System and method for measuring spectral parameters of luminous object

Abstract

The invention relates to the technical field of semiconductor photoelectric detection, particularly discloses a light-emitting object spectral parameter measurement system and method, and solves the technical problems that a conventional measurement scheme requires that the ratio of a spectral characteristic curve does not have a repetitive value and the measurement range is relatively narrow. The measuring system optical system is used for receiving the self-luminous light emitted by the luminous object and imaging the self-luminous light to an imaging surface to form a self-luminous optical image; the multi-band image acquisition module is arranged on a light path of the imaging surface and comprises at least three light filtering units with different transmission band characteristics, each light filtering unit is used for transmitting light of a specific band in the optical image, and the multi-band image acquisition module is used for acquiring the light of the corresponding transmission band in the optical image through each light filtering unit; multi-channel image data of self-illumination in different wave bands are obtained; wherein the multi-channel image data is used for being input into a preset mapping function, so that spectral parameters of the light-emitting object are obtained through fitting calculation.

Description

Technical Field

[0001] The present application relates to the field of semiconductor photoelectric detection technology, and in particular to a system and method for measuring spectral parameters of a luminous object. Background Art

[0002] The need for wavelength consistency testing is involved in wafer-level testing, chip screening after mass transfer, online sorting of production lines, and other links in the Micro LED display manufacturing process, as well as in the manufacturing process of terminal products such as AR / VR micro displays and ultra-high-definition screens.

[0003] In a traditional solution, a device and method for measuring a central wavelength plane are provided, which include: a luminous body to be measured, which emits the light to be measured; a filter switching unit, which switches a first filter and a second filter, thereby allowing the light to be measured to pass through the first filter and the second filter respectively; a photosensitive device, which detects the first light passing through the first filter to obtain a first measurement value, and detects the second light passing through the second filter to obtain a second measurement value, the first filter and the photosensitive device constitute a first optical system, and the second filter and the photosensitive device constitute a second optical system, within the wavelength range of the light to be measured, the ratio of the spectral characteristic curves of the first optical system and the second optical system has no repeated values, and the central wavelength of the light to be measured is obtained by the ratio of the first measurement value and the second measurement value.

[0004] In the above conventional scheme, the ratio of the spectral characteristic curves of the first optical system and the second optical system must have no repeated values, which places high demands on the optical system. In addition, this method can usually only be used in the range where the spectral response ratio is monotonic ( to The main wavelength is accurately measured within the range of 1 / 4 and 1 / 8, so the measurement wavelength range is narrow. Summary of the Invention

[0005] The embodiments of the present application provide a system and method for measuring the spectral parameters of a luminous object, which mainly solve the technical problems that traditional measurement solutions require that the ratio of spectral characteristic curves has no repeated values ​​and has a narrow measurement range.

[0006] In a first aspect, a system for measuring spectral parameters of a luminous object is provided, comprising: An optical system for receiving the self-luminous light emitted by the luminous object and imaging it onto an imaging surface to form a self-luminous optical image; A multi-band image acquisition module is disposed on the optical path of the imaging surface and includes at least three filter units with different transmission band characteristics. Each filter unit is used to transmit light in a specific band of the optical image. The multi-band image acquisition module collects light in the corresponding transmission band of the optical image through each filter unit, thereby obtaining multi-channel image data of the self-luminous light in different bands. The multi-channel image data is input into a preset mapping function to obtain the spectral parameters of the luminous object by fitting and calculation.

[0007] Furthermore, the multi-band image acquisition module is a combination structure of a single imaging device and a filter unit switcher, including: An independent filter unit switcher, wherein the filter unit switcher comprises at least three filter units, wherein the filter units are bandpass filter units, short-wave pass filter units or long-wave pass filter units, or at least three filter units are filter units having complementary wavelength characteristics; The filter unit switcher is driven by an adjustable structure to switch each of the at least three filter units in turn to the light path between the optical system and the single imaging device, so that the light of the corresponding band in the optical image is transmitted and collected by the single imaging device.

[0008] A single imaging device is used to perform photoelectric conversion on the optical image of a specific wavelength band after passing through each filter unit to obtain multi-channel image data of self-luminescence in different wavelength bands.

[0009] Furthermore, the multi-band image acquisition module is a combination structure of multiple imaging devices and a beam splitting device, including: At least three imaging devices, each of which is fixedly equipped with a filter unit having different transmission band characteristics, for transmitting light of corresponding wavelength bands in the optical image; a beam splitting device for splitting a light path containing an optical image output by the optical system into a plurality of paths and directing the light paths to respective imaging devices of the at least three imaging devices; At least three imaging devices synchronously collect light signals passing through corresponding filter units to obtain multi-channel image data of self-luminescence in different bands.

[0010] Furthermore, it also includes an image correction unit for aligning and synthesizing multi-channel image data, correcting the optical axis offset or imaging difference caused by the switching of the filter unit in the combination structure of a single imaging device and a filter switch, or correcting the optical axis offset or imaging difference caused by the difference in the optical path in the parallel structure of multiple imaging devices.

[0011] Furthermore, the luminous object spectral parameter measurement system further includes a data processing unit; The data processing unit is used to perform local statistical processing on the multi-channel image data within the coordinate area of ​​the imaging device. The local statistical processing includes the regional mean method, the regional median method or the regional fitting center value method; the eigenvalues ​​after statistical processing are input into the mapping function to obtain the spectral parameters of the corresponding area.

[0012] Furthermore, the mapping function is established through polynomial fitting, interpolation, neural network model or other machine learning methods, and the fitting relationship of the mapping function is derived from a data set generated by calibration of standard samples corresponding to the luminous object or simulation.

