Electronic equipment reliability prediction method based on failure physical Bayesian fusion

Abstract

The invention discloses an electronic equipment reliability prediction method based on failure physics Bayesian fusion, which relates to the field of electronic equipment reliability prediction, and comprises the following steps: collecting failure physical model parameters of each electronic equipment failure unit, and determining a failure physical model through failure mechanism analysis; carrying out reliability parameter calculation on the electronic equipment failure unit to obtain a failure physical prediction value; reliability analysis is carried out based on the electronic equipment reliability prediction manual, the failure rate of failure units of the electronic equipment is obtained, and the average failure-free working time of all the failure units is calculated; and performing system level reliability analysis by using a Bayesian formula to obtain system failure rate and system average fault time, and completing electronic equipment reliability prediction based on failure physical Bayesian fusion. The problems that in the prior art, when the reliability of the electronic equipment is predicted, it is difficult to consider a failure physical mechanism and actual test data at the same time, and prediction is inaccurate due to the lack of dynamic correction capacity are solved.

Description

Technical Field

[0001] The present invention relates to the field of electronic equipment reliability prediction, and in particular to an electronic equipment reliability prediction method integrating failure physics and Bayesian fusion. Background Art

[0002] Today, electronic equipment is widely used in high-reliability fields such as aerospace, automotive industry, and defense equipment. These complex electronic systems usually work in harsh environments such as high temperature, strong vibration, and strong electromagnetic interference, and are highly sensitive to failure. Once the key components of the system fail, it often causes system-level failures, bringing safety risks and huge maintenance costs. Therefore, reliability prediction technology has received more and more attention. In the design and development stage of electronic equipment, traditional reliability prediction methods mostly rely on life models based on statistical experience or large sample test inferences, which usually have many shortcomings. For example, many traditional methods only calculate the average life based on historical data or failure statistics, and the specific failure mechanisms of electronic components (such as thermal fatigue, vibration fatigue, material aging, etc.) are not considered in depth, making it difficult to accurately promote them on new technologies or new material products; obtaining reliable statistical data usually requires a large number of long-term experimental samples, resulting in very high experimental cycles and costs; at the same time, once electronic products are upgraded or the use environment changes, the original statistical models often need to be rebuilt.

[0003] In order to overcome the above difficulties, in recent years, there has been a research trend of combining the Physics of Failure (PoF) method with statistical analysis. The Physics of Failure model is based on an in-depth analysis of the material properties, structural characteristics and load environment of components, which can accurately characterize the failure process and make accelerated life predictions; while statistical analysis, especially the Bayesian method, can integrate prior knowledge (such as accelerated tests, historical experience, etc.) with actual service data (such as actual installation monitoring, failure occurrence time, etc.), so that the model parameters can be gradually corrected and updated under the condition of limited test samples, and reliability predictions that are closer to the real environment can be obtained. However, most of the current research is still at the stage of applying the Physics of Failure model and Bayesian estimation separately, lacking a systematic and operational fusion method to cope with multiple failure mechanisms in actual engineering, multi-unit level reliability assessment, and dynamic prediction requirements of complex electronic equipment systems. Especially in an environment where multiple failure modes coexist, how to effectively identify key failure units, establish accurate Physics of Failure equations, and continuously correct the model in combination with actual service data is still a technical challenge faced by the industry and academia. Summary of the invention

[0004] In view of the above-mentioned deficiencies in the prior art, the present invention provides an electronic device reliability prediction method that integrates failure physics and Bayesian fusion, which solves the problems in the prior art of predicting the reliability of electronic equipment, such as the difficulty in taking into account both the failure physics mechanism and the actual test data, and the inaccurate prediction caused by the lack of dynamic correction capability.

