A method for monitoring the end face reflectivity of a laser chip

Abstract

The application discloses a laser chip end face reflectivity monitoring method and relates to the technical field of computer processing.The method comprises the following steps: S01, obtaining a chip end face to be monitored and inputting the chip end face to a preset micro area division model to obtain a plurality of micro areas of the laser chip end face and recording preset reflectivity standard values of each micro area; S02, using a high-precision reflectivity measuring device to obtain actual reflectivity measuring values of each micro area; and S03, determining reflectivity deviation conditions of each micro area according to a comparison between the actual reflectivity measuring values and the preset reflectivity standard values of each micro area and integrating micro area information containing the reflectivity deviation conditions into multi-area reflectivity distribution information.The application combines gridded reflectivity space analysis with multi-source data fusion technology and realizes micron-level precision monitoring of a chip patch coverage range.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of computer processing, in particular to a laser chip end face reflectivity monitoring method. BACKGROUND

[0002] In the field of semiconductor laser manufacturing, the accurate control of end face reflectivity is a key factor determining the performance of the device. The traditional detection method mainly has two limitations:

[0003] First, the existing reflectivity measurement technology (such as Chinese public patent, publication number CN1242516A, published on January 26, 2000, a kind of mirror high reflectivity measurement method) only focuses on a single position or overall average reflectivity, but cannot identify the abnormal reflectivity distribution of the microscopic area;

[0004] Second, the deep learning technology used by the conventional image detection method (such as Chinese public patent, publication number CN116645587A, published on August 25, 2023, a kind of image recognition model generation method, image recognition method, system and chip) can identify macroscopic defects, but the sensitivity to nanoscale coating unevenness and patch microshift is insufficient. SUMMARY

[0005] In view of the above technical problems, the technical solution adopted by the present application is a laser chip end face reflectivity monitoring method, which comprises the following steps:

[0006] S01, obtaining the end face of the chip to be monitored and inputting it into a preset micro area division model to obtain a plurality of micro areas of the laser chip end face, and recording the preset reflectivity standard value of each micro area;

[0007] S02, using a high-precision reflectivity measurement device to obtain the actual reflectivity measurement value of each micro area;

[0008] S03, according to the comparison of the actual reflectivity measurement value of each micro area and the preset reflectivity standard value, determining the reflectivity deviation of each micro area, and integrating the micro area information containing the reflectivity deviation into multi-area reflectivity distribution information, the preset reflectivity standard value is the reflectivity value that each micro area should reach when the chip is designed;

[0009] S04, according to the multi-area reflectivity distribution information and the end face reflectivity actual detection information corresponding to each process node, detecting whether there is reflectivity abnormality at each process node of the chip end face; if there is reflectivity abnormality, determining the abnormal process node and the abnormal type;

[0010] Among them, the acquisition of the end face reflectivity actual detection information corresponding to each process node comprises the following steps:

[0011] At each key process node of chip manufacturing, the pose state information of the chip and the local reflectivity detection data of the end face under the pose are obtained;

[0012] According to the pose state information of the chip and the local reflectivity detection data, and in combination with a pre-established coordinate conversion model, actual reflectivity distribution information of the end face at each key process node under a global coordinate system is determined;

[0013] The actual reflectivity distribution information corresponding to each key process node is determined as the actual detection information of the end face reflectivity.

[0014] As preferred, the micro area division model comprises:

[0015] According to the circuit design diagram of the chip, the film plating process requirement, and the patch process specification, a key area of the end face that needs to be focused on is determined;

[0016] Based on the key area, in combination with the actual size of the chip and the manufacturing precision requirement, the end face is divided into a plurality of micro areas that are uniform in size and have clear boundaries;

[0017] A unique identification number is set for each micro area, and a preset reflectivity standard value thereof is recorded, so as to form a complete micro area division model.

[0018] As preferred, the actual reflectivity measurement value of each micro area in step S02 is measured by using a high-precision reflectivity measurement, comprising:

[0019] S21, high-precision reflectivity measurement equipment with nanometer measurement precision is loaded to detect data;

[0020] S22, the high-precision reflectivity measurement equipment detection data is calibrated, so that the measurement parameters are matched with the material characteristics and reflection characteristics of the chip end face;

[0021] S23, according to the identification number of the micro area division model, the reflectivity of each micro area is measured in sequence, the actual reflectivity measurement value of each micro area is recorded, and the measurement process is repeated for multiple times to reduce the measurement error.

