CN110298126A - A kind of polynary Copula power device method for evaluating reliability based on the physics of failure - Google Patents
A kind of polynary Copula power device method for evaluating reliability based on the physics of failure Download PDFInfo
- Publication number
- CN110298126A CN110298126A CN201910597236.0A CN201910597236A CN110298126A CN 110298126 A CN110298126 A CN 110298126A CN 201910597236 A CN201910597236 A CN 201910597236A CN 110298126 A CN110298126 A CN 110298126A
- Authority
- CN
- China
- Prior art keywords
- failure
- power device
- parameter
- copula
- physical model
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/30—Circuit design
- G06F30/36—Circuit design at the analogue level
- G06F30/367—Design verification, e.g. using simulation, simulation program with integrated circuit emphasis [SPICE], direct methods or relaxation methods
Landscapes
- Engineering & Computer Science (AREA)
- Computer Hardware Design (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Microelectronics & Electronic Packaging (AREA)
- Evolutionary Computation (AREA)
- Geometry (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The present invention relates to a kind of polynary Copula power device method for evaluating reliability based on the physics of failure, comprising the following steps: step 1: power device information is collected, and determines actual condition, internal structure and existing failure mechanism；Step 2: failure physical model application and model parameter randomization determine the edge distribution of each structural life-time；Step 3: power device reliability assessment compares Monte Carlo competing failure method；Its theory based on the physics of failure, from power device, existing failure mechanism and failure physical model are started under actual condition, the parameter lacked in model is obtained using emulation, the reliability expression of power device under more failure mechanisms is derived using polynary Archimedean Copula.Pass through comparison Monte Carlo-competing failure method for evaluating reliability, it was demonstrated that polynary Copula model belongs to component reliability assessment technique field it can be considered that cross-influential factors between each mechanism.
Description
(1) technical field:
The present invention relates to a kind of polynary Copula power device method for evaluating reliability based on the physics of failure, it is based on losing
The theory of physics is imitated, existing failure mechanism and failure physical model are started under actual condition from power device, analysis failure
Parameter value in physical model obtains the parameter of missing using emulation.Derive lose using polynary Archimedean Copula more
Imitate the reliability expression of power device under mechanism.By comparing Monte Carlo-competing failure method for evaluating reliability, it was demonstrated that more
First Copula model belongs to component reliability assessment technique field it can be considered that cross-influential factors between each mechanism.
(2) background technique:
As microelectronic component develops to small size, extensive and high-performance direction, its method for evaluating reliability is also mentioned
Higher requirement is gone out.Model-driven and data-driven two major classes can be divided into for the reliability evaluation of device at present.Based on mistake
The evaluation of effect physical model needs analysis elements device inside failure mechanism, understands physics, the chemical process for leading to failure, is to improve
The basic research of product reliability.
However, often obtaining most weak point using single failure physical model for the reliability evaluation of power device at present
The lifetime results of position, and there are complicated more ambient stress sections in a practical situation, the evaluation under simple stress is not accurate enough.
And consider that competing failure thought is then widely applied in the device reliability evaluation under the influence of failure mechanism, it is not intended that between failure mechanism
Existing cross-influential factors, such as temperature, power will affect multiple reliability of structures in device.For this purpose, this method considers
The power device reliability evaluation of each structural failure service life distribution correlation, proposes a kind of consideration caused by public influence factor
The power device method for evaluating reliability of more failure mechanisms, more failpoints.For the service life merged more failure mechanisms, consider each structure
The power device reliability evaluation for being distributed correlation provides Evaluation example, establishes higher efficiency and more inexpensive evaluation method stream
Journey.
(3) summary of the invention:
1, purpose: the object of the present invention is to provide a kind of, and the polynary Copula power device reliability based on the physics of failure is commented
Valence method.This method is distributed correlation in view of service life of the structure each inside power device under different failure mechanisms, examines simultaneously
Consider assessed cost and matter of time, the unknown parameter in failure physical model is obtained by finite element simulation, passes through Monte Carlo
Influence of the sampling consideration stochastic variable to each structural life-time of device, the reliability assessment for power device in engineering practice provide reason
By guidance.
