Method for predicting the calibratability of lens assembly to be assembled and camera module assembly method

Abstract

The present invention provides a method for predicting the calibratability of a lens assembly to be assembled, comprising: 1) obtaining measured defocus curves of multiple identification patterns on the central field of view and the edge field of view of the lens assembly to be assembled based on a test optical path; 2) obtaining the peak position of the defocus curve of each identification pattern; 3) determining the compensation parameters for focus correction of the lens assembly; 4) assuming that the astigmatism, field curvature, and peak value of the lens assembly itself remain unchanged, calculating the peak position of the simulated defocus curve based on each measured defocus curve according to the compensation parameters; and 5) calculating the clarity on each field of view axis corresponding to each identification pattern under the determined compensation parameters, and then judging whether the imaging quality of the lens assembly to be assembled meets the standards. The present application also provides a camera module assembly method based on the above-mentioned calibratability prediction method. The present application can improve the focus assembly efficiency of the photosensitive component and the lens assembly.

Description

Technical Field

[0001] The present invention relates to the technical field of camera modules, and in particular to a method for predicting the calibratability of a lens assembly to be assembled and a camera module assembly method. Background Art

[0002] With the popularization of mobile electronic devices, the relevant technologies of camera modules used in mobile electronic devices to help users obtain images (such as videos or images) have been rapidly developed and advanced, and in recent years, camera modules have been widely used in many fields such as medical care, security, industrial production, etc. In recent years, users have higher and higher requirements for the imaging quality of camera modules, and accordingly, the demand for camera modules with high imaging quality has also increased. In addition, in order to meet the diverse needs of taking pictures, more and more electronic terminals are equipped with array camera modules. Array camera modules include at least two camera modules, and some even have as many as four or five. This has led to a surge in the quality and quantity requirements of camera modules, posing a challenge to existing production capacity.

[0003] A camera module usually includes a photosensitive component and a lens component. The photosensitive component includes a photosensitive chip, sometimes also called an image sensor. The image sensor is attached to a circuit board, and the circuit board, the image sensor, the lens mount and other components mounted on the circuit board together constitute the photosensitive component. The lens assembly usually includes an optical lens. The current common method of assembling camera modules is usually to prefabricate the lens assembly and the photosensitive component separately, and then assemble the two (for example, attach) together. During the assembly process, the relative position of the lens assembly and the photosensitive component, especially the relative position of the optical axis of the optical lens and the photosensitive element, has a decisive influence on the imaging quality of the camera module, and the two need to be precisely positioned relative to each other. In low-pixel camera modules, mechanical alignment can be used to achieve assembly and fixation of the two, but the positioning accuracy of this method is not high, which may have a negative impact on the imaging quality, and therefore is often difficult to use in high-end product series of camera modules.

[0004] In order to achieve precise positioning of the optical component and the photosensitive component, active calibration is used to adjust the relative position of the photosensitive component and the lens component and assemble them, which will help improve the imaging quality of the camera module product. Specifically, one of the optical components or photosensitive components of the module (i.e., the camera module) can be used as a reference to actively adjust the other component so that the normal of the photosensitive chip is parallel to the optical axis of the lens assembly, and the center of the photosensitive chip coincides with the optical center of the lens assembly, so that the four corners and the central field of view of the module can achieve the best imaging clarity, thereby maximizing the imaging quality of the module and improving the imaging level. More specifically, one assembly method is that the photosensitive component to be assembled can be fixed in a suitable position, the photosensitive chip is lit, and the mechanical device clamps the lens assembly and adjusts it in six degrees of freedom. Another assembly method is to clamp the lens assembly and place the photosensitive component on an adjustment platform capable of moving with multiple degrees of freedom. By running the defocus curve, the relative position of the lens assembly with respect to the photosensitive component is adjusted to ensure that the center of the image is clear and the resolution of the four corners of the picture is uniform. The lens assembly is then fixed (for example, bonded) to the photosensitive component in the appropriate position. Assembly methods based on active calibration can effectively improve the imaging quality of the product. However, the current conventional active calibration method completes the assembly of the optical components and photosensitive components of a single module through multiple continuous steps. This production process is time-consuming and inefficient, and it is difficult to increase the UPH, making it difficult to adapt to the task of producing a large number of modules in a short period of time.

[0005] Specifically, during the active calibration process, it is often necessary to measure the defocus curve of the lens assembly to be assembled by moving the lens (for example, by moving the lens through a motor) or a photosensitive chip, determine the actual tilt of the lens assembly based on the defocus curve, and then use the clamping jaws that clamp the lens assembly to adjust the tilt of the lens assembly (for example, to level the tilt). However, there is a certain systematic error in the mechanical adjustment of the clamping jaws, so after the adjustment, it is necessary to move the lens or photosensitive chip again to measure the defocus curve of the lens assembly, so as to calculate the actual tilt of the lens assembly after adjustment based on the measured optical imaging data. When the actual tilt angle still does not meet the standard, it is necessary to use the clamping jaws for adjustment again and run the defocus curve again until the actual tilt angle of the lens assembly to be assembled is within a preset range (for example, within ±0.01°). Since running the defocus curve requires moving the lens or photosensitive chip multiple times and measuring the resolution of the optical system at multiple positions, each run of the defocus curve is time-consuming, resulting in a decrease in production efficiency. In particular, some lens assemblies undergoing assembly cannot meet image quality requirements through active calibration due to inherent defects (e.g., excessive manufacturing tolerances of their optical components or excessive assembly tolerances during the assembly of the individual optical components). For these lens assemblies (referred to as "NG lens assemblies"—lens assemblies that fail to meet pre-defined image quality requirements are referred to as "NG lens assemblies"), active calibration consumes a significant amount of time, severely impacting production efficiency.

[0006] To improve assembly efficiency, photosensitive components are often calibrated with adhesive so they can be bonded to the lens assembly immediately after calibration. However, if the active calibration ultimately reveals that the lens assembly being assembled is defective, it will be difficult to promptly replace it with a new lens assembly and assemble it with the glued photosensitive component, causing the potentially good photosensitive component to be scrapped as well. This increased scrap rate leads to higher costs. Summary of the Invention

[0007] The purpose of the present invention is to overcome the shortcomings of the prior art and provide a solution that can overcome the above-mentioned technical problems, quickly and accurately predict the calibrability of the lens assembly to be assembled, thereby improving the assembly efficiency of the camera module and reducing production costs.

[0008] To solve the above technical problems, the present invention provides a method for predicting the calibratability of a lens assembly to be assembled, comprising: 1) placing the lens assembly to be assembled in a test light path, and obtaining measured defocus curves of multiple identification patterns on the central field of view and the edge field of view of the lens assembly to be assembled; 2) obtaining the peak position of the defocus curve of each identification pattern; 3) determining compensation parameters for focus correction of the lens assembly; wherein the compensation parameters include parameters characterizing the posture and / or position adjustment of the lens assembly; 4) assuming that the astigmatism, field curvature, and peak value of the lens assembly itself remain unchanged, calculating the peak position of a simulated defocus curve based on each measured defocus curve according to the determined compensation parameters; wherein the simulated defocus curve is: a defocus curve after the inclination angle and axial position of the lens assembly are adjusted according to the determined compensation parameters; and 5) based on the peak position of the simulated defocus curve calculated in step 4), calculating the clarity on each field of view axis corresponding to each identification pattern under the determined compensation parameters, and then judging whether the imaging quality of the lens assembly to be assembled meets the standard.

