TWI792291B - Method of automatically setting optical parameters and automated optical inspection system using the same - Google Patents

Abstract

A method of automatically setting optical parameters, using Automatic Optical Inspection(AOI) System, the method includes: obtaining a recommended object image when the AOI system under a first recommended optical parameter set; performing computation on a standard image of a and a recommended image of the to-be-measured object according to an optimized error function to obtain a recommended error value between the standard image and the recommended image; determining whether the recommended error value converges, when determining that the recommended error value does not converge, performing computation according to the recommended error value and first recommended optical parameter set to obtain a second recommended optical parameter set; when the recommended error value converges, deciding the first recommended optical parameter set as the best optical parameter set of the AOI system.

Description

Optical parameter automatic setting method and automatic optical inspection using it system

The disclosure relates to a method for automatically setting optical parameters and an automatic optical inspection system using the method.

The optical inspection system can take pictures of the product to obtain the image of the product (object to be tested), and analyze the image to detect product defects. In order to improve the detection accuracy, the image captured by the automatic optical inspection system must have appropriate or optimal color information, so it is necessary to adjust the optical parameters of the optical inspection system according to the environment or the product to be inspected. The current optical parameter setting is mainly to adjust the optical parameters of the optical detection system on the spot by experts according to experience.

However, there is a risk of misjudgment when on-site experts adjust optical parameters based on experience. In addition, the judgments of different experts are usually not exactly the same as the optical parameters set, which may lead to unstable quality control. On the other hand, in the case of a large number of optical parameter items, manual adjustment of optical parameters by experts is quite time-consuming, which may reduce production speed and efficiency.

The present disclosure relates to a method for automatically setting optical parameters and an automatic optical inspection system using the method.

According to an embodiment of the present disclosure, a method for automatically setting optical parameters is proposed. The method for automatically setting optical parameters is suitable for an Automated Optical Inspection (AOI) system. The automatic optical inspection system includes a camera module, an error calculation module, an automatic setting module and an optical parameter recommendation module. The method for automatically setting optical parameters includes the following steps: the camera module is set to use a first recommended optical parameter set in the automatic optical inspection system to capture an image of an object to be measured to obtain a recommended image of the object to be measured; the error calculation module is based on a Optimizing the error function, performing calculations on a standard image of the DUT and a recommended image of the DUT to obtain a recommended error value between the standard image of the DUT and the recommended image of the UUT; the error calculation module determines whether the recommended error value is convergence. When it is judged that the recommended error value has not converged, the optical parameter recommendation module performs calculations based on the recommended error value and the first recommended optical parameter set to obtain a second recommended optical parameter set, and automatically sets the module based on the second recommended optical parameter set Setting the automatic optical inspection system; and, when judging that the recommended error values converge, the error calculation module determines that the first recommended optical parameter set is one of the best optical parameter sets of the automatic optical inspection system.

According to another embodiment of the present disclosure, an automatic optical inspection system is provided. The automatic optical detection system includes a camera module, an error calculation module, an optical parameter recommendation module and an automatic setting module. The camera module is configured to perform: taking an image of an object under test under a recommended optical parameter, so as to obtain a recommended image of the object under test. The error calculation module is configured to execute: perform an operation on a standard image of the DUT and a recommended image of the DUT according to an optimized error function, so as to obtain a recommended error between the standard image of the DUT and the recommended image of the DUT and, judging whether the recommended error value is convergent. The optical parameter recommendation module is configured to execute: when it is judged that the recommended error value has not converged, the operation is performed according to the recommended error value and the first recommended optical parameter set to obtain a second recommended optical parameter set. When it is judged that the recommended error value converges, the error calculation module is further used to execute: determine the recommended optical parameter as one of the best optical parameter sets of the automatic optical inspection system.

In order to have a better understanding of the above and other aspects of the present disclosure, the following specific embodiments are described in detail in conjunction with the attached drawings as follows:

100: Automatic Optical Inspection System

110:Optimize the module

120: Error calculation module

130: Optical parameter recommendation module

140: Camera module

150: Automatic setting module

160:Light source module

C1~C3: curve

D1, D2: database

F1: Training standard map

F2: Standard diagram of the object to be tested

f: error operation function

f _min : minimum error value

E: Expected value analysis function

EF: Optimizing the Error Function

ET ₁ , ET ₂ , ET ₃ , ET _m : initial error value

ER ₁ , ER ₂ , ER ₃ , ER ₄ , ER ₅ , ER ₆ , ER ₇ , ER _n : recommended error value

MR ₁ , MR ₂ , MR ₃ , MR ₄ , MR ₅ , MR ₆ , MR ₇ , MR _n : recommended image of the object to be measured

M11 _S : Expert Standard Image

M12 _t : non-expert standard image

MT _m : Initial DUT image

PR ₁ , PR ₂ , PR ₃ , PR ₄ , PR ₅ , PR ₆ , PR ₇ , PR _n : recommended optical parameter set

PT ₁ , PT ₂ , PT ₃ , PT _m : initial optical parameter group

U1 _s , U2, U2 _t , V1, V2, W1 _t : vector

S110~S190: steps

:優化權重 Λ,

: optimize weight

xR _j : candidate optical parameter set

xT _i : selected optical parameter group

x ⁽¹⁾ ,x ⁽²⁾ ,x ⁽³⁾ ,x ^(k) ,x ^(P) : optical parameter type

Corr(xT _i ,xR _j ): gap

Φ(xR _j ): expected value

FIG. 1 shows a schematic diagram of an automatic optical inspection system according to an embodiment of the present disclosure.

2A and 2B are flowcharts showing the automatic optical parameter setting method of the automatic optical inspection system in FIG. 1 .

Figures 3A(a)~3H(b) illustrate the process of obtaining the recommended optical parameters in Figures 2A and 2B.

Please refer to Figures 1-2 and 3A(a)-3H(b), Figure 1 shows a schematic diagram of an automated optical inspection (Automated Optical Inspection, AOI) system 100 according to an embodiment of the present disclosure, Figures 2A and 2B Figure 1 shows the flowchart of the automatic optical parameter setting method of the automatic optical inspection system 100, and Figures 3A(a) to 3H(b) show the schematic diagram of the process of obtaining the recommended optical parameter PR _n in Figures 2A and 2B .

