Method and device for detecting roundness of interference ring of MEMS spectrum chip and electronic equipment

Abstract

The invention relates to the technical field of industrial vision, discloses an interference ring roundness detection method and device of an MEMS spectrum chip and electronic equipment, and aims to solve the problems of low efficiency and poor accuracy of the existing method. The scheme mainly comprises the following steps: detecting a circular scribed line coordinate of a spectrum chip to be detected by using a chip circular scribed line detection model; calculating a circle center coordinate; converting the interference ring image of the spectrum chip to be detected to a polar coordinate, and performing graying processing on the obtained polar coordinate image; the obtained polar coordinate image is preprocessed; sliding window automatic threshold segmentation is carried out on the preprocessed polar coordinate image, and an obtained binary image is negated; fusing the obtained interference ring binary image with the polar coordinate grayscale image to obtain an interference ring grayscale image; and fitting an interference ring extreme value curve according to the interference ring grayscale image, and calculating the roundness of the interference ring according to the interference ring extreme value curve. The method improves the detection efficiency and accuracy, and is suitable for the quality detection of the MEMS spectrum chip.

Description

Technical Field

[0001] The present invention relates to the field of industrial vision technology, and in particular to a method, a device and an electronic device for detecting the roundness of an interference ring of a MEMS spectral chip. Background Art

[0002] MEMS spectroscopic chip is a miniaturized optical device based on micro-electromechanical system technology, which has multiple optical functions such as filtering, beam splitting, polarization, etc. It consists of a miniaturized mechanical structure and an electronic control system, which can process and control optical signals efficiently and accurately. The mechanical structures in MEMS spectroscopic chips mainly include reflectors, beam splitters, filters, etc. These structures are manufactured on the chip through micro-nano processing technology and have tiny sizes and high-precision shapes. When light passes through these structures, it will be affected by reflection, refraction or interference, resulting in different optical effects.

[0003] The interference ring of the MEMS spectroscopy chip is a ring-shaped pattern formed by the interference of light after voltage is applied to the chip. The circularity of the interference ring is an important evaluation parameter in the chip inspection process and one of the important evaluation indicators for measuring the quality of MEMS spectroscopy chips. The rounder the interference ring, the better the quality of the chip.

[0004] In the existing MEMS spectral chip interference ring roundness detection scheme, the interference ring image of the MEMS spectral chip is usually obtained, and then the interference ring shape in the interference ring image is manually observed to determine its roundness. This method is not only inefficient, but also cannot guarantee accuracy due to reliance on manual experience, inconsistent standards, visual fatigue, etc., which can easily cause product quality fluctuations. Summary of the invention

[0005] The present invention aims to solve the problems of low efficiency and poor accuracy in the existing interference ring roundness detection scheme of MEMS spectroscopy chip, and proposes a method, device and electronic equipment for detecting the roundness of the interference ring of MEMS spectroscopy chip.

[0006] The technical solution adopted by the present invention to solve the above technical problems is:

[0007] In a first aspect, the present invention provides a method for detecting the circularity of an interference ring of a MEMS spectroscopy chip, the method comprising:

[0008] Acquire a chip image of the spectral chip to be detected under red light;

[0009] Based on the chip image, the circular scribed line coordinates of the spectral chip to be detected are detected using a pre-trained chip circular scribed line detection model, and the coordinates of the center of the circle are calculated;

[0010] After applying voltage to the spectral chip to be detected, an interference ring image of the spectral chip to be detected is obtained;

[0011] Convert the interference ring image to polar coordinates according to the circle center coordinates to obtain a polar coordinate image, and grayscale the polar coordinate image to obtain a polar coordinate grayscale image;

[0012] Preprocessing the polar coordinate image;

[0013] Perform sliding window automatic threshold segmentation on the preprocessed polar coordinate image to obtain a binary image, and then invert the binary image to obtain a binary image of the interference ring;

[0014] The interference ring binary image is merged with the polar coordinate grayscale image to obtain an interference ring grayscale image;

[0015] An interference ring extreme value curve is fitted according to the interference ring grayscale image, and the interference ring circularity is calculated according to the interference ring extreme value curve.

