A failure physics-based electronic component reliability model construction method
By constructing a reliability model for electronic components based on failure physics, the problems of lag and environmental limitations in existing prediction models are solved, achieving more accurate reliability prediction and cost savings.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- XIDIAN UNIV
- Filing Date
- 2023-04-25
- Publication Date
- 2026-08-04
AI Technical Summary
Existing reliability prediction models for electronic components suffer from time lag, outdated technology, and limitations in application environment, resulting in significant prediction deviations under different conditions.
A reliability model for electronic components based on failure physics is constructed. By collecting device parameters, internal failure coefficient, external failure coefficient, quality coefficient, maturity coefficient, and environmental coefficient are obtained. Monte Carlo simulation and finite element simulation are combined to construct a reliability model to predict reliability.
It improves the accuracy and timeliness of reliability prediction, reduces data costs, and adapts to diverse application environments.
Smart Images
Figure CN116542094B_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of electronic component reliability model construction, specifically involving a method for constructing electronic component reliability models based on failure physics. Background Technology
[0002] With the development of integrated circuits, electronic components are becoming increasingly important in daily life, commercial production, and defense technology. Faced with the wide range and diversity of applications, electronic components are trending towards miniaturization, multifunctionality, and integration, making reliability research increasingly crucial.
[0003] In engineering applications, the reliability prediction model in GJB / Z 299C-2006 "Reliability Prediction Manual for Electronic Equipment" is typically used to predict the failure rate of electronic components. This reliability prediction model primarily considers the impact of internal and external failures on device failure rates. Internal failures include integrated circuit failure factors, while external failures include the impact of on-chip bonding wires and packaging methods. It also considers the influence of the quality factor and maturity factor on device failure rates, ultimately forming a relatively complete failure rate model.
[0004] However, reliability prediction manuals suffer from time lag, outdated technology, and limitations in application environments. GJB / Z 299C-2006, "Reliability Prediction Manual for Electronic Equipment," was published in 2006, and its failure rate data is primarily derived from statistical analysis of experimental data. Using this data to predict the reliability of current electronic components may result in significant deviations, and many current application conditions are not included in the reliability prediction manual. Summary of the Invention
[0005] To address the aforementioned problems in the existing technology, this invention provides a method for constructing a reliability model for electronic components based on failure physics. The technical problem to be solved by this invention is achieved through the following technical solution: This invention provides a method for constructing a reliability model for electronic components based on failure physics, comprising: Step 1: Collect electronic component parameters and determine the application environment of the electronic components. The electronic component parameters include: device manufacturing process, process parameters, transistor size, package type, package material parameters, and package process. Step 2: In the application environment, obtain the reliability correlation coefficients based on the electronic component parameters, including: internal failure coefficients. First external failure factor Second external failure coefficient quality coefficient Maturity coefficient and environmental coefficient ; Step 3: Based on the correlation relationship of the aforementioned reliability correlation coefficients, construct the reliability model as follows: ; in, The failure rate of electronic components; Internal failure coefficient; The first external failure factor; This is the second external failure factor; This is the quality coefficient; Maturity coefficient; For environmental factors; Step 4: Calculate the reliability correlation coefficients respectively, and obtain the failure rate of the electronic components according to the reliability model. .
[0006] In one embodiment of the present invention, in step two, the internal failure coefficient is obtained. ,include, Obtain the hot carrier injection failure rate respectively Negative bias temperature instability failure rate Time-dependent dielectric layer breakdown efficiency The internal failure coefficient was calculated. ,in: .
[0007] In one embodiment of the present invention, the hot carrier injection failure rate is obtained. ,include: Step 1: Establish the failure rate of the hot carrier injection. Lifetime model : ; in, The width of the channel; The effective diffusion coefficient; Carrier concentration; It is the first constant; Effective diffusion length; It is the second constant; This is the drain current; The minimum energy required to generate an interface trap; The mean free path of electrons; It represents the amount of charge; This represents the maximum electric field in the channel; Step 2: Apply the lifetime model Monte Carlo simulations were performed, and the simulated samples were fitted to determine that the sample lifetimes followed a log-normal distribution, thus obtaining the probability density function corresponding to the sample lifetimes. and its distribution function ; Step 3: Based on the distribution function The failure rate of the hot carrier injection was obtained. .
