Method for detecting defects of leds based on pl spectrum and related device

Abstract

Embodiments of the present application relate to the field of defect detection, and provide an LED defect detection method based on PL spectrum and related device, the method comprises: acquiring first spectrum information of a micro-LED chip to be detected; performing optimization processing on the first spectrum information to obtain second spectrum information; determining the center wavelength of the micro-LED chip to be detected according to the second spectrum information; and performing defect detection on the micro-LED chip to be detected according to the center wavelength to obtain a defect detection result. Therefore, the acquired spectrum can be optimized, and subsequent defect detection can be performed based on the optimized spectrum, thereby improving the accuracy of defect detection.

Description

Technical Field

[0001] This application relates to the field of defect detection technology, specifically to an LED defect detection method and related apparatus based on PL spectrum. Background Technology

[0002] In existing solutions, defect detection of micro LED chips mainly involves contact and non-contact methods. Contact detection is prone to causing damage (chip damage, contact probe damage), which leads to a decrease in the performance of subsequent detection. Non-contact detection typically uses the spectrum to determine the center wavelength and performs detection based on this. However, interference may exist in the detection environment, and directly using the spectrum to determine the center wavelength reduces the accuracy of subsequent detection. Summary of the Invention

[0003] This application provides an LED defect detection method and related apparatus based on PL spectrum, which can optimize the acquired spectrum and perform subsequent defect detection based on the optimized spectrum, thereby improving the accuracy of defect detection.

[0004] A first aspect of this application provides a method for detecting LED defects based on PL spectroscopy, the method comprising:

[0005] Obtain the first spectral information of the micro LED chip to be tested;

[0006] The first spectral information is optimized to obtain the second spectral information;

[0007] The center wavelength of the micro LED chip to be tested is determined based on the second spectral information.

[0008] Defect detection is performed on the micro LED chip to be tested based on the center wavelength to obtain the defect detection result.

[0009] In one possible implementation, optimizing the first spectral information to obtain the second spectral information includes:

[0010] Acquire the laser intensity during laser excitation of the micro LED chip under test; and acquire the detection environment information during the detection of the micro LED chip under test;

[0011] Spectral optimization information is generated based on the laser intensity and the detection environment information;

[0012] The first spectral information is optimized using the aforementioned spectral optimization information to obtain the second spectral information.

[0013] In one possible implementation, generating spectral optimization information based on the laser intensity and the detection environment information includes:

[0014] Obtain the offset information between the laser intensity and the preset laser intensity to obtain the first offset information;

[0015] The first environmental impact level is determined based on the detected environmental information;

[0016] The spectral optimization information is generated based on the first offset information and the first environmental impact degree.

[0017] In one possible implementation, generating the spectral optimization information based on the first offset information and the first environmental impact degree includes:

[0018] Global spectral optimization information is generated based on the first offset information;

[0019] The environmental loss factor is determined based on the first environmental impact level;

[0020] The global spectral optimization information is corrected using the environmental loss factor to obtain the spectral optimization information.

[0021] In one possible implementation, determining the first environmental impact degree based on the detected environmental information includes:

[0022] Extract the ambient light intensity and dust distribution information from the detected environmental information;

[0023] The first reference influence degree is determined based on the ambient light intensity;

[0024] Dust type, dust area, and dust concentration are extracted from the dust distribution information;

[0025] The second reference influence degree is determined based on the dust area and the dust concentration;

[0026] The third reference influence degree is determined based on the dust category;

[0027] Obtain the correlation information between ambient light intensity and dust.

[0028] The first environmental impact degree is determined based on the impact correlation information, the first reference impact degree, the second reference impact degree, and the third reference impact degree.

[0029] A second aspect of this application provides an LED defect detection device based on PL spectroscopy, the device comprising:

[0030] The acquisition unit is used to acquire the first spectral information of the micro LED chip to be tested.

[0031] An optimization unit is used to optimize the first spectral information to obtain the second spectral information;

[0032] The determining unit is used to determine the center wavelength of the micro LED chip to be detected based on the second spectral information.

[0033] The detection unit is used to perform defect detection on the micro LED chip to be tested according to the center wavelength and obtain the defect detection result.