[0013] Furthermore, the polynomial fitting adopts a second-order or third-order polynomial regression model to solve the model coefficients by minimizing the mean square error between the spectral parameter values ​​predicted by the model and the true spectral parameter values ​​of the standard sample; the polynomial coefficients are iteratively updated to reduce the above mean square error until the polynomial regression model converges.

[0014] In a second aspect, a method for measuring spectral parameters of a luminous object is provided, comprising: The luminous object emits spontaneous light, which is received by the optical system and imaged onto the imaging surface, forming a spontaneous luminous optical image. The optical image on the imaging surface is collected by a multi-band image acquisition module, which includes at least three filter units with different transmission band characteristics. Each filter unit transmits light in a specific band of the optical image. The light signals corresponding to the transmission band are collected by each filter unit, thereby obtaining multi-channel image data of the spontaneous light in different bands. The multi-channel image data is input into the preset mapping function, and the spectral parameters of the luminous object are obtained by fitting and calculation.

[0015] Furthermore, the multi-band image acquisition module is a combination structure of a single imaging device and a filter unit switcher, and the optical image is acquired by the multi-band image acquisition module, including: Driven by the adjustable structure of the filter unit switcher, each of the at least three filter units is switched in sequence to the optical path between the optical system and the single imaging device, so that light of the corresponding wavelength band in the optical image is transmitted; The single imaging device performs photoelectric conversion on the optical image of a specific wavelength band after being transmitted through each filter unit, collects images of the corresponding wavelength bands in sequence, and obtains multi-channel image data of self-luminescence in different wavelength bands.

[0016] Furthermore, the multi-band image acquisition module is a combination structure of a multi-imaging device and a beam splitting device, and the optical image is acquired by the multi-band image acquisition module, including: The light path containing the optical image output by the optical system is divided into multiple paths by a beam splitter, and the paths are respectively directed to the respective imaging devices; Multiple imaging devices synchronously collect light signals passing through corresponding filter units to obtain multi-channel image data of self-luminescence in different bands.

[0017] In one of the solutions provided in the present application, the embodiment of the present application introduces three or more filter units, and fits and inverts the spectral parameters through the multi-channel image data and mapping relationship corresponding to the three or more filter units. There is no need to require that the spectral response ratio has no repetition within the measurement range, which breaks through the problem of "no repetition of the ratio of the two channels" in the traditional solution. Moreover, the use of at least 3 or more filter units to fit the wavelength can make up for the problem of the monotonic interval of only two filter ratios, expand the detection wavelength range, and improve the detection range. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following briefly introduces the drawings required for use in the description of the embodiments of the present application. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0019] Figure 1 This is a typical wavelength response curve diagram of a three-phase optical filter; Figure 2 It is a response curve diagram in which the response curves of two wavelengths have two or more intersection points due to unstable manufacturing process; Figure 3 This is a schematic diagram of a single-phase filter cutting solution in one embodiment of the present application; Figure 4 This is a schematic diagram of a single-camera three-filter solution in one embodiment of the present application; Figure 5 This is a schematic diagram of a single-camera four-filter solution in one embodiment of the present application; Figure 6 This is a schematic diagram of a multi-camera solution in one embodiment of the present application; Figure 7 This is a schematic diagram of a multi-camera three-filter solution in one embodiment of the present application; Figure 8 This is a schematic diagram of a multi-camera four-filter solution in one embodiment of the present application. DETAILED DESCRIPTION

[0020] In order to make the technical problems, technical solutions and beneficial effects solved by this application more clearly understood, this application is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0021] The present application belongs to the field of semiconductor photoelectric detection technology, and specifically relates to a system and method for measuring the spectral parameters of luminous objects mainly used for Micro LED and Mini LED chips. Exemplarily, it is mainly used in links including but not limited to wafer-level detection in the Micro LED display manufacturing process, chip screening after mass transfer, online sorting of production lines, and wavelength consistency detection of terminal products such as AR / VR micro displays and ultra-high-definition screens. For example, it can meet the high-precision wavelength measurement requirements of ±0.5nm for micro-light-emitting devices below 50μm.

[0022] For example, taking the inspection of Micro LED display products as an example, Micro LED display technology is currently at a critical stage of industrialization, and its core manufacturing processes such as mass transfer and wafer-level inspection place extremely high demands on the optical performance testing of single Micro LED chips. The existing technology mainly faces the following technical problems closely related to this application: Some solutions, such as the wavelength detection method mentioned in the background technology, require that the ratio of the spectral characteristic curves of the first optical system and the second optical system of the system has no repeated values, which places high demands on the optical system, and this method is usually only able to detect the optical performance of the system in the range where the spectral response ratio is monotonic ( to The main wavelength is accurately measured within the range of 1 / 4 and 1 / 8, so the measurement wavelength range is narrow.

[0023] In the embodiment of the present application, a system and method for measuring the spectral parameters of a luminous object are provided, which are mainly used to solve the above-mentioned requirements that the ratio of the spectral characteristic curve has no repeated values ​​and can only be measured in the interval where the spectral response ratio is monotonic ( to The main wavelength cannot be accurately measured within the range of 100 nm (between 100 nm and 100 nm), so the measurement wavelength range is narrow.

[0024] Below, the embodiments of the present application are described in detail through various embodiments.