2, technical solution: the present invention is a kind of polynary Copula power device reliability evaluation side based on the physics of failure
Method, it includes the following steps:
Step 1: power device information is collected, and determines actual condition, internal structure and existing failure mechanism；
Step 2: failure physical model application and model parameter randomization determine the edge distribution of each structural life-time；
Step 3: power device reliability assessment compares Monte Carlo competing failure method；
Wherein, function under power device information, including analysis power device internal structure and actual condition is collected in step 1
Failure mechanism existing for rate device.Overcurrent, over-voltage, excessively high junction temperature can all cause to fail when due to power device work, this
All there is substantial connection with temperature.Excessively high junction temperature will cause PN junction breakdown, and lasting temperature alternating also will cause device interconnection knot
The fatigue failure of structure, if bonding line fails, weldering knot layer aging etc..Therefore main to consider chip and failure of both interconnection, it grinds
Study carefully failure physical model relevant to temperature, the service life for assessing each structure for subsequent applications failure physical model provides input.
Wherein, power device geometry, material, working environment and the identification obtained in step 2 based on research
Weak part and existing failure mechanism, consider that Monte Carlo randomization is applied in influence of the stochastic variable to device lifetime
The structural parameters of device, material parameter, technological parameter into.Using failure physical model as input, using the parameter value of randomization
Calculate the failure probability distributions that each structure is obeyed.The feature of these service life distribution is described using Gaussian Kernel Density function, it is close
Degree function and distribution function may be expressed as:
In formula, hXIndicate the window width of Marginal density function, calculation formula are as follows:
hx=0.9 × N-0.2×min(std,iqr/1.34)
In formula, N indicates that sample total, std are sample standard deviation, and iqr is quartile in sample sequence.
Wherein, consider that failure physical model input parameter exists in step 3 to intersect.Such as Fig. 3, temperature, power, electric current etc.
The life prediction value of multiple structures in power device may be all influenced, the calculated result of failure physical model can not be considered
It is complete independent.The edge distribution of each structural life-time obtained in step 2 is connected to obtain using multivariable Copula theory
Obtain joint distribution function.If thinking, the life value that the failure physical model of device is calculated after stochastic parameter is random becomes
Amount, obeys continuous edge distribution F1(t1), F2(t2) ..., Fn(tn)；Then by sklar theorem it is found that n failure physical model
The joint distribution function uniquely determined is obeyed in obtained cumulative failure distribution:
H(t1,t2,...,tn)=C (F1(t1), F2(t2) ..., Fn(tn))
With the dependency structure of Archimedean Copula race reflection life variance, unknown parameter in estimation function is needed.Here
Using Kendall τ coefficient ταRelationship between parameter alpha estimates parameter value.
If { (x1,y1),(x2,y2),...,(xn,yn) be continuous random vector (X, Y) n sample observations, (X, Y)
Copula function be Cα, kendall τ coefficient is τα, defined according to the Kendall τ of sample, ταEstimated value are as follows:
In Clayton copula functionIn Gumbel copula functionFrank function
Unknown parameter is acquired using Maximum-likelihood estimation.
According to AIC and BIC criterion, the optimal joint probability Distribution Model in power device service life is determined.
Derive that the device reliability comprising n failure mechanism meets series model:
With using following competing failure thought, the service life using the TTF minimum value of possible failure structure as device is carried out
No matter all advantageous from evaluation result reasonability and arithmetic speed comparison, this method be.
TTF=min { T1,T2,T3,,......TN}
(4) Detailed description of the invention:
Fig. 1 implementation steps flow diagram
Fig. 2 temperature cycles section
The key structure of Fig. 3 power device and existing typical failure mechanism
Fig. 4 excess carrier service life is distributed
Fig. 5 bonding wire service life is distributed
Fig. 6 welding layer thermal fatigue failure service life is distributed
Two method Reliability assessment Comparative result of Fig. 7
(5) specific embodiment:
Below in conjunction with attached drawing and specific implementation case, to the polynary Copula function of the present invention based on the physics of failure
Rate device reliability evaluation method is described in detail.
The present invention illustrates the polynary Copula power device based on the physics of failure by taking IRGB4059DPBF type IGBT device as an example
Part reliability evaluation process.