[0008] In one embodiment of the present invention, the failure rate of negative bias temperature instability is obtained. ,include: Step 1: Establish the failure rate of the negative bias temperature instability Lifetime model : ; in, This represents the change in threshold voltage. The change in threshold voltage caused by the charge of other oxide species besides interface traps; The rate at which interface traps are generated; Step 2: Apply the lifetime model Monte Carlo simulations were performed, and the simulated samples were fitted to determine that the sample lifetimes followed a log-normal distribution, thus obtaining the probability density function corresponding to the sample lifetimes. and its distribution function ; Step 3: Based on the distribution function The failure rate of the negative bias temperature instability is obtained. .
[0009] In one embodiment of the present invention, the time-dependent dielectric layer breakdown efficiency is obtained. ,include: Step 1: Establish the time-dependent dielectric layer breakdown efficiency. Lifetime model : ; in, It is the third constant; It is the fourth constant. A coefficient related to the nature of the defect. The voltage across the oxide layer; Step 2: Apply the lifetime model Monte Carlo simulations were performed, and the simulated samples were fitted to determine that the sample lifetimes followed a log-normal distribution, thus obtaining the probability density function corresponding to the sample lifetimes. and its distribution function ; Step 3: Based on the distribution function Obtain the time-dependent dielectric layer breakdown efficiency. .
[0010] In one embodiment of the present invention, in step two, a first external failure coefficient is obtained. ,include: Step 1: Establish an interference model of pin stress intensity for electronic components; Step 2: Determine the pin strength curve based on the material properties of the electronic component's pins; Step 3: Apply load to the interference model of the pin stress intensity and obtain the pin stress curve; Step 4: Obtain the first external failure coefficient based on the pin strength curve and the pin stress curve. : ; in, Pin strength curve; The pin stress curve; The probability of encapsulation failure when the mean stress is less than the mean material strength.
[0011] In one embodiment of the present invention, in step two, a second external failure coefficient is obtained. ,include: Step 1: Establish an interference model of bond wire stress intensity for electronic components; Step 2: Determine the bonding wire strength curve based on the material properties of the bonding wires of the electronic components; Step 3: Apply load simulation to the bond line stress intensity interference model to obtain the bond line stress curve; Step 4: Obtain the second external failure coefficient based on the bond line strength curve and the bond line stress curve. : ; in, This is the bond line strength curve; This is the bond line stress curve; This is the probability of bond failure when the mean stress is less than the mean material strength.
[0012] In one embodiment of the present invention, the quality coefficient and the maturity coefficient According to the technical manual.
[0013] In one embodiment of the present invention, the environmental factor The following formula is used to calculate: ; in, The first external failure factor is determined under general application environment conditions; This is the second external failure factor determined under general application environment conditions.
[0014] Compared with the prior art, the beneficial effects of the present invention are as follows: The present invention provides a method for constructing a reliability model for electronic components based on failure physics, which comprehensively considers the impact of quality coefficient, maturity coefficient, internal failure, external failure, and environmental coefficient on reliability. By performing failure physics analysis at the integrated circuit level and finite element simulation analysis of packaged devices under different application environments, the relevant parameters in the model are determined. Reliability prediction of electronic components can be performed through calculation and simulation, saving data costs and improving prediction reliability and timeliness.