[0025] In one embodiment, a system for measuring spectral parameters of a luminous object is provided, comprising: An optical system for receiving the self-luminous light emitted by the luminous object and imaging it onto an imaging surface to form a self-luminous optical image; A multi-band image acquisition module is disposed on the optical path of the imaging surface and includes at least three filter units with different transmission band characteristics. Each filter unit is used to transmit light in a specific band of the optical image. The multi-band image acquisition module collects light in the corresponding transmission band of the optical image through each filter unit, thereby obtaining multi-channel image data of the self-luminous light in different bands. The multi-channel image data is input into a preset mapping function to obtain the spectral parameters of the luminous object by fitting and calculation.

[0026] In this embodiment, the luminous object refers to the luminous object to be measured, including but not limited to luminous objects such as Micro LED and Mini LED chips. The luminous object to be measured emits self-luminous light, which is imaged onto the imaging surface through the optical system to form a self-luminous optical image. Exemplarily, the optical system can be an imaging system such as an objective lens, an imaging lens, or an anastigmatism system, and is not specifically limited thereto.

[0027] The system also includes a multi-band image acquisition module, disposed in the optical path of the imaging surface. The multi-band image acquisition module includes an imaging device and at least three filter units with different transmission band characteristics. Each filter unit is configured to transmit light in a specific wavelength band of the optical image. The multi-band image acquisition module uses each filter unit to respectively capture light in the corresponding transmission band in the optical image, thereby obtaining multi-channel image data of the spontaneous luminescence in different wavelength bands. For example, the system includes a first filter unit, a second filter unit, and a third filter unit with different transmission band characteristics. The first filter unit is configured to transmit light in a first wavelength band of the optical image. The multi-band image acquisition module uses the first filter unit to capture light in the optical image corresponding to the first wavelength band, thereby obtaining first-channel image data of the spontaneous luminescence in the first wavelength band. Similarly, second-channel image data corresponding to the second filter unit and third-channel image data corresponding to the third filter unit are obtained. The multi-channel image data is input into a preset mapping function to fit and calculate the spectral parameters of the luminescent object. Exemplarily, the spectral parameters of the luminescent object include one or more of a dominant wavelength, a center wavelength, and a peak wavelength, without limitation to the specific ones.

[0028] For example, the acquired multi-channel image data can be 、 、 , ..., as mapping function Input for mapping function obtained by pre-training or calibration , fitting and calculating the point or area The corresponding spectral parameters, for example, include one or more of a main wavelength, a center wavelength, and a peak wavelength.

[0029] It can be seen that the embodiment of the present application provides a new system for measuring the spectral parameters of luminous objects, which has at least the following advantages: it solves the problem that the traditional solution, which uses only two filters, must strictly ensure that the spectral response ratio is non-repetitive within the measurement range, otherwise it will lead to wavelength calculation errors, and solves the problem that the traditional solution has a monotonic range of the spectral response ratio ( to The main wavelength cannot be accurately measured within the range of 100 nm (between 100 nm and 100 nm), which leads to the problem of a narrow measurement wavelength range.

[0030] The embodiments of the present application introduce three or more filter units and utilize the multi-channel image data corresponding to the three or more filter units and the mapping relationship to fit and invert spectral parameters. This eliminates the requirement that the spectral response ratios must be non-repeated within the measurement range. This overcomes the problem of "non-repeated ratios of two channels" in traditional solutions, effectively avoids wavelength calculation errors, and greatly reduces the structural complexity of the optical system. Even if the spectral characteristic curves of some filters have intersections (i.e., repeated ratios), the multi-channel image data corresponding to the three or more filter units can be used to fit the mapping function to accurately invert the spectral parameters of the luminous object. like Figure 1 and Figure 2 As stated, Figure 1 This is a typical wavelength response curve of three filters, such as Figure 2 As shown in the figure, when the manufacturing process is unstable, filters 1 and 2 may have two or more intersection points, and the ratio is not unique. Curves 1, 2, and 3 represent the wavelength response curves of filters 1, 2, and 3 respectively. The figure is only a schematic diagram, and the spectral response curve of the filter can be other curves. The traditional solution uses two filters. For filters 1 and 2, the ratio must not have repeated values ​​within the detection range. Therefore, for the range that can be detected is and , resulting in a narrow detection range. For example, Figure 2 As shown, for Figure 2 In the case shown, filters 1 and 2 have two intersections, which does not meet the requirement of the traditional solution that the ratio has no repeated values. Therefore, the traditional solution has high requirements for the manufacturing process and quality of the filter. In the embodiment of the present application, taking three filter units as an example, the method of fitting the wavelength of at least three or more filter units can make up for the monotonic interval of the ratio of filters 1 and 2 being only and To solve the problem, the detection wavelength range is extended to and In addition, when the quality of the filter unit is poor, resulting in a non-unique ratio, filter 3 can also effectively form a complex mapping relationship with filters 1 and 2, and the system can also work normally, improving the detection range and accuracy, and having better detection robustness, and is applicable to a variety of luminous objects.

[0031] Moreover, general traditional spectral detection equipment is limited by optical resolution and sampling speed (seconds), making it difficult to achieve fast and accurate measurement of Micro LED single chips (such as Micro LED single chips below 50μm). The embodiments of the present application can achieve fast and accurate measurement of Micro LED single chips (such as Micro LED single chips below 50μm); in the wafer-level multi-chip synchronous detection scenario, optical crosstalk between adjacent light-emitting units causes wavelength measurement deviation; the embodiments of the present application can reduce the wavelength measurement deviation caused by optical crosstalk through multiple filtering units and mapping relationships.

[0032] It is also worth noting that the embodiments of the present application are not limited to the type of light to be detected, and can be relevant light or non-relevant light. They are also not limited to the detection field of view, and can detect the wavelength of every object in the shooting field of view.

