CN116682485A - Electromigration failure and life prediction method and device of magnetic memory and electronic equipment - Google Patents
Electromigration failure and life prediction method and device of magnetic memory and electronic equipment Download PDFInfo
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Abstract
The invention relates to a method and a device for predicting electromigration failure and service life of a magnetic memory, and electronic equipment, wherein the method comprises the following steps: obtaining a plurality of test data, wherein the plurality of test data are data obtained by performing electromigration failure test on a plurality of sample devices, the test data comprise external temperature, device temperature, applied current, device parameters and failure time, and the device parameters comprise device heavy metal layer materials, depth and thickness; inputting test data into a simulation model to obtain a plurality of simulated temperature variation amounts of different positions of the simulated device after a preset time period is passed; based on the test data and the plurality of simulated temperature variations, the electromigration failure and the service life of the magnetic memory are predicted by combining a black model. The invention can realize the re-quantization of the temperature of the magnetic memory device, in particular to the re-quantization of the temperature of the heavy metal layer of the device, so that the temperature of the re-quantized heavy metal layer of the device is closer to the actual temperature, and further, the electromigration failure and the service life of the magnetic memory are more accurately predicted by combining a black model.
Description
Technical Field
The present invention relates to the field of semiconductor manufacturing technologies, and in particular, to a method and apparatus for predicting electromigration failure and lifetime of a magnetic memory, and an electronic device.
Background
Electromigration of interconnect lines is one of the important causes of failure in the field of semiconductor fabrication. When the electromigration test is carried out on the device, current and temperature stress are applied to the device to accelerate the failure of the device, so that the purposes of electromigration failure and life prediction are achieved. Spin-orbit torque magnetic memories generally operate by switching the magnetic moment through a current in a heavy metal layer. The heavy metal layer of the magnetic memory can be regarded as a metal wire due to its extremely small cross-sectional area, which has a problem of electromigration.
Failure testing of electromigration is based on JEDEC specification standards and accelerated testing is performed with representative sample devices to predict lifetime under actual operating conditions. After median failure time is obtained through testing, based on a classical black model, failure time under corresponding actual working conditions is calculated and predicted through a formula.
However, the classical black model is not suitable for the magnetic memory, and the problem of high temperature at the middle position of the heavy metal layer of the magnetic memory after being electrified is not considered, so that the classical model is improper in characterization of temperature, and further calculation of relevant parameters is affected, and a prediction result of the whole model is deviated.
Disclosure of Invention
In view of the foregoing, it is desirable to provide a method, apparatus and electronic device for predicting electromigration failure and lifetime of magnetic storage, which are suitable for use in magnetic memories and can improve the accuracy of prediction.
The invention provides a method for predicting electromigration failure and service life of a magnetic memory, which comprises the following steps:
obtaining a plurality of test data, wherein the plurality of test data are data obtained by performing electromigration failure test on a plurality of sample devices, the test data comprise external temperature, device temperature, applied current, device parameters and failure time, and the device parameters comprise device heavy metal layer materials, depth and thickness;
inputting the test data into a simulation model to obtain a plurality of simulated temperature variation amounts of different positions of the simulated device after a preset time period, wherein the simulation model is a magnetic memory model constructed by simulation software;
based on the test data and the plurality of simulated temperature variations, a black model is combined to predict electromigration failure and life of the magnetic memory.
In one embodiment, the predicting electromigration failure and lifetime of the magnetic memory based on the test data and the plurality of simulated temperature variations in combination with a black model comprises:
updating the device temperature through the plurality of simulation temperature variation amounts, and updating the test data;
and fitting the updated test data with a black model to obtain electromigration failure and service life of the magnetic memory.
In one embodiment, the updating the device temperature by the plurality of simulated temperature variations includes:
and updating the temperature of the device according to the simulated temperature variation of the middle position of the heavy metal layer of the device.