[0015] The above description is merely an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described in detail below with reference to the accompanying drawings. Attached Figure Description
[0016] Figure 1 This is a flowchart of the reliability model construction provided in the embodiments of the present invention; Figure 2 This is a function graph of the cumulative failure probability density of hot carrier injection provided in the embodiments of the present invention; Figure 3 This is a diagram of the interference model of pin stress intensity of electronic components provided in the embodiments of the present invention. Detailed Implementation
[0017] To further illustrate the technical means and effects adopted by the present invention to achieve the intended purpose, the following describes in detail, with reference to the accompanying drawings and specific embodiments, a method for constructing a reliability model of electronic components based on failure physics proposed in accordance with the present invention.
[0018] The foregoing and other technical contents, features, and effects of the present invention will be clearly presented in the following detailed description of specific embodiments in conjunction with the accompanying drawings. Through the description of the specific embodiments, a more in-depth and concrete understanding can be gained of the technical means and effects adopted by the present invention to achieve its intended purpose. However, the accompanying drawings are for reference and illustration only and are not intended to limit the technical solutions of the present invention.
[0019] Example 1 In this embodiment, taking electronic components manufactured using a 90nm process as an example, with a normal operating life of 10 years, a reliability model is constructed and its reliability is calculated under a 1000G impact. The transistor size of the electronic component is 1×10⁻⁶. 8 It is on the order of magnitude, uses a QFP package structure, and employs gold wire bonding technology on-chip.
[0020] Please see Figure 1 , Figure 1 This is a flowchart of the reliability model construction provided in the embodiments of the present invention.
[0021] As shown in the figure, the method for constructing a reliability model for electronic components based on failure physics of the present invention includes: Step 1: Collect electronic component parameters and determine the application environment of the electronic components. Electronic component parameters include: device manufacturing process, process parameters, transistor size, package type, package material parameters, and packaging process.
[0022] In this embodiment, the parameters of the electronic components are determined through the component design manual and component design documents, and the application environment of the electronic components is determined based on the service life of the components and the application environment required for reliability analysis. The process parameters of the electronic components are shown in Table 1: Table 1 Process parameters of electronic components
[0023] The material parameters of the electronic components are shown in Table 2: Table 2 Material parameters of electronic components
[0024] Step 2: In the application environment, obtain the reliability correlation coefficient based on the parameters of the electronic components.
[0025] Table 3 shows the structures and related factors that cause electronic components to fail under different application environments. These factors affect the reliability of electronic components and are referred to as reliability correlation coefficients. Table 3. Major Failure Analysis of Electronic Components
[0026] In one optional implementation, the reliability correlation coefficient includes: internal failure coefficient. First external failure factor Second external failure coefficient quality coefficient Maturity coefficient and environmental coefficient ; Among them, internal failure coefficient To assess the impact of integrated circuits within electronic components on failure rates under general conditions; the first external failure factor. The impact of electronic component packaging structure (i.e., pins) on failure rate in a general environment; second external failure factor. This section describes the impact of on-chip bonding wires on failure rate of electronic components under general environmental conditions. The general environmental conditions refer to the normal operating or storage environment of electronic components in a well-conditioned environment with normal climatic conditions and near-zero mechanical stress, as specified by national standards.
[0027] quality coefficient The impact of manufacturing process quality, chip material quality, and casing quality on failure rate of electronic components; maturity coefficient. The impact of the maturity of electronic component manufacturing technology and processes on failure rate; environmental factor This study examines the impact of different application environments, such as shock, vibration, and high temperature, on the failure rate of electronic components.
[0028] Step 3: Based on the correlation relationship of the reliability correlation coefficients, construct the reliability model as follows: (1); in, The failure rate of electronic components; Internal failure coefficient; The first external failure factor; This is the second external failure factor; This is the quality coefficient; Maturity coefficient; For environmental factors; In an optional implementation, the internal failure factor First external failure factor Second external failure coefficient This also affects the reliability of electronic components; therefore, the three factors are related in parallel, which is reflected as an additive relationship in the reliability model. The production requirements standards for electronic components are expressed as a quality coefficient. The maturity process of a device from design to application is represented by a maturity coefficient. This also affects the failure rate of electronic components, so it is evaluated by multiplication in the reliability model.
