Method for constructing auxiliary inversion magnetic tunnel junction model

Abstract

Embodiments of the present application disclose a method for constructing an auxiliary flip magnetic tunnel junction model, including: constructing a magnetic tunnel junction device model based on a test data set; providing an auxiliary module model, and determining initial fitting parameters according to the value range of each initial parameter in the auxiliary module model; assigning the initial fitting parameters to the auxiliary module model, and connecting the assigned auxiliary module model to the magnetic tunnel junction device model to obtain an initial auxiliary flip magnetic tunnel junction model; performing simulation based on the initial auxiliary flip magnetic tunnel junction model to determine a simulation error; determining target fitting parameters of the auxiliary module model based on the simulation error, and assigning the target fitting parameters to the auxiliary module model included in the initial auxiliary flip magnetic tunnel junction model to obtain the auxiliary flip magnetic tunnel junction model.

Description

Technical Field

[0001] This application relates to the technical field of data processing, and more particularly, to a method for assisting in constructing a magnetic tunnel junction model. Background Art

[0002] The magnetic tunnel junction device (Magnetic Tunnel Junction, MTJ) is the core device of the spin-transfer torque magnetic random access memory (Spin-Transfer Torque Magnetic Random Access Memory, STT-MRAM) storage unit. STT-MRAM has the advantages of non-volatility, infinite erasability, and fast writing, and is expected to become the next-generation low-power general-purpose memory. As the first and foremost core device of MRAM, the magnetic tunnel junction acts as a storage medium in MRAM. Research on new MTJ devices has become one of the research hotspots in the field of spintronics.

[0003] The magnetic tunnel junction is a multi-layer structure composed of an ultra-thin insulating layer (also called the barrier layer) sandwiched between two ferromagnetic layers. The magnetic orientation of one ferromagnetic layer is fixed, called the fixed / reference layer (RL), while the other ferromagnetic layer has a magnetic orientation that can be parallel or anti-parallel to the RL layer, called the free layer FL. The magnetic orientation of FL can be changed by applying an external magnetic field or applying an appropriate current / voltage in a specific direction. The process of switching the relative magnetization intensity of FL from the P state to the AP state or vice versa is called MTJ writing / switching. When the magnetic directions of FL and RL are parallel, the resistance of the device is small, represented by low resistance (R P )). If the magnetic direction of FL is opposite to that of RL, the device is in a high-resistance state, represented by high resistance (R AP ).

[0004] The magnetic tunnel junction model can characterize the electrical characteristics and physical behaviors of the magnetic tunnel junction device. As the core component storage unit of the spin magnetic random access memory, a model of the magnetic tunnel junction is necessary to complete the simulation verification of the MRAM circuit, and the accuracy of the model affects the accuracy of the circuit design. Therefore, an accurate magnetic tunnel junction model is essential for MRAM circuit design and simulation. Moreover, an accurate magnetic tunnel junction model can not only be used to study the working characteristics of the magnetic tunnel junction, but also be used to simulate its static, transient characteristics, and even the working state of the entire circuit. Therefore, for circuit designers, if they can perform simulation analysis with the magnetic tunnel junction model before designing the circuit, it will save a large amount of product development costs and time for the designers. For manufacturers, an accurate magnetic tunnel junction model can be used to understand the internal working mechanism of the magnetic tunnel junction and to improve the device structure, thereby achieving the purpose of reducing costs and increasing efficiency.

[0005] There are mainly two types of models for magnetic tunnel junctions, namely static models and dynamic models. The static model is a relatively idealized model with simple modeling and fast simulation speed. However, the static model considers fewer physical effects and cannot fully simulate the actual working conditions of STT-MRAM. For example, regarding the critical current value for the magnetization direction reversal of the magnetic tunnel junction, in the description of the static model, when the current passing through the magnetic tunnel junction is greater than the critical current value for state reversal, the state of the magnetic tunnel junction will immediately reverse. However, in actual situations, the state of the magnetic tunnel junction is affected by factors such as thermal disturbance, and it is also possible for the write current to be less than the critical current for reversal and still cause a reversal. This part involves the research of the dynamic model of the magnetic tunnel junction. In addition, most existing models are based on ordinary magnetic tunnel junction devices, and it is impossible to accurately display the device characteristics of magnetic tunnel junction devices with assisted reversal. The MTJ-related circuit and system simulations require an assisted reversal magnetic tunnel junction model that can simulate the magnetic tunnel junction device with assisted reversal. Summary of the Invention

[0006] An embodiment of the present application provides a method for constructing an assisted reversal magnetic tunnel junction model, aiming to solve the problem of the lack of accurate modeling for magnetic tunnel junction devices with assisted reversal in the related art.

[0007] Other features and advantages of the present application will become apparent from the following detailed description, or will be learned in part through the practice of the present application.

[0008] According to one aspect of the embodiments of the present application, a method for constructing an assisted reversal magnetic tunnel junction model is provided. The method for constructing an assisted reversal magnetic tunnel junction model includes: constructing a magnetic tunnel junction device model based on a test data set; providing an auxiliary module model, and determining initial fitting parameters according to the value range of each initial parameter in the auxiliary module model; assigning the initial fitting parameters to the auxiliary module model to obtain an auxiliary parameter set, and accessing the assigned auxiliary module model into the magnetic tunnel junction device model in the form of transmitting the auxiliary parameter set to obtain an initial assisted reversal magnetic tunnel junction model; performing simulation based on the initial assisted reversal magnetic tunnel junction model to determine the simulation error; determining the target fitting parameters of the auxiliary module model based on the simulation error, and assigning the target fitting parameters to the auxiliary module model included in the initial assisted reversal magnetic tunnel junction model to obtain the assisted reversal magnetic tunnel junction model.

