Flip chip vibration signal denoising method and system

Abstract

The invention relates to the technical field of signal processing, and discloses a flip chip vibration signal denoising method and system, and the method comprises the steps: obtaining a vibration signal responded by a to-be-detected flip chip under the action of external ultrasonic excitation, carrying out the segmentation processing of the vibration signal, constructing an initial sparse dictionary according to the segmented vibration signal, and iteratively updating the sparse dictionary; in the iteration updating process, sparseness parameters are adjusted in real time along with the number of iterations, and soft threshold denoising is conducted on residual terms; introducing a feature projection matrix to perform joint modeling on each section of vibration signal, and constructing a joint optimization model according to the updated sparse dictionary and the corresponding sparse coefficient; alternately optimizing the sparse coefficient and the feature projection matrix in the joint optimization model to obtain an optimal solution of the sparse coefficient; and combining the updated sparse dictionary and the optimal solution of the sparse coefficient to reconstruct the vibration signal to obtain a denoised vibration signal. According to the method, transient features can be effectively extracted, expression of key features is enhanced, noise is suppressed, and denoising robustness and reconstruction precision are improved.

Description

Technical Field

[0001] The present invention relates to the field of signal processing technology, and in particular to a flip chip vibration signal denoising method and system. Background Art

[0002] Flip-chip packaging is an ideal packaging solution for high-end core chips due to its high terminal density, short interconnect paths, low resistance, and excellent high-frequency performance. However, as packaging density continues to increase, the size and spacing of solder joints are decreasing, and many of them are hidden inside the package, making solder joint defect detection increasingly difficult. Once a solder joint defect exists, it will seriously affect the electrical, thermal, and mechanical reliability of the packaged product, thereby threatening the stability and service life of the entire system. Therefore, to ensure the reliability of flip-chips, effective solder joint inspection is necessary. However, solder joints are often hidden between the chip and the substrate, making flip-chip solder joint defect detection difficult.

[0003] To detect solder joint defects, existing techniques combine ultrasonic excitation with vibration response analysis for nondestructive testing. This method offers advantages such as non-contact, non-destructive operation, ease of use, and rapid response, and has been gradually adopted for flip-chip solder joint defect detection. This method utilizes an ultrasonic transducer to apply ultrasonic excitation to the chip surface while simultaneously collecting the resulting vibration signal using a Doppler laser vibrometer. Defects within the chip's internal structure alter its local stiffness, causing changes in the vibration response. Analyzing this vibration signal effectively detects flip-chip defects.

[0004] However, in actual detection, due to the complex packaging structure of the flip chip, the small defect size, the significant edge effect and the presence of multi-source noise (material grain noise, system noise, etc.), the effective signal is easily submerged and the key features cannot be effectively extracted; at the same time, the multi-source noise is coupled with the transient characteristics of the defect, resulting in a significant reduction in the signal-to-noise ratio of the defect characteristics, making the signal denoising accuracy limited and the robustness poor. Summary of the Invention

[0005] To this end, the technical problem to be solved by the present invention is to overcome the deficiencies in the prior art and provide a flip-chip vibration signal denoising method and system, which can effectively extract transient features and enhance the expression of key features, suppress noise, and improve denoising robustness and reconstruction accuracy.

[0006] To solve the above technical problems, the present invention provides a flip chip vibration signal denoising method, comprising: The vibration signal of the flip chip under test under external ultrasonic excitation is obtained and processed in segments. An initial sparse dictionary is constructed based on the segmented vibration signal, and the sparse dictionary is iteratively updated. During the iterative update of the sparse dictionary, the sparsity parameter is adjusted in real time with the number of iterations. The residual term is subjected to soft threshold denoising based on the sparsity parameter adjusted in real time. The feature projection matrix is ​​introduced to jointly model each vibration signal, and a joint optimization model is constructed based on the updated sparse dictionary and the corresponding sparse coefficients. Alternately optimizing the sparse coefficients and the feature projection matrix in the joint optimization model to obtain an optimal solution for the sparse coefficients; The vibration signal is reconstructed by combining the updated sparse dictionary and the optimal solution of the sparse coefficients to obtain the denoised vibration signal.