[0029] Furthermore, the failure rate of electronic components varies under different application environments, taking environmental factors into account. The impact on the reliability of electronic components mainly affects the external failure factors among the failure factors. Therefore, based on the general environment, the first external failure factor is... Second external failure coefficient Multiply by the environmental factor The failure rate of electronic components under different environments was obtained; Step 4: Calculate the reliability correlation coefficients separately, and obtain the failure rate of electronic components based on the reliability model. .
[0030] Among them, failure rate It is a constant, on the order of 10. -6 The correlation between / h and the parameters of electronic components and application environment conditions is far greater than its correlation with time.
[0031] In this embodiment, the main factors affecting the failure rate of integrated circuits include: hot carrier injection (HCI), negative bias temperature instability (NBTI), and time-dependent dielectric breakdown (TDDB).
[0032] In this embodiment, the internal failure coefficient is obtained. This includes: obtaining the hot carrier injection failure rate respectively. Negative bias temperature instability failure rate Time-dependent dielectric layer breakdown efficiency The internal failure coefficient was calculated. ,in: (2); In an optional implementation, the hot carrier injection failure rate is obtained. ,include: Step 1: Establish hot carrier injection failure rate Lifetime model : (3); in, The width of the channel; The effective diffusion coefficient; Carrier concentration; It is the first constant; Effective diffusion length; It is the second constant; This is the drain current; The minimum energy required to generate an interface trap; The mean free path of electrons; It represents the amount of charge; This represents the maximum electric field in the channel; Step 2: Lifetime model Monte Carlo simulations were performed, and the simulated samples were fitted to determine that the sample lifetimes followed a log-normal distribution, thus obtaining the probability density function corresponding to the sample lifetimes. and its distribution function ; Step 3: Based on the distribution function Obtain the hot carrier injection failure rate .
[0033] The failure rate of hot carrier injection was obtained based on the lifetime model. The specific acquisition process is explained below. The sample parameters for the Monte Carlo simulation are set to 10000. Due to the instability of the photolithography process, the feature dimensions of each MOS device deviate. Therefore, the feature dimensions of the devices are set as random variables to obtain the random parameter matrix of the failure physics model: (4); The Monte Carlo method generates 10,000 samples according to a set normal distribution. Within acceptable error limits, the sample data is supplemented, and the simulated samples are fitted to determine that the failure physical model of a single sample lifetime follows a log-normal distribution. The expected value is obtained from the fitting results. and variance And obtain the probability density function corresponding to the sample lifetime. and its distribution function : (5); (6); Wherein, probability density function The function graph is as follows Figure 2 As shown.
[0034] In one optional implementation, transistors in the integrated circuit of the electronic component are connected in series, and their number is assumed to be... ,Pick =1×10 8 Since NMOS and PMOS transistors operate alternately in CMOS devices, let the stress-induced operating time of a single transistor be... Obtain the distribution function of the hot carrier injection failure rate of electronic components. : (7); Calculate the hot carrier injection failure rate The unit is 10 -6 / h.
[0035] In one alternative implementation, the negative bias temperature instability (NBTI) mechanism is as follows: when the PMOS device is biased under a negative bias, the Si-H bonds at the Si-SiO2 interface break, generating H atoms that diffuse out from the interface, leaving positively charged interface traps, thus causing the threshold voltage to change. The rise in concentration is called the stress process. When the negative bias is removed, H atoms diffuse back to the interface and recombine with Si+, which reduces the interface trap density. This process is called the recovery process.
[0036] In an optional implementation, the failure rate of negative bias temperature instability is obtained. ,include: Step 1: Establish the failure rate of temperature instability under negative bias Lifetime model : (8); in, This represents the change in threshold voltage. The change in threshold voltage caused by the charge of other oxide species besides interface traps; The generation rate of interface traps is calculated using the following formula: (9); in It is a constant; This refers to the thickness of the gate oxide layer; Gate capacitance; Gate voltage; Source-drain voltage; The electric field of the gate oxide layer; Here is the electric field parameter, with a value of 2 mV / cm; The activation energy is temperature-dependent and has a value of 0.12 eV. It is the fifth constant; This refers to the operating temperature.