[0009] In some examples, the parameters in the test data set include at least one of the following: structural parameters, electrical parameters, magnetic parameters, and process parameters; constructing a magnetic tunnel junction device model based on the test data set includes: according to the electrical characteristics and switching behavior characterized by the initial model of the magnetic tunnel junction device, replacing the parameters in the resistance model, temperature model, and switching probability model of the initial model of the magnetic tunnel junction device with the parameters of the test data set to obtain the magnetic tunnel junction device model.

[0010] In some examples, the same circuit analysis tool or hardware description language is used to describe the magnetic tunnel junction device model, the auxiliary module model, and the auxiliary switching magnetic tunnel junction model.

[0011] In some examples, determining the initial fitting parameters according to the value range of each initial parameter in the auxiliary module model includes: obtaining the value range of each initial parameter in the auxiliary module model; randomly selecting a specific value from the value range of each initial parameter as the initial fitting parameter.

[0012] In some examples, performing a simulation based on the initial model of the auxiliary switching magnetic tunnel junction and determining the simulation error by a numerical method includes: performing a simulation based on the initial model of the auxiliary switching magnetic tunnel junction to obtain simulation data; comparing the simulation data with the actual data to obtain fitting points; fitting the fitting points according to the numerical method to obtain a fitting function; and calculating the simulation error according to the fitting function.

[0013] In some examples, determining the target fitting parameters of the auxiliary module model based on the simulation error includes: comparing the simulation error with a confidence threshold; if the simulation error is lower than the confidence threshold, using the initial fitting parameter as the target fitting parameter.

[0014] In some examples, if the simulation error is higher than the confidence threshold, re-determining the initial fitting parameters by a numerical method according to the value range of each initial parameter in the auxiliary module model; and re-determining the simulation error according to the re-determined initial fitting parameters.

[0015] In some examples, after obtaining the auxiliary switching magnetic tunnel junction model, the method further includes: performing a modeling test based on the auxiliary switching magnetic tunnel junction model.

[0016] In the technical solution provided by the embodiments of the present application, the method for constructing an assisted flipping magnetic tunnel junction model includes: constructing a magnetic tunnel junction device model based on a test data set; providing an auxiliary module model, and determining initial fitting parameters according to the value range of each initial parameter in the auxiliary module model; assigning the initial fitting parameters to the auxiliary module model to obtain an auxiliary parameter set, and connecting the assigned auxiliary module model to the magnetic tunnel junction device model in the form of transmitting the auxiliary parameter set to obtain an initial assisted flipping magnetic tunnel junction model; performing simulation based on the initial assisted flipping magnetic tunnel junction model, and determining the simulation error by a numerical method; determining the target fitting parameters of the auxiliary module model based on the simulation error, and assigning the target fitting parameters to the auxiliary module model included in the initial assisted flipping magnetic tunnel junction model to obtain the assisted flipping magnetic tunnel junction model, simulating the characteristic that the flipping process of the magnetic tunnel junction device is accelerated due to the assisted flipping physical action exerted by the auxiliary physical field on the magnetic tunnel junction device by the assisted flipping magnetic tunnel junction model, and calibrating the parameters through the error between the simulation result of the assisted flipping magnetic tunnel junction device model and the test data set to obtain the target parameters for the assisted flipping magnetic tunnel junction device model, effectively reducing the modeling deviation caused by inaccurate model parameters, reducing the complexity of simulating the assisted flipping physical process, and improving the reliability, convergence and accuracy of the assisted flipping magnetic tunnel junction model. It has application value for the development of magnetic tunnel junction devices and the simulation of hybrid MTJ / CMOS circuits. Through partial parameter approximation processing and parameter calibration through simulation errors, the modeling accuracy of the assisted flipping magnetic tunnel junction device has been greatly improved. Therefore, according to the method of the present application, the problem that the assisted flipping magnetic tunnel junction device lacks an accurate model for instantaneous characteristic simulation is solved, and the purpose of accurately modeling the assisted flipping magnetic tunnel junction and quickly simulating the circuit time domain is achieved.

[0017] It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and cannot limit the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 is a schematic diagram of the basic process of a method for constructing an assisted flipping magnetic tunnel junction model shown in an exemplary embodiment of the present application. DETAILED DESCRIPTION

[0019] Here, the exemplary embodiments will be described in detail, and the examples are shown in the drawings. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. On the contrary, they are only examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims.

[0020] The block diagrams shown in the drawings are only functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor devices and / or microcontroller devices.

[0021] The flowcharts shown in the drawings are only illustrative and do not necessarily include all the content and operations / steps, nor do they have to be executed in the described order. For example, some operations / steps can be decomposed, while some operations / steps can be combined or partially combined, so the actual execution order may change according to the actual situation.

[0022] It should also be noted that: "a plurality of" mentioned in this application means two or more. "And / or" describes the association relationship of associated objects, indicating that three relationships can exist. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. The character " / " generally represents an "or" relationship between the front and back associated objects.

