A method and system for detecting optical window abnormality of a vertical cavity surface emitting laser

Abstract

This application discloses a method and system for detecting optical window anomalies in a vertical-cavity surface-emitting laser (VCSEL), belonging to the field of CCSEL optical window anomaly detection. The method involves: acquiring a first optical window intensity dataset corresponding one-to-one with each optical window position in the CCSEL to be tested; statistically calculating an anomaly judgment threshold corresponding to each optical window position based on the first optical window intensity datasets; and then comparing the emission intensity data of each optical window in the dataset corresponding to each optical window position with the corresponding anomaly judgment threshold one by one to obtain the anomaly detection result for each die at each optical window position. Therefore, by implementing this application, the problem of high false negative rate in the mass production detection of optical window defects in CCSELs can be solved in the prior art.

Description

Technical Field

[0001] This application relates to the field of optical window anomaly detection in vertical cavity surface-emitting lasers (VCSELs), and more particularly to a method and system for optical window anomaly detection in VCSELs. Background Technology

[0002] Vertical-cavity surface-emitting lasers (VCSELs) can integrate tens of thousands of dies, each containing several optical windows. If these windows have surface defects such as scratches, contamination, or black obstructions, the luminous intensity of the corresponding window will directly decrease, disrupting the uniformity of light output, weakening overall optical performance, and consequently reducing product yield and shipping reliability. Therefore, conducting VCSEL optical window anomaly detection and identifying defective windows is a necessary step to ensure the quality of mass-produced products and the stability of the control process.

[0003] Current technology uses a scheme that compares the luminous intensity of windows at different positions within a single die: first, the luminous intensity of all windows within the vertical-cavity surface-emitting laser (VCSEL) is tested; then, the average intensity of all windows within a single die is multiplied by a preset coefficient to obtain an anomaly threshold. Windows with intensities below this threshold are considered abnormal. However, for defects with small area proportions, this threshold can only be determined through manual visual inspection. Therefore, the current detection method has significant accuracy deficiencies. Specifically, the inherent luminous intensity of windows at different positions within the die fluctuates greatly, resulting in a large standard deviation. This makes the threshold setting based on the die's average luminous intensity lenient, unable to quantitatively and automatically detect small defects with an area proportion of less than 50%. Therefore, there is an urgent need for a technical solution that can address the high false negative rate in mass production defect detection scenarios for VCSEL windows. Summary of the Invention

[0004] This application provides a method and system for detecting optical window anomalies in a vertical cavity surface-emitting laser (VCSEL), which can solve the problem of high false negative rate in the mass production detection of optical window defects in VCSELs in the prior art.

[0005] In a first aspect, embodiments of the present invention provide a method for detecting optical window anomalies in a vertical-cavity surface-emitting laser, comprising:

[0006] A first optical window intensity dataset is obtained, which corresponds one-to-one with each optical window position in the vertical cavity surface-emitting laser to be detected; wherein, the vertical cavity surface-emitting laser includes a plurality of dies, and each of the dies includes the optical window positions in its detection area, and the first optical window intensity dataset includes the first optical window emission intensity data of each die at the optical window position corresponding to the first optical window intensity dataset.

[0007] Based on the intensity dataset of each first light window, the anomaly detection threshold corresponding to each light window position is calculated.

[0008] For each optical window position, the emission intensity data of each first optical window in the first optical window intensity dataset corresponding to the optical window position is compared with the anomaly detection threshold corresponding to the optical window position to obtain the anomaly detection result of each die in the vertical cavity surface emitter laser corresponding to the optical window position.

[0009] In this embodiment, by acquiring a first optical window intensity dataset that corresponds one-to-one with each optical window position and covers the luminous intensity of optical windows at the same position in each die, the inherent luminous intensity differences of optical windows at different positions within a single die can be eliminated by relying on the process consistency of optical windows at the same position. This reduces the fluctuation range of normal light intensity and provides a stable reference benchmark for anomaly judgment, thereby improving the inherent limitation of existing single-die comparison schemes that cannot identify small-sized defects due to excessive benchmark fluctuations. By calculating a dedicated anomaly judgment threshold for each optical window position, the independent light intensity distribution characteristics of different optical window positions can be adapted, making the judgment threshold more closely match the normal light intensity range of the corresponding position, thus improving the adaptability and accuracy of the anomaly judgment standard. By comparing the light intensity data of each die with the anomaly judgment threshold of the corresponding position one by one and outputting the anomaly detection results of the corresponding optical window positions of each die, anomaly screening of all dies in all optical window positions within a vertical cavity surface-emitting laser is achieved, which can reliably detect defects that occupy a small proportion of the optical window area. Therefore, the embodiments of this application can solve the problem of high false negative rate in the mass production detection of defects in vertical cavity surface-emitting laser (VCSEL) optical windows in the prior art.

[0010] As a preferred example of the first aspect, the acquisition of the first optical window intensity dataset corresponding one-to-one with the positions of each optical window in the vertical-cavity surface-emitting laser to be detected includes:

[0011] Obtain the second optical window intensity dataset corresponding to each of the dies in the vertical cavity surface-emitting laser to be tested; wherein, the second optical window intensity dataset includes the emission intensity data of the second optical window corresponding one-to-one with the position of each optical window;

[0012] Based on the position of each light window, the luminous intensity data of each second light window in each second light window intensity dataset are classified and integrated to obtain the first light window intensity dataset that corresponds one-to-one with each of the light window positions.

[0013] In this preferred example, by acquiring the second light window intensity dataset corresponding to each die, the original luminous intensity detection data of all light window positions within a single die can be completely preserved, providing a comprehensive data foundation for subsequent cross-die same-position comparisons. By classifying and integrating the light intensity data of each die according to the light window position, a statistical sample of light intensity of light windows at the same position is constructed, eliminating the data dispersion interference caused by the inherent light intensity differences of light windows at different positions within a single die, and providing reliable data support for subsequent calculation of anomaly judgment thresholds at each position and reducing the detection false negative rate.

[0014] As a preferred example of the first aspect, the step of classifying and integrating the luminous intensity data of each second light window in each second light window intensity dataset according to each light window position to obtain a first light window intensity dataset corresponding one-to-one with each light window position includes:

[0015] The first light window intensity dataset, corresponding one-to-one with each light window position, is obtained by traversing each light window position. During each traversal, the second light window intensity dataset corresponding to each die is traversed, and the second light window luminous intensity data corresponding to the current traversed light window position is extracted from the current traversed second light window intensity dataset according to the current traversed light window position. After traversing each die, the second light window luminous intensity data corresponding to the current traversed light window position in each die are integrated to form the first light window intensity dataset corresponding to the current traversed light window position.

[0016] In this preferred example, by traversing each optical window position and constructing a corresponding first optical window intensity dataset one by one, it is ensured that each optical window position independently forms a statistical sample, guaranteeing that the data and optical window positions are accurately correlated and avoiding data misalignment and confusion. During a single traversal, the luminous intensity data of the current optical window position is extracted and integrated for each die, which can aggregate the intensity data of optical windows at the same position, effectively eliminating the interference of inherent light intensity differences of optical windows at different positions within a single die. This provides a reliable data foundation for subsequent calculation of accurate anomaly judgment thresholds, helping to reduce the missed detection rate of small-sized defects in mass production inspection.

[0017] As a preferred example of the first aspect, the step of calculating the anomaly determination threshold corresponding to each light window position based on the intensity dataset of each first light window includes:

[0018] For each first light window intensity dataset, statistical calculations are performed on the luminous intensity data of each first light window in the first light window intensity dataset to obtain the median, upper quartile, and lower quartile of luminous intensity.

