Detection system of silicon optical chip

Abstract

The invention relates to the technical field of optical testing, in particular to a silicon optical chip detection system which comprises a structure visual angle regulation and control module, a reflection anomaly capture module, a direction dislocation verification module, a response distribution balance module and a structure state calibration module. According to the invention, through the extraction of the gray difference value and the continuous brightness change point in the image, and in combination with the kick boundary identification and the tangent plane construction, the precise positioning and the incident direction extraction of the microstructure abrupt change area are realized, the collection direction is rotated to suppress the gray fluctuation, and the optical stability of the edge alignment layer is improved. The method comprises the following steps: acquiring a brightness extension track, identifying a fracture position, screening reflection abnormal points in combination with diffusion amplitude difference, effectively enhancing the identification capability of heterogeneous reflection, arranging a brightness trend, carrying out included angle calculation, extracting a direction continuous offset track, realizing dynamic modeling of directional deviation, calculating an illumination difference value inside and outside a structure, and comparing a contour path. And the judgment precision of boundary response evolution and abnormal contour distribution is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of optical testing, and particularly to a detection system for a silicon photonics chip. Background Art

[0002] Optical testing technology includes test methods and detection processes for evaluating and verifying optical device systems and their performance. The core content is to quantitatively measure the optical characteristic parameters of optical components through specific means, including luminous flux distribution, spectral response, phase difference, reflectivity, transmittance, and optical coupling efficiency, etc. Optical testing methods are widely used in fields such as lasers, fiber optic sensors, optical communication modules, and silicon photonics chips, relying on precision test instruments and devices, such as interferometers, spectrometers, and detection array devices, etc., for high-precision data acquisition and analysis. The technical system in this field covers multiple aspects such as light source regulation, optical path design, detection method selection, and optimization of test process flow, aiming to accurately describe the structural and functional states of optical components and ensure their applicability and performance stability in complex systems.

[0003] Among them, the existing detection process for silicon photonics chips mainly relies on static single-frame image gray-scale comparison to complete edge structure detection, and it is unable to dynamically track the continuous evolution of diffraction characteristics over time or direction during the structural change process, often resulting in situations where the edge distortion cannot be recognized. During the reflection behavior analysis process, there is a lack of a fracture recognition standard based on the change pattern of the brightness trajectory, making it difficult to capture local irregular reflection anomalies generated in complex reflection surfaces, resulting in the inability to classify some weakly reflected distorted areas. The extraction of the brightness trend only uses the directional edge algorithm for processing, and does not have the ability to recognize the structural continuity in the angle dimension, making small-direction misalignments easily misjudged as boundary textures. The analysis method for the change in the light intensity response fails to establish an internal and external difference evolution chain of the structure, resulting in a delay in capturing the boundary energy conduction trend and affecting the judgment of local stability. In the structural form verification stage, the static contour path comparison method is used, and it is difficult to recognize contour micro-changes under the influence of stress migration or boundary drift, resulting in a low recognition granularity of abnormal structure distributions and prone to misjudgment or missed detection of some contour blocks. Summary of the Invention

[0004] The purpose of the present invention is to solve the drawbacks existing in the prior art, and to propose a detection system for a silicon photonics chip, which improves the discrimination accuracy of boundary response evolution and abnormal contour distribution.

[0005] To achieve the above purpose, the present invention provides a detection system for a silicon photonics chip, including: A structural perspective adjustment module, used to obtain the edge diffraction image area, extract the horizontal and vertical gray-scale differences and three brightness change points, calculate the trend difference, judge the jump boundary, construct the tangent plane and the incident direction, record the gray-scale fluctuation of consecutive frames and rotate the acquisition direction until the fluctuation is lower than the reference value, and generate an edge alignment image layer; The reflection anomaly capture module, based on the edge-aligned image layer, records the lighting uniformity, collects the reflection brightness trajectory, identifies and marks breaks, screens areas with more intense diffusion compared to the neighborhood, and generates a distribution map of abnormal reflection points; The direction misalignment verification module, based on the distribution map of abnormal reflection points, extracts the brightness trend, compares the angular difference with the neighborhood direction, screens segments with consistent directions to form a trajectory, and generates a continuous direction offset morphology map; The response distribution balance module, based on the continuous direction offset morphology map, extracts the brightness fluctuations in each area of the offset morphology map, calculates the difference in light intensity inside and outside the structure, determines whether the boundary difference continues to expand outward, and generates a regional response offset layer; The structure state calibration module, based on the regional response offset layer, compares the boundary drift area in the response layer with the initial contour path, identifies the stretching and compression differences, and generates a local abnormal map of the chip structure.

[0006] As a further solution of the present invention, the edge-aligned image layer includes the position of gray-level jump points, incident direction parameters, image stable fluctuation areas, and brightness difference structure information. The distribution map of abnormal reflection points includes the positions of brightness trajectory breaks, abnormal reflection intensity points, and neighborhood reflection abnormal diffusion areas. The continuous direction offset morphology map includes brightness trend paths, segments with over-limit angles, and continuous offset direction line segments. The regional response offset layer includes boundary response expansion areas, light intensity difference areas, and brightness fluctuation amplitude maps. The local abnormal map of the chip structure includes stretching abnormal areas, compression abnormal areas, and boundary contour drift areas.

[0007] As a further solution of the present invention, the structure perspective adjustment module includes: An edge extraction sub-module for obtaining the image area where the edge diffraction structure is located on the silicon optical chip, extracting the position differences of horizontal and vertical image gray-level values, calculating the position points in the gray-level difference grid that are greater than the brightness jump boundary threshold, constructing an edge path line, and obtaining the jump boundary difference coefficient; A trend recognition sub-module that extracts three consecutive gray-level change points based on the jump boundary difference coefficient, calculates the gray-level change direction index, screens groups of points with consistent directions to construct a brightness change trend path, and obtains the gray-level change trend value; A direction adjustment sub-module that constructs a structure tangent plane according to the gray-level change trend value and extracts the incident direction, records the range of regional gray-level fluctuations in consecutive frames, gradually rotates the acquisition direction and compares the gray-level fluctuations with the set reference fluctuation interval, screens the acquisition angle with the smallest gray-level fluctuations and establishes an image layer, and obtains the edge-aligned image layer.

[0008] As a further solution of the present invention, the specific calculation formula of the gray-level change direction index is: ; Among them, represents the grayscale change direction index, represents the sudden jump boundary strength coefficient, represents the original grayscale value of the th point position, is defined as taking when , otherwise taking , represents the direction consistency weight, represents the grayscale distribution normalization factor, represents the denominator offset constant.