[0033] In one embodiment, the optical filter units are bandpass filters, shortpass filters, or longpass filters, or at least three of the optical filter units are optical filter units with complementary wavelength characteristics. In this embodiment, the at least three optical filter units can be any of bandpass filters, shortpass filters, or longpass filters, which improves the adaptability and scalability of the solution. In addition, the at least three optical filter units with complementary wavelength characteristics have the following advantages: they can cover a wider wavelength range. Through the complementarity of different wavelength bands, spectral information of luminescent samples within a wider spectral band can be captured, meeting the detection requirements of wide-band luminescent objects; they improve the accuracy and robustness of wavelength measurement. The optical filter units with complementary wavelength bands can provide richer spectral features, which can better invert spectral parameters when combined with mapping function fitting. Even if some filters have characteristic deviations, the measurement accuracy can be guaranteed through the synergistic effect of multiple sets of data. Furthermore, the stringent requirements for the performance of individual filters are reduced. Even if the filters have non-ideal conditions such as intersection points, accurate measurement can still be achieved through fitting analysis of multi-channel image data.

[0034] It should be noted that the multi-band image acquisition module of the luminous object spectral parameter measurement system in the embodiment of the present application can be implemented in multiple ways. The embodiment of the present application provides at least two structural forms, each with its own characteristics, which are described below.

[0035] In one embodiment, the multi-band image acquisition module is a single imaging device solution. Specifically, the multi-band image acquisition module is a combination structure of a single imaging device and a filter unit switch, including: An independent filter unit switcher, the filter unit switcher comprising at least three filter units; The filter unit switcher is driven by an adjustable structure to switch each of the at least three filter units in turn to the optical path between the optical system and the single imaging device, so that the light of the corresponding band in the optical image is transmitted and collected by the single imaging device. In other words, a filter unit group is set between the optical system and the camera, and the filter unit group includes at least three (greater than or equal to three) filter units with different transmission band characteristics. Exemplarily, the switching of the filter units can be achieved by a mechanical wheel, an electric slide or a liquid crystal adjustable filter unit, which is not specifically limited.

[0036] A single imaging device is used to perform photoelectric conversion on an optical image of a specific wavelength band after passing through each filter unit, generating multi-channel image data of self-luminous light in different wavelength bands. A single imaging device can refer to a camera, particularly one primarily used in an optical system. Examples of such cameras include, but are not limited to, area scan and line scan cameras.

[0037] For example, the single imaging device is a single camera, and the filter unit is a filter. For example, Figure 3 As shown, the multi-band image acquisition module includes an independent filter unit switcher and a single camera. The filter unit switcher contains at least three filters (filter 1, filter 2, filter 3, ...). Driven by an adjustable structure, the filter unit switcher sequentially switches each of the at least three filters (filter 1, filter 2, filter 3, ...) into the optical path between the optical system and the single camera, allowing light in the corresponding wavelength band of the optical image to pass through and be captured by the single camera. The single camera is used to perform photoelectric conversion on the optical image of specific wavelength bands after passing through each filter (filter 1, filter 2, filter 3, ...), generating multi-channel image data of self-luminous light in different wavelength bands.

[0038] Specifically, in one example, Figure 4 As shown, the multi-band image acquisition module includes an independent filter unit switcher and a single camera. The filter unit switcher contains three filters (Filter 1, Filter 2, and Filter 3). The filter unit switcher, driven by an adjustable structure, sequentially switches each of the three filters (Filter 1, Filter 2, and Filter 3) into the optical path between the optical system and the single camera, allowing light in the corresponding wavelength band of the optical image to pass through and be captured by the single camera. The single camera is used to perform photoelectric conversion on the optical image of a specific wavelength band after passing through the three filters (Filter 1, Filter 2, and Filter 3), generating three channels of image data for self-luminescence in three different wavelength bands.

[0039] Specifically, in another example, Figure 5As shown, the multi-band image acquisition module includes an independent filter unit switcher and a single camera. The filter unit switcher contains four filters (Filter 1, Filter 2, Filter 3, Filter 4). Driven by an adjustable structure, the filter unit switcher sequentially switches each of the four filters (Filter 1, Filter 2, Filter 3, Filter 4) into the optical path between the optical system and the single camera, allowing light in the corresponding wavelength band of the optical image to pass through and be captured by the single camera. The single camera is used to perform photoelectric conversion on the optical image of a specific wavelength band after passing through the four filters (Filter 1, Filter 2, Filter 3, Filter 4), generating four channels of image data for the self-luminous light in four different wavelength bands.

[0040] It can be seen that in this embodiment, a specific implementation form of a multi-band image acquisition module is provided, which is a single imaging device solution. Filters of different bands can be replaced by mechanical switching of a filter unit switch or an electric slide. The structure is relatively simple, and no complex beam splitting device is required. The overall cost is low, and the installation and calibration requirements for the filter unit are not high. It is suitable for scenarios that do not require extreme detection speed, such as small batch sample detection, and there is no specific limitation.

[0041] In one embodiment, the multi-band image acquisition module is a multi-imaging device solution. Specifically, the multi-band image acquisition module is a combination structure of a multi-imaging device and a beam splitting device, including: At least three imaging devices are fixedly combined with filter units of different bands, that is, each imaging device is fixedly combined with a filter unit with different transmission band characteristics, which is used to transmit light of corresponding bands in the optical image; it should be noted that the imaging device of this embodiment can refer to a camera, and can refer to a camera mainly used in an optical system. Exemplarily, the camera includes but is not limited to area array cameras, line scan cameras, etc.