Step 1: power device information is collected；
Firstly, considering the Thermal cycling conditions that device may be undergone under actual condition, tried with reference to GJB128A temperature cycles
B conditions of proved recipe method determine environment section such as Fig. 2.Consider the inside key structure and existing failure mechanism such as Fig. 3 of device.It examines
Consider chip, three welding layer, bonding wire weak links, analysis corresponds to the physics of failure mould of failure mechanism under Fig. 2 environment section
Type.
Carrier determines many dynamics of device and static characteristic in IGBT device base region.Excess carrier concentration with
Temperature is increased and is increased, and when majority carrier concentration increases, minority carrier recombination slows, so that under device shutdown rate
Drop.The excess carrier service life obeys following formula in chip:
In formula, τ is hot carrier lifetime, unit s, NtFor carrier recombination centers density, unit cm-3, p is that cavity is close
Degree, unit cm-3, T refers to temperature, unit K.
Chip emission pole is connected by bonding wire with DCB layers of copper, constantly has thermal stress tired under temperature shock repeatedly
Product arrives the contact area of lead and DCB, it may appear that bonding wire falls off.Bonding line Root Stress indicates are as follows:
In formula, αwAnd αsThe thermal expansion coefficient of lead material and base material is respectively corresponded, Δ T is range of temperature.2L
For wire length, unit m；2D is lead mutual conductance, unit m；R is wire cross-sectional radius surface, unit m；EWFor lead elasticity modulus,
Unit Pa；
The relationship of bonding line thermal fatigue life and Root Stress is expressed as:
In formula, CwFor the tensile fatigue coefficient for being bonded wire material, usually 5~7；mwFor tensile fatigue coefficient.
Due to the inhomogeneities of solder itself and the thermal expansion coefficient difference of welding layer both sides material, caused by temperature fluctuation
Thermal stress makes the weakness of solder layer subtle crackle occur because of shear stress.Using Engelmaier heat fatigue model, calculate
The thermal fatigue life of welding layer:
In formula, Nf 1For welding layer thermal fatigue life under operating ambient temperature section；Δγ1For welding layer plasticity shear strain；
ε'fFor fatigue ductile coefficient；C is fatigue ductility index, is calculated as follows:
In formula, TDTime, unit min are immersed for high/low temperature.
Solder joint plasticity shear strain Δ γ1Calculation formula is as follows:
Δ α LT in formulac, Δ α LTsThe thermal diffusion for characterizing chip and copper base respectively, calculates such as formula:
In formula, LxFor solder joint maximum spacing on the direction x or pad array span, unit mm；LyMost for solder joint on the direction y
Big spacing or pad array span, unit mm；αcxFor the thermal expansion coefficient of device on the direction x, unit is ppm/ DEG C；αcyFor y
The thermal expansion coefficient of device on direction, unit are ppm/ DEG C；αsxFor the thermal expansion coefficient of circuit board on the direction x, unit ppm/
℃；αsyFor the thermal expansion coefficient of circuit board on the direction y, unit is ppm/ DEG C.Tc max, Tc minRespectively chip is highest and lowest
Temperature, Ts max, Ts minThe respectively highest and lowest temperature of substrate.
Step 2: failure physical model application and model parameter randomization；
The service life of chip, bonding wire and welding layer in failure physical model assessment power device in applying step one.Benefit
Default parameters in failure physical model are obtained with finite element simulation method: including each structure temperature, stress, strain.Emulation solves
The temperature of chip, welding layer, bonding line, parameter value such as table 1 in failure physical model.
1 failure physical model parameter value of table
The service life distribution of each structure under the influence of failure mechanism is determined using Monte Carlo stochastic parameter；It is taken out by angular distribution
The geometric dimension and material thermal parameters of sample device, by the temperature parameter of Normal distribution sampling structure, with Gaussian Kernel Density function
The feature of these distributions is described, obtains the lower three kinds of structures of each failure mechanism corresponding service life distribution such as Fig. 5, Fig. 6, Fig. 7.
Step 3: power device reliability assessment；
After determining marginal distribution function, select 3 Archimedean Copula races Typical Representative Gumbel, Clayton and
Frank Copula is calculated, and determines parameter alpha, corresponds to τ between acquiring 3 distributions using the definition of kendall ταWith parameter alpha and AIC
Value such as table 2:
2 Copula parameter Estimation of table and AIC value
Calculated result shows that Clayton function is optimal Copula function, according to the reliable of series model calculating device
Function is spent, this method compares Monte Carlo-competing failure method reliability calculating result such as Fig. 7.Contrast sample's number is
The reliability evaluation result of two methods and operation time such as table 3 when 50000.