[0023] As Figure 1 shown, Figure 1 is a method for constructing an auxiliary switching magnetic tunnel junction model shown in an embodiment of the present application. The method for constructing an auxiliary switching magnetic tunnel junction model includes:

[0024] S101. Construct a magnetic tunnel junction device model based on a test data set;

[0025] S102. Provide an auxiliary module model and determine initial fitting parameters according to the value range of each initial parameter in the auxiliary module model;

[0026] S103. Assign the initial fitting parameters to the auxiliary module model, and connect the assigned auxiliary module model to the magnetic tunnel junction device model to obtain an initial auxiliary switching magnetic tunnel junction model;

[0027] S104. Perform simulation based on the initial auxiliary switching magnetic tunnel junction model and determine the simulation error;

[0028] S105. Determine the target fitting parameters of the auxiliary module model based on the simulation error, and assign the target fitting parameters to the auxiliary module model included in the initial auxiliary switching magnetic tunnel junction model to obtain the auxiliary switching magnetic tunnel junction model.

[0029] In some examples, the magnetic tunnel junction device model, the auxiliary module model, and the auxiliary switching magnetic tunnel junction model are described using the HSPICE language.

[0030] In some examples, the parameters in the test data set include at least one of the following: structural parameters, electrical parameters, magnetic parameters, and process parameters; constructing a magnetic tunnel junction device model based on the test data set includes: through the electrical characteristics and switching behavior characterized by the initial model of the magnetic tunnel junction device, replacing the parameters in the resistance model, temperature model, TMR model, and switching probability model of the initial model of the magnetic tunnel junction device with the parameters of the test data set to obtain the magnetic tunnel junction device model. Among them, this magnetic tunnel junction device model is used to describe the magnetization switching effect of the magnetic tunnel junction device caused by the directional charge current. It can be understood that the magnetic tunnel junction device has two or more different resistance states. When the magnetization switching process occurs, the resistance state of the magnetic tunnel junction device changes; further, when one or more auxiliary physical fields other than the directional charge current are applied, an auxiliary switching physical effect occurs, resulting in an accelerated magnetization switching process.

[0031] Continuing with the above example, among them, the structural parameters include but are not limited to: the width L of the magnetic tunnel junction device x 、length L y 、the thickness L of the free layer z 、the thickness t of the oxide layer ox ;

[0032] The electrical parameters include but are not limited to: the tunneling magnetoresistance ratio TMR0 at zero bias voltage, the resistance-area product RA, the polarization factor P0 at zero temperature, the Debye length L D ;

[0033] The magnetic parameters include but are not limited to: the magnetization MS0 at zero temperature, the magnetocrystalline anisotropy constant K b 、the damping factor α, the initial magnetization angle θ0, the initial barrier energy E0;

[0034] The process parameters include but are not limited to: the critical switching current density-switching time J c -T, the longest switching time T max 、the auxiliary field duration T E .

[0035] Then, through the electrical characteristics and switching behavior characterized by the existing initial model of the magnetic tunnel junction device, replacing the parameters in the resistance model, TMR model, and switching probability model of the initial model of the magnetic tunnel junction device with the parameters of the test data set, and then obtaining the magnetic tunnel junction device model; among them, the magnetic tunnel junction device model obtained after replacement is:

[0036] Resistance model:

[0037] Parallel state low resistance

[0038] Antiparallel high resistance state

[0039] Temperature TMR model:

[0040]

[0041] Wherein, RA is the resistance area product, L x and L y are the width and length of the magnetic tunnel junction device, P0 is the polarization factor at zero temperature, α sp is a material-related parameter, V0 is a fitting parameter, and T is the ambient temperature;

[0042] Reversal probability model:

[0043]

[0044] Where θ c is the critical initial angle, E b is the oxide layer energy barrier, k B is the Boltzmann constant, and Δ is the thermal stability factor;

[0045] It can be understood that the auxiliary module model is used for the auxiliary flipping physical action exerted by the auxiliary physical field on the magnetic tunnel junction device; the input of the auxiliary module includes one or more of the pulse voltage signal V pulse , pulse duration T E , pulse rise delay T rise , pulse fall delay T down . The auxiliary module is also used to output an auxiliary parameter set, and the auxiliary parameter set includes: one or more of an angle variable set Δθ and a barrier variable set ΔE; assign the initial fitting parameter to the auxiliary module model, and connect the assigned auxiliary module model to the magnetic tunnel junction device model to obtain an initial model of the auxiliary flipping magnetic tunnel junction.

[0046] In some examples, the angle variable set Δθ characterizes the change in one or more magnetic moment directions when the magnetic tunnel junction device undergoes magnetization reversal; the barrier variable set ΔE characterizes the change in the height of one or more oxide layer barriers when the magnetic tunnel junction device undergoes magnetization reversal.

[0047] In some examples, the angle variable set Δθ and the barrier variable set ΔE are characterized using a controlled voltage source or a controlled current source.

[0048] In some examples, determining the initial fitting parameters according to the value ranges of each initial parameter in the auxiliary module model includes: obtaining the value ranges of each initial parameter in the auxiliary module model; randomly selecting specific values from the value ranges of each initial parameter as the initial fitting parameters.