[0042] A beam splitting device is used to split the light path containing the optical image output by the optical system into multiple paths and guide them respectively to each imaging device in at least three imaging devices; illustratively, the beam splitting device includes but is not limited to a polarization beam splitter, a dispersion prism or a half-reflecting half-mirror lens, which divides the same light path into multiple paths and guides them respectively to each imaging device.

[0043] At least three imaging devices synchronously collect light signals passing through corresponding filter units to obtain multi-channel image data of self-luminescence in different bands.

[0044] For example, the imaging device is a camera, and the filter unit is a filter. For example, Figure 6As shown, the multi-band image acquisition module includes at least three cameras, a beam splitter, and filters of different bands (filter 1, filter 2, filter 3, ...). The at least three cameras are respectively combined with the filters of different bands (filter 1, filter 2, filter 3, ...), that is, each camera is fixedly combined with a filter with different transmission band characteristics for transmitting light of corresponding bands in the optical image; the beam splitter is used to split the light path containing the optical image output by the optical system into multiple paths and guide them to each camera respectively. The at least three cameras synchronously collect light signals passing through the corresponding filters to obtain multi-channel image data of self-luminescence in different bands.

[0045] For example, Figure 7 As shown, the multi-band image acquisition module includes three cameras, a beam splitter, and filters of three bands (filter 1, filter 2, and filter 3). The three cameras are fixedly combined with three filters of different bands (filter 1, filter 2, and filter 3). That is, each camera is fixedly combined with filters with different transmission band characteristics for transmitting light of corresponding bands in the optical image; the beam splitter is used to split the light path containing the optical image output by the optical system into multiple paths and guide them to the three cameras respectively. The three cameras synchronously collect light signals passing through the corresponding filters to obtain three-channel image data corresponding to the self-luminescence in three different bands.

[0046] For example, Figure 8 As shown, the multi-band image acquisition module includes at least four cameras, a beam splitter, and four filters of different bands (filter 1, filter 2, filter 3, filter 4). The four cameras are fixedly combined with the four filters of different bands (filter 1, filter 2, filter 3, filter 4), that is, each camera is fixedly combined with a filter with different transmission band characteristics, which is used to transmit light of the corresponding band in the optical image; the beam splitter is used to split the light path containing the optical image output by the optical system into multiple paths and guide them to the four cameras respectively. The four cameras synchronously collect light signals passing through the corresponding filters to obtain four-channel image data corresponding to the self-luminescence in four different bands.

[0047] It can be seen that in this embodiment, another specific implementation form of the multi-band image acquisition module is provided, which is a multi-imaging device solution. In this embodiment, a filter unit switch is not required, but a beam splitter device is used to eliminate the mechanical switching process, which can significantly improve the acquisition speed and is particularly suitable for large-scale production line inspection or fast scanning scenarios.

[0048] In one embodiment, the luminous object spectral parameter measurement system provided in the embodiment of the present application also includes an image correction unit for aligning and synthesizing multi-channel image data, correcting the optical axis offset or imaging difference caused by the switching of the filter unit in the combination structure of a single imaging device and a filter switch, or correcting the optical axis offset or imaging difference caused by the difference in the optical path in the parallel structure of multiple imaging devices.

[0049] This embodiment also includes image registration and synthesis steps to correct the optical axis offset or imaging difference caused by the switching of the filter unit in the combination structure of a single imaging device and a filter switch, or to correct the optical axis offset or imaging difference caused by the difference in the optical path in the parallel structure of multiple imaging devices, thereby further improving the accuracy and precision of the multi-channel data obtained and effectively improving the measurement accuracy of the final spectral parameters such as the main wavelength.

[0050] In one embodiment, the luminous object spectral parameter measurement system further includes a data processing unit; The data processing unit is used to perform local statistical processing on the multi-channel image data within the coordinate area of ​​the imaging device. The local statistical processing includes the regional mean method, the regional median method or the regional fitting center value method; the characteristic value after statistical processing is input into the mapping function to obtain the spectral parameters of the corresponding area. In addition, in one example, the spectral parameters of the image data within the specified coordinate area can be obtained. For example, if the object to be measured is an array structure (such as a pixel array, a filter array, a microcavity array, etc.), local statistical processing can be performed on the multi-channel image data within the coordinate area of ​​the imaging device in each array unit area. For example, the multi-channel image data includes image data , fitting and calculating the point or area Corresponding spectral parameters, including one or more of dominant wavelength, central wavelength and peak wavelength; Regional mean method: in, Indicates the array unit area, Indicates the Channel image data at pixel points The pixel value at Indicates area Neidi The average value of the channel image data is used as the feature value.

[0051] Regional median method: This formula represents the indivual All pixels in , in The median of these pixel values ​​is taken as the feature value of the area under the channel. Indicates area Neidi The median value of the channel image data. median means taking the median.

[0052] Area fitting center value (such as the center point value after smooth fitting): This formula represents the indivual All pixels in , first fit in space, then take the output value of the fitting function at the center of the region. Indicates area Neidi The fitting center value of the channel image data. fit-cnter means taking the fitting center value.

[0053] The eigenvalues ​​after statistics Input into the trained mapping function: , thus obtaining the array unit The corresponding representative spectral parameters (such as main wavelength, central wavelength or peak wavelength, etc.) are used to realize regional spectral reconstruction of the entire array structure.

[0054] In addition, in one example, the coordinate area can be specified The image data in the image is processed and the spectral parameters of the area are fitted by combining the mapping function, including one or more of the main wavelength, central wavelength and peak wavelength. The results can be output in the form of pixel level or regional statistics.