[0034] In one possible implementation, the optimization unit is specifically used for:

[0035] Acquire the laser intensity during laser excitation of the micro LED chip under test; and acquire the detection environment information during the detection of the micro LED chip under test;

[0036] Spectral optimization information is generated based on the laser intensity and the detection environment information;

[0037] The first spectral information is optimized using the aforementioned spectral optimization information to obtain the second spectral information.

[0038] In one possible implementation, the optimization unit is specifically used for generating spectral optimization information based on the laser intensity and the detection environment information, specifically for:

[0039] Obtain the offset information between the laser intensity and the preset laser intensity to obtain the first offset information;

[0040] The first environmental impact level is determined based on the detected environmental information;

[0041] The spectral optimization information is generated based on the first offset information and the first environmental impact degree.

[0042] In one possible implementation, the optimization unit is specifically configured to: generate the spectral optimization information based on the first offset information and the first environmental impact degree.

[0043] Global spectral optimization information is generated based on the first offset information;

[0044] The environmental loss factor is determined based on the first environmental impact level;

[0045] The global spectral optimization information is corrected using the environmental loss factor to obtain the spectral optimization information.

[0046] In one possible implementation, regarding the determination of the first environmental impact degree based on the detected environmental information, the optimization unit is specifically used for:

[0047] Extract the ambient light intensity and dust distribution information from the detected environmental information;

[0048] The first reference influence degree is determined based on the ambient light intensity;

[0049] Dust type, dust area, and dust concentration are extracted from the dust distribution information;

[0050] The second reference influence degree is determined based on the dust area and the dust concentration;

[0051] The third reference influence degree is determined based on the dust category;

[0052] Obtain the correlation information between ambient light intensity and dust.

[0053] The first environmental impact degree is determined based on the impact correlation information, the first reference impact degree, the second reference impact degree, and the third reference impact degree.

[0054] A third aspect of this application provides a terminal including a processor, an input device, an output device, and a memory, wherein the processor, input device, output device, and memory are interconnected, wherein the memory is used to store a computer program, the computer program including program instructions, and the processor is configured to invoke the program instructions to execute the step instructions as described in the first aspect of this application.

[0055] A fourth aspect of this application provides a computer-readable storage medium storing a computer program for electronic data interchange, wherein the computer program causes a computer to perform some or all of the steps described in the first aspect of this application.

[0056] A fifth aspect of this application provides a computer program product, wherein the computer program product includes a non-transitory computer-readable storage medium storing a computer program operable to cause a computer to perform some or all of the steps described in the first aspect of this application. The computer program product may be a software installation package.

[0057] Implementing the embodiments of this application has at least the following beneficial effects:

[0058] By acquiring the first spectral information of the micro LED chip to be tested, optimizing the first spectral information to obtain the second spectral information, determining the center wavelength of the micro LED chip to be tested based on the second spectral information, and performing defect detection on the micro LED chip to be tested based on the center wavelength to obtain the defect detection result, the acquired spectrum can be optimized and subsequent defect detection can be performed based on the optimized spectrum, thus improving the accuracy of defect detection. Attached Figure Description

[0059] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0060] Figure 1 This application provides a schematic flowchart of an LED defect detection method based on PL spectroscopy.

[0061] Figure 2 A spectral diagram is provided for an embodiment of this application;

[0062] Figure 3 This is a schematic diagram of the structure of a terminal provided in an embodiment of this application;

[0063] Figure 4 This application provides a schematic diagram of the structure of an LED defect detection device based on PL spectrum. Detailed Implementation

[0064] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0065] The terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or apparatuses.

[0066] In this application, the reference to "embodiment" means that a specific feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a mutually exclusive, independent, or alternative embodiment. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described in this application can be combined with other embodiments.

[0067] Please see Figure 1 , Figure 1 This application provides a schematic flowchart of an LED defect detection method based on PL spectroscopy. Figure 1 As shown, the method includes:

[0068] 101. Obtain the first spectral information of the micro LED chip to be tested.