As shown in FIG. 1 , the automatic optical inspection system 100 includes an optimization module 110 , an error calculation module 120 , an optical parameter recommendation module 130 , a camera module 140 , an automatic setting module 150 and a light source module 160 . The camera module 140 is, for example, an optical camera or a linear optical scanner. The light source module 160 includes at least one light source (not shown), for example. At least one of the optimization module 110, the error calculation module 120, the optical parameter recommendation module 130, and the automatic setting module 150 is, for example, a physical circuit (circuit) formed by using a semiconductor process, or a firmware through a calculation processor to complete. In addition, at least one of the optimization module 110, the error calculation module 120, the optical parameter recommendation module 130 and the automatic setting module 150 can be integrated into a single module, or integrated in a controller (not shown) or calculation processing device (not shown).

As shown in FIG. 1, the camera module 140 captures an image of the object under the condition that the automatic optical inspection system 100 is set to the recommended optical parameter set PR _n (for example, the first recommended optical parameter set PR ₁ in FIG. 3B ). , to obtain the recommended DUT image MR _n . The error calculation module 120 is configured to perform calculations based on the optimized error function EF to perform calculations on the standard image F2 of the object to be measured and the image MR _n of the recommended object to be measured, so as to obtain the difference between the standard image F2 of the object to be measured and the image MR _n of the recommended object to be measured The recommended error value ER _n . Next, the error calculation module 120 determines whether the recommended error value ER _n is convergent. When judging that the recommended error value ER _n converges, the error calculation module 120 determines that the recommended optical parameter set PR _n (for example, the first recommended optical parameter set PR ₁ in FIG. 3B ) is the optimal optical parameter set for the automatic optical inspection system 100 ; When the error operation module 120 judges that the recommended error value ER _n is not converged, the optical parameter recommendation module 130 is configured to perform calculations based on the recommended error value ER _n and the first recommended optical parameter set PR _n to obtain another recommendation Optical parameter set PR _n+1 (for example, the second recommended optical parameter set PR ₂ in FIG. 3E ). In this way, the automatic optical inspection system 100 can automatically obtain the optimal optical parameter set, which improves the conventional manual dimming problem.

The "optical parameter set" in this article, for example, includes at least one optical parameter type, such as x ⁽¹⁾ , x ⁽²⁾ , x ⁽³⁾ , ..., x ^(P) and other P parameter types, where x ^{(1 )} , x ⁽²⁾ , x ⁽³⁾ , ..., x ^(P) can respectively represent different optical parameter types, such as the light source parameters of the light source module 160 and/or the camera parameters of the camera module 140, wherein the light source The parameters are optical parameter types such as color temperature, brightness, luminous angle, luminous time, and/or irradiating angle of the irradiating light, and the imaging parameters are optical parameter types such as exposure time of the camera module. P represents the number of optical parameter types, and the embodiment of the present disclosure does not limit the maximum value of P. In this embodiment, two types of optical parameters are taken as examples, such as x ⁽¹⁾ and x ⁽²⁾ , which are, for example, the exposure time of the camera module and the light emission time of the light source module, respectively. The lighting mode of the light source module 160 can be set by the automatic setting module 150 according to the relevant optical parameter type of the recommended optical parameter set PR _n , and the camera mode of the camera module 140 can be set by the automatic setting module 150 according to the recommended optical parameter set PR _n The relevant optical parameter type performs setting. The light source module 160 provides illumination light to the object under test (not shown), and the camera module 140 takes an image of the object under test to obtain a recommended image of the object under test. The aforementioned "object under test" is, for example, any product that can be inspected online (eg, defect inspection, product type inspection, etc.) through the AOI system, such as semiconductor chips, circuit boards, etc., but this disclosure does not limit the type of object under test.

The following is a further description of the automatic optical parameter setting method of the automatic optical inspection system 100 of the embodiment of the present disclosure by using the flow charts of FIGS. 2A and 2B .

In step S110, an optimized error function EF is obtained. The optimization error function EF of this embodiment is obtained through the operation of the optimization module 110 (as shown in FIG. 1 ). However, in other implementations, the optimization error function EF can be obtained in advance by performing calculations offline or offline, so the automatic optical inspection system may not include an optimization module.

In step S110, the optimization module 110 performs training standard image F1 (shown in Figure 1), at least one expert standard image M11 _S (shown in Figure 1), and at least one non-expert standard image M12 _t (shown in Figure 1 Perform calculations in Fig. 1) to obtain an optimized error function EF, wherein S is a positive integer equal to or greater than 1, and the maximum value of S depends on the total number of expert standard images M11 _S , which is not limited by the embodiments of this disclosure, and t is a positive integer equal to or greater than 1, and the maximum value of t depends on the total number of non-expert standard images M11 _t , which is not limited in the embodiments of the present disclosure. The number of the training standard image F1 is, for example, one, and the number of the expert standard image _M11S may be at least one. Each expert can select an image closest to the training standard map F1 from several images (for example, it can be the closest person in terms of various color information such as color temperature and brightness), and the images selected by each expert are defined as "Expert standard images", and the rest of the unselected images are defined as "non-expert standard images". The embodiment of the present disclosure does not limit the number of expert standard images M11 _S and the number of non-expert standard images M12 _t . As shown in FIG. 1 , the training standard image F1 , the expert standard image M11 _S and the non-expert standard image M12 _t can be stored in the first database D1 , and the standard image F2 of the DUT can be stored in the second database D2 . At least one of the first database D1 and the second database D2 may belong to the automatic optical inspection system 100 , but the embodiment of the present disclosure is not limited thereto.

