condition is met; the iteration stop condition is that the current iteration number reaches the set iteration number or the fitness value under the current iteration number is the minimum;

[0032] If the iteration stop condition is not met, the learning factor at the current iteration is updated according to the fitness value at the current iteration, and the updated learning factor is used as the learning factor at the next iteration, and "the real-time information of each individual particle in the particle swarm at the current iteration is determined based on the boundary constraint according to the learning factor at the current iteration" is returned;

[0033] If the iteration stop condition is met, the learning factor at the current iteration number is taken as the optimal learning factor;

[0034] The corresponding real-time information is determined according to the optimal learning factor, and then the corresponding optimized parasitic parameters are determined.

[0035] Optionally, the method for determining the dual-capacitance model specifically includes:

[0036] Constructing an electrostatically-actuated dual-capacitor model; the electrostatically-actuated dual-capacitor model comprises: a first power supply branch and a second power supply branch;

[0037] The electrostatic excitation dual capacitor model is converted from a time domain form to an s domain form by Laplace transformation to obtain impedance analysis data; the impedance analysis data includes: a series impedance value of a first power branch and a series impedance value of a second power branch;

[0038] Using an ESD simulator structure, based on the ground loop cable length and the cable circumference, determining the inductance parameters in the electrostatic excitation dual capacitor model;

[0039] Using MATLAB software, based on the current waveform under different voltage levels, pull-type transformation is performed to determine the resistance and capacitance parameters in the electrostatic excitation dual capacitor model;

[0040] Performing inverse Laplace transformation processing according to the inductance parameter, the resistance and capacitance parameters and the impedance analysis data to obtain a time domain current;

[0041] Using simulation software, the current waveform is reproduced and fitted according to the time domain current based on the electrostatic voltage to obtain a dual capacitor model.

[0042] In a second aspect, the present application provides a device for analyzing electrostatic protection characteristics of a TVS tube, comprising:

[0043] An information data acquisition module is used to acquire information data of the TVS tube; the information data includes: setting parameters and operating frequency range; the setting parameters include: breakdown voltage and clamping voltage;

[0044] A processing module is used to input the information data into a combined model to obtain electrostatic protection characteristic data; the electrostatic protection characteristic data includes: an impedance waveform curve and an electrostatic excitation waveform curve; the electrostatic protection characteristic data is used to characterize the electrostatic protection characteristics of the TVS tube; the combined model includes: an RLC circuit model and a dual-capacitor model; the RLC circuit model is a physical simulation model obtained by optimizing and fitting based on parasitic effects using a particle swarm optimization algorithm; the dual-capacitor model is a simulation model that uses simulation software to reproduce the current waveform corresponding to electrostatic excitation based on electrostatic voltage; the impedance waveform curve is a curve of impedance changes with operating frequency; the electrostatic excitation waveform curve is a curve based on the change of the set parameters with time.

[0045] In a third aspect, the present application provides a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-mentioned method for analyzing the electrostatic protection characteristics of the TVS tube.

[0046] According to the specific embodiments provided in this application, this application has the following technical effects:

[0047] The present application provides a method, device and equipment for analyzing the electrostatic protection characteristics of a TVS tube. Taking the measured impedance characteristics of the TVS and its application circuit as the optimization target, an RLC model considering parasitic parameters is constructed, and the parasitic parameters of the RLC model are quickly extracted based on the particle swarm optimization algorithm. By constructing a dual-capacitor model that meets different excitation characteristics, the numerical calculation and analysis of the electrostatic current excitation waveform is realized. In addition, a combined model based on the dual-capacitor model and the RLC model considering the parasitic parameters of the TVS tube is constructed, and finally the electrostatic protection circuit characteristic simulation of the TVS tube is realized to improve the accuracy and universality of the electrostatic protection characteristic analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0049] Figure 1 Flow chart of a method for analyzing the electrostatic protection characteristics of a TVS tube in one embodiment of the present application.

[0050] Figure 2 Schematic diagram of the RLC model of TVS tube parasitic parameters.

[0051] Figure 3 Schematic diagram of the process of optimizing parasitic parameters using particle swarm algorithm.

[0052] Figure 4 The figure is a schematic diagram of the operating steps of the electrostatic protection characteristics analysis method of TVS tubes in practical applications.

[0053] Figure 5 This is a schematic diagram of the real part curve of TVS tube impedance.

[0054] Figure 6 This is a schematic diagram of the imaginary part curve of the TVS tube impedance.

[0055] Figure 7 Schematic diagram of the real part fitting curve based on least squares and particle swarm algorithm.

[0056] Figure 8 Schematic diagram of the imaginary part fitting curve based on least squares and particle swarm algorithm.

