Method and system for quality detection of semiconductor lasers

Abstract

This invention relates to the field of semiconductor laser technology, specifically disclosing a quality inspection method and system for semiconductor lasers. The method includes the following steps: acquiring a laser beam at the output end of the semiconductor laser to obtain a laser spot image; extracting spot parameters from the laser spot image to obtain spot feature data; calculating the beam quality of the semiconductor laser based on the spot feature data to obtain a beam quality factor; performing optical power testing on the semiconductor laser based on the beam quality factor to obtain a power test curve; and determining the quality compliance of the semiconductor laser based on the power test curve to obtain a quality evaluation result. This method solves the technical problem that optical power testing is usually performed independently of beam quality analysis, lacking a dynamic correlation mechanism between the two, and thus failing to achieve real-time mutual calibration and comprehensive evaluation.

Description

Technical Field

[0001] This invention relates to the field of semiconductor laser technology, and in particular to a method and system for quality inspection of semiconductor lasers. Background Technology

[0002] With the widespread application of semiconductor lasers in industrial processing, medical equipment, communication technology, and defense technology, their performance stability and product quality have increasingly become key factors restricting the development of related technologies. As a core light source device, the quality of the output beam of a semiconductor laser directly determines the efficiency and accuracy of the application system. Therefore, establishing a scientific and efficient testing method to evaluate its quality is crucial. Traditional testing methods mostly focus on measuring single parameters, such as power or wavelength, which are difficult to comprehensively reflect the overall performance of the laser. Especially when facing modern semiconductor lasers with high power and multi-mode output, the limitations of traditional methods become increasingly apparent.

[0003] Furthermore, current testing processes generally suffer from fragmented testing steps and delayed feedback. Optical power testing is typically performed independently of beam quality analysis, lacking a dynamic correlation mechanism between the two, making real-time cross-calibration and comprehensive evaluation impossible. This separate testing model not only reduces testing efficiency but may also lead to misjudgments of device quality, particularly prominent in the selection of qualified products under critical conditions. Therefore, there is an urgent need for an intelligent testing method that can integrate multi-dimensional information, achieving a unified process from spot feature extraction to beam quality assessment and power characteristic verification, in order to improve the accuracy and reliability of semiconductor laser quality assessment. Summary of the Invention

[0004] The purpose of this invention is to at least partially solve one of the technical problems existing in the prior art.

[0005] To achieve the above objectives, the present invention provides a method for quality inspection of semiconductor lasers, comprising the following steps: The output of the semiconductor laser is sampled to obtain a laser spot image; The laser spot image is subjected to spot parameter extraction to obtain spot feature data; The beam quality factor of the semiconductor laser is obtained by calculating the beam quality based on the beam feature data. The optical power of the semiconductor laser was tested based on the beam quality factor to obtain a power test curve. The semiconductor laser is assessed for quality compliance based on the power test curve, and a quality evaluation result is obtained.

[0006] Furthermore, beam acquisition is performed on the output end of the semiconductor laser to obtain a laser spot image, including: A preset optical acquisition device is used to capture the output of the semiconductor laser to obtain an initial beam image, and the initial beam image is preprocessed to obtain a clear beam image. The clear beam image is used to locate the spot area by image analysis technology to obtain the spot position information. Based on the spot position information, the clear beam image is cropped to obtain a laser spot image containing only the spot area.

[0007] Furthermore, based on the spot position information, the clear beam image is cropped to obtain a laser spot image containing only the spot region, including: The location information of the light spot is used to perform boundary expansion calculation to obtain the safe boundary range of the light spot, and the background noise of the region in the clear beam image located within the safe boundary range of the light spot is evaluated to obtain the background interference features; Based on the background interference features, the clear beam image is adaptively cropped to obtain an initial cropped image of the beam spot. The edge integrity of the initial cropped image of the beam spot is then checked to obtain a laser beam spot image containing only the beam spot region.

[0008] Furthermore, the laser spot image is subjected to spot parameter extraction to obtain spot feature data, including: Gaussian fitting is performed on the laser spot image to obtain the light intensity distribution curve, and peak detection is performed on the light intensity distribution curve to obtain the center intensity data of the spot and the light intensity attenuation coefficient. Based on the light intensity distribution curve, the laser spot image is subjected to iso-intensity contour analysis to obtain multi-layer light intensity contour data, and the ellipticity of the multi-layer light intensity contour data is calculated to obtain the spot ellipticity parameter. The envelope area of ​​the laser spot is calculated based on the spot ellipticity parameter and the light intensity attenuation coefficient to obtain spot energy distribution data. The spot energy distribution data is then normalized to obtain spot feature data.

[0009] Furthermore, based on the light intensity distribution curve, iso-intensity contour analysis is performed on the laser spot image to obtain multi-layer light intensity contour data, including: Dynamic threshold segmentation is performed on the light intensity distribution curve to obtain multi-layer light intensity gradient data; Based on the multi-layer light intensity gradient data, contour tracking is performed on the laser spot image to obtain a set of light intensity contour lines, and spatial density calculation is performed on the set of light intensity contour lines to obtain light intensity interlayer distance data. Spatial feature analysis is performed on the light intensity interlayer distance data to obtain the spatial distribution characteristics of light intensity, and contour lines are reconstructed on the light intensity contour line set based on the spatial distribution characteristics of light intensity to obtain multi-layer light intensity contour line data.

[0010] Furthermore, based on the multi-layer intensity gradient data, contour tracking is performed on the laser spot image to obtain a set of intensity contour lines, including: Gradient direction vector calibration is performed on the multi-layer light intensity gradient data to obtain a directional gradient field matrix, and the correlation between the directional gradient field matrix and the pixel grayscale values ​​of the laser spot image is calculated to obtain a gradient-grayscale correlation coefficient matrix. Based on the gradient-grayscale correlation coefficient matrix, the laser spot image is screened for boundary seed points to obtain an initial boundary seed point set. Then, the initial boundary seed point set is verified for neighborhood gradient consistency to obtain an effective boundary seed point set. Based on the effective boundary seed point set, dynamic contour tracking is performed on the directional gradient field matrix to obtain single-intensity level contour lines. The single-intensity level contour lines are then summarized according to the hierarchical order of the intensity gradient data to obtain a set of intensity contour lines.

[0011] Furthermore, based on the beam feature data, the beam quality of the semiconductor laser is calculated to obtain the beam quality factor, including: Based on the laser spot feature data, the second moment of the laser spot is calculated to obtain the beam transmission matrix parameters, and the beam transmission matrix parameters are decomposed into eigenvalues ​​to obtain the beam quality evaluation coefficients. Optical path difference analysis is performed on the beam quality evaluation coefficient to obtain the beam phase distribution characteristics, and the beam diffraction limit is calculated based on the beam phase distribution characteristics to obtain the beam diffraction limit factor. The beam quality factor is obtained by performing a comprehensive calculation on the beam diffraction limit factor and the beam quality evaluation coefficient.

[0012] Furthermore, the optical power of the semiconductor laser is tested based on the beam quality factor to obtain a power test curve, including: The driving current of the semiconductor laser is adjusted in a stepwise manner based on the beam quality factor to obtain multiple sets of current adjustment parameters. The semiconductor laser is then subjected to time-series power acquisition based on the multiple sets of current adjustment parameters to obtain an initial power dataset. The initial power dataset is subjected to outlier removal using spatial filtering techniques to obtain filtered power data. The filtered power data is then weighted based on the beam quality factor to obtain a weighted power sequence. Time-domain interpolation is performed on the weighted power sequence to obtain continuous power variation data, and curve fitting is performed based on the continuous power variation data to obtain a power test curve.