[0016] Furthermore, the step of obtaining a chip image of the spectral chip to be detected under red light includes:

[0017] A collimated infrared light source is used to illuminate the spectral chip to be detected, and then a microscopic data collector is used to collect the chip image under red light.

[0018] Furthermore, the training method of the chip circular scribe line detection model includes:

[0019] Under a collimated infrared light source, images of each spectral chip sample are collected, and then the collected images of the spectral chip samples are annotated to obtain a training data set. An image processing algorithm model or a deep learning algorithm model is trained based on the training data set to obtain a chip circular line detection model.

[0020] Further, the polar coordinate image is preprocessed, including:

[0021] The polar coordinate image is subjected to image filtering and image enhancement processing, wherein the image filtering method includes a mean filtering method or a Gaussian filtering method; the image enhancement method includes a global enhancement algorithm, a local enhancement algorithm or a deep learning enhancement algorithm.

[0022] Furthermore, the preprocessed polar coordinate image is subjected to sliding window automatic threshold segmentation, including:

[0023] The polar coordinate image is vertically slid with the width of the polar coordinate image as the sliding window radius and the width of the polar coordinate image as the step length. The sliding window image at each step is segmented by OTSU automatic threshold to obtain a binary image.

[0024] Furthermore, the interference ring binary image is fused with the polar coordinate grayscale image, including:

[0025] The interference ring binary image is matrix-multiplied with the polar coordinate grayscale image to obtain the interference ring grayscale image.

[0026] Further, fitting an interference ring extreme value curve according to the interference ring grayscale image includes:

[0027] Determine the grayscale of each pixel in the interference ring grayscale image, determine the minimum grayscale value of each row, record the coordinates of the pixel corresponding to the minimum grayscale value of each row, and then use the least squares method to fit to obtain the interference ring extreme value curve.

[0028] Further, calculating the interference ring roundness according to the interference ring extreme value curve includes:

[0029] After smoothing and filtering the extreme value curve of the interference ring, the variance method is used to calculate the volatility of the extreme value curve of the interference ring. The volatility of the extreme value curve of the interference ring is the circularity of the interference ring. The closer the circularity of the interference ring is to 0, the better the circularity of the interference ring.

[0030] In a second aspect, the present invention provides an interference ring roundness detection device for a MEMS spectroscopy chip, the device comprising:

[0031] An acquisition module is used to obtain a chip image of the spectral chip to be detected under red light; and to obtain an interference ring image of the spectral chip to be detected after applying voltage to the spectral chip to be detected;

[0032] A prediction module, for detecting the circular scribed line coordinates of the spectral chip to be detected based on the chip image and using a pre-trained chip circular scribed line detection model, and calculating the coordinates of the center of the circle;

[0033] A conversion module, used for converting the interference ring image into polar coordinates according to the circle center coordinates to obtain a polar coordinate image, and graying the polar coordinate image to obtain a polar coordinate grayscale image;

[0034] An image processing module is used to preprocess the polar coordinate image; perform sliding window automatic threshold segmentation on the preprocessed polar coordinate image to obtain a binary image, invert the binary image to obtain an interference ring binary image; and fuse the interference ring binary image with the polar coordinate grayscale image to obtain an interference ring grayscale image;

[0035] The calculation module is used to fit the interference ring extreme value curve according to the interference ring grayscale image, and calculate the interference ring circularity according to the interference ring extreme value curve.

[0036] In a third aspect, the present invention provides an electronic device, the electronic device comprising a processor, a memory and a communication bus;

[0037] The communication bus is used to realize the connection and communication between the processor and the memory;

[0038] The processor is used to execute one or more programs in the memory to implement the steps of the interference ring circularity detection method of the MEMS spectroscopy chip as described in the first aspect.