[0037] Step 2: Lifetime model Monte Carlo simulations were performed, and the simulated samples were fitted to determine that the sample lifetimes followed a log-normal distribution, thus obtaining the probability density function corresponding to the sample lifetimes. and its distribution function ; Step 3: Based on the distribution function Obtain the failure rate of negative bias temperature instability .
[0038] In this embodiment, the failure rate of negative bias temperature instability is calculated based on the lifetime model. Specific steps and obtaining hot carrier injection failure rate The same applies, so I will not repeat it here.
[0039] In one alternative implementation, the mechanism of time-dependent dielectric layer breakdown (TDDB) is as follows: a chain consists of n links, and the strength of a single link is a random variable. Assuming that the strengths of each link are independent and have the same distribution, the breakage of the entire chain is only related to the strength of the weakest link.
[0040] In an optional implementation, the time-dependent dielectric layer breakdown efficiency is obtained. ,include: Step 1: Establish time-dependent dielectric layer breakdown efficiency Lifetime model : (10); in, It is the third constant; It is the fourth constant. A coefficient related to the nature of the defect. The voltage across the oxide layer; Step 2: Lifetime model Monte Carlo simulations were performed, and the simulated samples were fitted to determine that the sample lifetimes followed a log-normal distribution, thus obtaining the probability density function corresponding to the sample lifetimes. and its distribution function ; Step 3: Based on the distribution function Obtain the time-dependent dielectric layer breakdown efficiency. .
[0041] In this embodiment, the time-dependent dielectric layer breakdown efficiency is calculated based on the lifetime model. Specific steps and obtaining hot carrier injection failure rate The same applies, so I will not repeat it here.
[0042] Please see Figure 3 , Figure 3 This is a diagram of the interference model of pin stress intensity of electronic components provided in the embodiments of the present invention.
[0043] When electronic components are subjected to external forces, stress is generated within the material. The limit of the material's ability to withstand such forces is its strength. In electronic components, when the stress on the material exceeds its strength, the electronic component fails.
[0044] Furthermore, by representing the distribution functions of stress and strength in the same coordinate system, when the mean value of strength is greater than the mean value of stress, the overlapping part of the curves represents the "interference zone" between stress and strength. Within the "interference zone", the strength is less than the stress, which means failure occurs.
[0045] In this embodiment, the first external failure coefficient is obtained. ,include: Step 1: Establish an interference model of pin stress intensity for electronic components; Step 2: Determine the pin strength curve based on the material properties of the electronic component pins; Step 3: Perform load simulation on the pin stress intensity interference model to obtain the pin stress curve; In an optional implementation, the reliability of the material is determined based on the degree of interference between stress and intensity distribution using a stress-intensity interference model. The stress curve is obtained through finite element simulation. In the finite element simulation software, the electronic components are modeled and meshed. Then, based on the application conditions, binding constraints are applied to the bottom of the PCB board, and a 1000G load is applied to the components to obtain the stress distribution matrix of the pins. (11); Where n is the number of pins.
[0046] Step 4: Obtain the first external failure factor based on the pin strength curve and pin stress curve. : (12); in, Pin strength curve; The pin stress curve; The probability of encapsulation failure when the mean stress is less than the mean material strength.
[0047] In this embodiment, the second external failure coefficient is obtained. ,include: Step 1: Establish an interference model of bond wire stress intensity for electronic components; Step 2: Determine the bond wire strength curve based on the material properties of the bond wires of electronic components; Step 3: Apply load to the bond line stress intensity interference model and obtain the bond line stress curve; Similarly, using the stress intensity interference model, the stress distribution matrix of the bond line is obtained through simulation based on the degree of interference between stress and intensity distribution: (13); Where m is the number of bond lines.