[0005] In order to achieve the above-mentioned invention object, the technical solution adopted by the present invention is: a failure physics Bayesian fusion electronic equipment reliability prediction method, comprising the following steps: S1: Collect the failure physical model parameters of each electronic equipment failure unit and establish the failure physical model through failure mechanism analysis; S2: Calculate the reliability parameters of the failure unit of the electronic equipment based on the failure physical model to obtain the failure physical prediction value; S3: Perform reliability analysis based on the Electronic Equipment Reliability Prediction Manual to obtain the failure rate of failed units of electronic equipment and calculate the mean trouble-free working time of each failed unit; S4: Based on the failure physics prediction value, failure rate and mean trouble-free working time, the Bayesian formula is used to perform system-level reliability analysis to obtain the system failure rate and system mean failure time, and complete the reliability prediction of electronic equipment with failure physics Bayesian fusion.

[0006] Furthermore, S2 includes the following sub-steps: S21: Calculate the failure occurrence time of each failure unit of the electronic device under the failure physical model; S22: Compare the failure occurrence times under various failure physical models, and select the output value of the failure physical model with the shortest failure occurrence time as the failure physical prediction value.

[0007] Furthermore, in S21, the failure occurrence time of each failure unit of the electronic device under the failure physical model is calculated, and the formula is: The Coffin-Manson model is:

[0008]

[0009]

[0010] in, is the number of fatigue cycles, is the shear stress on the solder joint, is the fatigue ductility coefficient, is the fatigue ductility index, is the packaging factor, is the diagonal length, The height of the solder joint support. is the device thermal expansion coefficient, is the thermal expansion coefficient of the substrate, is the device temperature difference, is the substrate temperature difference, is the average temperature of the cycle, High temperature immersion time for temperature cycle; The TDDB model is:

[0011] in, is the fault occurrence time under the TDDB model, is a constant, is the dipole or polarizability coefficient in the medium, is the actual electric field strength applied to the dielectric layer, is the activation energy, is the Boltzmann constant, is the absolute temperature; The Corrosion model is:

[0012] in, is the failure occurrence time under the Corrosion model, is a constant, Relative humidity RH Related corrosion factors, is the activation energy; The EM failure life model is:

[0013] in, is the failure occurrence time under the EM failure life model, is the proportionality constant, is the applied current density, is the critical current density, is the EM index, is the activation energy; The HCI failure model is:

[0014] in, is the failure occurrence time under the HCI failure model, is a constant related to the material and device structure, is the peak substrate current, is the current index, is the activation energy.

[0015] Furthermore, S3 includes the following sub-steps: S31: Perform reliability analysis based on the Electronic Equipment Reliability Prediction Manual to obtain the failure rate of electronic equipment failure units. The formula is:

[0016]

[0017]

[0018] in, For reliability, is the failure rate, For time, is the failure distribution function, is the failure density function; S32: Based on the failure rate of the failure unit of the electronic equipment, the average trouble-free working time of each failure unit is calculated, and the formula is:

[0019] in, The mean trouble-free working time.

[0020] Furthermore, the S4 includes the following sub-steps: S41: taking the failure rate as the mean of the prior information and the failure physical prediction value as the observation data, the prior distribution of each failure unit is calculated; S42: Based on the prior distribution and likelihood function of each failed unit, the posterior distribution of each failed unit is calculated, and the posterior parameters are corrected to obtain the posterior corrected parameters, and the posterior mean and the mean failure time are calculated; S43: Based on the posterior mean and the mean failure time, obtain the system failure rate and the system mean failure time.

[0021] Furthermore, the prior distribution of each failed unit in S41 is:

[0022]

[0023] in, is the failure rate, is the prior information of the number of failures, is the prior information of failure time, and Gamma is the gamma distribution.

[0024] Furthermore, the likelihood function in S42 is:

[0025] in, is the likelihood function, is the cumulative running time, is the failure rate; The posterior distribution of each failure unit is:

[0026] in, is the posterior distribution; The a posteriori correction parameters are:

[0027]

[0028] in, and is the a posteriori correction parameter; The posterior mean satisfies: .

