Method and device for monitoring and controlling aperture of wet oxidation of VCSEL chip

Abstract

The present invention discloses a monitoring and control method and device for the wet oxidation aperture of a VCSEL chip, and relates to the field related to vertical cavity surface emitting lasers. The method includes: obtaining the expected beam diameter, the expected beam divergence angle, the expected beam profile, and the expected power in the barrel; collecting the oxidation aperture shape parameters and the oxidation aperture uniformity coefficient according to the application scenario type of the VCSEL chip; mapping through a beam quality predictor to generate a predicted beam diameter, a predicted beam divergence angle, a predicted beam profile, and a predicted power in the barrel; selecting wet oxidation control parameters that meet the expected beam diameter, the expected beam divergence angle, the expected beam profile, and the expected power in the barrel, and setting them as monitoring and control benchmark parameters; and performing wet oxidation monitoring. The method solves the technical problem of insufficient precision control in the existing wet oxidation aperture control of VCSEL chips, and achieves the technical effect of improving the precision control level of the oxidation aperture of VCSEL chips.

Description

Technical Field

[0001] The present application relates to the field of vertical cavity surface emitting lasers, and in particular to a method and device for monitoring and controlling the wet oxidation aperture of a VCSEL chip. Background Art

[0002] With the rapid development of optoelectronic technology, vertical cavity surface emitting laser (VCSEL), as a high-performance laser light source, has shown broad application prospects in many fields such as optical communication, data storage, lidar, 3D sensing, etc. The performance of VCSEL chips, especially the quality of their laser emission, is directly affected by the fine control of their internal structure, among which the precision control of the oxidation aperture is particularly critical. The oxidation aperture not only determines the emission path of the laser beam, but is also directly related to key parameters such as the diameter, divergence angle, profile, and power distribution of the beam, which in turn affects the overall performance and application effect of VCSEL. In the traditional VCSEL chip production process, the formation of the oxidation aperture and its wet oxidation control process mainly rely on empirical methods. This method usually relies on the experience accumulation of engineers and repeated debugging of process parameters. It lacks scientific quantitative standards and systematic control methods, resulting in insufficient precision control and affecting the stability and consistency of the product.

[0003] In the current related technologies, there is a technical problem of insufficient precision control in the wet oxidation aperture control of VCSEL chips. Summary of the invention

[0004] The present application provides a monitoring and control method and device for the wet oxidation aperture of a VCSEL chip, which responds to a user's request for optimization of the oxidation aperture of the VCSEL chip, clarifies the expected parameters of the light beam, collects the shape parameters and uniformity coefficient of the oxidation aperture according to the application scenario type of the VCSEL chip, and maps them through a beam quality predictor to generate predicted beam parameters. According to the prediction results, wet oxidation control parameters that meet the expected beam parameters are selected as benchmark parameters for monitoring and control, and real-time monitoring and adjustment are performed. Through precise quantitative control and real-time monitoring, the technical effect of improving the precision control level of the oxidation aperture of the VCSEL chip is achieved.

[0005] The present application provides a method for monitoring and controlling the aperture of a VCSEL chip wet oxidation, comprising:

[0006] In response to a VCSEL chip oxidation aperture optimization request submitted by a user end, an expected beam diameter, an expected beam divergence angle, an expected beam profile, and an expected power in a barrel are obtained; according to the VCSEL chip application scenario type of the VCSEL chip oxidation aperture optimization request, oxidation hole shape parameters and oxidation hole uniformity coefficients are collected; according to the oxidation hole shape parameters and the oxidation hole uniformity coefficients, mapping is performed through a beam quality predictor to generate a predicted beam diameter, a predicted beam divergence angle, a predicted beam profile, and a predicted power in a barrel; according to the predicted beam diameter, the predicted beam divergence angle, the predicted beam profile, and the predicted power in the barrel, wet oxidation control parameters that meet the expected beam diameter, the expected beam divergence angle, the expected beam profile, and the expected power in the barrel are selected and set as monitoring and control benchmark parameters; wet oxidation monitoring is performed according to the monitoring and control benchmark parameters.

[0007] In a possible implementation, according to the VCSEL chip application scenario type of the VCSEL chip oxidation aperture optimization request, the oxidation aperture shape parameter and the oxidation aperture uniformity coefficient are collected, and the following processing is performed:

[0008] According to the application scenario type of the VCSEL chip, a one-to-one corresponding historical cross-sectional data set of oxidation pore shape parameters and a historical cross-sectional data set of oxidation pore uniformity coefficient are collected; the historical cross-sectional data set of oxidation pore shape parameters is preprocessed by removing outliers to obtain the distribution interval of oxidation pore shape parameters; the historical cross-sectional data set of oxidation pore uniformity coefficient is preprocessed by removing outliers to obtain the distribution interval of oxidation pore uniformity coefficient; random assignment is performed according to the distribution interval of oxidation pore shape parameters and the distribution interval of oxidation pore uniformity coefficient to obtain the oxidation pore shape parameters and the oxidation pore uniformity coefficient.

[0009] In a possible implementation, according to the type of application scenario of the VCSEL chip, a one-to-one corresponding historical cross-sectional data set of oxidation hole shape parameters and a historical cross-sectional data set of oxidation hole uniformity coefficient are collected, and the following processing is performed:

[0010] Obtaining oxidation hole shape parameter attributes and oxidation hole uniformity coefficient attributes, wherein the oxidation hole shape parameter attributes include oxidation hole long diameter, oxidation hole short diameter, and oxidation hole depth, and the oxidation hole uniformity coefficient attributes include oxidation hole distribution spacing and oxidation hole distribution quantity; collecting historical cross-sectional data of the VCSEL chip application scenario type according to the oxidation hole long diameter, the oxidation hole short diameter, the oxidation hole depth, the oxidation hole distribution spacing, and the oxidation hole distribution quantity, to obtain the oxidation hole shape parameter historical cross-sectional data set and the oxidation hole uniformity coefficient historical cross-sectional data set.

[0011] In a possible implementation, according to the oxidation hole shape parameter and the oxidation hole uniformity coefficient, mapping is performed through a beam quality predictor to generate a beam prediction diameter, a beam prediction divergence angle, a beam prediction profile and a barrel prediction power, and the following processing is performed:

[0012] According to the oxidation hole long diameter parameter and the oxidation hole short diameter parameter of the oxidation hole shape parameter, mapping is performed through the beam diameter prediction channel of the beam quality predictor to obtain the predicted beam diameter; according to the oxidation hole shape parameter, mapping is performed through the beam divergence angle prediction channel of the beam quality predictor to obtain the predicted beam divergence angle; according to the oxidation hole uniformity coefficient, mapping is performed through the beam profile prediction channel of the beam quality predictor to obtain the predicted beam profile; according to the oxidation hole uniformity coefficient, mapping is performed through the barrel power prediction channel of the beam quality predictor to obtain the predicted barrel power.

[0013] In a possible implementation, according to the predicted beam diameter, the predicted beam divergence angle, the predicted beam profile and the predicted power in the barrel, a wet oxidation control parameter satisfying the expected beam diameter, the expected beam divergence angle, the expected beam profile and the expected power in the barrel is selected and set as a monitoring and control reference parameter, and the following processing is performed:

[0014] The predicted beam diameter, the predicted beam divergence angle, the predicted beam profile and the predicted power in the barrel are verified according to the expected beam diameter, the expected beam divergence angle, the expected beam profile and the expected power in the barrel to obtain a beam quality verification result; when the beam quality verification result is passed, the wet oxidation control parameters are optimized according to the oxidation hole shape parameters and the oxidation hole uniformity coefficient to obtain target wet oxidation control parameters; the target wet oxidation control parameters are set as the monitoring and control benchmark parameters; when the beam quality verification result is not passed, the oxidation hole shape parameters and the oxidation hole uniformity coefficient are updated according to the application scenario type of the VCSEL chip.