[0019] The anomaly detection threshold corresponding to the position of the light window is obtained by calculating based on the median of the luminous intensity, the upper quartile of the luminous intensity, and the lower quartile of the luminous intensity.

[0020] In this preferred example, by statistically calculating the median, upper quartile, and lower quartile of the luminous intensity for each first light window intensity dataset, the anti-outlier characteristics of the quartile statistical method can be utilized to avoid the interference of extreme data on the statistical benchmark, thus obtaining a robust reference benchmark that more closely matches the normal light intensity distribution. Based on the above statistics, the anomaly detection threshold for the corresponding light window position can be calculated, which can accurately define the lower boundary of the normal light intensity range in combination with the interquartile range. The threshold can be flexibly adapted to different detection accuracy requirements, accurately identify light intensity attenuation caused by small-sized defects, and effectively reduce the false negative rate in mass production detection scenarios.

[0021] As a preferred example of the first aspect, the calculation of the anomaly determination threshold corresponding to the position of the light window based on the median of the luminous intensity, the upper quartile of the luminous intensity, and the lower quartile of the luminous intensity includes:

[0022] The difference between the lower quartile and the upper quartile of the luminous intensity is calculated to obtain the difference value, and the difference value is multiplied by a preset coefficient to obtain the intermediate value.

[0023] The difference between the median luminous intensity and the intermediate value is calculated to obtain the anomaly detection threshold corresponding to the position of the light window.

[0024] In this preferred example, by subtracting the upper and lower quartiles of the luminous intensity and multiplying by a preset coefficient to obtain the median value, the dispersion of normal light intensity of the same position window can be quantified based on the interquartile range. The preset coefficient can be used to flexibly adjust the judgment sensitivity to adapt to different detection needs, and this statistic is not affected by extreme outliers, making the statistical benchmark more stable. Then, by subtracting the median value from the median of the luminous intensity to obtain the anomaly judgment threshold, the lower boundary of normal light intensity can be accurately defined based on a robust statistical benchmark. Compared with the traditional mean ratio threshold, it is closer to the actual data distribution and can effectively identify light intensity attenuation caused by small-sized defects, reducing the false negative rate of mass production detection.

[0025] As a preferred example of the first aspect, the preset coefficient ranges from 1.5 to 5.

[0026] As a preferred example of the first aspect, the step of comparing the emission intensity data of each first optical window in the first optical window intensity dataset corresponding to the optical window position with the anomaly detection threshold corresponding to the optical window position to obtain the anomaly detection result of each of the dies in the vertical cavity surface-emitting laser corresponding to the optical window position includes:

[0027] For each of the first light window's luminous intensity data, the luminous intensity data of the first light window is compared with the anomaly detection threshold corresponding to the light window;

[0028] If the luminous intensity data of the first light window is less than the anomaly determination threshold, then the chip corresponding to the luminous intensity data of the first light window is determined to be in an abnormal state at the light window position.

[0029] In this preferred example, by comparing the luminous intensity data of each first optical window with the anomaly judgment threshold of the corresponding optical window, it is possible to verify each die and each optical window position one by one. The judgment is based on the threshold obtained by statistical analysis of the light intensity at the same position, which replaces the coarse comparison method of uniform proportional threshold within a single die, ensuring the consistency and accuracy of the judgment scale. When the light intensity data is lower than the threshold, the optical window position of the corresponding die is judged to be abnormal. It can stably capture small light intensity attenuation caused by small-sized defects, realize the automatic quantitative detection of small defects, and effectively reduce the false negative rate of mass production inspection.

[0030] As a preferred example of the first aspect, after comparing the luminous intensity data of the first optical window with the anomaly detection threshold corresponding to the position of the optical window, the method further includes:

[0031] If the luminous intensity data of the first light window is greater than or equal to the anomaly determination threshold, then the die corresponding to the luminous intensity data of the first light window is determined to be in a normal state at the light window position.

[0032] In this preferred example, when the luminous intensity data of the first light window is greater than or equal to the anomaly determination threshold, the corresponding die is determined to be in a normal state at the position of the light window, which can form a complete quantitative determination closed loop with the anomaly determination rule.

[0033] As a preferred example of the first aspect, the light window anomaly detection method further includes:

[0034] The abnormal detection results of each die at each optical window position are integrated to obtain the optical window abnormal detection results of the vertical cavity surface-emitting laser.

[0035] The accuracy rate of the light window anomaly detection is obtained by statistically calculating the light window anomaly detection results and the preset number of actual defects.

[0036] In this preferred example, by integrating the abnormal detection results of each die at each optical window position, a systematic detection conclusion covering all dies and optical windows in the vertical cavity surface-emitting laser can be formed, providing a comprehensive judgment basis for mass production quality control; combined with the preset statistical calculation of the actual defect quantity, the actual performance of the detection scheme can be quantitatively evaluated.

[0037] In a second aspect, the present invention provides a system for detecting optical window anomalies in a vertical cavity surface-emitting laser, comprising a first detection module, a second detection module and a third detection module;

[0038] The first detection module is used to acquire a first optical window intensity dataset corresponding one-to-one with each optical window position in the vertical cavity surface-emitting laser to be detected; wherein, the vertical cavity surface-emitting laser includes a plurality of dies, and each of the dies includes each optical window position in its detection area, and the first optical window intensity dataset includes the first optical window emission intensity data of each die at the optical window position corresponding to the first optical window intensity dataset.

[0039] The second detection module is used to calculate the anomaly judgment threshold corresponding to each light window position based on the intensity dataset of each first light window.

[0040] The third detection module is used to compare the emission intensity data of each first optical window in the first optical window intensity dataset corresponding to each optical window position with the anomaly judgment threshold corresponding to the optical window position, so as to obtain the anomaly detection result of each of the diodes in the vertical cavity surface emitter laser corresponding to the optical window position.

[0041] In this embodiment, by acquiring a first optical window intensity dataset that corresponds one-to-one with each optical window position and covers the luminous intensity of optical windows at the same position in each die, the inherent luminous intensity differences of optical windows at different positions within a single die can be eliminated by relying on the process consistency of optical windows at the same position. This reduces the fluctuation range of normal light intensity and provides a stable reference benchmark for anomaly judgment, thereby improving the inherent limitation of existing single-die comparison schemes that cannot identify small-sized defects due to excessive benchmark fluctuations. By calculating a dedicated anomaly judgment threshold for each optical window position, the independent light intensity distribution characteristics of different optical window positions can be adapted, making the judgment threshold more closely match the normal light intensity range of the corresponding position, thus improving the adaptability and accuracy of the anomaly judgment standard. By comparing the light intensity data of each die with the anomaly judgment threshold of the corresponding position one by one and outputting the anomaly detection results of the corresponding optical window positions of each die, anomaly screening of all dies in all optical window positions within a vertical cavity surface-emitting laser is achieved, which can reliably detect defects that occupy a small proportion of the optical window area. Therefore, the embodiments of this application can solve the problem of high false negative rate in the mass production detection of defects in vertical cavity surface-emitting laser (VCSEL) optical windows in the prior art.

[0042] As a preferred example of the second aspect, the acquisition of the first optical window intensity dataset corresponding one-to-one with the positions of each optical window in the vertical-cavity surface-emitting laser to be detected includes:

[0043] Obtain the second optical window intensity dataset corresponding to each of the dies in the vertical cavity surface-emitting laser to be tested; wherein, the second optical window intensity dataset includes the emission intensity data of the second optical window corresponding one-to-one with the position of each optical window;

[0044] Based on the position of each light window, the luminous intensity data of each second light window in each second light window intensity dataset are classified and integrated to obtain the first light window intensity dataset that corresponds one-to-one with each of the light window positions.