[0039] The beneficial effects of the present invention are as follows: the method, device and electronic device for detecting the circularity of the interference ring of the MEMS spectral chip provided by the present invention detect the circular scribed lines through the circular scribed line detection model of the chip, and calculate the coordinates of the center of the circle, and use the coordinates of the center of the circle to convert the interference ring image to polar coordinates, and then use the sliding window method to obtain the interference ring area, and reflect the circularity of the interference ring according to the fluctuation of the extreme value curve of the interference ring, so as to realize the detection of the circularity of the interference ring of the MEMS spectral chip. Through the above process, the present invention does not require manual observation, and can automatically detect the circularity of the interference ring of the MEMS spectral chip, thereby quantifying the evaluation of the circularity of the interference ring of the spectral chip, and improving the detection efficiency and accuracy. And it can be deployed in the production line in the form of an algorithm module, and can completely replace manpower to realize the automation of the production line. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 A schematic flow chart of a method for detecting the circularity of an interference ring of a MEMS spectroscopy chip provided in an embodiment of the present invention;

[0041] Figure 2 A schematic diagram of an interference ring image provided by an embodiment of the present invention;

[0042] Figure 3 A schematic diagram of a polar coordinate image provided by an embodiment of the present invention;

[0043] Figure 4 A schematic diagram of a binary image provided by an embodiment of the present invention;

[0044] Figure 5 A schematic diagram of the structure of an interference ring roundness detection device for a MEMS spectroscopy chip provided in an embodiment of the present invention;

[0045] Figure 6 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0046] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0047] In some of the processes described in the specification of the present invention and the above-mentioned drawings, multiple operations appearing in a specific order are included, but it should be clearly understood that these operations may not be executed in the order in which they appear in this document or may be executed in parallel, and the sequence numbers of the operations are only used to distinguish different operations, and the sequence numbers themselves do not represent any execution order. In addition, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel.

[0048] The technical solution of the embodiment of the present invention is applicable to the application scenario of quality inspection of MEMS spectral chips. Since the current interference ring roundness inspection solution of MEMS spectral chips is basically to manually observe the interference ring image, the inventors have found through research that this method is inefficient and has poor accuracy due to reliance on manual experience, inconsistent standards, visual fatigue, etc.

[0049] In order to improve the efficiency and accuracy of detection, the technical scheme of the present invention is proposed. In an embodiment of the present invention, a chip image of a spectral chip to be detected under red light is obtained; based on the chip image, the circular line coordinates of the spectral chip to be detected are detected using a pre-trained chip circular line detection model, and the center coordinates are calculated; after applying voltage to the spectral chip to be detected, an interference ring image of the spectral chip to be detected is obtained; according to the center coordinates, the interference ring image is converted to polar coordinates to obtain a polar coordinate image, and the polar coordinate image is gray-scaled to obtain a polar coordinate grayscale image; the polar coordinate image is pre-processed; the pre-processed polar coordinate image is subjected to sliding window automatic threshold segmentation to obtain a binary image, the binary image is inverted to obtain an interference ring binary image; the interference ring binary image is fused with the polar coordinate grayscale image to obtain an interference ring grayscale image; the interference ring extreme value curve is fitted according to the interference ring grayscale image, and the interference ring circularity is calculated according to the interference ring extreme value curve.

[0050] Specifically, the present invention first detects the circular scribed lines of the spectral chip to be detected through a chip circular scribed line detection model and calculates the coordinates of the center of the circle. Then, the interference ring image is converted to polar coordinates using the coordinates of the center of the circle. Then, a sliding window method is used to obtain the interference ring area. Then, the image is inverted, fused, and the like to obtain an interference ring grayscale image. Finally, the interference ring circularity is calculated based on the interference ring extreme value curve, thereby realizing the detection of the interference ring circularity of the MEMS spectral chip and achieving the purpose of improving efficiency and accuracy.

[0051] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0052] Figure 1A method for detecting the circularity of an interference ring of a MEMS spectroscopy chip is shown in FIG. Figure 1 , the method comprises the following steps:

[0053] S1. Obtain a chip image of the spectral chip to be detected under red light.

[0054] In the embodiment of the present invention, a collimated infrared light source is used to illuminate the spectral chip to be detected, and then a microscopic data collector is used to collect the chip image under red light.

[0055] S2. Based on the chip image, the circular scribed line coordinates of the spectral chip to be detected are detected using a pre-trained chip circular scribed line detection model, and the coordinates of the center of the circle are calculated.

[0056] In an embodiment of the present invention, the training method of the chip circular scribe line detection model includes:

[0057] Under a collimated infrared light source, images of each spectral chip sample are collected, and then the collected images of the spectral chip samples are annotated to obtain a training data set. An image processing algorithm model or a deep learning algorithm model is trained based on the training data set to obtain a chip circular line detection model.