[0007] Furthermore, the sparsity parameter is adjusted in real time with the number of iterations, and the calculation method of the sparsity parameter adjusted in real time is: , in, is the initial factor, max iter is the maximum number of iterations, iter is the current iteration number, η For the iter The sparsity parameter is adjusted in real time in the iteration.

[0008] Furthermore, the soft threshold denoising is performed on the residual term in combination with the sparsity parameter adjusted in real time, specifically: In the iter In the iteration, the number of j 0 The residual term for the column atoms is: , in, is the first j 0 The residual term of the column atoms, is the number of the current sparse dictionary after soft threshold denoising j 0 The residual term of the column atoms, sign ( ) is the sign function, η For the iter The sparsity parameter is adjusted in real time in the iteration; Based on η The calculated coefficients, ; is the dynamic amplitude parameter, For The noise variance obtained by performing noise estimation.

[0009] Furthermore, the feature projection matrix is ​​introduced to jointly model each vibration signal, and a joint optimization model is constructed according to the updated sparse dictionary and the corresponding sparse coefficients, specifically: A standard sparse regression model of the vibration signal is constructed based on the updated sparse dictionary and the corresponding sparse coefficients. The optimization goal of the standard sparse regression model of the vibration signal is: , in, Y is the vibration signal, D is the updated sparse dictionary, is the sparse coefficient corresponding to the updated sparse dictionary, is the F norm, is the sparse weight of L1 norm, is the L1 norm; Introducing a feature projection matrix and establishing an optimization objective with the feature projection matrix, wherein the feature projection matrix is ​​used to assist the learning process of sparse coefficients; The optimization objective of the standard sparse regression model of the vibration signal and the optimization objective of the projection matrix with features are integrated to obtain a joint optimization model.

[0010] Furthermore, the optimization goal of the feature projection matrix is: , in, V is the feature projection matrix, T is the transpose operation, is the L2,1 norm, is the sparse weight of L2,1 norm.

[0011] Furthermore, the joint optimization model is: , in, is the i-th atom in the updated sparse dictionary, is the L2 norm, m is the number of atoms in the updated sparse dictionary, To control the projection consistency weight.

[0012] Furthermore, the alternate optimization of the sparse coefficients and the feature projection matrix in the joint optimization model is specifically as follows: Taking the updated sparse dictionary and feature projection matrix as fixed values, introducing the first auxiliary variable and constructing the first augmented Lagrangian function; Taking the updated sparse dictionary and the updated sparse coefficient as fixed values, introducing a second auxiliary variable and constructing a second augmented Lagrangian function; Use the alternating direction multiplication method to solve the updated sparse coefficients and feature projection matrix.

[0013] Furthermore, the first augmented Lagrangian function is: , in, Z is the first auxiliary variable, is the first augmented Lagrangian function, U is the first dual variable, is the first penalty parameter; The second augmented Lagrangian function is: , in, Z 1 is the second auxiliary variable, is the second augmented Lagrangian function, W is the second dual variable, ρ 1 is the second penalty parameter.

[0014] Furthermore, the alternating direction multiplier method is used to solve the updated sparse coefficients and feature projection matrix, specifically: The solution method for the updated sparse coefficients is: , in, is the sparse coefficient obtained by the k+1th round of iteration, is the first auxiliary variable obtained by the k+1th round of iteration, is the first dual variable solved in the k+1th round of iteration, is the identity matrix, is the soft threshold function; The solution to the characteristic projection matrix is: , in, is the feature projection matrix obtained by the k+1th round of iteration, is the i-th row of the feature projection matrix obtained by the k+1th round of iteration, , is the number of matrix rows of the feature projection matrix; is the second auxiliary variable obtained by the k+1th round of iteration, The i-th row of the second auxiliary variable obtained by the k+1th round of iteration, , is the number of matrix rows of the second auxiliary variable; is the second dual variable obtained by solving the k-th iteration, is the i-th row of the second dual variable obtained by the k-th iteration, , is the number of rows of the matrix of the second dual variable; The updated sparse coefficients and feature projection matrices are solved alternately until the maximum number of iterations is reached or the iteration end condition is met, and the sparse coefficients obtained at this time are taken as the optimal solution of the sparse coefficients.