[0049] Among them, assign the initial fitting parameters to the auxiliary module model, and connect the assigned auxiliary module model to the magnetic tunnel junction device model to obtain an initial model for assisting in flipping the magnetic tunnel junction; among them, provide the value ranges of each initial parameter of the auxiliary module, select specific values within the value ranges as the fitting parameters, assign the fitting parameters to the corresponding initial parameters of the initial model of the auxiliary module, and connect the assigned auxiliary module model to the magnetic tunnel junction device model in the form of transmitting an auxiliary parameter set;

[0050] Assign the initial fitting parameters to the auxiliary module model to obtain an auxiliary parameter set, and connect the assigned auxiliary module model to the magnetic tunnel junction device model in the form of transmitting the auxiliary parameter set. The objective function of adding an auxiliary flipping module in the magnetic tunnel junction model is:

[0051]

[0052] Among them, μ0 is the magnetic permeability, N dx and N dy are demagnetization factors related to geometry, MS0 is the magnetization intensity at zero temperature, T is the ambient temperature, T curie is the Curie temperature, β is a material-related parameter, H σ is the auxiliary energy field, L x 、L y 、L z are the width, length, and free layer thickness of the magnetic tunnel junction device respectively;

[0053] In some examples, assigning the initial fitting parameters to the auxiliary module model and connecting the assigned auxiliary module model to the magnetic tunnel junction device model includes:

[0054] Assign the initial fitting parameters to the auxiliary module model to obtain an auxiliary parameter set;

[0055] The parameters of the auxiliary parameter set have an angular variable set Δθ and a barrier variable set ΔE;

[0056] The auxiliary module model is connected to the magnetic tunnel junction device model in the form of transmitting the auxiliary parameter set.

[0057] In some examples, providing the auxiliary module model includes:

[0058] The auxiliary module model is determined by an externally applied auxiliary physical field;

[0059] After determining the auxiliary module model, an auxiliary flipped magnetic tunnel junction model is obtained by accessing the auxiliary module model;

[0060] The auxiliary flipped physical effect exerted by the auxiliary physical field on the magnetic tunnel junction device is characterized by the auxiliary flipped magnetic tunnel junction model.

[0061] In some examples, simulations are performed based on the initial auxiliary flipped magnetic tunnel junction model, and the simulation error is determined by numerical methods, including: performing simulations based on the initial auxiliary flipped magnetic tunnel junction model to obtain simulation data; comparing the simulation data with actual data to obtain fitting points; fitting the fitting points according to numerical methods to obtain a fitting function; and calculating the simulation error according to the fitting function.

[0062] Continuing from the above example, specifically, comparing the critical switching current density - switching time J simulation -T data obtained from the auxiliary flipped magnetic tunnel junction model with the critical switching current density - switching time J c -T data in the measured data set, and the process of obtaining the simulation error includes:

[0063] Step a: Performing simulations with the auxiliary flipped magnetic tunnel junction model obtained in the above steps to obtain a J simulation -T data curve X;

[0064] Step b: Obtaining the J c -T data curve Y of the auxiliary flipped magnetic tunnel junction device from the test data set;

[0065] Step c: Comparing curve X and curve Y to obtain fitting points

[0066] Step d: Fitting the fitting points according to the least squares method to obtain a fitting function f;

[0067] Step e: Calculating the simulation error according to the fitting function f:

[0068] In some examples, the target fitting parameters of the auxiliary module model are determined based on the simulation error, including: comparing the simulation error with a confidence threshold; if the simulation error is lower than the confidence threshold, taking the initial fitting parameters as the target fitting parameters.

[0069] That is, after the above step e, it is also necessary to compare the simulation error with the confidence threshold to determine whether to accept the fitting parameters as the target fitting parameters; if the simulation error is lower than the confidence threshold, taking the initial fitting parameters as the target fitting parameters.

[0070] In some examples, if the simulation error is higher than the confidence threshold, one or more of the shooting method, finite element method, and finite difference method in numerical methods are used to re-determine the initial fitting parameters according to the value range of each initial parameter in the auxiliary module model; and the simulation error is re-determined according to the re-determined initial fitting parameters, and then the re-determined simulation error is compared with the confidence threshold to determine whether to accept the fitting parameters as the target fitting parameters.

[0071] In some examples, if the simulation error is higher than the confidence threshold, the bisection method is used to re-determine the initial fitting parameters according to the value range of each initial parameter in the auxiliary module model on the basis of the initial fitting parameters of the initial parameters of the auxiliary module model; and the simulation error is re-determined according to the re-determined initial fitting parameters, and then the re-determined simulation error is compared with the confidence threshold to determine whether to accept the fitting parameters as the target fitting parameters.

[0072] In some examples, after obtaining the auxiliary flip magnetic tunnel junction model, the method further includes: performing a modeling test based on the auxiliary flip magnetic tunnel junction model.