[0029] The beneficial effects of the present invention are as follows: by combining the failure physics model (PoF) with the Bayesian statistical method, the present invention can make full use of the failure physics mechanism, and based on the mechanism models of thermal fatigue, vibration fatigue, material aging, etc. of components and solder joints, life modeling of key failure units is performed to improve the scientificity and accuracy of the prediction; and on the basis of the existing accelerated test and empirical priors, the failure data in the actual use process is integrated, and the reliability model is dynamically updated to make the prediction result more in line with the actual service environment; in addition, the data utilization efficiency is improved, and even in the case of a small test sample size or a limited test cycle, a relatively accurate life prediction and failure rate estimation can be obtained through the Bayesian fusion of prior distribution and actual observation data; finally, the system-level reliability analysis is realized, and on the basis of the unit-level failure distribution, the series-parallel or redundant structure inside the electronic equipment is combined to obtain the system-level reliability index and the mean time to failure (MTTF), etc., to provide a basis for design improvement and operation and maintenance decision-making. At the same time, it provides an efficient and accurate reliability evaluation method for the development and use of electronic equipment in high-reliability fields such as aviation, aerospace, and automobiles. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 This is a flow chart of the electronic device reliability prediction method based on failure physics and Bayesian fusion of the present invention. DETAILED DESCRIPTION

[0031] The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments.

[0032] like Figure 1 As shown, a failure physics Bayesian fusion electronic equipment reliability prediction method includes the following steps: S1: Collect the failure physical model parameters of each electronic equipment failure unit and establish the failure physical model through failure mechanism analysis; S2: Calculate the reliability parameters of the failure unit of the electronic equipment based on the failure physical model to obtain the failure physical prediction value; S3: Perform reliability analysis based on the Electronic Equipment Reliability Prediction Manual to obtain the failure rate of failed units of electronic equipment and calculate the mean trouble-free working time of each failed unit; S4: Based on the failure physics prediction value, failure rate and mean trouble-free working time, the Bayesian formula is used to perform system-level reliability analysis to obtain the system failure rate and system mean failure time, and complete the reliability prediction of electronic equipment with failure physics Bayesian fusion.

[0033] In one embodiment of the present invention, material failure physical model parameters are collected, and the signal processing module is divided into several failure units, including BGA solder joints, MLCC capacitors, metal film resistors, and bonding wires, which are recorded as C1, C2, C3, and C4 for the convenience of explanation of calculation. As shown in Tables 1-5, the parameter values ​​of the four key failure units under the failure physical model are shown.

[0034] Table 1 Coffin-Manson model parameters of four key failure units

[0035] Table 2 TDDB model parameters of four key failure units

[0036] Table 3 Corrosion model parameters of four key failure units

[0037] Table 4 EM failure physical model parameters of four key failure units

[0038] Table 5 HCI failure physical model parameters of four key failure units

[0039] The S2 includes the following sub-steps: S21: Calculate the failure occurrence time of each failure unit of the electronic device under the failure physical model; S22: Compare the failure occurrence times under various failure physical models, and select the output value of the failure physical model with the shortest failure occurrence time as the failure physical prediction value.

[0040] In S21, the failure occurrence time of each failure unit of the electronic device under the failure physical model is calculated, and the formula is: The Coffin-Manson model is:

[0041]

[0042]

[0043] in, is the number of fatigue cycles, is the shear stress on the solder joint, is the fatigue ductility coefficient, is the fatigue ductility index, is the packaging factor, is the diagonal length, The height of the solder joint support is is the device thermal expansion coefficient, is the thermal expansion coefficient of the substrate, is the device temperature difference, is the substrate temperature difference, is the average temperature of the cycle, High temperature immersion time for temperature cycle; For ease of calculation, the Coffin-Manson model uses a commonly used simplified form:

[0044] and The parameters of the Coffin-Manson model can all be obtained from experiments or literature. Different devices, packages, and materials will have different typical values. is the temperature in K. The time when the fault occurred It can be calculated by the following formula:

[0045] in, is the thermal cycle frequency; Calculate the number of fatigue cycles and the time when the failure occurred , as shown in Table 1, in addition, due to the calculated fatigue cycle number If it is too small, adjust the parameters manually.