[0009] Wherein, in the step 2), the measured defocus curves of the multiple identification patterns are fitted respectively, and then the peak position of the fitted defocus curve of each identification pattern is obtained; the step 3) also includes: determining the focus type and focus mode of the lens assembly for focus correction, wherein the focus type includes S focus, T focus or average focus; the focus type includes center focus or edge focus; in the step 4), the simulated defocus curve is: under the determined focus type and focus mode, the defocus curve after the inclination and axial position of the lens assembly are adjusted according to the determined compensation parameters. It should be noted that in some embodiments, when the measured defocus curve has high measurement accuracy, step 2) can be omitted, that is, the peak position and the corresponding peak value can be directly obtained by measuring the defocus curve. The peak value and peak position will be used for calculations in subsequent steps, which are used to simulate the posture adjustment (i.e., inclination adjustment) of the lens assembly and calculate the simulated defocus curve after the posture adjustment. In some embodiments, the lens assembly can be further simulated to adjust the axial position, and a simulated defocus curve after the axial position is adjusted or after both the inclination angle and the axial position are adjusted is calculated. The calculation basis may include: the original measured defocus curve of each identification pattern, and the peak position of the defocus curve after fitting of each identification pattern. The peak position represents the position where the corresponding identification pattern is the clearest image. Based on the position and the compensation parameter, the position where the imaging of each identification pattern is the clearest after the virtual correction of the lens assembly can be searched, that is, the peak position of the simulated defocus curve after the virtual correction. In this way, without actually moving the lens assembly, the defocus curve when the lens assembly is assumed to be corrected in posture (or posture and axial position) according to the compensation parameters can be estimated through simulation calculation, thereby helping to predict the calibrability of the lens assembly.

[0010] In which, in the measured defocus curve and the simulated defocus curve, the resolution is characterized by an SFR value; and the step 1) further includes: when the blade angle of the target plate of the test light path is not compatible with the SFR algorithm, rotating the blade angle based on affine transformation to adapt to the SFR algorithm, and then measuring the measured defocus curve.

[0011] Wherein, the step 1) includes the following sub-steps: 11) first using affine transformation to obtain the rotation matrix of the identification pattern, the rotation matrix can rotate the original blade angle of the current target plate to the target blade angle, and the target blade angle is within the angle range corresponding to the SFR algorithm; 12) then based on the rotation matrix, converting the original target plate image obtained by the test light path into a target plate image with the target blade angle; and 13) applying the SFR algorithm to the target plate image with the target blade angle to obtain the SFR value, and then obtain the measured defocus curve.

[0012] Wherein, in the step 1), the edge field of view is represented by four identification patterns located at the upper left, upper right, lower left, and lower right.

[0013] Among them, in the step 2), the method for obtaining the peak position of the fitted defocus curve includes the following steps: 21) finding the maximum value in the measured defocus curve and the axial position corresponding to the maximum value; 22) then using an Nth-order polynomial to fit the measured defocus curve to obtain the fitted defocus curve, where N is an integer; 23) then finding each maximum point of the fitted defocus curve and the axial position corresponding to the maximum point; and 24) when the difference between a maximum value in the fitted defocus curve and the maximum value of the measured defocus curve is less than the maximum value of the measured defocus curve multiplied by a preset threshold ratio, then directly determining the maximum value as the peak value of the fitted defocus curve, and obtaining the peak position of the fitted defocus curve.

[0014] Among them, the method for obtaining the peak position of the fitted defocus curve also includes: when step 24) cannot determine the peak value of the fitted defocus curve, executing step: 25) fitting the measured defocus curve again with a K-order polynomial to obtain the defocus curve after quadratic fitting, and finally obtaining the peak value and peak position based on the defocus curve after quadratic fitting; wherein K is less than N, N is 6, 7 or 8, and K is 4 or 5.

[0015] Wherein, in the step 24), when the defocus curve after fitting has multiple peaks, the average of the multiple peaks is calculated according to the centroid method to convert the multiple peak positions into a single peak position; when the defocus curve after fitting has only one peak or no peak is found, execute step: 25) fit the measured defocus curve again with a K-order polynomial to obtain the defocus curve after quadratic fitting, and finally obtain the peak value and peak position based on the defocus curve after quadratic fitting; wherein K is less than N, N is 6, 7 or 8, and K is 4 or 5.

[0016] Wherein, the step 25) also includes: selecting measured data points within the neighborhood of the maximum value from the measured defocus curve, and then performing the quadratic fitting on the measured defocus curve using a K-order polynomial based on the measured data points within the neighborhood of the maximum value.

[0017] Among them, in the step 3), the image plane tilt angle is obtained according to the clear imaging position corresponding to each identification pattern of the edge field of view, and then the tilt compensation amount and compensation direction for adjusting the image plane to a horizontal state are calculated, and the tilt compensation amount and compensation direction are set as the compensation parameters.

[0018] Wherein, in step 3), the compensation parameters are set according to an artificial intelligence algorithm.

[0019] Wherein, in the step 3), a human-computer interaction interface is provided and the user is prompted to input the compensation parameters.

[0020] Wherein, in the step 3), the compensation parameters include the axial position compensation amount and compensation direction, and the inclination compensation amount and compensation direction.

[0021] Wherein, in step 1), the edge field of view is characterized by four identification patterns located at the upper left, upper right, lower left, and lower right corners; step 4) includes the following sub-steps: 41) assuming that the tilt angle adjustment of the lens assembly does not change the astigmatism, field curvature, and peak resolution of the optical test system, based on the following four conditional component equations; the four conditions are:

[0022] Condition 1: (pLT + pRT) - (pLB + pRB) = W * tanθ y

[0023] Condition 2: (pLT + pLB) - (pRT + pRB) = H*tanθ x

[0024] Condition 3: (pLT+pRT+pLB+pRB) / 4=pCT+CF

[0025] Condition 4: For the corner with the smallest resolution difference from the central field of view among the four corners, its peak position remains unchanged during the virtual correction process; where CF represents field curvature, pLT, pRT, pLB, and pRB represent the peak positions corresponding to the upper left, upper right, lower left, and lower right marker patterns, respectively, pCT represents the peak position corresponding to the central field of view, W and H represent the distance between the centers of adjacent marker patterns in the edge field of view in the x-axis direction and the y-axis direction, respectively, and θ x and θ y It is the tilt angle component on the xoz plane and the tilt angle component on the yoz plane of the inclination angle of the virtually corrected lens assembly relative to the photosensitive surface of the photosensitive chip.

[0026] Among them, in the step 5), the resolution value of each focus type of each identification pattern in different fields of view is obtained based on the measured defocus curve of step 1) and the virtual focus position of the photosensitive chip, and the virtual focus position is the peak position of the simulated defocus curve calculated in step 4).

[0027] Wherein, in the step 5), the measured defocus curve is interpolated, and then the resolution value of each focus type of each identification pattern in different fields of view is obtained based on the virtual focus position.

[0028] Wherein, in the step 5), the measured defocus curve is interpolated using a cubic spline interpolation algorithm.

[0029] Wherein, in said step 1), in said test light path, image data is sensed by a standard photosensitive chip or a photosensitive chip in a photosensitive component to be assembled.

[0030] According to another aspect of the present application, a camera module assembly method is also provided, which includes: step A) based on any of the aforementioned calibratability prediction methods of the lens assembly to be assembled, predicting whether the current lens assembly to be assembled is calibratable; if it is not calibratable, abandoning the lens assembly to be assembled; if it is calibratable, executing step B); step B) assembling the lens assembly to be assembled that has passed the calibratability prediction with the photosensitive assembly to obtain a complete camera module.