[0015] The present invention also provides a flip chip vibration signal denoising system, comprising: A signal acquisition module is used to acquire the vibration signal of the flip chip under test under external ultrasonic excitation and process it in segments; The sparse dictionary module constructs an initial sparse dictionary based on the segmented vibration signal and iteratively updates the sparse dictionary. During the iterative update of the sparse dictionary, the sparsity parameter is adjusted in real time with the number of iterations, and the residual term is subjected to soft threshold denoising based on the sparsity parameter adjusted in real time. The denoising model construction module is used to introduce the feature projection matrix to jointly model each vibration signal and build a joint optimization model based on the updated sparse dictionary and the corresponding sparse coefficients; A sparse coefficient optimization module, configured to alternately optimize the sparse coefficients and the feature projection matrix in the joint optimization model to obtain an optimal solution for the sparse coefficients; The signal denoising module is used to reconstruct the vibration signal by combining the updated sparse dictionary and the optimal solution of the sparse coefficients to obtain a denoised vibration signal.

[0016] The above technical solution of the present invention has the following beneficial effects compared with the prior art: The present invention constructs a sparse dictionary and adjusts the sparsity parameters in real time during the iterative update process of the sparse dictionary, performs soft threshold denoising on the residual terms, suppresses the interference of noise on atomic updates, enhances the dictionary's sparse representation capability of transient features, and effectively extracts key features. At the same time, by constructing a joint optimization model and jointly optimizing the sparse coding and feature selection processes, the enhanced expression of key features and the effective suppression of noise are achieved, the denoising robustness and reconstruction accuracy are improved, and the denoising effect of the ultrasonic excitation vibration signal of the flip chip is effectively improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to make the content of the present invention more clearly understood, the present invention is further described in detail below based on specific embodiments of the present invention in conjunction with the accompanying drawings, wherein: Figure 1 Flowchart of the method in the preferred embodiment of the present invention.

[0018] Figure 2 This is a diagram showing the simulation experiment results of using different methods to reduce the noise of flip chip vibration signals in a preferred embodiment of the present invention.

[0019] Figure 3This is a simulation experiment result diagram of the SNR values ​​of vibration signals reconstructed using different methods under different noise intensities in a preferred embodiment of the present invention.

[0020] Figure 4 This is a simulation experiment result diagram of the RMSE value of the vibration signal reconstructed using different methods under different noise intensities in a preferred embodiment of the present invention. DETAILED DESCRIPTION

[0021] The present invention will be further described below with reference to the accompanying drawings and specific embodiments so that those skilled in the art can better understand the present invention and implement it. However, the embodiments are not intended to limit the present invention.

[0022] Reference Figure 1 As shown, the present invention discloses a flip chip vibration signal denoising method, comprising the following steps: S1: Obtaining a vibration signal of the flip chip to be tested in response to external ultrasonic excitation and preprocessing the signal. The vibration signal is a one-dimensional time series data containing solder joint status information and noise components.

[0023] S1-1: In this embodiment, the method for obtaining a vibration signal is as follows: placing a flip chip to be tested with a solder joint defect on an air-floating vibration isolation platform, using a signal generator to transmit a sinusoidal sweep frequency signal, which is amplified by a power amplifier and then excited by an air-coupled capacitive ultrasonic transducer to the flip chip to be tested; using a scanning Doppler laser vibrometer to measure the vibration of the surface of the flip chip to be tested, and obtaining a vibration signal of the upper surface of the flip chip to be tested.

[0024] S1-2: In this embodiment, the method for preprocessing the original vibration signal is: performing window processing on the vibration signal, and then performing segmentation processing: dividing the windowed vibration signal into n equal-length signal slices for subsequent dictionary training and sparse modeling.