In one embodiment, the predicting electromigration failure and lifetime of the magnetic memory based on the test data and the plurality of simulated temperature variations in combination with a black model comprises:
based on the simulation temperature variation amounts, combining the device parameters, and fitting to obtain temperature models aiming at different device parameters;
fusing the temperature model and a black model to obtain an improved model;
and fusing the test data with the improved model to obtain electromigration failure and service life of the magnetic memory.
In one embodiment, the temperature model is:
ΔT max ＝A×I 2 +B×S+C，
wherein I is a current passing through the heavy metal layer, S is a cross-sectional area of the heavy metal layer, and A, B, C is a constant.
In one embodiment, the improved model is:
wherein T is median failure time, ea is activation energy, P is a constant related to the density, resistivity and other factors of the heavy metal layer material, J is current density, T 0 For the initial temperature of the device at the beginning of the test, n is the current density index, k B Is the Boltzmann constant.
In one embodiment, the method further comprises:
and processing the test data, and calculating to obtain the cross-sectional area and the current density of the heavy metal layer.
The invention also provides a device for predicting electromigration failure and service life of a magnetic memory, which comprises:
the device comprises an acquisition module, a test module and a test module, wherein the acquisition module is used for acquiring a plurality of test data, the plurality of test data are data obtained by performing electromigration failure test on a plurality of sample devices, the test data comprise external temperature, device temperature, applied current, device parameters and failure time, and the device parameters comprise device heavy metal layer materials, depth and thickness;
the simulation module is used for inputting the test data into a simulation model to obtain a plurality of simulated temperature variation amounts of different positions of the simulated device after the preset time period, and the simulation model is a magnetic memory model constructed by simulation software;
and the prediction module is used for predicting electromigration failure and service life of the magnetic memory based on the test data and the simulation temperature variation quantities and combining a black model.
The invention also provides an electronic device comprising a memory and a processor, wherein the memory stores a computer program, and the processor realizes the electromigration failure and life prediction method of the magnetic memory when executing the computer program.
The invention also provides a computer readable storage medium storing a computer program which when executed by a processor implements a method of electromigration failure and lifetime prediction of a magnetic memory as defined in any one of the above.
According to the electromigration failure and life prediction method, the electromigration failure test device and the electronic equipment for the magnetic memory, the electromigration failure test is carried out on the plurality of sample devices to obtain a plurality of test data, each test data comprises the external temperature, the device temperature, the applied current, the device parameters and the failure time, the obtained plurality of test data are brought into the simulation model to obtain a plurality of simulated temperature variation amounts, and therefore the temperature of the magnetic memory device, particularly the temperature of the heavy metal layer of the device, can be re-quantized, the temperature of the re-quantized heavy metal layer of the device is enabled to be closer to the actual temperature, and then the black model (black equation) is combined, so that the electromigration failure and the life of the magnetic memory can be predicted more accurately.
Drawings
In order to more clearly illustrate the invention or the technical solutions of the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are some embodiments of the invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of electromigration failure and lifetime prediction of a magnetic memory in accordance with the present invention;
FIG. 2 is a graph of electromigration simulation temperature results for a magnetic memory in accordance with the present invention;
FIG. 3 is a second flowchart of the electromigration failure and lifetime prediction of a magnetic memory according to the present invention;
FIG. 4 is a graph showing the temperature change with the abscissa of the heavy metal layer depth and thickness of different magnetic memories;
FIG. 5 is a third flow chart of electromigration failure and life prediction of a magnetic memory in accordance with the present invention;
FIG. 6 is a graph showing the relationship between the depth, thickness, cross-sectional area and temperature of heavy metal layers of different magnetic memories;
FIG. 7 is a graph showing the relationship between the current and the temperature of heavy metal layers passing through different magnetic memories;
FIG. 8 is a schematic diagram of a magnetic memory electromigration failure and life prediction apparatus according to the present invention;
fig. 9 is an internal structural diagram of a computer device of one embodiment.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The electromigration failure and lifetime prediction method, apparatus and electronic equipment of the present invention are described below with reference to fig. 1 to 9.