3 liang of method evaluation results of table and operation time comparison
As seen from Table 3, consider that the reliability evaluation result of each failure physical model correlation is compared and be completely independent hypothesis
Under evaluation result it is higher.The MTTF result explanation of two methods does not consider the model calculation phase as caused by cross parameter
The reliability evaluation of closing property may judge the service life of device by accident.The operation time of two methods when contrast sample's number is 50000 simultaneously
It can be seen that this method compared with Monte Carlo competing failure evaluation method arithmetic speed faster.
Claims (4)
1. a kind of polynary Copula power device method for evaluating reliability based on the physics of failure, it is characterised in that: utilize failure
Physical model, Monte Carlo sampling, polynary Copula function evaluate reliability of the power device under actual condition, and
Common Monte Carlo competing failure evaluation method illustrates the advantage of this method in comparison engineering.The specific steps of this method are such as
Under:
Step 1: power device information is collected, and determines actual condition, internal structure and existing failure mechanism；
Step 2: failure physical model application and model parameter randomization determine the edge distribution of each structural life-time；
Step 3: power device reliability assessment compares Monte Carlo competing failure method.
2. power device information according to claim 1 is collected, actual condition, internal structure and existing failure machine are determined
Reason, it is characterised in that consider that overcurrent, over-voltage, excessively high junction temperature can all cause to fail when power device work, these all with temperature
Degree has substantial connection.Excessively high junction temperature will cause PN junction breakdown, and lasting temperature alternating also will cause the tired of device interconnected structure
Labor failure, if bonding line fails, weldering knot layer aging etc..Therefore main to consider failure of both chip and interconnection, research and temperature
Relevant failure physical model is spent, the service life for assessing each structure for subsequent applications failure physical model provides input.
3. failure physical model application according to claim 1 and model parameter randomization, determine the side of each structural life-time
Fate cloth, it is characterised in that based on research obtain power device geometry, material, working environment and identification it is thin
Weak position and existing failure mechanism consider that Monte Carlo randomization device is applied in influence of the stochastic variable to device lifetime
Structural parameters, material parameter, technological parameter into.Using failure physical model as input, using the parameter value calculation of randomization
The failure probability distributions that each structure is obeyed.The feature of these service life distribution, density letter are described using Gaussian Kernel Density function
Several and distribution function may be expressed as:
In formula, hXIndicate the window width of Marginal density function, calculation formula are as follows:
hx=0.9 × N-0.2×min(std,iqr/1.34)
In formula, N indicates that sample total, std are sample standard deviation, and iqr is quartile in sample sequence.
4. power device reliability assessment according to claim 1 compares Monte Carlo competing failure method, failure is considered
Physical model inputs parameter and there is intersection.Such as Fig. 3, temperature, power, electric current etc. may all influence multiple structures in power device
Life prediction value, the calculated result of failure physical model can not think to be completely independent.It is theoretical using multivariable Copula
The edge distribution of each structural life-time obtained in step 2 is connected to obtain joint distribution function.If thinking the mistake of device
The life value that effect physical model is calculated after stochastic parameter is stochastic variable, obeys continuous edge distribution F1(t1), F2
(t2) ..., Fn(tn)；Then by sklar theorem it is found that the cumulative failure distribution that n failure physical model obtains is obeyed and uniquely determined
Joint distribution function:
H(t1,t2,...,tn)=C (F1(t1), F2(t2) ..., Fn(tn))
Reflect the dependency structure of life variance, the unknown parameter in estimation function, using Kendall with Archimedean Copula race
τ coefficient ταRelationship between parameter alpha estimates parameter value:
In Clayton copula functionIn Gumbel copula functionFrank function it is unknown
Parameter is acquired using Maximum-likelihood estimation.
According to AIC and BIC criterion, the optimal joint probability Distribution Model in power device service life is determined.
Derive that the device reliability comprising n failure mechanism meets series model:
With using following competing failure thought, compared using the service life of TTF minimum value as the device of possible failure structure,
No matter all advantageous from evaluation result reasonability and arithmetic speed this method is.