[0013] Furthermore, based on the power test curve, the semiconductor laser is subjected to a quality compliance assessment to obtain a quality evaluation result, including: Fluctuation characteristic analysis is performed on the power test curve to obtain power stability parameters, and multi-dimensional threshold mapping is performed on the power stability parameters to obtain a quality feature vector; The semiconductor laser is subjected to multi-level quality assessment based on the quality feature vector to obtain the quality evaluation result.

[0014] The present invention also provides a quality inspection system for semiconductor lasers, comprising: The acquisition module is used to acquire the laser beam from the output end of the semiconductor laser to obtain a laser spot image; The extraction module is used to extract spot parameters from the laser spot image to obtain spot feature data; The calculation module is used to calculate the beam quality of the semiconductor laser based on the beam spot feature data to obtain the beam quality factor; The testing module is used to perform optical power testing on the semiconductor laser based on the beam quality factor and obtain a power test curve; The judgment module is used to determine the quality compliance of the semiconductor laser based on the power test curve and obtain the quality evaluation result.

[0015] This invention provides a quality inspection method for semiconductor lasers, comprising the following steps: acquiring a laser beam at the output end of the semiconductor laser to obtain a laser spot image; extracting spot parameters from the laser spot image to obtain spot feature data; calculating the beam quality of the semiconductor laser based on the spot feature data to obtain a beam quality factor; performing optical power testing on the semiconductor laser based on the beam quality factor to obtain a power test curve; and determining the quality compliance of the semiconductor laser based on the power test curve to obtain a quality evaluation result. This method solves the technical problem that optical power testing is usually performed independently of beam quality analysis, lacking a dynamic correlation mechanism between the two, and thus failing to achieve real-time mutual calibration and comprehensive evaluation. It realizes the calculation of the beam quality factor based on the extracted spot feature data, achieving intelligent conversion from image information to physical performance parameters, and can more realistically reflect the output characteristics of the laser under actual working conditions. It is particularly suitable for the technical effect of complex optical field analysis of multimode, high-power semiconductor lasers. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art 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 based on these drawings without creative effort.

[0017] Figure 1 This is a schematic diagram of the steps of a semiconductor laser quality detection method in one embodiment of the present invention; Figure 2 This is a schematic block diagram of the structure of a semiconductor laser quality inspection system according to an embodiment of the present invention.

[0018] The objectives, features, and advantages of this invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0019] The embodiments of the present invention are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention. The step numbers in the following embodiments are set only for ease of explanation, and there is no limitation on the order between the steps. The execution order of each step in the embodiments can be adaptively adjusted according to the understanding of those skilled in the art.

[0020] The following describes in detail, with reference to the accompanying drawings, a method for quality inspection of a semiconductor laser according to an embodiment of the present invention. First, the method for quality inspection of a semiconductor laser according to an embodiment of the present invention will be described in detail with reference to the accompanying drawings.

[0021] Figure 1 This invention provides a method for quality inspection of a semiconductor laser, comprising the following steps: Step S1: The output end of the semiconductor laser is sampled to obtain a laser spot image.

[0022] Specifically, the output of the semiconductor laser is sampled to obtain a laser spot image. This step first requires the semiconductor laser to be in normal operating condition, ensuring a stable laser beam output. Then, an optical element, such as a lens or mirror, is placed at a certain distance in front of the laser's output to guide and appropriately expand or focus the laser beam to match the receiving range of the subsequent imaging equipment. Next, a portion of the laser beam is guided to the imaging plane by a beam sampling device, such as a beam splitter, while the main beam can still be used for other tests or applications. The sampled laser beam is then incident on a high-resolution spot analysis camera equipped with a photosensitive chip suitable for the semiconductor laser wavelength, such as a CCD or CMOS sensor, and configured with appropriate attenuation. To prevent sensor saturation or damage, the camera is connected to the acquisition software. After starting the acquisition software, parameters such as exposure time and gain are adjusted to ensure that the captured image can clearly show the intensity distribution and edge features of the light spot. Finally, a digital laser spot image is formed and stored in the system for subsequent processing. For example, in the quality inspection process of a high-power 808nm semiconductor laser used for industrial cutting, the laser is powered on and driven to the rated operating current. A beam splitter is used to guide 10% of the output light into a CCD camera equipped with a neutral density attenuator and a corresponding wavelength response. The camera position is adjusted so that it is perpendicular to the optical axis, and a multimode light spot image with a bright center and asymmetrical extension around it is acquired. This image serves as the basic data for subsequent light spot parameter extraction.

[0023] Step S2: Extract the laser spot parameters from the laser spot image to obtain spot feature data.

[0024] Specifically, the laser spot image is processed to extract spot parameters and obtain spot feature data. This step involves first importing the acquired laser spot image into an image processing system. The software reads the pixel matrix information of the image and performs preprocessing operations, including background noise removal, dark current correction, and grayscale normalization, to improve image quality and ensure the accuracy of subsequent calculations. Next, the effective region of the spot is determined in the image. A threshold segmentation method is used to set a cutoff threshold based on the percentage of peak intensity, for example, selecting 13.5% of the maximum intensity as the boundary judgment standard, thereby defining the effective near-field or far-field contour of the spot. Then, based on this contour, multiple spatial parameters are calculated, including the centroid coordinates, major axis, and minor axis of the spot. The system calculates the length, ellipticity, standard deviation of intensity distribution, and second-order moment diameter of the laser beam. It also further calculates the energy concentration, eccentricity, and mode symmetry of the beam. All these parameters together constitute a set of quantifiable beam feature data to characterize the spatial distribution characteristics of the laser beam. For example, in the quality inspection of high-power 808nm semiconductor lasers used for industrial cutting, the system identifies non-circular beam regions by setting a 13.5% threshold for the acquired multimode beam image. It calculates the major axis as 1.8mm, minor axis as 1.2mm, ellipticity as 1.5, centroid offset as 0.15mm, and extracts the standard deviation of intensity distribution as 45.6. These numerical results serve as the beam feature data input for subsequent beam quality calculations.

[0025] Step S3: Calculate the beam quality of the semiconductor laser based on the beam spot feature data to obtain the beam quality factor.

[0026] Specifically, the beam quality of the semiconductor laser is calculated based on the beam feature data to obtain the beam quality factor. This step first calls the previously extracted beam feature data, including the second moment diameter, major axis length, minor axis size, and centroid distribution information of the beam. Combined with the known test distance, wavelength parameters, and optical system magnification, a mathematical model is established according to the second moment algorithm specified in international standard ISO 11146. By calculating the integral intensity distribution of the beam in the X and Y directions, the far-field divergence angles along the two orthogonal axes are solved. Simultaneously, the measured beam size is compared with the diffraction limit of an ideal Gaussian beam, using the formula M² = (measured beam parameters) / (ideal Gaussian beam parameters). The M² value of the actual beam, i.e., the beam quality factor, is derived. The closer the M² value is to 1, the higher the beam quality. The entire calculation process is automatically executed in the embedded processing unit or host computer software without manual intervention. For example, in the quality inspection process of a high-power 808nm semiconductor laser used for industrial cutting, the system calls the previously extracted major axis of 1.8mm, minor axis of 1.2mm and corresponding divergence angle data, substitutes them into the ISO 11146 standard algorithm, and calculates that the M² in the X direction is 2.3 and the M² in the Y direction is 3.1. The average of the two is taken to obtain the final beam quality factor of 2.7. This value serves as an important basis for guiding subsequent optical power testing and directly participates in the construction of the overall quality evaluation system.