[0057] Fig. 9 A schematic diagram of the structure of a computer device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0058] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0059] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0060] In an exemplary embodiment, Figure 1 As shown, a method for analyzing the electrostatic protection characteristics of a TVS tube is provided. The method is executed by a computer device, and can be specifically executed by a computer device such as a terminal or a server alone, or by a terminal and a server together. In an embodiment of the present application, the method is applied to a server as an example for explanation, and includes the following steps.

[0061] Step 100: Obtain information data of the TVS tube. The information data includes: setting parameters and operating frequency range; the setting parameters include: breakdown voltage and clamping voltage.

[0062] Step 200: Input the information data into the combined model to obtain electrostatic protection characteristic data. The electrostatic protection characteristic data includes: an impedance waveform curve and an electrostatic excitation waveform curve; the electrostatic protection characteristic data is used to characterize the electrostatic protection characteristics of the TVS tube; the combined model includes: an RLC circuit model and a dual capacitor model; the RLC circuit model is a physical simulation model obtained by optimizing and fitting based on parasitic effects using a particle swarm optimization algorithm; the dual capacitor model is a simulation model that uses simulation software to reproduce the current waveform corresponding to electrostatic excitation based on electrostatic voltage; the impedance waveform curve is a curve of impedance changes with operating frequency; the electrostatic excitation waveform curve is a curve based on the change of the set parameters over time.

[0063] In another exemplary embodiment of the present application, a method for determining an RLC circuit model specifically includes:

[0064] Obtain the operating frequency range of the test TVS tube; use a network analyzer to perform impedance testing on the test TVS tube within the corresponding operating frequency range to obtain the test impedance waveform curve.

[0065] Construct the RLC model of TVS tube parasitic parameters; the RLC model of TVS tube parasitic parameters is a physical model constructed based on the parasitic parameters of the TVS tube; the parasitic parameters include: parasitic inductance, breakdown resistance, RF resistance, junction capacitance and parasitic capacitance.

[0066] The particle swarm optimization algorithm is used to perform impedance analysis and iterative optimization of parasitic parameters on the RLC model of the TVS tube parasitic parameters based on the tested impedance waveform curve; the RLC model of the TVS tube parasitic parameters corresponding to the optimized parasitic parameters is determined as the RLC circuit model.

[0067] Among them, the particle swarm optimization algorithm is used to perform impedance analysis and iterative optimization of parasitic parameters on the RLC model of TVS tube parasitic parameters based on the tested impedance waveform curve, including:

[0068] The operating frequency range of the tested TVS tube is input into the TVS tube parasitic parameter RLC model to obtain the output impedance waveform curve.

[0069] The objective function is determined by the least square method; the objective function is the accumulation of the square sum of the errors of the real part and the imaginary part of the impedance corresponding to the impedance curve; the impedance curve includes: a tested impedance waveform curve and an output impedance waveform curve.

[0070] The particle swarm optimization algorithm is used to iteratively optimize the parasitic parameters with the minimum value of the objective function as the goal.

[0071] Specifically, the expression of the objective function is:

[0072] .

[0073] .

[0074] .

[0075] in, is the objective function; is the real part of the impedance corresponding to the output impedance waveform curve; The real part of the impedance corresponding to the impedance waveform curve tested; is the imaginary part of impedance corresponding to the output impedance waveform curve; The imaginary part of the impedance corresponding to the impedance waveform curve tested; is the breakdown resistance; is the RF resistance; is the junction capacitance; is the frequency; is the first real coefficient corresponding to the output impedance waveform curve; is the real second coefficient corresponding to the output impedance waveform curve; is the parasitic capacitance; is the imaginary coefficient corresponding to the output impedance waveform curve; is the serial number; The total number of information data.

[0076] In another exemplary embodiment of the present application, a particle swarm optimization algorithm is used to iteratively optimize the parasitic parameters with the minimum value of the objective function as the goal, specifically including:

[0077] Determine the initial parameters and the initial position of the individual particles; the initial parameters include: space dimension, learning factor, flight speed and set number of iterations; the individual particles are the information data in the combined model.

[0078] The particle swarm population is determined according to the spatial dimension; the particle swarm population is composed of multiple individual particles.

[0079] According to the learning factor at the current iteration number, the real-time information of each individual particle in the particle swarm at the current iteration number is determined based on the boundary constraint; the real-time information includes: real-time position and real-time flight speed.

[0080] According to the real-time information, the relative position and relative speed of each individual particle are calculated; the objective function is determined as the fitness function; based on the fitness function, the fitness value under the current number of iterations is determined according to the relative position and relative speed.

[0081] Determine whether the iteration stop condition is met; the iteration stop condition is that the current iteration number reaches the set iteration number or the fitness value under the current iteration number is the minimum.