[0009] As a further solution of the present invention, the reflection anomaly capture module includes: The illumination recording sub-module uniformly records the illumination area based on the edge-aligned image layer, collects the surface reflection intensity values in the illumination area, records the brightness values of each point position in the plane layer and arranges them in the order of the layer coordinates, constructs a brightness extension trajectory sequence based on the grayscale values of adjacent point positions, and generates a brightness extension trajectory value; The trajectory break sub-module calls the brightness extension trajectory value to judge whether there is a grayscale mutation position in the trajectory in the layer, detects whether the grayscale difference between the point positions before and after the mutation is greater than the brightness continuity threshold, records the coordinates where the break point is located, marks the break position to form a trajectory gap layer, and obtains a set of brightness trajectory break point positions; The anomaly screening sub-module screens the reflection areas around the break points according to the set of brightness trajectory break point positions, compares the brightness diffusion amplitude of the break points with the average diffusion amplitude in the neighborhood, screens the break points with the brightness diffusion amplitude greater than the diffusion intensity reference value, and marks the layer position coordinates to generate a distribution map of the set of abnormal reflection points.

[0010] As a further solution of the present invention, the direction misalignment verification module includes: The trend extraction sub-module extracts the brightness trend according to the distribution map of the set of abnormal reflection points in the order of layer arrangement, records the change direction of the brightness values of adjacent pixels, constructs a brightness trend sequence in combination with the point position order, and eliminates the segments with the brightness value fluctuation amplitude less than the brightness trend fluctuation threshold to obtain a brightness trend sequence value; The angle judgment sub-module calls the brightness trend sequence value to obtain the brightness extension direction in the adjacent area and measures the angle, calculates the included angle value between the adjacent trends and the extension direction, compares the angle difference with the set offset reference angle threshold, marks the position of the trend segment exceeding the threshold, and generates a direction angle deviation amount; The trajectory construction sub-module filters the running segments with the angle difference continuously less than the offset reference angle threshold according to the direction angle deviation amount, extracts the layer coordinates as connection nodes, connects the continuous segments to form a trajectory structure line and superimposes it on the original layer to obtain a continuous direction offset morphology map.

[0011] As a further solution of the present invention, the calculation formula for the included angle value between the adjacent running directions and the extension direction is specifically: ; Wherein, represents the included angle value between the th running direction and the th adjacent running direction and the extension direction, represents the running angle of the th position point in the th segment, represents the sum of the brightness values of all position points within the th segment, represents the running angle of the midpoint of the th segment, represents the direction angle of the th adjacent area, represents the th total number of position points participating in the calculation in the segment, represents the th average brightness value within the

[0012] As a further solution of the present invention, the response distribution balance module includes: The fluctuation extraction sub-module extracts the time series layer of the brightness values in the area according to the continuous direction offset morphology map, records the brightness change ranges of the pixel points in the structure area and the background area, eliminates the data segments with the change amplitude lower than the brightness fluctuation threshold, constructs a brightness fluctuation area distribution map, and generates the area brightness fluctuation interval value; The intensity difference sub-module calls the area brightness fluctuation interval value to extract the illumination intensity ranges of the two areas inside and outside the structure boundary, calculates the difference between the gray values of the two areas, and samples the brightness difference sequence outward according to the boundary to obtain the gray scale expansion relationship of the continuous pixel positions, and obtains the structure boundary illumination difference coefficient; The boundary diffusion sub-module judges the illumination difference change trend in the boundary direction according to the structure boundary illumination difference coefficient, judges the consistency of the difference growth direction at multiple adjacent boundary points, filters the continuously expanding outward area and superimposes it on the original layer to obtain the area response offset layer.

[0013] As a further solution of the present invention, the structure state calibration module includes: The boundary extraction sub-module extracts the coordinate order in the regional boundary layer based on the boundary drift region in the regional response offset layer, constructs the current contour path sequence, synchronously extracts the boundary path of the corresponding region in the initial contour image, records the point position mapping relationship between the two paths in the layer coordinate system, and obtains the regional boundary path control group; The contour comparison sub-module calls the regional boundary path control group, calculates the segment displacement direction and amplitude index between the current path and the initial path, judges whether there is a tendency of outward stretching and inward compression in the path direction, screens the boundary segments with the direction difference greater than the contour deformation judgment threshold, and obtains the contour differential offset coefficient; The abnormal marking sub-module locates the regional segments with concentrated contour direction differences according to the contour differential offset coefficient, screens the contour blocks including continuous direction differences exceeding the offset judgment interval, performs marked superposition on the corresponding layer regions, and obtains the local abnormal atlas of the chip structure.

[0014] As a further solution of the present invention, the calculation formula of the segment displacement direction and amplitude index between the current path and the initial path is specifically: ; Among them, represents the segment displacement direction and amplitude index between the current path and the initial path, represents the th point of the current path, represents the th point of the initial path, represents the direction difference of the th point, represents the total number of points.

[0015] Compared with the prior art, the advantages and positive effects of the present invention are as follows: In the present invention, through the extraction of the gray difference value and continuous brightness change points in the image, combined with the sudden jump boundary recognition and tangent plane construction, the accurate positioning of the micro-structure mutation region and the extraction of the incident direction are realized. The acquisition direction is rotated to suppress the gray fluctuation, the optical stability of the edge alignment layer is improved, the brightness expansion trajectory is collected and the fracture position is recognized, the reflection abnormal points are screened by combining the diffusion amplitude difference, the recognition ability of the inhomogeneous reflection is effectively enhanced, the brightness direction is arranged and the included angle is calculated, the direction continuous offset trajectory is extracted, the dynamic modeling of the directional deviation is realized, the illumination difference value inside and outside the structure is calculated and the contour paths are compared, and the discrimination accuracy of the boundary response evolution and abnormal contour distribution is improved. Brief Description of the Drawings

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0017] Figure 1 This is the system flow chart of the present invention.

[0018] Figure 2 This is the sub-module flow chart of the present invention. Detailed implementation manners

[0019] The following will describe the technical solutions in the present invention with reference to the drawings.

[0020] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to represent examples, illustrations or explanations. Any embodiment or design solution described as an "example" in the present invention should not be construed as being more preferred or having more advantages than other embodiments or design solutions. Exactly, the use of the word "example" is intended to present concepts in a specific way. In addition, in the embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one of the two.

[0021] In the embodiments of the present invention, "image" and "picture" can sometimes be used interchangeably. It should be noted that when the difference is not emphasized, the meanings they express are the same. "(of)", "corresponding", and "corresponding" can sometimes be used interchangeably. It should be noted that when the difference is not emphasized, the meanings they express are the same.