[0031] Wherein, in the step B), the assembly is implemented based on active calibration. During the active calibration, the actual posture and position of the lens assembly to be assembled are pre-adjusted using the compensation parameters obtained in the step A).

[0032] Compared with the prior art, this application has at least one of the following technical effects:

[0033] 1. This application can quickly and accurately predict the calibratability of the lens assembly to be assembled.

[0034] 2. In some embodiments of this application, uncalibrated lens assemblies that cannot be calibrated can be discarded based on pre-determined results, thereby avoiding the need to actively calibrate these uncalibrated lens assemblies (or perform other methods of actual focus correction) and occupying valuable production capacity in the focus assembly process. Therefore, this application can improve the focus assembly efficiency of the photosensitive component and lens assembly.

[0035] 3. In some embodiments of the present application, an NG lens assembly that cannot be calibrated can be abandoned based on the prediction results, thereby avoiding the waste of photosensitive components caused by the use of NG lens assemblies, thereby reducing production costs.

[0036] 4. In some embodiments of the present application, the target blade angle rotation can be simulated based on affine transformation, so that the SFR algorithm can be applied to more types of targets with different blade angles, with strong scalability and compatibility.

[0037] 5. In some embodiments of this application, a fast and stable axis value simulation algorithm is provided. By simulating the module axis values ​​in advance, the product is judged to be OK. OK products can adjust the tilt angle (i.e., adjust the TILT) based on the predicted results, and bad products are intercepted in advance, thereby improving the production efficiency of the camera module. In this case, OK products can be understood as qualified semi-finished products, and bad products can be understood as unqualified semi-finished products. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 A flow chart showing a method for predicting the calibratability of a lens assembly to be assembled according to one embodiment of the present application is shown;

[0039] Figure 2 A schematic diagram of a target plate used in one embodiment of the present application is shown;

[0040] Figure 3-5 The measured defocus curve, the fitted defocus curve, and the quadratically fitted defocus curve in some embodiments of the present application are shown;

[0041] Figure 6 The figure shows the defocus curves under different focus types and focus modes in one embodiment of the present application;

[0042] Figure 7 Shown based on Figure 6 The peak position of the simulated defocus curve under center focus calculated from the defocus curve;

[0043] Figure 8 Shown based on Figure 6 The defocus curve is calculated based on the simulated defocus curve under center focus and after introducing 0.03° tilt interference;

[0044] Figure 9 Shown based on Figure 6 The defocus curve is calculated from the simulated defocus curve under center focus and with 5 micron position interference introduced;

[0045] Figure 10 shows a defocus curve before interpolation in one embodiment of the present application;

[0046] Figure 11 shows the defocus curve after interpolation in one embodiment of the present application;

[0047] Figure 12 An example of a target plate in one embodiment of the present application is shown;

[0048] Figure 13 A schematic diagram showing the principle of rotating a single test block in the present application is shown. DETAILED DESCRIPTION

[0049] For a better understanding of the present application, various aspects of the present application will be described in more detail with reference to the accompanying drawings. It should be understood that these detailed descriptions are merely descriptions of exemplary embodiments of the present application and are not intended to limit the scope of the present application in any way. Throughout the specification, the same reference numerals refer to the same elements. The expression "and / or" includes any and all combinations of one or more of the associated listed items.

[0050] It should be noted that in this specification, the expressions of first, second, etc. are only used to distinguish one feature from another feature, and do not represent any limitation on the features. Therefore, without departing from the teaching of this application, the first subject discussed below can also be referred to as the second subject.

[0051] In the accompanying drawings, the thickness, size and shape of objects have been slightly exaggerated for ease of explanation. The accompanying drawings are only examples and are not drawn strictly to scale.

[0052] It should also be understood that the terms "comprises," "including," "having," "includes," and / or "comprising," when used in this specification, indicate the presence of the stated features, integers, steps, operations, elements, and / or parts, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, parts, and / or combinations thereof. In addition, when expressions such as "at least one of..." appear after a list of listed features, they modify the entire listed features rather than modifying the individual elements in the list. In addition, when describing embodiments of the present application, "may" is used to mean "one or more embodiments of the present application." And, the term "exemplary" is intended to refer to an example or illustration.

[0053] As used herein, the terms "substantially," "approximately," and similar terms are used as terms of approximation, not degree, and are intended to account for the inherent variations in measurements or calculations that would be recognized by those having ordinary skill in the art.

[0054] Unless otherwise defined, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by those skilled in the art to which this application belongs. It should also be understood that terms (such as those defined in commonly used dictionaries) should be interpreted as having a meaning consistent with their meaning in the context of the relevant technology and will not be interpreted in an idealized or overly formal sense unless expressly defined as such herein.

[0055] It should be noted that, unless there is any conflict, the embodiments and features in the embodiments of this application can be combined with each other.

[0056] The present invention will be further described below with reference to the accompanying drawings and specific embodiments.

[0057] The present application relates to a method for assembling a lens assembly and a photosensitive assembly into a camera module and a method for pre-calibrating the calibrability of a lens assembly to be assembled in the assembly of a camera module. In one embodiment, the lens assembly may include a motor and an optical lens, and the optical lens may be installed in a carrier of the motor, and the carrier may be moved in a controlled manner relative to the motor housing to achieve various functions such as autofocus, optical zoom, or optical image stabilization. The photosensitive assembly generally includes a photosensitive chip and a circuit board, and may also be referred to as a circuit board assembly. The motor base of the lens assembly may be attached to the surface of the circuit board, thereby assembling the lens assembly and the photosensitive assembly into a complete camera module, which may have various functions such as autofocus, optical zoom, or optical image stabilization. In another embodiment, the lens assembly may not have a motor, that is, the optical lens alone constitutes the lens assembly. The bottom surface of the optical lens may be bonded to the surface of the circuit board as an attachment surface, thereby assembling into a complete fixed-focus camera module. For ease of description, the attachment surface of the lens assembly may be referred to as the second bonding surface herein. In some embodiments, the photosensitive component may further include a filter component, which may include a lens holder and a filter mounted on the lens holder. The lens holder may be a molded lens holder directly formed on the surface of the circuit board, or may be pre-formed and then mounted on the circuit board. The bottom surface of the lens holder may be mounted on the surface of the circuit board. The top surface of the lens holder serves as an attachment surface (or referred to as a first bonding surface) for bonding with the lens assembly. That is, the top surface of the lens holder is bonded to the motor base or the bottom surface of the optical lens to form a complete camera module. In this article, calibratability refers to the ability to make the resolution of the lens assembly meet the standard by adjusting the position and posture. If it is determined that the resolution of the lens assembly can be made to meet the standard by adjusting the position and posture, the lens assembly to be assembled is considered to be calibrable; if it is determined that the resolution of the lens assembly cannot be made to meet the standard by adjusting the position and posture, the lens assembly to be assembled is considered to be non-calibrable.

[0058] The present application is further described below with reference to the accompanying drawings and specific embodiments.

[0059] Figure 1 The flowchart of the method for predicting the calibratability of the lens assembly to be assembled according to one embodiment of the present application is shown. Figure 1 The calibratability prediction method of this embodiment includes the following steps S1-S5.