[0082] If the iteration stop condition is not met, the learning factor at the current iteration number is updated according to the fitness value at the current iteration number, and the updated learning factor is used as the learning factor at the next iteration number, and returns "Based on the learning factor at the current iteration number, determine the real-time information of each individual particle in the particle swarm at the current iteration number based on the boundary constraints".

[0083] If the iteration stop condition is met, the learning factor at the current iteration number is taken as the optimal learning factor.

[0084] The corresponding real-time information is determined according to the optimal learning factor, and then the corresponding optimized parasitic parameters are determined.

[0085] In one embodiment, a method for determining a dual-capacitance model specifically includes:

[0086] An electrostatically-actuated dual-capacitor model is constructed; the electrostatically-actuated dual-capacitor model includes: a first power supply branch and a second power supply branch.

[0087] The electrostatic excitation dual capacitor model is converted from a time domain form to an s domain form by Laplace transformation to obtain impedance analysis data; the impedance analysis data includes: a series impedance value of a first power branch and a series impedance value of a second power branch.

[0088] The inductance parameters in the electrostatically excited dual-capacitor model are determined based on the ESD simulator structure and the ground loop cable length and cable circumference.

[0089] Using MATLAB software, pull-type transformation is performed based on the current waveform under different voltage levels to determine the resistance and capacitance parameters in the electrostatic excitation dual capacitor model.

[0090] According to the inductance parameters, resistance and capacitance parameters and impedance analysis data, Laplace inverse transformation is performed to obtain the time domain current.

[0091] Using simulation software, the current waveform is reproduced and fitted according to the time domain current based on the electrostatic voltage to obtain a dual capacitor model.

[0092] The core idea of ​​the analysis method mentioned in this application is to target the non-ideal characteristics of the TVS tube protection circuit by constructing an RLC circuit model that takes parasitic effects into account, namely, the RLC model, decomposing the impedance expression of the RLC model into real and imaginary parts, constructing the minimum square error sum of the measured impedance real part and the impedance imaginary part as the objective function based on the least squares method, and using the particle swarm optimization algorithm to fit the optimization parameters that meet the test data, so as to achieve the simulation of the non-ideal characteristics of the TVS tube protection circuit. In view of the problem that some simulation characteristic parameters of the existing standard electrostatic excitation model cannot fully meet the standard, a dual-capacitor model is constructed, the dual-capacitor model is converted from the time domain to the s domain, and MATLAB is used to numerically calculate and correct the model parameters to achieve the reproduction of the current excitation waveform, and then complete the simulation modeling in the simulation software. A combined RLC circuit model based on dual capacitors and considering the parasitic parameters of the TVS tube is built, and finally the analysis of the electrostatic protection characteristics of the TVS tube is realized, which solves the problem that the modeling of the TVS tube electrostatic protection circuit is not universal.

[0093] In practical applications, such as Figure 4 As shown, the method provided by the present application specifically includes the following steps.

[0094] Step 1. Determine the main characteristic parameters and operating frequency range of the TVS tube.

[0095] Step 2. Extract the impedance characteristics within the specified frequency range of the TVS tube circuit, fit the extracted impedance characteristics, and obtain the curves of the real and imaginary parts of the impedance changing with frequency.

[0096] Step 3. Extract TVS tube parasitic parameters based on particle swarm combinatorial optimization algorithm.

[0097] Step 3.1: Consider the parasitic parameters generated at the operating frequency and construct the TVS tube parasitic parameter RLC model. The schematic diagram of the TVS tube parasitic parameter RLC model is shown in Figure 2 .

[0098] Step 3.2: Impedance of TVS tube parasitic parameters RLC model analysis. is the resistance in the RLC model. Represents the imaginary part.

[0099] (1)

[0100] Total reactance of the circuit for:

[0101] (2)

[0102] in, is the inductive reactance of the circuit, is the capacitive reactance of the circuit.

[0103] Parasitic capacitance not connected in parallel Impedance The expression is:

[0104] (3)

[0105] At this time, the impedance of the corresponding TVS tube parasitic parameter RLC model is The expression is:

[0106] (4)

[0107] in parallel The overall impedance expression is:

[0108] (5)

[0109] in, is the parasitic inductance; is the breakdown resistance; is the RF resistance; is the junction capacitance; is the parasitic capacitance.

[0110] Step 3.3: Use the least squares method to construct the parasitic parameter optimization objective function.

[0111] Angular frequency ω With frequency f There is a relationship between ω=2πf , so the impedance and frequency have a univariate nonlinear function relationship. Using the least squares method, the impedance of formula (5) is separated into the following real and imaginary expressions. The real part expression is:

[0112] (6)

[0113] in:

[0114] (7)

[0115] (8)

[0116] The imaginary part expression is:

[0117] (9)

[0118] in:

[0119] (10)

[0120] Therefore, the objective function is the accumulation of the sum of squares of the two errors:

[0121] (11)

[0122] When the objective function takes the minimum value, the corresponding five parameters are the model parameter values ​​that best match the actual situation. This objective function is the fitness function, which serves as a criterion for judging the quality of particle positions.