[0022] In the embodiments of the present invention, sometimes subscripts such as W 1 may be written in a non-subscript form such as W1. When the difference is not emphasized, the meanings they express are the same.

[0023] To make the technical problems to be solved, technical solutions and advantages of the present invention clearer, the following will be described in detail with reference to the drawings and specific embodiments.

[0024] Please refer to Figure 1 and Figure 2 , a detection system for a silicon photonics chip, comprising: The structural perspective control module is used to obtain the image area where the edge diffraction structure on the silicon photonic chip is located, extract the position difference of the horizontal and vertical image grayscale values, and extract three consecutive brightness change points, calculate the difference in the change trend, and determine whether there is a sharp turning area based on the set brightness jump boundary. The structural cutting plane is constructed and the position incident direction is extracted, the grayscale fluctuation range of the area in the continuous frames is recorded, and the acquisition direction is gradually rotated until the fluctuation is lower than the reference fluctuation value to generate an edge alignment image layer; The reflection anomaly capture module records uniform illumination of the illuminated area based on the edge alignment image layer, collects the brightness expansion trajectory of the surface reflection intensity in the plane, determines whether the brightness trajectory is broken in the layer and marks the broken part, selects those with more drastic changes in brightness diffusion amplitude compared with the neighboring reflection area, and generates a distribution map of abnormal reflection point sets; The direction misalignment verification module extracts the brightness trend according to the distribution map of abnormal reflection point sets, calls the brightness extension direction of the adjacent area for angle comparison, calculates the angle and determines whether the angle difference exceeds the offset reference value, selects the segments with continuous and consistent directions to form an offset trajectory, and generates a continuous direction offset morphology map; The response distribution balance module extracts the brightness fluctuation regional distribution of each area based on the continuous direction offset morphology map, extracts the range of light intensity variation inside and outside the structure for difference calculation, determines whether the difference continues to expand outward in the boundary area, and generates a regional response offset layer; The structural state calibration module compares the contour trend of the current area with the corresponding boundary path of the initial contour image based on the boundary drift area in the regional response offset layer, screens the contour blocks with differences in tension and compression trends, and generates a local anomaly map of the chip structure.

[0025] The edge alignment image layer includes the grayscale jump point position, incident direction parameters, image stable fluctuation area, and brightness difference structure information. The abnormal reflection point set distribution map includes the brightness trajectory break position, abnormal reflection intensity points, and neighborhood reflection abnormal diffusion area. The continuous direction offset morphology map includes the brightness trend path, angle excess fragments, and continuous offset direction segments. The regional response offset layer includes the boundary response extension area, the illumination intensity difference area, and the brightness fluctuation amplitude map. The chip structure local abnormality map includes the stretching abnormality area, the compression abnormality area, and the boundary contour drift area.

[0026] See also Figure 1 and Figure 2 , the structural perspective control module includes: The edge extraction submodule is used to obtain the image area where the edge diffraction structure on the silicon photonic chip is located, extract the position difference of the horizontal and vertical image grayscale values, calculate the position points in the grayscale difference grid that are greater than the brightness jump boundary threshold, and construct the edge path line to obtain the jump boundary difference coefficient.

[0027] First, perform image denoising operations. For example, use a 3×3 median filter to remove scattered noise, and screen out image blocks with an average gray value less than 50 in the image gray scale range of 0 to 255, which are determined as areas where edge diffraction structures may exist. For example, in an image, there is an area with an average gray value of 38, then add this area to the candidate set. Subsequently, traverse the candidate area horizontally and vertically using a 3×3 window, calculate the difference of adjacent pixel gray values, and extract the position differences of the horizontal and vertical image gray values. For example, the difference between points (i,j) and (i+1,j) is 12, and the difference from (i,j+1) is 35, forming two sets of gray differences in two directions. Expand the above sets into an image grid, and then set the brightness jump boundary threshold, which is set according to the image noise level. For example, when the image standard deviation is less than 10, the threshold is set to 25; when the image standard deviation is greater than 10 and less than 20, it is set to 30; when it is greater than 20, it is set to 35. In this example, the image standard deviation is 14, and the threshold is set to 30. Select pixel points with gray differences greater than 30 as jump points according to this value, connect the jump points as edge paths where the distance between each other is less than 2 pixels, and connect between paths by judging the coordinate distance point by point. For example, the distance between points (i,j) and (i+1,j+1) is 1.4, which is less than 2, so connect them. Finally, form multiple edge paths, and then select the one with the largest gray jump intensity in the paths. For example, the differences on the path are 32, 35, 40, 45, 38 in sequence, with a total of 7 points, and take their average difference as 38.1, and this value is the jump boundary difference coefficient.

[0028] The trend recognition sub-module extracts three consecutive gray change points based on the jump boundary difference coefficient, calculates the gray change direction index, screens out point groups with the same direction to construct the brightness change trend path, and obtains the gray change trend value.

[0029] The specific calculation formula for the gray change direction index is: ; where, represents the gray change direction index, represents the jump boundary strength coefficient, represents the th original gray value of the point position, , represents the position weight distribution factor, is defined as taking when , otherwise take ( is the initial reference gray scale), represents the direction consistency weight, represents the gray distribution normalization factor, represents the denominator offset constant.

[0030] Parameter Definition and Value Source: (Sudden Jump Boundary Strength Coefficient): Set by monitoring the output amplitude of the image edge detection filter, with a value of . This coefficient is based on the ratio of the high threshold to the low threshold in Canny edge detection, and the reasonable range is , referring to the actual image processing standard.

[0031] (Point Gray Value): Directly collected by the image sensor, with a value of , , (8-bit gray scale range ).

[0032] (Position Weight Distribution Factor): Set based on the statistical analysis of the influence of spatial position on the trend, with a weight of , , . The weight value is based on the requirement of outlier suppression, with a higher weight for the middle point, and the sum is .

[0033] (Direction Symbol): When , output , otherwise output . The initial reference gray scale is calculated from the average gray scale of the previous frame of the image.

[0034] (Direction Consistency Weight): Set to according to the statistical significance of the product of direction symbols, referring to the balance relationship between the product of symbols and the noise tolerance in direction consistency detection, and the reasonable range is .

[0035] (Normalization Factor): Adjusted based on the dynamic range of the image gray scale, taking , calculated from the sum of the squares of the gray scale values and the calibration data of the device sensitivity, and the reasonable range is .