[0045] In this preferred example, by acquiring the second light window intensity dataset corresponding to each die, the original luminous intensity detection data of all light window positions within a single die can be completely preserved, providing a comprehensive data foundation for subsequent cross-die same-position comparisons. By classifying and integrating the light intensity data of each die according to the light window position, a statistical sample of light intensity of light windows at the same position is constructed, eliminating the data dispersion interference caused by the inherent light intensity differences of light windows at different positions within a single die, and providing reliable data support for subsequent calculation of anomaly judgment thresholds at each position and reducing the detection false negative rate.

[0046] As a preferred example of the second aspect, the step of classifying and integrating the luminous intensity data of each second light window in each second light window intensity dataset according to each light window position to obtain a first light window intensity dataset corresponding one-to-one with each light window position includes:

[0047] The first light window intensity dataset, corresponding one-to-one with each light window position, is obtained by traversing each light window position. During each traversal, the second light window intensity dataset corresponding to each die is traversed, and the second light window luminous intensity data corresponding to the current traversed light window position is extracted from the current traversed second light window intensity dataset according to the current traversed light window position. After traversing each die, the second light window luminous intensity data corresponding to the current traversed light window position in each die are integrated to form the first light window intensity dataset corresponding to the current traversed light window position.

[0048] In this preferred example, by traversing each optical window position and constructing a corresponding first optical window intensity dataset one by one, it is ensured that each optical window position independently forms a statistical sample, guaranteeing that the data and optical window positions are accurately correlated and avoiding data misalignment and confusion. During a single traversal, the luminous intensity data of the current optical window position is extracted and integrated for each die, which can aggregate the intensity data of optical windows at the same position, effectively eliminating the interference of inherent light intensity differences of optical windows at different positions within a single die. This provides a reliable data foundation for subsequent calculation of accurate anomaly judgment thresholds, helping to reduce the missed detection rate of small-sized defects in mass production inspection.

[0049] As a preferred example of the second aspect, the calculation of the anomaly determination threshold corresponding to each optical window position based on the intensity dataset of each first optical window includes:

[0050] For each first light window intensity dataset, statistical calculations are performed on the luminous intensity data of each first light window in the first light window intensity dataset to obtain the median, upper quartile, and lower quartile of luminous intensity.

[0051] The anomaly detection threshold corresponding to the position of the light window is obtained by calculating based on the median of the luminous intensity, the upper quartile of the luminous intensity, and the lower quartile of the luminous intensity.

[0052] In this preferred example, by statistically calculating the median, upper quartile, and lower quartile of the luminous intensity for each first light window intensity dataset, the anti-outlier characteristics of the quartile statistical method can be utilized to avoid the interference of extreme data on the statistical benchmark, thus obtaining a robust reference benchmark that more closely matches the normal light intensity distribution. Based on the above statistics, the anomaly detection threshold for the corresponding light window position can be calculated, which can accurately define the lower boundary of the normal light intensity range in combination with the interquartile range. The threshold can be flexibly adapted to different detection accuracy requirements, accurately identify light intensity attenuation caused by small-sized defects, and effectively reduce the false negative rate in mass production detection scenarios.

[0053] As a preferred example of the second aspect, the calculation of the anomaly determination threshold corresponding to the position of the light window based on the median of the luminous intensity, the upper quartile of the luminous intensity, and the lower quartile of the luminous intensity includes:

[0054] The difference between the lower quartile and the upper quartile of the luminous intensity is calculated to obtain the difference value, and the difference value is multiplied by a preset coefficient to obtain the intermediate value.

[0055] The difference between the median luminous intensity and the intermediate value is calculated to obtain the anomaly detection threshold corresponding to the position of the light window.

[0056] In this preferred example, by subtracting the upper and lower quartiles of the luminous intensity and multiplying by a preset coefficient to obtain the median value, the dispersion of normal light intensity of the same position window can be quantified based on the interquartile range. The preset coefficient can be used to flexibly adjust the judgment sensitivity to adapt to different detection needs, and this statistic is not affected by extreme outliers, making the statistical benchmark more stable. Then, by subtracting the median value from the median of the luminous intensity to obtain the anomaly judgment threshold, the lower boundary of normal light intensity can be accurately defined based on a robust statistical benchmark. Compared with the traditional mean ratio threshold, it is closer to the actual data distribution and can effectively identify light intensity attenuation caused by small-sized defects, reducing the false negative rate of mass production detection.

[0057] As a preferred example of the second aspect, the preset coefficient ranges from 1.5 to 5.

[0058] As a preferred example of the second aspect, the step of comparing the emission intensity data of each first optical window in the first optical window intensity dataset corresponding to the optical window position with the anomaly detection threshold corresponding to the optical window position to obtain the anomaly detection result of each of the dies in the vertical cavity surface-emitting laser corresponding to the optical window position includes:

[0059] For each of the first light window's luminous intensity data, the luminous intensity data of the first light window is compared with the anomaly detection threshold corresponding to the light window;

[0060] If the luminous intensity data of the first light window is less than the anomaly determination threshold, then the chip corresponding to the luminous intensity data of the first light window is determined to be in an abnormal state at the light window position.

[0061] In this preferred example, by comparing the luminous intensity data of each first optical window with the anomaly judgment threshold of the corresponding optical window, it is possible to verify each die and each optical window position one by one. The judgment is based on the threshold obtained by statistical analysis of the light intensity at the same position, which replaces the coarse comparison method of uniform proportional threshold within a single die, ensuring the consistency and accuracy of the judgment scale. When the light intensity data is lower than the threshold, the optical window position of the corresponding die is judged to be abnormal. It can stably capture small light intensity attenuation caused by small-sized defects, realize the automatic quantitative detection of small defects, and effectively reduce the false negative rate of mass production inspection.

[0062] As a preferred example of the second aspect, after comparing the luminous intensity data of the first light window with the anomaly detection threshold corresponding to the position of the light window, the method further includes:

[0063] If the luminous intensity data of the first light window is greater than or equal to the anomaly determination threshold, then the die corresponding to the luminous intensity data of the first light window is determined to be in a normal state at the light window position.

[0064] In this preferred example, when the luminous intensity data of the first light window is greater than or equal to the anomaly determination threshold, the corresponding die is determined to be in a normal state at the position of the light window, which can form a complete quantitative determination closed loop with the anomaly determination rule.

[0065] As a preferred example of the second aspect, the light window anomaly detection method further includes:

[0066] The abnormal detection results of each die at each optical window position are integrated to obtain the optical window abnormal detection results of the vertical cavity surface-emitting laser.

[0067] The accuracy rate of the light window anomaly detection is obtained by statistically calculating the light window anomaly detection results and the preset number of actual defects.

[0068] In this preferred example, by integrating the abnormal detection results of each die at each optical window position, a systematic detection conclusion covering all dies and optical windows in the vertical cavity surface-emitting laser can be formed, providing a comprehensive judgment basis for mass production quality control; combined with the preset statistical calculation of the actual defect quantity, the actual performance of the detection scheme can be quantitatively evaluated.

[0069] Another embodiment of this application provides a terminal device, including: a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the steps of the optical window anomaly detection method of the vertical cavity surface-emitting laser of this application.

