Method and device for inspecting aberration of optical system

Abstract

The invention discloses an optical system aberration checking method and device, and the method comprises the steps: providing a dot pattern array target plate, enabling a lens and a photosensitive chip to form an imaging system, shooting the dot pattern array target plate, collecting at least two frames of dot pattern array images shot by the imaging system, and carrying out the imaging of the dot pattern array target plate. An aberration simulation model is obtained according to shape information of dot patterns in dot pattern array images shot by at least two frames of an imageable system, the optical aberration of the imageable system is obtained according to the aberration simulation model, and the dot pattern array target plate covers a center view field area and a peripheral view field area of the imageable system. The sizes and the shapes of the dot patterns in the same view field area in the dot pattern array images shot by the two frames of imaging systems are different, and the axial distances of the photosensitive chip and the lens are different when the two frames of dot pattern array images are shot, so that excessive measurement conditions of the optical system are not depended on; the aberration of the full view field of the lens is recovered from the two recorded frames of defocus intensity images through the aberration simulation model, and a defocus curve is generated.

Description

Technical Field

[0001] The present application relates to a method for inspecting optical system aberrations, and in particular to a method for inspecting optical system aberrations that accurately reproduces an imaging system using a small amount of data. Background Art

[0002] Image clarity is one of the important indicators of the quality of a lens. In the prior art, the modulation transfer function (MTF) is an important indicator of lens image clarity. In the actual production of lenses or camera modules, multiple lenses are combined together to form a lens. The molding tolerance of the lens itself and the eccentricity error of the lens assembly often make the modulation transfer function of the actual lens extremely complex. If the manufacturer only controls the yield of the lens for the modulation transfer function, then the yield of the lens for the camera module is difficult to guarantee by only controlling the performance of the modulation transfer function.

[0003] In the prior art, the optical modulation transfer function test through-focus data (MTF-Through-Focus) analyzes the overall imaging performance of the camera module at different focus distances based on the focus position of the lens. In the prior art, camera module manufacturers often discover defects in the optical performance of camera modules or lenses by testing the optical modulation transfer function test through-focus data, which can also be used to trace the defects of camera modules or lenses. Therefore, the optical modulation transfer function test through-focus data has a very important significance and role for camera module manufacturers.

[0004] Engineers can use the optical modulation transfer function to test the defocus data (MTF-Through-Focus) to find quality problems in the assembly process or process of a single lens or a single camera module, and perform tracebacks and adjustments to improve the production of the camera module. At present, the defocus data is mainly obtained by changing the axial relative position of the lens and the photosensitive chip during the test, recording multiple frames of defocused images, and testing the modulation transfer function of each field of view at the current position, and finally obtaining a modulation transfer function curve in which the value of the modulation transfer function changes with the axial distance between the lens and the photosensitive chip. The whole process requires multiple measurements, and the overall test process is time-consuming. In addition, the optical modulation transfer function is not omnipotent, and there is no way to know the distortion, color reproduction, vignetting and anti-glare capabilities of the lens. Therefore, the existing optical modulation transfer function lacks a comprehensive description of the optical characteristics of the lens or camera module, and it is difficult to perform defect analysis to improve the image quality.

[0005] On the other hand, there is a solution in the prior art that uses a split lens to be actively calibrated after being clamped by a mechanism. The mechanism clamps part of the lens and changes the position between the lenses, thereby changing the optical performance of the split lens. During the active calibration process, it is also necessary to change the axial distance of the photosensitive chip relative to the split lens, record multiple frames of defocused images, and test the values ​​of the modulation transfer function of each field of view at the current position, so as to finally know whether the performance of the split lens can meet the requirements when the lens is in this relative position. Therefore, each time the position of the split lens is changed, it is necessary to measure the optical modulation transfer function test defocus data again. At this time, the optical modulation transfer function test defocus workload of the entire split lens active calibration process is doubled according to the number of adjustments of the adjusted split lens. Obviously, reducing the time of each optical modulation transfer function test defocus data can greatly reduce the time of active calibration of the split lens.

[0006] With the development of the industry, computational optics is an emerging research field. Computational optics is an organic combination of optical control and image processing. It mines the hidden information of the optical system by collecting data and calculating and reconstructing the indirect imaging method of the optical system. It has achieved remarkable results in many imaging fields. In the prior art, the point spread function (PSF) of the optical system with ideal imaging is a diffraction-limited Airy disk, while the shape of the point spread function of the aberration-containing system will change, which means that module manufacturers have the opportunity to mine the corresponding optical aberration information from the shape of the point spread function under the influence of aberration. The use of computational optics in production often requires the identification of each lens or module produced, and then the targeted assembly of each lens or module produced through a large number of images taken, so that each lens or module can obtain good imaging through computational optics.

[0007] There is an urgent need for an image processing method that can accurately reproduce lens aberrations with only a small amount of data. Without relying on interferometric measurement of the optical system, the powerful information processing capabilities of computational optics are used to recover the aberration of the lens' full field of view from the information graph of several frames of defocus curves recorded by the photosensitive chip, and generate defocus curves for each field of view. Summary of the invention

[0008] One object of the present application is to provide an optical system aberration inspection method, including providing a dot pattern array target plate, combining a lens and a photosensitive chip to form an imaging system and photographing the dot pattern array target plate, collecting at least two frames of dot pattern array images photographed by the imaging system, obtaining an aberration simulation model based on shape information of dot patterns in at least two frames of dot pattern array images photographed by the imaging system, and obtaining optical aberrations of the imaging system based on the aberration simulation model. The dot pattern array target plate covers a central field of view area and a peripheral field of view area of ​​the imaging system, the sizes and shapes of dot patterns in the same field of view area in the two frames of dot pattern array images photographed by the imaging system are different, and the axial distances between the photosensitive chip and the lens are different when photographing the two frames of dot pattern array images. The aberration of the full field of view of the lens can be restored from the two frames of recorded defocus intensity images through the aberration simulation model without relying on too many measurement conditions for the optical system, and a defocus curve can be generated.