[0060] In step S1, a lens assembly to be assembled is placed in a test light path to obtain a measured defocus curve for the lens assembly to be assembled. The test light path includes a target plate as a photographic object and a standard photosensitive chip. The standard photosensitive chip is used to receive imaging data from the target plate (specifically, multiple identification patterns on the target plate representing a specific field of view) of the lens assembly to be assembled, thereby obtaining resolution data that can characterize the imaging quality in the corresponding field of view. This resolution data can be, for example, an SFR value. In other embodiments, this resolution data can be, for example, an MTF value or a TV-Line value, or other parameters that can characterize resolution. A defocus curve is obtained by measuring the resolution data of each identification pattern on the target plate corresponding to each axial distance by varying the axial distance between the optical lens and the photosensitive chip in the test light path, and then plotting a curve for each identification pattern based on this measured data. In other words, each identification pattern can be measured to produce a corresponding measured defocus curve. In the measured defocus curve, the horizontal axis can represent the axial distance, and the vertical axis can represent the resolution data, such as the SFR value. Axial distance refers to the distance in the direction of the optical axis. In this embodiment, the lens assembly may be a motor lens assembly, that is, the lens assembly is provided with a motor, and the motor is at least suitable for moving the optical lens in the direction of the optical axis. In this way, when measuring the defocus curve, the axial distance between the optical lens and the photosensitive chip in the test light path can be changed by the motor. In another embodiment, the axial distance between the optical lens and the photosensitive chip in the test light path can also be changed by moving the standard photosensitive chip, thereby obtaining the measured defocus curve. In yet another embodiment, the axial distance between the optical lens and the photosensitive chip in the test light path can also be changed by simultaneously moving the motor and the standard photosensitive chip, thereby obtaining the measured defocus curve.

[0061] It should be noted that in step S1 , any measured defocus curve is actually composed of a plurality of discrete points, wherein each discrete point represents an axial distance value and its corresponding measured resolution data. Figure 2 The schematic diagram of the target plate used in one embodiment of the present application is shown. In this embodiment, each identification pattern represents at least two fields of view, the edge field of view and the center field of view, wherein the edge field of view can be, for example, 0.8 fields of view (of course, the edge field of view can also be other values). The edge field of view can be represented by four identification patterns, which are the upper left, upper right, lower left, and lower right identifications. In the test light path, the surface of the target plate is roughly perpendicular to the optical axis of the optical lens. When using the SFR algorithm to calculate the resolution, the identification pattern needs to have a certain inclination angle (refer to Figure 2), which is usually referred to as the knife edge angle in the industry. In this embodiment, each identification pattern can obtain a different defocus curve based on the focus type. In this article, the focus type refers to S-direction focus, T-direction focus or average focus (the average focus of the S direction and T direction). The S direction refers to the sagittal direction (that is, the radial direction of the lens), and the T direction refers to the meridian direction (that is, the tangent direction of the lens). S-direction focus refers to measuring the resolution in the S direction (such as SFR value) during the defocus process, and T-direction focus refers to measuring the resolution in the T direction (such as SFR value) during the defocus process. Average focus refers to measuring the resolution in the S direction and T direction during the defocus process and taking the average of the two. For each focus type, each identification pattern can measure a measured defocus curve. In this way, based on multiple identification patterns of the edge field of view and the central field of view, multiple measured defocus curves can be obtained. These measured defocus curves can be used to simulate partial posture and position adjustments of the lens assembly to be tested through numerical calculations in subsequent steps, so that the calibratability of the lens assembly to be tested can be predicted without having to perform actual posture and position adjustments.

[0062] Step S2, fitting the measured defocus curve to obtain the peak position of the fitted defocus curve. Since the measured data may be interfered with by various factors (such as environmental factors, tolerances of the measurement system, and manufacturing tolerances and assembly tolerances of the lens assembly itself), sometimes the measured defocus curve will have abnormal conditions such as multiple peaks, one-sidedness, and jitter. Therefore, in order to improve the accuracy of the pre-judgment of the lens assembly to be tested, the measured defocus curve can be fitted to obtain a functional analytical expression of the defocus curve, and then its peak position and peak value can be analyzed for use in subsequent steps.

[0063] Specifically, in step S1, during the defocusing process (i.e., the process of changing the axial distance between the optical lens and the photosensitive chip in the test light path), due to the light source environment, object distance, motor dynamic TILT, equipment vibration, etc., the defocus curve we obtain has multiple peaks, one side, jitter and other phenomena, which sometimes seriously affect the calculation of the peak position of the defocus curve. To address the above problems, this step adopts targeted curve fitting technology to accurately fit the true peak position of the curve and improve the module's defocus accuracy.

[0064] In this embodiment, the curve fitting technology includes: a) first finding the maximum value in the measured defocus curve and its corresponding index value. The index value can represent the axial distance (the axial direction is the z-axis direction, that is, the height direction of the optical lens). In this embodiment, the defocus curve moves the photosensitive chip in a certain step size to obtain the resolution value of the image captured by the photosensitive chip at a series of discrete axial positions, wherein the index value refers to the position of the photosensitive chip after each step. The maximum value here refers to the value of each peak position in the measured defocus curve. b) Then use the Nth-order polynomial to fit the curve to obtain the fitted defocus curve. c) Then find the maximum point of the defocus curve after fitting (that is, the maximum value and the maximum index). d) Then determine whether the peak value can be directly calculated based on the defocus curve after fitting. Among them, if the difference between a certain maximum value in the defocus curve after fitting and the maximum value in the measured defocus curve is less than the quotient of the maximum value in the measured defocus curve and the preset difference judgment coefficient M (the quotient of the maximum value in the measured defocus curve and the preset difference judgment coefficient M is the maximum value in the measured defocus curve multiplied by the preset threshold ratio), then the maximum value is directly determined to be the maximum value (i.e., peak value) of the defocus curve after fitting, and the axial position corresponding to the peak value is the peak position, and then step S3 is executed; if no peak meeting the above conditions is found in the defocus curve after fitting, sub-step e) is continued to search for the peak value and peak position. In this step, the difference judgment coefficient M is an empirical value. If M is too small, the fluctuation will be mistaken for the peak; if M is too large, the peak of multiple peaks will be missed; both situations may cause the final peak position to be inaccurately fitted. Therefore, in general, the value range of M is 6 to 12. In this embodiment, fluctuation refers to the fluctuation of the measured data caused by measurement tolerance in the actual measurement optical path and measurement system. Multimodality refers to the situation where the defocus curve has multiple peaks due to reasons inherent to the lens assembly (e.g., due to manufacturing or assembly tolerances). In step d), fluctuations introduced by measurement tolerances can be filtered out based on a threshold (this threshold can be associated with the measured maximum value, for example, the quotient of the maximum value in the measured defocus curve and a preset difference determination coefficient M), while retaining the multimodality in the defocus curve. Furthermore, sub-step e) is as follows.

[0065] e) When the peak value cannot be calculated directly, the curve is fitted with a K-order polynomial to obtain a defocus curve after quadratic fitting, and finally the peak value and peak position are obtained based on the defocus curve after quadratic fitting. Wherein K is less than N. In this embodiment, N can be 6-8, for example, and K can be 4-5, for example. After the peak value and peak position are determined, the astigmatism and field curvature can be further calculated. Both N and K are integers. In particular, the quadratic fitting can be a K-order polynomial fitting based on the measured data in the neighborhood of the maximum value position of the measured defocus curve. The neighborhood range can be, for example, the maximum value position of the measured data plus three measured data points before and after, and the measured data points far from the peak position can be discarded. Quadratic fitting based on the measured data in the neighborhood of the measured maximum value position can better restore the curve near the peak position, so that the obtained peak position and peak value are more accurate.

[0066] Figure 3-5 The measured defocus curve, the fitted defocus curve and the quadratic fitted defocus curve in some embodiments of the present application are shown. Figure 3 It shows that the measured defocus curve presents a unilateral shape. Figure 4 It shows that the measured defocus curve has fluctuations. Figure 5 The figure shows that the measured defocus curve has a multi-peak shape. Figure 3-5 In the figure, the measured defocus curve is called the original defocus curve, the fitted defocus curve is called the high-order fitting curve, and the quadratic fitting defocus curve adopts peak curve fitting, that is, a K-order polynomial fitting is performed based on the peak position neighborhood range of the measured data.