[0070] Another embodiment of this application also provides a computer-readable storage medium item, including: a stored computer program, which, when the computer program is running, controls the device where the computer-readable storage medium is located to perform the steps of the optical window anomaly detection method of the vertical cavity surface-emitting laser of this application. Attached Figure Description

[0071] To more clearly illustrate the technical solution of this application, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0072] Figure 1 A flowchart illustrating an embodiment of a method for detecting optical window anomalies in a vertical-cavity surface-emitting laser provided by the present invention;

[0073] Figure 2 A schematic diagram of the wafer-die-window hierarchical structure and quantity specifications of an embodiment of a method for detecting optical window anomalies in a vertical cavity surface-emitting laser provided by the present invention;

[0074] Figure 3 A schematic diagram of the normal light emission pattern of a vertical cavity surface-emitting laser (VCSEL) and a single-core optical window light emission array, as provided in this invention, is shown in an embodiment of a method for detecting abnormal optical windows in a VCSEL.

[0075] Figure 4 This invention provides a method for detecting optical window anomalies in a vertical cavity surface-emitting laser (VCSEL) according to the present invention, and a schematic diagram of the abnormal optical window emission pattern and a single-core optical window emission array.

[0076] Figure 5A schematic diagram of the optical window position arrangement in a single-chip embodiment of an embodiment of a method for detecting optical window anomalies in a vertical cavity surface-emitting laser provided by the present invention;

[0077] Figure 6 This is a module structure diagram of an embodiment of a vertical cavity surface-emitting laser (VCSEL) optical window anomaly detection system provided by the present invention. Detailed Implementation

[0078] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0079] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains; the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the application; the terms “comprising” and “having”, and any variations thereof, in the specification, claims, and foregoing description of the drawings are intended to cover non-exclusive inclusion.

[0080] In the description of the embodiments of this application, technical terms such as "first" and "second" are used only to distinguish different objects and should not be construed as indicating or implying relative importance or implicitly specifying the number, specific order, or primary and secondary relationship of the indicated technical features. In the description of the embodiments of this application, "multiple" means two or more, unless otherwise explicitly defined.

[0081] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0082] In the description of the embodiments in this application, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this document generally indicates that the preceding and following related objects have an "or" relationship.

[0083] In the description of the embodiments of this application, the term "multiple" refers to two or more (including two), similarly, "multiple sets" refers to two or more (including two sets), and "multiple pieces" refers to two or more (including two pieces).

[0084] In the description of the embodiments of this application, unless otherwise expressly specified and limited, technical terms such as "installation," "connection," "joining," and "fixing" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. For those skilled in the art, the specific meaning of the above terms in the embodiments of this application can be understood according to the specific circumstances.

[0085] Example 1

[0086] Please refer to Figure 1 To address the high false negative rate in existing technologies for mass production inspection of defects in vertical-cavity surface-emitting laser (VCSEL) optical windows, this application provides a method for detecting optical window anomalies in VCSELs, comprising:

[0087] S1. Obtain the first optical window intensity dataset corresponding one-to-one with each optical window position in the vertical cavity surface-emitting laser to be detected; wherein, the vertical cavity surface-emitting laser includes a plurality of dies, and each of the dies includes each optical window position in its detection area, and the first optical window intensity dataset includes the first optical window emission intensity data of each die at the optical window position corresponding to the first optical window intensity dataset.

[0088] In some embodiments, obtaining the first optical window intensity dataset corresponding one-to-one with each optical window position in the vertical-cavity surface-emitting laser to be detected includes:

[0089] Obtain the second optical window intensity dataset corresponding to each of the dies in the vertical cavity surface-emitting laser to be tested; wherein, the second optical window intensity dataset includes the emission intensity data of the second optical window corresponding one-to-one with the position of each optical window;

[0090] Based on the position of each light window, the luminous intensity data of each second light window in each second light window intensity dataset are classified and integrated to obtain the first light window intensity dataset that corresponds one-to-one with each of the light window positions.

[0091] In some embodiments, the step of classifying and integrating the luminous intensity data of each second light window in each second light window intensity dataset according to each light window position to obtain a first light window intensity dataset corresponding one-to-one with each light window position includes:

[0092] The first light window intensity dataset, corresponding one-to-one with each light window position, is obtained by traversing each light window position. During each traversal, the second light window intensity dataset corresponding to each die is traversed, and the second light window luminous intensity data corresponding to the current traversed light window position is extracted from the current traversed second light window intensity dataset according to the current traversed light window position. After traversing each die, the second light window luminous intensity data corresponding to the current traversed light window position in each die are integrated to form the first light window intensity dataset corresponding to the current traversed light window position.

[0093] Specifically, the luminous intensity of the entire wafer of the vertical-cavity surface-emitting laser (VCSEL) to be tested is first measured. A single wafer (VCSEL) integrates tens of thousands of dies. Within the testing area of ​​each die, several optical windows are arranged in fixed positions, and the positions of the optical windows on each die correspond to each other. After the test, the original optical window intensity dataset corresponding to each die is obtained. Each set of original data records the luminous intensity values ​​of all optical windows of that die according to their positions. Next, the original test data of the entire wafer is categorized and integrated based on the optical window positions. Each optical window position is traversed one by one. When traversing a single optical window position, the data is read sequentially... The original optical window intensity dataset of each die is taken, and the emission intensity data corresponding to the current traversal position is extracted from it. After the data extraction of all dies is completed, the emission intensity data of all dies at the same position are summarized into a set of independent datasets. This process is repeated to complete the data collection of all optical window positions. Finally, the first optical window intensity dataset corresponding to each optical window position is obtained. Each set of first optical window intensity datasets contains the emission intensity data of all dies in the wafer to be tested (the vertical cavity surface-emitting laser to be tested) at the corresponding optical window position. It can reflect the overall light intensity distribution of the optical window at the same position and can be directly used for the statistical calculation of the subsequent anomaly judgment threshold.

[0094] S2. Calculate the anomaly judgment threshold corresponding to each light window position based on the intensity dataset of each first light window;

[0095] In some embodiments, the step of calculating the anomaly determination threshold corresponding to each optical window position based on each first optical window intensity dataset includes:

[0096] For each first light window intensity dataset, statistical calculations are performed on the luminous intensity data of each first light window in the first light window intensity dataset to obtain the median, upper quartile, and lower quartile of luminous intensity.

[0097] The anomaly detection threshold corresponding to the position of the light window is obtained by calculating based on the median of the luminous intensity, the upper quartile of the luminous intensity, and the lower quartile of the luminous intensity.

[0098] In some embodiments, the step of calculating the anomaly detection threshold corresponding to the light window position based on the median of the luminous intensity, the upper quartile of the luminous intensity, and the lower quartile of the luminous intensity includes:

[0099] The difference between the lower quartile and the upper quartile of the luminous intensity is calculated to obtain the difference value, and the difference value is multiplied by a preset coefficient to obtain the intermediate value.

[0100] The difference between the median luminous intensity and the intermediate value is calculated to obtain the anomaly detection threshold corresponding to the position of the light window.

[0101] In some embodiments, the preset coefficient ranges from 1.5 to 5.

[0102] Specifically, after obtaining the first light window intensity dataset corresponding to each light window position, independent statistical calculations are performed for each dataset to obtain a unique anomaly detection threshold corresponding to each light window position. During the calculation, all luminous intensity data in a single first light window intensity dataset are first sorted by value. The median, upper quartile, and lower quartile of the luminous intensity at that light window position are then calculated based on the total number of data points. The median is selected based on the parity of the total number of data points, either as the middle value after sorting or as the average of the two middle values. The upper quartile corresponds to the luminous intensity value at the 75th percentile of the dataset, and the lower quartile corresponds to the luminous intensity value at the 25th percentile. The difference between the upper and lower quartiles is then calculated to obtain the interquartile range. This interquartile range is multiplied by a preset coefficient N to obtain an intermediate calculated value. Finally, the median luminous intensity at that position is subtracted from this intermediate calculated value, and the result is the anomaly detection threshold corresponding to that light window position. The preset coefficient N ranges from 1.5 to 5. In actual mass production testing, the value can be flexibly adjusted according to the sensitivity requirements for defect detection.