[0015] In a possible implementation, the predicted beam diameter, the predicted beam divergence angle, the predicted beam profile, and the predicted power in the barrel are verified according to the expected beam diameter, the expected beam divergence angle, the expected beam profile, and the expected power in the barrel to obtain a beam quality verification result, and the following processing is performed:

[0016] When the diameter deviation between the expected diameter of the light beam and the predicted diameter of the light beam is less than or equal to the diameter deviation threshold, and the divergence angle deviation between the expected divergence angle of the light beam and the predicted divergence angle of the light beam is less than or equal to the divergence angle deviation threshold, and the contour shape deviation between the expected profile of the light beam and the predicted profile of the light beam is less than or equal to the contour shape deviation threshold, and the bucket power deviation between the expected power in the barrel and the predicted power in the barrel is less than or equal to the bucket power deviation threshold, a first-level verification pass signal is generated; a first multiplication factor between the expected diameter of the light beam and the expected divergence angle of the light beam is calculated, and a second multiplication factor between the predicted diameter of the light beam and the predicted divergence angle of the light beam is calculated to obtain A third product factor of a diffraction-limited Gaussian beam diameter and a diffraction-limited Gaussian beam divergence angle of the same wavelength is obtained; a ratio of the first product factor to the third product factor is calculated, and set as an expected beam quality factor; a ratio of the second product factor to the third product factor is calculated, and set as a predicted beam quality factor; when a beam quality factor deviation between the expected beam quality factor and the predicted beam quality factor is less than or equal to a beam quality factor deviation threshold, a secondary verification pass signal is generated; when both the primary verification pass signal and the secondary verification pass signal are passed, the beam quality verification result is passed; otherwise, the beam quality verification result is failed.

[0017] In a possible implementation, when the beam quality verification result is passed, the wet oxidation control parameter optimization is performed according to the oxidation hole shape parameter and the oxidation hole uniformity coefficient to obtain the target wet oxidation control parameter, and the following processing is performed:

[0018] Obtain a wet oxidation control parameter distribution constraint interval, wherein the wet oxidation control parameter distribution constraint interval includes an oxidation temperature distribution constraint interval, an oxidation time distribution constraint interval, a nitrogen flow rate distribution constraint interval, a nitrogen concentration distribution constraint interval, a water vapor flow rate distribution constraint interval, and a water vapor concentration distribution constraint interval; according to the VCSEL chip model and the wet oxidation equipment model, collect a wet oxidation control historical cross-sectional data set that satisfies the oxidation pore shape parameter and the oxidation pore uniformity coefficient through networking; based on the oxidation temperature distribution constraint interval, the oxidation time distribution constraint interval, the nitrogen flow rate distribution constraint interval, the nitrogen concentration distribution constraint interval, the water vapor flow rate distribution constraint interval, and the water vapor concentration distribution constraint interval, perform normalized dimensionality reduction distribution on the wet oxidation control historical cross-sectional data set to obtain wet oxidation control particle distribution coordinates; perform outlier coordinate deletion on the wet oxidation control particle distribution coordinates to obtain wet oxidation control particle concentrated coordinates; extract the wet oxidation control parameter distribution interval of the wet oxidation control particle concentrated coordinates, and set it as the target wet oxidation control parameter.

[0019] In a possible implementation, based on the oxidation temperature distribution constraint interval, the oxidation time distribution constraint interval, the nitrogen flow distribution constraint interval, the nitrogen concentration distribution constraint interval, the water vapor flow distribution constraint interval, and the water vapor concentration distribution constraint interval, the wet oxidation control historical cross-section data set is normalized and dimensionally reduced to obtain the wet oxidation control particle distribution coordinates, and the following processing is performed:

[0020] Obtain oxidation temperature history section data of the first wet oxidation control history section data of the wet oxidation control history section data set; calculate the first deviation modulus of the oxidation temperature history section data and the minimum value of the oxidation temperature distribution constraint interval; calculate the second deviation modulus of the maximum value and the minimum value of the oxidation temperature distribution constraint interval; calculate the ratio of the first deviation modulus to the second deviation modulus, and set it as the oxidation temperature normalized coordinate; with the same processing process, obtain the oxidation time normalized coordinate, nitrogen flow normalized coordinate, nitrogen concentration normalized coordinate, water vapor flow normalized coordinate, water vapor concentration normalized coordinate; according to the oxidation temperature normalized coordinate, the oxidation time normalized coordinate, the nitrogen flow normalized coordinate, the nitrogen concentration normalized coordinate, the water vapor flow normalized coordinate, the water vapor concentration normalized coordinate, the first particle distribution coordinate is obtained, and added into the wet oxidation control particle distribution coordinate.

[0021] The present application also provides a monitoring and control device for the wet oxidation aperture of a VCSEL chip, comprising:

[0022] An oxidation aperture optimization request response module is used to respond to a VCSEL chip oxidation aperture optimization request submitted by a user end, and obtain an expected beam diameter, an expected beam divergence angle, an expected beam profile, and an expected power in the barrel; an oxidation aperture parameter acquisition module is used to acquire oxidation aperture shape parameters and oxidation aperture uniformity coefficients according to the VCSEL chip application scenario type of the VCSEL chip oxidation aperture optimization request; a beam quality prediction module is used to predict the beam quality through the oxidation aperture shape parameters and the oxidation aperture uniformity coefficients according to the oxidation aperture shape parameters and the oxidation aperture uniformity coefficients. The predictor performs mapping to generate a predicted beam diameter, a predicted beam divergence angle, a predicted beam profile and a predicted power in the barrel; a monitoring and control benchmark parameter acquisition module is used to select wet oxidation control parameters that meet the expected beam diameter, the expected beam divergence angle, the expected beam profile and the expected power in the barrel according to the predicted beam diameter, the predicted beam divergence angle, the predicted beam profile and the predicted power in the barrel, and set them as monitoring and control benchmark parameters; a wet oxidation monitoring module is used to perform wet oxidation monitoring according to the monitoring and control benchmark parameters.

[0023] The monitoring and control method and device for the wet oxidation aperture of a VCSEL chip proposed in the present application first respond to the VCSEL chip oxidation aperture optimization request submitted by the user end, obtain the expected beam diameter, the expected beam divergence angle, the expected beam profile and the expected power in the barrel, and then collect the oxidation hole shape parameters and the oxidation hole uniformity coefficient according to the VCSEL chip application scenario type of the VCSEL chip oxidation aperture optimization request, and then map the oxidation hole shape parameters and the oxidation hole uniformity coefficient through a beam quality predictor to generate a beam predicted diameter, a beam predicted divergence angle, a beam predicted profile and a predicted power in the barrel, and then select the wet oxidation control parameters that meet the expected beam diameter, the expected beam divergence angle, the expected beam profile and the expected power in the barrel according to the predicted beam diameter, the predicted beam divergence angle, the predicted beam profile and the predicted power in the barrel, and set them as monitoring and control benchmark parameters, and finally perform wet oxidation monitoring according to the monitoring and control benchmark parameters, thereby achieving the technical effect of improving the precision control level of the oxidation aperture of the VCSEL chip. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the technical solution of the embodiment of the present invention, the accompanying drawings of the embodiment of the present invention will be briefly introduced below. A flow chart is used in the present application to illustrate the operations performed by the device according to the embodiment of the present application. It should be understood that the previous or following operations are not necessarily performed accurately in order. On the contrary, various steps can be processed in reverse order or simultaneously as needed. At the same time, other operations can also be added to these processes, or one or more operations can be removed from these processes.

[0025] Figure 1 A schematic flow chart of a method for monitoring and controlling the aperture of a VCSEL chip wet oxidation provided in an embodiment of the present application.

[0026] Figure 2 A schematic diagram of the structure of a device for monitoring and controlling the wet oxidation aperture of a VCSEL chip provided in an embodiment of the present application.

[0027] Explanation of the accompanying drawings: oxidation aperture optimization request response module 10, oxidation aperture parameter acquisition module 20, beam quality prediction module 30, monitoring and control benchmark parameter acquisition module 40, wet oxidation monitoring module 50. DETAILED DESCRIPTION

[0028] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below.

[0029] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings. The described embodiments should not be regarded as limiting the present application. All other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of this application.

[0030] In the following description, reference is made to "some embodiments", which describe a subset of all possible embodiments, but it is understood that "some embodiments" may be the same subset or different subsets of all possible embodiments, and may be combined with each other without conflict, and the terms "first\second" involved are merely to distinguish similar objects and do not represent a specific ordering of objects. The terms "including" and "having" and any variations are intended to cover non-exclusive inclusions, for example, a process, method, device, product, or server that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products, or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those generally understood by technicians in the technical field of this application. The terms used herein are for the purpose of describing the embodiments of the present application only.