In one embodiment, the optimization module 110 aims at minimizing the error between the training standard image F1 and the expert standard image M11 _s and non-minimizing the error between the training standard image F1 and the non-expert standard image M12 _t , and obtains Optimize the optimization weight Λ of the error function EF. For example, the optimization module 110 sets the target error TE1 between the training standard image F1 and the expert standard image M11 _S to 0 (as shown in the following formula (1)) and the target error between the training standard image F1 and the non-expert standard image M12 _t TE2 is set as 1 (the following formula (2)) as the target, and the optimization weight Λ of the optimized error function EF is obtained (the following formula (3)). The error calculation function f of formulas (1) and (2) can use various error analysis techniques or mathematical methods to perform error analysis on the vectors in the error calculation function f, such as calculating weighted Euclidean distance or Mahalanobis distance. This is not limited.

f(V1 , U1 _s |Λ)=TE1...(1)

f(V1 , W1 _s |Λ)=TE2...(2)

e=Σ _S (f(V1 , U1 _s |Λ)) ² +Σ _t (1-f(V1 , W1 _s |Λ)) ² ...(3)

。最佳優化權重

例如是包含至少一常數，亦可以是包含多個常數的矩陣。本實施例中，向量V1、向量U1_S、向量W1_t為像素層級的特徵向量，例如表示RGB或HSV(色相、飽和度、明度)，然而在其他實施方式中，向量V1、向量U1_S、向量W1_t也可以是圖像層級的特徵向量，例如深度學習模型抽取出來的特徵向量。 In formula (1), the vector V1 is, for example, the feature vector at the pixel level of the training standard image F1, and the vector U1 _S is, for example, the feature vector at the pixel level of the S-th expert standard image M11 _S. In formula (2), the vector W1 _t is, for example, a pixel-level feature vector of the t-th non-expert standard image M12 _t . In formula (3), e is the total error value, and its calculation goal is to obtain the optimal weight Λ when the sum of the error values of all expert standard images and the error values of all non-expert standard images is 0 or close to 0. As shown in formula (4), the optimization module 110 can optimize the optimization weight Λ by using the mathematical method of argmin. For example, the optimization module 110 may determine the optimization weight Λ corresponding to the smallest total error value e as the best optimization weight

. optimal optimization weight

For example, it may contain at least one constant, or may be a matrix containing multiple constants. In this embodiment, the vector V1, the vector U1 _S , and the vector W1 _t are feature vectors at the pixel level, such as representing RGB or HSV (hue, saturation, lightness), but in other implementations, the vector V1, the vector U1 _S , The vector W1 _t may also be an image-level feature vector, such as a feature vector extracted by a deep learning model.

後，即可依據最佳優化權重

來建構優化誤差函數EF，如下式(5)。在式(5)中，向量V2為標準圖例如待測物標準圖)的像素層級的特徵向量，向量U2為待測物影像例如初始待測物影像及/或推薦待測物影像)的像素層級的特徵向量。本實施例中，向量V2及向量U2為像素層級的特徵向量，例如表示RGB或HSV(色相、飽和度、明度)，然而在其他實施方式中，向量V2及向量U2也可以是圖像層級的特徵向量，例如深度學習模型抽取出來的特徵向量。 In obtaining the optimal optimization weight

After that, the weights can be optimized according to the best

To construct the optimization error function EF, the following formula (5). In the formula (5), the vector V2 is the feature vector of the pixel level of the standard image (such as the standard image of the UUT), and the vector U2 is the pixel of the image of the UUT (such as the initial image of the UUT and/or the recommended image of the UUT) Hierarchy feature vectors. In this embodiment, the vector V2 and the vector U2 are feature vectors at the pixel level, such as representing RGB or HSV (hue, saturation, lightness), but in other implementations, the vector V2 and the vector U2 can also be at the image level Feature vectors, such as feature vectors extracted from deep learning models.

After substituting vector V2 and vector U2 into formula (5), calculate the error value between vector V2 and vector U2 (such as initial error value and/or recommended error value) according to the optimized error function EF, where the error value represents the image of the object under test For the difference with the standard image F2, the smaller the error value, the smaller the difference between the vector V2 and the vector U2 (for example, the closer the image is in terms of color information); otherwise, the larger.

After obtaining the optimized error function EF, steps S120˜S190 may be performed to determine the optical parameter set to automatically set the optical parameters. For clarity, the steps S120-S190 of the optical parameter automatic method in this embodiment are used to determine the optical parameter set, and the optical parameter set is illustrated as including two types of optical parameters, such as x ⁽¹⁾ and x ⁽²⁾ . In one embodiment, the method for determining the optical parameter set (such as steps S120-S190) is to determine x ⁽²⁾ when x ⁽¹⁾ is a fixed value (such as, Figs. 3A(a)-3H(b) coordinates on the horizontal axis). In another embodiment, the method for determining the optical parameter set (such as steps S120-S190) is to determine x ⁽¹⁾ and x ⁽²⁾ when both x ⁽¹⁾ and x ⁽²⁾ are variable. In other embodiments, the optical parameter set may include P optical parameter types x ⁽¹⁾ , x ⁽²⁾ , x ⁽³⁾ , ..., x ^(P) , and the method for determining the optical parameter set is based on x ^{( 1)} , x ⁽²⁾ , x ⁽³⁾ , ..., x ^(P) are varied in value.

In step S120 , the automatic optical inspection system 100 can calculate the standard image F2 of the object under test and the M initial object images MT _m according to the optimization error function EF to obtain the first recommended optical parameter set. The subscript m of the symbol MT _m represents the mth initial UUT image, where m is a positive integer ranging from 1 to M, and M is the total number of initial UUT images, and the embodiment of this disclosure does not limit the maximum value of M .

Step S120 may include steps S121-S123.

In step S121, when the automatic optical inspection system 100 is set with M initial optical parameter sets PT _m , the camera module 140 acquires M initial images MT _m of the object under test respectively, which are also set by the automatic optical inspection system 100. In the case of setting with the initial optical parameter set PT ₁ , the camera module 140 performs image capturing of the object to be measured to obtain the initial image MT ₁ of the object to be measured; then, when the automatic optical inspection system 100 is set with the initial optical parameter set PT ₂ In this case, the camera module 140 obtains the initial image MT ₂ of the object under test, so that the automatic optical inspection system 100 is successively set with M initial optical parameter sets PT _m , and the camera module 140 performs image capturing successively to obtain M initial Image MT _m of the object to be tested. The types of optical parameters included in the initial optical parameter set PT _m are not limited in the embodiments of the present disclosure. As shown in Figure 3A(a), three initial optical parameter sets PT _m in the embodiment of the disclosure are illustrated by taking three examples (that is, the case of M=3), which are initial optical parameter sets PT ₁ , PT ₂ and PT ₃ . The initial optical parameter groups PT ₁ , PT ₂ and PT ₃ can be expressed as PT ₁ [x ⁽¹⁾ , x ⁽²⁾ ], PT ₂ [x ⁽¹⁾ , x ⁽²⁾ ] and PT ₃ [x ^{(1) )} ,x ⁽²⁾ ], and the other optical parameter groups have similar expressions, which will not be repeated here.