[0027] Step S4: Perform optical power testing on the semiconductor laser based on the beam quality factor to obtain a power test curve.

[0028] Specifically, the optical power of the semiconductor laser is tested based on the beam quality factor to obtain a power test curve. This step involves first loading the calculated beam quality factor as an input parameter into the test control system. The system determines the current beam quality level according to a preset mapping relationship and dynamically sets the current scanning range and sampling density for the optical power test accordingly. For example, when the beam quality factor is 2.7, the system identifies the semiconductor laser as having a medium beam quality level and automatically configures the test start current to be 1.2 times the threshold current and the termination current to be 1.5 times the rated current. Within this range, the voltage is gradually increased in 10mA increments, maintaining a stable state for approximately 200 milliseconds after each current level is reached to ensure the output power reaches a steady state. Subsequently, the power is measured by a photoelectric power meter placed in the laser output path. The system collects the output power value under the corresponding current in real time. The power meter converts the analog signal into a digital signal and transmits it to the data acquisition module. All current-power data pairs are recorded synchronously in time sequence, and a continuously changing power test curve is plotted with current as the horizontal axis and power as the vertical axis. The entire test process strictly follows the laser's operating timing and safety specifications to avoid overdrive and damage to the device. For example, in the quality inspection of a high-power 808nm semiconductor laser used for industrial cutting, the system starts a preset medium quality test protocol based on an M² value of 2.7. Starting from 6A, the drive current is gradually increased in 10mA increments to 30A, and the output power at each point is collected synchronously. Finally, a power test curve is formed that shows typical threshold characteristics and is accompanied by a slight saturation trend. This curve fully reflects the power response behavior of the laser in the actual operating range.

[0029] Step S5: Based on the power test curve, the semiconductor laser is assessed for quality compliance to obtain a quality evaluation result.

[0030] Specifically, the semiconductor laser is assessed for quality compliance based on the power test curve to obtain a quality evaluation result. This step begins by using the aforementioned power test curve as input data. This curve records the continuous change in output power of the semiconductor laser under stepped current drive. By analyzing the morphological characteristics of the curve, it is determined whether it meets the preset technical specifications. In practice, it is checked whether the curve has a significant inflection point near the threshold current, whether it maintains a stable growth trend in the linear operating region, and whether abnormal fluctuations or premature saturation occur when approaching the rated current. At the same time, key parameters such as slope efficiency, maximum output power, and power fluctuation range are extracted and compared with standard thresholds. For example, in the application scenario of high-power 808nm semiconductor lasers for industrial cutting, the slope efficiency is specified to be no less than 0.75W / A, the maximum output power should reach more than 22W at 30A, and the power fluctuation should not exceed ±3%. If all indicators meet the requirements, it is judged as qualified; if any item exceeds the tolerance range, it is marked as unqualified. Finally, a quality evaluation result containing grade classification and judgment criteria is generated. This result is used to guide product grading or rework decisions.

[0031] In a specific embodiment, beam acquisition is performed at the output end of the semiconductor laser to obtain a laser spot image, including: A preset optical acquisition device is used to capture the output of the semiconductor laser to obtain an initial beam image, and the initial beam image is preprocessed to obtain a clear beam image. The clear beam image is used to locate the spot area by image analysis technology to obtain the spot position information. Based on the spot position information, the clear beam image is cropped to obtain a laser spot image containing only the spot area.

[0032] Specifically, the process involves acquiring a laser beam from the output of the semiconductor laser to obtain a laser spot image. This includes using a pre-set optical acquisition device to capture the beam from the output of the semiconductor laser, obtaining an initial beam image, preprocessing the initial beam image to obtain a clear beam image, locating the spot region in the clear beam image using image analysis technology to obtain spot position information, and cropping the clear beam image based on the spot position information to obtain a laser spot image containing only the spot region. This step is achieved by first powering on the semiconductor laser and stabilizing its laser beam output, then using a pre-set optical acquisition device to perform beam capture. This device consists of a beam splitter, an attenuation module, an imaging lens group, and a high dynamic range CCD camera. The beam splitter reflects a portion of the main output beam at a fixed ratio (e.g., 5%~10%) to the acquisition path, while the remaining beam continues to be used for subsequent testing or applications. The reflected light passes through a neutral density attenuator to prevent strong light damage to the sensor, and is then focused onto the CCD camera's photosensitive surface by the imaging lens. The camera adjusts its spectral response parameters according to the semiconductor laser's wavelength (e.g., 808nm). Set an appropriate exposure time (e.g., 50 μs) and gain value, and start the image acquisition software to trigger single-frame shooting, thereby acquiring an initial beam image containing background noise, optical distortion, and uneven intensity distribution. This initial beam image is then preprocessed to improve image quality. The preprocessing process includes reading the image grayscale matrix data, performing dark-field correction (subtracting the dark-field image matrix acquired under no-light conditions), background subtraction, using morphological opening or Gaussian filtering to separate low-frequency background light from high-frequency spot signals, and finally performing gamma correction and contrast adjustment on the image. Enhancement is performed to make the intensity difference between the center and edge of the light spot more clearly discernible, ultimately generating a clear light beam image with sufficient noise reduction, sharp boundaries, and a suitable dynamic range. Next, image analysis techniques are applied to locate the light spot region in the clear light beam image. By calculating the global maximum gray value of the image and setting a segmentation threshold based on a certain proportion (e.g., 13.5%), a connected component analysis algorithm is used to identify the largest connected region as the candidate region for the main light spot, and its centroid coordinates and circumscribed rectangle boundary are calculated to obtain the light spot position information. This information includes the center pixel coordinates (x, y) of the light spot in the image. The system first obtains the width and height of the enclosing region (y) and then performs a cropping operation on the clear beam image based on the beam position information. The circumscribed rectangle is expanded outwards by a certain multiple (e.g., 1.2 times) centered on the centroid. A subset of pixels corresponding to the row and column range is extracted from the image matrix to form a sub-image containing only the complete beam and its main energy distribution area. This sub-image is the laser beam image used for subsequent parameter extraction. For example, in the quality inspection process of a high-power 808nm semiconductor laser used for industrial cutting, the system acquires an initial beam image with a resolution of 1280×1024. After dark field correction and background subtraction, a clear image is obtained, and then...The 5% threshold method located the main laser spot in the center-right region of the image, with a bounding rectangle of 300×200 pixels. The system then expanded this region by a factor of 1.2 and cropped it to produce a 360×240 pixel laser spot image, ensuring that subsequent analysis was free from irrelevant background interference.

[0033] In a specific embodiment, the clear beam image is cropped based on the spot position information to obtain a laser spot image containing only the spot area, including: The location information of the light spot is used to perform boundary expansion calculation to obtain the safe boundary range of the light spot, and the background noise of the region in the clear beam image located within the safe boundary range of the light spot is evaluated to obtain the background interference features; Based on the background interference features, the clear beam image is adaptively cropped to obtain an initial cropped image of the beam spot. The edge integrity of the initial cropped image of the beam spot is then checked to obtain a laser beam spot image containing only the beam spot region.

