MEMS in-plane motion detection method and device, detection terminal and storage medium

Abstract

The invention provides an MEMS in-plane motion detection method and device, a detection terminal and a storage medium. The method comprises the following steps: acquiring a plurality of continuous vibration images of the MEMS device; for each vibration image, defining an ROR region and an ROI region of the device to obtain a marked vibration image; the marked vibration images form an image set; processing the image set by adopting an SOFI algorithm to obtain a plurality of primary microscopic images; and performing motion amplification on each primary microscopic image to obtain a plurality of images after motion amplitude amplification. According to the method, the ROR region and the ROI region are defined, the motion is detected in combination with the SOFI algorithm, the motion obtained through detection is amplified for the second time, and amplification visualization of the sub-nanometer displacement is achieved.

Description

Technical Field

[0001] The present invention relates to the field of non-contact measurement technology, and in particular to a MEMS in-plane motion detection method, device, detection terminal and storage medium. Background Art

[0002] Micro-Electro-Mechanical Systems (MEMS) are widely used in fields such as sensing, actuation and wireless communication, and are developing rapidly and widely used. Advances in semiconductor technology and micromachining have produced a series of new technologies, including MEMS design, manufacturing, experimentation and testing. High-resolution dynamic motion imaging is a necessary condition for testing and developing MEMS devices, but it is difficult to accurately image their dynamic response due to their ultra-high frequencies and sharp small movements.

[0003] In the prior art, laser interferometry is usually used to detect in-plane motion, but laser interferometry has been widely used for out-of-plane motion due to its wide frequency range (>3GHz) and sub-picometer accuracy (~70pm), as well as its advantages of non-contact and accessibility. Although out-of-plane motion can be measured with extremely high accuracy, high-frequency in-plane motion can only be imaged by stroboscopic photography technology and then image processed, resulting in a resolution much lower than out-of-plane motion, affecting the detection accuracy of MEMS in-plane motion imaging. Summary of the invention

[0004] The embodiments of the present invention provide a MEMS in-plane motion detection method, device, detection terminal and storage medium to solve the problem of detection accuracy of in-plane motion imaging by laser interferometry.

[0005] In a first aspect, an embodiment of the present invention provides a method for detecting MEMS in-plane motion, comprising:

[0006] Acquire multiple continuous vibration images of MEMS devices;

[0007] For each vibration image, define the ROR region and ROI region of the device to obtain a marked vibration image; each marked vibration image forms an image set;

[0008] The image set was processed using the SOFI algorithm to obtain multiple primary microscopic images;

[0009] Each primary microscopic image is motion-amplified to obtain a plurality of images with amplified motion amplitudes.

[0010] In a second aspect, an embodiment of the present invention provides a device for detecting a MEMS in-plane motion, comprising:

[0011] An image acquisition module, used to acquire multiple continuous vibration images of the MEMS device;

[0012] A labeling module is used to define the ROR area and the ROI area of ​​the device for each vibration image to obtain a labeled vibration image; each labeled vibration image forms an image set;

[0013] A motion detection module, used to process the image set using the SOFI algorithm to obtain a plurality of primary microscopic images;

[0014] The amplification module is used to perform motion amplification on each primary microscopic image to obtain multiple images with amplified motion amplitudes.

[0015] In a third aspect, an embodiment of the present invention provides a detection terminal, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the method for detecting MEMS in-plane motion provided in the first aspect or any possible implementation of the first aspect are implemented.

[0016] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the steps of the method for detecting MEMS in-plane motion provided in the first aspect or any possible implementation method of the first aspect.

[0017] The embodiments of the present invention provide a method, device, detection terminal and storage medium for detecting motion in a MEMS plane. The above-mentioned method for detecting motion in a MEMS plane includes: acquiring multiple continuous vibration images of a MEMS device; for each vibration image, defining the ROR area and ROI area of ​​the device to obtain a marked vibration image; each marked vibration image forms an image set; the image set is processed using the SOFI algorithm to obtain multiple primary microscopic images; each primary microscopic image is motion-amplified to obtain multiple images with amplified motion amplitudes. In the present invention, the ROR area and the ROI area are defined, the motion is detected in combination with the SOFI algorithm, and the detected motion is amplified twice, thereby realizing the amplified visualization of sub-nanometer displacement. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0019] Figure 1It is a flow chart of an implementation method of a MEMS in-plane motion detection method provided by an embodiment of the present invention;

[0020] Figure 2 is a vibration image of a MEMS device provided by an embodiment of the present invention;

[0021] Figure 3 yes Figure 2 The vibration image shown is the image after the ROI and ROR areas are marked;

[0022] Figure 4 is a displacement map spectrum diagram output by the SOFI algorithm provided in an embodiment of the present invention;

[0023] Figure 5 is a voltage waveform diagram of an excitation source provided in an embodiment of the present invention;

[0024] Figure 6 is a comparison chart of the detection method provided by the embodiment of the present invention and the vibration amplitude obtained by Polytec;

[0025] Figure 7 It is a comparison diagram of the amplitude and SNR detected by SOFI in the ROI area and the POR area provided by the embodiment of the present invention;

[0026] Figure 8 It is a structural schematic diagram of a MEMS in-plane motion detection device provided by an embodiment of the present invention;

[0027] Fig. 9 is a schematic diagram of a detection terminal provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0028] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present invention. However, it should be clear to those skilled in the art that the present invention may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present invention.

[0029] In order to make the purpose, technical solutions and advantages of the present invention more clear, specific embodiments will be described below in conjunction with the accompanying drawings.

[0030] See also Figure 1 , which shows a flow chart of a method for detecting in-plane motion of MEMS provided by an embodiment of the present invention, which is described in detail as follows:

[0031] The above-mentioned method for detecting the in-plane motion of MEMS includes:

[0032] S101: Acquire multiple continuous vibration images of the MEMS device;

[0033] The detection method provided by the embodiment of the present invention is mainly for ultra-high frequency and rapid small motion. The in-plane motion can be recorded in a Polytec MSA-600-V microsystem analyzer mounted on a vibration isolation table (ZVP10-08) using a stroboscopic video microscope. The image is captured by a bright field objective (A-MOB-050X) with a magnification of 50× and a total field of view of 200×150 (um×um).

[0034] Based on the stroboscopic photography technology, the vibration video of the high-speed moving DUT is shot. The video can be regarded as a set of continuous images. Each frame in the image set can be regarded as a two-dimensional discrete matrix of size m×n. The value of each matrix represents the intensity of the light signal collected by the pixel: 0~2^n (n is the number of camera parameter bits).

[0035] S102: for each vibration image, define the ROR region and ROI region of the device to obtain a marked vibration image; each marked vibration image forms an image set;

[0036] refer to Figure 2 Due to its structural characteristics, the device surface and the substrate have different reflective abilities to light (the substrate is darker than the device); based on this, refer to Figure 3 , define the ROR region (Region of Reference) and the ROI region (Region of Interest), where the region of interest (ROI) is configured to contain motion components and the reference region (ROR) is defined as a static region to eliminate global noise. Since the displacement of the ROR region can be considered unchanged within a small time scale, it can be considered that the actual change of the pixel is caused by camera and environmental noise. By removing the displacement of the ROR region, the algorithm's sensitivity to noise can be reduced, which is of great significance for measuring displacement at the pm level.

[0037] Based on this, this application defines ROI and ROR to reduce global noise.

[0038] S103: using the SOFI algorithm to process the image set to obtain a plurality of primary microscopic images;

[0039] Optical flow methods provide the most direct path from the optical signal to the only path, and have superior sensitivity, accuracy, and computational complexity when detecting devices with clear boundaries.

[0040] In the embodiment of the present invention, based on the traditional optical flow method, the spectral optical flow method SOFI (Spectral Optical Flow Imaging) is introduced to achieve low-noise and high-precision measurement of ultra-high frequency rapid smile vibrations.

[0041] After the SOFI algorithm, the spectrum and displacement analysis results of the device can be obtained. Figure 4 .

[0042] It should be noted that the contrast of the field of view can be adjusted according to the size and structural characteristics of the device, so that the substrate and the device have a more obvious light gradient. Define the size of the subset and the number of subsets.

[0043] S104: performing motion amplification on each primary microscopic image to obtain a plurality of images with amplified motion amplitudes.

[0044] For example, in a 200×140 (um×um) field of view, the naked eye cannot observe motion below 1um due to scale limitations, and Polytec cannot observe it either. However, the motion magnification algorithm can magnify the motion obtained by our algorithm by 800 times without distortion, so that the tiny motion observed by the naked eye can be seen.

[0045] The embodiment of the present invention defines ROI and ROR to reduce global noise, adopts SOFI for motion recognition, and performs secondary amplification, which can achieve low-noise and high-precision measurement of ultra-high frequency sharp and tiny vibrations with better robustness.

[0046] In a possible implementation, the calculation formula of the SOFI algorithm may include:

[0047]

[0048] s ROI =s RAW -s ROR

[0049] Among them, (x, y) is the coordinate of the detected point, t is the time, L0 and L A are the constant and the resonance in the spectral component of interest, ω l is the resonant frequency of the spectral component, is the light intensity gradient, I((x,y),L0) is the reference image, is the brightness scale, is the spectral component of the displacement.

[0050] The reflection intensity P(x,y) of each point for light intensity L is the light intensity r((x,y),L)

[0051] r((x,y),L)=L×P(x,y)

[0052] During the exposure time of the camera, the light intensity falling on a single pixel is integrated, which is the intensity value I(x,y) collected by the camera.

[0053]

[0054] When the device moves a small distance, the change in light intensity is approximately

[0055]

[0056] in, is the light intensity gradient.

[0057] Based on the principle of light intensity conservation, assume that the spectral component we are interested in is L(t) = L0 + L A sin(ω l t), the light intensity can be expressed as:

[0058]

[0059] Put both sides at the same time in [0,T e ], we can get

[0060]

[0061] The left side of the equation is the vibration image captured by the camera, I((x,y),L0) is the reference image, is the brightness scale, is the spectral component of the displacement, thus,

[0062]

[0063] Similarly, different spectral components are calculated using this method.

[0064] In the SOFI algorithm, we introduce the concept of subset to improve the resolution of the algorithm. For single pixel displacement calculation, the 2D displacement can be calculated by the equation:

[0065]

[0066] For a subset of N×N,

[0067]

[0068] The subset in the Region of interest (ROR) area will be defined as a fixed point, and the actual motion displacement of the subset in the Region of reference (ROI) area is:

[0069] s ROI =s RAW -s ROR

[0070] In the embodiment of the present invention, a pixel subset of size d*d (d is d pixels) is defined, and the pixel value range of the subset is [0, 255*d], that is, the calculation depth of the algorithm can be increased by d times. Therefore, by selecting a suitable subset size d, while ensuring detection accuracy, smaller movements can be detected.

[0071] In a possible implementation, S104 may include:

[0072] S1041: For any primary microscopic image, obtain the morphological features of the primary microscopic image at different scales; filter the primary microscopic image and enlarge the ROI area; fuse the enlarged image with the primary microscopic image and reconstruct and fuse the image pyramid to obtain an image with amplified motion amplitude corresponding to the primary microscopic image.

[0073] The present application performs Laplacian pyramid processing on each frame of the image to obtain morphological features of different scales, and each scale feature is expressed in the time domain as: a series of time-related pixel points.

[0074] This neural network uses bandpass filtering to achieve more accurate motion extraction and better noise resistance. At the same time, the embodiment of the present invention also performs amplification and image reconstruction to amplify the modes that are difficult to observe visually in the image, present the motion results in a clearer and easier to understand manner, and facilitate modal analysis.

[0075] In a possible implementation, obtaining morphological features of different scales of the primary microscopic image may include:

[0076] 1. Perform Laplace pyramid processing on the primary microscopic image to obtain morphological features of different scales of the primary microscopic image.

[0077] In a possible implementation, filtering the primary microscopic image may include:

[0078] 1. Design a bandpass filter based on the ROI area of ​​the primary microscopic image and perform secondary filtering on the area of ​​no interest.

[0079] In the embodiment of the present invention, secondary filtering is performed to extract signals in a specified frequency band and suppress signals of other frequencies to achieve more accurate motion extraction (for example, the vibration frequency of the device is known to be 50 Hz, but due to the influence of the camera and noise, the algorithm will detect signals of other frequencies. Through FFT, we can only extract the motion component of 50 Hz to remove the influence of other frequency components), so that the detection method provided in the embodiment of the present invention has better noise resistance.

[0080] In a possible implementation, a plurality of continuous vibration images may be acquired by a bright field objective lens with a magnification of 50 and a total field of view of 200×150 (um×um).

[0081] In a possible implementation manner, the vibration image may be an image with 500 frames per vibration cycle.

[0082] The above method is described below in conjunction with specific embodiments.

[0083] refer to Figure 5 , the excitation voltage is 5×5 (Vdc·Vac), and the vibration amplitude is 1340nm, which is identifiable under Polytec. Obviously, the data not only shows a high consistency with Polytec, but also shows a low background noise of 0.087nm and a high accuracy of the detection amplitude of 1334nm.

[0084] In the range where Polytec begins to fail, the detection method provided by the embodiment of the present invention can still detect amplitudes as low as 0.3nm, which is consistent with the theoretical model. Figure 6 The detection method provided by the embodiment of the present invention has a resolution as small as 0.273nm, which is the amplitude detected in the ROI area without applying any voltage. Under small excitations, the signal will deviate slightly from the fitting line, which may be due to the introduction of weak excitations by environmental noise or thermal noise that cannot be completely eliminated. Its noise resistance is demonstrated by using the substrate as a reference during the process. Reference Figure 7 ,The SNR under different voltages shows that when the amplitude is above 1nm, the SNR remains above 20dB, and when the amplitude is at the sub-nanometer scale, the SNR is around 11.36dB. The detection method provided by the embodiment of the present invention ensures a robust capacity that has never been achieved before, making it an ideal choice for sub-nanometer scale motion detection.

[0085] It should be understood that the order of execution of the steps in the above embodiment does not necessarily mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present invention.

[0086] The following is an embodiment of the device of the present invention. For details not described in detail therein, reference may be made to the corresponding method embodiment described above.

[0087] Figure 8 The structure diagram of the detection device of the MEMS plane motion provided by the embodiment of the present invention is shown. For the convenience of description, only the part related to the embodiment of the present invention is shown, which is described in detail as follows:

[0088] like Figure 8 As shown, the detection device for the motion in the MEMS plane includes:

[0089] An image acquisition module 21 is used to acquire multiple continuous vibration images of the MEMS device;

[0090] The labeling module 22 is used to define the ROR area and the ROI area of ​​the device for each vibration image, and obtain a labeled vibration image; each labeled vibration image forms an image set;

[0091] A motion detection module 23, used to process the image set using a SOFI algorithm to obtain a plurality of primary microscopic images;

[0092] The amplification module 24 is used to perform motion amplification on each primary microscopic image to obtain a plurality of images with amplified motion amplitudes.

[0093] In a possible implementation, the calculation formula of the SOFI algorithm may include:

[0094]

[0095] s ROI =s RAW -s ROR

[0096] Among them, (x, y) is the coordinate of the detected point, t is the time, L0 and L A are the constant and the resonance in the spectral component of interest, ω l is the resonant frequency of the spectral component, is the light intensity gradient, I((x,y),L0) is the reference image, is the brightness scale, is the spectral component of the displacement.

[0097] In a possible implementation, the amplification module 24 may include:

[0098] The amplification unit is used to obtain the morphological features of different scales of any primary microscopic image; filter the primary microscopic image and amplify the ROI area; fuse the amplified image with the primary microscopic image and reconstruct and fuse the image pyramid to obtain an image with amplified motion amplitude corresponding to the primary microscopic image.

[0099] In a possible implementation, the amplification unit may include:

[0100] The feature extraction subunit is used to perform Laplacian pyramid processing on the primary microscopic image to obtain morphological features of different scales of the primary microscopic image.

[0101] In a possible implementation, the amplification unit may include:

[0102] The filtering subunit is used to design a bandpass filter based on the ROI region of the primary microscopic image and perform secondary filtering on the region of no interest.

[0103] In a possible implementation, a plurality of continuous vibration images may be acquired by a bright field objective lens with a magnification of 50 and a total field of view of 200×150 (um×um).

[0104] In a possible implementation manner, the vibration image may be an image with 500 frames per vibration cycle.

[0105] Fig. 9 Schematic diagram of a terminal device 3 provided in an embodiment of the present invention. Fig. 9 As shown, the terminal device 3 of this embodiment includes: a processor 30 and a memory 31. The memory 31 is used to store a computer program 32, and the processor 30 is used to call and run the computer program 32 stored in the memory 31 to perform the steps in the above-mentioned various MEMS in-plane motion detection method embodiments, such as Figure 1 Alternatively, the processor 30 is used to call and run the computer program 32 stored in the memory 31 to implement the functions of each module / unit in the above-mentioned device embodiments, such as Figure 8 The functions of modules 21 to 24 are shown.

[0106] Exemplarily, the computer program 32 may be divided into one or more modules / units, one or more modules / units are stored in the memory 31, and are executed by the processor 30 to implement the present invention. One or more modules / units may be a series of computer program instruction segments that can implement specific functions, and the instruction segments are used to describe the execution process of the computer program 32 in the terminal device 3. For example, the computer program 32 may be divided into Figure 8 Modules / units 21 to 24 are shown.

[0107] The terminal device 3 may be a computing device such as a desktop computer, a notebook, a PDA, or a cloud server. The terminal device 3 may include, but is not limited to, a processor 30 and a memory 31. Those skilled in the art will appreciate that Fig. 9 It is only an example of terminal device 3 and does not constitute a limitation on terminal device 3. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the terminal may also include input and output devices, network access devices, buses, etc.

[0108] The processor 30 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0109] The memory 31 may be an internal storage unit of the terminal device 3, such as a hard disk or memory of the terminal device 3. The memory 31 may also be an external storage device of the terminal device 3, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the terminal device 3. Further, the memory 31 may also include both an internal storage unit and an external storage device of the terminal device 3. The memory 31 is used to store computer programs and other programs and data required by the terminal. The memory 31 may also be used to temporarily store data that has been output or is to be output.

[0110] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0111] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0112] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0113] In the embodiments provided by the present invention, it should be understood that the disclosed devices / terminals and methods can be implemented in other ways. For example, the device / terminal embodiments described above are only schematic, for example, the division of modules or units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0114] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0115] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0116] If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. Computer-readable media may include: any entity or device capable of carrying computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal and software distribution medium, etc.

[0117] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the protection scope of the present invention.

Claims

1. A method for detecting MEMS in-plane motion, characterized in that: include: Acquire multiple continuous vibration images of MEMS devices; For each vibration image, define the ROR region and ROI region of the device to obtain a marked vibration image; each marked vibration image forms an image set; The image set is processed using a SOFI algorithm to obtain a plurality of primary microscopic images; Each primary microscopic image is motion-amplified to obtain a plurality of images with amplified motion amplitudes.

2. The method for detecting MEMS in-plane motion according to claim 1, characterized in that: The calculation formula of the SOFI algorithm includes: s ROI =s RAW -s ROR Among them, (x, y) is the coordinate of the detected point, t is the time, L0 and L A are the constant and the resonance in the spectral component of interest, ω l is the resonant frequency of the spectral component, is the light intensity gradient, I((x,y),L0) is the reference image, is the brightness scale, is the spectral component of the displacement.

3. The method for detecting MEMS in-plane motion according to claim 1, characterized in that: The step of performing motion amplification on each primary microscopic image to obtain a plurality of images with amplified motion amplitudes includes: For any primary microscopic image, the morphological features of the primary microscopic image at different scales are obtained; the primary microscopic image is filtered and the ROI area is enlarged; the enlarged image is fused with the primary microscopic image and the image pyramid is reconstructed and fused to obtain an image with amplified motion amplitude corresponding to the primary microscopic image.

4. The method for detecting MEMS in-plane motion according to claim 3, characterized in that: The step of obtaining morphological features of different scales of the primary microscopic image comprises: The primary microscopic image is processed by Laplace pyramid to obtain morphological features of different scales of the primary microscopic image.

5. The method for detecting MEMS in-plane motion according to claim 3, characterized in that: The filtering of the primary microscopic image comprises: A bandpass filter is designed based on the ROI region of the primary microscopic image, and a secondary filtering is performed on the region of no interest.

6. The method for detecting MEMS in-plane motion according to any one of claims 1 to 5, characterized in that: The multiple continuous vibration images are obtained by a bright field objective lens with a magnification of 50 and a total field of view of 200×150 (um×um).

7. The method for detecting MEMS in-plane motion according to any one of claims 1 to 5, characterized in that: The vibration image is an image with 500 frames per vibration cycle.

8. A device for detecting MEMS in-plane motion, characterized in that: include: An image acquisition module, used to acquire multiple continuous vibration images of the MEMS device; A labeling module is used to define the ROR area and the ROI area of ​​the device for each vibration image to obtain a labeled vibration image; each labeled vibration image forms an image set; A motion detection module, used for processing the image set using a SOFI algorithm to obtain a plurality of primary microscopic images; The amplification module is used to perform motion amplification on each primary microscopic image to obtain multiple images with amplified motion amplitudes.

9. A detection terminal, characterized in that: The device comprises a processor and a memory, wherein the memory is used to store a computer program, and the processor is used to call and run the computer program stored in the memory to execute the steps of the method for detecting the in-plane motion of MEMS as claimed in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method for detecting the in-plane motion of MEMS as claimed in any one of claims 1 to 7 are implemented.

Images