[0067] Furthermore, in one embodiment of the present application, in sub-step d), when the fitted defocus curve has multiple peaks, the average value and average position of all valid peaks may be calculated (e.g., using the centroid method), and the average value and average position may be used as the peak value and peak position of the resolution curve of the lens assembly. In other words, multiple peak positions are converted into a single peak position, and multiple peaks are converted into a single peak, to facilitate data processing in subsequent steps.

[0068] Furthermore, in one embodiment of the present application, in the sub-step d), when the number of effective peaks in the fitted defocus curve (the defocus curve after high-order fitting) is not greater than 1 (actually there are two cases, the number of effective peak points of the high-order fitting curve is equal to 1 or equal to 0), peak curve fitting is performed, and the peak value and peak position are obtained based on the peak curve obtained by fitting. The difference between this embodiment and the embodiment in the previous text is that in the sub-step d), when there is no multi-peak phenomenon in the fitted defocus curve, step e) is further performed, and the final peak position and peak value are obtained based on the peak curve. Here, if the difference between a certain maximum value in the fitted defocus curve and the maximum value in the measured defocus curve is less than the quotient of the maximum value in the measured defocus curve and the preset difference judgment coefficient M, the maximum value point is an effective peak point (or simply an effective peak).

[0069] Step S3, determines the focus type, focus mode and compensation parameters for focus correction of the lens assembly. The focus type is as described in step S1. The focus mode refers to selecting center focus or edge focus. The edge focus can be, for example, upper left, upper right, lower left, or lower right focus, or it can be focused based on the average of the four. The center focus is focused based on the identification pattern of the center field of view. The compensation parameters refer to the parameters for adjusting the posture and position of the lens assembly. Posture adjustment is tilt adjustment, which can also be called tilt adjustment. In this step, position adjustment mainly refers to the adjustment of the axial position, that is, compensation of the axial position. It should be noted that the compensation parameters determined in this step are simulated adjustment amounts used for numerical calculations, rather than actual adjustments to the lens assembly and its test optical path.

[0070] Figure 6 The defocus curves under different focus types and focus modes in one embodiment of the present application are shown. These defocus curves can be obtained by fitting the corresponding measured data, to distinguish them from the simulated defocus curves obtained after virtual correction below. Figure 6 The defocus curve is marked as the defocus curve before simulation. Figure 6 In this step, the peak positions of the measured defocus curves marked on the upper left, upper right, lower left, and lower right correspond to the clearest imaging positions marked on the upper left, upper right, lower left, and lower right, respectively (referring to the axial position, which is the axial position in the image). Figure 6 (where Pos is the horizontal coordinate in the middle). Based on these four positions, the image plane tilt angle (i.e., image plane tilt) for clear imaging can be derived, and thus the optical axis tilt angle (optical axis tilt) of the lens assembly can be derived. In this embodiment, the compensation target is set to level the image plane tilt, that is, to adjust the optical axis to a vertical state. The tilt adjustment amount in the compensation parameters should be consistent with the image plane tilt angle derived from the peak position of the measured defocus curve, and the tilt adjustment direction in the compensation parameters should be opposite to the image plane tilt direction.

[0071] On the other hand, during the actual measurement of the lens assembly to be assembled in step S1, there may be axial deviation due to various factors (such as light source environment, object distance, motor dynamic tilt, equipment vibration, etc.), that is, the peak position of the measured defocus curve may not reflect the optimal focus position of the lens assembly to be assembled. Therefore, in this step, axial position compensation can also be used as one of the compensation parameters. The compensation amount and compensation direction of the axial position compensation can be determined manually or identified and set by the device based on artificial intelligence (AI).

[0072] Similarly, in another embodiment of the present application, the compensation amount and compensation direction of the tilt compensation of the lens assembly can also be determined manually, or identified and set by the device based on artificial intelligence (AI).

[0073] Furthermore, in each of the above embodiments, a human-computer interaction interface can be provided in the control center of the device, which includes an interactive interface graphic (such as an input box and its prompt text information) that prompts the user to input the inclination compensation amount and compensation direction, and an interactive interface graphic (such as an input box and its prompt text information) that prompts the user to input the axial position compensation amount and compensation direction, so as to realize manual input of compensation parameters.

[0074] In step S4, assuming that the astigmatism, field curvature, and peak value of the lens assembly itself remain unchanged (note that the peak value unchanged here refers to the unchanged peak resolution of each defocus curve, not the unchanged peak position), the peak position of a simulated defocus curve is calculated based on each measured defocus curve according to the determined focus type, focus mode, and compensation parameters. The simulated defocus curve refers to a defocus curve detected by the imaging system for detecting the lens assembly to be assembled, after adjusting the inclination and axial position of the lens assembly according to the determined compensation parameters under the determined focus type and focus mode. In this step, the simulated defocus curve is a virtual curve. Specifically, assuming that the inclination and axial position of the lens assembly to be assembled are adjusted according to the determined compensation parameters, and then a defocus run is performed for actual measurement, a corresponding defocus curve should be obtained. However, in this step, actual defocus run is not performed, but rather the lens assembly posture and position adjustment is simulated through numerical calculation (this numerical calculation-based simulation process is sometimes referred to as virtual correction in this article to distinguish it from the actual adjustment process of the lens assembly posture and position). Furthermore, numerical calculations can directly determine the peak position of the simulated defocus curve after virtual correction. This simulated defocus curve simulates the actual defocus curve, and its peak position also simulates the peak position of the actual defocus curve. This peak position represents the clearest imaging position under the set focus type and focus mode (note that the peak position refers to the axial position), which is also the virtual correction focus position.

[0075] Specifically, the method for calculating field curvature and astigmatism is as follows:

[0076] CF s =(pLT s +pRT s +pLB s +pRB s ) / 4-pCT s ;

[0077] CF t =(pLT t +pRT t +pLB t +pRB t ) / 4-pCT t ;

[0078] CF=(pLT+pRT+pLB+pRB) / 4-pCT;

[0079] Where CF represents field curvature, pLT, pRT, pLB, and pRB represent the peak positions corresponding to the upper left, upper right, lower left, and lower right identification patterns, respectively, pCT represents the peak position corresponding to the central field of view, the subscript s represents focus in the S direction, the subscript t represents focus in the T direction, and no subscript represents average focus.

[0080] XS LT =pLT s -pLT t ;

[0081] XS LB =pLB s -pLB t ;

[0082] XS RT =pRT s -pRT t ;

[0083] XS RB =pRB s -pRB t ;

[0084] Among them, XS represents astigmatism, XS LT 、XS RT 、XS LB 、XS RB represents the astigmatism of the upper left, upper right, lower left, and lower right marking patterns respectively, pLT, pRT, pLB, and pRB represent the peak positions corresponding to the upper left, upper right, lower left, and lower right marking patterns respectively, the subscript s represents the focus in the S direction, and the subscript t represents the focus in the T direction.