[0009] One object of the present application is to provide a method for inspecting the aberration of an optical system. In the step of obtaining an aberration simulation model based on the shape information of a dot pattern in at least two frames of a dot pattern array image taken by an imaging system, the aberration simulation model can obtain a simulated dot pattern shape based on the Zernike coefficient superposition fitting parameters. The difference between the simulated dot pattern shape and the shape of the actually taken dot pattern is less than a threshold value, thereby being able to indicate that the aberration-bearing image output by the aberration simulation model is close to the image taken by the actual optical system, and the aberration simulation model has the ability to closely describe the aberrations of the actual optical system.

[0010] One purpose of the present application is to provide an optical system aberration inspection method, wherein the aberration simulation model is optimized by the following steps: J1. randomly generating a set of vectors containing at least 15 numbers, simulating the first 15 items of the Zernike coefficients, and simulating a wavefront with an aberration; J2. using Fresnel diffraction to simulate the simulated dot pattern image formed on the image plane in the form of a light field by the wavefront with an aberration; J3. calculating the error between the simulated dot pattern image and the dot pattern in the actual collected image, and obtaining an evaluation function; J4. randomly generating a set of perturbations and adding them to the current Zernike coefficient vector A new vector is formed, and the evaluation function and the evaluation function increment of the image corresponding to the new vector are calculated; J5. The Zernike coefficient is updated to obtain a simulated image with superimposed random disturbance; J6. The error between the simulated image with superimposed random disturbance and the point pattern of the actual collected image is calculated to obtain the evaluation function. If the evaluation function is less than a given value, the simulation model is optimized, and a set of approximate solutions that meet the conditions are output as the aberration index of the optical system. If not, the coefficient is updated and then jumps to step J2 to continue the next round of iteration until the evaluation function is less than a given threshold, and the simulation model is optimized. In this way, the aberration simulation model can be optimized, and the aberration image output by the aberration simulation model is close to the image taken by the actual optical system. The aberration simulation model has the ability to describe the aberration of the actual optical system.

[0011] Further objectives and advantages of the present application will be fully reflected through understanding of the following description and drawings.

[0012] These and other purposes, features and advantages of the present application are fully reflected in the following detailed description, drawings and claims. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 is a flow chart of an optical system aberration inspection method according to the present application;

[0014] Figure 2A is a schematic diagram of a dot pattern array target according to an embodiment of the present application;

[0015] Figure 2B is a schematic diagram of a dot pattern array target according to another embodiment of the present application;

[0016] Figure 3 is a schematic diagram of a testing system according to an embodiment of the present application;

[0017] Figure 4A is a schematic diagram of a plurality of dot pattern shapes according to an embodiment of the present application;

[0018] Figure 4B is a schematic diagram of the shape of a dot pattern and the distance in the direction of the optical axis according to an embodiment of the present application;

[0019] Figure 5 is a dot pattern shape with different aberrations according to an embodiment of the present application;

[0020] Figure 6 is a flow chart of a method for optimizing a simulation model according to another embodiment of the present application;

[0021] Figure 7 is a schematic diagram of a root mean square error (MSE) between a simulated image output by a simulation model according to another embodiment of the present application and an actual captured image;

[0022] Figure 8 is a schematic diagram of a simulation model according to an embodiment of the present application;

[0023] Fig. 9 is a schematic diagram of comparison between a simulation model and an actual captured image according to another embodiment of the present application; DETAILED DESCRIPTION

[0024] Below, the present application is further described in conjunction with specific implementation methods. It should be noted that, under the premise of no conflict, the various embodiments or technical features described below can be arbitrarily combined to form new embodiments.

[0025] The term "comprising" is open ended. As used in the appended claims, the term does not exclude additional structures or steps.

[0026] In the description of the present application, it should be noted that directional words, such as the terms "center", "lateral", "longitudinal", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", "clockwise", "counterclockwise", etc., indicating directions and positional relationships are based on the directions or positional relationships shown in the accompanying drawings, which are only for the convenience of narrating the present application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and cannot be understood as limiting the specific scope of protection of the present application.

[0027] It should be noted that the terms "first", "second", etc. in the description and claims of the present application are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence.

[0028] The terms "including" and "having" and any variations thereof in the specification and claims of this application are intended to cover non-exclusive inclusions. For example, a process, method, system, product or apparatus comprising a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to these processes, methods, products or apparatuses.

[0029] It should be noted that, as used in this application, the terms "substantially", "approximately" and similar terms are used as terms to express approximation rather than as terms to express degree, and are intended to account for the inherent deviations in measurements or calculations that would be recognized by a person of ordinary skill in the art.

[0030] In the description of this application, it should also be noted that, unless otherwise clearly specified and limited, the terms "set", "install", "connect", and "connect" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, a contact connection, or an indirect connection through an intermediate medium, and it can be the internal connection of two elements. For ordinary technicians in this field, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0031] "Configured to" various units, circuits, or other components may be described or recited as "configured to" perform one or more tasks. In such contexts, "configured to" is used to imply a structure (e.g., circuitry) by indicating that the unit / circuit / component includes the structure that performs the one or more tasks during operation. In addition, "configured to" may include general structures (e.g., general circuitry) manipulated by software and / or firmware to operate in a manner that is capable of performing the one or more tasks to be solved. "Configured to" may also include adjusting a manufacturing process (e.g., a semiconductor fabrication facility) to manufacture a device (e.g., an integrated circuit) suitable for implementing or performing one or more tasks.

[0032] The terms used in the description herein are only for describing specific embodiments and are not intended to be limiting. As used in the specification and the appended claims, the singular forms of "one", "a kind of" and "the" are intended to also cover the plural forms, unless the context clearly indicates otherwise. It will also be understood that the term "and / or" used herein refers to and covers any and all possible combinations of one or more items in the items listed in association. It will also be understood that the terms "including" and / or "comprising" when used in this specification specify the presence of stated features, integers, steps, operations, elements and / or parts, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, parts and / or their grouping.

[0033] As used herein, the term "if" may be interpreted to mean "when" or "upon" or "in response to determining" or "in response to detecting," depending on the context. Similarly, the phrases "if it is determined that" or "if [a stated condition or event] is detected" may be interpreted to mean "upon determining that" or "in response to determining that" or "upon detecting [a stated condition or event]" or "in response to detecting [a stated condition or event]," depending on the context.

[0034] Exemplary Image Processing Method Description

[0035] Attached Figure 1 The flowchart of the optical system aberration inspection method of the present application is shown, which includes the following steps:

[0036] S1. Provide a dot pattern array target plate;

[0037] S2. The lens and the photosensitive chip form an imaging system and shoot a dot pattern array target plate;

[0038] S3. Collecting at least two frames of dot pattern array images captured by the imaging system;

[0039] S4. Obtaining an aberration simulation model based on shape information of the dot pattern in the dot pattern array image captured by at least two frames of the imaging system;

[0040] S5. Obtain the optical aberration of the imaging system according to the aberration simulation model; wherein the dot pattern array target plate covers the central field of view area and the peripheral field of view area of ​​the imaging system, the size and shape of the dot patterns in the same field of view area in the two frames of dot pattern array images taken by the imaging system are different, and the axial distances between the photosensitive chip and the lens are different when the two frames of dot pattern array images are taken. It is possible to restore the aberration of the full field of view of the lens through the aberration simulation model from the two frames of recorded defocus intensity maps, and generate a defocus curve without relying on too many measurement conditions for the optical system.

[0041] Computational optics is an emerging research field. Computational optics is an organic combination of optical control and image processing. It mines the hidden information of the optical system by collecting data to calculate and reconstruct the indirect imaging method of the optical system. It has achieved remarkable results in many imaging fields. In the prior art, the point spread function (PSF pointspread function) of the optical system with ideal imaging is a diffraction-limited Airy disk, while the shape of the point spread function of the aberration-containing system will change, which means that module manufacturers have the opportunity to mine the corresponding optical aberration information from the shape of the point spread function under the influence of aberration. The use of computational optics in production often requires the identification of each lens or module produced, and then the targeted assembly of each lens or module produced through a large number of images taken, so as to achieve good imaging of each lens or module through computational optics. With the development of electronic technology, the current computing power has been greatly enhanced. If an image processing method that can accurately reproduce lens aberrations with only a small amount of data is implemented. Without relying on interferometric measurement of the optical system, the powerful information processing capabilities of computational optics are used to recover the aberration of the lens's full field of view from the information graph of several frames of defocus curves recorded by the photosensitive chip, and generate defocus curves for each field of view, which can greatly improve the measurement efficiency of the optical system, thereby improving the production efficiency of manufacturers. The basic principle of this application is to simulate the shape of the aberrated dots when the dots are actually photographed by calculating the superimposed aberrations, thereby obtaining the types and number of superimposed aberrations as the types and number of actual aberrations of the optical system.

[0042] Attached Figure 2A FIG. 1 is a schematic diagram of a dot pattern array target according to an embodiment of the present application. In step S1, a dot pattern array target is provided. The dot pattern array target can refer to FIG. Figure 2AIn this embodiment, the dot pattern array target uses a single dot pattern as the information source of the point spread function (PSF point spread function), and different test points are formed at different distances from the central field of view 1A to the outside to test the point spread function of different fields of view. This method can reflect the point source response information of many fields of view in the overall picture.

[0043] Points at the same distance outward from the central field of view 1A can be considered as the test point pattern within the same test field of view. Figure 2A Each test field of view is schematically divided in the figure. For example, in the embodiment, the central field of view 1A, 0.3 field of view 2A, 0.5 field of view 3A, and 0.8 field of view 4A all include at least one test point. The dot pattern at the same distance from the central field of view 1A can be set at the upper left, upper right, lower right, and lower left corners of the screen, or the dot pattern at the same distance from the central field of view 1A can be set at the left, right, upper, and lower sides of the screen. As long as the points at the same distance outward from the central field of view 1A can be considered as the test dot pattern in the same test field of view, by analogy, there are 4 dot patterns in the 0.3 field of view, 0.5 field of view, and 0.8 field of view test areas. This test method can be used to measure important fields of view in the screen. According to some specifications of this application, a point light source array target plate can be designed according to test requirements.

[0044] Attached Figure 2B FIG. 1 is a schematic diagram of a dot pattern array target according to another embodiment of the present invention. In step S1, a dot pattern array target is provided. The dot pattern array target can refer to FIG. Figure 2B In this embodiment, the dot pattern array target uses a pattern of multiple dots as the information source of the point spread function. Different test view areas are formed from points at almost the same distance from the central view field 1B to test the point spread function in different view areas. This method can reflect the point source response information of many view areas in the overall picture. Figure 2B , points at the same distance outward from the central field of view 1B can be considered as the test point pattern within the same test field of view. Figure 2BEach test field of view is schematically divided in the figure. The central field of view 1B, 0.3 field of view 2B, 0.5 field of view 3B, and 0.8 field of view 4C all include at least 4 test points. The dot pattern at the same distance from the central field of view 1B can be set at the upper left, upper right, lower right, and lower left corners of the screen, or the dot pattern at the same distance from the central field of view 1B can be set at the left, right, upper, and lower sides of the screen. As long as the points at a similar distance from the central field of view 1B to the outside can be considered as the test dot pattern in the same test field of view, four arrays of patterns consisting of four points in the 0.3 field of view, 0.5 field of view, and 0.8 field of view test areas can be obtained by analogy. This test method can be used to measure important field of view areas in the screen. Using multiple points as the dot matrix test area instead of the attached Figure 2A The method of using a single point in the image as a test area can increase the area of ​​the test field of view. In addition to being able to measure important field of view areas in the image, this test method can also achieve, for example, setting four test points in the central field of view, which can increase the sampling points and improve the accuracy. The method of multiple test points can also cover more pixels in the image and reduce the impact of individual endpoints. According to some specifications of this application, the number of dot patterns of the point light source array target plate can be designed according to the test requirements. In this application, multiple dot patterns can be used as a single test area to increase the statistical number of point responses, and multiple points can be evenly arranged as a single test area to increase the statistical number of point responses. Both implementation methods can meet the test requirements of the point spread function.

[0045] In step S2, the lens and the photosensitive chip are combined into an imaging system and the dot pattern array target is photographed. The imaging system can refer to the attached Figure 3 As shown, the present application proposes a test system for a dot pattern array target, which includes a light source 10, a dot pattern array target 20, a relay lens 30, an optical lens 40, a photosensitive chip 50 and a defocusing mechanism 60, wherein the light emitted by the light source 10 is patterned after passing through the dot pattern of the dot pattern array target 20 to be uniformly propagated outward, and the light source 10 is suitable for adjusting the color temperature, changing the wavelength of the light source and changing the brightness. Since the light source 10 can be adjusted in color temperature, change the wavelength of the light source and change the brightness, different test environments can be provided to adapt to different test requirements.

[0046] The relay lens 30 is disposed between the optical lens 40 and the dot pattern array target plate 20. The relay lens 30 is used to amplify the optical path of the light from the dot pattern array target plate 20 to the optical lens 40, and is used to extend the test distance of the test system to adapt to the change of the mid-to-long focus of the test distance.

[0047] The photosensitive chip 50 is arranged at the lower side of the optical lens 40, and is used to receive the light modulated by the optical lens 40, so that the photosensitive chip 50 can take pictures. The defocusing mechanism 60 is arranged at the lower side of the photosensitive chip 50, so as to achieve defocusing processing of the photosensitive chip 50, so that the photosensitive chip 50 is located at a suitable back focus position of the optical lens 40 to obtain a clear image.

[0048] The dot pattern array target provided in the present application has a dot pattern that adapts to the pixel size of the photosensitive chip in the imaging system. In the present embodiment, the size of a single dot pattern is exactly the diameter of 2 pixels of the photosensitive chip, so as to realize the calculation of 4-in-1 pixels in the photosensitive chip, thereby enabling the scaling of the picture, and facilitating the use of high-pixel photosensitive chips to synthesize low-pixel photosensitive chips, thereby improving the versatility of the photosensitive chips. There is no need to change the photosensitive chips, only by changing the image synthesis algorithm, to achieve simulation of photosensitive chips of various specifications. On the other hand, by adapting the pixel size of the photosensitive chip, it is possible to use photosensitive chips of universal specifications to adapt to various testing requirements.

[0049] It is worth mentioning that those skilled in the art should be able to imagine that the test system of the present application can also include optical point response information of the lens of any field of view, any wavelength, and any defocus position, with the characteristics of high precision, strong stability, strong compatibility, etc., and the working axis (not shown in the figure) that drives the light source to move can drive the dot pattern array target plate to move, and the working axis (not shown in the figure) can drive the light source to adjust with 0.1um precision in the direction parallel to the imaging plane. In addition, the working axis can also drive the light source to move with 0.1um precision in the optical axis direction, so that the light source can be driven to move with higher precision. The light source can be driven by the working axis to move along the optical axis direction of the lens, and can also be driven by the working axis to move in a direction perpendicular to the optical axis plane of the lens, so that a dot pattern array target plate with different near and far distances relative to the imaging system can be provided, and a dot pattern array target plate with different offsets relative to the imaging system can also be provided. By changing the axial distance between the target plate and the photosensitive chip, the defocus data of the optical system can be obtained.

[0050] The lens and the photosensitive chip are combined into an imaging system and the point light source array image information is captured. The captured point light source array image can be used as the basis for computational optical calculations. The information in the point light source array image can be mined by capturing it, thereby obtaining a comprehensive description of the optical system. In this application, only two frames of defocus intensity graphs can be captured, and the aberrations of each sub-field of view can be reconstructed based on this, and the corresponding defocus curve can be generated. This application can reduce data collection work, rely on powerful computing power, and obtain various types of aberrations and defocus data of the optical system by obtaining a fitted aberration simulation model.

[0051] In step S3, at least two frames of dot pattern array images captured by the imaging system are collected. The point spread function (PSF) in the prior art describes the response of the imaging system to the point source. In the case of a point light source, regardless of whether the light emitted by the target is coherent or incoherent, the light intensity distribution on the image plane can be expressed as:

[0052] h=|F{p(λdξ,λdη)}| 2 (1)

[0053] Among them, F{.} represents Fourier transform, and p is the generalized pupil function of the corresponding field of view. Regardless of the central field of view or the field of view with aberrations at the edge, the PSF already implies the aberration information of the imaging system. In order to accurately measure the PSF function, the present application designs a corresponding dot pattern array target plate according to the optical parameters of the camera module and the relay lens. For the shape and arrangement of the dots in the dot pattern array target plate, refer to the aforementioned content. In the present application, the dot pattern of the dot pattern array target plate covers the central field of view area and the peripheral field of view area of ​​the imaging system. The aforementioned content mentions that the dot pattern covers the central field of view area and the peripheral field of view area of ​​the imaging system to make a complete evaluation of the entire field of view of the optical system. The dot patterns in the central field of view area and the peripheral field of view area can be used to evaluate the performance of the overall optical system.

[0054] It is worth mentioning that the shape of a single dot circle in the dot pattern array target in this embodiment can be mapped on the imaging surface to a diameter of 2 unit pixels of the photosensitive chips. Further, it can be considered that the diameter of a single dot circle in the dot pattern array target is the unit pixel size of 2 photosensitive chips. During imaging, the closer the size of a single dot circle in the dot pattern array target is to the pixel size, the higher the information volume and accuracy of the dot array imaging. In this embodiment, the mapping size of the dot array circle on the imaging surface reaches a diameter of 2 pixels, so that the imaging information of the dot array circle has a field of view coverage angle, thereby meeting the test requirements.

[0055] In step S3, acquiring at least two frames of dot pattern array images captured by the imaging system further includes the following steps:

[0056] C1. Change the distance between the photosensitive chip and the lens in the direction of the optical axis, and record at least two point light source array images at different distances in the direction of the optical axis.

[0057] In order to achieve the goal of reconstructing the optical parameters of the imaging system with at least 2 frames of images, the inventor found that in the process of producing lenses or camera modules, 2 frames of images cannot obtain the absolute distance of the photosensitive chip relative to the main surface of the imaging module. Only by measuring the optical modulation transfer function data at different distances by axial scanning can the optical modulation transfer function test defocus curve be established. Therefore, the focal length of the optical system or the position of the chip (imaging surface) is crucial in calculating the free propagation of the light field of the optical system by calculating the optical calculation. This application adopts a tricky way to obtain the axial distance.