[0123] Step 3.4: Particle swarm algorithm optimizes parasitic parameters, such as Figure 3 As shown, the specific steps are:

[0124] 1) Initialize the particle swarm population. The population size, that is, the total number of information data, is N , initialize the particle group velocity, and assume that each particle has D positions, then:

[0125] (12)

[0126] No. i The speed of a particle is also D dimensional vector, denoted as:

[0127] (13)

[0128] 2) Record individual historical optimal solutions and global optimal solutions.

[0129] The position that best meets the objective function requirement experienced by the i-th particle is called the individual historical optimal solution. , expressed as:

[0130] (14)

[0131] The position that best meets the objective function requirements experienced by all particles in the group is called the global optimal solution. , expressed as:

[0132] (15)

[0133] 3) Initialize the individual learning factor c1 and the collective learning factor c2. c1 represents the weight of the particle's next action derived from its own experience, that is, pushing the particle to the individual optimal position The acceleration weight, c2 represents the weight of the particle's next action derived from the experience of other particles, that is, pushing the particle to the optimal position of the group. The acceleration weight.

[0134] 4) Set the inertia weight . Using a linear change strategy, the formula is:

[0135] (16)

[0136] represents the maximum inertia weight, represents the minimum inertia weight, represents the current algebra, Represents the maximum number of generations of a cycle.

[0137] 5) Update the speed of the particle group and set a limit on the speed that exceeds the boundary setting. If the upper limit is exceeded, the principle of downward compatibility is adopted, that is:

[0138] (17)

[0139] If the particle speed exceeds the lower boundary, the upward compatibility principle is adopted, that is:

[0140] (18)

[0141] for The new velocity is calculated without bounds checking, is the maximum velocity of the j-th dimension, is the minimum velocity of the j-th dimension. is the particle number, represents the number of generations of the current population, , It is a random number in the range [0,1], which increases the randomness of the search.

[0142] 6) Update the position of the particle swarm and constrain the position that exceeds the boundary setting. The constraint principle is the same as the speed constraint principle, that is:

[0143] (19)

[0144] (20)

[0145] In the formula for The new position is calculated without bounds checking, is the maximum value of the j-th dimension position, is the minimum value of the j-th dimension.

[0146] 7) Perform iterative parameter optimization with the objective function in step 3.3 as the goal. Each particle calculates its fitness value in each cycle. If the calculated result is better than its historical fitness value, the result is taken as the historical optimal solution of the particle. If the calculated result is better than the historical fitness value of all particles in the group, the result is taken as the historical optimal solution of the group. .

[0147] 8) Set the maximum number of iterations to stop the condition and obtain the optimal solution for the RLC model parameters of the TVS tube parasitic parameters.

[0148] Step 4. Modeling of electrostatically excited dual-capacitor circuit model.

[0149] Step 4.1: Analyze the electrostatic excitation model and waveform specified in the standard. The standard excitation model generally consists of a resistor The discharge circuit formed by the capacitor and the resistor varies according to the discharge model to simulate the actual effect. For example, in the HBM model, the resistor R Take the human body resistance of 1.5kΩ and discharge the capacitor C Take 100pF, and the MM model, R Take 0Ω, capacitor C Take 200pF. Since the excitation model is too simple, the simulated waveform parameters cannot fully meet the standard waveform.

[0150] Step 4.2: Construct an electrostatic excitation dual-capacitor model and perform numerical analysis. The dual-capacitor model consists of two power supply branches. The first branch retains the standard electrostatic excitation model structure analyzed in step 4.1, and adds an inductor to improve the smoothness near the peak current. L 1 ; The second branch includes capacitor C 2 and another voltage source, and a protective resistor in series R 2 and ground inductance L 2 , to avoid too fast a rise time caused by too fast a capacitor discharge.

[0151] The time domain model is converted into an s domain model through Laplace transform, where: is the series impedance value of the first power branch, is the series impedance value of the second power branch, and the numerical analysis steps are:

[0152] (twenty one)

[0153] From Kirchhoff's current law:

[0154] (twenty two)

[0155] in, is the input current; is the output current.

[0156] Get including resistance The branch current The expression is:

[0157] (twenty three)

[0158] Finally, perform an inverse Laplace transform to obtain the current in the time domain , thus obtaining the current waveform. The current function corresponding to the current waveform The expression is:

[0159] (twenty four)

[0160] Step 4.3: Determine the inductor parameters from Step 4.2.