[0022] As preferred, according to the comparison between the actual reflectivity measurement value of each micro area and the preset reflectivity standard value, the reflectivity deviation of each micro area is determined in step S03, comprising:

[0023] S31, for each micro area, the difference between the actual reflectivity measurement value and the preset reflectivity standard value is calculated;

[0024] S32, set the allowed range of reflectivity deviation, if the difference is within the allowed range, it is determined that the micro area reflectivity is normal; if the difference exceeds the allowed range, according to the positive and negative and size of the difference, it is determined whether the micro area exists reflectivity too high, too low or fluctuation abnormal.

[0025] As preferred, at each key process node of chip manufacturing, the pose state information of the chip and the local reflectivity detection data of the end face under the pose are obtained, including:

[0026] In the chip manufacturing process, the key process nodes of the end face reflectivity are determined, and the key process nodes at least include the film coating process and the patch process.

[0027] At each key process node, the position and attitude information of the chip in the three-dimensional space are obtained by using a high-precision pose sensor, including the translation position and the rotation angle.

[0028] At the same time, the local reflectivity detection device is used to detect the reflectivity of the specific area of the chip end face, and the local reflectivity detection data under the pose is obtained.

[0029] As preferred, according to the pose state information of the chip and the local reflectivity detection data, the actual reflectivity distribution information of the end face of each key process node under the global coordinate system is determined by combining the pre-established coordinate conversion model, including:

[0030] The coordinate conversion model between the chip pose state and the global coordinate system is established in advance, and the coordinate conversion model is used to analyze the necessary data of each key process node, and the necessary data at least includes the installation mode and the motion trajectory.

[0031] The obtained chip pose state information is substituted into the coordinate conversion model, and the coordinates corresponding to the local reflectivity detection data are converted to the global coordinate system.

[0032] According to the converted coordinates and the local reflectivity detection data, the actual reflectivity value of each micro area under the current key process node is determined by combining the micro area division model.

[0033] As preferred, in step S04, according to the multi-region reflectivity distribution information and the end face reflectivity actual detection information corresponding to each process node, whether the chip end face exists reflectivity abnormality at each process node is detected, including:

[0034] S41, the end face reflectivity actual detection information corresponding to each process node is compared and analyzed with the multi-region reflectivity distribution information.

[0035] S42, for each micro area, whether the actual reflectivity change at each process node conforms to the expected process change rule is checked.

[0036] S43, if the actual reflectivity of a micro area at a certain process node deviates too much from the expected value, or multiple micro areas at the same process node have abnormal reflectivity trends, it is determined that the chip end face has reflectivity abnormalities at the process node;

[0037] S44, according to the distribution characteristics and changes of the reflectivity abnormalities, the type of the abnormalities is determined, and the type of the abnormalities at least includes reflectivity differences caused by uneven coating and local reflectivity changes caused by patch micro-offset.

[0038] The present application has at least the following beneficial effects:

[0039] 1. By establishing a micro area division model based on chip design parameters, combined with a reflectivity measurement device with nanometer precision, fine detection of the chip end face at a sub-micron level can be achieved. Each micro area has a preset reflectivity standard value and a measured value for comparison, which accurately identifies the small reflectivity deviation of the local area, breaks through the limitation of traditional overall detection that cannot locate micro defects, and significantly improves the quality control accuracy of the chip manufacturing process.

[0040] 2. Chip pose information and local reflectivity data are collected in real time at key process nodes, and are mapped to a global coordinate system through a preset coordinate conversion model to form a complete end face reflectivity distribution map under the process node. This enables clear tracking of the specific impact of each process on the reflectivity of different areas of the chip end face, real-time monitoring of process execution effects, timely detection of deviations introduced by the process, and immediate data feedback for process optimization, avoiding the accumulation of problems to subsequent processes causing greater losses.

[0041] 3. By comparing the actual reflectivity distribution information of each process node with the final multi-region reflectivity distribution information in time and space, it can not only determine whether there is an abnormality, but also accurately locate the specific process node where the abnormality occurs. At the same time, combined with the spatial distribution characteristics and deviation properties of the reflectivity deviation, the root cause type of the abnormality can be intelligently identified, greatly improving the accuracy and efficiency of fault diagnosis and providing a clear direction for rapid process adjustment. BRIEF DESCRIPTION OF DRAWINGS

[0042] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings needed in the embodiment description. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0043] Figure 1 A flow chart of a laser chip end face reflectivity monitoring method is provided for the first embodiment of the present application.