[0069] The first spectral information may include a spectrum, specifically such as... Figure 2 As shown. Figure 2 The spectrum of a micro-LED chip under test is shown. For example, the first spectral information can be obtained using a non-contact photoluminescence (PL) detection method. The detection principle is to use short-wavelength light such as ultraviolet (UV) light to excite the multiple quantum well layers of the micro-LED, causing electrons to undergo transitions upon radiation and emit light, thus generating a PL spectrum.

[0070] 102. Optimize the first spectral information to obtain the second spectral information.

[0071] Environmental factors and light source conditions can introduce errors into the detected first spectral information. Since chip testing demands extremely high precision and involves a large number of chips, even minor errors can lead to numerous false positives. Therefore, the first spectral information can be optimized by combining it with light source and environmental information to obtain a superior second spectral information for subsequent detection, thereby improving overall accuracy.

[0072] 103. Determine the center wavelength of the micro LED chip to be tested based on the second spectral information.

[0073] One approach is to use a common method based on spectral analysis to extract the center wavelength, thereby obtaining the center wavelength of the micro LED chip to be tested.

[0074] 104. Perform defect detection on the micro LED chip to be tested according to the center wavelength to obtain the defect detection result.

[0075] After extracting the center wavelength, defect detection can be performed using a pre-set defect detection model to obtain the defect detection results. This pre-trained defect detection model is used for defect detection of micro LED chips based on the center wavelength.

[0076] In this example, by acquiring the first spectral information of the micro LED chip to be tested, optimizing the first spectral information to obtain the second spectral information, determining the center wavelength of the micro LED chip to be tested based on the second spectral information, and performing defect detection on the micro LED chip to be tested based on the center wavelength, a defect detection result is obtained. Therefore, the acquired spectrum can be optimized and subsequent defect detection can be performed based on the optimized spectrum, thus improving the accuracy of defect detection.

[0077] In one possible implementation, a method for optimizing the first spectral information to obtain second spectral information includes:

[0078] A1. Obtain the laser intensity when the micro LED chip under test is laser-excited; and obtain the detection environment information when the micro LED chip under test is tested;

[0079] A2. Generate spectral optimization information based on the laser intensity and the detection environment information;

[0080] A3. The first spectral information is optimized using the spectral optimization information to obtain the second spectral information.

[0081] Because defect detection involves placing the micro LED chip to be tested on a testing stage and using a short-wavelength laser light source, the laser propagation path is directly exposed to the air rather than being tested in a vacuum. Therefore, differences in the testing environment can lead to certain deviations in the final test results, which may seriously affect the testing accuracy and cause false detections.

[0082] Therefore, the spectrum can be optimized by combining laser intensity and detection environment to obtain second spectral information.

[0083] Specifically, a method for generating spectral optimization information based on the laser intensity and the detection environment information includes:

[0084] A21. Obtain the offset information between the laser intensity and the preset laser intensity to obtain the first offset information;

[0085] A22. Determine the first environmental impact degree based on the aforementioned detection environment information;

[0086] A23. Generate the spectral optimization information based on the first offset information and the first environmental impact degree.

[0087] In the process of laser emission, the intensity of the laser emitted by the laser equipment may deviate due to factors such as equipment operation after a period of use. Therefore, it can be corrected.

[0088] The intensity of the emitted laser can be detected using a light intensity detection device, and the offset information can be calculated by comparing it with a preset laser intensity to obtain the first offset information. The first offset information can be understood as the difference between the laser intensity and the preset laser intensity; this difference can be positive, negative, or zero. The preset laser intensity is the initial laser intensity set when detecting the LED chip, using system default settings, etc.

[0089] When determining the first level of environmental impact, the specific measures can be as follows:

[0090] A221. Extract the ambient light intensity and dust distribution information from the detected environment information;

[0091] A222. Determine the first reference influence degree based on the ambient light intensity;

[0092] A223. Extract dust type, dust area, and dust concentration from the dust distribution information;

[0093] A224. Determine the second reference influence degree based on the dust area and the dust concentration;

[0094] A225. Determine the third reference influence degree based on the dust category;

[0095] A226. Obtain the correlation information between the ambient light intensity and dust;

[0096] A227. Determine the first environmental impact degree based on the impact correlation information, the first reference impact degree, the second reference impact degree, and the third reference impact degree.