As shown in FIG. 1, in one embodiment, a method for electromigration failure and lifetime prediction of a magnetic memory includes the steps of:
step S110, a plurality of test data are obtained, wherein the test data are data obtained by performing electromigration failure test on a plurality of sample devices, the test data comprise external temperature, device temperature, applied current, device parameters and failure time, and the device parameters comprise device heavy metal layer materials, depth and thickness.
Specifically, an electromigration test platform is built through an MPI high-low temperature machine, a power supply meter and a probe, different current and temperature stresses are respectively applied to a plurality of sample devices of a magnetic memory, a failure criterion is set to be resistance change 20% according to JEDEC interconnection line electromigration standard, failure time of the sample devices, applied current, external temperature, device temperature and device parameters in the electromigration process are recorded, wherein the device parameters mainly comprise parameters of a heavy metal layer of the device, including heavy metal layer materials, depth and thickness.
When the obtained test data are processed, the cross-sectional area of the heavy metal layer, the current density flowing through the heavy metal layer and other data can be calculated and obtained. The cross-sectional area (S) of the heavy metal layer is calculated to be related to the depth (D) and the thickness (H), and the calculation formula is as follows: s=d×h; the current density (J) flowing through the heavy metal layer is related to the current (I) flowing through the heavy metal layer and the cross-sectional area (S) of the heavy metal layer, and the calculation formula is j=i/S.
Step S120, inputting test data into a simulation model to obtain a plurality of simulated temperature variation amounts of different positions of the simulated device after a preset time period, wherein the simulation model is a magnetic memory model constructed by simulation software.
The device structure of the magnetic memory (mainly SOT-MTJ spin logic device) is simulated by simulation software. Simulation data of the simulation software is derived from the actual representative product data, and the simulation is performed by simulating the entire magnetic memory device and surrounding areas thereof, applying different temperature stress and current stress, and changing parameters of the magnetic memory device itself.
Specifically, test data are input into simulation data, and a plurality of simulated temperature variation amounts of different positions of the simulated device after a preset time period is passed are obtained, so that the temperature of the magnetic memory device is re-quantized.
Step S130, based on the test data and the plurality of simulated temperature variation amounts, the electromigration failure and the service life of the magnetic memory are predicted by combining a black model.
The conventional interconnect electromigration lifetime model uses the Black equation, but the conventional Black equation does not consider the sharp temperature rise in the middle of the heavy metal layer of the device. Referring to the simulation results of the heavy metal layer of the device of fig. 2, the intermediate temperature rises sharply, which results in the intermediate portion being relatively easily damaged, and the FIB (Focused Ion beam) test results actually performed on the electromigration-disabled device also show this. For magnetic memory devices, the sharp rise in temperature at the intermediate location has a greater effect on the temperature parameters in the Black equation. The Black equation generally uses the externally applied temperature as a temperature parameter that is not suitable for the electromigration case of the memory device. The Black equation has fitting distortion, which leads to electromigration failure of the magnetic memory and inaccurate parameter prediction of a life calculation model, and does not consider factors of actual temperature conditions. The generalized Black equation does not reflect the actual real temperature conditions and the accuracy of the predicted electromigration failure and lifetime calculation model of the magnetic storage is low.
According to the electromigration failure and life prediction method for the magnetic memory, electromigration failure tests are conducted on a plurality of sample devices to obtain a plurality of test data, each test data comprises an external temperature, a device temperature, an applied current, a device parameter and failure time, the obtained plurality of test data are brought into a simulation model to obtain a plurality of simulated temperature variation amounts, and therefore the temperature of the magnetic memory device, particularly the temperature of a heavy metal layer of the device, can be re-quantized, the temperature of the re-quantized heavy metal layer of the device is enabled to be closer to the actual temperature, and then the black model (black equation) is combined, so that electromigration failure and life of the magnetic memory can be predicted more accurately.