[0034] Specifically, based on the spot position information, the clear beam image is cropped to obtain a laser spot image containing only the spot area. This includes performing boundary expansion calculations on the spot position information to obtain the safe boundary range of the spot, and evaluating the background noise of the area in the clear beam image located within the safe boundary range of the spot to obtain background interference features. This step is implemented by first calling the spot position information determined by the aforementioned image analysis technology. This information includes the centroid coordinates of the spot (x0, y0) and the width W and height H of the original circumscribed rectangle. Boundary expansion calculations are performed based on this circumscribed rectangle, expanding the four sides of the rectangle outward by a certain proportion. For example, if the expansion proportion is 1.2 times, then the new left boundary is x0 - 0.6W, the right boundary is x0 + 0.6W, the upper boundary is y0 - 0.6H, and the lower boundary is y0 + 0.6H. 0.6H, thus forming a safe boundary range for the light spot covering the main light spot and its surrounding low-intensity energy diffusion area. This range ensures that the effective edge of the light spot will not be truncated during subsequent trimming, especially when processing multimode or highly divergent semiconductor laser spots, preserving sufficient spatial information. Then, within this safe boundary range, an inner region near the main light spot and an outer ring region near the boundary are divided. Statistical analysis is performed on the pixel grayscale values ​​of the outer ring region, calculating its mean, variance, and signal-to-noise ratio as background interference features. If the grayscale mean of this region is higher than a preset threshold (e.g., ...), ... If the noise level is less than 5% of the maximum intensity or the variance is large, it indicates significant stray light or ambient light pollution, which needs to be compensated for in subsequent clipping strategies. Based on the background interference features, the clear beam image is adaptively clipped to obtain the initial clipped image of the light spot. In specific execution, the size and position of the clipping window are dynamically adjusted according to the severity of the background interference features. If the background interference features show a low noise level, the original light spot safety boundary range is directly used for clipping to generate a preliminary initial clipped image of the light spot. If the background interference features indicate that there is edge blurring or low-frequency drift, the clipping window is reduced to 1 / 3 of the original bounding rectangle.The image is centered and aligned to reduce noise. Then, the initial cropped image of the laser spot is checked for edge integrity. This is done by detecting continuous high-intensity pixel bands or abruptly truncated intensity gradients around the image edges to determine if the laser spot is completely contained. If any edge experiences a sudden drop in intensity that doesn't return to zero, or exhibits asymmetric truncation, the edge is considered incomplete. The system automatically expands the boundary in that direction by 5% to 10%, regenerating the cropped image until all four edges meet the integrity requirements. Finally, it outputs a laser spot image containing only the laser spot area, eliminating most irrelevant background while fully preserving the laser spot structure. For example, in the quality inspection process of high-power 808nm semiconductor lasers used for industrial cutting, the system acquires a clear light... After analyzing the beam image, the main spot was determined to be located slightly to the right of the center, with an enclosing rectangle of 300×200 pixels. Boundary expansion calculations yielded a safe boundary of 360×240 pixels. Background noise assessment of the outer ring of this area revealed slight stray light interference on the left (mean grayscale value reaching 6% of maximum intensity). Therefore, an adaptive cropping mechanism was activated, fine-tuning the cropping window to 350×230 pixels and shifting it slightly to the right by 10 pixels to avoid the high-noise area. After generating the initial cropped image of the spot, edge integrity verification revealed incomplete intensity attenuation at the bottom edge. A 15-pixel margin was then added vertically, ultimately generating a 350×245-pixel laser spot image, ensuring the overall structure of the spot remained intact and background interference was minimized.

[0035] In a specific embodiment, spot parameters are extracted from the laser spot image to obtain spot feature data, including: Gaussian fitting is performed on the laser spot image to obtain the light intensity distribution curve, and peak detection is performed on the light intensity distribution curve to obtain the center intensity data of the spot and the light intensity attenuation coefficient. Based on the light intensity distribution curve, the laser spot image is subjected to iso-intensity contour analysis to obtain multi-layer light intensity contour data, and the ellipticity of the multi-layer light intensity contour data is calculated to obtain the spot ellipticity parameter. The envelope area of ​​the laser spot is calculated based on the spot ellipticity parameter and the light intensity attenuation coefficient to obtain spot energy distribution data. The spot energy distribution data is then normalized to obtain spot feature data.

[0036] Specifically, the laser spot image is processed to extract spot parameters and obtain spot feature data. This step first involves Gaussian fitting of the laser spot image. By treating the gray value of each pixel in the image as a digital representation of light intensity, an iterative optimization algorithm is used to adjust the spatial distribution of the two-dimensional Gaussian function to make it approximate the energy distribution of the actual spot as closely as possible in terms of shape, width, and symmetry. During the fitting process, the residual between the calculated value and the measured value is continuously compared until convergence, ultimately generating a continuous light intensity distribution curve. This curve accurately describes the intensity change trend from the brightest center region of the spot to the periphery. Then, peak detection is performed on the light intensity distribution curve. By scanning all data points on the curve, the points where the gray value reaches its maximum and decreases symmetrically around it are identified, and their corresponding intensity values ​​are recorded as the center intensity data of the spot. At the same time, the range of pixels crossed when the peak attenuates to a specific proportion (such as 50% or 13.5%) is analyzed. Combined with the spatial resolution of the imaging system, this is converted into the actual physical size, thereby determining the light intensity attenuation coefficient. This coefficient reflects the concentration and expansion speed of the beam energy in space. Next, based on the light intensity distribution curve, iso-intensity contour analysis is performed on the laser spot image. Multiple preset intensity percentage levels, such as peak intensities of 90%, 75%, 50%, 25%, and 10%, are selected as iso-value reference standards. Continuous boundary points reaching these intensity values ​​are searched layer by layer in the image. An edge tracking algorithm is used to connect these points to form a closed ring contour. Each layer corresponds to an intensity level, thereby constructing a set of multi-layer light intensity contour data that expands from the inside out. These contour lines truly reflect the spatial morphology of the light spot at different energy levels. Then, ellipticity calculation is performed on the multi-layer light intensity contour data. For each layer of contour lines, an optimal matching ellipse shape is fitted, and the ratio of its major axis to minor axis is calculated. If the ratio is close to 1.0, it indicates that the contour of that layer is close to a circle. If the ratio is greater than 1.2, it indicates that there is obvious stretching or distortion. The average ratio of all layers is taken as the ellipticity parameter of the light spot to quantify the overall non-circular symmetry of the light spot. Based on this, the envelope area of ​​the laser spot is calculated using the spot ellipticity parameter and the intensity attenuation coefficient. Taking into account the spatial expansion direction and energy attenuation characteristics of the spot, the principal axis direction and shape factor are determined by the ellipticity parameter, and the boundary range of the effective energy coverage is defined by the intensity attenuation coefficient. The area enclosed by all pixels within this boundary is calculated using pixel statistics methods to obtain the spot energy distribution data. This data represents the effective projected area occupied by the laser output energy in space and is an important basis for evaluating focusing ability and beam quality.The beam energy distribution data is then normalized and compared with the envelope area measured by a reference light source under standard conditions (such as rated current and room temperature). The relative ratio is calculated and adjusted to a uniform dimension range, for example, between 0.85 and 1.15, to eliminate systematic biases caused by equipment sensitivity, imaging distance, or ambient light interference. Finally, a set of standardized, horizontally comparable beam feature data is formed. This dataset contains information on multiple dimensions such as center intensity, attenuation characteristics, shape distortion, and energy distribution, providing complete input for subsequent beam quality calculations. For example, in the quality inspection of a high-power 808nm semiconductor laser used for industrial cutting, the system performs Gaussian fitting on the acquired laser spot image to obtain the intensity distribution curve. The detected center intensity is 247mW / cm², and the intensity attenuation coefficient corresponds to a full width at half maximum (FWHM) of 0.96 mm. Iso-intensity contour analysis extracts five clearly distinguishable contour lines. The ellipticity calculation yields an average major axis to minor axis ratio of 1.51, indicating that the spot has a distinct elliptical shape. Combining this ellipticity parameter and the attenuation coefficient, the effective envelope area is estimated to be 0.77 square centimeters. This area is then normalized to the standard reference value of 0.80 square centimeters, resulting in a normalized spot characteristic data of 0.962. This value, along with other parameters, serves as the input for subsequent beam quality factor calculations, ensuring the accuracy and consistency of the evaluation results.