[0044] Figure 2 The flowchart of S02 provided for the first embodiment of the present application is shown in the following table:

[0045] Figure 3 The flowchart of S04 provided for the first embodiment of the present application is shown in the following table. DETAILED DESCRIPTION

[0046] The technical solutions in the embodiments of the present application will be clearly and completely described in the following with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work fall within the scope of protection of the present application.

[0047] It should be noted that the terms "first", "second" and the like in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and do not necessarily indicate a specific order or a chronological sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the present application described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or server including a series of steps or units does not have to be limited to those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0048] Embodiment one

[0049] The embodiment provides a laser chip end face reflectivity monitoring method, and the method comprises the following steps, as shown in the following table: Figure 1

[0050] S01, an end face of a chip to be monitored is obtained and input into a preset micro area division model to obtain a plurality of micro areas of the end face of the laser chip, and a preset reflectivity standard value of each micro area is recorded;

[0051] Specifically, the micro area division model in the above embodiment comprises:

[0052] According to a circuit design diagram of the chip, a film plating process requirement and a patch process specification, a key area needing to be focused on the end face is determined;

[0053] Based on the key area, the end face is divided into a plurality of micro areas of uniform size and with clear boundaries in combination with an actual size of the chip and a manufacturing precision requirement;

[0054] A unique identification number is set for each micro area, and a preset reflectivity standard value thereof is recorded to form a complete micro area division model.​

[0055] The system loads the circuit design of the chip to be monitored, the film plating process requirements (such as film layer material, thickness, number of layers, target reflectivity), and the patch process specification (such as soldering material, precision requirement, stress control) in the above-mentioned embodiments. When the method is started, the system loads the circuit design of the chip to be monitored, the film plating process requirements (such as film layer material, thickness, number of layers, target reflectivity), and the patch process specification (such as soldering material, precision requirement, stress control). For example: the center area of the cavity surface of the laser resonant cavity (directly affecting the light output efficiency and mode), the edge area near the electrode or waveguide structure (easily affected by the film plating step effect or patch stress), and the interface of different materials or structure mutation (the reflectivity is easy to fluctuate).

[0056] The micro area division method is to take the identified key area as the core, combine the actual physical size (such as width and height) of the chip end face and the precision (such as photolithography resolution, film plating uniformity control ability, patch positioning precision) that can be achieved by the manufacturing process. The entire end face is uniformly divided into a large number (for example, hundreds to thousands) of small micro areas with clear geometric boundaries (such as square, hexagonal grid). The size of each area is usually in microns or sub-microns, which can capture the local reflectivity changes caused by film plating unevenness, slight contamination, stress points, etc.

[0057] A unique identification number (ID) is assigned to each micro area divided, and a spatial position index is established. According to the film plating process requirements and design targets, the preset reflectivity standard value of each micro area is calculated or specified. This value represents the reflectivity level that the specific small area should achieve under ideal manufacturing conditions. The output generates a structured micro area division model. The micro area division model includes a list of geometric coordinate boundaries of all micro areas, a list of corresponding unique IDs, and a list of preset reflectivity standard values corresponding to each ID. In essence, it is a "grid map" covering the entire end face with expected reflectivity values. A high-precision reflectivity measurement device (such as a confocal microscopic Raman spectrometer, a high-resolution optical coherence tomography OCT, and a special microscopic reflectometer) with nanoscale spatial resolution and optical precision is used for simulation. Before measurement, the device is accurately calibrated according to the materials (such as GaAs, InP) and expected reflectivity characteristics (such as high reflectivity HR film, anti-reflective AR film) of the chip end face, to ensure that the measured value is consistent with the true value.

[0058] The micro area division model generated above is loaded. The device drives the optical probe according to the micro area boundaries and unique ID sequence defined in the model, and positions to the center of each micro area or performs area scanning in sequence. In each micro area, the device performs reflectivity measurement operation to accurately obtain the actual reflectivity measurement value of the small area.