[0031] The present application embodiment provides a method for monitoring and controlling the aperture of a VCSEL chip wet oxidation, such as Figure 1 As shown, the method includes:

[0032] Step S100, in response to the VCSEL chip oxidation aperture optimization request submitted by the user end, obtain the expected beam diameter, the expected beam divergence angle, the expected beam profile and the expected power in the barrel. Specifically, receive the VCSEL chip oxidation aperture optimization request from the user end, and parse the expected beam diameter, expected beam divergence angle, expected beam profile and expected power in the barrel parameters contained in the request. Among them, the oxidation aperture is a specific aperture formed on the chip by wet oxidation and other technical means during the VCSEL chip manufacturing process, which is used to control the output characteristics of the light beam. The expected beam diameter is the beam diameter that the user wants the VCSEL chip to output; the expected beam divergence angle is the beam divergence angle that the user wants the VCSEL chip to output; the expected beam profile is the beam profile shape that the user wants the VCSEL chip to output; the expected power in the barrel is the output power that the user wants the VCSEL chip to output in a specific area (such as the barrel area of ​​the optical receiver).

[0033] Step S200, according to the VCSEL chip application scenario type of the VCSEL chip oxidation aperture optimization request, the oxidation hole shape parameter and the oxidation hole uniformity coefficient are collected. Specifically, according to the application scenario type of the VCSEL chip (such as communication, sensing, projection, etc.), the scope of data collection is determined. The historical data of the oxidation hole shape parameter and the oxidation hole uniformity coefficient corresponding to the current application scenario type are collected from the historical database. Among them, the oxidation hole shape parameter is a parameter describing the geometric characteristics of the oxidation hole, such as the long diameter, short diameter, depth, etc.; the oxidation hole uniformity coefficient is a parameter describing the uniformity of the oxidation hole distribution, such as the distribution spacing, the distribution number, etc.

[0034] In a possible implementation, according to the VCSEL chip application scenario type of the VCSEL chip oxidation aperture optimization request, the oxidation hole shape parameter and the oxidation hole uniformity coefficient are collected, and step S200 further includes step S210, according to the VCSEL chip application scenario type, collecting a one-to-one corresponding oxidation hole shape parameter historical cross-sectional data set and an oxidation hole uniformity coefficient historical cross-sectional data set. Specifically, the specific application scenario of the VCSEL chip is clarified, such as high-speed data transmission, 3D sensing, laser radar, etc. For a specific application scenario, the historical data of the oxidation hole shape parameter (such as diameter, shape factor, etc.) and the oxidation hole uniformity coefficient (an indicator for measuring the consistency of the oxidation hole size) related to the application scenario are retrieved from the historical database, and these data come from previous design iterations, tests or production records. Step S220, the oxidation hole shape parameter historical cross-sectional data set is preprocessed by outlier removal to obtain the oxidation hole shape parameter distribution interval. Specifically, each data point in the oxidation hole shape parameter historical cross-sectional data set is checked, and those abnormal values ​​(outliers) that are obviously deviated from most data points are identified and deleted, which may be caused by measurement errors, equipment failures or other atypical factors. Perform statistical analysis on the cleaned data set, calculate the mean, standard deviation and other statistics of the data, and determine the distribution of the data. Based on the results of the statistical analysis, determine a reasonable distribution interval, which contains most (such as 95% or 99%) of the data points, as a reference range for subsequent random assignment. Step S230, perform outlier removal preprocessing on the oxidation hole uniformity coefficient historical cross-section data set to obtain the oxidation hole uniformity coefficient distribution interval. Specifically, similar to step S220, but for the oxidation hole uniformity coefficient historical cross-section data set. By deleting outliers and performing statistical analysis, a reasonable oxidation hole uniformity coefficient distribution interval is determined. Step S240, randomly assign values ​​according to the oxidation hole shape parameter distribution interval and the oxidation hole uniformity coefficient distribution interval to obtain the oxidation hole shape parameter and the oxidation hole uniformity coefficient. Specifically, within the distribution interval determined in steps S220 and S230, a random sampling method is used to generate the values ​​of the oxidation hole shape parameter and the oxidation hole uniformity coefficient. The randomly generated parameter values ​​are assigned to the current VCSEL chip optimization process to explore possible better performance in a given application scenario. This implementation method collects and analyzes historical cross-sectional data sets to determine which oxidation pore shape parameters and uniformity coefficients are more ideal in different application scenarios. By randomly assigning values ​​within these distribution ranges, it ensures that the new optimization is close to the optimal solution from the beginning, thereby achieving the technical effect of accelerating the optimization process and improving optimization efficiency.

[0035] In a possible implementation, according to the type of application scenario of the VCSEL chip, a one-to-one corresponding historical cross-sectional data set of oxidation hole shape parameters and a historical cross-sectional data set of oxidation hole uniformity coefficient are collected, and step S210 further includes step S211, obtaining oxidation hole shape parameter attributes and oxidation hole uniformity coefficient attributes, wherein the oxidation hole shape parameter attributes include oxidation hole long diameter, oxidation hole short diameter, and oxidation hole depth, and the oxidation hole uniformity coefficient attributes include oxidation hole distribution spacing and oxidation hole distribution number. Specifically, determine the oxidation hole shape parameter attributes and oxidation hole uniformity coefficient attributes that need to be collected. Among them, the oxidation hole shape parameter attributes include the long diameter of the oxidation hole (the maximum diameter of the oxidation hole in the direction perpendicular to the light emission direction), the short diameter (if the shape is not completely circular, the diameter in the direction perpendicular to the long diameter) and the depth (the distance of the oxidation hole from the chip surface to its bottom), which directly affect the light emission mode and efficiency. The oxidation hole uniformity coefficient attribute includes the distribution spacing of the oxidation holes on the chip (the distance between adjacent oxidation holes, which affects the uniformity and resolution of light) and the distribution number (the total number of oxidation holes in a specific area, which affects the intensity and coverage of light). These parameters determine the uniformity and overall performance of light. The above attributes are recorded in a data structure for subsequent data collection and processing. Step S212, according to the oxidation hole long diameter, the oxidation hole short diameter, the oxidation hole depth, the oxidation hole distribution spacing, and the oxidation hole distribution number, the historical cross-sectional data of the VCSEL chip application scenario type is collected to obtain the oxidation hole shape parameter historical cross-sectional data set and the oxidation hole uniformity coefficient historical cross-sectional data set. Specifically, a historical database storing VCSEL chip design, manufacturing and test data is accessed, and according to the application scenario and the attributes defined in step S211, the historical data of the oxidation hole shape parameters and the oxidation hole uniformity coefficient related to the current application scenario are screened out from the database. The screened data is classified and sorted according to the application scenario and attributes to form an oxidation hole shape parameter historical cross-sectional data set and an oxidation hole uniformity coefficient historical cross-sectional data set. This implementation method achieves the technical effect of improving the pertinence and effectiveness of VCSEL chip optimization by accurately defining and collecting historical data on oxidation hole shape parameters and uniformity coefficient properties that are closely related to VCSEL chip application scenarios.

[0036] Step S300, mapping is performed through a beam quality predictor according to the oxidation hole shape parameters and the oxidation hole uniformity coefficient, generating a beam prediction diameter, a beam prediction divergence angle, a beam prediction profile and a barrel prediction power. Specifically, the beam quality predictor is a prediction tool based on machine learning, which is used to predict the beam quality according to the oxidation hole parameters. The oxidation hole shape parameters and the oxidation hole uniformity coefficient are input into the beam quality predictor, and the beam quality predictor predicts the diameter, divergence angle, profile and barrel power of the beam according to the input parameters through a mapping relationship.

[0037] In a possible implementation, mapping is performed through a beam quality predictor according to the oxidation hole shape parameter and the oxidation hole uniformity coefficient to generate a predicted beam diameter, a predicted beam divergence angle, a predicted beam profile and a predicted power in the barrel, and step S300 further includes step S310, mapping through a beam diameter prediction channel of the beam quality predictor according to the oxidation hole long diameter parameter and the oxidation hole short diameter parameter of the oxidation hole shape parameter to obtain the predicted beam diameter; step S320, mapping through a beam divergence angle prediction channel of the beam quality predictor according to the oxidation hole shape parameter to obtain the predicted beam divergence angle; step S330, mapping through a beam profile prediction channel of the beam quality predictor according to the oxidation hole uniformity coefficient to obtain the predicted beam profile; step S340, mapping through a barrel power prediction channel of the beam quality predictor according to the oxidation hole uniformity coefficient to obtain the predicted power in the barrel.