[0037] In a specific embodiment, iso-intensity contour analysis is performed on the laser spot image based on the light intensity distribution curve to obtain multi-layer light intensity contour data, including: Dynamic threshold segmentation is performed on the light intensity distribution curve to obtain multi-layer light intensity gradient data; Based on the multi-layer light intensity gradient data, contour tracking is performed on the laser spot image to obtain a set of light intensity contour lines, and spatial density calculation is performed on the set of light intensity contour lines to obtain light intensity interlayer distance data. Spatial feature analysis is performed on the light intensity interlayer distance data to obtain the spatial distribution characteristics of light intensity, and contour lines are reconstructed on the light intensity contour line set based on the spatial distribution characteristics of light intensity to obtain multi-layer light intensity contour line data.

[0038] Specifically, based on the light intensity distribution curve, iso-intensity contour analysis is performed on the laser spot image to obtain multi-layer light intensity contour data. This step first relies on dynamic threshold segmentation of the light intensity distribution curve to obtain multi-layer light intensity gradient data. During this process, the system reads the light intensity distribution curve generated by Gaussian fitting, which characterizes the intensity decay trend from the center to the edge of the spot. Based on the overall dynamic range of the spot, a set of non-uniformly distributed intensity thresholds is automatically set. These thresholds are not equally spaced but adaptively adjusted according to the gradient characteristics of light intensity changes. For example, denser thresholds are set in regions where light intensity decreases sharply (such as from the peak to the full width at half maximum), while sparser thresholds are used in the outer regions where light intensity tends to level off. This ensures higher analysis resolution in energy-concentrated areas. Each threshold corresponds to a light intensity gradient level, forming an ordered set of... Multi-layer intensity gradient data is generated, and then contour tracking of the laser spot image is performed based on the multi-layer intensity gradient data. Specifically, the intensity gradient of each layer is used as a segmentation threshold in the original laser spot image. An edge detection algorithm (such as the Canny operator or a chain code-based boundary tracking method) is used to identify all continuous boundary points with pixel intensities equal to or slightly higher than the threshold, thereby generating a closed or nearly closed intensity contour line. The contour lines of all layers together constitute an intensity contour line set. Then, the spatial density of the intensity contour line set is calculated to evaluate the relative distance between each contour line. During the calculation, several key radial paths (such as along the major axis, minor axis, and 45-degree diagonal direction) are selected, the average distance between two adjacent contour lines on these paths is measured, and their variance is calculated to obtain the intensity inter-layer distance data, which reflects the rate of change and uniformity of light intensity in space.Furthermore, spatial feature analysis is performed on the light intensity interlayer distance data to identify asymmetric, locally abrupt, or distorted regions of light intensity variation. For example, if the interlayer distance of multiple consecutive layers in a certain direction is significantly smaller than in other directions, it indicates that the light intensity attenuates faster in that region, possibly due to mode distortion or aberrations. This analysis result constitutes the spatial distribution characteristics of light intensity. Finally, based on these spatial distribution characteristics, contour lines are reconstructed from the light intensity contour set. The reconstruction process does not simply retain the original tracking results but rather performs smoothing corrections, breaks, or local densifications on the contour lines according to spatial features. For example, interpolated contour lines are inserted in regions of abrupt changes in interlayer distance to improve resolution, and spline interpolation is used at contour line breaks to obtain continuous contours. Ultimately, a set of geometrically continuous, reasonably distributed contour lines that faithfully reflect the true spatial variation of light intensity is generated. Multi-layer intensity contour data, such as in the quality inspection of high-power 808nm semiconductor lasers used in industrial cutting, involves the system dynamically thresholding the intensity distribution curve and setting seven non-uniform gradient levels (95%, 90%, 80%, 65%, 50%, 30%, and 15% of the peak value, respectively). Seven original contour lines are obtained through contour tracing. Spatial density calculations reveal that the average interlayer distance in the Y direction is approximately 18% smaller than that in the X direction, indicating faster divergence in the vertical direction. Based on this, spatial feature analysis confirms the existence of vertical compression distortion. The system then reconstructs the contour set, adding an interpolated contour line in the Y direction and performing B-spline smoothing. The final output is multi-layer intensity contour data containing eight optimized contour lines, providing a precise geometric basis for subsequent ellipticity calculations and energy distribution assessments.

[0039] In a specific embodiment, contour tracking is performed on the laser spot image based on the multi-layer light intensity gradient data to obtain a set of light intensity contour lines, including: Gradient direction vector calibration is performed on the multi-layer light intensity gradient data to obtain a directional gradient field matrix, and the correlation between the directional gradient field matrix and the pixel grayscale values ​​of the laser spot image is calculated to obtain a gradient-grayscale correlation coefficient matrix. Based on the gradient-grayscale correlation coefficient matrix, the laser spot image is screened for boundary seed points to obtain an initial boundary seed point set. Then, the initial boundary seed point set is verified for neighborhood gradient consistency to obtain an effective boundary seed point set. Based on the effective boundary seed point set, dynamic contour tracking is performed on the directional gradient field matrix to obtain single-intensity level contour lines. The single-intensity level contour lines are then summarized according to the hierarchical order of the intensity gradient data to obtain a set of intensity contour lines.

[0040] Specifically, contour tracking is performed on the laser spot image based on the multi-layer intensity gradient data to obtain a set of intensity contour lines. This step first involves calibrating the gradient direction vector of the multi-layer intensity gradient data to construct a directional field that guides the contour tracking direction. Specifically, the theoretical contour line direction corresponding to each intensity gradient layer is preset to a radial divergence or focusing mode. Combined with the actual intensity gradient distribution of the laser spot image, the gradient magnitude and direction of each pixel in the image are calculated using the Sobel or Scharr operator to form an initial gradient field. Then, based on the centroid position of the spot, the gradient direction of all pixels is vector-calibrated to ensure that the gradient direction is consistent with or opposite to the vector pointing from the centroid to that point, thereby eliminating light... To address local directional deviations caused by optical distortion or noise, a directional gradient field matrix is ​​generated that uniformly points towards the center of the laser spot or along the tangent of equipotential lines. Then, the correlation between the directional gradient field matrix and the pixel grayscale values ​​of the laser spot image is calculated. By comparing the consistency between the gradient direction and the grayscale change trend pixel by pixel, for example, if the grayscale value of a pixel is close to the intensity gradient value of a certain layer, and its gradient direction is perpendicular to the radial direction drawn from the centroid, then the point is determined to be near the contour line of that layer. Normalized cross-correlation or cosine similarity algorithms are used to calculate the response intensity of each pixel under multiple gradients, constructing a gradient-grayscale correlation coefficient matrix that reflects the degree of matching between the gradient direction and the grayscale distribution. The high coefficient regions in this matrix represent potential contour line locations. Based on this, the laser spot image is screened for boundary seed points based on the gradient-grayscale correlation coefficient matrix. Pixels with correlation coefficients higher than a preset threshold and located within the intensity range corresponding to the multi-level light intensity gradient are selected as candidate points to form an initial boundary seed point set. These seed points are distributed on the potential contour lines of different light intensity levels. Subsequently, the initial boundary seed point set is verified for neighborhood gradient consistency to exclude isolated noise or false edge points. In the verification process, a 3×3 or 5×5 neighborhood window is constructed with each seed point as the center. The angle deviation between the gradient direction of each point in the neighborhood and the tangent of the contour line is calculated. If the deviation of more than half of the neighborhood points is less than a set angle (such as 30 degrees), the seed point is retained; otherwise, it is discarded. Finally, a set of effective boundary seed points with continuous spatial distribution and consistent direction is formed. This set accurately identifies the starting position and key nodes of the contour lines at each level.Next, dynamic contour tracking is performed on the directional gradient field matrix based on the effective boundary seed point set. An improved chain code tracking or fast traversal method is used, starting from each effective boundary seed point and extending gradually along the pixel whose gradient direction best matches in its neighborhood. The tracking path always follows the guidance of the directional gradient field, ensuring the contour line extends continuously along the direction of equal intensity. The process stops when the path closes or exceeds the image boundary, generating a complete single-intensity level contour line. This process is executed layer by layer according to the hierarchical order of the multi-layer intensity gradient data. Each layer independently completes seed point guidance and field direction tracking. Finally, all single-intensity level contour lines are summarized to form... A set of well-structured and hierarchically distinct intensity contour lines is used, for example, in the quality inspection process of a high-power 808nm semiconductor laser used for industrial cutting. The system processes 7 layers of intensity gradient data, constructs a directional gradient field matrix, calculates the gradient-grayscale correlation coefficient matrix, selects an initial boundary seed point set of 320 points, retains 287 valid points after neighborhood consistency verification, and then completes layer-by-layer tracing from these points under the guidance of the directional gradient field, successfully generating 7 closed single-intensity hierarchical contour lines, which are then summarized into an intensity contour line set in descending order of intensity, accurately depicting the multi-level spatial structure characteristics of the laser's output spot.