[0059] For each micro-region in the model (indexed by its ID), its preset reflectance standard value is read, along with its preset reflectance standard value and its preset reflectance standard value. The calculated difference (or relative deviation percentage) is used as the reflectance deviation for that region. Information such as the ID, geometric location, preset standard value, actual measurement value, and calculated deviation value for each micro-region is integrated into a complete multi-region reflectance distribution information report / dataset. This report not only includes the overall reflectance level but, more importantly, reveals the spatial distribution of reflectance at the micro-scale of the end face and the details of local deviations from the design target.

[0060] S02. Using high-precision reflectivity measurement equipment, obtain the actual reflectivity measurement value of each micro-region, specifically including the following steps (e.g.) Figure 2 (as shown)

[0061] S21. Load detection data from a high-precision reflectivity measurement device with nanometer-level measurement accuracy;

[0062] S22. Calibrate the detection data of the high-precision reflectivity measurement equipment to match its measurement parameters with the material properties and reflectivity of the chip end face;

[0063] S23. According to the identification number of the micro-region division model, the reflectance of each micro-region is measured in sequence, the actual reflectance measurement value of each micro-region is recorded, and the measurement process is repeated multiple times to reduce measurement error.

[0064] Specifically, the detection framework for the chip's end face is first determined based on a micro-region segmentation model. This model divides the end face into several micro-regions (e.g., a 10μm × 10μm grid), each with a unique ID and a preset reflectivity standard value (e.g., region A01 standard value = 95%). A high-precision reflectivity measurement device (e.g., a confocal laser microscope) automatically positions the chip based on the region numbering sequence of this model, ensuring a strict correspondence between the measurement target and the designed region. The laser wavelength (e.g., 1550nm) and detector gain are adjusted according to the optical characteristics of the chip end face material (e.g., InP substrate) to eliminate substrate material interference. The measured value of each region (e.g., 93.5%) is compared with the model's preset standard value (95%) in real time, and the absolute deviation (-1.5%) and relative deviation (-1.58%) are calculated. If the deviation exceeds the allowable threshold (e.g., ±0.5%), the area is marked as abnormal (e.g., "A01: Reflectivity too low"). Finally, the deviation data of all areas are integrated to generate structured multi-area reflectivity distribution information (e.g., a list of {area ID, measured value, deviation value, abnormal status} in JSON format).

[0065] S03. Based on the comparison between the actual reflectance measurement value of each micro-region and the preset reflectance standard value, determine the reflectance deviation of each micro-region, integrate the information of each micro-region containing the reflectance deviation into multi-region reflectance distribution information, and the preset reflectance standard value is the reflectance value that each micro-region should achieve as specified in the chip design.

[0066] Specifically, the following steps are included:

[0067] S31. For each micro-region, calculate the difference between its actual reflectance measurement value and the preset reflectance standard value;

[0068] S32. Set the allowable range for reflectance deviation. If the difference is within the allowable range, the reflectance of the micro-region is determined to be normal. If the difference exceeds the allowable range, determine whether the micro-region has excessively high or low reflectance or abnormal fluctuations based on the sign and magnitude of the difference.

[0069] The above describes the process of obtaining the preset reflectivity standard value (design value) for each region in the micro-region division model and the actual reflectivity measurement value collected by high-precision measuring equipment. For each micro-region, the following calculation is performed: Deviation value = Actual reflectivity measurement value - Preset reflectivity standard value. For example, if the design standard value is 80% and the measured value is 75%, the deviation value is -5%. A preset allowable deviation threshold (e.g., ±2%) is set. If the deviation value is within the threshold (e.g., -2% vs. deviation ≤ +2%), it is marked as "normal". If it exceeds the threshold: Reflectivity too high: Deviation value > + upper allowable limit (e.g., +3% → excessively thick coating or abnormal material); Reflectivity too low: Deviation value < - lower allowable limit (e.g., -5% → missing coating or contamination); Abnormal fluctuation: Multiple measurements of the same region show excessive dispersion (e.g., standard deviation > 1% → equipment stability or process fluctuation).

[0070] S04. Based on the multi-region reflectivity distribution information and the actual reflectivity detection information of the end face corresponding to each process node, detect whether there is reflectivity abnormality on the chip end face at each process node; if reflectivity abnormality exists, determine the abnormal process node and the type of abnormality. The specific steps are as follows (e.g. Figure 3 (as shown)

[0071] S41. Compare and analyze the actual detection information of the end face reflectivity corresponding to each process node with the multi-region reflectivity distribution information.