[0085] Furthermore, the method for calculating the focus position after virtual correction is as follows: Assume that (pLT, pRT, pLB, pRB) are the peak positions corresponding to the upper left, upper right, lower left, and lower right marking patterns after virtual correction. The virtual correction satisfies the following conditions:

[0086] Condition 1: (pLT + pRT) - (pLB + pRB) = W * tanθ y

[0087] Condition 2: (pLT + pLB) - (pRT + pRB) = H*tanθ x

[0088] Condition 3: (pLT+pRT+pLB+pRB) / 4=pCT+CF

[0089] Condition 4: For the corner with the smallest resolution difference from the center of the visual field (i.e., one of the upper left, upper right, lower left, or lower right corners), its peak position remains unchanged during the virtual correction process. That is, the peak position of this corner remains unchanged before and after virtual correction. The resolution difference between the upper left corner and the center of the visual field can be expressed as abs(pLT - (pCT + CF)). Here, abs() represents the absolute value. The resolution differences between the other three corners and the center of the visual field can be expressed using similar expressions. For example, the resolution difference between the upper right corner and the center of the visual field can be expressed as abs(pRT - (pCT + CF)), the resolution difference between the lower left corner and the center of the visual field can be expressed as abs(pLB - (pCT + CF)), and the resolution difference between the lower right corner and the center of the visual field can be expressed as abs(pRB - (pCT + CF)).

[0090] Wherein, W and H are the spacing between the centers of the adjacent identification patterns in the edge field of view in the x-axis direction and the spacing in the y-axis direction, respectively. Herein, the x-axis and the y-axis are two coordinate axes perpendicular to the z-axis, and the x-axis and the y-axis are perpendicular to each other. (θ x ,θ y ) is the angle compensation value, that is, the tilt angle of the lens assembly after virtual correction (i.e., virtual tilt adjustment). Here, the tilt angle of the lens assembly refers to the tilt angle of the lens assembly relative to the photosensitive surface of the photosensitive chip. When the photosensitive chip is a standard photosensitive chip, the horizontal plane can be regarded as the photosensitive surface. θ x and θ y where is the tilt angle component of the lens assembly in the xoz plane and the tilt angle component in the yoz plane, respectively. pCT is the peak position of the original central field of view. In this embodiment, it is assumed that the peak position of the central field of view remains unchanged during the virtual correction process. CF is the field curvature, which also remains unchanged during the virtual correction process. Astigmatism also remains unchanged during the virtual correction process.

[0091] By using computer numerical simulation technology, the four corner peak positions (pLT, pRT, pLB, pRB) after virtual correction that simultaneously meet the above four conditions can be solved.

[0092] Figure 7 Shown based on Figure 6 The peak position of the simulated defocus curve under center focus calculated from the defocus curve. Figure 8 Shown based on Figure 6 The simulated defocus curve calculated from the defocus curve under center focus and after introducing a 0.03° tilt interference. The introduction of the 0.03° tilt interference can represent adjusting the tilt angle of the lens assembly by 0.03°.

[0093] Furthermore, in a modified embodiment, when the virtual correction includes the adjustment of the axial position, a field curvature compensation value (Δ S , Δ T ), the field curvature is corrected as follows:

[0094] CF S =CF s +Δ S CF T =CF t +Δ T CF=CF+(Δ S +Δ T ) / 2

[0095] Then the corrected S direction field curvature CF S , Corrected T-direction field curvature CF T The values ​​of the corrected average field curvature CF are substituted into the equations constructed based on the above four conditions to solve the virtual corrected focus position, that is, the virtual corrected four corner peak positions (pLT, pRT, pLB, pRB). In this modified embodiment, the field curvature compensation value (Δ S , Δ T ) can be obtained based on prior knowledge. For example, when the virtual correction includes adjusting the axial position of the lens assembly, the software system can display a human-computer interaction interface to prompt the user to input the adjustment amount of the axial position and the corresponding field curvature compensation value (Δ S , Δ T Then, according to the adjustment amount of the axial position input by the operator and the corresponding field curvature compensation value (Δ S , Δ T ), solve the equations constructed based on the above four conditions to obtain the four corner peak positions (pLT, pRT, pLB, pRB) after virtual correction.

[0096] Figure 9 Shown based on Figure 6The simulated defocus curve under center focus and with 5 micron position interference is calculated from the defocus curve. The 5 micron position interference can represent that the lens assembly or the photosensitive chip is virtually moved 5 microns along the z-axis. It should be noted that in the above-mentioned deformed embodiment, when the virtual correction includes the adjustment of the axial position, the introduced field curvature compensation value is only used for calculation, and it does not mean that the field curvature of the lens assembly to be tested itself has changed. The field curvature of the lens assembly itself is determined by the physical factors of the lens assembly itself, such as the lens shape, material, surface shape, assembly tolerance between lenses, etc., and usually will not change due to the adjustment of the position and posture (i.e., tilt) of the lens assembly. Similarly, the astigmatism and resolution peak of the lens assembly itself will not change due to the adjustment of the position and posture of the lens assembly.

[0097] Step S5, based on the peak position of the simulated defocus curve calculated in step S4 (i.e., the virtual corrected focus position), calculate the sharpness (also referred to as axis value) on each field of view axis corresponding to each marker pattern under the determined compensation parameters. The axis value is the resolution value of each field of view axis corresponding to the virtual corrected focus position. The resolution value of each focus type for each marker pattern can be obtained based on the measured defocus curve in step S1. After obtaining the axis values ​​of each field of view axis, these axis values ​​can be used to determine whether the imaging quality of the lens assembly to be assembled meets the requirements.

[0098] Furthermore, in one embodiment of the present application, each measured defocus curve of step S1 can be interpolated first, and then the corresponding axis value of each measured defocus curve can be found according to the virtual corrected focus point obtained in step S4. The interpolation can be implemented based on a cubic spline interpolation algorithm, for example. Since in step S1, what is obtained during the defocusing process is actually a series of discrete data based on a certain step length, and in step S5, the axial position corresponding to each field of view axis may be between the axial positions corresponding to the two discrete data. If the distance between the axial positions corresponding to the two discrete data is large (that is, the step length during defocusing is large), the error of the obtained axis value will increase. Figure 10 FIG. 1 shows a defocus curve before interpolation in one embodiment of the present application, wherein the peak position of the simulated defocus curve obtained after simulation is marked. Figure 11 FIG1 shows the defocus curve after interpolation in one embodiment of the present application. The peak position of the simulated defocus curve obtained after simulation is also marked. Figure 10 and Figure 11In this embodiment, a relatively sparse discrete data set can be converted into a relatively dense discrete data set through interpolation processing. After interpolation, the axial position spacing between adjacent discrete data is reduced, thereby reducing or eliminating the error in the axis value calculation. On the other hand, since the interpolation algorithm can be used to reduce the error in the axis value calculation, when a larger step size is used in step S1 to complete the defocus test (i.e., running defocus), the axis value calculation error can still be smaller, thereby ensuring the accuracy and stability of the calibratability prediction of the lens assembly to be assembled. At the same time, since the defocus test time of step S1 can be shortened, it also helps to improve the speed of the calibratability prediction of the lens assembly to be assembled.