[0041] In a specific embodiment, the beam quality of the semiconductor laser is calculated based on the beam spot feature data to obtain a beam quality factor, including: Based on the laser spot feature data, the second moment of the laser spot is calculated to obtain the beam transmission matrix parameters, and the beam transmission matrix parameters are decomposed into eigenvalues ​​to obtain the beam quality evaluation coefficients. Optical path difference analysis is performed on the beam quality evaluation coefficient to obtain the beam phase distribution characteristics, and the beam diffraction limit is calculated based on the beam phase distribution characteristics to obtain the beam diffraction limit factor. The beam quality factor is obtained by performing a comprehensive calculation on the beam diffraction limit factor and the beam quality evaluation coefficient.

[0042] Specifically, the beam quality of the semiconductor laser is calculated based on the beam feature data to obtain the beam quality factor. This step relies on the beam feature data obtained after the aforementioned normalization process. This data includes key information such as the beam center intensity of 247 mW / cm², the full width at half maximum (FWHM) corresponding to the intensity attenuation coefficient of 0.96 mm, the ellipticity parameter of 1.51, and the normalized envelope area of ​​0.962. Using this data, the second moment of the laser beam is calculated. Specifically, in the image coordinate system, the weighted square distance of the beam grayscale distribution relative to the centroid position is calculated in pixels, and the second moment widths in the X and Y directions are calculated. This process fully considers the non-uniform distribution characteristics of light intensity in space, and can more accurately reflect the energy diffusion trend, especially in multimode laser beams. The calculation results, combined with the known test distance of 1.2 meters and laser wavelength of 808 nanometers, derive the far-field divergence angle information, and combine it with the near-field dimensions to form the beam transmission matrix parameters. This matrix contains the coupling relationship between the spatial dimensions and angular broadening of the beam in the propagation direction, and fully describes its transmission behavior. Subsequently, the beam transmission matrix parameters are decomposed into eigenvalues. The two principal eigenvalues ​​of the matrix are solved by numerical calculation. These two values ​​represent the beam propagation trend in the optimal and worst focusing directions, respectively. Their geometric mean is extracted as the beam quality evaluation coefficient. For example, in the calculation, the propagation mode intensities corresponding to the two eigenvalues ​​are 1.83 and 2.01, respectively. Taking their average trend, the beam quality evaluation coefficient is 1.92. This coefficient is higher than the theoretical value of an ideal single-mode beam, indicating the existence of mode distortion or non-uniform divergence. Next, optical path difference analysis is performed on the beam quality evaluation coefficient. This coefficient is compared with the propagation characteristics of an ideal Gaussian beam to identify deviations in the actual beam propagation process on the wavefront. In specific implementation, the asymmetry reflected by the spot ellipticity parameter of 1.51 is used to inversely deduce the relative delay of each region on the beam cross-section. For example, in the edge region along the long axis, astigmatism or lens distortion leads to a longer optical path, resulting in local phase lag. The system reconstructs the two-dimensional beam phase distribution characteristics through an iterative algorithm. This feature map shows that the phase deviation at the right edge of the spot reaches 0.6 wavelengths, while that on the left is 0.4, indicating significant asymmetry. This suggests that there may be mechanical stress or encapsulation tilt in the cavity. Based on the beam phase distribution characteristics, the beam diffraction limit is calculated. Referring to the minimum divergence angle achievable by an ideal aberration-free beam under the same wavelength and initial beam width, the minimum achievable beam quality benchmark under the theoretical limit is calculated by combining an 808nm wavelength and a 0.96mm beam width. This benchmark is denoted as the beam diffraction limit factor. Under standard conditions, this factor is set to 0.97, representing the best performance level physically allowed under the current system configuration.Finally, a comprehensive beam quality calculation is performed based on the beam diffraction limit factor and the beam quality evaluation coefficient. The beam quality evaluation coefficient of 1.92 is proportionally calculated to the beam diffraction limit factor of 0.97 to obtain the final beam quality factor. This calculation process not only considers the statistical characteristics of the measured spot but also incorporates the influence of wavefront phase distortion, avoiding the misjudgments caused by traditional methods that rely solely on size measurements. For example, in the quality inspection process of a high-power 808nm semiconductor laser used for industrial cutting, the system calculates a beam quality factor of 1.98 based on the above parameters. This value is higher than the industry-standard threshold of 2.0 but is still within an acceptable range. This indicates that although the laser has slight aberrations, its overall performance is stable and can meet the requirements of medium- and high-precision cutting equipment. This result serves as the core basis for subsequent power testing strategy adjustments and quality compliance judgments, ensuring the scientific nature and closed-loop nature of the testing process.

[0043] In a specific embodiment, the optical power of the semiconductor laser is tested based on the beam quality factor to obtain a power test curve, including: The driving current of the semiconductor laser is adjusted in a stepwise manner based on the beam quality factor to obtain multiple sets of current adjustment parameters. The semiconductor laser is then subjected to time-series power acquisition based on the multiple sets of current adjustment parameters to obtain an initial power dataset. The initial power dataset is subjected to outlier removal using spatial filtering techniques to obtain filtered power data. The filtered power data is then weighted based on the beam quality factor to obtain a weighted power sequence. Time-domain interpolation is performed on the weighted power sequence to obtain continuous power variation data, and curve fitting is performed based on the continuous power variation data to obtain a power test curve.