In step S122, the error operation module 120 performs calculations on the standard image F2 of the object to be measured and the M initial images MT _m of the object to be measured according to the optimized error function EF, and respectively obtains the M initial images MT m of the object to be measured and the images MT _m to be measured respectively. M initial error values ET _m between the standard graphs F2 of the measured object, where m is a positive integer ranging from 1 to M. As shown in FIG. 3A(a), three initial error values ET _m in the embodiment of the present disclosure (ie, the case of M=3) are illustrated as examples, which are the initial error values ER ₁ , ER ₂ and ER ₃ .

Curve C1 in Figure 3A(a) represents the corresponding relationship between the optical parameter set and the error value in the 0th iteration, and each point in Figure 3A(b) represents the curve C1 between different iterations and the result of the 0th iteration Figures 3B~3H(b) have similar definitions for the smallest of several error values in (called "minimum error value"), and will not be described here. In the stage shown in Figure 3A(b) of the method for automatic setting of optical parameters, the recommended optical parameter set PR _n has not yet been introduced into the calculation (the iterative process of steps S140~S180 has not yet been executed), so the iteration times corresponding to the minimum error value is 0.

In step S123 , the optical parameter recommendation module 130 performs calculations according to the M initial error values ET _m and the M initial optical parameter sets PT _m to obtain the first recommended optical parameter set PR ₁ .

In the optical parameter automatic setting method, the optical parameter recommendation module 130 can use various implementation methods to determine the recommended optical parameter set. For example, the optical parameter recommendation module 130 can obtain the recommended optical parameter set according to formulas (6)-(7). However, as long as the recommended optical parameter set can be obtained, the method of obtaining the recommended optical parameter set in the embodiment of the present disclosure is not limited to using formulas (6)-(7).

In formulas (6) and (7), xR _j is the candidate optical parameter group, where j is a positive integer ranging from 1 to J, which is used to indicate that xR _j is the jth candidate optical parameter group, and J is the candidate optical parameter group The number of parameter groups. xR _j includes several types of optical parameters, which can be further represented by xR _j [x ⁽¹⁾ , x ⁽²⁾ , x ⁽³⁾ , . . . , x ^(P) ]. The candidate optical parameter set xR _j may be one of all the optical parameter sets that has not been used to perform calculations to obtain error values. The embodiments of the present disclosure do not limit the maximum value of J, which may be determined by the number of optical parameter types and/or its value range.

In formulas (6) and (7), xT _i is the selected optical parameter group, where i is a positive integer ranging from 1 to Q, which is used to indicate that xT _i is the i-th selected optical parameter group, where Q is Number of selected optical parameter sets. xT _i includes several types of optical parameters, which can be further represented by xT _i [x ⁽¹⁾ , x ⁽²⁾ , x ⁽³⁾ , . . . , x ^(P) ]. The selected optical parameter set xT _i includes the initial optical parameter set PT _m and/or the recommended optical parameter set PR _n for which error values have been obtained. For example, in Figure 3B (entering the first iteration), the selected optical parameter set xT _i includes the initial optical parameter sets PT ₁ , PT ₂ and PT ₃ in Figure 3A(a) (represented by solid circles ). Taking Figure 3D (entering the second iteration), the selected optical parameter set xT _i includes the initial optical parameter sets PT ₁ , PT ₂ and PT ₃ in Figure 3C(a) and the recommended optical parameter set PR ₁ ( are represented by solid dots). In formula (6), θ _k represents the weight of the kth optical parameter type x ^(k) , which is a constant, and the values of several θ _k of different optical parameter types x ^(k) can be different or the same . In addition, the aforementioned k is a positive integer ranging from 1 to Q, wherein Q is the number of selected optical parameter sets xT _i . The number of recommended optical parameter sets increases with the number of iterations, that is, the value of Q increases accordingly, and the maximum value of Q depends on the number of iterations. The embodiments of the present disclosure do not limit the maximum value of Q.

In formula (6), Corr(xT _i , xR _j ) represents the difference between the j-th candidate optical parameter set xR _j and the i-th selected optical parameter set xT _i . In the operation of the exponential function exp, when the gap between the j-th candidate optical parameter set xR _j and the i-th selected optical parameter set xT _i x T _i is larger, the value of Corr(xT _i , xR _j ) The smaller it is, the larger it will be.

In formula (7), Φ(xR _j ) represents the jth expected value (or probability) of the jth candidate optical parameter set xR _j compared to all selected optical parameter sets xT _i , and the expected value Φ(xR _j ) The value of is, for example, between 0 and 1. The expected value analysis function E is used to obtain the expected value of the j-th candidate optical parameter set xR _j compared to all selected optical parameter sets xT _i , which can use various statistical or probabilistic analysis techniques. Q represents the number of all selected optical parameter sets xT _i , for example, in the case of Figure 3B (executing the first iteration), the selected optical parameter sets xT _i are 3 (such as the number of solid circles), That is, Q is equal to 3; in the case of the 3D diagram (entering the second iteration), the selected optical parameter set xT _i is 4 (for example, the number of solid circles), that is, Q is equal to 4.

In formula (7), f _min represents the minimum of all error values corresponding to all selected optical parameter groups xT _i , for example, in Figure 3B (executing the first iteration), f _min is the current iteration The latest initial error value ET ₂ (as shown in Figure 3A(b)); in Figure 3D (executing the second iteration), f _min is the latest minimum error value of the current iteration, that is, the 3C ( b) The initial error value ET ₂ on the far right of the figure (that is, ET ₂ is the smallest of the initial error values ET ₁ ~ ET ₃ ); in figure 3F (executing the third iteration), f _min is the current iteration The next latest minimum error value, that is, the initial error value ET ₂ on the far right of Figure 3E(b) (ie, the smallest of the initial error values ET ₁ ~ ET ₃ and the recommended error value ET ₁ ).

