A system and method for measuring spectral parameters of luminous objects

Abstract

This application relates to the field of semiconductor optoelectronic detection technology, and in particular discloses a system and method for measuring the spectral parameters of luminescent objects, solving the technical problems of traditional measurement schemes requiring non-repeating ratios of spectral characteristic curves and having a narrow measurement range. The measurement system includes an optical system for receiving the self-emission emitted by the luminescent object and imaging it onto an imaging surface to form a self-emission 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 of a specific band in the optical image. The multi-band image acquisition module acquires light of the corresponding transmission band in the optical image through each filter unit, obtaining multi-channel image data of the self-emission in different bands. The multi-channel image data is input to a preset mapping function to fit and calculate the spectral parameters of the luminescent object.

Description

Technical Field

[0001] This application relates to the field of semiconductor optoelectronic detection technology, and in particular to a system and method for measuring the spectral parameters of luminescent objects. Background Technology

[0002] Wavelength consistency testing is required in various stages of Micro LED display manufacturing, including wafer-level inspection, chip screening after mass transfer, and online sorting on production lines, as well as in the manufacturing of terminal products such as AR / VR micro-displays and ultra-high-definition screens.

[0003] A conventional approach provides a device and method for measuring the center wavelength area. The device includes: a light source to be measured that emits light to be measured; a filter switching unit that switches between 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; and a photosensitive device that 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 overlapping value, and the center 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 aforementioned traditional approach, the ratio of the spectral characteristic curves of the first and second optical systems must not have overlapping values, which places high demands on the optical systems. Furthermore, this method is typically only applicable to the range where the spectral response ratio exhibits monotonicity. to The main wavelength is accurately measured within a certain range (between 1 and 2), thus the measurement wavelength range is relatively narrow. Summary of the Invention

[0005] This application provides a system and method for measuring the spectral parameters of luminescent objects, which mainly solves the technical problems of traditional measurement schemes requiring that the ratios of spectral characteristic curves have no repeating values ​​and that the measurement range is narrow.

[0006] Firstly, a system for measuring the spectral parameters of a luminescent object is provided, comprising:

[0007] An optical system is used to receive the self-emission emitted by a luminous object and image it onto an imaging surface to form a self-emission optical image;

[0008] A multi-band image acquisition module is set on the optical path of the imaging surface and contains at least three filter units with different transmission band characteristics. Each filter unit is used to transmit light of a specific band in the optical image. The multi-band image acquisition module acquires light of the corresponding transmission band in the optical image through each filter unit to obtain multi-channel image data of self-emission in different bands.

[0009] The multi-channel image data is used as input to a preset mapping function to fit and calculate the spectral parameters of the luminous object.

[0010] Furthermore, the multi-band image acquisition module is a combination structure of a single imaging device and a filter unit switcher, including:

[0011] An independent filter unit switcher, comprising at least three filter units, wherein the filter units are bandpass filter units, short-pass filter units, or long-pass filter units, or at least three filter units having complementary band characteristics;

[0012] 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 the light of the corresponding wavelength band in the optical image can pass through and be collected by the single imaging device.

[0013] A single imaging device is used to perform photoelectric conversion on optical images of specific wavelengths after they have been transmitted through various filter units, so as to obtain multi-channel image data of self-emitting light in different wavelengths.

[0014] Furthermore, the multi-band image acquisition module is a combination structure of multiple imaging devices and a beam splitting device, including:

[0015] At least three imaging devices, each of which is fixedly equipped with filter units with different transmission band characteristics, used to transmit light of the corresponding band in the optical image;

[0016] A beam splitter is used to divide the optical path containing the optical image output by the optical system into multiple paths and guide them to each of at least three imaging devices.

[0017] At least three imaging devices simultaneously acquire light signals passing through the corresponding filter units to obtain multi-channel image data of self-emitting light in different wavelength bands.

[0018] Furthermore, it also includes an image correction unit for registering and synthesizing multi-channel image data, correcting optical axis offset or imaging differences caused by the switching of filter units in the combined structure of a single imaging device and a filter switcher, or correcting optical axis offset or imaging differences caused by optical path differences in the parallel structure of multiple imaging devices.

[0019] Furthermore, the spectral parameter measurement system for luminescent objects also includes a data processing unit;

[0020] The data processing unit is used to perform local statistical processing on multi-channel image data within the coordinate area of ​​the imaging device. The local statistical processing includes the regional mean method, the regional mean method, or the regional fitting center value method. The statistically processed feature values ​​are input into the mapping function to obtain the spectral parameters of the corresponding region.

[0021] Furthermore, the mapping function is established through polynomial fitting, interpolation, neural network models, or other machine learning methods. The fitting relationship of the mapping function comes from the dataset generated by standard sample calibration or simulation corresponding to the luminescent object.

[0022] Furthermore, the polynomial fitting employs a second- or third-order polynomial regression model. 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 mean square error is reduced by iteratively updating the polynomial coefficients until the polynomial regression model converges.