[0036] (Denominator Offset Constant): A fixed constant introduced to prevent the denominator from being zero, taking , meeting the requirement of minimizing the denominator.

[0037] Example and Calculation Process, Numerator Calculation: ; Product of Direction Symbols: ; Direction item: ; Sum of numerators: ; Denominator calculation: ; Sum of denominators: ; Final result: .

[0038] Result explanation: This result indicates the direction consistency index , and the numerical value reflects the consistency strength of the grayscale change direction. When , it is determined that the direction consistency of the three points satisfies the threshold condition, and enters the stage of constructing the luminance change trend path. The product of the direction symbols in the numerator is negative, indicating that the grayscale of the third point decreases relative to the previous point, but the weighted grayscale accumulation term dominates the result and still passes the screening.

[0039] The direction adjustment sub-module constructs a structural tangent plane according to the grayscale change trend value, extracts the incident direction, records the grayscale fluctuation range of the region in consecutive frames, gradually rotates the acquisition direction, compares the grayscale fluctuation with the set reference fluctuation range, selects the acquisition angle with the smallest grayscale fluctuation, and establishes an image layer to obtain an edge-aligned image layer.

[0040] Construct a structural tangent plane according to the grayscale change trend value and extract the incident direction. First, take the start and end coordinates of the trend path to establish a direction vector. For example, if the start point is (20, 30) and the end point is (100, 60), then the main direction of the path is determined to be from the upper left to the lower right. Based on this direction, construct a set of incident directions perpendicular to it, take one direction every 5° from 0° to 180°, and obtain a total of 36 incident angles. Then, collect the average grayscale of this region in 10 consecutive frames of images in each direction. For example, in the 60° direction, the grayscales of the 10 frames of images are 70, 71, 69, 73, 68, 70, 72, 70, 69, 71 respectively. The grayscale fluctuation range is the maximum value minus the minimum value, that is, 73 minus 68 equals 5. Record the grayscale fluctuation value in this direction. Subsequently, set the grayscale fluctuation reference interval as [5, 15]. This setting is given according to the allowable stability tolerance of the system. When the image contrast is stable, it is set to 5, and it is expanded to 15 in the dynamic contrast scene. In this setting, if the grayscale fluctuation in the direction is within this interval, it is recorded as a candidate direction. Finally, select the angle with the smallest grayscale fluctuation among all candidate directions as the optimal acquisition direction. For example, the fluctuation in the 70° direction is 4.8, which is not within the set interval and is excluded. The fluctuation in the 60° direction is 5, which is accepted and is the minimum value, so select this direction to establish an image layer to form an edge-aligned image layer.

[0041] See also Figure 1 and Figure 2 , the reflection exception capture module includes: The illumination recording submodule records the uniform illumination of the illuminated area based on the edge alignment image layer, collects the surface reflection intensity value in the illuminated area, records the brightness value of each point in the plane layer and arranges them in order according to the layer coordinates, constructs a brightness extension trajectory sequence based on the grayscale values ​​of adjacent points, and generates a brightness extension trajectory value.

[0042] First, determine the target area in the layer where illumination measurement needs to be performed. This area is set as a fixed rectangular area by edge-aligning the contour boundary box of the structure jump area in the layer. For example, the area is set to a range of 60 pixels × 80 pixels. Illumination acquisition is performed point by point in this area. Each pixel receives a uniform light source incident from a standard angle, and its reflected brightness value is collected. The brightness value ranges from 0 to 255 grayscale levels. After recording the brightness value of each point, all brightness values ​​are arranged into a one-dimensional sequence from left to right and from top to bottom in the layer. The numbering method is P(i,j), where i is the row number and j is the column number. For example, the brightness of point (10,15) is 142, then the brightness of point (10,15) is 142. At the 595th position in the sequence, the brightness expansion trajectory is constructed, that is, the brightness difference between the current point and its four adjacent direction points (up, down, left, and right) is used as a reference, and the brightness expansion direction is recorded in sequence. In an actual example, if the brightness of point (10,15) is 142, the brightness of the point on its right is 145, the point below is 140, the left is 138, and the top is 143, then it is expanded to the right and up in the direction of the difference to form a brightness expansion trajectory. The trajectory prioritizes the direction according to the maximum brightness difference order. In this way, a continuous brightness jump path is generated, and each jump step value is recorded in sequence to form a brightness expansion trajectory value sequence, and finally the brightness expansion trajectory value of the area is obtained.

[0043] The trajectory break submodule calls the brightness extension trajectory value to determine whether there is a grayscale mutation position in the trajectory layer, detects whether the grayscale difference between the points before and after the mutation is greater than the brightness continuity threshold, and records the coordinates of the break point, marks the break position to form a trajectory gap layer, and obtains the brightness trajectory break point set.

[0044] Call the brightness expansion trajectory value to determine whether there are gray-scale mutation positions in the trajectory in the layer. In the sequence of brightness expansion trajectory values obtained in the previous step, calculate the brightness difference between each adjacent pair of points in turn, and set the brightness continuity threshold T_cont to determine the mutation position. The threshold is set according to the overall gray-scale change range of the image. When the standard deviation of the image gray-scale is less than 20, set T_cont to 12; when the standard deviation is between 20 and 35, set it to 18; when it exceeds 35, set it to 22. In a certain instance, the standard deviation of the image area is 28, so set T_cont to 18. When the brightness difference between any two points in the trajectory is greater than this value, it is regarded as a gray-scale mutation point. For example, if the consecutive brightness values in the trajectory are 142, 145, 147, 173, then the difference between 145 and 147 is 2, which is not a mutation, while the difference between 147 and 173 is 26, which is greater than the threshold 18, so it is determined as a mutation point. Record the layer coordinate position of this point. For example, this mutation point is located at (11, 17), mark it as a break point, and split this break point from the trajectory to generate a break gap. After performing the same operation on all trajectories in the entire layer, obtain the coordinate set of all break points, and mark the position of each break point on the layer in the form of image marker points. For example, mark this point with a red pixel value. Finally, summarize all the marked points to form a trajectory gap layer, and then obtain the set of brightness trajectory break point positions.

[0045] Anomaly screening sub-module, according to the set of brightness trajectory break point positions, screen the reflection areas around the break points, compare the brightness diffusion amplitude of the break points with the average diffusion amplitude in the neighborhood, screen the break points with a brightness diffusion amplitude greater than the diffusion intensity reference value, and mark the layer position coordinates to generate a distribution map of the set of abnormal reflection points.