[0099] Furthermore, in one embodiment of the present application, the resolution is characterized by an SFR value. However, different test items and different projects have different requirements for the test target, so there are different SFR algorithms for different targets. As customer requirements continue to change, there is a demand for calculating the SFR value under different target blade angles, and the traditional SFR algorithm generally has a test target blade angle of 3-8 degrees. If it exceeds this range, the calculation accuracy of the SFR algorithm will be affected. In response to the above problem, this embodiment proposes an SFR algorithm based on angle rotation. The algorithm introduces an angle rotation step on the basis of the traditional SFR algorithm. Without changing the clarity of the test block, the blade angle of the test block is rotated to a range of 3-8 degrees, and then the traditional SFR algorithm is used to perform SFR calculation, thereby obtaining the SFR value under any blade angle. Figure 12An example of a target plate in one embodiment of the present application is shown. Specifically, the identification pattern in the target plate is generally roughly in the shape of a rectangular block, so it can be called a test block. And there is a certain inclination angle between the edges of these test blocks and the edges of the target plate, and this inclination angle is generally referred to as the blade angle of the target plate. In this embodiment, the blade angles of the test blocks at the four corners of the target plate are not within the range of 3-8 degrees. Assuming that the software system carried by the assembly equipment adopts the traditional SFR algorithm, and its corresponding blade angle is 3-8 degrees, then the test image of this type of target plate (also referred to as the target plate image) cannot be directly used for SFR calculation using the traditional SFR algorithm. To address this issue, in this embodiment, step S1 may include: first, using an affine transformation to determine a test block rotation matrix. This rotation matrix can rotate the original blade angle of the test block of the target in the test light path to a target blade angle, wherein the target blade angle is within the angle range corresponding to the SFR algorithm; then, based on the rotation matrix, converting the original target image into a target image with the target blade angle (in this step, cubic polynomial interpolation can be used to interpolate the rotated coordinates to obtain the rotated image coordinates); and finally, applying the SFR algorithm to the target image with the target blade angle to obtain the SFR value. Typically, the target is a transparent hard plastic sheet with a special pattern printed on it. A light box is placed above the target paper to illuminate the target downward. Then, an imaging system (which can be composed of a lens assembly to be assembled and a standard photosensitive chip for testing) is placed below the target paper to photograph the target paper from bottom to top. In this embodiment, the target plate is actually stationary. After the imaging system captures the target plate paper, the captured image information is adjusted by an algorithm so that the original blade angle of the test block on the original target plate paper is rotated to the target blade angle that is compatible with the SFR algorithm. Therefore, this embodiment can adapt to different target plate blade angles, improve the compatibility of the SFR algorithm, and improve accuracy. It should be noted that in this embodiment, for the target plate image captured in the test light path, each test block can be rotated individually so that it has a target blade angle (for example, a blade angle within 3-8 degrees). The rotation matrix can be obtained based on the principle of affine transformation. Figure 13 The schematic diagram of the principle of rotating a single test block in this application is shown. Figure 13For a single test block, the coordinate system transformation can be performed first, and the coordinate origin o can be moved to the center of the test block. Then, each position point on the test block is rotated by an angle θ based on the new coordinate origin. The rotation can be achieved based on an affine transformation, that is, the original coordinates of each position point are mapped to the new coordinates after the affine transformation. Finally, the rotated single test block is merged into the new target image. The new target image here is the new target image after each test block is rotated so that the blade angle meets the requirements of the SFR algorithm. During the rotation process, the only change of each position point is the position coordinate, and the value of the image data of the position point (such as the value representing the brightness, the value representing the color, and other various values ​​of the image data) remains unchanged.

[0100] Furthermore, in the above embodiment, the central field of view is represented by an identification pattern (i.e., a test block) located in the center, and the edge field of view is represented by four identification patterns located in the upper left, upper right, lower left, and lower right, respectively. However, it should be noted that in some modified embodiments of the present application, the edge field of view can also be represented by a larger number of identification patterns. For example, the edge field of view can be represented by eight identification patterns evenly distributed on the field of view ring. In addition, in some other modified embodiments of the present application, the edge field of view can also be represented by four identification patterns located in the upper, lower, left, and right directions, respectively. In some further modified embodiments of the present application, more fields of view can be set on the target plate, for example, the central field of view, the 0.6 field of view, and the 0.8 field of view can be set at the same time.

[0101] Furthermore, in some embodiments of the present application, the method for predicting the calibratability of a lens assembly to be assembled can also be directly applied during active calibration. In this embodiment, in step S1, the standard photosensitive chip in the test light path is replaced by the photosensitive chip in the photosensitive assembly to be assembled. In other words, the resolution data measured during the defocusing process in this embodiment is the image resolution data output by the actual photosensitive assembly to be assembled. In this embodiment, steps S2-S5 can be consistent with the previous embodiment and will not be repeated here.

[0102] Furthermore, according to an embodiment of the present application, a method for assembling a camera module based on the above-mentioned calibratability prediction method is provided, and the assembly method includes:

[0103] In step A, based on the aforementioned method for predicting the calibratability of the lens assembly to be assembled, whether the current lens assembly to be assembled is calibratable is predicted. If it is not calibratable, the lens assembly to be assembled is abandoned. If it is calibratable, step B is executed.

[0104] In step B, the lens assembly to be assembled and the photosensitive assembly that have been pre-judged by calibratability are assembled to obtain a complete camera module. The assembly process can be implemented based on active calibration, wherein the data obtained in the pre-judgment process of the lens assembly to be assembled can be used to pre-adjust the posture and position of the lens assembly to be assembled, and the adjustment amount and adjustment direction can be consistent with the compensation parameters in the previous step 3. It should be noted that the pre-adjustment here is an actual physical adjustment of the posture and position of the lens assembly to be assembled, and no virtual calculation is performed. After the pre-adjustment, active calibration can be continued to be performed, and the relative position of the lens assembly to be assembled and the photosensitive assembly with the best imaging quality can be finally determined, and then the lens assembly and the photosensitive assembly can be assembled (for example, by bonding or welding) based on the relative position determined by the active calibration to obtain a complete camera module.

[0105] That is, in this embodiment, high-precision axis value simulation can be used to eliminate defective modules in the module before the actual detection and production of the module, so as to avoid defective modules occupying module production time and production materials, greatly improving the production efficiency of the module and reducing the production cost of the module. If the NG module is involved in the production, on the one hand, it wastes production materials, and on the other hand, the internal components of the module after processing (such as dispensing) are difficult or difficult to recycle, which wastes the components that could have been used.

[0106] In addition, it should be noted that in some embodiments of the present application, when the measured defocus curve has a high measurement accuracy, step S2 can be omitted, that is, the peak position and the corresponding peak value can be directly obtained by measuring the defocus curve. The peak value and peak position will be used for calculations in subsequent steps, and the calculation is used to simulate the posture adjustment of the lens assembly (i.e., tilt adjustment), and calculate the simulated defocus curve after the posture adjustment. In some embodiments, the lens assembly can be further simulated to adjust the axial position, and calculate the simulated defocus curve after the axial position adjustment or the tilt and axial position are adjusted. The calculation basis may include: the original measured defocus curve of each identification pattern, and the peak position of the fitted defocus curve of each identification pattern. The peak position represents the position where the corresponding identification pattern is the clearest imaging. Based on the position and compensation parameters, the position where the imaging of each identification pattern after virtual correction of the lens assembly is the clearest, that is, the peak position of the simulated defocus curve after virtual correction can be searched. In this way, without actually moving the lens assembly, the defocus curve when the lens assembly is assumed to be corrected in posture (or posture and axial position) according to the compensation parameters can be estimated through simulation calculation, thereby helping to predict the calibratability of the lens assembly.

[0107] Finally, it should be noted that the above embodiments are intended only to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions to the technical solutions of the present invention do not depart from the spirit and scope of the technical solutions of the present invention and are intended to be encompassed by the claims of the present invention.