[0023] Secondly, a method for measuring the spectral parameters of a luminescent object is provided, including:

[0024] The luminescent object emits self-luminescence, which is received by the optical system and imaged onto the imaging surface to form a self-luminescent optical image. The optical image on the imaging surface is acquired by a multi-band image acquisition module. The multi-band image acquisition module contains at least three filter units with different transmission band characteristics. Each filter unit transmits light of a specific band in the optical image. The light signal of the corresponding transmission band is acquired by each filter unit to obtain multi-channel image data of self-luminescence in different bands.

[0025] Multi-channel image data is input into a preset mapping function, and the spectral parameters of the luminous object are obtained by fitting and calculation.

[0026] Furthermore, the multi-band image acquisition module is a combination structure of a single imaging device and a filter unit switcher. The multi-band image acquisition module acquires optical images, including:

[0027] Driven by the adjustable structure of the filter unit switcher, each of the at least three filter units is sequentially switched 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 can pass through.

[0028] A single imaging device performs photoelectric conversion on optical images of specific wavelengths after transmission through each filter unit, and sequentially acquires images of corresponding wavelengths to obtain multi-channel image data of self-emitting light in different wavelengths.

[0029] Furthermore, the multi-band image acquisition module is a combination structure of multiple imaging devices and a beam splitter. The multi-band image acquisition module acquires optical images, including:

[0030] The optical path containing the optical image output by the optical system is divided into multiple paths by a beam splitter, which are then directed to each imaging device.

[0031] Multiple imaging devices simultaneously acquire light signals passing through corresponding filter units to obtain multi-channel image data of self-emitting light in different wavelength bands.

[0032] In one of the solutions provided in this application, the embodiments of this application introduce three or more filter units. By fitting and retrieving the spectral parameters through the multi-channel image data and mapping relationship corresponding to the three or more filter units, it is not necessary to require that the spectral response ratio has no repetition within the measurement range. This overcomes the problem of "no repetition of the ratio between the two channels" in the traditional solution. Moreover, by using at least three or more filter units to fit the wavelength, the problem of the monotonic range of the ratio of only two filters can be made up for, thereby expanding the detection wavelength range and improving the detection range. Attached Figure Description

[0033] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the description of the embodiments of this application 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.

[0034] Figure 1 This is a schematic diagram of the wavelength response curve of a typical three-camera filter;

[0035] Figure 2 This is a schematic diagram of a response curve where two wavelengths of the wave have two or more intersection points due to unstable manufacturing process.

[0036] Figure 3 This is a schematic diagram of a single-camera filter cutting scheme according to one embodiment of this application;

[0037] Figure 4 This is a schematic diagram of a single-camera three-filter scheme according to one embodiment of this application;

[0038] Figure 5 This is a schematic diagram of a single-camera four-filter scheme according to one embodiment of this application;

[0039] Figure 6 This is a schematic diagram of a multi-camera scheme according to an embodiment of this application;

[0040] Figure 7 This is a schematic diagram of a multi-camera three-filter scheme in one embodiment of this application;

[0041] Figure 8 This is a schematic diagram of a multi-camera four-filter scheme in one embodiment of this application. Detailed Implementation

[0042] To make the technical problems, technical solutions, and beneficial effects solved by this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0043] This application belongs to the field of semiconductor optoelectronic detection technology, specifically relating to a system and method for measuring the spectral parameters of light-emitting objects, mainly used for Micro LED and Mini LED chips. Exemplarily, it is mainly applied to wafer-level inspection, chip screening after mass transfer, and online sorting on production lines in the Micro LED display manufacturing process, as well as 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.

[0044] For example, taking the testing of Micro LED display products as an example, Micro LED display technology is currently in a critical stage of industrialization. Its core manufacturing processes, such as mass transfer and wafer-level testing, place extremely high demands on the optical performance testing of individual Micro LED chips. Existing technologies mainly face the following technical problems closely related to this application: some solutions, such as the wavelength detection method mentioned in the background, require that the ratio of the spectral characteristic curves of the first and second optical systems of the system has no overlapping values, placing high demands on the optical system. Furthermore, this method typically only works within the range where the spectral response ratio has monotonicity (…). to The main wavelength is accurately measured within a certain range (between 1 and 2), thus the measurement wavelength range is relatively narrow.

[0045] In this application embodiment, a system and method for measuring the spectral parameters of a luminescent object are provided, mainly used to solve the above-mentioned requirement that the ratio of the spectral characteristic curve has no repeating values, and that it can only be measured in the range where the spectral response ratio has monotonicity ( to The technical problem of accurately measuring the main wavelength within (between) and thus having a narrow measurement wavelength range.

[0046] The embodiments of this application will now be described in detail through various examples.