[0046] Screen the reflection areas around the break points according to the set of break point positions on the brightness trajectory. First, select each break point in the set of break point positions on the trajectory. Establish a neighborhood window with a radius of 3 pixels centered on it, which contains a total of 49 pixels of 7×7. Record the brightness values of all pixels in this neighborhood, and calculate the average diffusion amplitude of this area, that is, the difference between the maximum brightness and the minimum brightness in the neighborhood. For example, if the break point is located at (12, 18), the maximum brightness in its neighborhood is 190 and the minimum brightness is 130, and the average diffusion amplitude is obtained as 60. At the same time, extract the difference between the two values of the break point's own brightness mutation before and after in the trajectory as the brightness diffusion amplitude of this point. For example, if the brightness values before and after the break point are 150 and 185 respectively, the diffusion amplitude is 35. Then compare the brightness diffusion amplitude of the break point with the neighborhood average diffusion amplitude. If this value is greater than the set diffusion intensity reference value, mark this point as an abnormal reflection point. The diffusion intensity reference value is set according to the system allowable error and is set to 30 in most applications. When the diffusion amplitude of the break point is greater than 30, it is determined as abnormal. For example, the diffusion amplitude of the aforementioned point is 35, which is greater than 30. Record its coordinates (12, 18) and mark the corresponding position in the layer. Then, draw a layer distribution map for all abnormal reflection points in the order of coordinates, and uniformly represent their positions with green highlighted pixels. Finally, generate a distribution map of the abnormal reflection point set.

[0047] Please refer to Figure 1 and Figure 2 , the direction misalignment verification module includes: A trend extraction sub-module that extracts the brightness trend according to the order of layer arrangement in the distribution map of the abnormal reflection point set, records the change direction of the brightness values of adjacent pixels, constructs a brightness trend sequence in combination with the point position order, and eliminates the segments with a brightness value fluctuation amplitude less than the brightness trend fluctuation threshold to obtain the brightness trend sequence value.

[0048] First, traverse the abnormal reflection points in the layer in the coordinate order from left to right horizontally and from top to bottom vertically. Construct a preliminary brightness change path by recording the pixel coordinate differences and corresponding brightness value differences between each pair of adjacent points. Between every two consecutive points, form a one-way segment according to the direction of brightness value increase or decrease (increase is recorded as "+", decrease is recorded as "-", and no change is recorded as "0"), and record it with the coordinate vector direction as the walking direction. For example, if the brightness of point A is 145, the brightness of point B is 139, the brightness difference is -6, and the direction vector is from (20, 30) to (21, 31), then record the walking direction as downward to the right with brightness decreasing. Continuously record all such direction and brightness relationships in the entire layer to obtain the initial sequence of brightness directions. Subsequently, set a brightness direction fluctuation threshold to filter out segments with relatively small change amplitudes. The setting of this threshold is based on the overall brightness change degree of the layer. When the brightness standard deviation is less than 20, it is set to 5; when it is between 20 and 35, it is set to 8; when it is greater than 35, it is set to 10. In the example, if the brightness standard deviation is 24, then the fluctuation threshold is set to 8. Eliminate all segments with brightness change amplitudes less than 8. For example, if the brightness of a certain segment changes from 152 to 146, only changing by 6 which is less than the threshold 8, then eliminate this segment and retain other segments with larger change amplitudes. Finally, organize and obtain the brightness direction sequence values.

[0049] The included angle judgment sub-module calls the brightness direction sequence values to obtain the brightness extension directions of adjacent regions and measures the angles, calculates the included angle values between adjacent walking directions and the extension directions, compares the angle differences with the set offset reference angle threshold, marks the positions of the walking segments that exceed the threshold, and generates the direction included angle deviation amount.

[0050] The specific calculation formula for the included angle value between the adjacent walking direction and the extension direction is: ; Among them, represents the included angle value between the th walking segment and the th adjacent walking direction and the extension direction, represents the walking angle of the th position point in the th segment, represents the sum of the brightness values of all position points in the th segment, represents the walking angle of the midpoint in the th segment, represents the direction angle of the th adjacent region, represents the th total number of position points participating in the calculation in the segment, represents the th average value of the brightness values in the adjacent region.

[0051] Parameter Acquisition and Value Setting: : The orientation angle of the th position point in the th segment. It is calculated by extracting the coordinates of the two endpoints of the line segment through image processing techniques such as edge detection and Hough transform. The angle value range is usually between 0° and 180°.

[0052] : The sum of the brightness values of all position points within the th segment. It is obtained by traversing all the pixel points of the th segment and accumulating their brightness values (gray values). The brightness value range is from 0 to 255.

[0053] : The orientation angle of the midpoint of the th segment. It is obtained by selecting the central position point of the th segment and calculating its orientation angle.

[0054] : The orientation angle of the th adjacent region. It is obtained by analyzing the structural characteristics of the adjacent region and calculating its main direction. The angle value range is usually between 0° and 180°.

[0055] : The total number of position points participating in the calculation in the th segment. It is obtained by counting the number of pixel points in the th segment.

[0056] : The average value of the brightness values within the th adjacent region. It is obtained by traversing all the pixel points in the th adjacent region and calculating the average value of their brightness values. The brightness value range is from 0 to 255.

[0057] Specific Value Setting: : [30°, 35°, 40°, 45°, 50°]; : Set to 1000; : 45°; : 40°; : 5; : 200.

[0058] Formula Calculation Process: Calculate : ; Calculate ; Calculate : ; Calculate the numerator: ; Calculate the denominator: ; Final calculation : .

[0059] The result shows that the included angle value between the direction of the th segment and the extension direction of the th adjacent region is approximately 5.23. This value is used to determine whether the angle difference exceeds the set offset reference angle threshold, and then mark the position of the direction segment exceeding the threshold to generate the direction angle deviation amount.

[0060] The trajectory construction sub-module screens the direction segments with the angle difference continuously less than the offset reference angle threshold according to the direction angle deviation amount, extracts the layer coordinates as connection nodes, connects the continuous segments to form a trajectory structure line and superimposes it on the original layer to obtain a continuous direction offset morphology map.