Claims

1. A method for predicting the calibratability of a lens assembly to be assembled, characterized in that: include: 1) placing the lens assembly to be assembled in a test light path, and obtaining measured defocus curves of multiple identification patterns in the central field of view and the peripheral field of view of the lens assembly to be assembled; 2) fitting the measured defocus curves of the plurality of marking patterns respectively, and then obtaining the resolution peak position corresponding to each marking pattern according to the fitted defocus curve of the marking pattern; 3) determining focus correction compensation parameters for the lens assembly; wherein the compensation parameters include parameters characterizing the posture and / or position adjustment of the lens assembly; 4) determining a focus type and a focus mode for focus correction of the lens assembly, wherein the focus type includes S focus, T focus, or average focus, and the focus mode includes center focus or edge focus; Under the premise that the astigmatism, field curvature, and peak value of the lens assembly itself remain unchanged, the peak position of the simulated defocus curve is calculated based on each measured defocus curve according to the determined compensation parameter; wherein the simulated defocus curve is: a defocus curve obtained by adjusting the inclination angle and axial position of the lens assembly according to the determined compensation parameter under the determined focus type and focus mode; as well as 5) Based on the peak position of the simulated defocus curve calculated in step 4), calculate the clarity on each field axis corresponding to each identification pattern under the determined compensation parameters, and then determine whether the imaging quality of the lens assembly to be assembled meets the standard.

2. The calibratability prediction method according to claim 1, characterized in that: In the measured defocus curve and the simulated defocus curve, the resolution is characterized by an SFR value; the step 1) also includes: when the blade angle of the target plate of the test light path is not compatible with the SFR algorithm, rotating the blade angle based on an affine transformation to adapt to the SFR algorithm, thereby measuring the measured defocus curve.

3. The calibratability prediction method according to claim 2, characterized in that: The step 1) includes the following sub-steps: 11) First, using affine transformation, a rotation matrix of the marking pattern is obtained, wherein the rotation matrix can rotate the original blade angle of the current target plate to a target blade angle, and the target blade angle is within the angle range corresponding to the SFR algorithm; 12) then converting the original target plate image obtained by the test light path into a target plate image having the target knife-edge angle based on the rotation matrix; as well as 13) Applying the SFR algorithm to the target plate image with the target blade angle to obtain the SFR value, and then obtaining the measured defocus curve.

4. The calibratability prediction method according to claim 1, wherein: In the step 1), the edge field of view is represented by four identification patterns located at the upper left, upper right, lower left, and lower right.

5. The calibratability prediction method according to claim 1, wherein: In step 2), the method for obtaining the peak position of the fitted defocus curve comprises the following steps: 21) Finding the maximum value in the measured defocus curve and the axial position corresponding to the maximum value; 22) Then, the measured defocus curve is fitted using an N-order polynomial to obtain a fitted defocus curve, where N is an integer; 23) Then, find each maximum point of the defocus curve after fitting and the axial position corresponding to the maximum point; and 24) When the difference between a certain maximum value in the fitted defocus curve and the maximum value of the measured defocus curve is less than the maximum value of the measured defocus curve multiplied by a preset threshold ratio, the maximum value is directly determined to be the peak value of the fitted defocus curve, and the peak position of the fitted defocus curve is obtained.

6. The method for predicting calibratability according to claim 5, wherein: The method for obtaining the peak position of the fitted defocus curve further includes: when step 24) fails to determine the peak of the fitted defocus curve, executing the steps: 25) Fitting the measured defocus curve again with a K-order polynomial to obtain a quadratic-fitted defocus curve, and finally obtaining a peak value and a peak position based on the quadratic-fitted defocus curve; wherein K is less than N, N is 6, 7, or 8, and K is 4 or 5.

7. The calibratability prediction method according to claim 5, characterized in that: In the step 3), the image plane tilt angle is obtained according to the clear imaging position corresponding to each identification pattern of the edge field of view, and then the tilt compensation amount and compensation direction for adjusting the image plane to a horizontal state are calculated, and the tilt compensation amount and compensation direction are set as the compensation parameters.

8. The calibratability prediction method according to claim 5, characterized in that: In step 3), the compensation parameters are set according to an artificial intelligence algorithm or a human-computer interaction interface is provided to prompt the user to input the compensation parameters.

9. The method for predicting calibratability according to claim 5, wherein: In step 24), when the fitted defocus curve has multiple peaks, the average of the multiple peaks is calculated according to the centroid method to convert the multiple peak positions into a single peak position; When the defocus curve after fitting has only one peak or no peak is found, perform the following steps: 25) Fitting the measured defocus curve again with a K-order polynomial to obtain a quadratic-fitted defocus curve, and finally obtaining a peak value and a peak position based on the quadratic-fitted defocus curve; wherein K is less than N, N is 6, 7, or 8, and K is 4 or 5.

10. The calibratability prediction method according to claim 5, characterized in that: In step 3), the compensation parameters further include an axial position compensation amount and compensation direction, and an inclination angle compensation amount and compensation direction.

11. The method for predicting calibratability according to claim 5, wherein: In step 1), the edge field of view is characterized by four identification patterns located at the upper left, upper right, lower left, and lower right corners; The step 4) includes the following sub-steps: 41) Assuming that the tilt adjustment of the lens assembly does not change the astigmatism, field curvature, and peak resolution of the optical test system, the following four conditions are used to construct the equation system; the four conditions are: Condition 1: (pLT + pRT) - (pLB + pRB) = W * tanθ y Condition 2: (pLT + pLB) - (pRT + pRB) = H*tanθ x Condition 3: (pLT+pRT+pLB+pRB) / 4=pCT+CF Condition 4: For the corner with the smallest resolution difference from the central field of view among the four corners, its peak position remains unchanged during the virtual correction process; Wherein, CF represents field curvature, pLT, pRT, pLB, and pRB represent the peak positions corresponding to the upper left, upper right, lower left, and lower right marking patterns, respectively, pCT represents the peak position corresponding to the center field of view, W and H represent the spacing between the centers of adjacent marking patterns in the edge field of view in the x-axis direction and the y-axis direction, respectively, θ x and θ y It is the tilt angle component on the xoz plane and the tilt angle component on the yoz plane of the inclination angle of the virtually corrected lens assembly relative to the photosensitive surface of the photosensitive chip.

12. The calibratability prediction method according to claim 5, characterized in that: In the step 5), the resolution values ​​of each focus type of each identification pattern in different fields of view are obtained based on the measured defocus curve of step 1) and the virtual focus position of the photosensitive chip, and the virtual focus position is the peak position of the simulated defocus curve calculated in step 4).

13. The calibratability prediction method according to claim 12, characterized in that: In the step 5), the measured defocus curve is interpolated, and then the resolution value of each focus type of each identification pattern in different fields of view is obtained based on the virtual focus position.

14. The method for predicting calibratability according to claim 13, wherein: In the step 5), a cubic spline interpolation algorithm is used to interpolate the measured defocus curve.

15. The calibratability prediction method according to claim 1, characterized in that: In the step 1), in the test light path, image data is sensed by a standard photosensitive chip or a photosensitive chip in a photosensitive component to be assembled.

16. The calibratability prediction method according to claim 6 or 9, characterized in that: The step 25) further includes: selecting measured data points within the neighborhood of the maximum value of the measured defocus curve, and then performing the quadratic fitting on the measured defocus curve using a K-order polynomial based on the measured data points within the neighborhood of the maximum value.

17. A camera module assembly method, characterized in that: include: Step A) pre-judging whether the lens assembly to be assembled is calibratable based on the method for pre-judging the calibratability of the lens assembly to be assembled according to any one of claims 1 to 16; if not, abandoning the lens assembly to be assembled; if so, executing step B); Step B) Assembling the lens assembly to be assembled that has passed the calibratability prediction with the photosensitive assembly to obtain a complete camera module.

18. The camera module assembly method according to claim 17, wherein: In the step B), the assembly is implemented based on active calibration. During the active calibration, the actual posture and position of the lens assembly to be assembled are pre-adjusted using the compensation parameters obtained in the step A).

Images