[0038] Specifically, the beam quality predictor is a machine learning model that integrates multiple prediction channels and is used to predict the beam quality based on the oxidation hole parameters. Among them, the beam diameter prediction channel inputs the long diameter and short diameter of the oxidation hole and outputs the predicted beam diameter; the beam divergence angle prediction channel inputs the oxidation hole shape parameters and outputs the predicted beam divergence angle; the beam profile prediction channel inputs the oxidation hole uniformity coefficient and outputs the predicted beam profile; the barrel power prediction channel inputs the oxidation hole uniformity coefficient and the receiving area parameters and outputs the predicted barrel power. The model training process is described below using the beam diameter prediction channel as an example: a large amount of oxidation hole parameters and corresponding beam diameter data after wet oxidation of VCSEL chips are collected, the data is cleaned, noise and outliers are removed, and the collected data is normalized. A neural network model is selected according to the data characteristics, the model is trained using the preprocessed data, the prediction performance is optimized by adjusting the model parameters and training strategies, and the prediction accuracy and generalization ability of the model are evaluated using the test set to ensure that the model meets actual needs. Finally, the trained model is deployed in the beam quality predictor for real-time prediction of beam quality. This implementation utilizes specialized prediction channels to process different beam quality parameters. Each channel is optimized for its specific task. Through training with a large amount of historical data, it is able to capture complex optical phenomena and physical laws, thereby more accurately predicting the quality characteristics of the beam, achieving the technical effect of improving the accuracy and efficiency of the prediction.

[0039] Step S400, based on the predicted beam diameter, the predicted beam divergence angle, the predicted beam profile and the predicted power in the barrel, select the wet oxidation control parameters that meet the expected beam diameter, the expected beam divergence angle, the expected beam profile and the expected power in the barrel, and set them as monitoring and control benchmark parameters. Specifically, the predicted beam diameter, divergence angle, profile and power in the barrel are compared with the user's expected values. Select the oxidation hole shape parameters and oxidation hole uniformity coefficient corresponding to the predicted beam quality parameters that can meet the user's expected values, and obtain the wet oxidation control parameters corresponding to the oxidation hole shape parameters and oxidation hole uniformity coefficient, and set them as monitoring and control benchmark parameters. Among them, the wet oxidation control parameters are parameters used to control the shape and uniformity of the oxidation aperture during the wet oxidation process, such as oxidation temperature, oxidation time, gas flow rate and concentration, etc.

[0040] In a possible implementation, according to the predicted beam diameter, the predicted beam divergence angle, the predicted beam profile and the predicted power in the barrel, a wet oxidation control parameter that satisfies the expected beam diameter, the expected beam divergence angle, the expected beam profile and the expected power in the barrel is selected and set as a monitoring and control reference parameter, and step S400 further includes step S410, according to the expected beam diameter, the expected beam divergence angle, the expected beam profile and the expected power in the barrel, the predicted beam diameter, the predicted beam divergence angle, the predicted beam profile and the predicted power in the barrel are verified to obtain a beam quality verification result. Specifically, the expected value and the predicted value of the beam are compared one by one, and the difference or deviation between them is calculated by mathematical methods. If all the predicted values ​​are within a certain tolerance range of the expected value, the verification result is passed; otherwise, it is not passed. Step S420, when the beam quality verification result is passed, the wet oxidation control parameter is optimized according to the oxidation hole shape parameter and the oxidation hole uniformity coefficient to obtain the target wet oxidation control parameter. Specifically, according to the verified oxidation hole shape parameters and uniformity coefficients, and the control parameter range of wet oxidation, an optimization algorithm is used to search within the control parameter range to find a set of control parameters that can make the beam quality (predicted value) closest to the expected value, that is, the target wet oxidation control parameters. Step S430, the target wet oxidation control parameters are set as the monitoring and control reference parameters. Specifically, the target wet oxidation control parameters obtained by optimization are set as the monitoring and control reference parameters in the wet oxidation process. Step S440, when the beam quality verification result is not passed, the oxidation hole shape parameters and the oxidation hole uniformity coefficient are updated according to the application scenario type of the VCSEL chip. Specifically, if the beam quality verification result is not passed, the shape parameters and uniformity coefficients of the oxidation holes are readjusted according to the specific application scenario of the VCSEL chip. The beam quality prediction and verification are re-performed according to the updated oxidation hole shape parameters and oxidation hole uniformity coefficients until the beam quality verification result passes. This implementation method ensures that the wet oxidation process can more accurately meet the expected requirements of the beam quality through beam quality verification, thereby achieving the technical effect of improving the performance and quality of the product.

[0041] In a possible implementation, the predicted beam diameter, the predicted beam divergence angle, the predicted beam profile and the predicted power in the barrel are verified according to the expected beam diameter, the expected beam divergence angle, the expected beam profile and the expected power in the barrel to obtain a beam quality verification result, and step S410 further includes step S411, when the diameter deviation between the expected beam diameter and the predicted beam diameter is less than or equal to the diameter deviation threshold, and the divergence angle deviation between the expected beam divergence angle and the predicted beam divergence angle is less than or equal to the divergence angle deviation threshold, and the contour shape deviation between the expected beam profile and the predicted beam profile is less than or equal to the contour shape deviation threshold, and the power deviation between the expected power in the barrel and the predicted power in the barrel is less than or equal to the power deviation threshold, a first-level verification pass signal is generated. Specifically, the diameter deviation, the divergence angle deviation, the profile shape deviation and the power deviation in the barrel respectively represent the difference between the actual predicted value and the expected value. If all deviations meet the threshold conditions, a first-level verification pass signal is generated.

[0042] Step S412, calculate the first product factor of the expected beam diameter and the expected beam divergence angle, calculate the second product factor of the predicted beam diameter and the predicted beam divergence angle, and obtain the third product factor of the diffraction-limited Gaussian beam diameter and the diffraction-limited Gaussian beam divergence angle of the same wavelength; Step S413, calculate the ratio of the first product factor to the third product factor, set it as the expected beam quality factor, calculate the ratio of the second product factor to the third product factor, and set it as the predicted beam quality factor; Step S414, when the beam quality factor deviation between the expected beam quality factor and the predicted beam quality factor is less than or equal to the beam quality factor deviation threshold, generate a secondary verification pass signal.

[0043] Specifically, the first product factor (the product of the expected beam diameter and the expected beam divergence angle) and the second product factor (the product of the predicted beam diameter and the predicted beam divergence angle) are calculated. At the same time, the diameter and divergence angle of the diffraction-limited Gaussian beam of the same wavelength are obtained, and their product is calculated as the third product factor. The ratio of the first product factor to the third product factor (the expected beam quality factor) and the ratio of the second product factor to the third product factor (the predicted beam quality factor) are calculated respectively. These two ratios are parameters for quantifying the beam quality, reflecting the degree of closeness of the beam quality to the diffraction-limited Gaussian beam quality. The closer the value is to 1, the better the beam quality is, and the closer it is to the ideal diffraction-limited Gaussian beam (an ideal beam model with the best beam quality). The beam quality factor deviation between the expected beam quality factor and the predicted beam quality factor is calculated, and it is determined whether it is less than or equal to the beam quality factor deviation threshold. If so, a secondary verification pass signal is generated.

[0044] Step S415, when both the first-level verification pass signal and the second-level verification pass signal are passed, the beam quality verification result is passed; step S416, otherwise, the beam quality verification result is failed. Specifically, if both the first-level verification pass signal and the second-level verification pass signal are generated, the beam quality verification result is passed. If any first-level verification fails, the beam quality verification result is failed. This implementation method comprehensively evaluates the quality of the beam through conventional beam parameter comparison and beam quality factor comparison, ensures that the predicted beam performance meets actual needs, and achieves the technical effect of improving the accuracy of beam quality verification.