TTF=min { T1,T2,T3,,......TN}。
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910597236.0A CN110298126B (en) | 2019-07-04 | 2019-07-04 | Multi-element Copula power device reliability evaluation method based on failure physics |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910597236.0A CN110298126B (en) | 2019-07-04 | 2019-07-04 | Multi-element Copula power device reliability evaluation method based on failure physics |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110298126A true CN110298126A (en) | 2019-10-01 |
| CN110298126B CN110298126B (en) | 2021-11-02 |
Family
ID=68030198
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910597236.0A Active CN110298126B (en) | 2019-07-04 | 2019-07-04 | Multi-element Copula power device reliability evaluation method based on failure physics |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110298126B (en) |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112446139A (en) * | 2020-11-05 | 2021-03-05 | 西安电子科技大学 | Accelerated test profile optimization method and system, electromechanical product, medium and terminal |
| CN112613269A (en) * | 2020-12-28 | 2021-04-06 | 武汉理工大学 | System optimal redundancy calculation method based on irrelevance coverage model |
| CN112699579A (en) * | 2020-12-09 | 2021-04-23 | 电子科技大学 | IGBT state monitoring method based on finite element simulation |
| CN113282853A (en) * | 2021-05-26 | 2021-08-20 | 北京字跳网络技术有限公司 | Comment preloading method and device, storage medium and electronic equipment |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102103658A (en) * | 2010-12-03 | 2011-06-22 | 北京航空航天大学 | Practical electronic product service life evaluation model parameter high-accuracy extraction method |
| US20120143564A1 (en) * | 2010-12-01 | 2012-06-07 | Xerox Corporation | System and method for predicting remaining useful life of device components |
| CN103336901A (en) * | 2013-06-27 | 2013-10-02 | 中国人民解放军国防科学技术大学 | Statistical analysis technique for accelerated life test related to ineffectiveness in competition |
| CN103955568A (en) * | 2014-04-17 | 2014-07-30 | 北京航空航天大学 | Physics-of-failure-based MOS (metal oxide semiconductor) device reliability simulation evaluation method |
| CN106529090A (en) * | 2016-12-12 | 2017-03-22 | 中国航天标准化研究所 | Evaluation method of reliability of aerospace electronic product |
| CN107506337A (en) * | 2017-10-12 | 2017-12-22 | 中国人民解放军海军航空工程学院 | Reliability statistics estimating method based on polynary acceleration degraded data |
| CN109149982A (en) * | 2018-08-21 | 2019-01-04 | 南方电网科学研究院有限责任公司 | Modularization level converter power Module Reliability appraisal procedure |
| CN109840310A (en) * | 2019-01-14 | 2019-06-04 | 中国空间技术研究院 | A kind of three-dimensionally integrated level components service life integrated evaluating method of spacecraft |
-
2019
- 2019-07-04 CN CN201910597236.0A patent/CN110298126B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20120143564A1 (en) * | 2010-12-01 | 2012-06-07 | Xerox Corporation | System and method for predicting remaining useful life of device components |
| CN102103658A (en) * | 2010-12-03 | 2011-06-22 | 北京航空航天大学 | Practical electronic product service life evaluation model parameter high-accuracy extraction method |
| CN103336901A (en) * | 2013-06-27 | 2013-10-02 | 中国人民解放军国防科学技术大学 | Statistical analysis technique for accelerated life test related to ineffectiveness in competition |
| CN103955568A (en) * | 2014-04-17 | 2014-07-30 | 北京航空航天大学 | Physics-of-failure-based MOS (metal oxide semiconductor) device reliability simulation evaluation method |
| CN106529090A (en) * | 2016-12-12 | 2017-03-22 | 中国航天标准化研究所 | Evaluation method of reliability of aerospace electronic product |
| CN107506337A (en) * | 2017-10-12 | 2017-12-22 | 中国人民解放军海军航空工程学院 | Reliability statistics estimating method based on polynary acceleration degraded data |
| CN109149982A (en) * | 2018-08-21 | 2019-01-04 | 南方电网科学研究院有限责任公司 | Modularization level converter power Module Reliability appraisal procedure |
| CN109840310A (en) * | 2019-01-14 | 2019-06-04 | 中国空间技术研究院 | A kind of three-dimensionally integrated level components service life integrated evaluating method of spacecraft |
Non-Patent Citations (2)
| Title |
|---|