[0161] According to the actual ESD simulator structure of the ground loop cable length l , Cable circumference p , the ground inductance can be estimated L 2 for:

[0162] (25)

[0163] in, is the magnetic permeability of air, p is the circumference of the conductor cross section, l The length of the wire.

[0164] Initial setup L 1 = L 2 , compare the current peak value, rise time and fall time obtained in step 4.4 with the waveform in the standard, and complete L 1 of fine-tuning.

[0165] Step 4.4: Determine the resistance and capacitance parameters in step 4.2.

[0166] Selection criteria C 1 , R 1 . R 2 The effect on the waveform is small, set R 2 = R 1 , R 3 For load, choose different C 2 , perform pull-type transformation in MATLAB to obtain the current waveform under different voltage levels, and compare the MATLAB fitting results with the waveform in the standard to correct C 2 Parameters until the current waveform meets the waveform parameter characteristics specified in the standard.

[0167] Step 4.5: Establish an electrostatic current excitation dual capacitor model. According to the values ​​fitted by MATLAB, establish an electrostatic excitation simulation model for reproduction to ensure that the dual capacitor model meets the current requirements under different voltage levels.

[0168] Step 5. According to the RLC model obtained by extracting the TVS tube parasitic parameters based on particle swarm combinatorial optimization in step 3 and the electrostatic current excitation dual capacitor model obtained in step 4, a combined model for TVS tube electrostatic protection simulation is established.

[0169] Step 6: Simulate the electrostatic protection characteristics of the TVS tube based on the combined model.

[0170] In practical applications, the method provided in this application is adopted, and the HBM electrostatic protection application scenario is taken as an example to realize the simulation modeling of the HBM human body electrostatic protection characteristics of a certain type of TVS tube, as follows.

[0171] S1. Determine the characteristic parameters of a certain type of TVS tube to be used. The breakdown voltage of the TVS tube is 24V, the clamping voltage is no more than 55V, the junction capacitance is 30pF, and the operating frequency of the application is 1MHz~100MHz.

[0172] S2. Extract the impedance of the TVS tube in the 1MHz~100MHz frequency band, use a network analyzer to test the impedance, and fit the test frequency points and impedance values ​​to obtain the impedance real and imaginary curves of 1MHz~100MHz. Figure 5 and Figure 6 shown.

[0173] S3. Extract TVS tube parasitic parameters based on particle swarm combinatorial optimization algorithm.

[0174] S3.1: Construct an RLC model of the TVS electrostatic protection circuit taking into account the parasitic parameters generated at the operating frequency.

[0175] S3.2: RLC model impedance analysis, see formula (1) to formula (5) for details.

[0176] S3.3: The objective function is constructed using least squares to separate the impedance of formula (5) into real and imaginary parts, as shown in formulas (6) to (10). The objective function is the cumulative sum of square errors of the real and imaginary parts of the impedance, as shown in formula (11).

[0177] S3.4: Particle swarm algorithm is used to optimize parasitic parameters. The population size is set to 100, and the position boundary is the range of inductance, resistance, and capacitance of the TVS tube. The lower boundary is 0. According to the actual situation of the TVS tube, the upper boundary sets the maximum capacitance value to pF The maximum inductance is μHThe resistance is not more than 50Ω. The particle group velocity is set to 10%~20% of its position change range. c 1 = c 2 =2. The impedance of 100 TVS tubes at 1MHz~100MHz measured in step 2 is taken as the test set, the algorithm loop number is set to 200 times, and the iteration is performed in MATLAB. The final optimization parameter values ​​are shown in Table 1.

[0178] Table 1 Optimization parameter values ​​based on particle swarm algorithm

[0179]

[0180] Depend on Figure 7 and Figure 8 It can be seen that after the parasitic parameters are optimized, the fitting curve after the real and imaginary impedance waveforms are fitted is basically consistent with the measured curve.

[0181] S4, electrostatically excited dual capacitor modeling.

[0182] S4.1: Analyze the HBM electrostatic excitation model and waveform in the standard, with current peak, rise time, and fall time as the main parameters. The rise time is the time required for the maximum current to rise from 10% to 90%. The standard stipulates 2 ns ~10 ns The fall time is the time required for the maximum current to drop from 100% to 36.8%. The standard stipulates that 130 ns ~170 ns The peak discharge current increases with the increase of test voltage. The reference electrostatic excitation model in the HBM standard is simulated in the simulation software PSpise, and 2 kV Pulse power supply, cycle 1 ns , pulse width 1 ns , the rise and fall times are both 0.1 ns The peak value of the output current waveform is 1.31 A , the rise time is 2.34 ns , the fall time is 150.9 ns ,Compared to the standard discharge waveform, the rise time of the classic circuit model is too short, and its waveform near the peak current is too sharp and not smooth enough, and cannot reproduce the standard waveform well.