[0025] S2: Construct an initial sparse dictionary based on the segmented vibration signal and iteratively update the sparse dictionary.

[0026] S2-1: randomly extracting samples from the segmented vibration signal to construct an initial sparse dictionary. In this embodiment, the initial sparse dictionary is an over-complete dictionary.

[0027] S2-2: Perform sparse representation on the vibration signal based on the initial sparse dictionary, and obtain the sparse representation model as follows: , Where Y is the vibration signal, , is the i-th signal slice, n is the number of signal slices, is the initial sparse dictionary, is the initial sparse coefficient, For noise.

[0028] S2-3: Use the K-SVD algorithm to iteratively update the sparse dictionary in alternating iterations of sparse representation and sparse dictionary update. In this embodiment, a dynamic sparse contraction mechanism is designed to be introduced during the iterative update of the sparse dictionary. Specifically, the sparsity parameter is adjusted in real time with the number of iterations, and the residual term is soft-thresholded denoised based on the real-time adjusted sparsity parameter. This can suppress the interference of noise on the dictionary atomic update process and improve the sparse representation capability of transient features.

[0029] The calculation method of the sparsity parameter adjusted in real time is: , in, is the initial factor, The value of depends on the actual setting. iter is the current number of iterations, max iter is the maximum number of iterations, η For the iter The sparsity parameter is adjusted in real time in the iteration.

[0030] In the iter In the iteration, the number of j 0 The residual term for the column atoms is: , in, is the first j 0 The residual term of the column atoms, is the number of the current sparse dictionary after soft threshold denoising j 0 The residual term of the column atoms, sign ( ) is the sign function, η For the iter The sparsity parameter is adjusted in real time in the iteration; Based on η The calculated coefficients, ; is the dynamic amplitude parameter, For The noise variance obtained by performing noise estimation. In this embodiment, , For the residual term The signal energy obtained by variance estimation is , MAD ( ) indicates the median absolute deviation method.

[0031] S3: The feature projection matrix is ​​introduced to jointly model each vibration signal, and a joint optimization model is constructed based on the updated sparse dictionary and the corresponding sparse coefficients.

[0032] S3-1: According to the updated sparse dictionary and the corresponding sparse coefficients (i.e. The standard sparse regression model of the vibration signal is constructed after the S2-2 iterative update. The optimization goal of the standard sparse regression model of the vibration signal is: , in, Y is the vibration signal, D is the updated sparse dictionary, is the sparse coefficient corresponding to the updated sparse dictionary, is the F norm, is the sparse weight of L1 norm, is the L1 norm; in the optimization objective expression, Limit the reconstruction error of vibration signals on sparse dictionaries to ensure the integrity of the basic structure; is the L1 regularization term, which is used to improve sparsity and suppress redundant expressions.

[0033] S3-2: Introduce the feature projection matrix and establish the optimization objective with the feature projection matrix as follows: , in, V is the feature projection matrix, T is the transpose operation, is the L2,1 norm, is the sparse weight of L2,1 norm; in the optimization objective expression with the feature projection matrix, constraint distribution, making it closer to the signal structure after noise suppression; Encourage V The feature projection matrix is ​​used to further reduce redundancy and noise by sparsifying the rows and retaining only the most representative features. In this embodiment, the initial feature projection matrix is ​​a randomly initialized matrix. The feature projection matrix enhances the ability of the sparse coefficients to express key signal features and further suppresses redundancy and noise interference. The feature projection matrix is ​​used to assist in the learning process of the sparse coefficients to improve their ability to express key transient features.

[0034] S3-3: Integrate the optimization objectives of the standard sparse regression model of the vibration signal and the optimization objectives of the feature projection matrix to obtain a joint optimization model, that is, the optimization objectives of the dual sparse regression model are: , in, is the i-th atom in the updated sparse dictionary, is the L2 norm, m is the number of atoms in the updated sparse dictionary. In this embodiment, the number of atoms in the sparse dictionary is set to m =2 n ; In order to control the projection consistency weight, in this embodiment .