[0097] The environment is typically an indoor setting, or one with some dust control and cleanliness, but not absolutely dust-free or light-free. Therefore, ambient light intensity and dust particles can affect laser propagation. Dust can cause attenuation, and ambient light can cause light pollution, both impacting accuracy. Therefore, corrections can be made by combining information on ambient light intensity and dust distribution.

[0098] Different ambient light intensities have different effects on the emitted laser, and this relationship can be used to determine the first reference influence corresponding to the current ambient light intensity.

[0099] Dust distribution information includes the type of dust, the area affected by the dust, and the dust concentration. This information can be obtained through dust detection sensors. The dust area can be understood as the dust distribution zone. The dust concentration in this zone must be greater than a preset concentration threshold. If the concentration is less than the preset threshold, it can be considered to have a minor impact and can be ignored to improve processing efficiency. The preset concentration threshold is set based on empirical values ​​or historical data.

[0100] The second and third reference influence values ​​are calculated only when the dust area overlaps with the detected laser propagation path. If there is no overlap, the second and third reference influence values ​​can be set to zero, meaning no influence. Specifically, the higher the dust concentration, the stronger its attenuation of the laser, the larger the value of the second reference influence value, and the greater the required spectral enhancement. Different dust types attenuate laser propagation differently; the third reference influence value can be determined based on the type. Stronger attenuation results in a larger value of the third reference influence value, and thus, a greater required spectral enhancement.

[0101] When determining the correlation between ambient light intensity and dust, the specific steps are as follows: Higher ambient light intensity leads to increased intensity of the reflected portion after absorption by dust, and secondary reflections between dust particles further amplify the adverse effects. Therefore, the correlation between ambient light intensity and dust concentration can be determined under the current environmental conditions. Higher ambient light intensity and higher dust concentration result in a stronger correlation, requiring greater enhancement efforts. This correlation primarily considers secondary reflections and refractions between dust particles.

[0102] Finally, the first and second reference impact degrees are fused using the impact correlation information, and then summed with the third reference impact degree to obtain the first environmental impact degree.

[0103] During the fusion process, the sum of the first and second reference influence values ​​can be multiplied by the influence association information to obtain the fused influence value. Finally, the fused influence value is summed with the third reference influence value to obtain the first environmental influence value, thus accurately obtaining the first environmental influence value.

[0104] Specifically, a method for generating the spectral optimization information based on the first offset information and the first environmental impact degree includes:

[0105] A231. Generate global spectral optimization information based on the first offset information;

[0106] A232. Determine the environmental loss factor based on the first environmental impact degree;

[0107] A233. The global spectral optimization information is corrected using the environmental loss factor to obtain the spectral optimization information.

[0108] Specifically, a global spectral optimization information generation model can be used to generate global spectral optimization information based on the input first offset information. This global spectral optimization information generation model is pre-trained and used to generate global spectral optimization information. Global spectral optimization information can be understood as performing global optimization on the spectral image, such as global enhancement of the spectral image.

[0109] Specifically, the greater the environmental impact, the larger the environmental loss factor, and the greater the enhancement required. Finally, the product of the environmental loss factor and the optimized value in the global spectral optimization information can be determined as the final optimized value (spectral optimization information). This final optimized value is then used to perform global enhancement processing on the spectral image. During global enhancement, the final optimized value can be scaled down to optimize the first spectral information, resulting in second spectral information. This significantly improves the accuracy of the second spectral information, thereby enhancing the accuracy of subsequent defect detection.

[0110] For examples consistent with the above embodiments, please refer to... Figure 3 , Figure 3 A schematic diagram of a terminal structure provided in an embodiment of this application is shown in the figure. It includes a processor, an input device, an output device, and a memory. The processor, input device, output device, and memory are interconnected. The memory is used to store a computer program, which includes program instructions. The processor is configured to call the program instructions. The program includes instructions for performing the following steps.

[0111] Obtain the first spectral information of the micro LED chip to be tested;

[0112] The first spectral information is optimized to obtain the second spectral information;

[0113] The center wavelength of the micro LED chip to be tested is determined based on the second spectral information.