[0047] In one embodiment, a system for measuring the spectral parameters of a luminescent object is provided, comprising:

[0048] An optical system is used to receive the self-emission emitted by a luminous object and image it onto an imaging surface to form a self-emission optical image;

[0049] A multi-band image acquisition module is set on the optical path of the imaging surface and contains at least three filter units with different transmission band characteristics. Each filter unit is used to transmit light of a specific band in the optical image. The multi-band image acquisition module acquires light of the corresponding transmission band in the optical image through each filter unit to obtain multi-channel image data of self-emission in different bands.

[0050] The multi-channel image data is used as input to a preset mapping function to fit and calculate the spectral parameters of the luminous object.

[0051] In this embodiment, the light-emitting object refers to the light-emitting object under test, including but not limited to light-emitting objects made of Micro LED and Mini LED chips. The light-emitting object under test emits self-emission, which is imaged onto an imaging surface by an optical system to form a self-emitting optical image. Exemplarily, the optical system can be an imaging system such as an objective lens, an imaging lens, or an astigmatism correction system, etc., and is not specifically limited.

[0052] The system also includes a multi-band image acquisition module disposed on the optical path of the imaging surface. This module includes an imaging device and at least three filter units with different transmission band characteristics. Each filter unit transmits light of a specific band in the optical image. The multi-band image acquisition module acquires light of the corresponding transmission band in the optical image through each filter unit, obtaining multi-channel image data of self-emission in different bands. For example, it may include three filter units with different transmission band characteristics: a first filter unit, a second filter unit, and a third filter unit. The first filter unit transmits light of the first band in the optical image. The multi-band image acquisition module acquires light of the corresponding first band in the optical image through the first filter unit, obtaining first-channel image data of self-emission in the first band. Similarly, it obtains second-channel image data corresponding to the second filter unit and third-channel image data corresponding to the third filter unit. The multi-channel image data is input to a preset mapping function to fit and calculate the spectral parameters of the luminescent object. For example, the spectral parameters of the luminescent object include one or more of the dominant wavelength, center wavelength, and peak wavelength, without specific limitations.

[0053] For example, the acquired multi-channel image data , , ... as mapping functions The input is used to obtain a mapping function through pre-training or calibration. Fitting and calculating the point or region The corresponding spectral parameters, for example, include one or more of the dominant wavelength, center wavelength, and peak wavelength.

[0054] As can be seen, this application provides a new system for measuring the spectral parameters of luminescent objects, which has at least the following advantages: it solves the problem that traditional solutions, which use only two filters, require strict assurance that the spectral response ratio does not repeat within the measurement range, otherwise leading to errors in wavelength calculation; and it also solves the problem of traditional solutions in the range where the spectral response ratio has monotonicity (…). to The inability to accurately measure the main wavelength within a certain range (between two wavelengths) results in a narrow measurement wavelength range.

[0055] This application embodiment introduces three or more filter units and uses the multi-channel image data and mapping relationship corresponding to the three or more filter units to fit and inverse the spectral parameters. It does not require that the spectral response ratio be non-repeating within the measurement range, thus overcoming the problem of "no repetition of the ratio between the two channels" in the traditional solution. It 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., the ratios are repetitive), they can still be fitted with the multi-channel image data and mapping function corresponding to the three or more filter units to accurately inverse the spectral parameters of the luminescent object.

[0056] like Figure 1 and Figure 2 The above, Figure 1 This is a typical wavelength response curve for three filters, such as... Figure 2 As shown, unstable manufacturing processes can lead to filters 1 and 2 having two or more intersection points, resulting in a non-unique ratio. Curves 1, 2, and 3 represent the wavelength response curves of filters 1, 2, and 3, respectively (the figures are for illustrative purposes only; the spectral response curves of the filters can be other curves). Traditional methods using two filters require that the ratio of filters 1 and 2 has no overlapping values ​​within the detection range. Therefore, for a detectable range of... and This results in a narrower detection range. For example, such as... Figure 2 As shown, for Figure 2 The situation shown has two intersection points between filters 1 and 2, which does not meet the requirement of no repeating values ​​in the ratio of traditional schemes. Therefore, traditional schemes have high requirements for the manufacturing process and quality of the filters. In the embodiments of this application, taking three filter units as an example, the method of fitting the wavelength with at least three or more filter units can compensate for the monotonic range of the ratio of filters 1 and 2 being only a few wavelengths. and The problem is that the detection wavelength range is expanded to and Furthermore, even when the quality of the filter unit is poor, resulting in a non-unique ratio, filter 3 can effectively form a complex mapping relationship with filters 1 and 2, and the system can still work normally, improving the detection range and accuracy, enhancing detection robustness, and making it suitable for a variety of luminous objects.

[0057] Moreover, conventional spectral detection equipment is limited by optical resolution and sampling speed (seconds), making it difficult to achieve rapid and accurate measurement of Micro LED single chips (such as Micro LED single chips below 50μm). The embodiments of this application can achieve rapid and accurate measurement of Micro LED single chips (such as Micro LED single chips below 50μm). In the scenario of simultaneous detection of multiple chips at the wafer level, optical crosstalk between adjacent light-emitting units causes wavelength measurement deviation. The embodiments of this application can reduce wavelength measurement deviation caused by optical crosstalk through multiple filter units and mapping relationships.