[0073] The method for constructing an auxiliary switching magnetic tunnel junction model provided in this example includes: constructing a magnetic tunnel junction device model based on a test data set; providing an auxiliary module model, and determining initial fitting parameters according to the value range of each initial parameter in the auxiliary module model; assigning the initial fitting parameters to the auxiliary module model to obtain an auxiliary parameter set, and connecting the assigned auxiliary module model to the magnetic tunnel junction device model in the form of transmitting the auxiliary parameter set to obtain an initial auxiliary switching magnetic tunnel junction model; performing simulation based on the initial auxiliary switching magnetic tunnel junction model to determine the simulation error; determining the target fitting parameters of the auxiliary module model based on the simulation error, and assigning the target fitting parameters to the auxiliary module model included in the initial auxiliary switching magnetic tunnel junction model to obtain the auxiliary switching magnetic tunnel junction model, simulating the characteristic that the flipping process of the magnetic tunnel junction device is accelerated due to the auxiliary flipping physical action exerted by the auxiliary physical field on the magnetic tunnel junction device by the auxiliary switching magnetic tunnel junction model, and calibrating the parameters through the error between the simulation result of the auxiliary switching magnetic tunnel junction device model and the test data set to obtain the initial parameters for the auxiliary switching magnetic tunnel junction device model, effectively reducing the modeling deviation caused by inaccurate model parameters and improving the reliability of the auxiliary switching magnetic tunnel junction model. It has application value for the development of magnetic tunnel junction devices and the simulation of hybrid MTJ / CMOS circuits. Through partial parameter approximation processing and parameter calibration through simulation errors, the modeling accuracy of the auxiliary switching magnetic tunnel junction device has been greatly improved. Therefore, according to the method of this application, the problem of the lack of accurate modeling parameters for the auxiliary switching magnetic tunnel junction device is solved, and the purpose of accurately modeling the auxiliary switching magnetic tunnel junction is achieved.

[0074] For a better understanding of the present invention, this embodiment provides a more specific example;

[0075] A method for simulating and optimizing an auxiliary switching magnetic tunnel junction device provided according to this embodiment includes the following steps:

[0076] Step 1: Provide a magnetic tunnel junction device model, where the magnetic tunnel junction device model includes at least one parameter of the test data set;

[0077] Step 2: Provide the auxiliary module model, provide the value range of each initial parameter of the auxiliary module, and randomly select specific values within the value range as the initial fitting parameters;

[0078] Step 3: Assign the initial fitting parameters to the corresponding initial parameters of the auxiliary module model to obtain an auxiliary parameter set, and connect the assigned auxiliary module model to the magnetic tunnel junction device model in the form of transmitting the auxiliary parameter set for simulation;

[0079] Step 4: Determine the simulation error, and compare the critical switching current density - switching time, i.e., J simulation -T, with the critical switching current density - switching time of the test data set, i.e., J c -T, to obtain the fitting points: And fit the fitting points according to the least squares method to obtain the fitting function f, and calculate the simulation error according to the fitting function f: wherein, T max is the longest switching time in the test data set of the magnetic tunnel junction device, and compare the simulation error with the confidence threshold to determine whether to accept the fitting parameter as the target characteristic parameter;

[0080] Step 5: Iterate through the above Steps 2 to 4 to obtain a simulation error less than the confidence threshold, and substitute the fitting parameter corresponding to the simulation error value as the target parameter into the initial model of the auxiliary switching magnetic tunnel junction to obtain the final auxiliary switching magnetic tunnel junction model.

[0081] According to the technical solution of this example, in Step 1 of the method,

[0082] The test data set of the magnetic tunnel junction device includes at least one of the following physical parameters:

[0083] Structural parameters: the width L of the magnetic tunnel junction device x , length L y , free layer thickness L z , oxide layer thickness t ox ;

[0084] Electrical parameters: tunneling magnetoresistance ratio TMR0 at zero bias voltage, resistance - area product RA, polarization factor P0 at zero temperature, Debye length L D ;

[0085] Magnetic parameters: magnetization MS0 at zero temperature, magnetic crystal anisotropy constant K b , damping factor α, initial magnetization angle θ0, initial barrier energy E0;

[0086] Process parameters: critical switching current density - switching time J c -T, longest switching time T max , auxiliary field duration T E .

[0087] According to the technical solution of this example, in Step 1 of the method, when obtaining the test data set of the auxiliary switching magnetic tunnel junction device, the Debye length of CoFeB and Ru is approximated using the Debye length of Au;

[0088] According to the technical solution of this example, the process of obtaining the initial model of the magneto-tunnel junction device in the first step based on the test data set is as follows:

[0089] Based on the electrical characteristics and switching behavior characterized by the existing magneto-tunnel junction device model, replace the parameters in the resistance model, TMR model, and switching probability model of the magneto-tunnel junction model with the parameters of the test data set respectively.

[0090] According to the solution of this example, in the first step, the initial model of the magneto-tunnel junction device obtained based on the test data set is:

[0091] Resistance model:

[0092] Low resistance in parallel state

[0093] High resistance in antiparallel state

[0094] Temperature TMR model:

[0095]

[0096] Among them, RA is the resistance-area product, L x , L y are the width and length of the magneto-tunnel junction device, P0 is the polarization factor at zero temperature, α sp is a material-related parameter, V0 is a fitting parameter, and T is the ambient temperature;

[0097] Switching probability model:

[0098]

[0099] Among them, θ c is the critical initial angle, E b is the energy barrier of the oxide layer, k B is the Boltzmann constant, and Δ is the thermal stability factor;

[0100] According to the technical solution of the present invention, in the second step, the objective function of adding an auxiliary switching module model to the magneto-tunnel junction device model is:

[0101]

[0102] Among them, μ0 is the magnetic permeability, N dx and N dy are the demagnetization factors related to geometry, MS0 is the magnetization intensity at zero temperature, T is the ambient temperature, T curie is the Curie temperature, β is a material-related parameter, H σ is the auxiliary energy field, L x , L y , Lz They are the width, length, and free layer thickness of the magnetic tunnel junction device, respectively;

[0103] According to the technical solution of the present invention, in the fourth step, comparing the J simulation -T data obtained from the auxiliary flipping magnetic tunnel junction model with the J c -T data of the test data set and obtaining the simulation error includes the following steps:

[0104] Step a: Randomly select specific values within the value range of each initial parameter of the auxiliary module model to replace the initial parameter to obtain an initial model of the auxiliary flipping magnetic tunnel junction;

[0105] Step b: Perform simulation with the initial model of the auxiliary flipping magnetic tunnel junction obtained in step a to obtain the J simulation -T data curve X;

[0106] Step c: Obtain the J c -T data curve Y of the auxiliary flipping magnetic tunnel junction from the test data set;

[0107] Step d: Compare curve X and curve Y to obtain fitting points

[0108] Step e: Fit the fitting points according to the least squares method to obtain a fitting function f;

[0109] Step f: Calculate the simulation error according to the fitting function f: And compare the simulation error with the confidence threshold to determine whether to accept the fitting parameter as the target characteristic parameter;

[0110] For the first embodiment, the confidence threshold is taken as 0.1, and the simulation error between the simulation line X of the auxiliary flipping magnetic tunnel junction device model and the curve Y of the test data set is less than the confidence threshold. Therefore, this example effectively reduces the simulation deviation caused by inaccurate model parameters of the auxiliary flipping magnetic tunnel junction device and has broad application prospects in the field of auxiliary flipping magnetic tunnel junction modeling and MRAM design and simulation.

[0111] According to the technical solution of the example, the physical parameters required to simulate the initial model of the auxiliary flipping magnetic tunnel junction will be classified. Before experimentally measuring the physical parameters of the auxiliary flipping magnetic tunnel junction, the Debye length of Au is used to approximate the Debye lengths of CoFeB and Ru to handle the Debye lengths of some materials that cannot be known; thus, various physical parameters of the auxiliary flipping magnetic tunnel junction are obtained.

[0112] According to the technical solution of this example, the characteristics of the magnetic tunnel junction device being switched under an externally applied physical field are simulated according to the auxiliary flipping module, and the target parameters for the auxiliary flipping magnetic tunnel junction device model are obtained by calibrating the simulation error between the simulation results of the auxiliary flipping magnetic tunnel junction device model and the test data set, effectively reducing the modeling deviation caused by inaccurate model parameters and improving the reliability of the auxiliary flipping magnetic tunnel junction model. It has application value for the development of magnetic tunnel junction devices and the simulation of hybrid MTJ / CMOS circuits.

[0113] According to the technical solution of this example, the results of simulating the auxiliary flipping magnetic tunnel junction device are confirmed. The process of accelerating the magnetization flipping of the magnetic tunnel junction device caused by the flipping characteristics of the at least one or more physical quantity-assisted magnetic tunnel junction device is described based on the initial model of the magnetic tunnel junction with a 90% confidence level.

[0114] According to the technical solution of this example, the simulation results of the above-mentioned auxiliary flipping magnetic tunnel junction device model with at least 90% confidence level prove that through partial parameter approximation processing and parameter calibration through simulation error, the modeling accuracy of the auxiliary flipping magnetic tunnel junction device has been greatly improved. Thus, according to the method of the present invention, the problem of the lack of accurate modeling parameters for the auxiliary flipping magnetic tunnel junction device is solved, and the purpose of accurately modeling the auxiliary flipping magnetic tunnel junction is achieved.

[0115] This example also provides a method for simulating the magnetization flipping of a magnetic tunnel junction device, and the method includes:

[0116] Including a first model that describes the magnetization flipping effect of the magnetic tunnel junction device caused by a directional charge current;

[0117] The magnetic tunnel junction device,

[0118] Has two or more different resistance states, and when the magnetization flipping process occurs, the resistance state of the magnetic tunnel junction device changes;

[0119] Further, when one or more auxiliary physical fields other than the directional charge current are applied, an auxiliary flipping physical effect occurs, resulting in an accelerated magnetization flipping process;

[0120] Including an auxiliary module,

[0121] Which can be applied to the first model to describe the auxiliary flipping physical effect exerted by the auxiliary physical field on the magnetic tunnel junction device;

[0122] Further, an auxiliary parameter set is output to the first model, and the auxiliary parameter set includes one or more of an angle variable set Δθ and a barrier variable set ΔE.

[0123] Furthermore, the auxiliary module and the first model form a second model.

[0124] Among them, the first model: includes a set of parameters to be assisted, which serves as the basic element for the assisted magnetization reversal of the first model. The set of parameters to be assisted includes one or more of: angular parameters, barrier parameters;

[0125] Furthermore, the input of the first model includes a first set of input parameters and the set of parameters to be assisted;

[0126] Furthermore, the first set of input parameters includes: the directional drive signal for driving the reversal;

[0127] Furthermore, the output of the first model includes a first set of output parameters, including: the time when the first model generates magnetization reversal, the directional drive signal that triggers the magnetization reversal effect of the first model;

[0128] Furthermore, there is a first mapping relationship between the first set of input parameters and the first set of output parameters obtained by applying the first set of input parameters to the magnetic tunnel junction device;

[0129] Furthermore, the first set of input parameters and the set of parameters to be assisted form a third set of input parameters;

[0130] Furthermore, there is a third mapping relationship between the third set of input parameters and the third set of output parameters obtained by applying the first set of input parameters and the set of parameters to be assisted to the magnetic tunnel junction device.