[0061] Screen the direction segments with the angle difference continuously less than the offset reference angle threshold according to the direction angle deviation amount. First, screen out the segments with the included angle value exceeding the reference threshold among all the marked direction segments in the previous stage, and retain the segments with the continuous angle difference less than the threshold as candidate trajectory segments. During the screening process, take each unmarked deviation segment as the starting point and continuously search backward whether the next segment is also unmarked. If three or more consecutive segments all meet the condition that the included angle value is lower than the threshold, they are constructed into connectable trajectory segments. For example, if the angles of segments a, b, and c are 12 degrees, 14 degrees, and 17 degrees respectively, all less than the threshold of 20 degrees, they are selected as the basis for trajectory connection. Then, extract the coordinate positions of their endpoints in the layer from each segment. For example, the endpoint of segment a is (30, 45), and the starting point of segment b is (30, 45), and they are directly connected to construct a sequence of trajectory connection nodes. Connect each segment in the order of layer coordinates to form a trajectory structure line. The trajectory structure line is superimposed on the original layer in the form of a single-pixel-width path, and a complete continuous direction trajectory is formed. In the layer image, the connection path is given a specific gray value such as 200 to distinguish the gray information of the original structure layer, realizing the visible superposition between layers, and finally obtaining a continuous direction offset morphology map.

[0062] Please refer to Figure 1 and Figure 2 , the response distribution balance module includes: The fluctuation extraction sub-module extracts the time-series layer of the brightness values in the region according to the continuous direction offset morphology diagram, records the brightness change ranges of the pixel points in the structure region and the background region, eliminates the data segments with a change amplitude lower than the brightness fluctuation threshold, constructs the brightness fluctuation region distribution diagram, and generates the regional brightness fluctuation interval value.

[0063] According to the continuous direction offset morphology diagram, the time-series layer of the brightness values in the region is extracted. First, the sampling ranges are set for the structure region and the background region in the layer respectively. The structure region consists of the pixel points within the closed figure connected by continuous edges, and the background region is the annular belt region extending 5 pixels outward from the structure boundary. The system records the brightness values of all pixel points in these two types of regions at each time sampling point (such as each frame of the image), and constructs the time-series gray vector of each point. In an actual scenario, if the cycle is 10 frames of images, the gray levels of point P in 10 frames are [152, 154, 156, 153, 155, 158, 160, 162, 159, 157]. Calculate the difference between the maximum and minimum values of this sequence to obtain the brightness change amplitude of 160 - 152 = 8. Subsequently, a brightness fluctuation threshold is set to screen out the points with insufficient fluctuations. This threshold is set according to the background noise range. If the average noise amplitude of the image is less than 4, the threshold is set to 6; when the noise amplitude is between 4 and 8, it is set to 9; if it is higher than 8, it is set to 12. In the current example, the noise amplitude is 6, and the fluctuation threshold is set to 9. Compare the brightness fluctuation range of each pixel point. If it is less than 9, the data segment of this point is eliminated. For example, the fluctuation of the above point P is 8, which is lower than 9, so it is removed. After performing the same judgment on all pixel points, the remaining high-fluctuation points are located and marked, and then they are restored to the layer according to the spatial position to form the brightness fluctuation region distribution diagram. Calculate the maximum, minimum, and average values of the fluctuation amplitudes in each region, and summarize them as the regional brightness fluctuation interval value.

[0064] The intensity difference sub-module calls the regional brightness fluctuation interval value to extract the illumination intensity ranges of the two regions inside and outside the structure boundary, calculates the difference between the gray values of the two regions, and samples the brightness difference sequence outward from the boundary to obtain the gray-scale expansion relationship of continuous pixel positions, and obtains the illumination difference coefficient of the structure boundary.

[0065] The structure for extracting the range of luminance fluctuations in the calling area obtains the illumination intensity ranges of two areas inside and outside the structure boundary. First, identify the structure boundary line in the layer and divide the pixels according to inside the boundary (inside the structure) and outside the boundary (inside the background band). In each type of area, calculate the maximum luminance value and the minimum luminance value of all remaining points to form the illumination intensity range. For example, the luminance of the points inside the structure is between [145, 168], and outside the structure is between [110, 132]. Then, calculate the luminance difference between the areas as the mean value inside the structure minus the mean value outside the structure. If the mean value inside the structure is 156 and the mean value outside the structure is 121, the difference is 35. Subsequently, select boundary points at intervals of 1 pixel on the structure boundary, extend 3 pixels outward, obtain the luminance values of the continuous pixels outside the boundary, and record the differences from the structure boundary points to construct a luminance difference sequence. For example, if the luminance of the boundary point is 155, and the luminances of the three points outward are 140, 130, and 125 in sequence, the difference sequence is 15, 25, 30. This sequence is used to reflect the expansion trend of the gray-scale change at the boundary. Finally, record the maximum value in the continuous luminance differences and its relative position, and synthesize the average difference on each boundary line to form the structure boundary illumination difference coefficient. This coefficient is the regional response intensity reflection of the continuous gray-scale differences.

[0066] Boundary diffusion sub-module, according to the structure boundary illumination difference coefficient, judge the change trend of the illumination difference in the boundary direction, judge the consistency of the difference growth direction at multiple adjacent boundary points, screen the continuously expanding areas and superimpose them on the original layer to obtain the regional response offset layer.

[0067] According to the structure boundary illumination difference coefficient, judge the change trend of the illumination difference in the boundary direction, extract the continuous difference increase and decrease change directions from each point on the structure boundary and its outward expanding luminance difference sequence. For example, if the luminance difference of a group of points is 12, 17, 22, record it as continuously increasing. If another group is 22, 19, 15, it is continuously decreasing. Compare the difference directions of all boundary points, and set the consistency judgment range to at least 3 consecutive points with the same direction. That is, if it continuously shows an increase or decrease from point A to point C on the boundary, it is considered to have consistency. Screen out all point segments that meet the consistency conditions as candidate expansion areas. Subsequently, mark the corresponding outer expansion boundary coordinates of the candidate area and reconstruct the layer pixel structure, superimpose the area identifier on the original layer, and uniformly use a high-brightness gray value such as 200 or a specified label value to cover the original pixel gray scale to form a new layer. Finally, obtain the regional response offset layer.

[0068] Please refer to Figure 1 and Figure 2 , the structure state calibration module includes: The boundary extraction sub-module extracts the coordinate order in the regional boundary layer based on the boundary drift region in the regional response offset layer, constructs the current contour path sequence, synchronously extracts the boundary path of the corresponding region in the initial contour image, records the point position mapping relationship of the two paths in the layer coordinate system, and obtains the regional boundary path control group.