[0058] In practical applications, a microscopic data collector can be used to collect images of each spectral chip sample under red light, and then the open source Labelimg annotation tool is used to annotate the circular scribed area of ​​the chip with a rectangular frame. The annotation format is xml, and a training data set is obtained. Finally, the training data set is used to train the image processing algorithm model or the deep learning algorithm model. The embodiment of the present invention uses the YOLO target detection model for training.

[0059] After the circular line detection model of the chip is obtained through training, the chip image of the spectral chip to be detected under red light is input into the circular line detection model to obtain the circular line coordinates of the spectral chip to be detected, and then the center coordinates of the silicon crystal area are calculated based on the circular line coordinates.

[0060] S3. After applying voltage to the spectral chip to be detected, an interference ring image of the spectral chip to be detected is obtained.

[0061] In the embodiment of the present invention, after voltage is applied to the spectrum chip to be detected, the interference ring image of the spectrum chip to be detected under red light is obtained. Similarly, a collimated infrared light source can be used to illuminate the pressured spectrum chip to be detected, and then a microscopic data collector can be used to collect the interference ring image under red light. The obtained interference ring image can be found in Figure 2 .

[0062] S4. Convert the interference ring image to polar coordinates according to the circle center coordinates to obtain a polar coordinate image, and grayscale the polar coordinate image to obtain a polar coordinate grayscale image.

[0063] In practical applications, the center coordinates of the circle are used as the poles of the polar coordinates, and the silicon crystal area of ​​the spectral chip is converted to the corresponding position of the polar coordinates to obtain a polar coordinate image. The obtained polar coordinate image can be found in Figure 3 Then the polar coordinate image is grayed to obtain a polar coordinate grayscale image.

[0064] S5. Preprocessing the polar coordinate image.

[0065] In an embodiment of the present invention, the polar coordinate image is preprocessed, including: performing image filtering and image enhancement processing on the polar coordinate image, the image filtering method includes a mean filtering method or a Gaussian filtering method; the image enhancement method includes a global enhancement algorithm, a local enhancement algorithm or a deep learning enhancement algorithm.

[0066] S6. Perform sliding window automatic threshold segmentation on the preprocessed polar coordinate image to obtain a binary image, and invert the binary image to obtain an interference ring binary image.

[0067] Among them, sliding window automatic threshold segmentation is an image processing method, which is mainly used to automatically determine the segmentation threshold in the image, so as to segment the image into two parts: foreground and background. This method realizes automatic segmentation by moving the sliding window on the image and calculating the threshold in each window. The basic principle of the sliding window method is to define a window of fixed size on the image and then move the window in each direction of the image. In each window, a threshold is calculated according to the distribution of pixel values ​​in the window to segment the foreground and background. By sliding the window, the entire image can be covered, thus realizing automatic segmentation.

[0068] In an embodiment of the present invention, the preprocessed polar coordinate image is subjected to sliding window automatic threshold segmentation, including: using the polar coordinate image width as the sliding window radius and the polar coordinate image width as the step length to perform longitudinal sliding window on the polar coordinate image, performing OTSU automatic threshold segmentation on the sliding window image at each step, and obtaining a binary image. The obtained binary image is shown in FIG. Figure 4 .

[0069] S7, fusing the interference ring binary image with the polar coordinate grayscale image to obtain an interference ring grayscale image.

[0070] In an embodiment of the present invention, the interference ring binary image and the polar coordinate grayscale image are fused, including: performing matrix dot multiplication on the interference ring binary image and the polar coordinate grayscale image to obtain the interference ring grayscale image.

[0071] S8. Fitting an interference ring extreme value curve according to the interference ring grayscale image, and calculating the interference ring circularity according to the interference ring extreme value curve.

[0072] In an embodiment of the present invention, fitting an interference ring extreme value curve according to the interference ring grayscale image includes: determining the grayscale of each pixel in the interference ring grayscale image, determining the minimum grayscale value of each row, recording the coordinates of the pixel points corresponding to the minimum grayscale value of each row, and then using the least squares method for fitting to obtain the interference ring extreme value curve.

[0073] Calculating the circularity of the interference ring according to the extreme value curve of the interference ring includes: after smoothing and filtering the extreme value curve of the interference ring, using the variance method to calculate the volatility of the extreme value curve of the interference ring, the volatility of the extreme value curve of the interference ring is the circularity of the interference ring, and the closer the circularity of the interference ring is to 0, the better the circularity of the interference ring is.