[0072] S42. For each micro-region, check whether the actual reflectance change at each process node conforms to the expected process change pattern.

[0073] S43. If the actual reflectance of a certain micro-region at a certain process node deviates too much from the expected value, or if multiple micro-regions show an abnormal reflectance trend at the same process node, it is determined that the chip end face has an abnormal reflectance at that process node.

[0074] S44. Based on the distribution characteristics and changes of reflectance anomalies, determine the anomaly type. The anomaly type includes at least reflectance differences caused by uneven coating and local reflectance changes caused by micro-offset of the patch.

[0075] The above embodiments match the actual reflectivity distribution information (including measured values ​​of micro-regions) obtained from each process node in the global coordinate system with the multi-region reflectivity distribution information (design standard value + deviation), ensuring that the data of the same micro-region in different processes can be accurately compared. For example, the measured value of region A in the coating process will be compared longitudinally with the design standard value and the value of the region after the mounting process. Based on the chip process knowledge base, a preset "expected process change law" is established (e.g., the reflectivity should increase uniformly by 10% after coating, and the reflectivity of the edge region after mounting is allowed to fluctuate by ±2%). The system automatically checks whether the reflectivity change of each micro-region at each process node conforms to this law (e.g., whether the reflectivity increment from process B to process C is within the preset range). Anomaly detection and classification: Single-point anomaly: If the reflectivity of a certain micro-region in a specific process deviates from the expected value by more than a threshold (e.g., >5%), an alarm is triggered. Group anomaly: If multiple adjacent regions show similar abnormal trends in the same process (e.g., the collective reflectivity of the edge region is low), it is judged as a systemic process problem.

[0076] The multi-region reflectivity distribution information includes the preset reflectivity standard value (design expectation value) and / or the final measured reflectivity value and its deviation for each micro-region. It represents the reflectivity distribution that the chip end face should achieve under ideal conditions or after manufacturing.

[0077] The actual reflectivity information of the end face at each process node is the actual data obtained at the end of each key process node (such as after coating, after patching, etc.) during the manufacturing process. It contains the actual reflectivity value of each micro-region of the entire end face, mapped in the global coordinate system at that node.

[0078] The "actual inspection information" and "multi-region reflectivity distribution information" of the same process node are compared. The key here is that the comparison is conducted within the same global coordinate system and the same micro-region division. This means that the actual performance of each tiny region at each process node can be accurately compared with the design expectations or final requirements.

[0079] The analysis should not only look at the absolute reflectance value of a certain process node, but more importantly, look at the reflectance change trajectory of each micro-region (region i) in the manufacturing process (spanning different process nodes).

[0080] The system checks the reflectivity value of region i: Does the change from process node A to node B meet process expectations? (For example, is the increase in reflectivity of this region within a reasonable range after the coating process?) At a specific process node X, does the value of region i suddenly deviate from its previous trend or design standard? This time-series analysis by region and by process is key; it reveals the dynamic impact of the process on each tiny region.

[0081] Exception detection logic:

[0082] Significant single-point deviation: In a specific micro-region (region j) at a specific process node (node ​​Y), the deviation between the actual reflectance value and the expected value (from "multi-region information") exceeds a preset allowable threshold. This indicates that a local problem may have occurred in that region at that process.

[0083] Anomalous cluster trends: Multiple micro-regions (e.g., a ring around the edge of an end face, or regions arranged along a specific direction) exhibit reflectivity deviations simultaneously or in similar patterns at the same process node (node ​​Z) (e.g., all are too high, all are too low, or the fluctuations are increasing). This strongly suggests a systemic or global problem in the process at that process node (node ​​Z) (e.g., equipment parameter drift, fixture offset, etc.).

[0084] Anomaly Type Diagnosis: Once an anomaly is identified, the system further analyzes its spatial distribution characteristics and the timing of its occurrence in the process chain to infer the root cause (anomaly type): Is the microscopic region of the anomaly randomly distributed? Is it concentrated in a specific area (e.g., center, edge, specific quadrant)? Does it exhibit a gradient change (e.g., gradually changing from one end to the other)? Does it present a symmetrical or asymmetrical pattern? At which process node did the anomaly first appear? Compared to the previous process node, is the change abrupt or gradual? What are the process characteristics of this node?