[0183] S4.2: Construct the HBM electrostatic excitation dual capacitor model. The first branch includes the resistor in the standard R 1 and capacitor C 1 And the inductor that improves the curve smoothness near the peak current L1 The second branch consists of capacitor C 2 , protection resistor R 2 and ground inductance L 2 , R 3 is the load resistance. The model is subjected to Laplace transformation for numerical calculation, as shown in formula (21) to formula (24).

[0184] S4.3: Determine the inductor parameters in S4.2.

[0185] The ground loop cable in the actual HBM electrostatic simulator is generally 2 m , perimeter 2 cm , according to formula (22), determine L 2 =2.5 ,set up L 1 =2.5 .

[0186] S4.4: Determine the resistance and capacitance parameters in S4.2.

[0187] According to the HBM electrostatic excitation standard requirements, set C 1 =100pF, R 1 =1.5 kΩ . R 1 , C 2 It has a great influence on the discharge current. R 2 The change of resistance value has little effect on the waveform. R 2 = R 1 , C 2 In 1 pF ~100 pF Take the value in the middle, perform pull-type transformation in MATLAB, and select the one with the best overlap according to the overlap degree of the waveforms. C 2 is 100 pF .

[0188] S4.5: Establish HBM electrostatic current excitation dual capacitor simulation model. Setting 2 kV Pulse voltage, cycle 1 ns , pulse width 1 ns , rising and falling edges are 0.1 ns, build a dual capacitor model in pSpice, and finally get a peak current of 1.426 A , rise time 3.8 ns , fall time 131.43 ns , meeting the requirements specified in the standard.

[0189] S5. Build the RLC model obtained by extracting the TVS tube parasitic parameters based on particle swarm combinatorial optimization in step S3 and the HBM electrostatic current excitation dual capacitor simulation model obtained in step S4, substitute the optimized and extracted model parameters, and obtain the TVS tube parasitic parameter extraction and electrostatic protection simulation model based on particle swarm combinatorial optimization.

[0190] S6. Exciting the combined simulation model, that is, the combined model, it can be concluded that when the electrostatic voltage exceeds the breakdown voltage of the TVS tube, the TVS tube absorbs transient energy and clamps the voltage at 45 V , 200 ns Then, as the charge release voltage decreases, the TVS tube returns to normal state. The protection capability of the TVS tube can be evaluated through transient analysis of waveform power changes.

[0191] Based on the same inventive concept, the embodiment of the present application also provides a TVS tube protection characteristic analysis device for implementing the above-mentioned TVS tube protection characteristic analysis method. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above-mentioned method, so the specific limitations in the embodiments of the one or more TVS tube protection characteristic analysis devices provided below can refer to the limitations of the TVS tube protection characteristic analysis method above, and will not be repeated here.

[0192] In an exemplary embodiment, a device for analyzing electrostatic protection characteristics of a TVS tube is provided, comprising: an information data acquisition module and a processing module.

[0193] The information data acquisition module is used to acquire the information data of the TVS tube; the information data includes: setting parameters and operating frequency range; the setting parameters include: breakdown voltage and clamping voltage.

[0194] The processing module is used to input information data into the combined model to obtain electrostatic protection characteristic data; the electrostatic protection characteristic data includes: impedance waveform curve and electrostatic excitation waveform curve; the electrostatic protection characteristic data is used to characterize the electrostatic protection characteristics of the TVS tube; the combined model includes: RLC circuit model and dual capacitor model; the RLC circuit model is a physical simulation model obtained by optimizing and fitting based on parasitic effects using a particle swarm optimization algorithm. The dual capacitor model is a simulation model that uses simulation software to reproduce the current waveform corresponding to electrostatic excitation based on electrostatic voltage; the impedance waveform curve is a curve of impedance changes with operating frequency; the electrostatic excitation waveform curve is a curve based on the change of the set parameters over time.

[0195] Beneficial effects:

[0196] 1) This application constructs an RLC model of the TVS tube considering parasitic effects, constructs an objective function with the impedance characteristic measurement results of different frequency bands as the optimization target, and optimizes and fits the RLC model parameters through the particle swarm optimization algorithm to obtain model parameters that meet the actual impedance characteristics, effectively improving the applicability of modeling non-ideal characteristics of TVS tube protection circuits in different working ranges.

[0197] 2) By constructing a dual-capacitor model to perform numerical simulation of the current waveform, the defect that some characteristic parameters in the classic electrostatic model simulation cannot fully meet the standards is solved. By correcting the parameter values ​​in the dual-capacitor model, it can be used to simulate electrostatic discharge models such as the human body model (HBM), machine model (MM), and charge device model (CDM).

[0198] 3) The method mentioned in this application is universal and can be used to evaluate the protection effect of different TVS tubes and application circuits under different electrostatic excitations.