[0045] In a possible implementation, when the beam quality verification result is passed, the wet oxidation control parameter optimization is performed according to the oxidation hole shape parameter and the oxidation hole uniformity coefficient to obtain the target wet oxidation control parameter, and step S420 further includes step S421, obtaining the wet oxidation control parameter distribution constraint interval, wherein the wet oxidation control parameter distribution constraint interval includes the oxidation temperature distribution constraint interval, the oxidation time distribution constraint interval, the nitrogen flow distribution constraint interval, the nitrogen concentration distribution constraint interval, the water vapor flow distribution constraint interval, and the water vapor concentration distribution constraint interval. Specifically, according to process knowledge, equipment specifications and experimental data, reasonable distribution constraint intervals are set for each control parameter of wet oxidation (oxidation temperature, oxidation time, nitrogen flow, nitrogen concentration, water vapor flow, water vapor concentration). These intervals limit the range of parameter adjustment to ensure that the operation is carried out within a safe and effective range. The constraint interval of each parameter is recorded as the basis for subsequent data processing and optimization. Step S422, according to the VCSEL chip model and the wet oxidation equipment model, the wet oxidation control historical cross-sectional data set that meets the oxidation pore shape parameters and the oxidation pore uniformity coefficient is collected online. Specifically, according to the VCSEL chip model and the wet oxidation equipment model, the historical data range to be collected is determined. Through the network connection to the database or data warehouse, the wet oxidation control historical data that meets the conditions is automatically or manually downloaded, and these data include control parameters and results in multiple experiments or production processes. The collected data is sorted to form a wet oxidation control historical cross-sectional data set, and each cross section represents the control parameters and results in an experiment or production process. Step S423, based on the oxidation temperature distribution constraint interval, the oxidation time distribution constraint interval, the nitrogen flow distribution constraint interval, the nitrogen concentration distribution constraint interval, the water vapor flow distribution constraint interval, and the water vapor concentration distribution constraint interval, the wet oxidation control historical cross-sectional data set is normalized and reduced in dimension distribution to obtain the wet oxidation control particle distribution coordinates. Specifically, each control parameter in the wet oxidation control historical cross-sectional data set is normalized according to its distribution constraint interval, that is, the actual value is converted into a relative value between 0 and 1. Perform distribution analysis on the normalized data to determine the distribution of each control parameter, providing a basis for subsequent optimization. Step S424, delete the outlier coordinates of the wet oxidation control particle distribution coordinates to obtain the wet oxidation control particle centralized coordinates. Specifically, perform density distribution analysis on each control parameter point to calculate its density or frequency in the data set. Based on the results of the density analysis, identify those points with low density and a long distance from other points as outliers. Delete the identified outliers from the data set to obtain more centralized and representative wet oxidation control particle centralized coordinates.Step S425, extract the wet oxidation control parameter distribution interval of the concentrated coordinates of the wet oxidation control particles, and set it as the target wet oxidation control parameters. Specifically, the concentrated coordinates of the wet oxidation control particles are analyzed to determine the distribution of each control parameter. According to the analysis results, the reasonable distribution intervals of each control parameter are extracted, and these intervals can cover most of the concentrated coordinate points and meet the requirements of the oxidation pore shape parameters and the oxidation pore uniformity coefficient. The extracted wet oxidation control parameter distribution interval is set as the target wet oxidation control parameter to guide subsequent experiments or production processes. This implementation method scientifically and systematically determines the target wet oxidation control parameters by setting reasonable distribution constraint intervals for each control parameter, collecting historical data, performing normalized dimensionality reduction distribution, deleting outlier coordinates, etc., and achieves the technical effect of improving the accuracy and reliability of the wet oxidation control parameter optimization results.

[0046] In a possible implementation, the wet oxidation control historical section data set is normalized and reduced in dimension based on the oxidation temperature distribution constraint interval, the oxidation duration distribution constraint interval, the nitrogen flow distribution constraint interval, the nitrogen concentration distribution constraint interval, the water vapor flow distribution constraint interval, and the water vapor concentration distribution constraint interval to obtain the wet oxidation control particle distribution coordinates, and step S423 further includes step S4231, obtaining the oxidation temperature historical section data of the first wet oxidation control historical section data of the wet oxidation control historical section data set. Specifically, extract the first wet oxidation control historical section data (any record in the data set) from the wet oxidation control historical section data set. Separate the oxidation temperature-related data from the historical section data to form the oxidation temperature historical section data. Step S4232, calculate the first deviation modulus of the oxidation temperature historical section data and the minimum value of the oxidation temperature distribution constraint interval. Specifically, determine the minimum value (i.e., the lower limit of temperature) of the oxidation temperature distribution constraint interval. Calculate the absolute value of the difference between the temperature value in the oxidation temperature historical section data and the lower limit of temperature to obtain the first deviation modulus. Step S4233, calculate the second deviation modulus of the maximum value and the minimum value of the oxidation temperature distribution constraint interval. Specifically, determine the maximum value (i.e., the upper temperature limit) and the minimum value (i.e., the lower temperature limit) of the oxidation temperature distribution constraint interval. Calculate the absolute value of the difference between the upper temperature limit and the lower temperature limit to obtain the second deviation modulus. Step S4234, calculate the ratio of the first deviation modulus to the second deviation modulus, and set it as the normalized coordinate of the oxidation temperature. Specifically, divide the first deviation modulus by the second deviation modulus to obtain a ratio, and map this ratio to the interval [0, 1] to obtain the normalized coordinate of the oxidation temperature.

[0047] Step S4235, with the same processing process, obtain the normalized coordinates of oxidation time, nitrogen flow rate, nitrogen concentration, water vapor flow rate, and water vapor concentration. Specifically, repeat the process of steps S4231 to S4234, but process the oxidation time, nitrogen flow rate, nitrogen concentration, water vapor flow rate, and water vapor concentration control parameters respectively, and calculate the corresponding normalized coordinates for each control parameter. Step S4236, according to the normalized coordinates of the oxidation temperature, the normalized coordinates of the oxidation time, the normalized coordinates of the nitrogen flow rate, the normalized coordinates of the nitrogen concentration, the normalized coordinates of the water vapor flow rate, and the normalized coordinates of the water vapor concentration, obtain the first particle distribution coordinates, and add them to the wet oxidation control particle distribution coordinates. Specifically, the normalized coordinates of all control parameters are combined together to form a multidimensional vector. This multidimensional vector is used as the first particle distribution coordinate and added to the wet oxidation control particle distribution coordinate set. This implementation method converts the wet oxidation control historical cross-section data set into a form that is easier to process and analyze through normalization and dimensionality reduction processing, thereby reducing computational complexity and achieving the technical effect of improving optimization efficiency.

[0048] Step S500, wet oxidation monitoring is performed according to the monitoring and control benchmark parameters. Specifically, a wet oxidation process is started on the VCSEL chip, and chemical etching is performed according to the monitoring and control benchmark parameters to form the desired oxidation pore size. During the wet oxidation process, various sensors and monitoring equipment are used to monitor the shape parameters (such as long diameter, short diameter), uniformity coefficient and other key parameters (such as temperature, concentration, etc.) of the oxidation pores in real time. If it is found that the actual oxidation process deviates from the monitoring and control benchmark parameters, or the shape and uniformity of the oxidation pores do not meet the requirements, the control parameters of the wet oxidation are adjusted in time to restore or optimize the oxidation process. When the wet oxidation process reaches the predetermined target and the shape and uniformity of the oxidation pores meet the requirements, the wet oxidation process is stopped. The embodiment of the present application adopts a method of responding to a user terminal's request for optimization of the oxidation aperture of a VCSEL chip, clarifying the desired parameters of the light beam, collecting the shape parameters and uniformity coefficient of the oxidation aperture according to the application scenario type of the VCSEL chip, and mapping them through a beam quality predictor to generate predicted beam parameters. According to the prediction results, wet oxidation control parameters that meet the desired beam parameters are selected as reference parameters for monitoring and regulation, and real-time monitoring and adjustment are performed. Through precise quantitative control and real-time monitoring, the technical effect of improving the precision control level of the oxidation aperture of the VCSEL chip is achieved.

[0049] In the above, refer to Figure 1 The method for monitoring and controlling the aperture of a VCSEL chip wet oxidation according to an embodiment of the present invention is described in detail. Figure 2A device for monitoring and controlling the wet oxidation aperture of a VCSEL chip according to an embodiment of the present invention is described.

[0050] The monitoring and control device for wet oxidation aperture of a VCSEL chip according to an embodiment of the present invention is used to solve the technical problem of insufficient precision control in the existing wet oxidation aperture control of a VCSEL chip, and achieve the technical effect of improving the precision control level of the oxidation aperture of the VCSEL chip. The monitoring and control device for wet oxidation aperture of a VCSEL chip includes: an oxidation aperture optimization request response module 10, an oxidation aperture parameter acquisition module 20, a beam quality prediction module 30, a monitoring and control reference parameter acquisition module 40, and a wet oxidation monitoring module 50.