)表示待測物標準圖F2的像素層級的特徵向量V2與第i張已選影像的像素層級的特徵向量U2_i，其中已選影像是指已被用以執行運算取得誤差值的影像，例如在第3B圖(執行第1次疊代)中，已選影像即是初始待測物影像MT₁(攝像模組140在基於第3A(a)圖之第1個初始光學參數組PT₁之設定下取得)、初始待測物影像MT₂(攝像模組140在基於第3A(a)圖之第2個初始光學參數組PT₂之設定下取得)及初始待測物影像MT₃(攝像模組140在基於第3A(a)圖之第3個初始光學參數組PT₃之設定下取得)；在第3D圖中(執行第2次疊代)來說，已選影像即是初始待測物影像MT₁、MT₂及MT₃，以及第一個推薦待測物影像MR₁(攝像模組140在基於第3C(a)圖之第一個推薦光 學參數組PR₁之設定下取得)。在本實施例中，特徵向量V2及特徵向量U2_i為像素層級的特徵向量，例如表示RGB或HSV(色相、飽和度、明度)，然而在其他實施方式中，向量V2及向量U2_i也可以是圖像層級的特徵向量，例如深度學習模型抽取出來的特徵向量。 In formula (7), EF(V2 , U2 _i |

) represents the pixel-level feature vector V2 of the standard image F2 of the object under test and the pixel-level feature vector U2 _i of the i-th selected image, where the selected image refers to the image that has been used to perform calculations to obtain error values, for example In Figure 3B (executing the first iteration), the selected image is the initial image MT ₁ of the object to be measured (the camera module 140 is based on the first initial optical parameter set PT ₁ in Figure 3A(a) setting), the initial image of the object to be measured MT ₂ (the camera module 140 is obtained based on the setting of the second initial optical parameter set PT ₂ in Figure 3A(a)), and the initial image of the object to be measured MT ₃ (the Module 140 is obtained under the setting of the 3rd initial optical parameter set PT ₃ based on Fig. 3A(a)); in Fig. 3D (executing the second iteration), the selected image is the initial The images MT ₁ , MT ₂ and MT ₃ of the object to be measured, and the first recommended image MR ₁ of the object to be measured (the camera module 140 is obtained based on the setting of the first recommended optical parameter set PR ₁ in FIG. 3C(a) ). In this embodiment, the feature vector V2 and the feature vector U2 _i are feature vectors at the pixel level, such as representing RGB or HSV (hue, saturation, lightness), but in other implementations, the vector V2 and the vector U2 _i can also be is the feature vector at the image level, such as the feature vector extracted by the deep learning model.

As shown in formulas (6) and (7), when the gap between the j-th candidate optical parameter set xR _j and the i-th selected optical parameter set xT _i is larger (that is, Corr(xT _i , xR The smaller the value of _j ), the larger the expected value Φ(x _j ), which means that the jth candidate optical parameter set xR _j has a higher probability of producing an error value smaller than f _min . Each set of candidate optical parameter sets xR _j can obtain an expected value Φ(xR _j ). The optical parameter recommendation module 130 can use J different values of candidate optical parameter groups xR _j to obtain corresponding J expected values Φ(xR _j ), and determine the jth corresponding to the largest of the J expected values Φ(xR _j ). The candidate optical parameter sets xR _j are recommended optical parameter sets PR _n .

Referring to Figures 3B-3C(b), the process of obtaining the first recommended optical parameter set PR ₁ based on formulas (6)-(7) is illustrated as follows. The optical parameter recommendation module 130 first obtains several expected values Φ(xR _j ) of several candidate optical parameter sets xR _j compared to all selected optical parameter sets xT _i , and then determines (selects) these expected values Φ(xR The largest candidate optical parameter set xR _j in _j ) is the first recommended optical parameter set. The process of obtaining the expected value Φ(xR ₁ ) of the first candidate optical parameter set xR ₁ is illustrated as follows.

)；並且運算取得待測物標準圖F2之像素層級之特徵向量V2與第1張已選影像(如，第1張初始待測物影像MT₁)的像素層級的特徵向量U2₁的初始誤差值ET₁(初始誤差值ET₁為式(7)之EF(V2,U2₁|

)的結果值)。 As shown in Figure 3A(a), when the number of selected optical parameter sets xT _i is 3 (Q=3), and the initial optical parameter set PT _m includes PT ₁ , PT ₂ and PT ₃ , execute The operation of formula (6). At this time, when i is equal to 1 and j is equal to 1, the optical parameter recommendation module 130 performs the operation of formula (6) to obtain the first selected optical parameter set xT ₁ (for example, the initial optical parameter set PT ₁ ) and the value of Corr(xT ₁ ,xR ₁ ) of the first candidate optical parameter set xR ₁ (calculation formula:

); and obtain the initial error between the pixel-level feature vector V2 of the standard image F2 of the UUT and the pixel-level feature vector U2 ₁ of the first selected image (for example, the first initial UUT image MT ₁ ) value ET ₁ (the initial error value ET ₁ is EF(V2 , U2 ₁ |

) for the resulting value).

)；並且運算取得待測物標準圖F2之特徵向量V2與第2張已選影像(如，第2張初始待測物影像MT₂)的特徵向量U2₂的初始誤差值ET₂(初始誤差值ET₂為式(7)之EF(V2,U2₂|

)的結果值)。 The optical parameter recommendation module 130 executes the operation of formula (6) when i is equal to 2 and j is equal to 1, and obtains the second selected optical parameter set xT ₂ (for example, the initial optical parameter set PT ₂ ) and the first _The value ₍ calculation _formula :

_{); and obtain the initial error value ET 2 ( initial} error The value ET ₂ is EF(V2 , U2 ₂ |

) for the resulting value).

)；並且執行運算取得待測物標準圖F2之特徵向量V2與第3(即，i=3)張已選影像(如，第3張初始待測物影像MT₃)的特徵向量U2₃的初始誤差值ET₃(初始誤差值ET₃為式(7)之EF(V2,U2₃|

)的結果值)。 The optical parameter recommendation module 130 executes the operation of formula (6) when i is equal to 3 (the value of Q) and j is equal to 1, and obtains the third selected optical parameter set xT ₃ (for example, the initial optical parameter set PT ₃ ) and the value of Corr(xT ₃ ,xR ₁ ) of the first candidate optical parameter set xR ₁ (calculation formula:

); and perform calculations to obtain the characteristic vector V2 of the standard image F2 of the _UUT and the eigenvector U2 3 of the third (ie, i=3) selected image (such as the third initial UUT image MT ₃ ) The initial error value ET ₃ (the initial error value ET ₃ is EF(V2 , U2 ₃ |

) for the resulting value).

之商值、

之商值及

之商值，並在取得此些商值之加總(和值計算式：

)後，運算式(7)之期望值分析函數E，取得(或計算出)第1個候選光學參數組xR₁之期望值Φ(xR₁)。 Then, the optical parameter recommendation module 130 can obtain

the business value of

The commercial value of and

quotient value, and obtain the sum of these quotient values (sum value calculation formula:

), calculate the expected value analysis function E of formula (7), and obtain (or calculate) the expected value Φ(xR ₁ ) of the first candidate optical parameter set xR ₁ .