As shown in FIG. 3, in one embodiment, predicting electromigration failure and lifetime of a magnetic memory based on test data and a plurality of simulated temperature variations, in combination with a black model, includes:
step S131, updating the device temperature through a plurality of simulation temperature variation amounts, and updating test data.
Specifically, the device temperature is updated according to the simulated temperature variation of the middle position of the heavy metal layer of the device. Fig. 2 is a graph of electromigration simulation temperature results of a magnetic memory, which shows simulation results of a heavy metal layer of the magnetic memory, and referring to fig. 4, a graph of temperature change along with abscissa is shown in the case of different depths and thicknesses of heavy metal layers of the magnetic memory, wherein (a.u.) is normalization processing. From fig. 2 in combination with fig. 4, it can be seen that, by taking a temperature-dependent cut-off along the x-axis, the temperature is highest at the intermediate position, and the important research position can be determined as the intermediate position of the device, and the electromigration is most likely to occur at the highest point of the intermediate position due to the extremely strong correlation between the electromigration and the temperature. The temperature change quantity of the middle position of the heavy metal layer is maximum, the middle temperature rises sharply, and the middle part is easy to damage, so that the temperature of the middle position of the heavy metal layer of the device is an important parameter affecting electromigration failure and service life of the magnetic memory, updated test data is used, and the temperature of the heavy metal layer of the device is closer to the actual temperature.
And step S132, fitting the updated test data with a black model to obtain electromigration failure and service life of the magnetic memory.
The Black equation has fitting distortion, which leads to electromigration failure of the magnetic memory and inaccurate parameter prediction of a life calculation model, mainly because factors of actual temperature conditions are not considered. The temperature of the heavy metal layer of the device is closer to the actual temperature according to the updated test data, so that electromigration failure and service life of the magnetic memory obtained by fitting are more accurate.
As shown in FIG. 5, in one embodiment, predicting electromigration failure and lifetime of a magnetic memory based on test data and the plurality of simulated temperature variations in combination with a black model comprises:
step S133, fitting to obtain temperature models aiming at different device parameters based on a plurality of simulated temperature variation amounts and combining the device parameters.
Wherein, the black model is:
wherein t is median failure time, ea is activation energy (eV), P is a constant related to the density, resistivity and other factors of the heavy metal layer material, and J is current density (A/cm 2 ) T is absolute temperature (K), n is current density index, K B Is the Boltzmann constant.
Referring to fig. 6, the relationship between the depth, thickness, cross-sectional area and temperature variation of heavy metal layers of different magnetic memories is shown, wherein (a.u.) is normalization. The electromigration temperature change data of the magnetic memory obtained through simulation under different heavy metal layer depths, thicknesses and cross sectional areas are used, and the electromigration temperature change data are obtained through data fitting with the data of the heavy metal layer depths, the thickness, the cross sectional areas and the like. And the maximum temperature variation of the heavy metal layer of the magnetic memory is in direct proportion to the depth, thickness and cross-sectional area of the heavy metal layer of the magnetic memory. Referring to fig. 7, a graph of the current versus temperature change of the heavy metal layer passing through the magnetic memory is shown, wherein (a.u.) is normalization processing. The graph is obtained by using electromigration temperature change data of the magnetic memory under different currents of the overweight metal layer obtained through simulation and performing data fitting with the currents of the overweight metal layer. And the maximum temperature variation of the heavy metal layer of the magnetic memory and the square of the current passing through the heavy metal layer are in direct proportion to linear relation. Combining the related variables of the black equation to obtain parameters related to the highest temperature, namely current and the cross-sectional area of the heavy metal layer of the magnetic memory, thereby obtaining a temperature model:
ΔT max ＝A×I 2 +B×S+C，
wherein I is a current passing through the heavy metal layer, S is a cross-sectional area of the heavy metal layer, and A, B, C is a constant.