[0069] Based on the boundary drift region in the regional response offset layer, first extract the boundary region in the layer that is determined to have a continuously expanding change in brightness difference. According to the spatial coordinate arrangement order of each pixel point in the layer, extract the boundary points from left to right and from top to bottom in sequence, and construct a continuous and closed boundary path. In a typical example, the drift region is an approximately elliptical structure, and the sequence of its boundary points is: (45,30), (45,31), (46,32)…(60,50). Record this boundary point sequence as the current contour path sequence. At the same time, extract the boundary path of the corresponding region of the chip in the initial state image. Identify the alignment by comparing the coordinates of the region center point and the region label number, and find the boundary point sequence of the same region in the original layer, for example: (45,30), (45,31), (46,31)…(59,49). Map each point between the current contour path and the initial contour path one by one, and establish a point position pairing relationship through relative order correspondence between each pair of points. If 40 boundary points are extracted for a certain region in the current layer, and 38 points in the initial layer, then interpolate and complete the shorter path by inserting the average position points to align the number of point positions and ensure the integrity of the pairing relationship, forming a regional boundary path control group, and record each point position mapping relationship table. For example, the 10th point in the current layer corresponds to the 10th point in the initial layer. These comparison relationships are used for subsequent path displacement and deformation analysis.

[0070] The contour comparison sub-module calls the regional boundary path control group, calculates the displacement direction and amplitude index between the current path and the initial path, judges whether there is an outward stretching and inward compression trend in the path direction, and screens the boundary segments with a direction difference greater than the contour deformation judgment threshold to obtain the contour differential offset coefficient.

[0071] The calculation formula for the displacement direction and amplitude index between the current path and the initial path is specifically: ; Among them, represents the displacement direction and amplitude index between the current path and the initial path, represents the th point of the current path, represents the th point of the initial path, represents the direction difference of the th point, represents the total number of points.

[0072] The path point coordinate data is obtained in real time through a high-precision image contour extraction module. The image resolution is 1920×1080. The path contour boundary is obtained by a binary edge detection algorithm, and path points are sampled at every 10-pixel interval. 100 points in a path are selected for comparison, that is 。

[0073] Parameter description and quantization basis: : The coordinate value of the -th point on the current path, in pixels, is obtained by collecting at the position numbered i in the contour boundary image; : The coordinate value of the -th point on the initial path, in pixels, is obtained from the boundary point sequence of the standard initial path set by the system; : Direction difference, representing the absolute value of the angle between the direction angle of the current point and the direction angle of the initial point, in degrees, is obtained by calculating the tangent direction of the line connecting two consecutive points and comparing them. The direction angle is quantified clockwise with north as 0 degrees.

[0074] Direction angle is calculated by the formula: ; where is the current point, is its next point. The direction difference is calculated as: ; The change in the direction value is based on ±15 degrees, and boundary segments with an error greater than this value will be considered deformed.

[0075] Actual numerical example calculation process: 3 groups of example path points are sampled for demonstration: Point 1: Current point , initial point ; Point 2: Current point , initial point ; Point 3: Current point , initial point .

[0076] Calculate the displacement modulus (Euclidean distance): ; ; .

[0077] Direction angle calculation. Assume the subsequent points are as follows: Subsequent to Point 1: , ; Subsequent to the initial point: , , direction difference ; Similar calculations yield: , .

[0078] Substitute the data into the formula: .

[0079] This result indicates that the average differential offset coefficient of the current path compared to the initial path in the paragraph is 70.7, indicating an obvious path offset trend in the selected point samples. The degree of offset has a multiplicative enhancement with the direction difference value, comprehensively reflecting the differential offset intensity of the deformation boundary.

[0080] Abnormal marking sub-module. Locate the regional segments where the contour trend differences are concentrated based on the contour differential offset coefficient. Screen the contour blocks that include consecutive trend difference values exceeding the offset judgment interval, perform marked superposition on the corresponding layer areas, and obtain the local abnormal atlas of the chip structure.

[0081] First, extract the path segments with large offsets that appear consecutively from the contour path. Determine the concentrated area of trend differences by statistically analyzing the consistency of the offset direction of each segment with the direction of adjacent segments. In a typical example, if the offset directions of five consecutive path segments are expanding outwards, and the offset amplitudes of each segment are 3.5, 3.7, 3.9, 4.2, and 4.0 pixels, all greater than the set threshold of 3 pixels, and the directions are continuously consistent, it is judged as a concentrated area of difference segments. Subsequently, set the offset judgment interval as "starting from 3 pixels and continuously appearing in 3 segments or more" as the screening condition. The segments that meet the conditions are classified as abnormal contour blocks. After recording the start and end coordinates of this contour block, map them to the original structure of the layer, extract the smallest bounding rectangle area corresponding to this block. For example, this area includes the layer range from (48, 32) to (55, 40). Define this range as the marked block area in the image data and assign a unified highlighted gray-scale marking value, such as 230, to overwrite the original pixel values to complete the layer modification. The same operation is performed on all contour blocks that meet the screening conditions to complete the marking process of all contour abnormal areas, and finally form a complete local abnormal atlas of the chip structure.

[0082] As described above, it is only the specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention can easily think of changes or substitutions, which should all be covered within the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claimed rights.

Claims

1. A detection system for a silicon photonic chip, characterized in that: include: The structural perspective control module is used to obtain the edge diffraction image area, extract the horizontal and vertical grayscale differences and three brightness change points, calculate the trend difference, determine the jump boundary, construct the cutting plane and the incident direction, record the grayscale fluctuation of continuous frames and rotate the acquisition direction until the fluctuation is lower than the reference value, and generate the edge alignment image layer; A reflection anomaly capture module records illumination uniformity, collects reflection brightness trajectories, identifies and marks fractures, selects areas with more intense diffusion than neighboring areas, and generates a distribution map of abnormal reflection point sets based on the edge alignment image layer; The direction misalignment verification module extracts the brightness trend based on the abnormal reflection point set distribution map, compares the angle difference with the neighborhood direction, selects the segments with the same direction to form a trajectory, and generates a continuous direction offset morphology map; The response distribution balance module extracts the brightness fluctuation of each area of ​​the offset morphology map based on the continuous direction offset morphology map, calculates the difference in light intensity inside and outside the structure, determines whether the boundary difference continues to expand outward, and generates a regional response offset layer; The structural state calibration module compares the boundary drift area in the response layer with the initial contour path based on the regional response offset layer, identifies the tensile and compressive differences, and generates a local abnormality map of the chip structure.

2. The silicon photonic chip detection system according to claim 1, characterized in that: The edge alignment image layer includes the grayscale jump point position, incident direction parameters, image stability fluctuation area, and brightness difference structure information; the abnormal reflection point set distribution map includes the brightness trajectory break position, abnormal reflection intensity points, and neighborhood reflection abnormal diffusion areas; the continuous direction offset morphology map includes the brightness trend path, angle excess fragments, and continuous offset direction segments; the regional response offset layer includes the boundary response extension area, the illumination intensity difference area, and the brightness fluctuation amplitude map; the chip structure local abnormality map includes the stretching abnormality area, the compression abnormality area, and the boundary contour drift area.