[0131] Among them, by obtaining a data source set for performing tests on the magnetic tunnel junction device, the data source set is stored in a database; among them, the data source set includes one or more of: test data sets, simulation data sets;

[0132] Furthermore, the data source set contains at least one set of the third set of input parameters and the corresponding third set of output parameters, that is, the target output parameter set.

[0133] Furthermore, the data source set reflects the third mapping relationship. Specifically, there is a third mapping relationship between at least one set of the third set of input parameters and the target output parameter set;

[0134] Furthermore, the third mapping relationship reflects the assisted flipping physical effect exerted by the assisted physical field on the magnetic tunnel junction device.

[0135] Among them, the second model and the data source set include one or more of the following parameters:

[0136] Structural parameters: the width L of the magnetic tunnel junction device x and the length Ly 、Free layer thickness L z 、Oxide layer thickness t ox ;

[0137] Electrical parameters: Tunneling magnetoresistance ratio TMR0 at zero bias voltage, resistance - area product RA, polarization factor P0 at zero temperature, Debye length L D ;

[0138] Magnetic parameters: Magnetization MS0 at zero temperature, magnetocrystalline anisotropy constant K b 、Damping factor α, initial magnetization angle θ0, initial barrier energy E0;

[0139] Process parameters: Critical switching current density - switching time J c -T, longest switching time T max 、Auxiliary field duration T E .

[0140] Among them, the auxiliary switching physical effect includes the indirect exchange effect (Ruderman - Kittel - Kasuya - Yosida, RKKY); further, the triggered condition is that one or more of the following external auxiliary physical fields need to be applied: electric field, light field, magnetic field, electromagnetic field.

[0141] Among them, the auxiliary module corresponds to the external conditions required for the auxiliary switching physical effect with the magnetic tunnel junction device;

[0142] Further, the input of the auxiliary module includes a second input parameter set;

[0143] Further, the second input parameter set includes one or more of: voltage signal, current signal, optical wave signal, electromagnetic signal;

[0144] Optionally, the second input parameter set includes: pulse voltage signal V pulse 、Pulse duration T E 、Pulse rise delay T rise 、Pulse fall delay T down one or more of;

[0145] Further, the output of the auxiliary module includes the auxiliary parameter set;

[0146] Further, there is a second mapping relationship between the second input parameter set and the auxiliary parameter set obtained by applying the second input parameter set to the auxiliary module;

[0147] Further, the auxiliary module includes a target component parameter set;

[0148] Among them, for the auxiliary module, optionally, the set of angular variables Δθ characterizes the change in the direction of one or more magnetic moments when the magnetization of the magnetic tunnel junction device is reversed;

[0149] Optionally, the set of barrier variables ΔE characterizes the change in the height of one or more oxide barriers when the magnetization of the magnetic tunnel junction device is reversed.

[0150] Among them, the second model can be equivalent to the total set of a circuit element;

[0151] Furthermore, the total set includes: the set of equivalent circuit elements of the auxiliary module, that is, the auxiliary set; the set of equivalent circuit elements of the first model, that is, the model set;

[0152] Specifically, the total set includes at least one of the following circuit elements: one or more of a voltage source, a resistor, a capacitor, a controlled voltage source, and a controlled current source;

[0153] Furthermore, the circuit elements are connected in series, in parallel, or in series-parallel to form the total set;

[0154] Optionally, the same circuit analysis tool or hardware description language needs to be used for description;

[0155] Furthermore, after being described by the circuit analysis tool or the hardware description language, it can be applied in electronic design automation (EDA) software or circuit analysis software;

[0156] Specifically, the circuit analysis tool includes SPICE;

[0157] Optionally, the hardware description language includes Verilog A and System C.

[0158] Among them, the auxiliary set can be equivalent to a set of circuit elements, including one or more circuit elements;

[0159] Furthermore, it includes one or more controlled sources. Optionally, the controlled sources include: a controlled voltage source or a controlled current source;

[0160] Furthermore, the signals in the second input parameter set control the controlled sources;

[0161] Furthermore, the output of the auxiliary parameter set is associated with the control voltage or control current of one or more sets of controlled source sets;

[0162] Furthermore, the output of the controlled source set reflects the second mapping relationship.

[0163] Among them, the set of component parameters,

[0164] When a set of third input parameter sets included in the data source set and the target component parameter set are applied, the mapping relationship between the third output parameter set obtained by the second model and the third input parameter set conforms to the third mapping relationship;

[0165] Further, the target component parameter set of the auxiliary set is determined by implementing the following steps, including:

[0166] Step A1: Receive the data source set of the magnetic tunnel junction device, where the data source set includes the third input parameter set and the target output parameter set;

[0167] Step A2: In response to an initial component parameter set setting module being configured in the circuit analysis tool or the hardware description language, set at least one initial component parameter set, and fill in the initial component parameter set in the form of a component parameter set to generate a mapping form;

[0168] Step A3: Use the component parameter set to fit the second model; specifically, the second model maps one-dimensional fitting of any set component parameter set, two-dimensional fitting of any two set component parameter sets, and three-dimensional fitting of three or more set component parameter sets in the circuit analysis tool or the hardware description language;

[0169] Step A4: Adjust the initial component parameter set through a correction method during the fitting process, and repeat Step A3 so that the mapping relationship between the third input parameter set and the third output parameter set conforms to the third mapping relationship, achieving the purpose of selecting the target component parameter set.

[0170] Optionally, the correction method includes one or more of: the shooting method, the finite difference method, and the finite element method.

[0171] Those skilled in the art will readily think of other implementation manners of the present application after considering the specification and practicing the embodiments disclosed herein. The present application is intended to cover any variations, uses, or adaptations of the present application, which follow the general principles of the present application and include the common general knowledge or conventional technical means in the technical field not disclosed in the present application.

[0172] The above content is only a preferred exemplary embodiment of the present application and is not used to limit the implementation manner of the present application. Those of ordinary skill in the art can easily make corresponding changes or modifications according to the main concept and spirit of the present application. Therefore, the protection scope of the present application should be subject to the protection scope required by the claims.

Claims

1. A method for constructing an auxiliary flipped magnetic tunnel junction model, characterized in that The method for constructing an auxiliary flip magnetic tunnel junction model includes: Constructing a magnetic tunnel junction device model based on a test data set; Providing an auxiliary module model and determining initial fitting parameters according to the value ranges of each initial parameter in the auxiliary module model; Assigning the initial fitting parameters to the auxiliary module model and connecting the assigned auxiliary module model to the magnetic tunnel junction device model to obtain an initial auxiliary flip magnetic tunnel junction model; Performing simulation based on the initial auxiliary flip magnetic tunnel junction model to determine the simulation error; Determining the target fitting parameters of the auxiliary module model based on the simulation error and assigning the target fitting parameters to the auxiliary module model included in the initial auxiliary flip magnetic tunnel junction model to obtain the auxiliary flip magnetic tunnel junction model; The constructing a magnetic tunnel junction device model based on a test data set includes: Based on the electrical characteristics and flip behavior characterized by the initial magnetic tunnel junction device model, replacing the parameters in the resistance model, temperature TMR model, and flip probability model of the initial magnetic tunnel junction device model with the parameters of the test data set to obtain the magnetic tunnel junction device model; The parameters in the test data set include at least one of structural parameters, electrical parameters, magnetic parameters, and process parameters; Assigning the initial fitting parameters to the auxiliary module model and connecting the assigned auxiliary module model to the magnetic tunnel junction device model includes: Assigning the initial fitting parameters to the auxiliary module model to obtain an auxiliary parameter set; The auxiliary parameter set includes one or more of an angle variable set and a barrier variable set; The auxiliary module model is connected to the magnetic tunnel junction device model in the form of transmitting the auxiliary parameter set; Providing an auxiliary module model includes: The auxiliary module model is determined by an externally applied auxiliary physical field; After determining the auxiliary module model, obtaining an auxiliary flip magnetic tunnel junction model by connecting it to the magnetic tunnel junction device model; Characterizing, through the auxiliary flip magnetic tunnel junction model, the process in which the auxiliary physical field accelerates the flipping process of the magnetic tunnel junction device due to the auxiliary flipping physical action exerted by the auxiliary physical field on the magnetic tunnel junction device; The initial model of the magnetic tunnel junction device obtained according to the test data set is: Resistance model: Parallel state low resistance ; Antiparallel state high resistance ; Temperature TMR model: ； Among them, is the product of resistance and area, , are the width and length of the magnetic tunnel junction device, is the polarization factor at zero temperature, is a material-related parameter, is a fitting parameter, is the ambient temperature; Flip probability model: ； wherein is the critical initial angle, is the energy barrier of the oxide layer, is the Boltzmann constant, is the thermal stability factor; The objective function of adding an auxiliary flip module model to the magnetic tunnel junction device model is: ; wherein, is the magnetic permeability, and are the demagnetization factors related to geometry, is the magnetization at zero temperature, is the ambient temperature, is the Curie temperature, is a material-related parameter, is the auxiliary energy field, , , are the width, length, and free layer thickness of the magnetic tunnel junction device, respectively.

2. The method for constructing an auxiliary flipped magnetic tunnel junction model according to claim 1, wherein Determining initial fitting parameters according to the value ranges of each initial parameter in the auxiliary module model includes: Obtaining the value ranges of each initial parameter in the auxiliary module model; Randomly selecting specific values from the value ranges of each initial parameter as the initial fitting parameters.

3. The method for constructing an auxiliary flip magnetic tunnel junction model according to claim 1, wherein Performing simulation based on the initial auxiliary flip magnetic tunnel junction model to determine the simulation error includes: Performing simulation based on the initial auxiliary flip magnetic tunnel junction model to obtain simulation data; Comparing the simulation data with actual data to obtain fitting points; Performing fitting on the fitting points according to a numerical method to obtain a fitting function; Calculating the simulation error according to the fitting function.

4. The method for constructing an auxiliary flipped magnetic tunnel junction model according to claim 3, wherein Determining the target fitting parameters of the auxiliary module model based on the simulation error includes: Comparing the simulation error with a confidence threshold; If the simulation error is lower than the confidence threshold, taking the initial fitting parameters as the target fitting parameters.

5. The method for constructing an auxiliary flip magnetic tunnel junction model according to claim 4, wherein If the simulation error is higher than the confidence threshold, re-determining the initial fitting parameters according to the value range of each initial parameter in the auxiliary module model; And re-determining the simulation error according to the re-determined initial fitting parameters.

6. The method for constructing an auxiliary flipped magnetic tunnel junction model according to claim 5, wherein After obtaining the auxiliary flip magnetic tunnel junction model, the method further includes: Performing a modeling test based on the auxiliary flip magnetic tunnel junction model.

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