[0035] S4: The dual sparse regression model is iteratively solved using the Alternating Direction Method of Multipliers (ADMM). The optimal solution for the sparse coefficients is obtained by alternately optimizing the sparse coefficients and the feature projection matrix in the joint optimization model. This improves the sparse coefficients' ability to express defect information and suppresses background noise interference.

[0036] S4-1: Take the updated sparse dictionary and feature projection matrix as fixed values, introduce the first auxiliary variable and construct the first augmented Lagrangian function as follows: , in, Z is the first auxiliary variable, is the first augmented Lagrangian function, U is the first dual variable, is the first penalty parameter.

[0037] S4-2: Take the updated sparse dictionary and the updated sparse coefficient as fixed values, introduce the second auxiliary variable and construct the second augmented Lagrangian function as follows: , in, Z 1 is the second auxiliary variable, is the second augmented Lagrangian function, W is the second dual variable, ρ 1 is the second penalty parameter.

[0038] S4-3: Use the alternating direction multiplication method to solve the updated sparse coefficients and feature projection matrix.

[0039] The solution method for the updated sparse coefficients is: , in, is the sparse coefficient obtained by the k+1th round of iteration, is the first auxiliary variable obtained by the k+1th round of iteration, is the first dual variable solved in the k+1th round of iteration, is the identity matrix, Soft threshold function; The solution to the characteristic projection matrix is:

[0040] in, is the feature projection matrix obtained by the k+1th round of iteration, is the i-th row of the feature projection matrix obtained by the k+1th round of iteration, , is the number of matrix rows of the feature projection matrix; is the second auxiliary variable obtained by the k+1th round of iteration, The i-th row of the second auxiliary variable obtained by the k+1th round of iteration, , is the number of matrix rows of the second auxiliary variable; is the second dual variable obtained by solving the k-th iteration, is the i-th row of the second dual variable obtained by the k-th iteration, , is the number of rows of the matrix of the second dual variable; The updated sparse coefficients and feature projection matrices are solved alternately, and the solution is gradually converged by fixing one variable and optimizing the other variable in each round until the maximum number of iterations is reached or the iteration end condition is met. The sparse coefficients obtained at this time are regarded as the optimal solution of the sparse coefficients.

[0041] S5: Reconstruct the vibration signal by combining the updated sparse dictionary and the optimal solution of the sparse coefficients. The denoised vibration signal is: , in, is the reconstructed vibration signal, that is, the denoised vibration signal, is the optimal solution for sparse coefficients. include n The reconstructed vibration signal slices are spliced ​​together to form a complete denoised vibration signal.

[0042] Through the sparse reconstruction process in the present invention, background noise can be effectively suppressed while ensuring the integrity of the signal structure, the ability to identify defect-related transient features can be improved, and higher-fidelity signal restoration can be achieved.

[0043] The present invention also discloses a flip chip vibration signal denoising system, comprising: A signal acquisition module is used to acquire the vibration signal of the flip chip under test under external ultrasonic excitation and process it in segments; The sparse dictionary module constructs an initial sparse dictionary based on the segmented vibration signal and iteratively updates the sparse dictionary. During the iterative update of the sparse dictionary, the sparsity parameter is adjusted in real time with the number of iterations, and the residual term is subjected to soft threshold denoising based on the sparsity parameter adjusted in real time. The denoising model construction module is used to introduce the feature projection matrix to jointly model each vibration signal and build a joint optimization model based on the updated sparse dictionary and the corresponding sparse coefficients; A sparse coefficient optimization module, configured to alternately optimize the sparse coefficients and the feature projection matrix in the joint optimization model to obtain an optimal solution for the sparse coefficients; The signal denoising module is used to reconstruct the vibration signal by combining the updated sparse dictionary and the optimal solution of the sparse coefficients to obtain a denoised vibration signal.

[0044] The present invention also discloses a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, a flip chip vibration signal denoising method is implemented.