According to the aforementioned principle, the optical parameter recommendation module 130 can obtain J expected values Φ(xR _j ) in J different candidate optical parameter sets xR _j . The optical parameter recommendation module 130 can select the largest one from J expected values Φ(xR _j ), and decide that the largest one is the first (ie, n=1) recommended optical parameter set PR ₁ , as shown in FIG. 3B Show.

After obtaining the first recommended optical parameter set PR ₁ , step S124 is executed, and the automatic setting module 150 sets the automatic optical inspection system 100 according to the first recommended optical parameter set PR ₁ . The automatic setting module 150 can set the light source module 160 and the camera module 140 according to the recommended optical parameter set PR ₁ . For example, taking x (1) of the recommended optical parameters PR ₁ [x ⁽¹⁾ , x ⁽²⁾ ] as the exposure time and x ^{( 2)} as the light emission time, the automatic setting module 150 sets the exposure of the camera module 140 The time is the value of x ⁽¹⁾ in the recommended optical parameter set PR ₁ , and the light emitting time of the light source module 160 is set to be the value of x ⁽²⁾ in the recommended optical parameter set PR ₁ . The automatic setting module 150 can automatically set the light source module 160 and/or the camera module 140 according to the recommended optical parameter set without manual operation, which can reduce the time required for automatic setting of optical parameters and increase the time required for automatic setting of optical parameters. efficiency. In another embodiment, if manual setting is used, the automatic optical inspection system 100 can optionally omit the automatic setting module 150 .

In step S130 , the error calculation module 120 sets the initial value of n to 1. The order of execution between steps S130 and S124 is not limited, and may be executed first or at the same time.

In step S140 , the camera module 140 performs imaging of the object under test under the setting of the first recommended optical parameter set PR ₁ in the automatic optical inspection system 100 , so as to obtain the first recommended image MR ₁ of the object under test.

In step S150, the error calculation module 120 calculates the pixel-level feature vector V2 of the standard image F2 of the test object and the pixel-level feature of the first recommended test object image _MR1 according to the optimized error function EF of formula (5). The vector U2 performs calculations to obtain the first recommended error value ER ₁ between the standard image F2 of the UUT and the first recommended UUT image MR ₁ , as shown in FIG. 3C(a).

As shown in FIG. 3C(a), the optical parameter recommendation module 130 can use a curve fitting method to obtain a fitting curve C2 between the initial error values ET ₁ , ET ₂ , ET ₃ and the recommended error value ER _n .

In step S160 , the error calculation module 120 determines whether the recommended error value converges. When the error calculation module 120 judges that the recommended error value is convergent, the process proceeds to step S190; when it judges that the recommended error value is not convergent, the process proceeds to step S170.

In step S160 of an embodiment, when the recommended error value is equal to or smaller than a convergence threshold or the difference between any two of several recommended error values after several iterations varies within a convergence variation range, then The error calculation module 120 determines that the error value has converged. The aforementioned "convergence threshold value" is, for example, any value between 0.1~0.3 (including the endpoint value), the "number of iterations" is, for example, equal to or greater than 3 times, and the "convergence variation The "range" is, for example, any value between 0.1-0.3 (including the endpoint value), but the embodiments of the present disclosure are not limited thereto.

As shown in Figure 3C(b), the first recommended error value ER ₁ obtained through step S150 is not smaller than the minimum error value obtained in the previous iteration (here, it is obtained in the 0th iteration Among the multiple initial error values, the minimum initial error value ET ₂ ), therefore, after the first iteration, the minimum error value is still the initial error value ET ₂ . Since the first recommended error value ER ₁ is not smaller than the minimum error value of the previous iteration (that is, the initial error value ET ₂ of the 0th iteration), the error calculation module 120 judges that the minimum error value has not yet converged, The flow goes to step S170.

In step S170, the optical parameter recommendation module 130 performs calculations based on n recommended error values, n recommended optical parameter sets, M initial error values, and M test initial optical parameter sets to obtain the n+1th recommended optical parameter set parameter group. In other words, the optical parameter recommendation module 130 uses equations (6) and (7) to calculate all selected optical parameter sets xT _i (including n recommended optical parameter sets and M test initial optical parameter sets) and all calculated optical parameter sets Error values (including n recommended error values and M initial error values), and the next (ie, n+1) recommended optical parameter set PR _n+1 (the n+1th recommended optical parameter set) is obtained.

The following further illustrates the process of obtaining the second recommended optical parameter set PR ₂ through formulas (6)~(7) (corresponding to Figures 3D~3E(b)). During the process, the optical parameter recommendation module 130 first calculates and obtains several expected values Φ(xR _j ) of each of several candidate optical parameter sets xR _j compared to all selected optical parameter sets xT _i , and then determines these expected values Φ The candidate optical parameter set xR _j with the maximum value in (xR _j ) is the second recommended optical parameter set PR ₂ . The following takes the first candidate optical parameter set xR ₁ as an example to illustrate the process of calculating the expected value Φ(xR _j ).

As shown in Figure 3C(a), the number of selected optical parameter sets xT _i is 4 (Q=4), including 3 initial optical parameter sets PT ₁ , PT ₂ and PT ₃ and 1 recommended optical parameter set _pt1 .

)，且運算待測物標準圖F2之像素層級之特徵向量V2與第1(即，i=1)張已選影像(如，第1張初始待測物影像MT₁)的像素層級的特徵向量U2₁的初始誤差值ET₁(初始誤差值ET₁為式(7)之EF(V2,U2₁|

)的運算結果值)。 When i is equal to 1 and j is equal to 1, formula (6) is calculated, and the optical parameter recommendation module 130 calculates the first (i.e., i=1) selected optical parameter set xT ₁ according to formula (6) (for example, the initial Optical parameter set PT ₁ ) and the value of Corr(xT ₁ , xR ₁ ) of the first (ie, j=1) candidate optical parameter set xR ₁ (calculation formula:

), and calculate the pixel-level feature vector V2 of the standard image F2 of the DUT and the pixel-level features of the first (ie, i=1) selected image (for example, the first initial DUT image MT ₁ ) The initial error value ET ₁ of the vector U2 ₁ (the initial error value ET ₁ is EF(V2 , U2 ₁ |

) operation result value).