[0044] Specifically, the optical power of the semiconductor laser is tested based on the beam quality factor to obtain a power test curve. This step relies on the beam quality factor calculated earlier. This factor serves as a core input parameter to guide the current control strategy for subsequent power testing. During execution, the corresponding current adjustment mode is determined by looking up a table or through a preset function mapping relationship based on the beam quality factor's numerical range. For example, when the beam quality factor is 1.91, the system determines that the semiconductor laser belongs to a medium-high beam quality level and then initiates a medium-precision stepped adjustment protocol. The driving current starts from the threshold current (e.g., 5A) and is divided into multiple adjustment intervals with non-uniform spacing. A smaller step size (e.g., 0.5A) is set in the low current range (5A~15A), and a larger step size (e.g., 0.5A) is set in the high current range (15A~15A). The current adjustment parameters are gradually increased from A to 30A to 1.0A or 1.5A to generate a set of multiple current adjustment parameters adapted to the beam quality state. These parameters include not only the target current value, but also the stabilization time (e.g., 200ms) for each step and the voltage feedback calibration mechanism. Subsequently, the semiconductor laser is subjected to time-series power acquisition according to the multiple sets of current adjustment parameters. At each set current level, after the current stabilizes, the data acquisition system is started, and the laser output power is read in real time through a high-precision photoelectric power meter. 5 to 10 continuous power samples are collected at each level, and the corresponding timestamps and current values ​​are recorded. All data are organized in chronological order into a structured initial power dataset, which contains the original response characteristics of the laser under different driving conditions. Based on this, spatial filtering techniques are used to remove outliers from the initial power dataset to improve data reliability. Specifically, the initial power dataset is treated as a discrete set of two-dimensional points (current-power pairs). A local density-based filtering algorithm, such as DBSCAN or moving average combined with the 3σ criterion, is used to identify isolated points or abrupt changes that deviate from the main trend curve by more than a set threshold. For example, if a set of power values ​​is collected at a certain current level [1.21W, 1.23W, 1.22W, 1.85W, ...], ... [1.24W], of which 1.85W significantly deviates from neighboring values ​​and is identified as an outlier and removed. The remaining valid data are retained to form filtered power data, ensuring that subsequent analysis is not affected by transient interference or sensor jitter. Then, the filtered power data is weighted based on the beam quality factor to construct a weighted power sequence. The allocation principle is: the closer the beam quality factor is to the ideal value (e.g., 1.0), the more stable the beam mode is, and the higher the reliability of the power data in the corresponding current range, and the greater the weight is given. Conversely, the weight is reduced. For example, in the testing of an 808nm industrial laser, if the beam quality factor corresponding to a certain current range is low (e.g., M²>2.5), the weight of the power data in that range is set to 0.7, while the weight of the range M²<2.0 is set to 1.0. Based on this, the system multiplies each point in the filtered power data by its corresponding weight to generate a set of weighted power sequences that reflect the influence of beam stability.Subsequently, time-domain interpolation is performed on the weighted power sequence. Spline interpolation or PCHIP (piecewise cubic Hermite interpolation) methods are used to insert several intermediate points between adjacent data points, transforming the originally discrete power data into high-density continuous power variation data. This data sequence can more realistically reflect the laser's output response during continuous current variation. Finally, curve fitting is performed based on the continuous power variation data. The least squares method is used to fit a polynomial function (such as a quadratic or cubic polynomial) or a physical driving model (such as a PI linear-saturation model) to obtain a smooth, continuous curve that conforms to... The power test curve of the laser's physical characteristics fully presents the power growth trend, slope efficiency, and saturation characteristics from the threshold to the rated current range. For example, in the application scenario of a high-power 808nm semiconductor laser for industrial cutting, the system sets the step current parameter according to M²=1.91, collects an initial power dataset of 26 points, removes 3 outliers by spatial filtering, performs weighted processing, and then obtains continuous data at 0.1A intervals through interpolation. Finally, a power test curve with a clear threshold inflection point and a slight saturation trend is fitted, providing an accurate basis for subsequent quality compliance judgment.

[0045] In a specific embodiment, the semiconductor laser is assessed for quality compliance based on the power test curve to obtain a quality evaluation result, including: Fluctuation characteristic analysis is performed on the power test curve to obtain power stability parameters, and multi-dimensional threshold mapping is performed on the power stability parameters to obtain a quality feature vector; The semiconductor laser is subjected to multi-level quality assessment based on the quality feature vector to obtain the quality evaluation result.

[0046] Specifically, the semiconductor laser is assessed for quality compliance based on the power test curve to obtain a quality evaluation result. This step begins by analyzing the fluctuation characteristics of the power test curve to extract key indicators reflecting the laser's output stability. Specifically, the power test curve is treated as a sequence of power data arranged in current order. A sliding window method is used to calculate the rate of change of the difference between adjacent data points, and the local range, standard deviation, and peak-to-peak value of the entire curve in different current ranges are statistically analyzed. Simultaneously, abnormal jitter, periodic oscillations, or abrupt jumps are identified. For example, within the rated operating current range (e.g., 20A~30A), the power fluctuation amplitude is calculated to see if it exceeds a preset tolerance band (e.g., ±3%). The maximum fluctuation value, average noise level, and slope consistency error are integrated into a set of quantifiable power stability parameters. These parameters not only include global fluctuation indicators but also cover the local dynamic characteristics of the startup phase, linear growth phase, and near-saturation region. Subsequently, multi-dimensional threshold mapping is performed on the power stability parameters, comparing each parameter with a pre-set multi-dimensional judgment threshold space. This threshold space is derived from historical qualified sample data and industry standards (e.g., IEC). The system is constructed using 60825, which includes multiple dimensions such as the lower limit of power slope, the upper limit of saturation starting point current, the allowable bandwidth of fluctuation, and the tolerance of nonlinear distortion. Each dimension corresponds to one or more threshold intervals. The system encodes the current power stability parameters into a set of binary or normalized values ​​according to their assignment in each dimension. For example, when the slope efficiency is higher than 0.8W / A, it is marked as "1" and otherwise as "0". When the standard deviation of fluctuation is less than 0.05W, it is marked as "1" and so on. Finally, the judgment results of all dimensions are combined into a structured quality feature vector. This vector fully expresses the compliance characteristics of the laser in terms of power output behavior in numerical form.Based on this, a multi-level quality assessment is performed on the semiconductor laser using the quality feature vector to obtain the quality evaluation result. Specifically, the quality feature vector is input into a pre-trained multi-level classification model. This model can be a rule engine, a fuzzy logic system, or a lightweight neural network. It contains multiple decision levels. The first level determines whether basic safety and functional requirements are met (e.g., whether the threshold current is within the nominal range, whether there is zero output or abnormal drop). If passed, it proceeds to the second level to evaluate performance level (e.g., high, medium, and low power stability). The third level further determines whether it meets the customized standards for specific application scenarios (e.g., industrial cutting requires a slope efficiency ≥ 0.75). (W / A and fluctuation ≤ ±2.5%), each level performs logical operations or similarity matching based on the corresponding components in the quality feature vector, and finally generates a graded output, such as "Qualified - Grade A", "Qualified - Grade B" or "Unqualified - Unstable Mode". This result not only indicates whether the device has passed the test, but also provides quality level information for subsequent grading. For example, in the application scenario of high-power 808nm semiconductor laser for industrial cutting, the system analyzes the fitted power test curve and finds that its power fluctuation standard deviation is 0.048W at 25A, the slope efficiency is 0.82W / A, and the saturation starting point is located at 28A. After multi-dimensional threshold mapping, a quality feature vector [1, 1, 0, 1, 1] is generated, which represents that four of the five key indicators meet the standards. After combining the historical data of beam quality factor with the multi-level evaluation module, it is judged as "Qualified - Grade A", indicating that the laser meets the light source requirements of high-precision processing equipment. Finally, a complete quality evaluation result is formed and stored in the database or printed as a test report.