| ROMEO, JOSE S.等: ""Bayesian bivariate survival analysis using the power variance function copula"", 《LIFETIME DATA ANALYSIS》 * |
| 万博 等: ""航空电子产品可靠性仿真预计数据处理方法研究"", 《电子产品可靠性与环境试验》 * |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112446139A (en) * | 2020-11-05 | 2021-03-05 | 西安电子科技大学 | Accelerated test profile optimization method and system, electromechanical product, medium and terminal |
| CN112446139B (en) * | 2020-11-05 | 2024-05-10 | 西安电子科技大学 | Acceleration test profile optimization method, acceleration test profile optimization system, electromechanical product, medium and terminal |
| CN112699579A (en) * | 2020-12-09 | 2021-04-23 | 电子科技大学 | IGBT state monitoring method based on finite element simulation |
| CN112613269A (en) * | 2020-12-28 | 2021-04-06 | 武汉理工大学 | System optimal redundancy calculation method based on irrelevance coverage model |
| CN112613269B (en) * | 2020-12-28 | 2023-02-24 | 武汉理工大学 | System optimal redundancy calculation method based on irrelevance coverage model |
| CN113282853A (en) * | 2021-05-26 | 2021-08-20 | 北京字跳网络技术有限公司 | Comment preloading method and device, storage medium and electronic equipment |
| CN113282853B (en) * | 2021-05-26 | 2024-04-16 | 北京字跳网络技术有限公司 | Comment preloading method and device, storage medium and electronic equipment |
Also Published As
| Publication number | Publication date |
|---|---|
| CN110298126B (en) | 2021-11-02 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110298126A (en) | A kind of polynary Copula power device method for evaluating reliability based on the physics of failure | |
| CN108072821B (en) | The real-time online prediction technique of semiconductor power device dynamic junction temperature | |
| Pedersen et al. | Dynamic modeling method of electro-thermo-mechanical degradation in IGBT modules | |
| Celaya et al. | Towards prognostics of power MOSFETs: Accelerated aging and precursors of failure | |
| TW201133671A (en) | Valuation method of dielectric breakdown lifetime of gate insulating film, valuation device of dielectric breakdown lifetime of gate insulating film and program for evaluating dielectric breakdown lifetime of gate insulating film | |
| EP3531146B1 (en) | Wire-bonded power semi-conductor module monitoring | |
| CN107045993B (en) | Electro-migration testing device, electro-migration testing system and its test method | |
| Qin et al. | A lifetime prediction method for IGBT modules considering the self-accelerating effect of bond wire damage | |
| CN105550397B (en) | A kind of IGBT module state evaluating method based on damage voltage | |
| CN104297586A (en) | Aerospace component ground and ontrack service life associativity test evaluation method | |
| Hu et al. | Cost-effective prognostics of IGBT bond wires with consideration of temperature swing | |
| CN111458617B (en) | Semiconductor device reliability detection method, semiconductor device reliability detection device, computer equipment and medium | |
| Bespalov et al. | MOSFETs selection software to form parallel and series connections | |
| CN116579189B (en) | IGBT power module service life prediction method and device | |
| CN113646647B (en) | Method for evaluating the thermal load of a converter | |
| Babel et al. | Condition monitoring and failure prognosis of IGBT inverters based on on-line characterization | |
| Chen et al. | A comprehensive analytical and experimental investigation of wire bond life for IGBT modules | |
| Hong et al. | Prognosis for insulated gate bipolar transistor based on Gaussian process regression | |
| Zhuang et al. | A new reliability assessment model for power electronic modules | |
| El Helou et al. | Thermal modeling and experimental validation of heat sink design for passive cooling of BEOL IC structures | |
| CN110703066B (en) | Method and system for predicting transistor failure | |
| Mohammadi et al. | A new approach for electrothermal analysis of electronic circuits | |
| Vaccaro et al. | Remaining Useful Lifetime Prediction of Discrete Power Devices by Means of Artificial Neural Networks | |
| CN104977519A (en) | Device hot carrier injection effect test method | |
| JP2019124638A (en) | Inspection device, inspection system, intelligence power module, method for inspection, and program |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |