A multi-objective prediction method for thermal fatigue life and electrical performance of wire bonds

Abstract

This invention discloses a multi-objective prediction method for wirebond thermal fatigue life and electrical performance, belonging to the field of microwave interconnect technology. The method includes: determining the physical and structural parameters of the wirebond interconnect structure; obtaining a mathematical description of the wirebond morphology; establishing a thermal analysis model and a structural-electromagnetic analysis model for the wirebond interconnect structure; designing comprehensive experiments and obtaining various performance indicators; establishing a mapping relationship model from wire geometry parameters to various performance indicators; establishing a thermal fatigue life prediction model and completing reliability life prediction. This method can be used to accurately parameterize wirebond interconnect structural models, rapidly achieve multi-objective prediction of wirebond thermal fatigue life and electrical performance, and guide the structural design and optimization of wirebond packages.

Description

Technical Field

[0001] The present invention relates to the field of microwave interconnection technology, and in particular to a multi-objective prediction method for wire bonding thermal fatigue life and electrical performance. Background Art

[0002] The continuous advancement of semiconductor technology is driving the need for electronic equipment to be lightweight, miniaturized, and perform better. Active phased array radars, in particular, require compact, lightweight, high-performance, and highly reliable microwave components due to their specialized operating environments. Wire bonding is often used in traditional high-density microwave assemblies to interconnect chips, electronic components, and microwave transmission lines. To meet the demands of high density, high operating frequency, high reliability, and miniaturization for microwave assemblies, the conductivity and reliability of wire bonding must be continuously improved. Lead structure is a major factor influencing the electrical performance and reliability of microwave assemblies. Parameters such as lead diameter, arch height, pin placement, and platform length all significantly impact the thermal reliability and microwave transmission of microwave assemblies.

[0003] Wirebond packaging has a wide range of applications in practical engineering. Current research, both domestically and internationally, focuses on three main areas: First, focusing on the manufacturing process and process parameters of wirebond packaging, studying the reliability of wirebond interconnect structures; second, focusing on the physical and structural parameters of wirebonds, studying the reliability of wirebond interconnect structures under different service environments, such as temperature and vibration; and third, focusing on the signal integrity issues caused by interconnect leads in circuits, primarily using software simulation tools to evaluate their performance. However, judging performance often requires considerable time to establish interconnect structure models and perform software simulations. Furthermore, due to the limited predictive effect, it is often impossible to comprehensively consider the impact of the service environment and electrical performance, and provide specific regulatory guidance. These issues are the greatest obstacles to improving the prediction and regulation of wirebond interconnect performance in engineering practice, limiting the development of wirebond packaging.

[0004] The above problems need to be solved urgently. To this end, the present invention provides a multi-objective prediction method for thermal fatigue life and electrical performance of wire bonding. Summary of the Invention

[0005] The technical problem to be solved by the present invention is: how to solve the problems existing in the above-mentioned prior art and realize multi-objective prediction of wire bonding thermal fatigue life and electrical performance. A multi-objective prediction method for wire bonding thermal fatigue life and electrical performance is provided, which can be used to accurately parameterize the wire bonding interconnection structure model, quickly realize multi-objective prediction of wire bonding thermal fatigue life and electrical performance, and guide the structural design and optimization of wire bonding packaging.

[0006] The present invention solves the above technical problems through the following technical solutions, which include the following steps:

[0007] Step 1: Determine the geometric parameters and physical properties of the wire bonding interconnect structure based on the specific requirements of the interconnection in the high-frequency microwave component;

[0008] Step 2: Parametrically characterize the geometric parameters of each interconnect structure in the microwave component to obtain a mathematical description of the wire bonding morphology;

[0009] Step 3: Based on the determined geometric parameters, physical parameters, and parametric characterization of the wire bond interconnection in the microwave component, a thermal analysis model and a structural-electromagnetic analysis model of the wire bond interconnection structure are established respectively;

[0010] Step 4: Based on the control factors, temperature cycling reliability, and electromagnetic transmission performance indicators of microwave components, a comprehensive experiment was designed to test the wire bond interconnect morphological parameters, temperature cycling reliability, and electromagnetic transmission performance. The average stress and strain variation range of the wire bond interconnect structure during the thermal simulation process was obtained for each experimental item, as well as the insertion loss of the wire bond interconnect structure obtained from the electromagnetic simulation.

[0011] Step 5: Based on comprehensive experimental data, a CNN-based convolutional neural network mapping model is established to map the wire bond interconnect morphological parameters to temperature cycling reliability indicators and electromagnetic transmission performance indicators, and the electromagnetic transmission performance prediction of the wire bond interconnect structure is completed.

[0012] Step 6: Establish a thermal fatigue life prediction model. Based on the mapping relationship model between the wire bonding interconnection morphological parameters and the temperature cycle reliability index obtained in step 5, complete the thermal fatigue life prediction of the wire bonding interconnection structure.

[0013] Furthermore, in the step 1, the geometric parameters of the wire bonding interconnection structure are determined, including the wire diameter D, the arch height h1, the wire span L, the platform length xL, the pad diameter D1, the pad height h3, the chip length L1, the chip width W1, the chip height h2, the dielectric substrate length L2, the dielectric substrate width W2, and the dielectric substrate height h4; the physical parameters of the wire bonding interconnection structure are determined, including the signal transmission frequency f, the dielectric constant ε of each component g , loss tangent θ g , density d g , elastic modulus E g , Poisson's ratio λ g , thermal expansion coefficient α g , and the tensile strength of the lead σ b , cross-sectional shrinkage rate ψ.

[0014] Furthermore, in step 2, the specific processing process is as follows:

[0015] Step 2a: Obtain a mathematical description of the first segment of the wire bond using the formula f(x)=y, where x represents the position of the first segment of the wire along the x-axis, x=0, and y represents the position of the first segment of the wire along the y-axis, y∈[h2+h3,h1+h2+h3];

[0016] Step 2b: Obtain a mathematical description of the second segment of the wire bond using the formula f(x) = h1 + h2 + h3, where x is in the range [0, xL].

[0017] Step (2c): By formula Get the mathematical description of the third segment of the wire bond, where x is in the range [xL,L].

[0018] Furthermore, in step three, the specific processing process is as follows:

[0019] Step 3a: Based on the geometric parameters and physical properties determined in Step 1 and the parametric characterization of the wire bonds in Step 2, a thermal analysis model of the wire bond interconnect structure is established in ANSYS APDL software;

[0020] Step 3b: Based on the geometric parameters and physical parameters determined in step 1 and the parametric characterization of the wire bonding in step 2, a wire bonding interconnect structure-electromagnetic analysis model is established in full-wave 3D electromagnetic simulation software.

[0021] Furthermore, in step 4, the specific processing process is as follows:

[0022] Step 4a: Based on the wire bonding interconnection morphology, the wire diameter D, the arch height h1, the wire span L, and the platform length xL are used as control factors for the wire bonding interconnection structure;

[0023] Step 4b: Determine the range of the four control factors for wire bonding interconnection: wire diameter D∈[D min ,D max ]、Lead arch height h1∈[h min ,h max ]、Lead span L∈[L min ,L max ]、Lead platform length xL∈[xL min ,xL max ],in,() min Indicates the minimum value of a certain control factor, () max Indicates the maximum value of a certain control factor;

[0024] Step 4c: Using a Latin hypercube uniform sampling method, extract N sampling points within the variation range of the four control factors to obtain N sets of wire bond interconnection morphological parameter data, where N is a positive integer;

[0025] Step 4d: Using the average stress and strain variation range of the wire bond interconnect structure during the thermal simulation as a temperature cycling reliability indicator, and using the insertion loss as an electromagnetic transmission performance indicator;

[0026] Step 4e: Use the N sets of data obtained by Latin Hypercube sampling as input parameters to the ANSYS APDL software to conduct comprehensive experiments on the wire bond interconnect morphological parameters and temperature cycling reliability, and obtain the average stress and strain variation range of the wire bond interconnect structure during the thermal simulation process;

[0027] Step 4f: Use the N sets of data obtained by Latin hypercube sampling as input parameters to input into full-wave 3D electromagnetic wave simulation software to conduct comprehensive experiments on wire bond interconnect morphological parameters and electromagnetic transmission performance to obtain insertion loss.

[0028] Furthermore, in step 5, the specific processing process is as follows:

[0029] Step 5a: Randomly arrange the obtained wire bond interconnect morphological parameters and the comprehensive experimental data of the thermal analysis model and the structural-electromagnetic analysis model, and divide the first 90% of the data into a training set and the remaining 10% of the data into a test set;

[0030] Step 5b: The obtained wire bond interconnect morphological parameters and the four control factors of the wire bond interconnect structure from the comprehensive experimental data of the thermal analysis model and the structural-electromagnetic analysis model (wire diameter D, arch height h1, wire span L, and platform length xL) are used as inputs to the CNN convolutional neural network mapping relationship model. The average stress of the wire bond interconnect structure, the strain variation range of the wire bond interconnect structure, and the insertion loss of the wire bond interconnect structure are used as outputs.

[0031] Step 5c: Use the formula Normalize the input and output data, limit the data to [0,1], and eliminate the influence of each parameter dimension. 标准 represents the normalized value of an experimental data, x represents the actual value of an experimental data, and x max Indicates the maximum value of a certain experimental data range, x min Indicates the minimum value of a certain experimental data range;

[0032] Step 5d: Establish a CNN convolutional neural network topology with 4 inputs and 3 outputs. The network structure includes an input layer, a convolutional layer, an activation layer, a maximum pooling layer, a fully connected layer, and a regression layer. The activation layer uses the RELU activation function with the mathematical expression f(x) = max(0, x);

[0033] Step 5e: Input the experimental data into the network and use the SGDM gradient descent method to train the network;

[0034] Step 5f: Denormalize the output prediction value and save the model. Establish a mapping relationship between the wire bonding interconnection morphological parameters and the average stress of the wire bonding interconnection structure, the strain variation range of the wire bonding interconnection structure, and the insertion loss of the wire bonding interconnection structure. The model relationship is as follows:

[0035] σ m =Y1(D,h1,L,xL)

[0036] Δε=Y2(D,h1,L,xL)

[0037] S 21 =Y3(D,h1,L,xL)

[0038] Among them, σ m is the predicted average stress of the wire bonding interconnection structure, Δε is the predicted strain variation range of the wire bonding interconnection structure, S 21 To predict the insertion loss of the wire-bonded interconnect structure, Y1 is the mapping relationship between the four control factors and the average stress of the wire-bonded interconnect structure, Y2 is the mapping relationship between the four control factors and the strain variation range of the wire-bonded interconnect structure, and Y3 is the mapping relationship between the four control factors and the insertion loss of the wire-bonded interconnect structure. The electromagnetic transmission performance of the wire-bonded interconnect structure can be predicted through the mapping relationship Y3.

[0039] Furthermore, in step six, the specific processing process is as follows:

[0040] Step 6a: The thermal fatigue life of the wire bond interconnect structure is predicted from three aspects: junction temperature fluctuation, plastic strain, and elastic deformation. Then, a thermal fatigue life prediction model is established. The calculation formula is as follows:

[0041]

[0042] Among them, Δε is the strain variation range, σ' r is the material fatigue strength coefficient, σ m is the average stress in one cycle, N f is the number of failure cycles, i.e. thermal fatigue life, b is the fatigue strength index, ε r ' is the fatigue ductility coefficient, c is the fatigue ductility index, and E is the elastic modulus;

[0043] Step 6b: Determine the material fatigue strength coefficient, fatigue strength index, fatigue ductility coefficient, and fatigue ductility index using the following formulas:

[0044] b=-0.12

[0045] c=-0.6

[0046] σ' r =3.5σ r

[0047]

[0048] Among them, σ r is the fracture strength coefficient, ε r is the fracture ductility coefficient;

[0049] The calculation formulas for the fracture strength coefficient and fracture ductility coefficient are as follows:

[0050] σ r =σ b ln(1+ψ)

[0051] ε r = -ln(1-ψ)

[0052] Among them, σb is the tensile strength, ψ is the cross-sectional reduction rate;

[0053] Step 6c: Substitute the parameter calculation formulas in step 6b into the thermal fatigue life prediction model calculation formula in step 6a to obtain:

[0054]

[0055] Step 6d: Substitute the average stress and strain variation range of the wire bonding interconnection structure predicted by the corresponding mapping relationship model into the calculation formula of the thermal fatigue life prediction model to calculate the predicted thermal fatigue life of the wire bonding interconnection structure.

[0056] Compared with the prior art, the present invention has the following advantages:

[0057] 1. The present invention uses ANSYS APDL software and full-wave three-dimensional electromagnetic wave simulation software to establish precise parameterized models for thermal analysis and structural-electromagnetic analysis of wire-bonded interconnect structures, respectively. A comprehensive experiment on the relationship between wire-bonded interconnect morphological parameters and temperature cycling reliability and electrical performance is designed to obtain reliability indicators and electromagnetic transmission performance indicators of wire-bonded packages under service environments. A multi-objective prediction model is established based on a CNN convolutional neural network, linking wire-bonded interconnect structural parameters to temperature cycling reliability indicators and electromagnetic transmission performance indicators. This model can be used to study the influence of wire-bonded interconnect structural parameters on their reliability and signal transmission performance, solving the current problem of being unable to quickly perform precise parameterized modeling based on wire-bonded interconnect structural parameters and simultaneously accurately predict the temperature cycling reliability and electromagnetic transmission performance of wire-bonded interconnect structures.

[0058] 2. By establishing a multi-objective prediction model based on the CNN convolutional neural network, the mapping relationship between the wire bonding interconnection structural parameters and the temperature cycling reliability index and the electromagnetic transmission performance index can be quickly and accurately obtained. It can be used to judge the rationality of the wire bonding interconnection structure design scheme and its impact on reliability and electromagnetic transmission performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 1 is a flow chart of a multi-objective prediction method for wire bonding thermal fatigue life and electrical performance according to an embodiment of the present invention;

[0060] Figure 2 This is a dimensioned diagram of a substrate and a chip in a thermal analysis model of a wire bonding interconnect structure according to an embodiment of the present invention;

[0061] Figure 3 is a dimensioned diagram of a single bonding wire and pad in a thermal analysis model of a wire bonding interconnect structure according to an embodiment of the present invention;

[0062] Figure 4 This is a dimensioned diagram of the wire bonding interconnection structure-electromagnetic analysis model in an embodiment of the present invention. DETAILED DESCRIPTION

[0063] The following is a detailed description of an embodiment of the present invention. This embodiment is implemented based on the technical solution of the present invention, and provides a detailed implementation method and specific operation process. However, the protection scope of the present invention is not limited to the following embodiment.

[0064] Example 1

[0065] This embodiment provides a technical solution: a multi-objective prediction method for wire bonding thermal fatigue life and electrical performance, with the following specific steps:

[0066] Step 1: Determine the physical and structural parameters of the wire bonding interconnect structure

[0067] The structural parameters of the thermal analysis model of wire bonding interconnection structure are as follows: Figure 2 、 Figure 3 As shown in Figure 2, the structural parameters include the length, width, height, diameter, span and platform length of the dielectric substrate, chip, lead, and pad. The physical parameters include the density, elastic modulus, Poisson's ratio, thermal expansion coefficient, etc. of each part of the material. The working parameters include the maximum temperature T during thermal analysis. max , minimum temperature T min ;

[0068] Wire bonding interconnect structure - Structural parameters of electromagnetic analysis model are as follows Figure 4As shown in the figure, the structural parameters include the length, width, height, diameter, span and platform length of the dielectric substrate, chip, lead and pad; the physical properties include the relative dielectric constant and loss tangent of each part of the material; and the operating parameters include the signal transmission frequency f of the wire bonding interconnection structure.

[0069] Step 2: Obtain a mathematical description of the wire bond morphology

[0070] In step 2, the specific processing process is as follows:

[0071] (2a): The mathematical description of the morphology of the first segment of the wire bond is obtained by the formula f(x)=y, where x represents the position of the first segment of the wire in the x-axis direction, x=0, and y represents the position of the first segment of the wire in the y-axis direction, and the variable range is y∈[h2+h3,h1+h2+h3];

[0072] (2b): The mathematical description of the morphology of the second section of the wire bond is obtained by the formula f(x) = h1 + h2 + h3, where x is variable in the range [0, xL];

[0073] (2c): By formula Get a mathematical description of the morphology of the third segment of the wire bond, where x is in the range [xL,L].

[0074] Step 3: Establish thermal analysis model and structural-electromagnetic analysis model of wire bond interconnect structure

[0075] In step three, the specific processing process is as follows:

[0076] (3a): Based on the geometric parameters (structural parameters) and physical parameters determined in step 1, and the parametric characterization of wire bonding in step 2, a thermal analysis model of the wire bonding interconnection structure is established in ANSYS Apdl software;

[0077] (3b): Based on the geometric parameters and physical parameters determined in step 1 and the parametric characterization of wire bonding in step 2, a wire bonding interconnect structure-electromagnetic analysis model is established in full-wave 3D electromagnetic wave simulation software.

[0078] Step 4: Design a comprehensive experiment and obtain various performance indicators

[0079] In step 4, the specific processing process is as follows:

[0080] (4a): Based on the wire bonding interconnection morphology, the wire diameter D, arch height h1, wire span L, and platform length xL are used as control factors for wire bonding interconnection;

[0081] (4b): Determine the variable range of the four control factors of wire bonding interconnection: wire diameter D∈[D min,D max ]、Lead arch height h1∈[h min ,h max ]、Lead span L∈[L min ,L max ]、Lead platform length xL∈[xL min ,xL max ],in,() min Indicates the minimum value of a certain control factor, () max Indicates the maximum value of a certain control factor;

[0082] (4c): 200 sampling points were extracted within the range of the four geometric parameters (control factors) using the Latin hypercube uniform sampling method to obtain 200 sets of structural parameter data;

[0083] (4d): Based on the actual research on wire bond packaging of (high-frequency) microwave components, the average stress and strain variation range of the wire bond interconnect structure during thermal simulation is used as the temperature cycling reliability indicator, and the insertion loss is used as the electromagnetic transmission performance indicator;

[0084] (4e): 200 sets of data obtained by Latin hypercube sampling were input into ANSYS APDL software to conduct comprehensive experiments on wire bond interconnect morphological parameters and temperature cycling reliability, and to obtain the average stress and strain variation range of the wire bond interconnect structure during thermal simulation;

[0085] (4f): 200 sets of data obtained by Latin hypercube sampling are input as input parameters into full-wave 3D electromagnetic wave simulation software to conduct comprehensive experiments on wire bond interconnect morphological parameters and electromagnetic transmission performance to obtain insertion loss.

[0086] In (4e), the wire bond interconnect morphology parameters are the four control factors mentioned above.

[0087] Step 5: Establish a mapping relationship model from wire bonding interconnect morphological parameters to various performance indicators

[0088] In step five, the specific processing process is as follows:

[0089] (5a): Randomly arrange the obtained wire bond interconnect morphological parameters and the comprehensive experimental data of the thermal analysis model and the structural-electromagnetic analysis model, and divide the first 90% of the obtained 200 groups of experimental data into a training group, and the remaining 10% of the data into a test group; (The comprehensive experimental data specifically includes data such as thermal analysis results and structural-electromagnetic analysis results under different wire bond interconnect morphological parameters. The experimental items refer to the experimental items corresponding to the four control factors mentioned above)

[0090] (5b): The obtained wire bonding interconnection morphological parameters and the four control factors of the wire bonding interconnection structure in the comprehensive experimental data of the thermal analysis model and the structural-electromagnetic analysis model, namely the wire diameter D, the arch height h1, the wire span L and the platform length xL, are used as the input of the CNN convolutional neural network mapping relationship model, and the average stress of the wire bonding interconnection structure, the strain variation range of the wire bonding interconnection structure and the insertion loss of the wire bonding interconnection structure are used as the output;

[0091] (5c) Using the formula Normalize the input and output data, limit the data to [0,1], and eliminate the influence of each parameter dimension. 标准 represents the normalized value of an experimental data, x represents the actual value of an experimental data, and x max Indicates the maximum value of a certain experimental data range, x min Indicates the minimum value of a certain experimental data range;

[0092] (5d) Establish a CNN convolutional neural network topology with 4 inputs and 3 outputs. The network structure includes an input layer, a convolutional layer, an activation layer, a maximum pooling layer, a fully connected layer, and a regression layer. The activation layer uses the RELU activation function with the mathematical expression f(x) = max(0, x).

[0093] (5e): The network is trained using the SGDM gradient descent method. The initial learning rate is set to 0.01, and the learning rate reduction factor is set to 0.1. The experimental data is put into the network for training and testing. 30 samples are set for each training, and the maximum number of training times is 1200.

[0094] (5f): Denormalize the output prediction value and save the model. Establish a mapping relationship between the wire bonding interconnection morphological parameters and the average stress of the wire bonding interconnection structure, the strain variation range of the wire bonding interconnection structure, and the insertion loss of the wire bonding interconnection structure. The model relationship is as follows:

[0095] σ m =Y1(D,h1,L,xL)

[0096] Δε=Y2(D,h1,L,xL)

[0097] S 21 =Y3(D,h1,L,xL)

[0098] Among them, σ m is the predicted average stress of the wire bonding interconnection structure, Δε is the predicted strain variation range of the wire bonding interconnection structure, S 21To predict the insertion loss of the wire-bonded interconnect structure, Y1 is the mapping relationship between the four control factors and the average stress of the wire-bonded interconnect structure, Y2 is the mapping relationship between the four control factors and the strain variation range of the wire-bonded interconnect structure, and Y3 is the mapping relationship between the four control factors and the insertion loss of the wire-bonded interconnect structure. The electromagnetic transmission performance of the wire-bonded interconnect structure can be predicted through the mapping relationship Y3.

[0099] Step 6: Establish a thermal fatigue life prediction model and complete the reliability life prediction

[0100] In step six, the specific processing process is as follows:

[0101] (6a): The thermal fatigue life of wire bond interconnect structures is predicted from three aspects: junction temperature fluctuation, plastic strain, and elastic deformation. The calculation formula is as follows:

[0102]

[0103] Among them, Δε is the strain variation range, σ' r is the material fatigue strength coefficient, σ m is the average stress in one cycle, N f is the number of failure cycles, i.e. thermal fatigue life, b is the fatigue strength index, ε r ' is the fatigue ductility coefficient, c is the fatigue ductility index, and E is the elastic modulus;

[0104] (6b): According to the summary of fatigue tests on various metal materials by some researchers, the four constant values ​​of material fatigue strength coefficient, fatigue strength index, fatigue ductility coefficient and fatigue ductility index can be determined by the following formula:

[0105] b=-0.12

[0106] c=-0.6

[0107] σ' r =3.5σ r

[0108]

[0109] Among them, σ r is the fracture strength coefficient, ε r is the fracture ductility coefficient;

[0110] (6c): The calculation formulas for the fracture strength coefficient and fracture ductility coefficient are as follows:

[0111] σ r =σ b ln(1+ψ)

[0112] εr = -ln(1-ψ)

[0113] Among them, σb is the tensile strength, ψ is the cross-sectional reduction rate;

[0114] (6d): Based on the above information, the thermal fatigue life of wire bond interconnection structure under thermal load is calculated as follows:

[0115]

[0116] Among them, σ m is the predicted average stress, Δε is the predicted strain variation range, and the predicted average stress and strain variation range are substituted into the thermal fatigue life prediction model (i.e., the calculation formula in 6(a) above) to calculate the predicted thermal fatigue life of the wire bonding interconnection structure.

[0117] Example 2

[0118] The advantages of the present invention can be further illustrated by the following simulation experiments:

[0119] 1. Determine the basic parameters of wire bonding interconnection structure

[0120] In this embodiment, the thermal analysis simulation takes the wire bonding interconnection structure as an example with a temperature cycle of 100°C at the highest temperature and -25°C at the lowest temperature, the physical parameters as shown in Table 1, and the structural parameters as shown in Table 2. Figure 2 、 Figure 3 shown.

[0121] Table 1 Thermal analysis physical properties of wire bonding interconnection structure

[0122]

[0123] Table 2 Thermal analysis structural parameters of wire bonding interconnection structure

[0124]

[0125] In this embodiment, the electromagnetic analysis is performed using a wire bonding interconnect structure with an operating frequency of 5 GHz and physical properties and structural parameters as shown in Table 3. Figure 4 shown.

[0126] Table 3 Basic parameters of electromagnetic analysis model for wire bonding interconnection structure

[0127]

[0128]

[0129] 2. Predicting Thermal Fatigue Life and Electrical Performance of Wire Bond Interconnect Structures

[0130] Based on the multi-objective prediction method for wire bonding thermal fatigue life and electrical performance proposed in this invention, a mapping relationship model is established between the wire bonding interconnection morphological parameters and the average stress of the wire bonding interconnection structure, the strain variation range of the wire bonding interconnection structure, and the insertion loss of the wire bonding interconnection structure. The model relationship is as follows:

[0131] σ m =Y1(D,h1,L,xL)

[0132] Δε=Y2(D,h1,L,xL)

[0133] S 21 =Y3(D,h1,L,xL)

[0134] According to the mapping relationship S 21 =Y3(D, h1, L, xL) can complete the prediction of the electromagnetic transmission performance of the wire bonding interconnection structure. Based on the multi-objective prediction method for wire bonding thermal fatigue life and electrical performance proposed in this invention, the thermal fatigue life prediction formula is as follows:

[0135]

[0136] Among them, the four constant values ​​of material fatigue strength coefficient, fatigue strength index, fatigue ductility coefficient and fatigue ductility index can be determined by the following formula:

[0137] b=-0.12

[0138] c=-0.6

[0139] σ' r =3.5σ r

[0140]

[0141] The calculation formulas for the fracture strength coefficient and fracture ductility coefficient are as follows:

[0142] σ r =σ b ln(1+ψ)

[0143] ε r = -ln(1-ψ)

[0144] By substituting the average stress and strain variation range of the wire bonding interconnection structure predicted by the CNN convolutional neural network into the thermal fatigue life prediction formula, the thermal fatigue life of the wire bonding interconnection structure can be calculated.

[0145] The obtained formula was packaged in MATLAB software, and the basic material parameter tables of the thermal analysis model and electromagnetic analysis model of the wire bonding interconnection structure were called. The thermal fatigue life of the wire bonding interconnection structure under the current thermal and electromagnetic conditions was obtained to be 2.1764×10 5 The insertion loss is -4.1041dB and the calculation time is 1min54s.

[0146] 3. Comparative Analysis of Simulation Results

[0147] The precise parameterized model of the wire bonding interconnection structure was established in ANSYS Apdl software using the thermal analysis model structure and physical property parameters of the wire bonding interconnection structure in Tables 1 and 2. Through thermal simulation, the average stress of the wire bonding interconnection structure under the current physical, structural, and thermal parameters of the wire bonding interconnection was found to be 103.9747 MPa and the strain variation range was 0.002258. The average stress and strain variation range were substituted into the thermal fatigue life calculation formula, and the thermal fatigue life was calculated to be 2.3423×10 5 The simulation and calculation time is 85min21s;

[0148] Using the wire-bond interconnect structure-electromagnetic analysis model structure and physical property parameters in Table 3, an accurate parameterized model of the wire-bond interconnect structure was established in the full-wave three-dimensional electromagnetic wave simulation software. Through electromagnetic simulation, the insertion loss of the current wire-bond interconnect structure under the physical, structural, and thermal parameters was obtained to be -4.0533 dB, and the simulation time was 2 minutes and 43 seconds.

[0149] Comparing the prediction method proposed in the present invention with the results obtained using thermal simulation software ANSYS Apdl and electromagnetic simulation software HFSS, it can be seen that the thermal fatigue life error and insertion loss error predicted by the wire-bond interconnect structure electrical performance prediction method proposed in the present invention are very small, with high prediction accuracy. From the perspective of analysis time, the time consumed by the prediction method proposed in the present invention is only 2.16% of the time consumed by simulation using thermal simulation software and electromagnetic software, which greatly saves time costs. This is particularly evident when analyzing a large number of wire-bond interconnect structures simultaneously in engineering practice. Therefore, the wire-bond interconnect structure performance prediction method proposed in the present invention can achieve accurate prediction of the thermal fatigue life and signal transmission performance of the wire-bond interconnect structure, effectively reducing the analysis time cost, which is beneficial for guiding the design and optimization of wire-bond interconnect structures.

[0150] Although the embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are illustrative and are not to be construed as limitations on the present invention. A person skilled in the art may change, modify, replace and modify the above embodiments within the scope of the present invention.

Claims

1. A multi-objective prediction method for wire bonding thermal fatigue life and electrical performance, characterized by: The following steps are involved: Step 1: Determine the geometric parameters and physical properties of the wire bonding interconnect structure based on the specific requirements of the interconnection in the high-frequency microwave component; In the step 1, the geometric parameters of the wire bonding interconnection structure are determined, including the wire diameter D, the arch height h1, the wire span L, the platform length xL, the pad diameter D1, the pad height h3, the chip length L1, the chip width W1, the chip height h2, the dielectric substrate length L2, the dielectric substrate width W2, and the dielectric substrate height h4; the physical parameters of the wire bonding interconnection structure are determined, including the signal transmission frequency f, the dielectric constant ε of each component g , loss tangent θ g , density d g , elastic modulus E g , Poisson's ratio λ g , thermal expansion coefficient α g , and the tensile strength of the lead σ b , section shrinkage ψ; Step 2: Parametrically characterize the geometric parameters of each interconnect structure in the microwave component to obtain a mathematical description of the wire bonding morphology; In step 2, the specific processing process is as follows: Step 2a: Obtain a mathematical description of the first segment of the wire bond using the formula f(x)=y, where x represents the position of the first segment of the wire along the x-axis, x=0, and y represents the position of the first segment of the wire along the y-axis, y∈[h2+h3,h1+h2+h3]; Step 2b: Obtain a mathematical description of the second segment of the wire bond using the formula f(x) = h1 + h2 + h3, where x is in the range [0, xL]. Step (2c): By formula Get the mathematical description of the third segment of the wire bond, where x is in the range [xL,L]; Step 3: Based on the determined geometric parameters, physical parameters, and parametric characterization of the wire bond interconnection in the microwave component, a thermal analysis model and a structural-electromagnetic analysis model of the wire bond interconnection structure are established respectively; Step 4: Based on the control factors, temperature cycling reliability, and electromagnetic transmission performance indicators of microwave components, a comprehensive experiment was designed to test the wire bond interconnect morphological parameters, temperature cycling reliability, and electromagnetic transmission performance. The average stress and strain variation range of the wire bond interconnect structure during the thermal simulation process was obtained for each experimental item, as well as the insertion loss of the wire bond interconnect structure obtained from the electromagnetic simulation. Step 5: Based on comprehensive experimental data, a CNN-based convolutional neural network mapping model is established to map the wire bond interconnect morphological parameters to temperature cycling reliability indicators and electromagnetic transmission performance indicators, and the electromagnetic transmission performance prediction of the wire bond interconnect structure is completed. Step 6: Establish a thermal fatigue life prediction model. Based on the mapping relationship model between the wire bonding interconnection morphological parameters and the temperature cycle reliability index obtained in step 5, complete the thermal fatigue life prediction of the wire bonding interconnection structure.

2. The multi-objective prediction method for thermal fatigue life and electrical performance of wire bonding according to claim 1, characterized in that: In step three, the specific processing process is as follows: Step 3a: Based on the geometric parameters and physical properties determined in Step 1 and the parametric characterization of the wire bonds in Step 2, a thermal analysis model of the wire bond interconnect structure is established in ANSYS APDL software; Step 3b: Based on the geometric parameters and physical parameters determined in step 1 and the parametric characterization of the wire bonding in step 2, a wire bonding interconnect structure-electromagnetic analysis model is established in full-wave 3D electromagnetic simulation software.

3. The multi-objective prediction method for wire bonding thermal fatigue life and electrical performance according to claim 2, characterized in that: In step 4, the specific processing process is as follows: Step 4a: Based on the wire bonding interconnection morphology, the wire diameter D, the arch height h1, the wire span L, and the platform length xL are used as control factors for the wire bonding interconnection structure; Step 4b: Determine the range of the four control factors for wire bonding interconnection: wire diameter D∈[D min ,D max ]、Lead arch height h1∈[h min ,h max ]、Lead span L∈[L min ,L max ]、Lead platform length xL∈[xL min ,xL max ],in,() min Indicates the minimum value of a certain control factor, () max Indicates the maximum value of a certain control factor; Step 4c: Using a Latin hypercube uniform sampling method, extract N sampling points within the variation range of the four control factors to obtain N sets of wire bond interconnection morphological parameter data, where N is a positive integer; Step 4d: Using the average stress and strain variation range of the wire bond interconnect structure during the thermal simulation as a temperature cycling reliability indicator, and using the insertion loss as an electromagnetic transmission performance indicator; Step 4e: Use the N sets of data obtained by Latin Hypercube sampling as input parameters to the ANSYS APDL software to conduct comprehensive experiments on the wire bond interconnect morphological parameters and temperature cycling reliability, and obtain the average stress and strain variation range of the wire bond interconnect structure during the thermal simulation process; Step 4f: Use the N sets of data obtained by Latin hypercube sampling as input parameters to input into full-wave 3D electromagnetic wave simulation software to conduct comprehensive experiments on wire bond interconnect morphological parameters and electromagnetic transmission performance to obtain insertion loss.

4. The multi-objective prediction method for wire bonding thermal fatigue life and electrical performance according to claim 3, characterized in that: In step 5, the specific processing process is as follows: Step 5a: Randomly arrange the obtained wire bond interconnect morphological parameters and the comprehensive experimental data of the thermal analysis model and the structural-electromagnetic analysis model, and divide the first 90% of the data into a training set and the remaining 10% of the data into a test set; Step 5b: The obtained wire bond interconnect morphological parameters and the four control factors of the wire bond interconnect structure from the comprehensive experimental data of the thermal analysis model and the structural-electromagnetic analysis model (wire diameter D, arch height h1, wire span L, and platform length xL) are used as inputs to the CNN convolutional neural network mapping relationship model. The average stress of the wire bond interconnect structure, the strain variation range of the wire bond interconnect structure, and the insertion loss of the wire bond interconnect structure are used as outputs. Step 5c: Use the formula Normalize the input and output data, limit the data to [0,1], and eliminate the influence of each parameter dimension. 标准 represents the normalized value of an experimental data, x represents the actual value of an experimental data, and x max Indicates the maximum value of a certain experimental data range, x min Indicates the minimum value of a certain experimental data range; Step 5d: Establish a CNN convolutional neural network topology with 4 inputs and 3 outputs. The network structure includes an input layer, a convolutional layer, an activation layer, a maximum pooling layer, a fully connected layer, and a regression layer. The activation layer uses the RELU activation function with the mathematical expression f(x) = max(0, x); Step 5e: Input the experimental data into the network and use the SGDM gradient descent method to train the network; Step 5f: Denormalize the output prediction value and save the model. Establish a mapping relationship between the wire bonding interconnection morphological parameters and the average stress of the wire bonding interconnection structure, the strain variation range of the wire bonding interconnection structure, and the insertion loss of the wire bonding interconnection structure. The model relationship is as follows: s m =Y1(D,h1,L,xL) Δε=Y2(D,h1,L,xL) S 21 =Y3(D,h1,L,xL) Among them, σ m is the predicted average stress of the wire bonding interconnection structure, Δε is the predicted strain variation range of the wire bonding interconnection structure, S 21 To predict the insertion loss of the wire-bonded interconnect structure, Y1 is the mapping relationship between the four control factors and the average stress of the wire-bonded interconnect structure, Y2 is the mapping relationship between the four control factors and the strain variation range of the wire-bonded interconnect structure, and Y3 is the mapping relationship between the four control factors and the insertion loss of the wire-bonded interconnect structure. The electromagnetic transmission performance of the wire-bonded interconnect structure can be predicted through the mapping relationship Y3.

5. The multi-objective prediction method for wire bonding thermal fatigue life and electrical performance according to claim 4, characterized in that: In step six, the specific processing process is as follows: Step 6a: The thermal fatigue life of the wire bond interconnect structure is predicted from three aspects: junction temperature fluctuation, plastic strain, and elastic deformation. Then, a thermal fatigue life prediction model is established. The calculation formula is as follows: Among them, Δε is the strain variation range, σ' r is the material fatigue strength coefficient, σ m is the average stress in one cycle, N f is the number of failure cycles, i.e. thermal fatigue life, b is the fatigue strength index, ε' r is the fatigue ductility coefficient, is the fatigue ductility index, E is the elastic modulus; Step 6b: Determine the material fatigue strength coefficient, fatigue strength index, fatigue ductility coefficient, and fatigue ductility index using the following formulas: b＝-0.12 c＝-0.6 in r =3.5σ r in, σr is the fracture strength coefficient, ε r is the fracture ductility coefficient; The calculation formulas for the fracture strength coefficient and fracture ductility coefficient are as follows: s r =s b ln(1+ψ) e r =-ln(1-ψ) Among them, σ b is the tensile strength, ψ is the section reduction rate; Step 6c: Substitute the parameter calculation formulas in step 6b into the thermal fatigue life prediction model calculation formula in step 6a to obtain: Step 6d: Substitute the average stress and strain variation range of the wire bonding interconnection structure predicted by the corresponding mapping relationship model into the calculation formula of the thermal fatigue life prediction model to calculate the predicted thermal fatigue life of the wire bonding interconnection structure.

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