[0045] The present invention also discloses a device, comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements a flip chip vibration signal denoising method when executing the computer program.

[0046] The present invention can effectively improve the distinguishability and signal-to-noise ratio of flip-chip vibration signals in complex noise environments. Compared with existing sparse representation methods, it has significant advantages in robustness, feature preservation, and denoising accuracy. The advantages of the present invention are as follows: 1. The present invention uses a dynamic sparse shrinkage mechanism to perform adaptive soft threshold denoising on the residual terms in the dictionary update phase, and adaptively adjusts the sparsity parameter through an iterative process, thereby suppressing the interference of background noise on the dictionary atom update and enhancing the dictionary's ability to represent transient features.

[0047] 2. By constructing a dual sparse regression model that jointly optimizes the sparse coefficients and the feature projection matrix, a trade-off between noise suppression and feature preservation is achieved in the sparse regression model. The projection matrix is ​​used to guide the sparse representation to be optimized in a cleaner direction, effectively improving the accuracy and stability of noise suppression.

[0048] 3. Reconstructing the vibration signal based on the dynamic sparse contraction mechanism and the dual sparse regression model can effectively improve the denoising robustness and reconstruction accuracy, and effectively improve the denoising effect of the ultrasonic excitation vibration signal of the flip chip.

[0049] To further demonstrate the advantages of the present invention, in this embodiment, the method of the present invention and the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) method, the Ensemble Empirical Mode Decomposition (EEMD) method, the K-SVD standard sparse reconstruction method (K-SVD method), and the classic L1 regularized sparse representation method (L1 method) in the prior art are used to perform noise reduction on the simulated vibration signal of the flip chip under -5dB noise. The spectrum diagrams after noise reduction by different methods are shown in FIG. Figure 2 As shown, Figure 2 The CEEMDAN in the figure represents the spectrum after noise reduction using the CEEMDAN method. Figure 2 The EEMD in the figure represents the spectrum after noise reduction using the EEMD method. Figure 2 The K-SVD in the figure represents the spectrum after noise reduction using the K-SVD standard sparse reconstruction method. Figure 2 The L1 in the figure represents the spectrum after noise reduction using the classic L1 regular sparse representation method. Figure 2 The present invention in FIG. 1 shows a spectrum diagram after noise reduction processing using the method of the present invention.

[0050] from Figure 2 It can be seen that although the CEEMDAN method can achieve multimodal decomposition, the first-order resonant frequency is significantly attenuated due to modal aliasing, and the core features are submerged by noise. The EEMD method uses noise-assisted decomposition, but the unified noise addition and averaging strategy ignores local sparsity, resulting in the loss of 3rd-5th order high-frequency components. It is also susceptible to interference from square wave noise, which causes pseudo-peaks to be misidentified as characteristic frequencies, reducing the accuracy of feature extraction. When using the K-SVD method, the sparse representation of noise and effective signal is not separable enough, resulting in a high proportion of noise atoms in the learned dictionary and significant noise residue in the reconstructed signal. Although the L1 method can better highlight the resonant frequency, it still has some noise interference in the low-amplitude region. In contrast, the method of the present invention is more effective in local noise suppression, can fully preserve the main features of the signal, and the reconstructed signal has the highest similarity with the original signal. In summary, this method can stably extract key features under multi-source noise interference and has higher robustness and stability.

[0051] To verify the applicability of the proposed method under varying noise intensities, this example applies noise of varying intensities to a simulated vibration signal and quantitatively compares the noise reduction effectiveness of each method. The quality of the reconstructed signal is evaluated using the signal-to-noise ratio (SNR) and root mean square error (RMSE). Generally, a higher SNR value and a lower RMSE indicate better denoising results. Figure 3 is the SNR value of the vibration signal reconstructed using different methods under different noise intensities, Figure 4 is the RMSE value of the vibration signal reconstructed using different methods under different noise intensities.