[0058] It is also worth noting that the embodiments of this application are not limited to the type of light to be detected, and can be either correlated light or uncorrelated light, nor are they limited to the detection field of view, and can detect the wavelength of each object within the shooting field of view.

[0059] In one embodiment, the aforementioned filter units are bandpass filter units, short-pass filter units, or long-pass filter units, or at least three filter units are filter units with complementary wavelength characteristics. In this embodiment, the at least three filter units can be any form of bandpass filter units, short-pass filter units, or long-pass filter units, improving the adaptability and scalability of the solution. In addition, the at least three filter units are filter units with complementary wavelength characteristics, which has the following advantages: it can cover a wider wavelength range. Through the complementarity of different wavelength bands, the spectral information of the luminescent sample in a wider spectral range can be captured, meeting the detection requirements of wide-band luminescent objects; it improves the accuracy and robustness of wavelength measurement. The filter units with complementary wavelength bands can provide richer spectral features, and when combined with mapping function fitting, the spectral parameters can be better inverted. Even if some filters have characteristic deviations, the measurement accuracy can be guaranteed through the synergistic effect of multiple sets of data; it reduces the stringent requirements on the performance of individual filters. Even if there are non-ideal situations such as filter intersections, accurate measurement can still be achieved through fitting analysis of multi-channel image data.

[0060] It should be noted that the multi-band image acquisition module of the luminescent object spectral parameter measurement system in this application embodiment can be implemented in various ways. This application embodiment provides at least two structural forms, each with its own characteristics, which will be described below.

[0061] 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 switcher, including:

[0062] An independent filter unit switcher, which contains at least three filter units;

[0063] 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 the light of the corresponding wavelength band in the optical image can pass through and be collected by the single imaging device. In other words, a filter unit group is set between the optical system and the camera. The filter unit group contains at least three (greater than or equal to three) filter units with different transmission wavelength band characteristics. For example, the switching of the filter units can be achieved by a mechanical rotary wheel, an electric slide, or a liquid crystal adjustable filter unit, and the specific method is not limited.

[0064] A single imaging device is used to perform photoelectric conversion on a specific band optical image transmitted through various filter units to obtain self-emissive multi-channel image data in different bands. A single imaging device can refer to a camera, specifically a camera used primarily in optical systems. For example, such a camera includes, but is not limited to, area scan, line scan, and other similar cameras.

[0065] For example, taking a single imaging device as a single camera and a filter unit specifically as a filter as an example:

[0066] For example, such as 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 of the corresponding band in the optical image to pass through and be acquired by the single camera. The single camera is used to perform photoelectric conversion on the optical image of a specific band after transmission through each filter (filter 1, filter 2, filter 3, ...), obtaining multi-channel image data of self-emitting light in different bands.

[0067] Specifically, in one example, such as 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). Driven by an adjustable structure, the filter unit switcher 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 of the corresponding band in the optical image to pass through and be acquired by the single camera. The single camera is used to perform photoelectric conversion on the specific band optical image transmitted through the three filters (filter 1, filter 2, and filter 3), obtaining three channels of self-emitting image data in three different bands.

[0068] Specifically, in another example, such as 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, and filter 4). Driven by an adjustable structure, the filter unit switcher sequentially switches each of the four filters (filter 1, filter 2, filter 3, and filter 4) into the optical path between the optical system and the single camera, allowing light of the corresponding band in the optical image to pass through and be acquired by the single camera. The single camera is used to perform photoelectric conversion on the optical image of a specific band after transmission through the four filters (filter 1, filter 2, filter 3, and filter 4), obtaining four channels of self-emitting image data in four different bands.

[0069] As can be seen, this embodiment provides a specific implementation of a multi-band image acquisition module, which is a single imaging device solution. Different band filters can be replaced by mechanical switching of the filter unit switcher or electric slide. The structure is relatively simple, without the need for a complex beam splitting device, and the overall cost is low. Moreover, the installation and calibration requirements of the filter unit are not high, making it suitable for scenarios where the detection speed requirement is not extreme, such as small batch sample detection, without specific limitations.

[0070] 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:

[0071] At least three imaging devices are fixedly combined with filter units of different wavelengths, that is, each imaging device is fixedly combined with filter units of different transmission wavelength characteristics for transmitting light of the corresponding wavelength in the optical image; it should be noted that the imaging device in this embodiment may refer to a camera, which may refer to a camera mainly used in optical systems. For example, the camera includes, but is not limited to, area scan, line scan, etc.

[0072] A beam splitter is used to divide an optical path containing an optical image output from an optical system into multiple paths and guide them to each of at least three imaging devices. For example, the beam splitter includes, but is not limited to, a polarizing beam splitter, a dispersive prism, or a semi-reflective mirror, which divides the same optical path into multiple paths and guides them to each imaging device.