[0074] Specifically, for each interference ring grayscale image area in the interference ring grayscale image, the grayscale minimum value is calculated row by row, the minimum value coordinates of each row are recorded, and the least squares method is used to fit the extreme value coordinates of all rows to form an interference ring extreme value curve. Then the interference ring extreme value curve is smoothed and filtered to reduce the influence of noise on the roundness calculation. The variance method is used to calculate the volatility of the smoothed interference ring extreme value curve, which is the interference ring roundness of the spectral chip to be tested. The closer its value is to 0, the better its roundness and the better the chip quality. Among them, the volatility of the interference ring extreme value curve can be determined by calculating the volatility index, such as standard deviation, variance and root mean square error.

[0075] Based on the above method, the present invention does not require manual observation and can automatically detect the circularity of the MEMS spectral chip interference ring, thereby quantifying the evaluation of the circularity of the spectral chip interference ring, improving the detection efficiency and accuracy. And it can be deployed to the production line in the form of an algorithm module, which can completely replace manpower to achieve production line automation.

[0076] Figure 5 A schematic diagram showing the structure of an interference ring roundness detection device for a MEMS spectral chip is shown in FIG. Figure 5 , the device comprises:

[0077] An acquisition module is used to obtain a chip image of the spectral chip to be detected under red light; and to obtain an interference ring image of the spectral chip to be detected after applying voltage to the spectral chip to be detected;

[0078] A prediction module, for detecting the circular scribed line coordinates of the spectral chip to be detected based on the chip image and using a pre-trained chip circular scribed line detection model, and calculating the coordinates of the center of the circle;

[0079] A conversion module, used for converting the interference ring image into polar coordinates according to the circle center coordinates to obtain a polar coordinate image, and graying the polar coordinate image to obtain a polar coordinate grayscale image;

[0080] An image processing module is used to preprocess the polar coordinate image; perform sliding window automatic threshold segmentation on the preprocessed polar coordinate image to obtain a binary image, invert the binary image to obtain an interference ring binary image; and fuse the interference ring binary image with the polar coordinate grayscale image to obtain an interference ring grayscale image;

[0081] The calculation module is used to fit the interference ring extreme value curve according to the interference ring grayscale image, and calculate the interference ring circularity according to the interference ring extreme value curve.

[0082] It can be understood that since the interference ring roundness detection device for the MEMS spectral chip described in the embodiment of the present invention is a device for implementing the interference ring roundness detection method for the MEMS spectral chip described in the embodiment, for the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method, which will not be repeated here.

[0083] Figure 6 A schematic diagram of the structure of an electronic device is shown. Figure 6 , the electronic device includes a processor, a memory and a communication bus;

[0084] The communication bus is used to realize the connection and communication between the processor and the memory;

[0085] The processor is used to execute one or more programs in the memory to implement the steps of the interference ring circularity detection method of the MEMS spectroscopy chip as described in the embodiment of the present invention.

[0086] It can be understood that since the electronic device described in the embodiment of the present invention is a device for implementing the interference ring roundness detection method of the MEMS spectral chip described in the embodiment, for the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method, which will not be repeated here.

Claims

1. A method for detecting the roundness of an interference ring of a MEMS spectral chip, characterized in that: The method comprises: Acquire a chip image of the spectral chip to be detected under red light; Based on the chip image, the circular scribed line coordinates of the spectral chip to be detected are detected using a pre-trained chip circular scribed line detection model, and the coordinates of the center of the circle are calculated; After applying voltage to the spectral chip to be detected, an interference ring image of the spectral chip to be detected is obtained; Convert the interference ring image to polar coordinates according to the circle center coordinates to obtain a polar coordinate image, and grayscale the polar coordinate image to obtain a polar coordinate grayscale image; Preprocessing the polar coordinate image; Perform sliding window automatic threshold segmentation on the preprocessed polar coordinate image to obtain a binary image, and then invert the binary image to obtain a binary image of the interference ring; The interference ring binary image is merged with the polar coordinate grayscale image to obtain an interference ring grayscale image; An interference ring extreme value curve is fitted according to the interference ring grayscale image, and the interference ring circularity is calculated according to the interference ring extreme value curve.