For xmA working current, y ℃ working temperature and znm 2 The simulation experiment is carried out by using different simulation models with cross sectional areas to obtain related data such as simulation temperature, simulation current, simulation device parameters and the like, and the formula is fitted by using multiple linear regression analysis to obtain the following formula (wherein the numerical values are normalized):
ΔT max ＝2750×I 2 -S+8800(a.u.)
where, (a.u.) is normalized.
In step S134, the temperature model and the black model are fused to obtain an improved model.
Specifically, the black model is modified according to the temperature model to obtain an improved model, namely a magnetic memory electromigration failure and life prediction model:
wherein T is median failure time, ea is activation energy, A is a constant related to the density, resistivity and other factors of the heavy metal layer material, J is current density, T 0 For the initial temperature of the device at the beginning of the test, n is the current density index, k B Is the Boltzmann constant.
And S135, fusing the test data with the improved model to obtain electromigration failure and service life of the magnetic memory.
Specifically, the improved model can be used for predicting the electromigration lifetime MTF and collecting parameters (depth D, thickness H, cross-sectional area S and the like of the heavy metal layer) of the device and currents of the overweight metal layer under different conditions. After collecting the parameters and currents of the device itself under different conditions, the predicted failure average time of the chip under the millions can be obtained. The parameters (cross-sectional area S and the like) of the device under different conditions are obtained, the development of a process can be controlled through data analysis, and the occurrence of electromigration at high temperature is reduced.
The electromigration failure and life predicting device for the magnetic memory provided by the invention is described below, and the electromigration failure and life predicting device for the magnetic memory and the electromigration failure and life predicting method for the magnetic memory described above can be correspondingly referred to each other.
As shown in FIG. 8, in one embodiment, a magnetic memory electromigration failure and life prediction apparatus includes an acquisition module 810, a simulation module 820, and a prediction module 830.
The obtaining module 810 is configured to obtain a plurality of test data, where the plurality of test data is data obtained by performing electromigration failure test on a plurality of sample devices, the test data includes an external temperature, a device temperature, an applied current, a device parameter and a failure time, and the device parameter includes a device heavy metal layer material, a depth and a thickness.
The simulation module 820 is configured to input test data into a simulation model, and obtain a plurality of simulated temperature variations of different positions of the simulated device after a preset period of time, where the simulation model is a magnetic memory model constructed by simulation software.
The prediction module 830 is configured to predict electromigration failure and lifetime of the magnetic memory based on the test data and the plurality of simulated temperature variations in combination with a black model.
In this embodiment, the prediction module 830 is specifically configured to:
updating the device temperature through the plurality of simulation temperature variation amounts, and updating the test data;
and fitting the updated test data with a black model to obtain electromigration failure and service life of the magnetic memory.
In this embodiment, the prediction module 830 is specifically further configured to:
and updating the temperature of the device according to the simulated temperature variation of the middle position of the heavy metal layer of the device.
In this embodiment, the prediction module 830 is specifically configured to:
based on the simulation temperature variation amounts, combining the device parameters, and fitting to obtain temperature models aiming at different device parameters;
fusing the temperature model and a black model to obtain an improved model;
and fusing the test data with the improved model to obtain electromigration failure and service life of the magnetic memory.
Wherein, the temperature model is:
ΔT max ＝A×I 2 +B×S+C，
wherein I is a current passing through the heavy metal layer, S is a cross-sectional area of the heavy metal layer, and A, B, C is a constant.
The improved model is as follows:
wherein t is median failure time, ea is activation energy, P is a constant related to factors such as density, resistivity and the like of the heavy metal layer material, J is current density,T 0 for the initial temperature of the device at the beginning of the test, n is the current density index, k B Is the Boltzmann constant.
In this embodiment, the electromigration failure and lifetime prediction apparatus of the magnetic memory is further configured to:
and processing the test data, and calculating to obtain the cross-sectional area and the current density of the heavy metal layer.