[0114] Defect detection is performed on the micro LED chip to be tested based on the center wavelength to obtain the defect detection result.

[0115] The above mainly describes the solutions of the embodiments of this application from the perspective of the method execution process. It is understood that, in order to achieve the above functions, the terminal includes the corresponding hardware structure and / or software modules for executing each function. Those skilled in the art should readily recognize that, in conjunction with the units and algorithm steps of the various examples described in the embodiments provided herein, this application can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed in hardware or by computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0116] This application embodiment can divide the terminal into functional units according to the above method example. For example, each function can be divided into a separate functional unit, or two or more functions can be integrated into one processing unit. The integrated unit can be implemented in hardware or as a software functional unit. It should be noted that the unit division in this application embodiment is illustrative and only represents one logical functional division. In actual implementation, there may be other division methods.

[0117] For those consistent with the above, please refer to Figure 4 , Figure 4 This application provides a schematic diagram of the structure of an LED defect detection device based on PL spectroscopy. For example... Figure 4 As shown, the device includes:

[0118] Acquisition unit 401 is used to acquire the first spectral information of the micro LED chip to be tested;

[0119] Optimization unit 402 is used to optimize the first spectral information to obtain second spectral information;

[0120] Determining unit 403 is used to determine the center wavelength of the micro LED chip to be detected based on the second spectral information;

[0121] The detection unit 404 is used to perform defect detection on the micro LED chip to be tested according to the center wavelength and obtain the defect detection result.

[0122] In one possible implementation, the optimization unit 402 is specifically used for:

[0123] Acquire the laser intensity during laser excitation of the micro LED chip under test; and acquire the detection environment information during the detection of the micro LED chip under test;

[0124] Spectral optimization information is generated based on the laser intensity and the detection environment information;

[0125] The first spectral information is optimized using the aforementioned spectral optimization information to obtain the second spectral information.

[0126] In one possible implementation, the optimization unit 402 is specifically configured to: generate spectral optimization information based on the laser intensity and the detection environment information;

[0127] Obtain the offset information between the laser intensity and the preset laser intensity to obtain the first offset information;

[0128] The first environmental impact level is determined based on the detected environmental information;

[0129] The spectral optimization information is generated based on the first offset information and the first environmental impact degree.

[0130] In one possible implementation, the optimization unit 402 is specifically configured to: generate the spectral optimization information based on the first offset information and the first environmental impact degree;

[0131] Global spectral optimization information is generated based on the first offset information;

[0132] The environmental loss factor is determined based on the first environmental impact level;

[0133] The global spectral optimization information is corrected using the environmental loss factor to obtain the spectral optimization information.

[0134] In one possible implementation, the optimization unit 402 is specifically configured to: determine the first environmental impact degree based on the detected environmental information;

[0135] Extract the ambient light intensity and dust distribution information from the detected environmental information;

[0136] The first reference influence degree is determined based on the ambient light intensity;

[0137] Dust type, dust area, and dust concentration are extracted from the dust distribution information;

[0138] The second reference influence degree is determined based on the dust area and the dust concentration;

[0139] The third reference influence degree is determined based on the dust category;

[0140] Obtain the correlation information between ambient light intensity and dust.

[0141] The first environmental impact degree is determined based on the impact correlation information, the first reference impact degree, the second reference impact degree, and the third reference impact degree.

[0142] This application also provides a computer storage medium storing a computer program for electronic data interchange, which causes a computer to perform some or all of the steps of any of the PL spectrum-based LED defect detection methods described in the above method embodiments.

[0143] This application also provides a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program that causes a computer to perform some or all of the steps of any of the PL spectrum-based LED defect detection methods described in the above method embodiments.

[0144] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0145] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0146] In the several embodiments provided in this application, it should be understood that the disclosed apparatus can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual couplings, direct couplings, or communication connections may be through some interfaces; indirect couplings or communication connections between devices or units may be electrical or other forms.

[0147] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0148] Furthermore, the functional units in the various embodiments of the application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software program module.