[0073] At least three imaging devices simultaneously acquire light signals passing through the corresponding filter units to obtain multi-channel image data of self-emitting light in different wavelength bands.

[0074] For example, taking a camera as the imaging device and a filter unit specifically as a filter as an example:

[0075] For example, such as 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 combined with 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 to transmit light of the corresponding band in the optical image. The beam splitter is used to divide the optical path containing the optical image output by the optical system into multiple paths and guide them to each camera. The at least three cameras simultaneously acquire the light signals transmitted through the corresponding filters to obtain multi-channel image data of self-emitted light in different bands.

[0076] For example, such as Figure 7 As shown, the multi-band image acquisition module includes three cameras, a beam splitter, and three filters (filter 1, filter 2, and filter 3) for three different bands. The three cameras are fixedly combined with the three filters (filter 1, filter 2, and filter 3) for different bands, that is, each camera is fixedly combined with a filter with different transmission band characteristics to transmit light of the corresponding band in the optical image. The beam splitter is used to divide the optical 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 acquire the light signals transmitted through the corresponding filters to obtain three-channel image data corresponding to the self-emission in three different bands.

[0077] For example, such as Figure 8 As shown, the multi-band image acquisition module includes at least four cameras, a beam splitter, and four filters (filter 1, filter 2, filter 3, and filter 4) of different bands. The four cameras are fixedly combined with the four filters (filter 1, filter 2, filter 3, and filter 4) of different bands, that is, each camera is fixedly combined with a filter with different transmission band characteristics to transmit light of the corresponding band in the optical image. The beam splitter is used to divide the optical 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 acquire the light signals transmitted through the corresponding filters to obtain four-channel image data of self-emission corresponding to the four different bands.

[0078] As can be seen, this embodiment provides another specific implementation of the multi-band image acquisition module, namely, a multi-imaging device solution. In this embodiment, there is no need for a filter unit switcher. Instead, a beam splitting device is used to eliminate the mechanical switching process, which can significantly improve the acquisition speed. It is particularly suitable for mass production line inspection or rapid scanning scenarios.

[0079] In one embodiment, the spectral parameter measurement system for luminescent objects provided in this application further includes an image correction unit, used to register and synthesize multi-channel image data, correct optical axis offset or imaging difference caused by the switching of filter units in the combined structure of a single imaging device and a filter switcher, or correct optical axis offset or imaging difference caused by optical path differences in the parallel structure of multiple imaging devices.

[0080] This embodiment also includes an image registration and synthesis step, which corrects the optical axis offset or imaging difference caused by the switching of filter units in the combined structure of a single imaging device and a filter switcher, or corrects the optical axis offset or imaging difference caused by the optical path difference in the parallel structure of multiple imaging devices, further improving the accuracy and precision of the obtained multi-channel data, and effectively improving the measurement accuracy of the final spectral parameters such as the dominant wavelength.

[0081] In one embodiment, the spectral parameter measurement system for luminescent objects further includes a data processing unit;

[0082] The data processing unit performs local statistical processing on multi-channel image data within the coordinate region of the imaging device. This local statistical processing includes methods such as regional mean, regional median, or regional fitting center value. The processed feature values ​​are then input into a mapping function to obtain the spectral parameters of the corresponding region. Additionally, in one example, the data processing can be performed on data within a specified coordinate region.

[0083] For example, if the object under test is an array structure (such as a pixel array, filter array, microcavity array, etc.), local statistical processing can be performed on the multi-channel image data within the coordinate region of the imaging device in each array unit region. For example, the multi-channel image data includes image data. Fitting and calculating the point or region The corresponding spectral parameters include one or more of the dominant wavelength, center wavelength, and peak wavelength;

[0084] Regional mean method:

[0085]

[0086] in, Indicates the first Each array cell region Indicates the first Channel image data at pixels Pixel value at that location, Indicates the region Inner The average value of the channel image data is used as the feature value.

[0087] Regional median method:

[0088]

[0089] This formula represents the first... indivual All pixels In the The median of these pixel values ​​is taken as the feature value of the region. Indicates the region Inner The median value of the channel image data; median indicates taking the median.

[0090] Region fitting center values ​​(e.g., center point values ​​after smooth fitting):

[0091]

[0092] This formula represents the first... indivual All pixels First, a spatial fit is performed, then the output value of the fitted function at the center of the region is taken. Indicates the region Inner The fitting center value of the channel image data; fit-center indicates taking the fitting center value.

[0093] The statistically derived eigenvalues Input into the trained mapping function: Thus, array units are obtained. The corresponding representative spectral parameters (such as dominant wavelength, center wavelength, or peak wavelength) enable regional-level spectral reconstruction of the entire array structure.

[0094] Additionally, in one example, a specified coordinate region can be selected. The image data within the region is processed, and the spectral parameters of the region are fitted using a mapping function, including one or more of the dominant wavelength, center wavelength, and peak wavelength. The results can be output in pixel-level or regional statistical form.