[0051] The oxidation aperture optimization request response module 10 is used to respond to the VCSEL chip oxidation aperture optimization request submitted by the user end, and obtain the expected beam diameter, the expected beam divergence angle, the expected beam profile and the expected power in the barrel; the oxidation aperture parameter acquisition module 20 is used to collect the oxidation aperture shape parameters and the oxidation aperture uniformity coefficient according to the VCSEL chip application scenario type of the VCSEL chip oxidation aperture optimization request; the beam quality prediction module 30 is used to map the oxidation aperture shape parameters and the oxidation aperture uniformity coefficient through a beam quality predictor to generate a beam predicted diameter, a beam predicted divergence angle, a beam predicted profile and a predicted power in the barrel; the monitoring and control reference parameter acquisition module 40 is used to select the wet oxidation control parameters that meet the expected beam diameter, the expected beam divergence angle, the expected beam profile and the expected power in the barrel according to the predicted beam diameter, the predicted beam divergence angle, the predicted beam profile and the predicted power in the barrel, and set them as monitoring and control reference parameters; the wet oxidation monitoring module 50 is used to perform wet oxidation monitoring according to the monitoring and control reference parameters.

[0052] The specific configuration of the oxidation hole parameter acquisition module 20 will be described in detail below. As described above, according to the VCSEL chip application scenario type of the VCSEL chip oxidation aperture optimization request, the oxidation hole shape parameter and the oxidation hole uniformity coefficient are collected, and the oxidation hole parameter acquisition module 20 may further include: a historical cross-sectional data set acquisition unit is used to collect a one-to-one corresponding oxidation hole shape parameter historical cross-sectional data set and an oxidation hole uniformity coefficient historical cross-sectional data set according to the VCSEL chip application scenario type; an oxidation hole shape parameter distribution interval acquisition unit is used to perform outlier removal preprocessing on the oxidation hole shape parameter historical cross-sectional data set to obtain the oxidation hole shape parameter distribution interval; an oxidation hole uniformity coefficient distribution interval acquisition unit is used to perform outlier removal preprocessing on the oxidation hole uniformity coefficient historical cross-sectional data set to obtain the oxidation hole uniformity coefficient distribution interval; a random assignment unit is used to perform random assignment according to the oxidation hole shape parameter distribution interval and the oxidation hole uniformity coefficient distribution interval to obtain the oxidation hole shape parameter and the oxidation hole uniformity coefficient.

[0053] Among them, according to the VCSEL chip application scenario type, a one-to-one corresponding historical cross-sectional data set of oxidation hole shape parameters and a historical cross-sectional data set of oxidation hole uniformity coefficient are collected, and the historical cross-sectional data set collection unit may further include: an attribute acquisition subunit is used to obtain oxidation hole shape parameter attributes and oxidation hole uniformity coefficient attributes, wherein the oxidation hole shape parameter attributes include oxidation hole long diameter, oxidation hole short diameter, oxidation hole depth, and the oxidation hole uniformity coefficient attributes include oxidation hole distribution spacing and oxidation hole distribution number; the historical cross-sectional data collection subunit is used to collect historical cross-sectional data of the VCSEL chip application scenario type according to the oxidation hole long diameter, the oxidation hole short diameter, the oxidation hole depth, the oxidation hole distribution spacing, and the oxidation hole distribution number, to obtain the oxidation hole shape parameter historical cross-sectional data set and the oxidation hole uniformity coefficient historical cross-sectional data set.

[0054] The specific configuration of the beam quality prediction module 30 will be described in detail below. As described above, according to the oxidation hole shape parameter and the oxidation hole uniformity coefficient, the beam quality predictor is used to map and generate the beam prediction diameter, the beam prediction divergence angle, the beam prediction profile and the barrel prediction power. The beam quality prediction module 30 may further include: a beam prediction diameter acquisition unit is used to map the oxidation hole long diameter parameter and the oxidation hole short diameter parameter of the oxidation hole shape parameter through the beam diameter prediction channel of the beam quality predictor to obtain the beam prediction diameter; a beam prediction divergence angle acquisition unit is used to map according to the oxidation hole shape parameter through the beam divergence angle prediction channel of the beam quality predictor to obtain the beam prediction divergence angle; a beam prediction profile acquisition unit is used to map according to the oxidation hole uniformity coefficient through the beam profile prediction channel of the beam quality predictor to obtain the beam prediction profile; a barrel prediction power acquisition unit is used to map according to the oxidation hole uniformity coefficient through the barrel power prediction channel of the beam quality predictor to obtain the barrel prediction power.

[0055] The specific configuration of the monitoring and control reference parameter acquisition module 40 will be described in detail below. As described above, according to the predicted beam diameter, the predicted beam divergence angle, the predicted beam profile and the predicted power in the barrel, the wet oxidation control parameters that meet the expected beam diameter, the expected beam divergence angle, the expected beam profile and the expected power in the barrel are selected and set as monitoring and control reference parameters. The monitoring and control reference parameter acquisition module 40 may further include: a beam quality verification unit for verifying the predicted beam diameter, the predicted beam divergence angle, the predicted beam profile and the expected power in the barrel according to the expected beam diameter, the expected beam divergence angle, the expected beam profile and the expected power in the barrel. The predicted power in the bucket is verified to obtain a beam quality verification result; the wet oxidation control parameter optimization unit is used to optimize the wet oxidation control parameters according to the oxidation hole shape parameters and the oxidation hole uniformity coefficient when the beam quality verification result is passed, so as to obtain the target wet oxidation control parameters; the monitoring and control benchmark parameter determination unit is used to set the target wet oxidation control parameters to the monitoring and control benchmark parameters; the oxidation hole parameter updating unit is used to update the oxidation hole shape parameters and the oxidation hole uniformity coefficient according to the application scenario type of the VCSEL chip when the beam quality verification result is failed.

[0056] Wherein, the predicted beam diameter, the predicted beam divergence angle, the predicted beam profile and the predicted power in the barrel are verified according to the expected beam diameter, the expected beam divergence angle, the expected beam profile and the expected power in the barrel to obtain a beam quality verification result, and the beam quality verification unit may further include: a first-level verification pass signal generating subunit for generating a first-level verification pass signal when the diameter deviation between the expected beam diameter and the predicted beam diameter is less than or equal to a diameter deviation threshold, and the divergence angle deviation between the expected beam divergence angle and the predicted beam divergence angle is less than or equal to a divergence angle deviation threshold, and the contour shape deviation between the expected beam profile and the predicted beam profile is less than or equal to a contour shape deviation threshold, and the bucket power deviation between the expected power in the barrel and the predicted power in the barrel is less than or equal to a bucket power deviation threshold; a multiplication factor acquisition subunit for calculating the beam period The method comprises the following steps: calculating a first product factor of the expected beam diameter and the expected divergence angle of the beam, calculating a second product factor of the predicted beam diameter and the predicted divergence angle of the beam, and obtaining a third product factor of the diffraction-limited Gaussian beam diameter and the diffraction-limited Gaussian beam divergence angle of the same wavelength; a beam quality factor calculation subunit is used to calculate the ratio of the first product factor to the third product factor, which is set as the expected beam quality factor, and calculating the ratio of the second product factor to the third product factor, which is set as the predicted beam quality factor; a secondary verification pass signal generation subunit is used to generate a secondary verification pass signal when the beam quality factor deviation between the expected beam quality factor and the predicted beam quality factor is less than or equal to the beam quality factor deviation threshold; a verification subunit is used to, when both the first verification pass signal and the second verification pass signal are passed, the beam quality verification result is passed, otherwise, the beam quality verification result is failed.