[0055] In this embodiment, local statistical processing is performed on multi-channel image data within the coordinate region of the imaging device. The processed eigenvalues ​​are input into a mapping function to obtain the spectral parameters of the corresponding region. This approach can adapt to different detection requirements, and the pixel-level output can accurately provide spectral information corresponding to each pixel. This approach is suitable for analyzing subtle wavelength variations within a luminous object, such as wavelength uniformity testing within a Micro LED chip. Regional statistical processing aggregates data from a specific region (such as the imaging range of a single chip) to obtain representative spectral parameters for that region, meeting the efficiency and consistency requirements of batch testing, such as rapid screening of wafer-level chips. Furthermore, this approach enhances the flexibility and applicability of data processing. For array-type luminous structures (such as pixel arrays), each array unit region can be processed separately, ensuring measurement accuracy within the region while efficiently analyzing the spectral characteristics of the entire array, balancing local detail with overall efficiency. Furthermore, it enhances the reliability of the results. Regional statistics (such as mean and median) can reduce the impact of noise or local outliers on spectral parameter calculation, while pixel-level processing provides data support for detailed analysis. The combination of these two approaches can more comprehensively reflect the spectral characteristics of luminous objects.

[0056] In one embodiment, the mapping function is established by polynomial fitting, interpolation, a neural network model or other machine learning methods, and the fitting relationship of the mapping function is derived from a data set generated by calibration of a standard sample corresponding to the luminous object or simulation.

[0057] This embodiment uses methods such as polynomial fitting, interpolation, and neural networks to construct the mapping function in the embodiments of this application, and determines the fitting relationship based on standard sample calibration or simulation data sets. This has the following main advantages: Due to the large differences in the spectral characteristics of different light-emitting objects (such as LEDs and laser devices), they may exhibit linear, nonlinear, or even complex segmented characteristics. Polynomial fitting is suitable for processing simple nonlinear relationships and has high computational efficiency. Machine learning methods such as neural networks can capture high-dimensional, strongly coupled spectral response patterns and are particularly suitable for complex mapping relationships under the synergistic effect of multiple filters. This diversity allows the scheme to flexibly select models based on the specific detection object, avoiding dependence on a single solution. Traditional spectral measurement requires the establishment of precise optical physics models (such as strict filter spectral response functions and optical path attenuation models). However, in actual systems, non-ideal factors such as filter characteristic drift, optical path scattering, and noise interference exist, which are difficult to fully characterize through physical models. The embodiments of the present application, based on a mapping function (based on calibration or simulation data), can directly learn patterns from the relationship between input (multi-channel image data) and output (spectral parameters), automatically accommodating the aforementioned non-ideal factors, reducing the accuracy requirements for the optical system's physical parameters, and improving measurement stability. The standard sample has known, precise spectral parameters. Using its collected multi-channel image data as the "input-output" sample pair to fit the mapping function, the accuracy of the standard sample can be transferred to the actual measurement, ensuring consistency between the model output and the actual spectral parameters. This "calibration-fitting" mechanism can effectively eliminate systematic errors and meet high-precision detection requirements (such as the Mini LED wavelength consistency requirement of ±0.5nm).

[0058] It's also worth noting that simulation can generate data for extreme conditions (such as ultra-wide wavelength ranges and unique spectral shapes) that are difficult to generate using physical standard samples. This complements calibration data and enables mapping functions to maintain accuracy across a wider range of operating conditions. Lightweight models such as polynomial fitting and interpolation offer fast computational speeds, making them suitable for real-time online testing (such as high-speed sorting on production lines). This allows solutions to flexibly balance efficiency and accuracy, adapting to the full range of scenarios, from rapid screening to sophisticated analysis.

[0059] In summary, this mapping function construction method not only ensures the accuracy and robustness of the measurement, but also expands the scope of application of the solution through flexibility and generalization capabilities, while taking into account cost and efficiency.

[0060] In one embodiment, the polynomial fitting adopts a second-order or third-order polynomial regression model, and the model coefficients are solved by minimizing the mean square error between the spectral parameter values ​​predicted by the model and the true spectral parameter values ​​of the standard sample; the above mean square error is reduced by iteratively updating the polynomial coefficients until the polynomial regression model converges.

[0061] In the embodiments of this application, a second- or third-order polynomial regression model is used, and the coefficients are iteratively solved until convergence by minimizing the mean squared error (MSE). This has the following key advantages: Second- or third-order polynomials are low-order models with relatively simple functional forms. They can capture the mainstream nonlinear relationships between spectral parameters and multi-channel image data (such as the quadratic curve characteristics of filter response) without overfitting noise or local fluctuations in the calibration data. This ensures the stability of the model in actual detection. Solving the coefficients of low-order polynomials requires minimal matrix operations and achieves rapid iterative convergence. In speed-sensitive scenarios such as in-line inspection (such as Mini LED wafer sorting), this high efficiency ensures that the spectral parameter calculation time for each frame of image is controlled in milliseconds, meeting the core requirements of high-speed detection. By iteratively adjusting the polynomial coefficients (e.g., decreasing the MSE with each coefficient update) until the MSE no longer decreases significantly (converges), the model can be ensured to approximate the true mapping relationship between "multi-channel image data → spectral parameters" as closely as possible. This approach can also adaptively accommodate slight fluctuations in the calibration data (e.g., measurement noise in standard samples), smoothing errors through multiple iterations to improve model stability. It is also worth noting that the relationship between the spectral parameters of luminous objects (e.g., peak wavelength) and multi-filter channel image data is typically weakly nonlinear (rather than strongly nonlinear) due to the influence of the optical system (e.g., filter response, optical path attenuation). Second- or third-order polynomials are sufficient to characterize these weakly nonlinear relationships (e.g., the quadratic variation of filter transmittance with wavelength), eliminating the need for more complex models (e.g., high-order polynomials or neural networks), simplifying model complexity while ensuring accuracy.