)，且運算待測物標準圖F2之特徵向量V2與第2(即，i=2)張已選影像(如，第2張初始待測物影像MT₂)的特徵向量U2₂的初始誤差值ET₂(初始誤差值ET₂為式(7)之EF(V2,U2₂|

)的結果值)。 When i is equal to 2 and j is equal to 1, formula (6) is calculated, and the optical parameter recommendation module 130 calculates the second (i.e., i=2) selected optical parameter set xT ₂ according to formula (6) (for example, the initial Optical parameter set PT ₂ ) and the value of Corr(xT ₂ , xR ₁ ) of the first (ie, j=1) candidate optical parameter set xR ₁ (calculation formula:

), and calculate the initial error between the eigenvector V2 of the standard image F2 of the UUT and the eigenvector U2 2 of the second (ie, i=2) selected image (for example, the second initial UUT image MT _{2 )} value ET ₂ (the initial error value ET ₂ is EF(V2, U2 ₂ |

) for the resulting value).

)，且運算待測物標準圖F2之特徵向量V2與第3(即，i=3)張已選影像(如，第3張初始待測物影像MT₃)的特徵向量U2₃的初始誤差值ET₃(初始誤差值ET₃為式(7)之EF(V2,U2₃|

)的結果值)。 When i is equal to 3 and j is equal to 1, formula (6) is calculated, and the optical parameter recommendation module 130 calculates the third (that is, 1=3) selected optical parameter set xT ₃ according to formula (6) (for example, the initial Optical parameter group PT ₃ ) and the value of Corr(xT 3 _, xR 1 ) of the first (ie, j=1) candidate optical parameter group xR ₁ (calculation _formula : Corr( xT ₃ , xR ₁ )=

), and calculate the initial error between the eigenvector V2 of the standard image F2 of the _UUT and the eigenvector U2 3 of the third (ie, i=3) selected image (for example, the third initial UUT image MT ₃ ) value ET ₃ (the initial error value ET ₃ is EF(V2 , U2 ₃ |

) for the resulting value).

)，且運算待測物標準圖F2之特徵向量V2與第4(即，i=4)張已選影像(如，推薦光學參數組PR₁)的特徵向量U2₄的推薦誤差值ER₁(推薦誤差值ER₁為式(7)之EF(V2,U2₄|

)的結果值)。 When i is equal to 4 and j is equal to 1, formula (6) is calculated, and the optical parameter recommendation module 130 calculates the 4th (i.e., i=4) selected optical parameter set xT ₄ according to formula (6) (for example, recommended Optical parameter set PR ₁ ) and the value of Corr(xT ₄ , xR ₁ ) of the first (ie, j=1) candidate optical parameter set xR ₁ (calculation formula:

), _and calculate the recommended error value ER _{1 (} The recommended error value ER ₁ is EF(V2 , U2 ₄ |

) for the resulting value).

之商值、

之商值、

之商值及

之商值，並運算此些商值之加總(計算式：

)後，再進一步依據式(7)之期望值分析函數E執行運算，取得第1個候選光學參數組xR₁之期望值Φ(xR₂)。 Then, the optical parameter recommendation module 130 can use the aforementioned calculation results to perform calculations to obtain

the business value of

the business value of

The commercial value of and

quotient value, and calculate the sum of these quotient values (calculation formula:

), and further perform _{operations according to the expected value analysis function E of formula (7), and obtain the expected value Φ(xR 2 )} of the first candidate optical parameter set xR 1 .

According to the aforementioned process, the optical parameter recommendation module 130 can obtain J expected values Φ(xR _j ) in J different candidate optical parameter sets xR _j . The optical parameter recommendation module 130 can select the largest one from J expected values Φ(xR _j ), and determine xR _j of the one with the largest expected value as the second (ie, n+1) recommended optical parameter set PR ₂ , as shown in the first 3D figure shown.

After the second recommended optical parameter set PR ₂ is obtained, step S175 is executed.

In step S175 , the automatic setting module 150 sets the automatic optical inspection system 100 according to the second (n+1th) recommended optical parameter set PR ₂ .

In step S180, the value of n is accumulated (n=2), and then the next iteration is executed, that is, step S140 is executed again. In the second (n=2) iteration, the camera module 140 When the detection system is set with the second recommended optical parameter set PR ₂ , image acquisition is performed on the object to be measured, so as to obtain the second recommended image MR ₂ of the object to be measured. Then, step S150 is executed. In the second iteration, the optical parameter recommendation module 130 performs calculations on the standard image F2 of the object to be measured and the second recommended image _MR2 of the object to be measured according to the optimized error function EF in formula (5). , and obtain the second recommended error value ER ₂ between the standard image F2 of the test object and the second recommended image MR ₂ of the test object, as shown in FIG. 3E(a). In FIG. 3E(a), the optical parameter recommendation module 130 can use a curve fitting method to obtain a fitting curve C3 between the initial error values ET ₁ , ET ₂ , and ET ₃ and the recommended error values ER ₁ and ER ₂ . Step S160 is executed, and the optical parameter recommendation module 130 determines whether the recommended error value converges. When it is judged that the recommended error value is converged, step S190 is executed; when it is judged that the recommended error value is not converged, step S170 is executed. As shown in Figure 3E(b), the second recommended error value ER ₂ is not less than the recommended error value ER ₁ and the minimum error value determined by the previous iterations (0th iteration and 1st iteration) (initial error value ET ₂ ), the optical parameter recommendation module 130 judges that the recommended error value has not yet converged, and then executes step S170.

In step S170, the optical parameter recommendation module 130 adopts the above-mentioned process of determining the recommended optical parameter set, calculates and obtains the third recommended optical parameter set PR ₃ , and executes step S175 to set the automatic optics according to the third recommended optical parameter set PR ₃ The detection system 100, and step S180 to update n (n=3) to enter the next iteration (the third iteration). In the third iteration, similar to the above-mentioned second iteration, steps S140-S160 are executed to obtain the third recommended error value ER ₃ (as shown in Figures 3F and 3G(a)). Then execute S160, as shown in Figure 3G(b), although the third recommended error value ER ₃ is smaller than the recommended error values ER ₁ and ER ₂ of the previous iteration results, and the minimum error value ET ₂ , but the third The recommended error value ER ₃ is not lower than the convergence threshold, and the optical parameter recommendation module 130 judges that the recommended error value has not yet converged, and enters the next round of iteration, and executes steps S170~S180 to obtain the first iteration of the next iteration. n recommended optical parameter sets PR _n .