[0057] Wherein, when the beam quality verification result is passed, the wet oxidation control parameter optimization is performed according to the oxidation hole shape parameter and the oxidation hole uniformity coefficient to obtain the target wet oxidation control parameter, and the wet oxidation control parameter optimization unit may further include: a wet oxidation control parameter distribution constraint interval acquisition subunit is used to obtain the wet oxidation control parameter distribution constraint interval, wherein the wet oxidation control parameter distribution constraint interval includes the oxidation temperature distribution constraint interval, the oxidation time distribution constraint interval, the nitrogen flow distribution constraint interval, the nitrogen concentration distribution constraint interval, the water vapor flow distribution constraint interval, and the water vapor concentration distribution constraint interval; the wet oxidation control historical cross-section data set acquisition subunit is used to collect the oxidation hole shape parameters and the oxidation hole uniformity coefficient according to the VCSEL chip model and the wet oxidation equipment model through the Internet. The wet oxidation control historical cross-sectional data set; the wet oxidation control particle distribution coordinate acquisition subunit is used to perform normalized dimensionality reduction distribution on the wet oxidation control historical cross-sectional data set based on the oxidation temperature distribution constraint interval, the oxidation duration distribution constraint interval, the nitrogen flow rate distribution constraint interval, the nitrogen concentration distribution constraint interval, the water vapor flow rate distribution constraint interval, and the water vapor concentration distribution constraint interval to obtain the wet oxidation control particle distribution coordinates; the wet oxidation control particle concentrated coordinate acquisition subunit is used to delete outlier coordinates of the wet oxidation control particle distribution coordinates to obtain the wet oxidation control particle concentrated coordinates; the wet oxidation control parameter distribution interval extraction subunit is used to extract the wet oxidation control parameter distribution interval of the wet oxidation control particle concentrated coordinates, and set it as the target wet oxidation control parameter.

[0058] Among them, based on the oxidation temperature distribution constraint interval, the oxidation time distribution constraint interval, the nitrogen flow distribution constraint interval, the nitrogen concentration distribution constraint interval, the water vapor flow distribution constraint interval, and the water vapor concentration distribution constraint interval, the wet oxidation control historical cross-section data set is normalized and dimensionally reduced to obtain the wet oxidation control particle distribution coordinates. The wet oxidation control particle distribution coordinates acquisition subunit may further include: an oxidation temperature historical cross-section data acquisition microunit for obtaining oxidation temperature historical cross-section data of the first wet oxidation control historical cross-section data of the wet oxidation control historical cross-section data set; a first deviation modulus calculation microunit for calculating the first deviation modulus of the oxidation temperature historical cross-section data and the minimum value of the oxidation temperature distribution constraint interval; a second deviation modulus calculation microunit for calculating Calculate the second deviation modulus of the maximum and minimum values ​​of the oxidation temperature distribution constraint interval; the oxidation temperature normalized coordinate setting microunit is used to calculate the ratio of the first deviation modulus to the second deviation modulus, and set it as the oxidation temperature normalized coordinate; the other parameter normalized coordinate acquisition microunit is used to obtain the oxidation time normalized coordinate, the nitrogen flow normalized coordinate, the nitrogen concentration normalized coordinate, the water vapor flow normalized coordinate, and the water vapor concentration normalized coordinate with the same processing process; the first particle distribution coordinate acquisition microunit is used to obtain the first particle distribution coordinate according to the oxidation temperature normalized coordinate, the oxidation time normalized coordinate, the nitrogen flow normalized coordinate, the nitrogen concentration normalized coordinate, the water vapor flow normalized coordinate, and the water vapor concentration normalized coordinate, and add it into the wet oxidation control particle distribution coordinate.

[0059] The device for monitoring and controlling the aperture of wet oxidation of a VCSEL chip provided in an embodiment of the present invention can execute the method for monitoring and controlling the aperture of wet oxidation of a VCSEL chip provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0060] Although the present application makes various references to certain modules in the device according to the embodiments of the present application, any number of different modules may be used and run on the user terminal and / or server, and the various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present invention.

[0061] The above specific implementation manner does not constitute a limitation to the protection scope of the present application. It should be understood by those skilled in the art that various modifications, combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present application should be included in the protection scope of the present application. In some cases, the actions or steps recorded in the present application can be performed in an order different from that in the embodiment and can still achieve the desired results. In addition, the process depicted in the accompanying drawings does not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

Claims

1. A method for monitoring and controlling the aperture of a VCSEL chip wet oxidation, characterized in that: include: In response to a VCSEL chip oxidation aperture optimization request submitted by a user, obtain a desired beam diameter, a desired beam divergence angle, a desired beam profile, and a desired power in the barrel; Determine the scope of data collection according to the VCSEL chip application scenario type of the VCSEL chip oxidation aperture optimization request, and collect historical data of oxidation hole shape parameters and oxidation hole uniformity coefficients corresponding to the current application scenario type from a historical database as oxidation hole shape parameters and oxidation hole uniformity coefficients, wherein the oxidation hole shape parameters include oxidation hole long diameter, oxidation hole short diameter, and oxidation hole depth, and the oxidation hole uniformity coefficient includes oxidation hole distribution spacing and oxidation hole distribution quantity; According to the oxidation hole shape parameter and the oxidation hole uniformity coefficient, mapping is performed through a beam quality predictor to generate a beam prediction diameter, a beam prediction divergence angle, a beam prediction profile and a barrel prediction power, wherein the beam quality predictor is a prediction tool based on machine learning, and the oxidation hole shape parameter and the oxidation hole uniformity coefficient are input into the beam quality predictor, and the beam quality predictor predicts the diameter, divergence angle, profile and barrel power of the beam according to the input parameters through a mapping relationship; According to the predicted beam diameter, the predicted beam divergence angle, the predicted beam profile and the predicted power in the barrel, select the wet oxidation control parameters corresponding to the oxidation hole shape parameters and the oxidation hole uniformity coefficient that meet the expected beam diameter, the expected beam divergence angle, the expected beam profile and the expected power in the barrel, and set them as monitoring and control benchmark parameters, wherein the wet oxidation control parameters are parameters used to control the shape and uniformity of the oxidation aperture during the wet oxidation process, including oxidation temperature, oxidation time, gas flow rate and concentration; Wet oxidation monitoring is performed according to the monitoring and regulating benchmark parameters.

2. The method for monitoring and controlling the aperture of a VCSEL chip wet oxidation according to claim 1, characterized in that: According to the VCSEL chip application scenario type of the VCSEL chip oxidation aperture optimization request, the oxidation aperture shape parameters and the oxidation aperture uniformity coefficient are collected, including: According to the application scenario type of the VCSEL chip, a one-to-one corresponding historical cross-sectional data set of oxidation hole shape parameters and a historical cross-sectional data set of oxidation hole uniformity coefficient are collected; Performing outlier removal preprocessing on the oxidation pore shape parameter historical cross-section data set to obtain the oxidation pore shape parameter distribution interval; Performing outlier removal preprocessing on the oxidation pore uniformity coefficient historical cross-section data set to obtain the oxidation pore uniformity coefficient distribution interval; Random assignment is performed according to the oxidation pore shape parameter distribution interval and the oxidation pore uniformity coefficient distribution interval to obtain the oxidation pore shape parameter and the oxidation pore uniformity coefficient.

3. The method for monitoring and controlling the aperture of a VCSEL chip wet oxidation according to claim 2, characterized in that: According to the application scenario type of the VCSEL chip, a one-to-one corresponding historical cross-sectional data set of oxidation hole shape parameters and a historical cross-sectional data set of oxidation hole uniformity coefficient are collected, including: Obtaining oxidation pore shape parameter attributes and oxidation pore uniformity coefficient attributes, wherein the oxidation pore shape parameter attributes include oxidation pore long diameter, oxidation pore short diameter, and oxidation pore depth, and the oxidation pore uniformity coefficient attributes include oxidation pore distribution spacing and oxidation pore distribution quantity; According to the oxidation hole long diameter, the oxidation hole short diameter, the oxidation hole depth, the oxidation hole distribution spacing, and the oxidation hole distribution number, historical cross-sectional data of the VCSEL chip application scenario type are collected to obtain the oxidation hole shape parameter historical cross-sectional data set and the oxidation hole uniformity coefficient historical cross-sectional data set.

4. The method for monitoring and controlling the aperture of a VCSEL chip wet oxidation according to claim 1, characterized in that: According to the oxidation hole shape parameter and the oxidation hole uniformity coefficient, mapping is performed through a beam quality predictor to generate a beam prediction diameter, a beam prediction divergence angle, a beam prediction profile and a barrel prediction power, including: According to the oxidation hole long diameter parameter and the oxidation hole short diameter parameter of the oxidation hole shape parameter, mapping is performed through the beam diameter prediction channel of the beam quality predictor to obtain the beam prediction diameter; According to the oxidation hole shape parameters, mapping is performed through the beam divergence angle prediction channel of the beam quality predictor to obtain the beam predicted divergence angle; According to the oxidation hole uniformity coefficient, mapping is performed through a beam profile prediction channel of the beam quality predictor to obtain the beam prediction profile; According to the oxidation hole uniformity coefficient, mapping is performed through the barrel power prediction channel of the beam quality predictor to obtain the barrel predicted power.