[0062] It can be seen that this embodiment achieves a balance between accuracy, efficiency, and stability through the combination of "low-order model + MSE optimization + iterative convergence", which not only meets the accuracy requirements of spectral measurement, but also adapts to the real-time requirements of industrial scenarios, while reducing the risk of model overfitting and the difficulty of engineering implementation.

[0063] Specifically, in one embodiment, by modeling training samples with known labels, a second-order or third-order polynomial regression model is used to establish a nonlinear mapping relationship between channel grayscale values ​​and target spectral parameters. Taking a second-order polynomial and three-channel image data as an example, the mapping function form is as follows: Represents three-channel image data, coefficient ~ The solution is obtained by minimizing the mean square error between the spectral parameter values ​​predicted by the model and the true spectral parameter values ​​of the standard sample, and the gradient descent algorithm is used for optimization training. The loss function is defined as follows: Indicates the The true spectral parameter values ​​of the samples; represents the spectral parameter values ​​predicted by the model; a represents the polynomial coefficients, and m is the number of samples used for fitting.

[0064] In this example, in order to optimize the model, the above loss function based on sample mean square error (MSE) is introduced. By performing gradient descent optimization on the loss function, the coefficients are iteratively updated. , the update formula is: in, represents the learning rate, Indicates the Sample pair The partial derivative of a parameter.

[0065] Through iterative convergence of the above process, the optimal polynomial model can be obtained for spectral parameter estimation at the regional or pixel level, which is particularly suitable for high-precision detection tasks in array structures.

[0066] From the above embodiments, it can be seen that the embodiments of the present application can not only solve the problems mentioned in the background solution, but also solve the problem that traditional spectral detection equipment is limited by optical resolution and sampling speed (seconds), making it difficult to achieve fast and accurate measurement of single Micro LED chips below 50μm; effectively reduce the wavelength measurement deviation caused by optical crosstalk between adjacent light-emitting units in the wafer-level multi-chip synchronous detection scenario; and improve the detection efficiency to meet the mass production line's detection speed requirements of tens of thousands of chips per hour.

[0067] A system for measuring spectral parameters of a luminous object provided in an embodiment of the present application is described above. A method for measuring spectral parameters of a luminous object provided in an embodiment of the present application is described below.

[0068] In one embodiment, a method for measuring spectral parameters of a luminous object is provided, comprising: The luminous object emits spontaneous light, which is received by the optical system and imaged onto the imaging surface, forming a spontaneous luminous optical image. The optical image on the imaging surface is collected by a multi-band image acquisition module, which includes at least three filter units with different transmission band characteristics. Each filter unit transmits light in a specific band of the optical image. The light signals corresponding to the transmission band are collected by each filter unit, thereby obtaining multi-channel image data of the spontaneous light in different bands. The multi-channel image data is input into the preset mapping function, and the spectral parameters of the luminous object are obtained by fitting and calculation.

[0069] In one embodiment, the multi-band image acquisition module is a combination of a single imaging device and a filter unit switcher. The multi-band image acquisition module is used to acquire an optical image, including: Driven by the adjustable structure of the filter unit switcher, each of the at least three filter units is switched in sequence to the optical path between the optical system and the single imaging device, so that light of the corresponding wavelength band in the optical image is transmitted; The single imaging device performs photoelectric conversion on the optical image of a specific wavelength band after being transmitted through each filter unit, collects images of the corresponding wavelength bands in sequence, and obtains multi-channel image data of self-luminescence in different wavelength bands.

[0070] In one embodiment, the multi-band image acquisition module is a combination of a multi-imaging device and a beam splitting device. The multi-band image acquisition module is used to acquire an optical image, including: The light path containing the optical image output by the optical system is divided into multiple paths by a beam splitter, and the paths are respectively directed to the respective imaging devices; Multiple imaging devices synchronously collect light signals passing through corresponding filter units to obtain multi-channel image data of self-luminescence in different bands.

[0071] In one embodiment, an image correction unit is further included, which aligns and synthesizes multi-channel image data to correct the optical axis offset or imaging difference caused by the switching of the filter unit in the combination structure of a single imaging device and a filter switch, or to correct the optical axis offset or imaging difference caused by the difference in the optical path in the parallel structure of multiple imaging devices.

[0072] In one embodiment, the luminous object spectral parameter measurement system also includes a data processing unit; the data processing unit performs local statistical processing on the multi-channel image data within the coordinate area of ​​the imaging device, and the local statistical processing includes the regional mean method, the regional median method or the regional fitting center value method; the characteristic value after statistical processing is input into the mapping function to obtain the spectral parameters of the corresponding area.

[0073] In one embodiment, the mapping function is established by polynomial fitting, interpolation, a neural network model or other machine learning methods, and the fitting relationship of the mapping function is derived from a data set generated by calibration of a standard sample corresponding to the luminous object or simulation.

[0074] In one embodiment, the polynomial fitting adopts a second-order or third-order polynomial regression model, and the model coefficients are solved by minimizing the mean square error between the spectral parameter values ​​predicted by the model and the true spectral parameter values ​​of the standard sample; the polynomial coefficients are iteratively updated to reduce the above mean square error until the polynomial regression model converges.