[0199] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Fig. 9As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. Among them, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device 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 the computer program in the non-volatile storage medium. The database of the computer device is used to store the protection characteristic analysis data of the TVS tube. The input / output interface of the computer device is used to exchange information between the processor and the external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for analyzing the electrostatic protection characteristics of a TVS tube is implemented.

[0200] Those skilled in the art will understand that Fig. 9 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components. In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.

[0201] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0202] In an exemplary embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0203] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0204] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0205] The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but is not limited thereto.

[0206] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0207] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A method for analyzing the electrostatic protection characteristics of a TVS tube, characterized in that: The electrostatic protection characteristic analysis method of the TVS tube includes: Acquire information data of the TVS tube; the information data includes: setting parameters and operating frequency range; the setting parameters include: breakdown voltage and clamping voltage; The information data is input into the combined model to obtain electrostatic protection characteristic data; the electrostatic protection characteristic data includes: an impedance waveform curve and an electrostatic excitation waveform curve; the electrostatic protection characteristic data is used to characterize the electrostatic protection characteristics of the TVS tube; the combined model includes: an RLC circuit model and a dual capacitor model; the RLC circuit model is a physical simulation model obtained by optimizing and fitting based on parasitic effects using a particle swarm optimization algorithm; the dual capacitor model is a simulation model that uses simulation software to reproduce the current waveform corresponding to electrostatic excitation based on electrostatic voltage; the impedance waveform curve is a curve of impedance changes with operating frequency; the electrostatic excitation waveform curve is a curve based on the change of the set parameters with time; The method for determining the RLC circuit model specifically includes: Get the operating frequency range of the tested TVS tube; Using a network analyzer to perform an impedance test on the test TVS tube within a corresponding operating frequency range to obtain a test impedance waveform curve; Constructing a TVS tube parasitic parameter RLC model; the TVS tube parasitic parameter RLC model is a physical model constructed based on the parasitic parameters of the TVS tube; the parasitic parameters include: parasitic inductance, breakdown resistance, radio frequency resistance, junction capacitance and parasitic capacitance; The particle swarm optimization algorithm is used to perform impedance analysis and iterative optimization of parasitic parameters on the RLC model of TVS tube parasitic parameters based on the tested impedance waveform curve. The TVS tube parasitic parameter RLC model corresponding to the optimized parasitic parameters is determined as the RLC circuit model; The method for determining the dual-capacitance model specifically includes: Constructing an electrostatically-actuated dual-capacitor model; the electrostatically-actuated dual-capacitor model comprises: a first power supply branch and a second power supply branch; The electrostatic excitation dual capacitor model is converted from a time domain form to an s domain form by Laplace transformation to obtain impedance analysis data; the impedance analysis data includes: a series impedance value of a first power branch and a series impedance value of a second power branch; Using an ESD simulator structure, based on the ground loop cable length and the cable circumference, determining the inductance parameters in the electrostatic excitation dual capacitor model; Using MATLAB software, based on the current waveform under different voltage levels, pull-type transformation is performed to determine the resistance and capacitance parameters in the electrostatic excitation dual capacitor model; Performing inverse Laplace transformation processing according to the inductance parameter, the resistance and capacitance parameters and the impedance analysis data to obtain a time domain current; Using simulation software, the current waveform is reproduced and fitted according to the time domain current based on the electrostatic voltage to obtain a dual capacitor model.

2. The method for analyzing the electrostatic protection characteristics of a TVS tube according to claim 1, characterized in that: The particle swarm optimization algorithm is used to perform impedance analysis and iterative optimization of parasitic parameters on the RLC model of TVS tube parasitic parameters based on the tested impedance waveform curve, including: Input the operating frequency range of the tested TVS tube into the TVS tube parasitic parameter RLC model to obtain the output impedance waveform curve; The objective function is determined by the least square method; the objective function is the accumulation of the square sum of the errors of the real part and the imaginary part of the impedance corresponding to the impedance curve; the impedance curve includes: a tested impedance waveform curve and an output impedance waveform curve; The particle swarm optimization algorithm is adopted to iteratively optimize the parasitic parameters with the minimum value of the objective function as the goal.

3. The method for analyzing the electrostatic protection characteristics of a TVS tube according to claim 2, characterized in that: The expression of the objective function is: ； ； ； in, is the objective function; is the real part of the impedance corresponding to the output impedance waveform curve; The real part of the impedance corresponding to the impedance waveform curve tested; is the imaginary part of impedance corresponding to the output impedance waveform curve; The imaginary part of the impedance corresponding to the tested impedance waveform curve; is the breakdown resistance; is the RF resistance; is the junction capacitance; is the frequency; is the first real coefficient corresponding to the output impedance waveform curve; is the real second coefficient corresponding to the output impedance waveform curve; is the parasitic capacitance; is the imaginary coefficient corresponding to the output impedance waveform curve; is the serial number; The total number of information data.