Fig. 9 illustrates a schematic physical structure of an electronic device, which may be an intelligent terminal, and an internal structure diagram thereof may be as shown in fig. 9. The electronic device includes a processor, a memory, and a network interface connected by a system bus. Wherein the processor of the electronic device is configured to provide computing and control capabilities. The memory of the electronic device includes a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The network interface of the electronic device is used for communicating with an external terminal through a network connection. The computer program, when executed by a processor, implements a method for electromigration failure and life prediction of a magnetic storage, the method comprising:
obtaining a plurality of test data, wherein the plurality of test data are data obtained by performing electromigration failure test on a plurality of sample devices, the test data comprise external temperature, device temperature, applied current, device parameters and failure time, and the device parameters comprise device heavy metal layer materials, depth and thickness;
inputting test data into a simulation model to obtain a plurality of simulated temperature variation amounts of different positions of a simulated device after a preset time period is elapsed, wherein the simulation model is a magnetic memory model constructed by simulation software;
based on the test data and the plurality of simulated temperature variations, the electromigration failure and the service life of the magnetic memory are predicted by combining a black model.
It will be appreciated by those skilled in the art that the structure shown in fig. 9 is merely a block diagram of a portion of the structure associated with the present invention and is not limiting of the electronic device to which the present invention is applied, and that a particular electronic device may include more or fewer components than shown, or may combine certain components, or have a different arrangement of components.
In another aspect, the present invention also provides a computer storage medium storing a computer program, which when executed by a processor, implements a method for electromigration failure and lifetime prediction of a magnetic memory, the method comprising:
obtaining a plurality of test data, wherein the plurality of test data are data obtained by performing electromigration failure test on a plurality of sample devices, the test data comprise external temperature, device temperature, applied current, device parameters and failure time, and the device parameters comprise device heavy metal layer materials, depth and thickness;
inputting test data into a simulation model to obtain a plurality of simulated temperature variation amounts of different positions of a simulated device after a preset time period is elapsed, wherein the simulation model is a magnetic memory model constructed by simulation software;
based on the test data and the plurality of simulated temperature variations, the electromigration failure and the service life of the magnetic memory are predicted by combining a black model.
In yet another aspect, a computer program product or computer program is provided, the computer program product or computer program comprising computer instructions stored in a computer readable storage medium. A processor of an electronic device reads the computer instructions from a computer readable storage medium, the processor executing the computer instructions to implement a method for electromigration failure and lifetime prediction of a magnetic memory, the method comprising:
obtaining a plurality of test data, wherein the plurality of test data are data obtained by performing electromigration failure test on a plurality of sample devices, the test data comprise external temperature, device temperature, applied current, device parameters and failure time, and the device parameters comprise device heavy metal layer materials, depth and thickness;
inputting test data into a simulation model to obtain a plurality of simulated temperature variation amounts of different positions of a simulated device after a preset time period is elapsed, wherein the simulation model is a magnetic memory model constructed by simulation software;
based on the test data and the plurality of simulated temperature variations, the electromigration failure and the service life of the magnetic memory are predicted by combining a black model.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in embodiments provided herein may include non-volatile and/or volatile memory. The nonvolatile memory can include Read Only Memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory.
By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), memory bus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), among others.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The foregoing examples illustrate only a few embodiments of the invention and are described in detail herein without thereby limiting the scope of the invention. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the invention, which are all within the scope of the invention. Accordingly, the scope of protection of the present invention is to be determined by the appended claims.
Claims (10)
1. A method for electromigration failure and lifetime prediction of a magnetic memory, the method comprising:
obtaining a plurality of test data, wherein the plurality of test data are data obtained by performing electromigration failure test on a plurality of sample devices, the test data comprise external temperature, device temperature, applied current, device parameters and failure time, and the device parameters comprise device heavy metal layer materials, depth and thickness;
inputting the test data into a simulation model to obtain a plurality of simulated temperature variation amounts of different positions of the simulated device after a preset time period, wherein the simulation model is a magnetic memory model constructed by simulation software;
based on the test data and the plurality of simulated temperature variations, a black model is combined to predict electromigration failure and life of the magnetic memory.