[0075] It should be noted that for more details of the above-mentioned method for measuring spectral parameters of luminous objects, including implementation details and technical effects, please refer to the description of the aforementioned embodiments, which will not be repeated here.

[0076] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0077] The above-described embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present application, and should all be included in the scope of protection of the present application.

Claims

1. A system for measuring spectral parameters of a luminous object, characterized in that: include: An optical system for receiving the self-luminous light emitted by the luminous object and imaging it onto an imaging surface to form a self-luminous optical image; a multi-band image acquisition module, disposed in the optical path of the imaging surface, comprising at least three filter units having different transmission band characteristics, each filter unit being configured to transmit light of a specific wavelength band in the optical image. The multi-band image acquisition module collects light of the corresponding transmission wavelength band in the optical image through each filter unit, thereby obtaining multi-channel image data of the self-luminescence in different wavelength bands; The multi-channel image data is used to be input into a preset mapping function to obtain the spectral parameters of the luminous object by fitting calculation.

2. The luminous object spectral parameter measurement system according to claim 1, characterized in that: The multi-band image acquisition module is a combination of a single imaging device and a filter unit switcher, including: An independent filter unit switcher, wherein the filter unit switcher comprises at least three filter units, wherein the filter units are bandpass filter units, short-wave pass filter units, or long-wave pass filter units, or at least three filter units are filter units having complementary wavelength characteristics; The filter unit switcher is driven by an adjustable structure to sequentially switch each of the at least three filter units to the optical path between the optical system and the single imaging device, so that light of a corresponding wavelength band in the optical image passes through and is collected by the single imaging device; The single imaging device is used to perform photoelectric conversion on the optical image of a specific wavelength band after being transmitted through each filter unit, so as to obtain multi-channel image data of the self-luminescence in different wavelength bands.

3. The luminous object spectral parameter measurement system according to claim 1, wherein: The multi-band image acquisition module is a combination of a multi-imaging device and a beam splitting device, including: At least three imaging devices, each of which is fixedly equipped with a filter unit having different transmission band characteristics, for transmitting light of corresponding wavelength bands in the optical image; a beam splitting device for splitting the light path containing the optical image output by the optical system into a plurality of paths and directing the light paths to each of the at least three imaging devices respectively; The at least three imaging devices synchronously collect light signals passing through corresponding filter units to obtain multi-channel image data of the spontaneous light in different wavelength bands.

4. The luminous object spectral parameter measurement system according to claim 2 or 3, characterized in that: It also includes an image correction unit for aligning and synthesizing the multi-channel image data, correcting the optical axis offset or imaging difference caused by switching the filter unit in the combination structure of a single imaging device and a filter switch, or correcting the optical axis offset or imaging difference caused by the difference in the optical path in the parallel structure of multiple imaging devices.

5. The luminous object spectral parameter measurement system according to claim 1, wherein: The luminous object spectral parameter measurement system also includes a data processing unit; The data processing unit is used to perform local statistical processing on the multi-channel image data within the coordinate area of ​​the imaging device, and the local statistical processing includes the regional mean method, the regional median method or the regional fitting center value method; the characteristic value after statistical processing is input into the mapping function to obtain the spectral parameters of the corresponding area.

6. The luminous object spectral parameter measurement system according to claim 1, wherein: The mapping function is established by polynomial fitting, interpolation, neural network model or other machine learning methods, and the fitting relationship of the mapping function is derived from a data set generated by calibration or simulation of a standard sample corresponding to the luminous object.

7. The luminous object spectral parameter measurement system according to claim 6, characterized in that: The polynomial fitting adopts a second-order or third-order polynomial regression model, and solves the model coefficients by minimizing the mean square error between the spectral parameter values ​​predicted by the model and the true spectral parameter values ​​of the standard sample; the above mean square error is reduced by iteratively updating the polynomial coefficients until the polynomial regression model converges.

8. A method for measuring spectral parameters of a luminous object, characterized in that: include: The luminous object emits self-luminescence, which is received by the optical system and imaged onto the imaging surface, forming a self-luminous optical image; The optical image on the imaging surface is collected by a multi-band image acquisition module, wherein the multi-band image acquisition module includes at least three filter units with different transmission band characteristics, each filter unit transmits light of a specific band in the optical image, and each filter unit collects light signals of the corresponding transmission band respectively to obtain multi-channel image data of the spontaneous light in different bands; The multi-channel image data is input into a preset mapping function, and the spectrum parameters of the luminous object are obtained by fitting calculation.

9. The method for measuring spectral parameters of a luminous object according to claim 8, wherein: The multi-band image acquisition module is a combination structure of a single imaging device and a filter unit switcher. The optical image is acquired by the multi-band image acquisition module, including: Driven by the adjustable structure of the filter unit switcher, each of the at least three filter units is switched in sequence to the optical path between the optical system and the single imaging device, so that light of the corresponding wavelength band in the optical image is transmitted; The single imaging device performs photoelectric conversion on the optical image of a specific wavelength band after being transmitted through each filter unit, collects images of corresponding wavelength bands in sequence, and obtains multi-channel image data of the self-luminescence in different wavelength bands.

10. The method for measuring spectral parameters of a luminous object according to claim 8, wherein: The multi-band image acquisition module is a combination structure of a multi-imaging device and a beam splitting device. The multi-band image acquisition module is used to acquire the optical image, including: Splitting the optical path containing the optical image output by the optical system into multiple paths by a beam splitter, and guiding the paths to the respective imaging devices; Multiple imaging devices synchronously collect light signals passing through corresponding filter units to obtain multi-channel image data of the self-luminescence in different wavelength bands.

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