[0085] In summary, according to Example 1, by establishing a micro-region segmentation model based on chip design parameters and combining it with nanometer-precision reflectivity measurement equipment, sub-micron level fine-grained inspection of the chip end face is possible. Each micro-region has a preset standard reflectivity value and a measured value for comparison, accurately identifying minute reflectivity deviations in local areas. This overcomes the limitation of traditional overall inspection in locating micro-defects, significantly improving the quality control accuracy of the chip manufacturing process. Chip pose information and local reflectivity data are collected in real time at key process nodes and mapped to the global coordinate system using a preset coordinate transformation model, forming a complete end-face reflectivity distribution map for that process node. This allows for clear tracking of the specific impact of each process on the reflectivity of different areas of the chip end face, real-time monitoring of process execution effects, timely detection of deviations introduced by processes, and immediate data feedback for process optimization, preventing problems from accumulating to subsequent processes and causing greater losses. By performing spatiotemporal comparative analysis of the actual reflectivity distribution information at each process node with the final multi-region reflectivity distribution information, it is possible not only to determine whether anomalies exist but also to accurately locate the specific process node where the anomaly occurred. At the same time, by combining the spatial distribution characteristics and properties of reflectivity deviation, the root cause of the anomaly can be intelligently identified, which greatly improves the accuracy and efficiency of fault diagnosis and provides a clear direction for rapid process adjustment.

[0086] Example 2

[0087] Based on the above embodiment one, the acquisition of actual end-face reflectivity detection information corresponding to each process node includes the following steps:

[0088] A. At each critical process node in chip manufacturing, acquire the chip's pose state information and the local reflectivity detection data of the lower end face at that pose, including the following steps:

[0089] In the chip manufacturing process, the key process nodes that determine the reflectivity of the end face are identified. The key process nodes include at least the coating process and the chip mounting process.

[0090] At each critical process node, high-precision pose sensors are used to acquire the chip's position and orientation information in three-dimensional space, including translation position and rotation angle;

[0091] Simultaneously, a local reflectivity detection device is used to detect the reflectivity of a specific area on the chip end face, obtaining local reflectivity detection data under that pose.

[0092] B. Based on the chip's pose state information and local reflectivity detection data, combined with a pre-established coordinate transformation model, determine the actual reflectivity distribution information of each key process node end face in the global coordinate system, including:

[0093] A coordinate transformation model between the chip's pose state and the global coordinate system is pre-established. This model is used to analyze the necessary data for each critical process node. The necessary data includes at least the mounting method and motion trajectory.

[0094] The acquired chip pose state information is substituted into the coordinate transformation model to transform the coordinates corresponding to the local reflectivity detection data to the global coordinate system.

[0095] Based on the transformed coordinates and local reflectivity detection data, combined with the micro-region segmentation model, the actual reflectivity value of each micro-region at the current key process node is determined.

[0096] C. Determine the actual reflectivity distribution information corresponding to each key process node as the actual end-face reflectivity detection information.

[0097] Specifically, in the chip manufacturing process, nodes that have a decisive impact on the reflectivity of the chip surface are selected (such as the coating process affecting film uniformity, and the mounting process affecting optical alignment). High-precision pose sensors (such as laser interferometers / visual positioning systems) are used to capture the chip's translational coordinates (X, Y, Z) and rotation angle (θ) in three-dimensional space in real time. x ,θ y ,θ z ), and establish the spatial pose matrix of the chip.

[0098] Using local reflectivity detection equipment (such as a digital twin system based on an optical simulation model or a high-resolution confocal microscope), only a specific area of ​​the end face (such as the edge, center point, or coating sensitive area) is scanned, and the reflectivity value and spatial coordinates (relative to the chip coordinate system) of the local area are output.

[0099] A transformation relationship between the global coordinate system (with the chip design reference plane as the origin) and the process pose coordinate system is established beforehand. Model inputs include: installation parameters of process nodes (such as fixture offsets). The local reflectivity detection data (including position information) obtained in step A is substituted into the coordinate transformation model for affine transformation. The transformed global coordinates are spatially matched with the micro-region partitioning model (mesh regions defined in Claim2), and the actual reflectivity value of each micro-region is calculated using an interpolation algorithm, integrating them into a full-face reflectivity distribution map. The full-face reflectivity distribution map in the global coordinate system generated in step B is stored as structured data categorized by process node. In the chip mounting process, chip tilt was detected, causing an abnormally high reflectivity at the edge of the end face (actually due to the change in the incident angle of the detection optical path caused by the angular offset). By restoring the true reflectivity distribution through coordinate transformation in step B, it was found that the actual value in this area was 15% lower than the standard value, confirming that uneven mounting pressure caused micro-cracks in the film layer—a problem that traditional methods cannot distinguish between process defects and detection errors.