3. The detection system of silicon photonic chip according to claim 1, characterized in that: The structural perspective control module includes: The edge extraction submodule is used to obtain the image area where the edge diffraction structure on the silicon photonic chip is located, extract the position difference of the horizontal and vertical image grayscale values, calculate the position points in the grayscale difference grid that are greater than the brightness jump boundary threshold, and construct the edge path line to obtain the jump boundary difference coefficient; The trend recognition submodule extracts three consecutive grayscale change points based on the jump boundary difference coefficient, calculates the grayscale change direction index, selects the point group with the same direction to construct the brightness change trend path, and obtains the grayscale change trend value; The direction adjustment submodule constructs a structural cutting plane and extracts the incident direction according to the grayscale change trend value, records the regional grayscale fluctuation range in continuous frames, gradually rotates the acquisition direction and compares the grayscale fluctuation with the set reference fluctuation range, selects the acquisition angle with the smallest grayscale fluctuation and establishes an image layer to obtain an edge-aligned image layer.

4. The detection system of silicon photonic chip according to claim 3, characterized in that: The grayscale change direction index calculation formula is specifically: ； in, Represents the grayscale change direction indicator, represents the sudden boundary strength coefficient, Representative The original gray value of each point, , represents the position weight distribution factor, Defined as Time , otherwise take , represents the directional consistency weight, represents the grayscale distribution normalization factor, Represents the denominator offset constant.

5. The detection system of silicon photonic chip according to claim 3, characterized in that: The reflection anomaly capture module includes: The illumination recording submodule records uniform illumination of the illumination area based on the edge alignment image layer, collects the surface reflection intensity value in the illumination area, records the brightness value of each point in the plane layer and arranges them in order of the layer coordinates, constructs a brightness extension trajectory sequence according to the grayscale values ​​of adjacent points, and generates a brightness extension trajectory value; The track break submodule calls the brightness extension track value to determine whether there is a grayscale mutation position in the track layer, detects whether the grayscale difference between the points before and after the mutation is greater than the brightness continuity threshold, and records the coordinates of the break point, marks the break position to form a track gap layer, and obtains a brightness track break point set; The abnormal screening submodule screens the reflection area around the breakpoint according to the brightness trajectory breakpoint set, compares the brightness diffusion amplitude of the breakpoint with the average diffusion amplitude in the neighborhood, screens the breakpoints whose brightness diffusion amplitude is greater than the diffusion intensity reference value, and marks the layer position coordinates to generate a distribution map of the abnormal reflection point set.

6. The silicon photonic chip detection system according to claim 5, characterized in that: The direction misalignment verification module includes: The direction extraction submodule extracts the brightness direction according to the layer arrangement order based on the abnormal reflection point set distribution map, records the direction of change of the brightness value of adjacent pixels, constructs a brightness direction sequence based on the point sequence, and removes the segments whose brightness value fluctuation amplitude is less than the brightness direction fluctuation threshold to obtain the brightness direction sequence value; The angle judgment submodule calls the brightness trend sequence value to obtain the brightness extension direction of the adjacent area and performs angle measurement, calculates the angle value between the adjacent trend and the extension direction, compares the angle difference with the set offset reference angle threshold, marks the trend segment position exceeding the threshold, and generates the direction angle deviation; The trajectory construction submodule selects the direction segments whose angle difference is continuously less than the offset reference angle threshold according to the direction angle deviation, extracts the layer coordinates as the connection nodes, connects the continuous segments to form the trajectory structure line and superimposes it on the original layer to obtain the continuous direction offset morphology map.

7. The silicon photonic chip detection system according to claim 6, characterized in that: The specific calculation formula for the angle between the adjacent strike direction and the extension direction is: ； in, Representative The direction of the paragraph The angle between the adjacent strike direction and the extension direction, Representative Section The direction angle of the position point, Representative The sum of the brightness values ​​of all points in the segment, Representative The direction angle of the midpoint of the segment, Representative The direction angles of adjacent regions, Representative The total number of position points involved in the calculation in the segment, Representative The average brightness value of adjacent areas.

8. The silicon photonic chip detection system according to claim 6, characterized in that: The response distribution balancing module includes: The fluctuation extraction submodule extracts the time series layer of the brightness value in the area according to the continuous direction offset morphology map, records the brightness variation range of the pixels in the structure area and the background area, removes the data segments whose variation range is lower than the brightness fluctuation threshold, constructs the brightness fluctuation area distribution map, and generates the regional brightness fluctuation interval value; The intensity difference submodule calls the regional brightness fluctuation interval value to extract the illumination intensity range of the two regions inside and outside the structure boundary, calculates the difference in grayscale values ​​of the two regions, and samples the brightness difference sequence outward from the boundary to obtain the grayscale expansion relationship of continuous pixel positions, and obtains the illumination difference coefficient of the structure boundary; The boundary diffusion submodule determines the illumination difference change trend in the boundary direction according to the structural boundary illumination difference coefficient, makes consistency judgment on the difference growth direction at multiple adjacent boundary points, screens the area that continues to expand outward and overlays the original layer to obtain the regional response offset layer.

9. The silicon photonic chip detection system according to claim 8, characterized in that: The structural state calibration module comprises: The boundary extraction submodule extracts the coordinate sequence in the regional boundary layer and constructs the current contour path sequence based on the boundary drift area in the regional response offset layer, simultaneously extracts the boundary path of the corresponding area in the initial contour image, records the point mapping relationship between the two paths in the layer coordinate system, and obtains the regional boundary path control group; The contour comparison submodule calls the regional boundary path control group, calculates the displacement direction and amplitude index between the current path and the initial path, determines whether there is an outward stretching and inward compression trend in the path direction, and selects the boundary segment whose direction difference is greater than the contour deformation judgment threshold to obtain the contour difference deviation coefficient; The abnormal marking submodule locates the regional fragments where the contour trend difference is concentrated according to the contour difference offset coefficient, screens the contour blocks whose continuous trend difference exceeds the offset judgment interval, marks and superimposes the corresponding layer areas, and obtains the local abnormality map of the chip structure.

10. The silicon photonic chip detection system according to claim 9, characterized in that: The calculation formula of the displacement direction and amplitude index between the current path and the initial path is as follows: ； in, Represents the displacement direction and amplitude index between the current path and the initial path. Represents the current path Points, Represents the initial path Points, Representative The direction difference of the points, Represents the total number of points.

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