[0052] from Figure 3 It can be seen that as the noise intensity increases, the SNR values ​​of the reconstructed signals of each method show a downward trend. Compared with the CEEMDAN method, EEMD method, K-SVD method and L1 method, the method of the present invention can obtain a higher reconstructed SNR under the same input SNR conditions, showing a significant advantage. Figure 4 It can be seen that as the noise intensity increases, the RMSE values ​​of each method, that is, the reconstruction error, all show an upward trend, but the method of the present invention always maintains the lowest error under various SNR conditions.

[0053] It can be seen from the above simulation experiments that the present invention has higher robustness and stability and higher reconstruction accuracy than the signal reconstruction method in the prior art, which proves the beneficial effects of the present invention.

[0054] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0055] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxesFigure 1 A device that provides the functions specified in a block or multiple blocks.

[0056] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0057] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 The steps for the function specified in one or more boxes.

[0058] Obviously, the above embodiments are merely examples for clarity of explanation and are not intended to limit the implementation methods. Those skilled in the art will appreciate that other variations or modifications can be made based on the above description. It is not necessary and impossible to enumerate all implementation methods here. Obvious variations or modifications arising therefrom remain within the scope of protection of the present invention.

Claims

1. A flip chip vibration signal denoising method, characterized in that: include: The vibration signal of the flip chip under test under external ultrasonic excitation is obtained and processed in segments. An initial sparse dictionary is constructed based on the segmented vibration signal, and the sparse dictionary is iteratively updated. During the iterative update process of the sparse dictionary, the sparsity parameter is adjusted in real time with the number of iterations, and the residual term is subjected to soft threshold denoising based on the sparsity parameter adjusted in real time; The feature projection matrix is ​​introduced to jointly model each vibration signal, and a joint optimization model is constructed based on the updated sparse dictionary and the corresponding sparse coefficients. The joint optimization model is: ， in, Y is the vibration signal, D is the updated sparse dictionary, is the sparse coefficient corresponding to the updated sparse dictionary, is the F norm, is the sparse weight of L1 norm, V is the feature projection matrix, T is the transpose operation, is the L1 norm, is the i-th atom in the updated sparse dictionary, is the L2 norm, m is the number of atoms in the updated sparse dictionary, To control the projection consistency weight, is the L2,1 norm, is the sparse weight of L2,1 norm; Alternately optimizing the sparse coefficients and the feature projection matrix in the joint optimization model to obtain an optimal solution for the sparse coefficients; The vibration signal is reconstructed by combining the updated sparse dictionary and the optimal solution of the sparse coefficients to obtain the denoised vibration signal.

2. The flip chip vibration signal denoising method according to claim 1, wherein: The sparsity parameter is adjusted in real time with the number of iterations, and the calculation method of the sparsity parameter adjusted in real time is: ， in, is the initial factor, max iter is the maximum number of iterations, iter is the current iteration number, η For the iter The sparsity parameter is adjusted in real time in the iteration.

3. The flip chip vibration signal denoising method according to claim 1, wherein: The soft threshold denoising of the residual term is performed in combination with the sparsity parameter adjusted in real time, specifically: In the iter In the iteration, the number of j 0 The residual term for the column atoms is: ， in, The first j 0 The residual term of the column atoms, is the number of the current sparse dictionary after soft threshold denoising j 0 The residual term of the column atoms, sign ( ) is the sign function, η For the iter The sparsity parameter is adjusted in real time in the iteration; Based on η The calculated coefficients, ; is the dynamic amplitude parameter, For The noise variance obtained by performing noise estimation.

4. The flip chip vibration signal denoising method according to claim 1, wherein: The feature projection matrix is ​​introduced to jointly model each vibration signal, and a joint optimization model is constructed according to the updated sparse dictionary and the corresponding sparse coefficients, specifically: A standard sparse regression model of the vibration signal is constructed based on the updated sparse dictionary and the corresponding sparse coefficients. The optimization goal of the standard sparse regression model of the vibration signal is: ； Introducing a feature projection matrix and establishing an optimization objective with the feature projection matrix, wherein the feature projection matrix is ​​used to assist the learning process of sparse coefficients; The optimization objective of the standard sparse regression model of the vibration signal and the optimization objective of the projection matrix with features are integrated to obtain a joint optimization model.