[0048] Step 4: Obtain the second external failure factor based on the bond line strength curve and bond line stress curve. : (14); in, This is the bond line strength curve; This is the bond line stress curve; This is the probability of bond failure when the mean stress is less than the mean material strength.
[0049] In one alternative implementation, the first external failure factor is determined for different application environments of the electronic components. Second external failure coefficient Thermal fatigue and vibration can also be used for simulation to determine this.
[0050] For fatigue life caused by thermal fatigue, the Coffin-Manson formula is used for calculation: (15); in, It is the shear plastic strain amplitude; This represents the fatigue toughness index after temperature and frequency correction. The load cycle frequency; The fatigue toughness coefficient has an empirical value of 0.325.
[0051] In one alternative implementation, the Palmgren-Miner theory is used to predict the fatigue life caused by vibration, by introducing a critical damage value. Determine the fatigue failure of the structure, and consider... =1, from which the fatigue life of the structure can be obtained: (16); in, This is the critical damage value; The cumulative fatigue damage value of the structure is: (17); in, This indicates that the stress level on the SN curve is... The number of fatigue failure cycles at that time Indicates stress level as The number of cycles that actually occur within 1 second.
[0052] In this embodiment, the quality coefficient By consulting the technical manuals (GD597A, GB / T4589.1) and Table 4, we obtained the following: Table 4 Quality Coefficient
[0053]
[0054] In this embodiment, maturity coefficient Based on the usage of electronic components and Table 5, we can conclude that: Table 5 Maturity Coefficient
[0055]
[0056] In this embodiment, the environmental factor The main influence on the first external failure factor Second external failure coefficient Regarding the internal failure coefficient The impact is not significant, as can be seen by comparing the ratio of the sum of external failure coefficients in practical application environments and general environments: (18); in, The first external failure factor is determined under general application environment conditions; This is the second external failure factor determined under general application environment conditions.
[0057] The failure physics-based electronic component reliability model construction method of this invention comprehensively considers the impact of quality coefficient, maturity coefficient, internal failure, external failure, and environmental coefficient on reliability. By performing failure physics analysis at the integrated circuit level and finite element simulation analysis of packaged devices under different application environments, the relevant parameters in the model are determined. Reliability prediction of electronic components can be performed through calculation and simulation, saving data costs and improving prediction reliability and timeliness.
[0058] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations are intended to cover non-exclusive inclusion, such that an article or device comprising a list of elements includes not only those elements but also other elements not expressly listed. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the article or device comprising said element. Terms such as "connected" or "linked" are not limited to physical or mechanical connections but can include electrical connections, whether direct or indirect. The orientations or positional relationships indicated by terms such as "upper," "lower," "left," and "right" are based on the orientations or positional relationships shown in the accompanying drawings and are used only for the convenience of describing the invention and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as limiting the invention.
[0059] The above description, in conjunction with specific preferred embodiments, provides a further detailed explanation of the present invention. It should not be construed that the specific implementation of the present invention is limited to these descriptions. For those skilled in the art, various simple deductions or substitutions can be made without departing from the concept of the present invention, and all such modifications and substitutions should be considered within the scope of protection of the present invention.