5. The method for monitoring and controlling the aperture of a VCSEL chip wet oxidation according to claim 1, characterized in that: According to the predicted beam diameter, the predicted beam divergence angle, the predicted beam profile, and the predicted power in the barrel, wet oxidation control parameters corresponding to the oxidation hole shape parameters and the oxidation hole uniformity coefficient that satisfy the expected beam diameter, the expected beam divergence angle, the expected beam profile, and the expected power in the barrel are selected and set as monitoring and control benchmark parameters, including: Verify the predicted beam diameter, the predicted beam divergence angle, the predicted beam profile and the predicted power in the barrel according to the expected beam diameter, the expected beam divergence angle, the expected beam profile and the expected power in the barrel to obtain a beam quality verification result; When the beam quality verification result is passed, optimizing the wet oxidation control parameters according to the oxidation hole shape parameters and the oxidation hole uniformity coefficient to obtain target wet oxidation control parameters; Setting the target wet oxidation control parameter as the monitoring and regulation reference parameter; When the beam quality verification result is failed, updating the oxidation hole shape parameter and the oxidation hole uniformity coefficient according to the application scenario type of the VCSEL chip; Wherein, when the beam quality verification result is passed, the wet oxidation control parameter optimization is performed according to the oxidation hole shape parameter and the oxidation hole uniformity coefficient to obtain the target wet oxidation control parameter, including: Obtaining a wet oxidation control parameter distribution constraint interval, wherein the wet oxidation control parameter distribution constraint interval includes an oxidation temperature distribution constraint interval, an oxidation time distribution constraint interval, a nitrogen flow distribution constraint interval, a nitrogen concentration distribution constraint interval, a water vapor flow distribution constraint interval, and a water vapor concentration distribution constraint interval; According to the VCSEL chip model and the wet oxidation equipment model, online collection of wet oxidation control historical cross-section data sets that meet the oxidation hole shape parameters and the oxidation hole uniformity coefficient; Based on the oxidation temperature distribution constraint interval, the oxidation time distribution constraint interval, the nitrogen flow rate distribution constraint interval, the nitrogen concentration distribution constraint interval, the water vapor flow rate distribution constraint interval, and the water vapor concentration distribution constraint interval, the wet oxidation control historical cross-section data set is normalized and reduced in dimension to obtain wet oxidation control particle distribution coordinates; Deleting outlier coordinates of the wet oxidation control particle distribution coordinates to obtain concentrated coordinates of the wet oxidation control particles; The wet oxidation control parameter distribution interval of the concentrated coordinates of the wet oxidation control particles is extracted and set as the target wet oxidation control parameter.

6. The method for monitoring and controlling the aperture of a VCSEL chip wet oxidation according to claim 5, characterized in that: The predicted beam diameter, the predicted beam divergence angle, the predicted beam profile, and the predicted power in the barrel are verified according to the expected beam diameter, the expected beam divergence angle, the expected beam profile, and the expected power in the barrel to obtain a beam quality verification result, including: When the diameter deviation between the expected diameter of the light beam and the predicted diameter of the light beam is less than or equal to the diameter deviation threshold, and the divergence angle deviation between the expected divergence angle of the light beam and the predicted divergence angle of the light beam is less than or equal to the divergence angle deviation threshold, and the contour shape deviation between the expected profile of the light beam and the predicted profile of the light beam is less than or equal to the contour shape deviation threshold, and the power deviation in the bucket between the expected power in the bucket and the predicted power in the bucket is less than or equal to the power deviation threshold, a first-level verification pass signal is generated; Calculating a first product factor of the expected beam diameter and the expected beam divergence angle, calculating a second product factor of the predicted beam diameter and the predicted beam divergence angle, and obtaining a third product factor of the diffraction-limited Gaussian beam diameter and the diffraction-limited Gaussian beam divergence angle of the same wavelength; Calculating a ratio of the first multiplication factor to the third multiplication factor, setting the ratio as an expected beam quality factor, calculating a ratio of the second multiplication factor to the third multiplication factor, setting the ratio as a predicted beam quality factor; When a beam quality factor deviation between the expected beam quality factor and the predicted beam quality factor is less than or equal to a beam quality factor deviation threshold, generating a secondary verification pass signal; When both the first-level verification pass signal and the second-level verification pass signal pass, the beam quality verification result is passed; Otherwise, the beam quality verification result is failed.

7. The method for monitoring and controlling the aperture of a VCSEL chip wet oxidation according to claim 5, characterized in that: Based on the oxidation temperature distribution constraint interval, the oxidation time distribution constraint interval, the nitrogen flow distribution constraint interval, the nitrogen concentration distribution constraint interval, the water vapor flow distribution constraint interval, and the water vapor concentration distribution constraint interval, the wet oxidation control historical cross-section data set is normalized and reduced in dimension to obtain the wet oxidation control particle distribution coordinates, including: Obtain oxidation temperature historical section data of the first wet oxidation control historical section data of the wet oxidation control historical section data set; Calculating a first deviation modulus value between the oxidation temperature historical cross-section data and a minimum value of the oxidation temperature distribution constraint interval; Calculating a second deviation modulus value of a maximum value and a minimum value of the oxidation temperature distribution constraint interval; Calculate the ratio of the first deviation modulus value to the second deviation modulus value and set it as the oxidation temperature normalized coordinate; Using the same processing process, the normalized coordinates of oxidation time, nitrogen flow rate, nitrogen concentration, water vapor flow rate, and water vapor concentration are obtained; According to the normalized coordinates of the oxidation temperature, the normalized coordinates of the oxidation time, the normalized coordinates of the nitrogen flow, the normalized coordinates of the nitrogen concentration, the normalized coordinates of the water vapor flow, and the normalized coordinates of the water vapor concentration, the first particle distribution coordinates are obtained and added to the wet oxidation control particle distribution coordinates.

8. A monitoring and control device for the wet oxidation aperture of a VCSEL chip, characterized in that: The device is used to implement the method for monitoring and controlling the aperture of a VCSEL chip wet oxidation according to any one of claims 1 to 7, and the device comprises: An oxidation aperture optimization request response module, wherein the oxidation aperture optimization request response module is used to respond to a VCSEL chip oxidation aperture optimization request submitted by a user end, and obtain an expected beam diameter, an expected beam divergence angle, an expected beam profile, and an expected power in the barrel; An oxidation hole parameter acquisition module, wherein the oxidation hole parameter acquisition module is used to determine the scope of data acquisition according to the VCSEL chip application scenario type of the VCSEL chip oxidation hole diameter optimization request, and to collect historical data of oxidation hole shape parameters and oxidation hole uniformity coefficients corresponding to the current application scenario type from a historical database as oxidation hole shape parameters and oxidation hole uniformity coefficients, wherein the oxidation hole shape parameters include oxidation hole long diameter, oxidation hole short diameter, and oxidation hole depth, and the oxidation hole uniformity coefficient includes oxidation hole distribution spacing and oxidation hole distribution quantity; A beam quality prediction module, wherein the beam quality prediction module is used to generate a beam prediction diameter, a beam prediction divergence angle, a beam prediction profile, and a barrel prediction power by mapping the oxidation hole shape parameters and the oxidation hole uniformity coefficient through a beam quality predictor, wherein the beam quality predictor is a prediction tool based on machine learning, and the oxidation hole shape parameters and the oxidation hole uniformity coefficient are input into the beam quality predictor, and the beam quality predictor predicts the diameter, divergence angle, profile, and barrel power of the beam according to the input parameters through a mapping relationship; A monitoring and control reference parameter acquisition module, the monitoring and control reference parameter acquisition module is used to select the oxidation hole shape parameters and the oxidation hole uniformity coefficient corresponding to the oxidation hole shape parameters that meet the expected beam diameter, the expected beam divergence angle, the expected beam profile and the expected power in the barrel according to the predicted beam diameter, the predicted beam divergence angle, the predicted beam profile and the predicted power in the barrel, and set them as monitoring and control reference parameters, wherein the wet oxidation control parameters are parameters used to control the shape and uniformity of the oxidation aperture during the wet oxidation process, including oxidation temperature, oxidation time, gas flow rate and concentration; A wet oxidation monitoring module is used to perform wet oxidation monitoring according to the monitoring and control benchmark parameters.

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