Chip defect detection method and system based on resonance frequency band energy entropy

Abstract

The application relates to the technical field of flip-chip defect detection, and specifically provides a chip defect detection method and system based on resonance frequency band energy entropy, which comprises the following steps: acquiring a sample chip; acquiring a vibration signal from the sample chip according to an air-coupled ultrasonic transducer and a Doppler laser vibration meter; performing wavelet packet decomposition on the vibration signal to generate a low-frequency vibration signal; generating a low-frequency vibration signal spectrum according to the low-frequency vibration signal; dividing the low-frequency vibration signal spectrum to generate a modal number, a center frequency and a penalty factor according to a Teager energy operator and a multiple extreme value segmentation method; inputting the modal number, the penalty factor and an initial center frequency into an improved VMD algorithm to generate a low-frequency resonance frequency band energy entropy; and generating a defect detection result according to the low-frequency resonance frequency band energy entropy. The combination method of the Teager energy operator and the multiple extreme value segmentation not only solves the problems of modal aliasing and the like, but also realizes accurate decomposition of the signal.

Description

Technical Field

[0001] This invention relates to the field of flip-chip defect detection technology, and in particular to a chip defect detection method and system based on resonant frequency band energy entropy. Background Technology

[0002] Integrated circuit manufacturing is the core and foundation of the modern electronic information industry, and microelectronic packaging and interconnection are the most critical and challenging aspects of integrated circuit manufacturing. Flip-chip packaging, with its superior performance, has become one of the mainstream microelectronic packaging technologies and is receiving increasing attention and research. However, with the development of ultra-fine pitch and high density solder joints in flip-chips, along with the introduction of Low-K materials and the requirement for lead-free packaging materials, the thermal / stress mismatch at the bonding sites of flip-chips is becoming more severe, making them more prone to manufacturing defects and fatigue failure. Therefore, non-destructive testing of flip-chips is crucial to ensure chip reliability.

[0003] When defects occur in flip-chip bonding, structural characteristics change, solder joint constraints decrease, and amplitude increases. The resonant frequency contains more defect information, and energy shifts, making resonant frequency band extraction essential. In recent years, numerous adaptive signal decomposition algorithms have been proposed. HUANG et al. proposed the Empirical Mode Decomposition (EMD) algorithm, which recursively decomposes the fault signal into multiple IMF components. However, EMD suffers from mode aliasing, over-decomposition, and under-decomposition problems. Variational Mode Decomposition (VMD) employs a non-recursive decomposition method, reducing mode aliasing caused by recursive decomposition in EMD. Furthermore, VMD possesses adaptive filtering characteristics. However, the selection of VMD algorithm parameters can affect decomposition accuracy.

[0004] It should be noted that the information disclosed in the background section above is only used to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0005] Therefore, the technical problem to be solved by the present invention is to overcome the problem that the existing technology has limited defect feature information for flip-chip obtained by single-frequency ultrasonic excitation, which makes it difficult to detect typical defects.

[0006] To address the aforementioned technical problems, a first aspect of the present invention provides a chip defect detection method based on resonant band energy entropy, the method comprising: acquiring a sample chip;

[0007] Vibration signals are obtained from the sample chip using an air-coupled ultrasonic transducer and a Doppler laser vibrometer.

[0008] The vibration signal is decomposed into a wavelet packet to generate a low-frequency vibration signal;

[0009] A low-frequency vibration signal spectrum is generated based on the low-frequency vibration signal.

[0010] The spectrum of the low-frequency vibration signal is divided into modes, initial center frequency, and penalty factor according to the Teager energy operator and the multiple extremum segmentation method.

[0011] The modal number, the penalty factor, and the initial center frequency are input into the improved VMD algorithm to generate the low-frequency resonant band energy entropy;

[0012] Based on the detection results of the low-frequency resonant band energy entropy generation defect.

[0013] In one embodiment of the present invention, the step of acquiring vibration signals from the sample chip using an air-coupled ultrasonic transducer and a Doppler laser vibrometer includes:

[0014] The sample chip is excited by the air-coupled ultrasonic transducer to generate an excited sample chip;

[0015] The vibration signal is generated by measuring the vibration on the surface of the excitation sample chip using a Doppler laser vibrometer.

[0016] In one embodiment of the present invention, the step of performing wavelet packet decomposition on the vibration signal to generate a low-frequency vibration signal includes:

[0017] The vibration signal is decomposed into sub-signals by a three-level decomposition using a wavelet packet decomposition algorithm.

[0018] The sub-signal is reconstructed to generate a low-frequency vibration signal.

[0019] In one embodiment of the present invention, the step of dividing the spectrum of the low-frequency vibration signal according to the Teager energy operator and the multiple extremum segmentation method to generate the number of modes, the initial center frequency, and the penalty factor includes:

[0020] The low-frequency vibration signal spectrum is converted into an energy spectrum according to the Teager energy operator;

[0021] Extract the energy envelope from the energy spectrum according to the Hibernate transform;

[0022] The energy envelope is smoothed by using a sliding filter to generate a smooth energy envelope;

[0023] The smooth energy envelope is processed according to the multiple extremum segmentation method to generate the initial center frequency, the number of modes, and the penalty factor.

[0024] In one embodiment of the present invention, the step of processing the smooth energy envelope according to the multiple extremum segmentation method to generate the initial center frequency, the number of modes, and the penalty factor includes:

[0025] Extreme values ​​are extracted from the smooth energy envelope; the extreme values ​​include the initial maximum, the initial maximum point, the initial minimum, and the initial minimum point.

[0026] Iterate through the initial minimum points and calculate the number of initial maximum points between adjacent initial minimum points;

[0027] Calculate the spacing between adjacent initial maxima to generate the first spacing;

[0028] Based on the first interval, the initial maximum value and the initial maximum value point are filtered to generate the maximum value and the maximum value point;

[0029] The initial center frequency is generated based on the maximum point;

[0030] Calculate the spacing between adjacent initial minima to generate a second spacing;

[0031] The initial minimum value and the initial minimum point are filtered according to the second interval to generate the minimum value and the minimum point;

[0032] The modality number and the penalty factor are generated based on the minimum point.

[0033] In one embodiment of the present invention, the step of generating a low-frequency vibration signal spectrum based on the low-frequency vibration signal includes:

[0034] The low-frequency vibration signal is subjected to Fourier transform to generate the low-frequency vibration signal spectrum.

[0035] In one embodiment of the present invention, the step of inputting the mode number, the penalty factor, and the initial center frequency into the improved VMD algorithm to generate the low-frequency resonant band energy entropy includes:

[0036] The modality number, the penalty factor, and the initial center frequency are input into the improved VMD algorithm to generate IMF components;

[0037] Calculate the energy based on the IMF components;

[0038] Based on Shannon entropy theory, the IMF components and energies are used to calculate the energy entropy of the low-frequency resonant band.

[0039] A second aspect of the present invention provides a chip defect detection system based on resonant band energy entropy, applied to a method proposed in any one of the first aspects above, the system comprising: a data acquisition module, a signal conversion module, and a detection module;

[0040] The data acquisition module is configured to: acquire a sample chip; acquire vibration signals from the sample chip using an air-coupled ultrasonic transducer and a Doppler laser vibrometer; perform wavelet packet decomposition on the vibration signals to generate low-frequency vibration signals; and generate a low-frequency vibration signal spectrum based on the low-frequency vibration signals.

[0041] The signal conversion module is configured to divide the spectrum of the low-frequency vibration signal according to the Teager energy operator and the multiple extremum segmentation method to generate the number of modes, the initial center frequency and the penalty factor;

[0042] The detection module is configured to: input the modality number, the penalty factor, and the initial center frequency into the improved VMD algorithm to generate the low-frequency resonant band energy entropy; and generate defect detection results based on the low-frequency resonant band energy entropy.

[0043] A third aspect of the present invention provides an electronic device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the program, implements the method described in the first aspect or any possible implementation thereof.

[0044] A fourth aspect of the present invention provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method described in the first aspect or any possible implementation thereof.

[0045] The technical solution of the present invention has the following advantages compared with the prior art:

[0046] This invention discloses a chip defect detection method and system based on resonant band energy entropy. It decomposes the low-frequency vibration signal of a flip-chip using an improved VMD algorithm and obtains the resonant band energy entropy from the decomposed resonant band. Based on the range of the resonant band energy entropy of the low-frequency vibration signal under four states of the flip-chip, solder joint defects can be identified quickly and efficiently. Furthermore, a combination of the Teager energy operator and multiple extremum segmentation is employed to quickly and accurately determine the number of modes, the initial center frequency of each modal component, and the penalty factor. This not only solves problems such as mode aliasing but also achieves precise signal decomposition, fully leveraging the advantages of the VMD algorithm. Attached Figure Description

[0047] To make the content of this invention easier to understand, the invention will be further described in detail below with reference to specific embodiments and accompanying drawings.

[0048] Figure 1This is a flowchart of a chip defect detection method and system based on resonant band energy entropy provided by the present invention;

[0049] Figure 2 This invention provides a chip defect detection method and system based on resonant band energy entropy, and a line graph showing the resonant band energy entropy value.

[0050] Figure 3 This is a system architecture diagram of a chip defect detection method and system based on resonant frequency band energy entropy provided by the present invention. Detailed Implementation

[0051] 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 and implement the present invention. However, the embodiments described are not intended to limit the present invention.

[0052] Furthermore, the described embodiments are merely some, not all, of the embodiments of this application. The components of the embodiments of this application described and illustrated herein can typically be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of the application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0053] Reference Figure 1 As shown, this invention provides a chip defect detection method based on resonant frequency band energy entropy, the method comprising:

[0054] S100, acquire sample chip;

[0055] In step S100, the sample chip exhibits various flip-chip solder joint defects, including missing solder joints, cold solder joints, and solder joint cracks. A Pac Tech Pac2.1 test chip was used as the sample chip. The bare chip measures 10mm × 10mm × 0.52mm, with 184 solder joints evenly distributed on its lower surface. Each solder joint is made of SnAg3Cu0.5 alloy, has a diameter of 120µm, and a spacing of 200µm. With the aid of an MC001-YR2010, some solder joints and pads were manually removed using fine-tipped tweezers to identify two typical defects: cold solder joints and missing solder joints. The locations of these defects were then marked. To identify solder joint cracks, thermal cycling experiments were conducted on the sample chip using ETS Medium-to-Large Thermal Shock Chambers, and the solder joint condition was observed using a scanning acoustic microscope. Using a multi-purpose micro-sub-meter placement machine from Finetech in Germany, the sample chips were soldered onto the substrate using reflow soldering. The substrate was a PacTech PCB310 organic solder mask laminate.

[0056] S200, vibration signals are acquired from the sample chip using an air-coupled ultrasonic transducer and a Doppler laser vibrometer;

[0057] In step S200, the step of obtaining vibration signals from the sample chip using an air-coupled ultrasonic transducer and a Doppler laser vibrometer includes: exciting the sample chip using the air-coupled ultrasonic transducer to generate an excited sample chip; and measuring the vibration on the surface of the excited sample chip using a Doppler laser vibrometer to generate the vibration signal.

[0058] In practical applications, sample chips with solder joint defects are placed on an air-floating vibration isolation platform. A signal generator produces a scanning voltage signal with a frequency of 0-640 kHz, which is then amplified by a power amplifier to increase its energy. The amplified signal is input to an air-coupled capacitive ultrasonic transducer to convert it into ultrasonic waves, which are emitted at a 45° incident angle onto the center of the flip-chip surface, fully exciting it and causing it to vibrate. This vibrating chip is designated as the excited sample chip. A Doppler laser vibrometer is used to measure the vibration of the excited sample chip at a sampling frequency of 1.28 MHz to obtain the vibration signal data of the chip under test.

[0059] S300, perform wavelet packet decomposition on the vibration signal to generate a low-frequency vibration signal;

[0060] In step S300, the step of performing wavelet packet decomposition on the vibration signal to generate a low-frequency vibration signal includes: performing three-level decomposition on the vibration signal using a wavelet packet decomposition algorithm to generate sub-signals; and reconstructing the sub-signals to generate a low-frequency vibration signal.

[0061] In practical applications, the wavelet packet decomposition algorithm is used to perform three-level decomposition on the vibration signal of the sample chip. After decomposition, eight sub-signals are obtained. The first three sub-signals of the eight sub-signals are reconstructed to obtain the low-frequency vibration signal of the sample chip.

[0062] S400, generate a low-frequency vibration signal spectrum based on the low-frequency vibration signal;

[0063] In step S400, the step of generating a low-frequency vibration signal spectrum based on the low-frequency vibration signal includes: performing a Fourier transform on the low-frequency vibration signal to generate a low-frequency vibration signal spectrum.

[0064] In practical applications, the low-frequency vibration signal of the sample chip is converted into a low-frequency vibration signal spectrum through Fourier transform.

[0065] S500, the low-frequency vibration signal spectrum is divided according to the Teager energy operator and the multiple extremum segmentation method to generate the number of modes, the initial center frequency and the penalty factor;

[0066] In step S500, the step of dividing the low-frequency vibration signal spectrum according to the Teager energy operator and the multiple extremum segmentation method to generate the number of modes, the initial center frequency, and the penalty factor includes: converting the low-frequency vibration signal spectrum into an energy spectrum according to the Teager energy operator; extracting the energy envelope from the energy spectrum according to the Hibernate transform; smoothing the energy envelope using a sliding filter to generate a smooth energy envelope; and processing the smooth energy envelope according to the multiple extremum segmentation method to generate the initial center frequency, the number of modes, and the penalty factor.

[0067] The steps of processing the smooth energy envelope according to the multiple extremum segmentation method to generate the initial center frequency, the number of modes, and the penalty factor include: extracting extrema from the smooth energy envelope; the extrema include initial maxima, initial maximum points, initial minimums, and initial minimum points; traversing the initial minimum points and calculating the number of initial maximum points between adjacent initial minimum points; calculating the spacing between adjacent initial maxima to generate a first spacing; filtering the initial maxima and initial maximum points according to the first spacing to generate maxima and maximum points; generating the initial center frequency according to the maximum points; calculating the spacing between adjacent initial minimums to generate a second spacing; filtering the initial minimums and initial minimum points according to the second spacing to generate minimums and minimum points; and generating the number of modes and the penalty factor according to the minimum points.

[0068] In practical applications, the discrete Teager energy operator is used to convert the low-frequency vibration signal spectrum x(n) into an energy spectrum Ψ for energy concentration, thereby reducing the influence of irrelevant factors and highlighting the characteristic information of the resonant frequency band. The energy spectrum Ψ is shown in equation (1):

[0069] Ψ[x(n)]=[x(n)] 2 -x(n-1)x(n+1) (1);

[0070] Where x(n) is the spectrum of the low-frequency vibration signal. To facilitate the acquisition of effective segmentation points and local maximum peak points, Hilbert transform is used to extract the energy envelope of the sample chip's vibration signal energy spectrum. The energy envelope is then smoothed using a sliding filter with a sliding step size of 100 to obtain a smooth energy envelope. The smooth energy envelope is then processed using a multiple extremum segmentation method to obtain local maximum peak points and effective segmentation points. The frequency corresponding to the local maximum peak point is used as the center frequency, and the frequency corresponding to the effective segmentation point is used as the segmentation boundary to segment the spectrum of the sample chip's low-frequency vibration signal. Three extremum extractions are performed on the smooth energy envelope to obtain more than a dozen initial maxima, initial maximum points, initial minimum points, and initial minimum points.

[0071] The algorithm iterates through the initial minimum points, calculating the number of initial maximum points between adjacent initial minimum points. If the number of initial maximum points is zero, the first initial minimum point on the right is discarded, and the iteration and calculation are repeated. If the number of initial maximum points is one, no changes are made, and the iteration and calculation continue. If the number of maximum points is more than one, dummy initial minimum points are inserted between adjacent initial maximum points, and the iteration and calculation are repeated.

[0072] Based on the characteristics of the smoothed energy envelope of the sample chip, the distance between adjacent initial maxima is calculated to generate a first gap. An appropriate threshold is set according to the power supply frequency of the sample chip, and the magnitudes of the corresponding maxima are compared. Then, the required maxima and maxima points are selected based on the first gap. Similarly, based on the characteristics of the smoothed energy envelope of the sample chip, the distance between adjacent initial minima is calculated to generate a second gap. An appropriate threshold is set according to the power supply frequency of the sample chip, and the magnitudes of the corresponding minima are compared. Then, the required minima and minima points are selected based on the generated second gap.

[0073] The maximum point is the frequency corresponding to the local maximum peak point, which is used to solve for the initial center frequency. The minimum point is the frequency corresponding to the effective segmentation point, which is used to divide the frequency band and obtain the frequency band division information. Based on the frequency band division information, the initial center frequency and penalty factor corresponding to each eigenmode function are estimated. The formula for calculating the initial center frequency is shown in equation (2):

[0074]

[0075] Among them, f k f represents the frequency corresponding to the local maximum peak point in the k-th frequency band. s Indicates the sampling frequency.

[0076] The formula for calculating the penalty factor is shown in equation (3):

[0077]

[0078] Among them, f kw f represents the bandwidth of the k-th frequency band. s Indicates the sampling frequency.

[0079] S600, the modal number, the penalty factor and the initial center frequency are input into the improved VMD algorithm to generate the low-frequency resonant band energy entropy;

[0080] In step S600, the step of inputting the mode number, the penalty factor, and the initial center frequency into the improved VMD algorithm to generate the low-frequency resonant band energy entropy includes: inputting the mode number, the penalty factor, and the initial center frequency into the improved VMD algorithm to generate IMF components; calculating the energy sum based on the IMF components; and calculating the low-frequency resonant band energy entropy based on Shannon entropy theory, the IMF components, and the energy sum.

[0081] In practical applications, to improve the adaptability of the VMD algorithm, the traditional VMD algorithm is improved by changing the single-value inputs of the penalty factor and initial center frequency to matrix inputs, resulting in an improved VMD algorithm. This improved algorithm can input the corresponding parameters of each modal component of the sample chip's vibration signal. The obtained modal number, penalty factor, and initial center frequency are input into the improved VMD algorithm to decompose the low-frequency vibration signal of the sample chip, obtaining the low-frequency resonant band energy entropy of the sample chip. Specifically, the improved VMD algorithm decomposes the vibration signal of the flip-chip sample into n IMF components, where the energy contained in each IMF component is:

[0082] E i =∫|u i (t)| 2 dt (4);

[0083] Where i = 1, 2, 3...n, therefore the sum of the energies E of the n IMF components is:

[0084]

[0085] According to Shannon entropy theory, the low-frequency resonant band energy entropy of the sample chip can be expressed as:

[0086]

[0087] Where, p i p represents the ratio of the energy of the i-th IMF component to the total energy. i =E i / E.

[0088] like Figure 2 As shown, the improved VMD algorithm is used to analyze the low-frequency vibration signals of flip-chips under four states in sequence, and the energy entropy of the vibration signals under each state is calculated.

[0089] S700, based on the energy entropy of the low-frequency resonant band, generates the defect detection result.

[0090] In step S700, the defect detection results of the flip-chip can be accurately obtained based on the obtained low-frequency resonant band energy entropy. This method can accurately distinguish the four states of flip-chips and effectively identify and locate solder joint defects. Unlike traditional manual visual inspection, this method has a higher degree of automation, better objectivity and accuracy of the detection results, providing a method for achieving rapid and efficient flip-chip solder joint defect detection.

[0091] Secondly, referring to Figure 3 As shown, this application provides a chip defect detection system based on resonant frequency band energy entropy, the system comprising: a data acquisition module 100, a signal conversion module 200, and a detection module 300;

[0092] The data acquisition module 100 is configured to: acquire a sample chip; acquire vibration signals from the sample chip based on an air-coupled ultrasonic transducer and a Doppler laser vibrometer; perform wavelet packet decomposition on the vibration signals to generate low-frequency vibration signals; and generate a low-frequency vibration signal spectrum based on the low-frequency vibration signals.

[0093] The signal conversion module 200 is configured to divide the spectrum of the low-frequency vibration signal according to the Teager energy operator and the multiple extremum segmentation method to generate the number of modes, the initial center frequency and the penalty factor;

[0094] The detection module 300 is configured to: input the modality number, the penalty factor, and the initial center frequency into the improved VMD algorithm to generate the low-frequency resonant band energy entropy; and generate defect detection results based on the low-frequency resonant band energy entropy.

[0095] The effects of applying the aforementioned method in the above system can be found in the description of the aforementioned method embodiments, and will not be repeated here.

[0096] A third aspect of the present invention provides an electronic device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the program, implements the method described in the first aspect or any possible implementation thereof.

[0097] A fourth aspect of the present invention provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method described in the first aspect or any possible implementation thereof.

[0098] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0099] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0100] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0101] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0102] Obviously, the above embodiments are merely illustrative examples for clear explanation and are not intended to limit the implementation. Those skilled in the art will recognize that other variations or modifications can be made based on the above description. It is neither necessary nor possible to exhaustively list all possible implementations here. However, obvious variations or modifications derived therefrom are still within the scope of protection of this invention.

Claims

1. A chip defect detection method based on resonant frequency band energy entropy, characterized in that, The method includes: Obtain sample chips; Vibration signals are obtained from the sample chip using an air-coupled ultrasonic transducer and a Doppler laser vibrometer. The vibration signal is decomposed into a wavelet packet to generate a low-frequency vibration signal; A low-frequency vibration signal spectrum is generated based on the low-frequency vibration signal. The spectrum of the low-frequency vibration signal is divided into modes, initial center frequency, and penalty factor according to the Teager energy operator and the multiple extremum segmentation method. The modal number, the penalty factor, and the initial center frequency are input into the improved VMD algorithm to generate the low-frequency resonant band energy entropy; Based on the detection results of the low-frequency resonant band energy entropy generation defect; The low-frequency vibration signal spectrum is divided into modes, an initial center frequency, and a penalty factor according to the Teager energy operator and the multiple extremum segmentation method. This includes: converting the low-frequency vibration signal spectrum into an energy spectrum using the Teager energy operator; extracting an energy envelope from the energy spectrum using the Hibernate transform; smoothing the energy envelope using a sliding filter to generate a smooth energy envelope; and processing the smooth energy envelope using the multiple extremum segmentation method to generate the initial center frequency, the number of modes, and the penalty factor. The smooth energy envelope is processed according to the multiple extremum segmentation method to generate the initial center frequency, the number of modes, and the penalty factor. This includes: extracting extrema from the smooth energy envelope; the extrema include initial maxima, initial maximum points, initial minimums, and initial minimum points; traversing the initial minimum points and calculating the number of initial maximum points between adjacent initial minimum points; calculating the spacing between adjacent initial maxima to generate a first spacing; filtering the initial maxima and initial maximum points according to the first spacing to generate maxima and maximum points; generating the initial center frequency based on the maximum points; calculating the spacing between adjacent initial minimums to generate a second spacing; filtering the initial minimums and initial minimum points according to the second spacing to generate minimums and minimum points; and generating the number of modes and the penalty factor based on the minimum points. The process of inputting the mode number, the penalty factor, and the initial center frequency into the improved VMD algorithm to generate the low-frequency resonant band energy entropy includes: inputting the mode number, the penalty factor, and the initial center frequency into the improved VMD algorithm to generate IMF components; calculating the energy sum based on the IMF components; and calculating the low-frequency resonant band energy entropy based on Shannon entropy theory, the IMF components, and the energy sum.

2. The chip defect detection method based on resonant band energy entropy according to claim 1, characterized in that, The steps for acquiring vibration signals from the sample chip using an air-coupled ultrasonic transducer and a Doppler laser vibrometer include: The sample chip is excited by the air-coupled ultrasonic transducer to generate an excited sample chip; The vibration signal is generated by measuring the vibration on the surface of the excitation sample chip using a Doppler laser vibrometer.

3. The chip defect detection method based on resonant band energy entropy according to claim 1, characterized in that, The steps of performing wavelet packet decomposition on the vibration signal to generate a low-frequency vibration signal include: The vibration signal is decomposed into sub-signals by a three-level decomposition using a wavelet packet decomposition algorithm. The sub-signal is reconstructed to generate a low-frequency vibration signal.

4. The chip defect detection method based on resonant band energy entropy according to claim 1, characterized in that, The steps for generating a low-frequency vibration signal spectrum based on the low-frequency vibration signal include: The low-frequency vibration signal is subjected to Fourier transform to generate the low-frequency vibration signal spectrum.

5. A chip defect detection system based on resonant frequency band energy entropy, characterized in that, The chip defect detection method based on resonant band energy entropy according to any one of claims 1 to 4, the system includes: a data acquisition module, a signal conversion module and a detection module; The data acquisition module is configured to: acquire a sample chip; acquire vibration signals from the sample chip using an air-coupled ultrasonic transducer and a Doppler laser vibrometer; perform wavelet packet decomposition on the vibration signals to generate low-frequency vibration signals; and generate a low-frequency vibration signal spectrum based on the low-frequency vibration signals. The signal conversion module is configured to divide the spectrum of the low-frequency vibration signal according to the Teager energy operator and the multiple extremum segmentation method to generate the number of modes, the initial center frequency and the penalty factor; The detection module is configured to: input the modality number, the penalty factor, and the initial center frequency into the improved VMD algorithm to generate the low-frequency resonant band energy entropy; and generate defect detection results based on the low-frequency resonant band energy entropy.

6. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps of the chip defect detection method based on resonant band energy entropy as described in any one of claims 1 to 4.

7. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the steps of a chip defect detection method based on resonant band energy entropy as described in any one of claims 1 to 4.

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