[0149] If the integrated unit is implemented as a software program module and sold or used as an independent product, it can be stored in a computer-readable storage device (CMD). Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned memory includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0150] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage device, which may include: a flash drive, a read-only memory, a random access memory, a magnetic disk, or an optical disk, etc.

[0151] The embodiments of this application have been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of this application. The description of the above embodiments is only for the purpose of helping to understand the method and core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for detecting LED defects based on PL spectroscopy, characterized in that, The method includes: Obtain the first spectral information of the micro LED chip to be tested; The first spectral information is optimized to obtain the second spectral information; The center wavelength of the micro LED chip to be tested is determined based on the second spectral information. Defect detection is performed on the micro LED chip to be tested based on the center wavelength to obtain the defect detection result.

2. The LED defect detection method based on PL spectroscopy according to claim 1, characterized in that, The step of optimizing the first spectral information to obtain the second spectral information includes: Acquire the laser intensity during laser excitation of the micro LED chip under test; and acquire the detection environment information during the detection of the micro LED chip under test; Spectral optimization information is generated based on the laser intensity and the detection environment information; The first spectral information is optimized using the aforementioned spectral optimization information to obtain the second spectral information.

3. The LED defect detection method based on PL spectroscopy according to claim 2, characterized in that, The step of generating spectral optimization information based on the laser intensity and the detection environment information includes: Obtain the offset information between the laser intensity and the preset laser intensity to obtain the first offset information; The first environmental impact level is determined based on the detected environmental information; The spectral optimization information is generated based on the first offset information and the first environmental impact degree.

4. The LED defect detection method based on PL spectrum according to claim 3, characterized in that, The step of generating the spectral optimization information based on the first offset information and the first environmental impact degree includes: Global spectral optimization information is generated based on the first offset information; The environmental loss factor is determined based on the first environmental impact level; The global spectral optimization information is corrected using the environmental loss factor to obtain the spectral optimization information.

5. The LED defect detection method based on PL spectroscopy according to claim 3, characterized in that, The step of determining the first environmental impact degree based on the detection environment information includes: Extract the ambient light intensity and dust distribution information from the detected environmental information; The first reference influence degree is determined based on the ambient light intensity; Dust type, dust area, and dust concentration are extracted from the dust distribution information; The second reference influence degree is determined based on the dust area and the dust concentration; The third reference influence degree is determined based on the dust category; Obtain the correlation information between ambient light intensity and dust. The first environmental impact degree is determined based on the impact correlation information, the first reference impact degree, the second reference impact degree, and the third reference impact degree.

6. An LED defect detection device based on PL spectrum, characterized in that, The device includes: The acquisition unit is used to acquire the first spectral information of the micro LED chip to be tested. An optimization unit is used to optimize the first spectral information to obtain the second spectral information; The determining unit is used to determine the center wavelength of the micro LED chip to be detected based on the second spectral information. The detection unit is used to perform defect detection on the micro LED chip to be tested according to the center wavelength and obtain the defect detection result.

7. The LED defect detection device based on PL spectrum according to claim 6, characterized in that, The optimization unit is specifically used for: Acquire the laser intensity during laser excitation of the micro LED chip under test; and acquire the detection environment information during the detection of the micro LED chip under test; Spectral optimization information is generated based on the laser intensity and the detection environment information; The first spectral information is optimized using the aforementioned spectral optimization information to obtain the second spectral information.

8. The LED defect detection device based on PL spectrum according to claim 7, characterized in that, In generating spectral optimization information based on the laser intensity and the detection environment information, the optimization unit is specifically used for: Obtain the offset information between the laser intensity and the preset laser intensity to obtain the first offset information; The first environmental impact level is determined based on the detected environmental information; The spectral optimization information is generated based on the first offset information and the first environmental impact degree.

9. A terminal, characterized in that, The system includes a processor, an input device, an output device, and a memory, which are interconnected. The memory is used to store a computer program, which includes program instructions. The processor is configured to invoke the program instructions to perform the method as described in any one of claims 1-5.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, the computer program including program instructions that, when executed by a processor, cause the processor to perform the method as described in any one of claims 1-5.

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