[0100] Example 3

[0101] This invention provides a non-transitory computer-readable storage medium storing at least one instruction or at least one program segment, which is loaded and executed by a processor to implement the following steps:

[0102] The end face of the chip to be monitored is obtained and input into a preset micro-region division model to obtain multiple micro-regions of the laser chip end face, and the preset reflectivity standard value of each micro-region is recorded.

[0103] Using high-precision reflectivity measurement equipment, the actual reflectivity measurement value of each micro-region is obtained;

[0104] Based on the comparison between the actual reflectance measurement value of each micro-region and the preset reflectance standard value, the reflectance deviation of each micro-region is determined. The information of each micro-region containing the reflectance deviation is integrated into multi-region reflectance distribution information. The preset reflectance standard value is the reflectance value that each micro-region should achieve as specified in the chip design.

[0105] Based on the multi-regional reflectivity distribution information and the actual detection information of the reflectivity of the end face corresponding to each process node, detect whether there is reflectivity abnormality on the chip end face at each process node; if reflectivity abnormality exists, determine the abnormal process node and the type of abnormality.

[0106] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0107] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is used as an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0108] Example 4

[0109] This invention provides an electronic device, including a processor and a memory, wherein the memory stores at least one instruction or at least one program segment, and the at least one instruction or the at least one program segment is loaded and executed by the processor to implement the following steps:

[0110] The end face of the chip to be monitored is obtained and input into a preset micro-region division model to obtain multiple micro-regions of the laser chip end face, and the preset reflectivity standard value of each micro-region is recorded.

[0111] Using high-precision reflectivity measurement equipment, the actual reflectivity measurement value of each micro-region is obtained;

[0112] Based on the comparison between the actual reflectance measurement value of each micro-region and the preset reflectance standard value, the reflectance deviation of each micro-region is determined. The information of each micro-region containing the reflectance deviation is integrated into multi-region reflectance distribution information. The preset reflectance standard value is the reflectance value that each micro-region should achieve as specified in the chip design.

[0113] Based on the multi-regional reflectivity distribution information and the actual detection information of the reflectivity of the end face corresponding to each process node, detect whether there is reflectivity abnormality on the chip end face at each process node; if reflectivity abnormality exists, determine the abnormal process node and the type of abnormality.

[0114] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A method for monitoring the reflectivity of a laser chip end face, characterized in that, The method includes the following steps: S01. Obtain the end face of the chip to be monitored and input it into a preset micro-region division model to obtain multiple micro-regions about the end face of the laser chip, and record the preset reflectivity standard value of each micro-region. S02. Using a high-precision reflectivity measurement device, obtain the actual reflectivity measurement value of each of the micro-regions; S03. Based on the comparison between the actual reflectance measurement value of each micro-region and the preset reflectance standard value, determine the reflectance deviation of each micro-region, and integrate the information of each micro-region containing the reflectance deviation into multi-region reflectance distribution information. The preset reflectance standard value is the reflectance value that each micro-region should achieve as specified in the chip design. S04. Based on the multi-regional reflectivity distribution information and the actual detection information of the reflectivity of the end face corresponding to each process node, detect whether there is an abnormal reflectivity of the chip end face at each process node. If an abnormal reflectivity is found, identify the abnormal process node and the type of abnormality; The acquisition of the actual detection information of the end face reflectivity corresponding to each process node includes the following steps: At each critical process node in chip manufacturing, acquire the chip's pose state information and the local reflectivity detection data of the lower end face in that pose; Based on the chip's pose state information and local reflectivity detection data, combined with a pre-established coordinate transformation model, the actual reflectivity distribution information of each key process node end face in the global coordinate system is determined. The actual reflectivity distribution information corresponding to each key process node is determined as the actual end-face reflectivity detection information.

2. The method for monitoring the reflectivity of a laser chip end face according to claim 1, characterized in that, The micro-region partitioning model includes: Based on the chip's circuit design diagram, coating process requirements, and surface mount technology specifications, identify the key areas on the end face that require special attention. Based on the aforementioned key area, and considering the actual size and manufacturing precision requirements of the chip, the end face is divided into multiple micro-regions of uniform size with clearly defined boundaries. Each micro-region is assigned a unique identifier and its preset reflectance standard value is recorded to form a complete micro-region division model.