5. The flip chip vibration signal denoising method according to claim 4, characterized in that: The optimization goal of the feature projection matrix is: 。 6. The flip chip vibration signal denoising method according to claim 1, wherein: The alternating optimization of the sparse coefficients and the feature projection matrix in the joint optimization model is specifically as follows: Taking the updated sparse dictionary and feature projection matrix as fixed values, introducing the first auxiliary variable and constructing the first augmented Lagrangian function; Taking the updated sparse dictionary and the updated sparse coefficient as fixed values, introducing a second auxiliary variable and constructing a second augmented Lagrangian function; Use the alternating direction multiplication method to solve the updated sparse coefficients and feature projection matrix.

7. The flip chip vibration signal denoising method according to claim 6, wherein: The first augmented Lagrangian function is: ， in, Z is the first auxiliary variable, is the first augmented Lagrangian function, U is the first dual variable, is the first penalty parameter; The second augmented Lagrangian function is: ， in, Z 1 is the second auxiliary variable, is the second augmented Lagrangian function, W is the second dual variable, ρ 1 is the second penalty parameter.

8. The flip chip vibration signal denoising method according to claim 7, wherein: The updated sparse coefficients and feature projection matrix are solved using the alternating direction multiplication method, specifically: The solution method for the updated sparse coefficients is: ， in, is the sparse coefficient obtained by the k+1th round of iteration, is the first auxiliary variable obtained by the k+1th round of iteration, is the first dual variable solved in the k+1th round of iteration, is the identity matrix, is the soft threshold function; The solution to the characteristic projection matrix is: ， in, is the feature projection matrix obtained by the k+1th round of iteration, is the i-th row of the feature projection matrix obtained by the k+1th round of iteration, , is the number of matrix rows of the feature projection matrix; is the second auxiliary variable obtained by the k+1th round of iteration, The i-th row of the second auxiliary variable obtained by the k+1th round of iteration, , is the number of matrix rows of the second auxiliary variable; is the second dual variable obtained by solving the k-th iteration, is the i-th row of the second dual variable obtained by the k-th iteration, , is the number of rows of the matrix of the second dual variable; The updated sparse coefficients and feature projection matrices are solved alternately until the maximum number of iterations is reached or the iteration end condition is met, and the sparse coefficients obtained at this time are taken as the optimal solution of the sparse coefficients.

9. A flip chip vibration signal denoising system, characterized in that: include: A signal acquisition module is used to acquire the vibration signal of the flip chip under test under external ultrasonic excitation and process it in segments; The sparse dictionary module constructs an initial sparse dictionary based on the segmented vibration signal and iteratively updates the sparse dictionary; During the iterative update process of the sparse dictionary, the sparsity parameter is adjusted in real time with the number of iterations, and the residual term is subjected to soft threshold denoising based on the sparsity parameter adjusted in real time; The denoising model construction module is used to introduce the feature projection matrix to jointly model each vibration signal, and to construct a joint optimization model based on the updated sparse dictionary and the corresponding sparse coefficients. The joint optimization model is: ， in, Y is the vibration signal, D is the updated sparse dictionary, is the sparse coefficient corresponding to the updated sparse dictionary, is the F norm, is the sparse weight of L1 norm, V is the feature projection matrix, T is the transpose operation, is the L1 norm, is the i-th atom in the updated sparse dictionary, is the L2 norm, m is the number of atoms in the updated sparse dictionary, To control the projection consistency weight, is the L2,1 norm, is the sparse weight of L2,1 norm; A sparse coefficient optimization module, configured to alternately optimize the sparse coefficients and the feature projection matrix in the joint optimization model to obtain an optimal solution for the sparse coefficients; The signal denoising module is used to reconstruct the vibration signal by combining the updated sparse dictionary and the optimal solution of the sparse coefficients to obtain a denoised vibration signal.

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