[0058] In the present application, when the dot pattern is used as the test reference, the inventor finds that the dot pattern has different sizes at different distances in the optical axis direction between the photosensitive chip and the lens. In this embodiment, the size of the point spread function of the central field of view can be used to determine the distance in the optical axis direction between the photosensitive chip and the lens, so that the data of the point spread function and the distances in different optical axis directions between the photosensitive chip and the lens can be obtained. Figure 4A It is a schematic diagram of the shapes of multiple dot patterns according to one embodiment of the application.

[0059] Attached Figure 4A In the figure, when the image of point A is in focus, the shape of the dot pattern is a smaller circle, and the image of point B shown in the figure is when the distance between the photosensitive chip and the lens in the optical axis direction is dx, and the shape of the dot pattern is a slightly larger circle. Correspondingly, the image of point B' shown in the figure is when the distance between the photosensitive chip and the lens in the optical axis direction is -dx, and the shape of the dot pattern is also a slightly larger circle. Except for the direction, the image of point B' is roughly the same as the image of point B. Similarly, the images of points C, D, E, F, and G are shown when they are dy, dz, du, dv, and dw away from the focal point A, respectively. Similarly, the images of points C', D', E', F', and G' are shown when they are in the negative directions of -dy, -dz, -du, -dv, and -dw. Through the attached Figure 4A In the invention, the inventors found that two frames of defocused images can be collected, the axial offset △z of the collection positions of the two frames can be combined, and the size of the dots at the two axial positions in the central field of view (r1, r2) can be recorded. The size of the dots of the ideal focal plane imaging (focus) can be calculated according to the imaging formula. Finally, the axial position of the current collected image is solved by (r1, r2, △z), and the distance between the current image plane and the focal plane is determined.

[0060] Attached Figure 4B is a schematic diagram of the dot pattern shape and the distance in the optical axis direction according to an embodiment of the present application, Figure 4BThis is a schematic diagram of calculating the axial offset based on the dot graphic information in the two frames of defocused images. Combined with the above content, this application proposes to collect two frames of defocused images, record the size of the dots at two axial positions in the central field of view, and calculate the axial offset of the two frames of image collection positions. As mentioned above, the size of the dots in the ideal focal plane image can be calculated based on the imaging magnification and the object size and recorded as r0. When the image is not taken at the in-focus position, the size of the dots at the two axial positions in the central field of view is (r1, r2). The corresponding geometric relationship is as follows: Figure 4B Finally, the axial position of the current captured image is solved by (r1, r2, △z), and the distance between the current image plane and the focal plane is determined. In this embodiment, the distance between the first plane and the lens pupil plane can be d=f+x, where x=(△z(r1-r0)) / (r2-r1). The distances of other similar planes can also be calculated in a similar manner.

[0061] In step S3, collecting at least two frames of point light source array images of the imaging system further includes the following steps:

[0062] C2. Determine the distance between the image plane and the focal plane according to the dot size in the point light source array image at two different optical axis direction distances. In step S3, the size and shape of the dot pattern in the same field of view area in the dot pattern array image captured by the two frames of imaging system are different. The size of the dot pattern in the same field of view area in the dot pattern array image captured by the two frames of imaging system is different, so as to obtain the distance between the image plane and the focal plane, thereby establishing the defocus data. In addition, the shape of the dot pattern in the same field of view area in the dot pattern array image captured by the two frames of imaging system is different, so as to obtain the aberration information in the image. Therefore, it is required that the size and shape of the dot pattern in the same field of view area in the dot pattern array image captured by the two frames of imaging system in this application are different.

[0063] Attached Figure 4A It illustrates that in this embodiment, the dot pattern in the central field of view simulates the size change of the imaging dot pattern at different distances in the optical axis direction from the photosensitive chip and the lens in the central field of view after parallel light is incident on the module. We can analyze the size change of the dot pattern in the whole process according to the post-focus section, focal plane, and pre-focus section. It can be seen from the figure that within a certain axial range, the size change of the dots is linear.

[0064] This application provides a method for judging the distance between the imaging plane and the focal plane within the focal length range of clear imaging, based on the distance offset of the two captured frames and the moving photosensitive chip and lens in different optical axis directions between the two frames, and based on the size of the dots at two axial positions in the central field of view in the two frames. The size of the dots imaged on the ideal focal plane can be calculated based on the imaging magnification and the object size and is recorded as r0. The corresponding geometric relationship is as follows: Figure 4B As shown. The axial position of the current captured image is solved from (r1, r2, Δz), and the distance between the current image plane and the focal plane is determined. The distance between the first plane and the lens pupil plane is d = f + x, where In this embodiment, the distance offset of the photosensitive chip and the lens in different optical axis directions is directionally and quantitatively controlled by the mechanism of the device, so the value of △z can be known, and r1 and r2 in the two frames of images belong to the sizes of circles in the two images respectively, so they can be determined from the sizes of the circles in the two images, and the circles in the imaging can be conveniently fitted by the existing technology and then identified according to the pixel size. In this way, the different distances in the optical axis direction of the photosensitive chip and the lens of the two images taken can be known.

[0065] In step S3, collecting at least two frames of point light source array images of the imaging system further includes the following steps:

[0066] C3. Establish the defocus data set for point spread function test based on the determined distance between the image plane and the focal plane

[0067] The data of the point spread function test is correlated with the axial distance between the corresponding photosensitive chip and the lens, so as to establish a data set to facilitate the subsequent establishment of an optical modulation transfer function test defocus curve.

[0068] Attached Figure 5 The diagram illustrates the ideal dot pattern in the present embodiment and various actual test dot patterns caused by the different numbers of low-order or high-order aberrations or the different types of aberrations in the actual test process. In the figure, A represents the ideal dot pattern as a circle, B represents the irregular circle actually captured due to aberrations, C represents the irregular circle actually captured due to aberrations, D represents the irregular circle actually captured due to aberrations, E represents the irregular circle actually captured due to aberrations, F represents the irregular circle actually captured due to aberrations, G represents the irregular circle actually captured due to aberrations, H represents the irregular circle actually captured due to aberrations, and I represents the irregular circle actually captured due to aberrations.

[0069] The inventors found that in the formula h=|F{p(λdξ,λdη)}| 2 ------(1) Under different aberrations, the intensity point spread function distribution is different. Figure 5 It can be clearly seen that the shape distribution of the dot patterns corresponding to different aberrations is quite different. Therefore, the inventors have found that a model that can mine the corresponding aberration information can be trained from the different forms of the dot patterns by model fitting.

[0070] In step S4, obtaining the aberration simulation model according to the shape information of the dot pattern in at least two frames of the dot pattern array images taken by the imaging system also includes the following steps:

[0071] D1. Reconstruct the optical parameters of the imaging system;

[0072] From formula (1), we can see that different aberrations correspond to different point spread function distributions. From the perspective of complex factors, the final imaging shape of the dot pattern may be a superposition of low-order to high-order aberrations. Figure 5 The simulation results of point spread functions generated by different aberrations on the focal plane are shown. We can clearly see that the distribution of point spread functions corresponding to different aberrations is quite different. This means that the corresponding aberration information can be mined from the different forms of the point patterns actually obtained.

[0073] The imaging information of the dot pattern obtained by the capture is actually determined by the point spread function (PSF) of the lens test. Therefore, the imaging information of the dot pattern obtained by the capture in the present application can obtain the corresponding point spread function (PSF). In the aforementioned process, at least two frames of images are used to obtain the axial distance, thereby establishing a data relationship between the point spread function (PSF) and the defocus.

[0074] For an imaging system, its point spread function (PSF) and optical transfer function are a pair of Fourier transforms, and both can be calculated from the generalized pupil function. The pupil function of an ideal lens imaging system without aberration is as follows:

[0075]

[0076] Where a is the size of the pupil, and the wavefront at the exit pupil is a converging Gaussian wavefront. For a system with aberrations, the generalized pupil function can be expressed as:

[0077]

[0078] Wavefront distortion can usually be represented by a linear combination of a series of orthogonal polynomials, such as Zernike polynomials, KL polynomials, etc. Among them, Zernike polynomials have become a widely used polynomial basis function due to their orthogonality in the circular domain. In addition, the low-order modes of Zernike polynomials correspond to Seidel aberrations in optical design. Therefore, in our scheme, Zernike polynomials are used to describe the wavefront distortion at the pupil:

[0079]

[0080] a iis the coefficient of the polynomial. By accurately obtaining the coefficients of the Zernike polynomial, the optical aberration of the imaging system can be described. After the parameters of the optical system are determined using the central field of view in the second step, it is not necessary to retake the image when processing the aberration of the edge field of view, and the two previously collected defocus images are still used. This is mainly because the number of pixels occupied by the defocus PSF on the detector is greater than that at the focal plane, which adds more useful information, so it is beneficial for solving aberrations. In addition, the wavefront change caused by defocus is a known quantity added artificially, which can be quantitatively given by the formula with the defocus distance, and will not hinder the aberration solution process. The phase part of the pupil function can be corrected as follows:

[0081] Where Δθ is the defocus difference. When the image of the central field of view is used to solve the axial distance, the defocus amount has been determined. So far, the relationship between the defocus PSF and the aberration has been completely established according to formula (1).

[0082] In order to solve the aberration of different fields of view, a uniformly distributed dot pattern array is produced in this application. The response of each point on the object plane on the image plane is the performance of the point spread function of the corresponding field of view. Figure 5 The specific performance of the dot pattern array during the actual shooting process is shown. The point spread function distribution diagrams of several randomly selected different fields of view show that the point spread function shapes of different fields of view are obviously different. This different shape is caused by different aberrations in different fields of view, which confirms that the performance of the point spread function obtained by actual shooting can be simulated through computational optics in the future.

[0083] When processing different field of view areas, the PSF of the corresponding field of view is first selected, and then an optimization algorithm is used to find a set of coefficients so that the point spread function generated by the corresponding aberration under the experimental parameters is highly similar to the h actually collected, then the module aberration at this time can be described by this set of coefficients. The aberration is optimized and solved using the stochastic parallel gradient descent algorithm (SPGD). The optimization method of the simulation model of another embodiment of the present application includes the following steps:

[0084] J1. Randomly generate a set of vectors containing at least 15 numbers, simulate the first 15 terms of the Zernike coefficients, and simulate a wavefront with aberration;

[0085] J2. Using Fresnel diffraction to simulate the aberrated wavefront to form a simulated dot pattern image on the image plane in the form of a light field;

[0086] J3. Calculate the error between the simulated dot pattern image and the dot pattern in the actual captured image, and obtain the evaluation function;

[0087] J4. Randomly generate a set of perturbations, add them to the current Zernike coefficient vector to form a new vector, and calculate the evaluation function and evaluation function increment of the image corresponding to the new vector;

[0088] J5. Update the Zernike coefficients to obtain a simulation image with superimposed random disturbances;

[0089] J6. Calculate the error between the simulated image with superimposed random disturbance and the point pattern of the actual collected image, and obtain the evaluation function. If the evaluation function is less than a given value, optimize the simulation model and output a set of approximate solutions that meet the conditions as the aberration index of the optical system. If not, update the coefficients and jump to step J2 to continue the next round of iteration until the evaluation function is less than a given threshold and the simulation model is optimized.

[0090] Step J1 is to use the current powerful computing power to substitute random values ​​and use the Zernike coefficients to produce a wavefront with aberrations. In step J2, the generated wavefront with aberrations is passed through the light field to produce an image with aberrations based on a dot pattern. In step J3, the shape difference between the simulated dot pattern image and the actually shot dot pattern image is verified, and the difference value between the two is determined. In step J4, a set of disturbances is randomly generated to generate more aberrated wavefronts again. In step J5, the Zernike coefficients are updated to obtain a simulated image with superimposed random disturbances. The final goal is to confirm in step J6 that when the difference between the generated image and the actual image is less than a threshold, the simulation model is considered to be optimized, and the optimized simulation model is close to the actual image. The aberrations of the optimized simulation model can be considered to be the aberrations of the actual optical system.

[0091] Figure 6 is a flow chart of a method for optimizing a simulation model according to another embodiment of the present application, which is basically the flow of the above-mentioned method for optimizing a simulation model. The more detailed flow is as follows:

[0092] Step 1: Randomly generate a set of vectors containing at least 15 numbers, simulate the first 15 terms of the Zernike coefficients, and simulate a wavefront with aberration;

[0093] Step 2: Using Fresnel diffraction to simulate the aberrated wavefront to form a simulated dot pattern image on the image plane in the form of a light field;

[0094] Step 3: Calculate the error between the simulated dot pattern image and the dot pattern in the actual captured image, and obtain the evaluation function J(u N );

[0095] Step 4: Randomly generate a set of perturbations and generate the image again

[0096] Step 5: Calculate the performance evaluation function J(un+δun)

[0097] Step 6: Evaluation function increment: δJn=J(un+δun)-J(un)

[0098] Step5: Update the Zernike coefficients,

[0099] Step 6: Determine if the evaluation function J(u N ) is less than the given value, the program terminates and outputs a set of approximate solutions that meet the conditions; if not, it updates the coefficients and jumps to step 2 to continue the next round of iteration.

[0100] It is easier to understand that Figure 6 The right half of the figure also shows the comparison process between the actual image and the simulated image. Even if the simulated image is not similar to the actual image at the beginning, through hundreds of thousands or millions of random tests, a simulated image similar to the actual image can always be generated and the simulation model can be optimized.

[0101] Figure 7 This is a schematic diagram of the root mean square error (MSE) between a simulated image output by a simulation model according to another embodiment of the present application and an actual captured image. The ordinate is the difference value and the abscissa is the number of training times. It can be seen that a difference value of approximately 5% can be achieved above 1200 times.

[0102] Figure 8 It is a schematic diagram of a simulation model according to an embodiment of the present application. In the figure, A1 is the central area, and is a dot with basically no aberration, A2 is the 0.3 field of view area, and the dot shape changes greatly, A3 is the 0.5 field of view area, and the dot shape also changes, A4 is the 0.7 field of view area, and the dot shape changes even more. By identifying the dots in the actual captured image and classifying them into the simulation model that has been established after identification, the aberration analysis of various fields of view can be realized.

[0103] In addition to using the error MSE of the two images, the evaluation function can also calculate the structural similarity SSIM of the two images. The former decreases with the iterative error, while the latter is the opposite.

[0104] After completing the previous content, the optical parameters of the current system and the wave aberration information of each field of view have been obtained. The light intensity distribution of the infinite point light source at different axial defocus positions, that is, PSF, can be calculated by formula (1), and the axial defocus amount is reflected in. PSF and optical transfer function OTF are a pair of Fourier transforms, and the modulation transfer function MTF is the amplitude of OTF:

[0105]

[0106] In simple terms, the MTF curve reflects the system's ability to transmit different spatial frequency components during the imaging process, with the horizontal axis being the spatial frequency. The defocus curve is the distribution of the MTF values ​​at different positions along the axis at a fixed spatial frequency. Figure 6 The numerical simulation results of aberration and defocus curve, from left to right are the coefficients of Zernike polynomial, wavefront aberration, and the generated defocus curve.

[0107] Experimentally, according to the above three steps (1) calculation of system parameters, (2) optimization algorithm to solve aberrations, (3) and calculation of defocus curve, data collection was also carried out on the real lens module. Fig. 9 It is a schematic diagram of the comparison between the simulation model and the actual captured image according to another embodiment of the present application, which is the processing result of a random module in the 0.2 field of view. (a) The left side of the figure is the curve of the change of the similarity evaluation function with the number of iterations, (a) The right side of the figure is the two-dimensional distribution of the wavefront aberration solved by the algorithm, Figure (b) is the Zernike polynomial coefficient corresponding to the aberration, (c) Figure is the two defocus PSFs of the actual shooting, and (d) Figure is the defocus PSF corresponding to the solved aberration. The higher the similarity between (c) and (d), the closer the aberration found by the algorithm is to the aberration of the real module. The optimization process is to update the coefficients of the Zernike polynomial through continuous iteration so that the corresponding defocus PSF is constantly close to Figure (c). Figure (e) is the defocus curve calculated by the aberration in Figure (a). For this module, the result of our actual measurement with SFR is that the optimal position of the center field of view is 440code, the optimal position of S / T in the 0.2 field of view is (422, 435)code, and 1code corresponds to 0.64um. The optimal center position we calculated through computational optics is 440 code, and the optimal position of the 0.2 field of view is (422, 440) code, which is highly consistent with the results of SFR measurement.

[0108] By obtaining the aberration of the optical system, we can divide the test standards of the batch of camera modules into qualified products, excellent products, limit products and defective products, and obtain the normal distribution parameters of the batch of camera modules according to the test standards. In this embodiment, the distribution of qualified products, excellent products, limit products and defective products can meet most product standard scenarios, so that the normal distribution curve for product distribution can be fitted and the parameters of the normal distribution can be obtained.

[0109] The point spread function (PSF) model is tested on qualified products, excellent products, limit products and defective products respectively, and the point spread function (PSF) models of qualified products, excellent products, limit products and defective products are obtained respectively. In this embodiment, the distribution of qualified products, excellent products, limit products and defective products can meet most product standard scenarios, and the point spread function (PSF) models of qualified products, excellent products, limit products and defective products are obtained for differentiated matching.

[0110] The point spread functions (PSFs) of qualified products, excellent products, limit products, and defective products are added to the standard image set, and convolution fusion is used to form simulated real-life images of qualified products, excellent products, limit products, and defective products, so that the differentiation of the training set used for deep learning is more obvious, and the gradient distribution of the training set is more obvious.

[0111] In one embodiment of the present application, it is possible to ensure that the simulated image is close to the actual shooting of the camera module, and to ensure that the input data for deep learning are images that are close to the color temperature, wavelength, brightness, etc. of the light source in the actual environment. The input image is always guaranteed to be close to the actual shooting of the product, thereby improving the accuracy of the model. On the other hand, the output image is based on a standard image set, so that a complete method from the input image to the output image can be established.

[0112] On the other hand, after obtaining the product point spread function (PSF) model, the point spread function (PSF) model itself has a certain parameter adjustment space, such as using the method of introducing factors in equal proportion to increase or reduce the influence of the point spread function (PSF) model, so that the point spread function (PSF) model of the gradient product can be obtained, and the influence of the point spread function (PSF) model can be modified by modifying the factors according to the proportional model of the gradient product, thereby affecting the output of the training set. Obviously, this factor can be achieved by relying on the normal distribution parameters of the camera module.

[0113] The present application also proposes a device for inspecting the aberration of an optical system using any one of the above-mentioned embodiments, and by completing the method of any one of the above-mentioned embodiments, the inspection of the optical system is achieved.

[0114] The present application also proposes a camera module produced using the optical system aberration inspection method of any one of the above-mentioned embodiments. The module produced by the optical system aberration inspection method of the present application has a higher yield and higher production efficiency.

[0115] The above describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions only describe the principles of the present invention. The present invention may be subject to various changes and improvements without departing from the spirit and scope of the present invention. These changes and improvements fall within the scope of the present invention to be protected. The scope of protection claimed by the present invention is defined by the attached claims and their equivalents.

Claims

1. A method for inspecting optical system aberrations, characterized in that: The following steps are involved: S1. Provide a dot pattern array target plate; S2. The lens and the photosensitive chip form an imaging system and shoot a dot pattern array target plate; S3. Collecting at least two frames of dot pattern array images captured by the imaging system; S4. Obtaining an aberration simulation model based on shape information of the dot pattern in the dot pattern array image captured by at least two frames of the imaging system; S5. Obtaining optical aberrations of the imaging system according to the aberration simulation model; In step S2, the dot pattern of the dot pattern array target plate covers the central field of view area and the peripheral field of view area of ​​the imaging system. In step S3, the sizes and shapes of the dot patterns in the same field of view in the two frames of dot pattern array images captured by the imaging system are different. In step S3, the axial distances between the photosensitive chip and the lens are different when capturing two frames of dot pattern array images.

2. The optical system aberration inspection method according to claim 1, wherein: In step S4, the aberration simulation model is obtained according to the shape information of the dot pattern in at least two frames of dot pattern array images captured by the imaging system: The aberration simulation model can obtain a simulated point pattern shape according to Zernike coefficient superposition fitting parameters, and the difference between the simulated point pattern shape and the shape of the point pattern actually photographed is less than a threshold value.

3. The optical system aberration inspection method according to claim 1, wherein: The aberration simulation model is established by the following steps: J1. Randomly generate a set of vectors containing at least 15 numbers, simulate the first 15 terms of the Zernike coefficients, and simulate a wavefront with aberration; J2. Using Fresnel diffraction to simulate the aberrated wavefront to form a simulated dot pattern image on the image plane in the form of a light field; J3. Calculate the error between the simulated dot pattern image and the dot pattern in the actual captured image, and obtain the evaluation function; J4. Randomly generate a set of perturbations, add them to the current Zernike coefficient vector to form a new vector, and calculate the evaluation function and evaluation function increment of the image corresponding to the new vector; J5. Update the Zernike coefficients to obtain a simulation image with superimposed random disturbances; J6. Calculate the error between the simulated image with superimposed random disturbance and the point pattern of the actual collected image, and obtain the evaluation function. If the evaluation function is less than a given value, optimize the simulation model and output a set of approximate solutions that meet the conditions as the aberration index of the optical system. If not, update the coefficients and jump to step J2 to continue the next round of iteration until the evaluation function is less than a given threshold and the simulation model is optimized.

4. The optical system aberration inspection method according to claim 3, wherein: The step of acquiring at least two frames of dot pattern array images captured by the imaging system also includes the following steps: C1. Changing the distance between the photosensitive chip and the lens in the optical axis direction, and recording at least two dot pattern array images at different distances in the optical axis direction; C2. determining the distance between the image plane and the focal plane according to the sizes of dots at the same position in the dot pattern array images at two different distances in the optical axis direction; C3. According to the determined distance between the image plane and the focal plane, a point spread function and a defocus data set are established.

5. The optical system aberration inspection method according to claim 4, wherein: In the step of determining the distance between the image plane and the focal plane according to the sizes of dots at the same position in the dot pattern array images at two different distances in the optical axis direction: The size of the dot imaged on the ideal focal plane is calculated based on the imaging magnification and the object size and is recorded as r0. When the image captured is not in focus, the sizes of the dots at the two axial positions in the central field of view are (r1, r2). The axial position of the current captured image is solved by (r1, r2, △z), and the distance between the current image plane and the focal plane is determined.

6. The optical system aberration inspection method according to claim 5, wherein the imaging system includes a light source, a dot pattern array target, a relay lens, an optical lens, a photosensitive chip and a defocusing mechanism, the light emitted by the light source is patterned after passing through the dot pattern of the dot pattern array target so as to propagate outward evenly, the relay lens is arranged between the optical lens and the dot pattern array target, the relay lens is used to amplify the optical path of the light from the dot pattern array target to the optical lens, the photosensitive chip is arranged on the lower side of the optical lens, the photosensitive chip is used to receive the light modulated by the optical lens so that the photosensitive chip can take pictures, and the defocusing mechanism is arranged on the lower side of the photosensitive chip.

7. The optical system aberration inspection method according to claim 6, wherein: In the aberration simulation model, a simulated point pattern shape is obtained by superimposing fitting parameters according to the Zernike coefficients. The shape of the simulated point pattern and the shape of the point pattern actually captured have a difference less than a threshold. The shape difference is the root mean square difference between the shape of the calculated simulated point pattern and the shape of the point pattern of the actual captured image. The threshold is 5%.

8. The optical system aberration inspection method according to claim 7, wherein: According to the aberration distribution of each field of view of the aberration simulation model, the corresponding defocus curve of each field of view is obtained.

9. An apparatus using the optical system aberration inspection method according to any one of claims 1 to 8.

10. A camera module produced using the optical system aberration inspection method according to any one of claims 1 to 8.

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