3. The method for monitoring the reflectivity of a laser chip end face according to claim 1, characterized in that, The actual reflectance measurement value of each micro-region in step S02 is obtained using high-precision reflectance measurement, including: S21. Load detection data from a high-precision reflectivity measurement device with nanometer-level measurement accuracy; S22. The detection data of the high-precision reflectivity measurement device is calibrated to match its measurement parameters with the material properties and reflectivity of the chip end face; S23. According to the identification number of the micro-region division model, the reflectance of each micro-region is measured in sequence, the actual reflectance measurement value of each micro-region is recorded, and the measurement process is repeated multiple times to reduce measurement error.

4. The method for monitoring the reflectivity of a laser chip end face according to claim 1, characterized in that, Step S03, which involves determining the reflectance deviation of each micro-region by comparing the actual reflectance measurement value with the preset reflectance standard value, includes: S31. For each of the micro-regions, calculate the difference between its actual reflectance measurement value and the preset reflectance standard value; S32. Set the allowable range for reflectance deviation. If the difference is within the allowable range, the reflectance of the micro-region is determined to be normal. If the difference exceeds the allowable range, determine whether the micro-region has excessively high or low reflectance or abnormal fluctuations based on the sign and magnitude of the difference.

5. The method for monitoring the reflectivity of a laser chip end face according to claim 1, characterized in that, The acquisition of chip pose state information and local reflectivity detection data of the lower end face at each key process node of chip manufacturing includes: The key process nodes that determine the factors that influence the reflectivity of the end face in the chip manufacturing process, the key process nodes including at least the coating process and the mounting process. At each of the key process nodes, a high-precision pose sensor is used to acquire the chip's position and orientation information in three-dimensional space, including translation position and rotation angle. Simultaneously, a local reflectivity detection device is used to detect the reflectivity of a specific area on the chip end face, obtaining local reflectivity detection data under that pose.

6. The method for monitoring the reflectivity of a laser chip end face according to claim 1, characterized in that, Based on the chip's pose state information and local reflectivity detection data, combined with a pre-established coordinate transformation model, the actual reflectivity distribution information of each key process node end face in the global coordinate system is determined, including: A coordinate transformation model between the chip pose state and the global coordinate system is pre-established. The coordinate transformation model is used to analyze the necessary data for each key process node. The necessary data includes at least the installation method and motion trajectory. The acquired chip pose state information is substituted into the coordinate transformation model to transform the coordinates corresponding to the local reflectivity detection data to the global coordinate system. Based on the transformed coordinates and local reflectivity detection data, and combined with the micro-region division model, the actual reflectivity value of each micro-region at the current key process node is determined.

7. The method for monitoring the reflectivity of a laser chip end face according to claim 1, characterized in that, Step S04, which involves detecting whether there are reflectivity anomalies on the chip end face at each process node based on the multi-region reflectivity distribution information and the actual detection information of the end face reflectivity corresponding to each process node, includes: S41. Compare and analyze the actual detection information of the end face reflectivity corresponding to each process node with the multi-region reflectivity distribution information. S42. For each micro-region, check whether the actual reflectance change at each process node conforms to the expected process change pattern. S43. If the actual reflectance of a certain micro-region at a certain process node deviates too much from the expected value, or if multiple micro-regions show an abnormal reflectance trend at the same process node, then it is determined that the chip end face has an abnormal reflectance at that process node. S44. Based on the distribution characteristics and changes of the reflectance anomaly, determine the anomaly type, which includes at least reflectance differences caused by uneven coating and local reflectance changes caused by micro-offset of the patch.

8. A non-transitory computer-readable storage medium, wherein the non-transitory computer-readable storage medium stores at least one instruction or at least one program segment, characterized in that, The at least one instruction or the at least one program segment is loaded and executed by the processor to implement the laser chip end-face reflectivity monitoring method as described in any one of claims 1-7.

9. An electronic device, characterized in that, The method includes a processor and a memory, wherein the memory stores at least one instruction or at least one program, and the at least one instruction or the at least one program is loaded and executed by the processor to implement the laser chip end face reflectivity monitoring method as described in any one of claims 1-7.

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