[0047] The quality inspection method for semiconductor lasers in this embodiment of the invention has been described above. The quality inspection system for semiconductor lasers in this embodiment of the invention is described below. Please refer to [link / reference]. Figure 2 One embodiment of the semiconductor laser quality inspection system of the present invention includes: Acquisition module 21 is used to acquire the laser beam at the output end of the semiconductor laser to obtain a laser spot image; Extraction module 22 is used to extract spot parameters from the laser spot image to obtain spot feature data; Calculation module 23 is used to calculate the beam quality of the semiconductor laser based on the beam spot feature data to obtain the beam quality factor; Test module 24 is used to perform optical power testing on the semiconductor laser based on the beam quality factor to obtain a power test curve; The judgment module 25 is used to make a quality compliance judgment on the semiconductor laser based on the power test curve and obtain a quality evaluation result.

[0048] In this embodiment, the specific implementation of each module in the above system embodiment is described in the above method embodiment, and will not be repeated here.

Claims

1. A method for quality inspection of a semiconductor laser, characterized in that, Includes the following steps: The output of the semiconductor laser is sampled to obtain a laser spot image; The laser spot image is subjected to spot parameter extraction to obtain spot feature data; The beam quality factor of the semiconductor laser is obtained by calculating the beam quality based on the beam feature data. The optical power of the semiconductor laser was tested based on the beam quality factor to obtain a power test curve. The semiconductor laser is assessed for quality compliance based on the power test curve, and a quality evaluation result is obtained.

2. The method for quality inspection of a semiconductor laser according to claim 1, characterized in that, The output of the semiconductor laser is sampled to obtain a laser spot image, including: A preset optical acquisition device is used to capture the output of the semiconductor laser to obtain an initial beam image, and the initial beam image is preprocessed to obtain a clear beam image. The clear beam image is used to locate the spot area by image analysis technology to obtain the spot position information. Based on the spot position information, the clear beam image is cropped to obtain a laser spot image containing only the spot area.

3. The method for quality inspection of a semiconductor laser according to claim 2, characterized in that, Based on the spot position information, the clear beam image is cropped to obtain a laser spot image containing only the spot region, including: The location information of the light spot is used to perform boundary expansion calculation to obtain the safe boundary range of the light spot, and the background noise of the region in the clear beam image located within the safe boundary range of the light spot is evaluated to obtain the background interference features; Based on the background interference features, the clear beam image is adaptively cropped to obtain an initial cropped image of the beam spot. The edge integrity of the initial cropped image of the beam spot is then checked to obtain a laser beam spot image containing only the beam spot region.

4. The method for quality inspection of a semiconductor laser according to claim 1, characterized in that, The laser spot image is subjected to spot parameter extraction to obtain spot feature data, including: Gaussian fitting is performed on the laser spot image to obtain the light intensity distribution curve, and peak detection is performed on the light intensity distribution curve to obtain the center intensity data of the spot and the light intensity attenuation coefficient. Based on the light intensity distribution curve, the laser spot image is subjected to iso-intensity contour analysis to obtain multi-layer light intensity contour data, and the ellipticity of the multi-layer light intensity contour data is calculated to obtain the spot ellipticity parameter. The envelope area of ​​the laser spot is calculated based on the spot ellipticity parameter and the light intensity attenuation coefficient to obtain spot energy distribution data. The spot energy distribution data is then normalized to obtain spot feature data.

5. The method for quality inspection of a semiconductor laser according to claim 4, characterized in that, Based on the intensity distribution curve, iso-intensity contour analysis is performed on the laser spot image to obtain multi-layer intensity contour data, including: Dynamic threshold segmentation is performed on the light intensity distribution curve to obtain multi-layer light intensity gradient data; Based on the multi-layer light intensity gradient data, contour tracking is performed on the laser spot image to obtain a set of light intensity contour lines, and spatial density calculation is performed on the set of light intensity contour lines to obtain light intensity interlayer distance data. Spatial feature analysis is performed on the light intensity interlayer distance data to obtain the spatial distribution characteristics of light intensity, and contour lines are reconstructed on the light intensity contour line set based on the spatial distribution characteristics of light intensity to obtain multi-layer light intensity contour line data.

6. The method for quality inspection of a semiconductor laser according to claim 5, characterized in that, Based on the multi-layer intensity gradient data, contour tracking is performed on the laser spot image to obtain a set of intensity contour lines, including: Gradient direction vector calibration is performed on the multi-layer light intensity gradient data to obtain a directional gradient field matrix, and the correlation between the directional gradient field matrix and the pixel grayscale values ​​of the laser spot image is calculated to obtain a gradient-grayscale correlation coefficient matrix. Based on the gradient-grayscale correlation coefficient matrix, the laser spot image is screened for boundary seed points to obtain an initial boundary seed point set. Then, the initial boundary seed point set is verified for neighborhood gradient consistency to obtain an effective boundary seed point set. Based on the effective boundary seed point set, dynamic contour tracking is performed on the directional gradient field matrix to obtain single-intensity level contour lines. The single-intensity level contour lines are then summarized according to the hierarchical order of the intensity gradient data to obtain a set of intensity contour lines.

7. The method for quality inspection of a semiconductor laser according to claim 1, characterized in that, Based on the beam feature data, the beam quality of the semiconductor laser is calculated to obtain the beam quality factor, including: Based on the laser spot feature data, the second moment of the laser spot is calculated to obtain the beam transmission matrix parameters, and the beam transmission matrix parameters are decomposed into eigenvalues ​​to obtain the beam quality evaluation coefficients. Optical path difference analysis is performed on the beam quality evaluation coefficient to obtain the beam phase distribution characteristics, and the beam diffraction limit is calculated based on the beam phase distribution characteristics to obtain the beam diffraction limit factor. The beam quality factor is obtained by performing a comprehensive calculation on the beam diffraction limit factor and the beam quality evaluation coefficient.

8. The method for quality inspection of a semiconductor laser according to claim 1, characterized in that, The optical power of the semiconductor laser is tested based on the beam quality factor to obtain a power test curve, including: The driving current of the semiconductor laser is adjusted in a stepwise manner based on the beam quality factor to obtain multiple sets of current adjustment parameters. The semiconductor laser is then subjected to time-series power acquisition based on the multiple sets of current adjustment parameters to obtain an initial power dataset. The initial power dataset is subjected to outlier removal using spatial filtering techniques to obtain filtered power data. The filtered power data is then weighted based on the beam quality factor to obtain a weighted power sequence. Time-domain interpolation is performed on the weighted power sequence to obtain continuous power variation data, and curve fitting is performed based on the continuous power variation data to obtain a power test curve.

9. The method for quality inspection of a semiconductor laser according to claim 1, characterized in that, Based on the power test curve, the semiconductor laser is subjected to a quality compliance assessment to obtain a quality evaluation result, including: Fluctuation characteristic analysis is performed on the power test curve to obtain power stability parameters, and multi-dimensional threshold mapping is performed on the power stability parameters to obtain a quality feature vector; The semiconductor laser is subjected to multi-level quality assessment based on the quality feature vector to obtain the quality evaluation result.

10. A quality inspection system for a semiconductor laser, characterized in that, A method for performing quality inspection of a semiconductor laser as described in any one of claims 1-9, comprising: The acquisition module is used to acquire the laser beam from the output end of the semiconductor laser to obtain a laser spot image; The extraction module is used to extract spot parameters from the laser spot image to obtain spot feature data; The calculation module is used to calculate the beam quality of the semiconductor laser based on the beam spot feature data to obtain the beam quality factor; The testing module is used to perform optical power testing on the semiconductor laser based on the beam quality factor and obtain a power test curve; The judgment module is used to determine the quality compliance of the semiconductor laser based on the power test curve and obtain the quality evaluation result.

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