[0103] S3. For each optical window position, compare the emission intensity data of each first optical window in the first optical window intensity dataset corresponding to the optical window position with the anomaly detection threshold corresponding to the optical window position to obtain the anomaly detection result of each die in the vertical cavity surface emitter laser corresponding to the optical window position.

[0104] In some embodiments, comparing the emission intensity data of each first optical window in the first optical window intensity dataset corresponding to the optical window position with the anomaly detection threshold corresponding to the optical window position to obtain the anomaly detection result of each of the dies in the vertical cavity surface-emitting laser corresponding to the optical window position includes:

[0105] For each of the first light window's luminous intensity data, the luminous intensity data of the first light window is compared with the anomaly detection threshold corresponding to the light window;

[0106] If the luminous intensity data of the first light window is less than the anomaly determination threshold, then the chip corresponding to the luminous intensity data of the first light window is determined to be in an abnormal state at the light window position.

[0107] In some embodiments, after comparing the luminous intensity data of the first light window with the anomaly detection threshold corresponding to the position of the light window, the method further includes:

[0108] If the luminous intensity data of the first light window is greater than or equal to the anomaly determination threshold, then the die corresponding to the luminous intensity data of the first light window is determined to be in a normal state at the light window position.

[0109] Specifically, after obtaining the anomaly detection threshold for each optical window position, a comparison and judgment operation is performed for each optical window position one by one. The first optical window intensity dataset corresponding to that optical window position is retrieved, and the luminous intensity data of the first optical window corresponding to each die in the dataset is compared with the anomaly detection threshold for that optical window position. When the luminous intensity data of a certain die at the current optical window position is less than the anomaly detection threshold for that position, it is determined that the optical window position of that die is in an abnormal state. The corresponding optical window may have scratches, dirt, or black obstruction, which will cause a decrease in luminous intensity. For appearance defects, if the luminous intensity data of the die is greater than or equal to the anomaly judgment threshold, it is determined that the die is in a normal state at that optical window position. After completing the comparison and judgment of all die data at the current optical window position, the anomaly detection results of all dies at that optical window position can be obtained. After completing the judgment process of all optical window positions in sequence according to the same comparison logic, the anomaly detection results of each die in the vertical cavity surface emitting laser under test at each optical window position can be obtained completely. The entire comparison process can be completed automatically by the program without the need for manual visual inspection. It can reliably detect small-sized defects accounting for 5% to 50% of the optical window area, effectively improving the detection rate and execution efficiency of mass production inspection.

[0110] In some embodiments, the light window anomaly detection method further includes:

[0111] The abnormal detection results of each die at each optical window position are integrated to obtain the optical window abnormal detection results of the vertical cavity surface-emitting laser.

[0112] The accuracy rate of the light window anomaly detection is obtained by statistically calculating the light window anomaly detection results and the preset number of actual defects.

[0113] Specifically, after completing the point-by-point comparison and judgment of all optical window positions, the anomaly detection results of each die at all optical window positions are collected one by one according to the die number and the optical window position sequence number, and integrated to form a complete optical window anomaly detection result covering all dies and all optical window positions of the entire vertical cavity surface-emitting laser wafer to be tested. The result can clearly mark the optical window position where each die has an anomaly, and can also statistically analyze the anomaly distribution of the entire wafer by position dimension, providing complete data support for product yield screening and process stability analysis. When verifying the detection effect based on this, the preset number of real defects for the corresponding test samples is imported in advance. This is the total number of light windows with appearance defects such as scratches, stains, and black obstructions that have been confirmed by manual calibration or other reliable methods in the early stage. Then, the number of correctly detected abnormalities that match the real defect points in this abnormality detection result is divided by the total number of real defects in the sample to obtain the light window abnormality detection accuracy rate corresponding to this detection scheme. During the verification process, the number of missed detections and false alarms can also be counted simultaneously, and the missed detection rate and false alarm rate can be calculated separately. The missed detection rate is the proportion of light windows with real defects that were not detected to the total number of real defects, and the false alarm rate is the proportion of normal light windows that were mistakenly judged as abnormal to the total number of light windows participating in the detection. In actual mass production applications, the preset coefficient values ​​in the threshold calculation process can be adjusted by combining the preset control index requirements and comparing the accuracy rate, missed detection rate, and false alarm rate under different parameters to select the optimal threshold scheme that meets the mass production requirements and ensure that the detection accuracy meets the actual standards of production control.

[0114] Furthermore, to fully illustrate the feasibility of this application, a mass-produced VCSEL wafer inspection scenario, in which 60,000 dies are integrated on a single wafer (vertical cavity surface-emitting laser) and each die contains 52 optical windows, is used as an example. The complete implementation process of the solution is explained in conjunction with actual mass production test data:

[0115] Before discussing the solution, it should be noted that this solution is applied to the wafer-level mass production inspection scenario of vertical-cavity surface-emitting lasers (VCSELs). A single wafer integrates a large number of dies with uniform specifications. Within the inspection area of ​​each die, several optical windows are arranged in fixed positions. The hierarchical structure and corresponding quantity specifications of the wafer, die, and optical windows are as follows: Figure 2 As shown, a single wafer can accommodate approximately 60,000 dies, with 52 optical windows evenly arranged within each die. The positions of the optical windows on each die correspond one-to-one, and the process characteristics of optical windows at the same position are consistent, providing a foundation for subsequent cross-die comparisons of light intensity data at the same position. Under normal conditions, the optical window surfaces are defect-free, emitting uniform and full light without localized obstruction or gaps. The emission morphology of a single normal optical window and the complete optical window emission array of a single die are shown in the figure. Figure 3As shown, each light window exhibits a complete and uniform circular luminous surface, and the overall luminous intensity is within the normal fluctuation range. However, when surface defects such as scratches, contamination, or black obstructions appear on the light window surface, the defective area will block light from escaping, reducing the effective light-emitting area of ​​the corresponding light window and directly causing a significant decrease in the luminous intensity of that light window. The luminous morphology of a single abnormal light window and the corresponding luminous array performance of the diode are shown in the figure. Figure 4 As shown, the light window in the red box in the lower right corner has a local light loss due to surface defects. Its overall light intensity is significantly lower than that of other normal light windows in the die. This difference in light intensity attenuation caused by defects is the basis for the automatic detection of light window abnormalities in this solution.

[0116] like Figure 5 As shown in Table 1, the positions of each optical window are represented. Based on these positions, the raw luminous intensity of the optical windows across the entire wafer is first tested and the dataset is collected. The wafer to be inspected is first subjected to a full-wafer optical window luminous intensity test to obtain the raw optical window intensity dataset for each die. Within each dataset, the luminous intensity values ​​of 52 optical windows on a single die are recorded according to their positions. Existing conventional inspection methods use data from optical windows at different positions within a single die for comparison and judgment. According to the measured statistics, as shown in Table 1, among 10 randomly selected dies, the mean luminous intensity of the 52nd optical window within a single die is in the range of 4,592,598 to 4,640,289, and the standard deviation within a single die is generally around 394,295.0 to 410,510.0. The data dispersion is large, directly resulting in a wide fluctuation range of the judgment benchmark, and only large defects occupying more than 50% of the optical window area can be identified.