4. The method for analyzing the electrostatic protection characteristics of a TVS tube according to claim 2, characterized in that: The particle swarm optimization algorithm is used to iteratively optimize the parasitic parameters with the minimum value of the objective function as the goal, specifically including: Determine initial parameters and initial positions of individual particles; the initial parameters include: spatial dimension, learning factor, flight speed and set number of iterations; the individual particles are information data in the combined model; Determine a particle swarm population according to the spatial dimension; the particle swarm population is composed of a plurality of the particle individuals; According to the learning factor at the current iteration number, the real-time information of each individual particle in the particle swarm at the current iteration number is determined based on the boundary constraint; the real-time information includes: real-time position and real-time flight speed; Calculating the relative position and relative speed of each individual particle according to the real-time information; Determine the objective function as a fitness function; Based on the fitness function, determining a fitness value at a current number of iterations according to the relative position and the relative speed; Determine whether the iteration stop condition is met; the iteration stop condition is that the current iteration number reaches the set iteration number or the fitness value under the current iteration number is the minimum; If the iteration stop condition is not met, the learning factor at the current iteration number is updated according to the fitness value at the current iteration number, and the updated learning factor is used as the learning factor at the next iteration number, and "according to the learning factor at the current iteration number, the real-time information of each individual particle in the particle swarm at the current iteration number is determined based on the boundary constraint" is returned; If the iteration stop condition is met, the learning factor at the current iteration number is taken as the optimal learning factor; The corresponding real-time information is determined according to the optimal learning factor, and then the corresponding optimized parasitic parameters are determined.

5. A device for analyzing the electrostatic protection characteristics of a TVS tube, characterized in that: The electrostatic protection characteristic analysis device of the TVS tube comprises: An information data acquisition module is used to acquire information data of the TVS tube; the information data includes: setting parameters and operating frequency range; the setting parameters include: breakdown voltage and clamping voltage; A processing module is used to input the information data into the combined model to obtain electrostatic protection characteristic data; the electrostatic protection characteristic data includes: an impedance waveform curve and an electrostatic excitation waveform curve; the electrostatic protection characteristic data is used to characterize the electrostatic protection characteristics of the TVS tube; the combined model includes: an RLC circuit model and a dual-capacitor model; the RLC circuit model is a physical simulation model obtained by optimizing and fitting based on parasitic effects using a particle swarm optimization algorithm; the dual-capacitor model is a simulation model that uses simulation software to achieve the reproduction of the current waveform corresponding to electrostatic excitation based on electrostatic voltage; the impedance waveform curve is a curve of impedance changes with operating frequency; the electrostatic excitation waveform curve is a curve based on the change of the set parameters with time; The method for determining the RLC circuit model specifically includes: Get the operating frequency range of the tested TVS tube; Using a network analyzer to perform an impedance test on the test TVS tube within a corresponding operating frequency range to obtain a test impedance waveform curve; Constructing a TVS tube parasitic parameter RLC model; the TVS tube parasitic parameter RLC model is a physical model constructed based on the parasitic parameters of the TVS tube; the parasitic parameters include: parasitic inductance, breakdown resistance, radio frequency resistance, junction capacitance and parasitic capacitance; The particle swarm optimization algorithm is used to perform impedance analysis and iterative optimization of parasitic parameters on the RLC model of TVS tube parasitic parameters based on the tested impedance waveform curve. The TVS tube parasitic parameter RLC model corresponding to the optimized parasitic parameters is determined as the RLC circuit model; The method for determining the dual-capacitance model specifically includes: Constructing an electrostatically-actuated dual-capacitor model; the electrostatically-actuated dual-capacitor model comprises: a first power supply branch and a second power supply branch; The electrostatic excitation dual capacitor model is converted from a time domain form to an s domain form by Laplace transformation to obtain impedance analysis data; the impedance analysis data includes: a series impedance value of a first power branch and a series impedance value of a second power branch; Using an ESD simulator structure, based on the ground loop cable length and the cable circumference, determining the inductance parameters in the electrostatic excitation dual capacitor model; Using MATLAB software, based on the current waveform under different voltage levels, pull-type transformation is performed to determine the resistance and capacitance parameters in the electrostatic excitation dual capacitor model; Performing inverse Laplace transformation processing according to the inductance parameter, the resistance and capacitance parameters and the impedance analysis data to obtain a time domain current; Using simulation software, the current waveform is reproduced and fitted according to the time domain current based on the electrostatic voltage to obtain a dual capacitor model.

6. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the electrostatic protection characteristic analysis method of the TVS tube according to any one of claims 1 to 4.

Images