[0095] In this embodiment, local statistical processing is performed on multi-channel image data within the coordinate region of the imaging device. The processed feature values ​​are then input into a mapping function to obtain the spectral parameters of the corresponding region. This approach adapts to different detection needs, and the pixel-level output is accurate to the spectral information corresponding to each pixel. On one hand, it is suitable for analyzing subtle wavelength differences in local areas of luminescent objects, such as wavelength uniformity detection within Micro LED chips. On the other hand, the regional statistical form can summarize 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 detection, such as rapid screening of wafer-level chips. Furthermore, it enhances the flexibility and applicability of data processing. For array-type luminescent structures (such as pixel arrays), each array unit region can be processed separately, ensuring measurement accuracy within the region while efficiently completing the spectral characteristic analysis of the entire array, balancing local details with overall efficiency. Moreover, it enhances the reliability of the results. Regional statistics (such as mean, median, etc.) can reduce the impact of noise or local anomalies on the calculation of spectral parameters, while pixel-level processing provides data support for fine analysis. The combination of these two forms can more comprehensively reflect the spectral characteristics of luminescent objects.

[0096] In one embodiment, the mapping function is established through polynomial fitting, interpolation, neural network models or other machine learning methods, and the fitting relationship of the mapping function is derived from the dataset generated by standard sample calibration or simulation corresponding to the luminescent object.

[0097] This embodiment employs polynomial fitting, interpolation, and neural networks to construct the mapping function in this application, and determines the fitting relationship based on standard sample calibration or simulation datasets. This approach offers several advantages: Due to the significant differences in spectral characteristics among different luminescent objects (such as LEDs and laser devices), they may exhibit linear, nonlinear, or even complex piecewise features. Polynomial fitting is suitable for handling simple nonlinear relationships and offers 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 for flexible model selection based on the specific detection object, avoiding dependence on a single approach. Traditional spectral measurements require the establishment of precise optical physical models (such as rigorous filter spectral response functions and optical path attenuation models), but actual systems exhibit non-ideal factors such as filter characteristic drift, optical path scattering, and noise interference, which are difficult to fully characterize through physical models. The embodiments of this application, based on mapping functions (based on calibration or simulation data), can directly learn patterns from the correlation between input (multi-channel image data) and output (spectral parameters), automatically incorporating the aforementioned non-ideal factors, reducing the requirements for the accuracy of the physical parameters of the optical system, and improving measurement stability. Standard samples have known, precise spectral parameters; using their acquired multi-channel image data as the "input-output" sample pair to fit the mapping function allows the accuracy of the standard sample to be transferred to the actual measurement, ensuring the consistency between the model output and the true spectral parameters. This "calibration-fitting" mechanism effectively eliminates systematic errors and meets the requirements of high-precision detection (such as the requirement of ±0.5nm wavelength consistency for Mini LEDs).

[0098] It's also worth noting that simulations can generate extreme state data (such as ultra-wide wavelength ranges and special spectral shapes) that are difficult to prepare with physical standard samples, supplementing the deficiencies of calibration data and enabling the mapping function to maintain accuracy under a wider range of operating conditions. Lightweight models such as polynomial fitting and interpolation offer fast computation speeds, making them suitable for real-time online detection (such as high-speed sorting on production lines); this allows for a flexible trade-off between efficiency and accuracy, adapting to all scenarios from rapid screening to precise analysis.

[0099] In summary, this method of constructing mapping functions ensures both the accuracy and robustness of measurements, while also expanding the applicability of the solution through flexibility and generalization capabilities, thus balancing cost and efficiency.

[0100] In one embodiment, the polynomial fitting employs a second- or third-order polynomial regression model. 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 mean square error is reduced by iteratively updating the polynomial coefficients until the polynomial regression model converges.

[0101] In this embodiment, a second- or third-order polynomial regression model is employed, and the coefficients are iteratively solved by minimizing the mean squared error (MSE) until convergence. This offers several advantages: Second- or third-order polynomials are low-order models with relatively simple functional forms, capable of capturing the mainstream nonlinear relationship 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 model's stability in actual detection. Solving the coefficients of low-order polynomials involves fewer matrix operations and faster iterative convergence. In speed-sensitive scenarios such as online production line inspection (e.g., Mini LED wafer sorting), this efficiency ensures that the calculation time for spectral parameters of each frame is controlled within milliseconds, meeting the core requirements of high-speed detection. By iteratively adjusting the polynomial coefficients (e.g., reducing the MSE with each coefficient update) until the MSE no longer decreases significantly (convergence), the model can be guaranteed to approximate the true mapping relationship between "multi-channel image data → spectral parameters" as closely as possible. It can adaptively accommodate slight fluctuations in calibration data (e.g., measurement noise from standard samples), smoothing errors through multiple iterations and improving model stability. It is also worth noting that the relationship between the spectral parameters of a luminescent object (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 (filter response, optical path attenuation). Second- or third-order polynomials are sufficient to characterize this weakly nonlinear relationship (e.g., the quadratic variation of filter transmittance with wavelength), eliminating the need for more complex models (e.g., higher-order polynomials or neural networks), thus simplifying model complexity while maintaining accuracy.

[0102] As can be seen, this embodiment achieves a balance between accuracy, efficiency, and stability through the combination of "low-order model + MSE optimization + iterative convergence". It 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.

[0103] Specifically, in one embodiment, a nonlinear mapping relationship between channel gray values ​​and target spectral parameters is established by modeling training samples with known labels using a second- or third-order polynomial regression model. Taking second-order polynomial and three-channel image data as an example, the mapping function form is as follows:

[0104]

[0105] Represents three-channel image data, coefficients ~ The loss function is calculated by minimizing the mean square error between the model's predicted spectral parameter values ​​and the true spectral parameter values ​​of the standard samples, and optimization is performed using the gradient descent algorithm. The loss function is defined as follows:

[0106]

[0107] Indicates the first The true spectral parameter values ​​of each sample; This represents the spectral parameter values ​​predicted by the model;

[0108] a represents the polynomial coefficients, and m is the number of samples used for fitting.

[0109] In this example, to optimize the model, the aforementioned loss function based on the sample mean squared error (MSE) is introduced. The coefficients are then iteratively updated by performing gradient descent optimization on this loss function. The updated formula is:

[0110]

[0111] in, Indicates the learning rate. Indicates the first The nth sample pair Partial derivatives of each parameter.

[0112] By iteratively converging through the above process, the optimal polynomial model can be obtained, which can be used for spectral parameter estimation at the regional or pixel level, and is especially suitable for high-precision detection tasks in array structures.

[0113] As can be seen from the above embodiments, the embodiments in this application 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 (second level), making it difficult to achieve fast and accurate measurement of Micro LED single chips below 50μm; effectively reduce wavelength measurement deviation caused by optical crosstalk between adjacent light-emitting units in wafer-level multi-chip synchronous detection scenarios; and also improve detection efficiency to meet the detection speed requirements of mass production lines for tens of thousands of chips per hour.

[0114] The above describes a system for measuring the spectral parameters of a luminescent object according to an embodiment of this application. The following describes a method for measuring the spectral parameters of a luminescent object according to an embodiment of this application.

[0115] In one embodiment, a method for measuring the spectral parameters of a luminescent object is provided, comprising:

[0116] The luminescent object emits self-luminescence, which is received by the optical system and imaged onto the imaging surface to form a self-luminescent optical image. The optical image on the imaging surface is acquired by a multi-band image acquisition module. The multi-band image acquisition module contains at least three filter units with different transmission band characteristics. Each filter unit transmits light of a specific band in the optical image. The light signal of the corresponding transmission band is acquired by each filter unit to obtain multi-channel image data of self-luminescence in different bands.

[0117] Multi-channel image data is input into a preset mapping function, and the spectral parameters of the luminous object are obtained by fitting and calculation.

[0118] In one embodiment, the multi-band image acquisition module is a combination of a single imaging device and a filter unit switch. The multi-band image acquisition module acquires optical images, including:

[0119] Driven by the adjustable structure of the filter unit switcher, each of the at least three filter units is sequentially switched 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 can pass through.

[0120] A single imaging device performs photoelectric conversion on optical images of specific wavelengths after transmission through each filter unit, and sequentially acquires images of corresponding wavelengths to obtain multi-channel image data of self-emitting light in different wavelengths.

[0121] In one embodiment, the multi-band image acquisition module is a combination of a multi-imaging device and a beam splitter. The multi-band image acquisition module acquires optical images, including:

[0122] The optical path containing the optical image output by the optical system is divided into multiple paths by a beam splitter, which are then directed to each imaging device.

[0123] Multiple imaging devices simultaneously acquire light signals passing through corresponding filter units to obtain multi-channel image data of self-emitting light in different wavelength bands.

[0124] In one embodiment, the system further includes an image correction unit that registers and synthesizes multi-channel image data to correct optical axis offsets or imaging differences caused by the switching of filter units in the combined structure of a single imaging device and a filter switcher, or to correct optical axis offsets or imaging differences caused by optical path differences in the parallel structure of multiple imaging devices.

[0125] In one embodiment, the spectral parameter measurement system for luminescent objects further includes a data processing unit; the data processing unit performs local statistical processing on multi-channel image data within the coordinate area of ​​the imaging device, including the regional mean method, the regional mean method, or the regional fitting center value method; the statistically processed feature values ​​are input to the mapping function to obtain the spectral parameters of the corresponding region.

[0126] In one embodiment, the mapping function is established through polynomial fitting, interpolation, neural network models or other machine learning methods, and the fitting relationship of the mapping function is derived from the dataset generated by standard sample calibration or simulation corresponding to the luminescent object.

[0127] In one embodiment, the polynomial fitting employs a second- or third-order polynomial regression model. 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 mean square error is reduced by iteratively updating the polynomial coefficients until the polynomial regression model converges.

[0128] It should be noted that more details of the above-described method for measuring the spectral parameters of luminescent objects, including implementation details and technical effects, can be found in the description of the foregoing embodiments, and will not be repeated here.

[0129] It should be understood that the sequence number of each step in the above embodiments does not imply 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.

[0130] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A system for measuring the spectral parameters of a luminescent object, characterized in that, include: An optical system is used to receive the self-emission emitted by a luminous object and image it onto an imaging surface to form a self-emission 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 of a specific band in the optical image. The multi-band image acquisition module acquires light of the corresponding transmission band in the optical image through each filter unit to obtain multi-channel image data of the self-emitting light in different bands. The multi-channel image data is used to input a preset mapping function to fit and calculate the spectral parameters of the luminescent object. 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, the filter unit switcher comprising at least three filter units, the filter units being bandpass filter units, short-pass filter units, or long-pass filter units, or at least three filter units having complementary band 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 the light of the corresponding wavelength band in the optical image can pass through and be collected by the single imaging device. The single imaging device is used to perform photoelectric conversion on the optical image of a specific band after it has been transmitted through each filter unit, so as to obtain the self-luminous multi-channel image data in different bands. or; The multi-band image acquisition module is a combination of a multi-imaging device and a beam splitter, including: At least three imaging devices, each of which is fixedly equipped with filter units with different transmission band characteristics, used to transmit light of the corresponding band in the optical image; A beam splitter is used to divide the optical path containing the optical image output by the optical system into multiple paths and guide them to each of the at least three imaging devices. The at least three imaging devices simultaneously acquire light signals transmitted through the corresponding filter units to obtain multi-channel image data of the self-emitting light in different wavelength bands.

2. The spectral parameter measurement system for luminescent objects as described in claim 1, characterized in that, It also includes an image correction unit, used to register and synthesize the multi-channel image data, correcting optical axis offset or imaging differences caused by filter unit switching in the combined structure of single imaging device and filter unit switcher, or correcting optical axis offset or imaging differences caused by optical path differences in the combined structure of multi-imaging device and beam splitter.

3. The spectral parameter measurement system for luminescent objects as described in claim 1, characterized in that, The spectral parameter measurement system for luminescent objects also includes a data processing unit; The data processing unit is used to perform local statistical processing on multi-channel image data within the coordinate area of ​​the imaging device. The local statistical processing includes the regional mean method, the regional mean method, or the regional fitting center value method. The statistically processed feature values ​​are input into the mapping function to obtain the spectral parameters of the corresponding region.

4. The spectral parameter measurement system for luminescent objects as described in claim 1, characterized in that, The mapping function is established through polynomial fitting, interpolation, neural network models or other machine learning methods, and the fitting relationship of the mapping function comes from the dataset generated by standard sample calibration or simulation corresponding to the luminescent object.

5. The spectral parameter measurement system for luminescent objects as described in claim 4, characterized in that, The polynomial fitting employs a second- or third-order polynomial regression model. The model coefficients are solved by minimizing the mean square error between the spectral parameter values ​​predicted by the model and the actual spectral parameter values ​​of the standard sample. The mean square error is reduced by iteratively updating the polynomial coefficients until the polynomial regression model converges.

6. A method for measuring the spectral parameters of a luminescent object, characterized in that, include: A luminous object emits its own light, which is received by an optical system and imaged onto an imaging surface to form a self-luminous optical image; The optical image on the imaging surface is acquired by a multi-band image acquisition module. The multi-band image acquisition module contains at least three filter units with different transmission band characteristics. Each filter unit transmits light of a specific band in the optical image. The light signal of the corresponding transmission band is acquired by each filter unit to obtain multi-channel image data of the self-emission in different bands. The multi-channel image data is input into a preset mapping function, and the spectral parameters of the luminescent object are obtained by fitting and calculation. The multi-band image acquisition module is a combination of a single imaging device and a filter unit switcher. The acquisition of optical images via the multi-band image acquisition module includes: Driven by the adjustable structure of the filter unit switcher, each of the at least three filter units is sequentially switched 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 can pass through. A single imaging device performs photoelectric conversion on the optical image of a specific band after it is transmitted through each filter unit, and sequentially acquires images of the corresponding bands to obtain multi-channel image data of the self-luminous emission in different bands. or; The multi-band image acquisition module is a combination of a multi-imaging device and a beam splitter. The acquisition of optical images via the multi-band image acquisition module includes: The optical path containing the optical image output by the optical system is divided into multiple paths by a beam splitter, which are then directed to each imaging device. Multiple imaging devices simultaneously acquire light signals passing through corresponding filter units to obtain multi-channel image data of the self-luminous material in different wavelength bands.

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