[0117] Table 1. Statistical data on luminous intensity of the inner window of a single tube.

[0118] This solution uses the optical window position as a dimension to classify and integrate test data from 60,000 dies across the entire wafer. It iterates through 52 optical window positions, sequentially reading the original optical window intensity dataset for each die and extracting the luminous intensity data corresponding to the current position. After traversing all dies, the luminous intensity data from all positions are aggregated into a single independent first optical window intensity dataset. Taking actual test data as an example, as shown in Table 2, the statistical sample size for a single optical window position can reach 60,032 dies. Although the mean luminous intensity at different positions has inherent differences, the standard deviation of the data at the same position is only 130,746.1~155,301.5, far lower than the standard deviation within a single die. This significantly reduces data dispersion and provides a stable statistical benchmark for the accurate identification of small-sized defects.

[0119] Table 2. Statistical data on luminous intensity across the same filament window.

[0120] Next, specific anomaly detection thresholds are calculated for each light window position. For each set of first light window intensity datasets, the quartile statistical method is used for calculation: First, the luminescence intensity data in the dataset are sorted in ascending order of value. Based on the sample size, the median luminescence intensity at that position (corresponding to the 50th percentile), the upper quartile Q3 (corresponding to the 75th percentile), and the lower quartile Q1 (corresponding to the 25th percentile) are obtained. Then, the detection threshold at that position is calculated using the formula "anomaly detection threshold = median luminescence intensity - N × (Q3 - Q1)". The empirical range of the coefficient N is 1.5~5, and the range of 1.5~4 is recommended for mass production scenarios. It can be flexibly adjusted according to the detection sensitivity requirements.

[0121] Taking the measured data at positions 49 and 50 as examples, the specific calculations are as follows: the median luminous intensity of the 49th optical window is 4,719,419, Q3 is 4,793,254, and Q1 is 4,663,347; the median luminous intensity of the 50th optical window is 4,726,793, Q3 is 4,786,607, and Q1 is 4,668,481. When N=3, the anomaly detection threshold for window 49 is 4719419 - 3×(4793254-4663347)=4329716, and the anomaly detection threshold for window 50 is 4726793 - 3×(4786607-4668481)=4372427. When N=4, the anomaly detection threshold for window 49 is 4719419 - 4×(4793254-4663347)=4199815, and the anomaly detection threshold for window 50 is 4726793 - 4×(4786607-4668481)=4254305. A smaller N value results in a higher detection threshold and stronger detection sensitivity, while a larger N value results in a lower detection threshold and lower false alarm risk. The optimal parameter can be selected as needed.

[0122] After threshold calculation, comparison and judgment are performed at each optical window position, and the detection results are output. For each optical window position, the luminous intensity data of each die in the corresponding first optical window intensity dataset is compared numerically with the anomaly judgment threshold for that position: if the luminous intensity of a die at the current position is less than the anomaly judgment threshold, the optical window position corresponding to that die is determined to be in an abnormal state, with appearance defects such as scratches, contamination, or black obstruction that cause a decrease in luminous intensity; if the luminous intensity is greater than or equal to the threshold, it is determined to be in a normal state. After completing the comparison of all 52 optical window positions, the anomaly detection results for all 60,000 dies in the entire wafer at each optical window position can be obtained.

[0123] To quantify and verify the detection performance of the solution, as shown in Table 3, 3000 die samples labeled with real defects (1359 of which were real defects) were selected for comparative testing. The test results are as follows: The original single-die mean ratio method could only detect 639 defects, with a detection accuracy of 78.20% and a false negative rate of 21.8%, and it could not automatically identify small-sized defects. When using this solution and taking N=3, a total of 1330 anomalies were detected, of which 998 were real defects, the detection accuracy improved to 88.93%, and the false negative rate dropped to 0. When taking N=4, a total of 958 anomalies were detected, all of which matched real defects, the detection accuracy reached 98.67%, the false negative rate was only 1.3%, and the false alarm rate was 0. All indicators meet the recommended requirements for mass production control (accuracy > 90%, false negative rate < 2%, false alarm rate < 5%).

[0124] Table 3 Comparison of the performance of the original method and the new method in detecting optical window anomalies

[0125] Based on actual testing results, the original solution could only automatically detect defects occupying more than 50% of the light window area, with an overall detection rate of about 40%. This solution, relying on the statistical benchmark of cross-die at the same position, can automatically and stably identify small-sized defects occupying 5% to 50% of the light window area, increasing the overall detection rate to 90%. It can effectively solve the problem of high missed detection rate of small defects in mass production testing scenarios, and the overall calculation logic is clear and can be directly embedded into mass production testing software systems for practical application.

[0126] In summary, the embodiments of this application, by acquiring a first optical window intensity dataset that corresponds one-to-one with each optical window position and covers the luminous intensity of optical windows at the same position in each die, can rely on the process consistency of optical windows at the same position to eliminate the data dispersion caused by the inherent difference in luminous intensity of optical windows at different positions within a single die, reduce the fluctuation range of normal light intensity, and provide a stable reference benchmark for anomaly judgment. This improves the inherent limitation of existing single-die comparison schemes that cannot identify small-sized defects due to excessive benchmark fluctuations. By calculating a dedicated anomaly judgment threshold for each optical window position, it can adapt to the independent light intensity distribution characteristics of different optical window positions, making the judgment threshold more closely match the normal light intensity range of the corresponding position, and improving the adaptability and accuracy of the anomaly judgment standard. By comparing the light intensity data of each die with the anomaly judgment threshold of the corresponding position one by one and outputting the anomaly detection results of the corresponding optical window positions of each die, anomaly screening of all dies in the vertical cavity surface-emitting laser at all optical window positions is realized, and defects occupying a small proportion of the optical window area can be reliably detected. Therefore, the embodiments of this application can solve the problem of high false negative rate in the mass production detection of defects in vertical cavity surface-emitting laser (VCSEL) optical windows in the prior art.

[0127] Example 2

[0128] like Figure 6 As shown, based on the above method embodiments, corresponding device embodiments are provided;

[0129] One embodiment of the present invention provides an optical window anomaly detection system for a vertical cavity surface-emitting laser, including a first detection module 61, a second detection module 62 and a third detection module 63;

[0130] The first detection module 61 is used to acquire a first optical window intensity dataset corresponding one-to-one with each optical window position in the vertical cavity surface-emitting laser to be detected; wherein, the vertical cavity surface-emitting laser includes a plurality of dies, and each of the dies includes the optical window positions in its detection area, and the first optical window intensity dataset includes the first optical window emission intensity data of each die at the optical window position corresponding to the first optical window intensity dataset.

[0131] The second detection module 62 is used to calculate based on the intensity dataset of each first light window to obtain the anomaly judgment threshold corresponding to each light window position.

[0132] The third detection module 63 is used to compare the emission intensity data of each first optical window in the first optical window intensity dataset corresponding to each optical window position with the anomaly judgment threshold corresponding to the optical window position, so as to obtain the anomaly detection result of each of the diodes in the vertical cavity surface emitter laser corresponding to the optical window position.