2. The method for detecting the circularity of the interference ring of the MEMS spectroscopy chip according to claim 1, characterized in that: The step of obtaining a chip image of the spectral chip to be detected under red light comprises: A collimated infrared light source is used to illuminate the spectral chip to be detected, and then a microscopic data collector is used to collect the chip image under red light.

3. The method for detecting the circularity of the interference ring of the MEMS spectroscopy chip according to claim 1, characterized in that: The training method of the chip circular scribe line detection model includes: Under a collimated infrared light source, images of each spectral chip sample are collected, and then the collected images of the spectral chip samples are annotated to obtain a training data set. An image processing algorithm model or a deep learning algorithm model is trained based on the training data set to obtain a chip circular line detection model.

4. The method for detecting the circularity of the interference ring of the MEMS spectroscopy chip according to claim 1, characterized in that: Preprocessing the polar coordinate image includes: The polar coordinate image is subjected to image filtering and image enhancement processing, wherein the image filtering method includes a mean filtering method or a Gaussian filtering method; the image enhancement method includes a global enhancement algorithm, a local enhancement algorithm or a deep learning enhancement algorithm.

5. The method for detecting the circularity of the interference ring of the MEMS spectroscopy chip according to claim 1, characterized in that: Perform sliding window automatic threshold segmentation on the preprocessed polar coordinate image, including: The polar coordinate image is vertically slid with the width of the polar coordinate image as the sliding window radius and the width of the polar coordinate image as the step length. The sliding window image at each step is segmented by OTSU automatic threshold to obtain a binary image.

6. The method for detecting the circularity of the interference ring of the MEMS spectroscopy chip according to claim 1, characterized in that: The interference ring binary image is fused with the polar coordinate grayscale image, including: The interference ring binary image is matrix-multiplied with the polar coordinate grayscale image to obtain the interference ring grayscale image.

7. The method for detecting the circularity of the interference ring of the MEMS spectroscopy chip according to claim 1, characterized in that: Fitting an interference ring extreme value curve according to the interference ring grayscale image includes: Determine the grayscale of each pixel in the interference ring grayscale image, determine the minimum grayscale value of each row, record the coordinates of the pixel corresponding to the minimum grayscale value of each row, and then use the least squares method to fit to obtain the interference ring extreme value curve.

8. The method for detecting the circularity of the interference ring of the MEMS spectroscopy chip according to claim 1, characterized in that: Calculating the circularity of the interference ring according to the interference ring extreme value curve includes: After smoothing and filtering the extreme value curve of the interference ring, the variance method is used to calculate the volatility of the extreme value curve of the interference ring. The volatility of the extreme value curve of the interference ring is the circularity of the interference ring. The closer the circularity of the interference ring is to 0, the better the circularity of the interference ring.

9. An interference ring roundness detection device for a MEMS spectral chip, characterized in that: The device comprises: An acquisition module is used to obtain a chip image of the spectral chip to be detected under red light; and to obtain an interference ring image of the spectral chip to be detected after applying voltage to the spectral chip to be detected; A prediction module, for detecting the circular scribed line coordinates of the spectral chip to be detected based on the chip image and using a pre-trained chip circular scribed line detection model, and calculating the coordinates of the center of the circle; A conversion module, used for converting the interference ring image into polar coordinates according to the circle center coordinates to obtain a polar coordinate image, and graying the polar coordinate image to obtain a polar coordinate grayscale image; An image processing module is used to preprocess the polar coordinate image; perform sliding window automatic threshold segmentation on the preprocessed polar coordinate image to obtain a binary image, invert the binary image to obtain an interference ring binary image; and fuse the interference ring binary image with the polar coordinate grayscale image to obtain an interference ring grayscale image; The calculation module is used to fit the interference ring extreme value curve according to the interference ring grayscale image, and calculate the interference ring circularity according to the interference ring extreme value curve.

10. An electronic device, characterized in that: The electronic device includes a processor, a memory and a communication bus; The communication bus is used to realize the connection and communication between the processor and the memory; The processor is used to execute one or more programs in the memory to implement the steps of the interference ring circularity detection method of the MEMS spectroscopy chip as described in any one of claims 1 to 8.

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