[0046] The TDDB model is commonly seen in dielectric layer breakdown or metal-dielectric interface degradation within a chip, and can be expressed using the following empirical equation: The TDDB model is:

[0047] in, is the fault occurrence time under the TDDB model, is a constant, is the dipole or polarizability coefficient in the medium, is the actual electric field strength applied to the dielectric layer, is the activation energy, is the Boltzmann constant, is the absolute temperature; The Corrosion model often occurs in the metallization layer, wire bonding area, etc. The model formula is as follows: The Corrosion model is:

[0048] in, is the failure occurrence time under the Corrosion model, is a constant, Relative humidity RH Related corrosion factors, is the activation energy; The EM failure life model is:

[0049] in, is the failure occurrence time under the EM failure life model, is the proportionality constant, is the applied current density, is the critical current density, is the EM index, is the activation energy; The HCI failure model is:

[0050] in, is the failure occurrence time under the HCI failure model, is a constant related to the material and device structure, is the peak substrate current, is the current index, is the activation energy.

[0051] Table 6 shows the time of failure occurrence calculated by each failure physical model.

[0052] Table 6 Failure occurrence time calculated by each failure physical model

[0053]

[0054] In summary, observing the life prediction of the five failure mechanisms of each component, the TTF calculated by the Coffin-Manson thermal fatigue model is numerically significantly smaller than that of the other four models. This mechanism will fail first and dominate. Finally, the life calculated by the Coffin-Manson model is selected as the physical prediction value of failure.

[0055] The S3 includes the following sub-steps: S31: Perform reliability analysis based on the Electronic Equipment Reliability Prediction Manual to obtain the failure rate of electronic equipment failure units and the failure rate It can be obtained by looking up the table, as shown in Table 7: Table 7 Failure rate of failed units obtained by looking up the table

[0056]

[0057] The reliability is calculated as follows:

[0058] The failure distribution function is as follows:

[0059] The failure density function is as follows:

[0060] in, For reliability, is the failure rate, For time, is the failure distribution function, is the failure density function; S32: Based on the failure rate of the failure unit of the electronic equipment, the average trouble-free working time of each failure unit is calculated, and the formula is:

[0061] in, The mean time between failures.

[0062] As shown in Table 8, the average trouble-free working time of each failed unit is: Table 8 Average trouble-free working time of each failed unit ( )

[0063] The S4 comprises the following sub-steps: S41: taking the failure rate as the mean of the prior information and the failure physical prediction value as the observation data, the prior distribution of each failure unit is calculated; S42: Based on the prior distribution and likelihood function of each failed unit, the posterior distribution of each failed unit is calculated, and the posterior parameters are corrected to obtain the posterior corrected parameters, and the posterior mean and the mean failure time are calculated; S43: Based on the posterior mean and the mean failure time, obtain the system failure rate and the system mean failure time.

[0064] The prior distribution of each failed unit in S41 is:

[0065]

[0066] in, is the failure rate, is the prior information of the number of failures, is the prior information of failure time, Gamma is the gamma distribution; In order to simplify the calculation, let ,then:

[0067] As shown in Table 9, these are the prior parameters of each failure unit: Table 9 Prior parameters of each failed unit

[0068] That is, the prior for each device is: .

[0069] The likelihood function in S42 is:

[0070] in, is the likelihood function, is the cumulative running time, is the failure rate; The posterior distribution of each failure unit is:

[0071] in, is the posterior distribution; The a posteriori correction parameters are:

[0072]

[0073] in, and is the a posteriori correction parameter; The posterior mean satisfies: .

[0074] Finally, the posterior correction parameters and calculation parameters of each failed unit are obtained through calculation, as shown in Table 10, where the unit of the posterior mean is The unit of mean time to failure is .

[0075] Table 10 A posteriori correction parameters of each failed unit

[0076] This system adopts series system analysis, and the system failure rate is:

[0077] The mean time between failures of the system for: .

[0078] Those skilled in the art will appreciate that the embodiments described herein are intended to help readers understand the principles of the present invention, and should be understood that the protection scope of the present invention is not limited to such specific statements and embodiments. Those skilled in the art can make various other specific variations and combinations that do not deviate from the essence of the present invention based on the technical revelations disclosed by the present invention, and these variations and combinations are still within the protection scope of the invention.