2. The method of claim 1, wherein the predicting the electromigration failure and lifetime of the magnetic memory based on the test data and the plurality of simulated temperature variations in combination with a black model comprises:
updating the device temperature through the plurality of simulation temperature variation amounts, and updating the test data;
and fitting the updated test data with a black model to obtain electromigration failure and service life of the magnetic memory.
3. The method of claim 2, wherein updating the device temperature with the plurality of simulated temperature variations comprises:
and updating the temperature of the device according to the simulated temperature variation of the middle position of the heavy metal layer of the device.
4. The method of claim 1, wherein the predicting the electromigration failure and lifetime of the magnetic memory based on the test data and the plurality of simulated temperature variations in combination with a black model comprises:
based on the simulation temperature variation amounts, combining the device parameters, and fitting to obtain temperature models aiming at different device parameters;
fusing the temperature model and a black model to obtain an improved model;
and fusing the test data with the improved model to obtain electromigration failure and service life of the magnetic memory.
5. The method of claim 4, wherein the temperature model is:
ΔT max ＝A×I 2 +B×S+C，
wherein I is a current passing through the heavy metal layer, S is a cross-sectional area of the heavy metal layer, and A, B, C is a constant.
6. The method of claim 4, wherein the improved model is:
wherein T is median failure time, ea is activation energy, P is a constant related to the density, resistivity and other factors of the heavy metal layer material, J is current density, T 0 For the initial temperature of the device at the beginning of the test, n is the current density index, k B Is the Boltzmann constant.
7. The method of any one of claims 1 to 6, further comprising:
and processing the test data, and calculating to obtain the cross-sectional area and the current density of the heavy metal layer.
8. A magnetic memory electromigration failure and life prediction apparatus, the apparatus comprising:
the device comprises an acquisition module, a test module and a test module, wherein the acquisition module is used for acquiring a plurality of test data, the plurality of test data are data obtained by performing electromigration failure test on a plurality of sample devices, the test data comprise external temperature, device temperature, applied current, device parameters and failure time, and the device parameters comprise device heavy metal layer materials, depth and thickness;
the simulation module is used for inputting the test data into a simulation model to obtain a plurality of simulated temperature variation amounts of different positions of the simulated device after the preset time period, and the simulation model is a magnetic memory model constructed by simulation software;
and the prediction module is used for predicting electromigration failure and service life of the magnetic memory based on the test data and the simulation temperature variation quantities and combining a black model.
9. An electronic device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any one of claims 1 to 7 when the computer program is executed.
10. A computer readable storage medium storing a computer program, characterized in that the computer program when executed by a processor implements the steps of the method of any one of claims 1 to 7.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116991646A (en) * | 2023-09-27 | 2023-11-03 | 致真存储(北京)科技有限公司 | Magnetic memory life prediction method and device, electronic equipment and storage medium |
| CN118133577A (en) * | 2024-05-07 | 2024-06-04 | 上海燧原智能科技有限公司 | Temperature correction method, device, equipment and medium in link electromigration test |
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116991646A (en) * | 2023-09-27 | 2023-11-03 | 致真存储(北京)科技有限公司 | Magnetic memory life prediction method and device, electronic equipment and storage medium |
| CN116991646B (en) * | 2023-09-27 | 2023-12-29 | 致真存储(北京)科技有限公司 | Magnetic memory life prediction method and device, electronic equipment and storage medium |
| CN118133577A (en) * | 2024-05-07 | 2024-06-04 | 上海燧原智能科技有限公司 | Temperature correction method, device, equipment and medium in link electromigration test |
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