[0133] In this embodiment, by acquiring a first optical window intensity dataset that corresponds one-to-one with each optical window position and covers the luminous intensity of optical windows at the same position in each die, the inherent luminous intensity differences of optical windows at different positions within a single die can be eliminated by relying on the process consistency of optical windows at the same position. This reduces the fluctuation range of normal light intensity and provides a stable reference benchmark for anomaly judgment, thereby improving the inherent limitation of existing single-die comparison schemes that cannot identify small-sized defects due to excessive benchmark fluctuations. By calculating a dedicated anomaly judgment threshold for each optical window position, the independent light intensity distribution characteristics of different optical window positions can be adapted, making the judgment threshold more closely match the normal light intensity range of the corresponding position, thus improving the adaptability and accuracy of the anomaly judgment standard. By comparing the light intensity data of each die with the anomaly judgment threshold of the corresponding position one by one and outputting the anomaly detection results of the corresponding optical window positions of each die, anomaly screening of all dies in all optical window positions within a vertical cavity surface-emitting laser is achieved, which can reliably detect defects that occupy a small proportion of the optical window area. Therefore, the embodiments of this application can solve the problem of high false negative rate in the mass production detection of defects in vertical cavity surface-emitting laser (VCSEL) optical windows in the prior art.

[0134] In some embodiments, obtaining the first optical window intensity dataset corresponding one-to-one with each optical window position in the vertical-cavity surface-emitting laser to be detected includes:

[0135] Obtain the second optical window intensity dataset corresponding to each of the dies in the vertical cavity surface-emitting laser to be tested; wherein, the second optical window intensity dataset includes the emission intensity data of the second optical window corresponding one-to-one with the position of each optical window;

[0136] Based on the position of each light window, the luminous intensity data of each second light window in each second light window intensity dataset are classified and integrated to obtain the first light window intensity dataset that corresponds one-to-one with each of the light window positions.

[0137] In this preferred example, by acquiring the second light window intensity dataset corresponding to each die, the original luminous intensity detection data of all light window positions within a single die can be completely preserved, providing a comprehensive data foundation for subsequent cross-die same-position comparisons. By classifying and integrating the light intensity data of each die according to the light window position, a statistical sample of light intensity of light windows at the same position is constructed, eliminating the data dispersion interference caused by the inherent light intensity differences of light windows at different positions within a single die, and providing reliable data support for subsequent calculation of anomaly judgment thresholds at each position and reducing the detection false negative rate.

[0138] In some embodiments, the step of classifying and integrating the luminous intensity data of each second light window in each second light window intensity dataset according to each light window position to obtain a first light window intensity dataset corresponding one-to-one with each light window position includes:

[0139] The first light window intensity dataset, corresponding one-to-one with each light window position, is obtained by traversing each light window position. During each traversal, the second light window intensity dataset corresponding to each die is traversed, and the second light window luminous intensity data corresponding to the current traversed light window position is extracted from the current traversed second light window intensity dataset according to the current traversed light window position. After traversing each die, the second light window luminous intensity data corresponding to the current traversed light window position in each die are integrated to form the first light window intensity dataset corresponding to the current traversed light window position.

[0140] In this preferred example, by traversing each optical window position and constructing a corresponding first optical window intensity dataset one by one, it is ensured that each optical window position independently forms a statistical sample, guaranteeing that the data and optical window positions are accurately correlated and avoiding data misalignment and confusion. During a single traversal, the luminous intensity data of the current optical window position is extracted and integrated for each die, which can aggregate the intensity data of optical windows at the same position, effectively eliminating the interference of inherent light intensity differences of optical windows at different positions within a single die. This provides a reliable data foundation for subsequent calculation of accurate anomaly judgment thresholds, helping to reduce the missed detection rate of small-sized defects in mass production inspection.

[0141] In some embodiments, the step of calculating the anomaly determination threshold corresponding to each optical window position based on each first optical window intensity dataset includes:

[0142] For each first light window intensity dataset, statistical calculations are performed on the luminous intensity data of each first light window in the first light window intensity dataset to obtain the median, upper quartile, and lower quartile of luminous intensity.

[0143] The anomaly detection threshold corresponding to the position of the light window is obtained by calculating based on the median of the luminous intensity, the upper quartile of the luminous intensity, and the lower quartile of the luminous intensity.

[0144] In this preferred example, by statistically calculating the median, upper quartile, and lower quartile of the luminous intensity for each first light window intensity dataset, the anti-outlier characteristics of the quartile statistical method can be utilized to avoid the interference of extreme data on the statistical benchmark, thus obtaining a robust reference benchmark that more closely matches the normal light intensity distribution. Based on the above statistics, the anomaly detection threshold for the corresponding light window position can be calculated, which can accurately define the lower boundary of the normal light intensity range in combination with the interquartile range. The threshold can be flexibly adapted to different detection accuracy requirements, accurately identify light intensity attenuation caused by small-sized defects, and effectively reduce the false negative rate in mass production detection scenarios.

[0145] In some embodiments, the step of calculating the anomaly detection threshold corresponding to the light window position based on the median of the luminous intensity, the upper quartile of the luminous intensity, and the lower quartile of the luminous intensity includes:

[0146] The difference between the lower quartile and the upper quartile of the luminous intensity is calculated to obtain the difference value, and the difference value is multiplied by a preset coefficient to obtain the intermediate value.

[0147] The difference between the median luminous intensity and the intermediate value is calculated to obtain the anomaly detection threshold corresponding to the position of the light window.

[0148] In this preferred example, by subtracting the upper and lower quartiles of the luminous intensity and multiplying by a preset coefficient to obtain the median value, the dispersion of normal light intensity of the same position window can be quantified based on the interquartile range. The preset coefficient can be used to flexibly adjust the judgment sensitivity to adapt to different detection needs, and this statistic is not affected by extreme outliers, making the statistical benchmark more stable. Then, by subtracting the median value from the median of the luminous intensity to obtain the anomaly judgment threshold, the lower boundary of normal light intensity can be accurately defined based on a robust statistical benchmark. Compared with the traditional mean ratio threshold, it is closer to the actual data distribution and can effectively identify light intensity attenuation caused by small-sized defects, reducing the false negative rate of mass production detection.

[0149] In some embodiments, the preset coefficient ranges from 1.5 to 5.

[0150] In some embodiments, comparing the emission intensity data of each first optical window in the first optical window intensity dataset corresponding to the optical window position with the anomaly detection threshold corresponding to the optical window position to obtain the anomaly detection result of each of the dies in the vertical cavity surface-emitting laser corresponding to the optical window position includes:

[0151] For each of the first light window's luminous intensity data, the luminous intensity data of the first light window is compared with the anomaly detection threshold corresponding to the light window;

[0152] If the luminous intensity data of the first light window is less than the anomaly determination threshold, then the chip corresponding to the luminous intensity data of the first light window is determined to be in an abnormal state at the light window position.

[0153] In this preferred example, by comparing the luminous intensity data of each first optical window with the anomaly judgment threshold of the corresponding optical window, it is possible to verify each die and each optical window position one by one. The judgment is based on the threshold obtained by statistical analysis of the light intensity at the same position, which replaces the coarse comparison method of uniform proportional threshold within a single die, ensuring the consistency and accuracy of the judgment scale. When the light intensity data is lower than the threshold, the optical window position of the corresponding die is judged to be abnormal. It can stably capture small light intensity attenuation caused by small-sized defects, realize the automatic quantitative detection of small defects, and effectively reduce the false negative rate of mass production inspection.

[0154] In some embodiments, after comparing the luminous intensity data of the first light window with the anomaly detection threshold corresponding to the position of the light window, the method further includes:

[0155] If the luminous intensity data of the first light window is greater than or equal to the anomaly determination threshold, then the die corresponding to the luminous intensity data of the first light window is determined to be in a normal state at the light window position.