Claims
1. A failure physics-based electronic component reliability model construction method, characterized by, include: Step 1: Collect electronic component parameters and determine the application environment of the electronic components. The electronic component parameters include: device manufacturing process, process parameters, transistor size, package type, package material parameters, and package process. Step 2: In the application environment, obtain the reliability correlation coefficients based on the electronic component parameters, including: internal failure coefficients. First external failure factor Second external failure coefficient quality coefficient Maturity coefficient and environmental coefficient ; Step 3: Based on the correlation relationship of the aforementioned reliability correlation coefficients, construct the reliability model as follows: ； wherein, is a failure rate of the electronic component; is an internal failure coefficient; is a first external failure coefficient; is a second external failure coefficient; is a quality coefficient; is a maturity coefficient; is an environmental coefficient; Step four: calculating the reliability correlation coefficient respectively, obtaining the failure rate of the electronic components according to the reliability model ; In the step two, the internal failure coefficient is acquired , comprising, hot carrier injection failure rate negative bias temperature instability failure rate time dependent dielectric breakdown failure rate internal failure coefficient wherein: ； In the second step, the first external failure coefficient is acquired , comprising: Step 1: Establish an interference model of pin stress intensity for electronic components; Step 2: Determine the pin strength curve based on the material properties of the electronic component's pins; Step 3: Apply load to the interference model of the pin stress intensity and obtain the pin stress curve; Step 4: obtaining the first external failure coefficient from the pin strength curve and the pin stress curve : ； wherein, is a pin strength curve; is a pin stress curve; is a probability of package failure when the mean value of stress is less than the mean value of material strength. In the second step, a second external failure coefficient is obtained comprising: Step 1: Establish an interference model of bond wire stress intensity for electronic components; Step 2: Determine the bonding wire strength curve based on the material properties of the bonding wires of the electronic components; Step 3: Apply load simulation to the bond line stress intensity interference model to obtain the bond line stress curve; Step 4: obtaining the second external failure coefficient from the bond wire strength curve and the bond wire stress curve : ； wherein, is the bond wire strength curve; is the bond wire stress curve; is the probability of bond failure when the average stress is less than the average strength of the material.
2. The method for constructing a reliability model for electronic components based on failure physics according to claim 1, characterized in that, Acquiring hot carrier injection failure rate comprising: Step 1 : Establishing the hot carrier injection failure rate lifetime model : ； in, The width of the channel; The effective diffusion coefficient; Carrier concentration; It is the first constant; The effective diffusion length; It is the second constant; This is the drain current; The minimum energy required to generate an interface trap; The mean free path of electrons; It represents the amount of charge; This represents the maximum electric field in the channel; Step 2: Apply the lifetime model Monte Carlo simulations were performed, and the simulated samples were fitted to determine that the sample lifetimes followed a log-normal distribution, thus obtaining the probability density function corresponding to the sample lifetimes. and its distribution function ; Step 3: obtaining the hot carrier injection failure rate from the distribution function . 3. The failure physics based electronic component reliability model building method of claim 2, wherein, Acquiring negative bias temperature instability failure rate comprises: Step 1 : Establishing the Negative Bias Temperature Instability Failure Rate Lifetime Model : ； wherein, is a threshold voltage change amount; is a threshold voltage change amount caused by the charge of other oxide species other than the interface traps; is a generation rate of the interface traps; Step 2: Monte Carlo simulation is performed on the life model and the sample generated by simulation is fitted to determine that the sample life is subject to lognormal distribution, and the probability density function corresponding to the sample life is obtained and its distribution function ; Step 3: obtaining the negative-bias temperature instability failure rate from the distribution function . 4. The failure physics based electronic component reliability model building method of claim 3, wherein, Time-dependent dielectric breakdown failure rate comprising: Step 1: Establish the time-dependent dielectric layer breakdown efficiency. Lifetime model : ； wherein, is a third constant; is a fourth constant, is a coefficient related to the nature of the defect, is the voltage across the oxide layer; Step 2: Monte Carlo simulation is performed on the life model The sample life is subject to a lognormal distribution, and the probability density function corresponding to the sample life is obtained by fitting the samples generated by the simulation and its distribution function ; Step 3: obtaining the time-dependent dielectric layer breakdown failure rate from the distribution function . 5. The failure physics based electronic component reliability model building method of claim 1, wherein, said quality coefficient and said maturity coefficient Obtained from the technical manual.
6. The failure physics based electronic component reliability model building method of claim 1, wherein, environmental coefficient is calculated according to the following formula: ； wherein, is a first external failure coefficient determined for general application environmental conditions; is a second external failure coefficient determined for general application environmental conditions.