Claims

1. A method for electronic device reliability prediction based on failure physics and Bayesian fusion, characterized in that: The following steps are involved: S1: Collect the failure physical model parameters of each electronic equipment failure unit and establish the failure physical model through failure mechanism analysis; S2: Calculate the reliability parameters of the failure unit of the electronic equipment based on the failure physical model to obtain the failure physical prediction value; S3: Perform reliability analysis based on the Electronic Equipment Reliability Prediction Manual to obtain the failure rate of failed units of electronic equipment and calculate the mean trouble-free working time of each failed unit; S4: Based on the failure physics prediction value, failure rate and mean trouble-free working time, the Bayesian formula is used to perform system-level reliability analysis to obtain the system failure rate and system mean failure time, and complete the reliability prediction of electronic equipment with failure physics Bayesian fusion.

2. The electronic device reliability prediction method based on failure physics and Bayesian fusion according to claim 1 is characterized in that: The S2 includes the following sub-steps: S21: Calculate the failure occurrence time of each failure unit of the electronic device under the failure physical model; S22: Compare the failure occurrence times under various failure physical models, and select the output value of the failure physical model with the shortest failure occurrence time as the failure physical prediction value.

3. The electronic device reliability prediction method based on failure physics and Bayesian fusion according to claim 2 is characterized in that: In S21, the failure occurrence time of each failure unit of the electronic device under the failure physical model is calculated, and the formula is: The Coffin-Manson model is: in, is the number of fatigue cycles, is the shear stress on the solder joint, is the fatigue ductility coefficient, is the fatigue ductility index, is the packaging factor, is the diagonal length, The height of the solder joint support. is the device thermal expansion coefficient, is the thermal expansion coefficient of the substrate, is the device temperature difference, is the substrate temperature difference, is the average cycle temperature, High temperature immersion time for temperature cycle; The TDDB model is: in, is the fault occurrence time under the TDDB model, is a constant, is the dipole or polarizability coefficient in the medium, is the actual electric field strength applied to the dielectric layer, is the activation energy, is the Boltzmann constant, is the absolute temperature; The Corrosion model is: in, is the failure occurrence time under the Corrosion model, is a constant, Relative humidity RH Related corrosion factors, is the activation energy; The EM failure life model is: in, is the failure occurrence time under the EM failure life model, is the proportionality constant, is the applied current density, is the critical current density, is the EM index, is the activation energy; The HCI failure model is: in, is the failure occurrence time under the HCI failure model, is a constant related to the material and device structure, is the peak substrate current, is the current index, is the activation energy.

4. The electronic device reliability prediction method based on failure physics and Bayesian fusion according to claim 1 is characterized in that: The S3 includes the following sub-steps: S31: Perform reliability analysis based on the Electronic Equipment Reliability Prediction Manual to obtain the failure rate of electronic equipment failure units. The formula is: in, For reliability, is the failure rate, For time, is the failure distribution function, is the failure density function; S32: Based on the failure rate of the failure unit of the electronic equipment, the average trouble-free working time of each failure unit is calculated, and the formula is: in, The mean time between failures.

5. The electronic device reliability prediction method based on failure physics and Bayesian fusion according to claim 1 is characterized in that: The S4 includes the following sub-steps: S41: taking the failure rate as the mean of the prior information and the failure physical prediction value as the observation data, the prior distribution of each failure unit is calculated; S42: Based on the prior distribution and likelihood function of each failed unit, the posterior distribution of each failed unit is calculated, and the posterior parameters are corrected to obtain the posterior corrected parameters, and the posterior mean and the mean failure time are calculated; S43: Based on the posterior mean and the mean failure time, obtain the system failure rate and the system mean failure time.

6. The electronic device reliability prediction method based on failure physics and Bayesian fusion according to claim 5 is characterized in that: The prior distribution of each failed unit in S41 is: in, is the failure rate, is the prior information of the number of failures, is the prior information of failure time, and Gamma is the gamma distribution.

7. The electronic device reliability prediction method based on failure physics and Bayesian fusion according to claim 6 is characterized in that: The likelihood function in S42 is: in, is the likelihood function, is the cumulative running time, is the failure rate; The posterior distribution of each failure unit is: in, is the posterior distribution; The a posteriori correction parameters are: in, and is the a posteriori correction parameter; The posterior mean satisfies: 。

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