[0156] In this preferred example, when the luminous intensity data of the first light window is greater than or equal to the anomaly determination threshold, the corresponding die is determined to be in a normal state at the position of the light window, which can form a complete quantitative determination closed loop with the anomaly determination rule.

[0157] In some embodiments, the light window anomaly detection method further includes:

[0158] The abnormal detection results of each die at each optical window position are integrated to obtain the optical window abnormal detection results of the vertical cavity surface-emitting laser.

[0159] The accuracy rate of the light window anomaly detection is obtained by statistically calculating the light window anomaly detection results and the preset number of actual defects.

[0160] In this preferred example, by integrating the abnormal detection results of each die at each optical window position, a systematic detection conclusion covering all dies and optical windows in the vertical cavity surface-emitting laser can be formed, providing a comprehensive judgment basis for mass production quality control; combined with the preset statistical calculation of the actual defect quantity, the actual performance of the detection scheme can be quantitatively evaluated.

[0161] For more detailed steps and working principles of this embodiment, please refer to the relevant description in Embodiment 1, but not limited to these descriptions.

[0162] It should be noted that the device embodiments described above are merely illustrative, and some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Furthermore, in the accompanying drawings of the device embodiments provided in this application, the connection relationships between modules indicate that they have communication connections, which can specifically be implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement this without any creative effort.

[0163] Example 3

[0164] Based on the above embodiments of the optical window anomaly detection method for vertical cavity surface-emitting lasers, another embodiment of this application provides a terminal device, which includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the optical window anomaly detection method for vertical cavity surface-emitting lasers according to any embodiment of this application.

[0165] For example, in this embodiment, the computer program can be divided into one or more modules, which are stored in the memory and executed by the processor to complete this application. The one or more module units may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program in the terminal device.

[0166] The terminal device may be a desktop computer, laptop, handheld computer, or cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0167] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the terminal device, connecting all parts of the terminal device via various interfaces and lines.

[0168] Example 4

[0169] Based on the above-described method embodiments, another embodiment of this application provides a computer-readable storage medium including a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to execute the optical window anomaly detection method of any of the above-described method embodiments of this application.

[0170] The modules / units integrated in the device / terminal equipment, if implemented as software functional units and sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc.

Claims

1. A method for detecting an optical window abnormality of a vertical cavity surface emitting laser, characterized by, include: A first optical window intensity dataset is obtained, which corresponds one-to-one with each optical window position in the vertical cavity surface-emitting laser to be detected; wherein, the vertical cavity surface-emitting laser includes a plurality of dies, and each of the dies includes the optical window positions in its detection area, and the first optical window intensity dataset includes the first optical window emission intensity data of each die at the optical window position corresponding to the first optical window intensity dataset. Based on the intensity dataset of each first light window, the anomaly detection threshold corresponding to each light window position is calculated. For each optical window position, the emission intensity data of each first optical window in the first optical window intensity dataset corresponding to the optical window position is compared with the anomaly detection threshold corresponding to the optical window position to obtain the anomaly detection result of each die in the vertical cavity surface emitter laser corresponding to the optical window position.

2. The method for detecting optical window anomalies in a vertical-cavity surface-emitting laser as described in claim 1, characterized in that, The acquisition of the first optical window intensity dataset, which corresponds one-to-one with the position of each optical window in the vertical-cavity surface-emitting laser to be detected, includes: Obtain the second optical window intensity dataset corresponding to each of the dies in the vertical cavity surface-emitting laser to be tested; wherein, the second optical window intensity dataset includes the emission intensity data of the second optical window corresponding one-to-one with the position of each optical window; Based on the position of each light window, the luminous intensity data of each second light window in each second light window intensity dataset are classified and integrated to obtain the first light window intensity dataset that corresponds one-to-one with each of the light window positions.

3. The method for detecting optical window anomalies in a vertical-cavity surface-emitting laser as described in claim 2, characterized in that, The step of classifying and integrating the luminous intensity data of each second light window in each second light window intensity dataset according to the position of each light window to obtain the first light window intensity dataset corresponding one-to-one with each light window position includes: The first light window intensity dataset, corresponding one-to-one with each light window position, is obtained by traversing each light window position. During each traversal, the second light window intensity dataset corresponding to each die is traversed, and the second light window luminous intensity data corresponding to the current traversed light window position is extracted from the current traversed second light window intensity dataset according to the current traversed light window position. After traversing each die, the second light window luminous intensity data corresponding to the current traversed light window position in each die are integrated to form the first light window intensity dataset corresponding to the current traversed light window position.

4. The method for detecting optical window anomalies in a vertical-cavity surface-emitting laser as described in claim 1, characterized in that, The step of calculating the anomaly detection threshold corresponding to each optical window position based on the intensity dataset of each first optical window includes: For each first light window intensity dataset, statistical calculations are performed on the luminous intensity data of each first light window in the first light window intensity dataset to obtain the median, upper quartile, and lower quartile of luminous intensity. The anomaly detection threshold corresponding to the position of the light window is obtained by calculating based on the median of the luminous intensity, the upper quartile of the luminous intensity, and the lower quartile of the luminous intensity.

5. The method for detecting optical window anomalies in a vertical-cavity surface-emitting laser as described in claim 4, characterized in that, The step of calculating the anomaly detection threshold corresponding to the light window position based on the median of the luminous intensity, the upper quartile of the luminous intensity, and the lower quartile of the luminous intensity includes: The difference between the lower quartile and the upper quartile of the luminous intensity is calculated to obtain the difference value, and the difference value is multiplied by a preset coefficient to obtain the intermediate value. The difference between the median luminous intensity and the intermediate value is calculated to obtain the anomaly detection threshold corresponding to the position of the light window.

6. The method for detecting optical window anomalies in a vertical-cavity surface-emitting laser as described in claim 5, characterized in that, The preset coefficient has a value range of 1.5 to 5.

7. The method for detecting optical window anomalies in a vertical-cavity surface-emitting laser as described in claim 1, characterized in that, The step of comparing the emission intensity data of each first optical window in the first optical window intensity dataset corresponding to the optical window position with the anomaly detection threshold corresponding to the optical window position to obtain the anomaly detection result of each of the dies in the vertical cavity surface-emitting laser corresponding to the optical window position includes: For each of the first light window's luminous intensity data, the luminous intensity data of the first light window is compared with the anomaly detection threshold corresponding to the light window; If the luminous intensity data of the first light window is less than the anomaly determination threshold, then the chip corresponding to the luminous intensity data of the first light window is determined to be in an abnormal state at the light window position.

8. The method for detecting optical window anomalies in a vertical-cavity surface-emitting laser as described in claim 7, characterized in that, After comparing the luminous intensity data of the first light window with the anomaly detection threshold corresponding to the position of the light window, the method further includes: If the luminous intensity data of the first light window is greater than or equal to the anomaly determination threshold, then the die corresponding to the luminous intensity data of the first light window is determined to be in a normal state at the light window position.

9. A method for detecting optical window anomalies in a vertical-cavity surface-emitting laser as described in any one of claims 1-8, characterized in that, The method for detecting abnormal light windows also includes: The abnormal detection results of each die at each optical window position are integrated to obtain the optical window abnormal detection results of the vertical cavity surface-emitting laser. The accuracy rate of the light window anomaly detection is obtained by statistically calculating the light window anomaly detection results and the preset number of actual defects.

10. A system for detecting optical window anomalies in a vertical-cavity surface-emitting laser, characterized in that, The optical window anomaly detection system is used to perform the optical window anomaly detection method for a vertical cavity surface-emitting laser as described in any one of claims 1-9.

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