As shown in Figure 3H(a), the optical parameter recommendation module 130 sequentially obtains the fourth recommended error value ER ₄ and the fifth (ie, The fifth recommended error value ER ₅ of the n=5) iteration, the sixth recommended error value ER 6 of the sixth (ie, n=6) iteration, and the _seventh (ie, n=7) iteration Substitute the seventh recommended error value ER ₇ . As shown in Figure 3H(b), in step S160 of the seventh iteration, the seventh recommended error value ER ₇ is lower than the aforementioned convergence threshold, and the optical parameter recommendation module 130 judges that the recommended error value has reached Convergence, then execute step S190, the optical parameter recommendation module 130 determines the minimum recommended error value of the seventh iteration, that is, the seventh recommended optical parameter set ER ₇ corresponding to the seventh recommended error value ER ₇ is automatic optics Optimal optical parameter set of detection system 100 .

The light source module 160 and the camera module 140 of the automatic optical inspection system 100 can operate on the production line according to the optimal optical parameter set, so that the product image obtained by the camera module 140 has the best quality (such as color presented) to improve the accuracy of the automatic optical inspection system 100 in detecting products.

To sum up, the embodiments of the present disclosure propose a method for automatically setting optical parameters and an automatic optical inspection system using the same, using machine learning technology to automatically obtain the optimal optical parameter set. In the application of the method for automatically setting optical parameters according to the disclosed embodiments, tens of thousands, hundreds of thousands or even more iterations must be performed on P or all optical parameters in a short period of time, and the calculation process is complicated. It must be executed by a computer, not by a human brain. Since the computer is used to execute the automatic setting method of optical parameters, at least one of all optical parameters in the automatic optical inspection system that may affect the presentation (or quality) of the product image captured by the camera can be considered during the execution process, so as to improve the performance of the camera. The quality of captured product images increases the detection accuracy. Compared with manual dimming, the disclosed embodiments have at least one of the following technical effects: (1). Avoid misjudgment due to human factors, thus improving the stability of dimming; (2). Reduce the time required for dimming ; (3). Reduce labor costs; (4). The number of optical parameter types added to the dimming process can be large; (5). Increase the detection accuracy of the automatic optical detection system.

To sum up, although the present disclosure has been disclosed above with embodiments, it is not intended to limit the present disclosure. Those with ordinary knowledge in the technical field to which this disclosure belongs, without departing from Various changes and modifications may be made within the spirit and scope of this disclosure. Therefore, the scope of protection of this disclosure should be defined by the scope of the appended patent application.

S110~S190: steps

Claims (16)

A method for automatically setting optical parameters, suitable for an automatic optical inspection (Automated Optical Inspection, AOI) system, the automatic optical inspection system includes a camera module, an error calculation module, an automatic setting module and an optical parameter recommendation module set, the method for automatically setting optical parameters includes: the camera module performs image capturing on an object under test when the automatic optical inspection system is set with a first recommended optical parameter set, so as to obtain a recommended image of the object under test; The error calculation module executes calculations on a standard image of the DUT and the recommended image of the DUT according to an optimized error function, so as to obtain a recommended error value between the standard image of the DUT and the recommended image of the DUT; The error calculation module judges whether the recommended error value is converged; when it is judged that the recommended error value is not converged, the optical parameter recommendation module executes an operation according to the recommended error value and the first recommended optical parameter set to obtain a second recommending an optical parameter set, and the automatic setting module sets the automatic optical inspection system according to the second recommended optical parameter set; and when judging that the recommended error value converges, the error calculation module determines that the first recommended optical parameter set is One of the optimal optical parameter sets for the automatic optical inspection system. The method for automatically setting optical parameters as described in Claim 1, further comprising: the camera module respectively obtains a plurality of initial images of the object under test when the automatic optical inspection system is set with a plurality of initial optical parameter sets ; The error calculation module performs calculations on the standard image of the DUT and the initial images of the DUT according to the optimized error function, so as to obtain a complex number between the initial images of the DUT and the standard image of the DUT. an initial error value; and the optical parameter recommendation module performs calculations according to the initial error values and the initial optical parameter sets to obtain the first recommended optical parameter set. The method for automatically setting optical parameters as described in claim 2 further includes: when it is judged that the recommended error value has not converged, the optical parameter recommendation module also performs calculations based on these initial error values and these initial optical parameter sets, so as to The second recommended optical parameter set is acquired. The optical parameter automatic setting method as described in claim 2, further comprising: the optical parameter recommendation module calculates and obtains a plurality of expected values of a plurality of candidate optical parameter sets compared with the initial optical parameter sets; and the optical parameter recommendation The module determines the maximum of the expected values as the first recommended optical parameter set. The method for automatically setting optical parameters as described in Claim 4, wherein the step of calculating and obtaining the expected values of the candidate optical parameter sets compared with the initial optical parameter sets includes: the optical parameter recommendation module obtains each of the a plurality of differences between candidate optical parameter sets compared to each of the initial optical parameter sets; and The optical parameter recommendation module calculates and obtains the expected values according to the gaps and the initial error values. The method for automatically setting optical parameters as described in Claim 1, wherein the automatic optical inspection system further includes a light source module, and the method for automatically setting optical parameters further includes: the automatic setting module sets the optimal optical parameter set A camera module and/or the light source module. The method for automatically setting optical parameters as described in Claim 1, wherein the automatic optical inspection system also includes an optimization module, and the method for automatically setting optical parameters further includes: the optimization module performs a training standard image, an expert standard image and An operation is performed on a non-expert standard image to obtain the optimized error function. The method for automatically setting optical parameters as described in Claim 7, further comprising: the optimization module to minimize the error between the training standard image and the expert standard image and to minimize the error between the training standard image and the non-expert standard image The error between is the target, and the complex number of optimization weights of the optimization error function is obtained through the operation. An automatic optical inspection system, comprising: a camera module configured to perform imaging of an object under test under a first recommended optical parameter set to obtain a recommended image of the object under test; an error calculation module, configured to perform an operation on a DUT standard image and the recommended DUT image according to an optimized error function to obtain the DUT standard A recommended error value between the image and the recommended object under test image, and determine whether the recommended error value converges; and an optical parameter recommendation module configured to execute according to the recommended error value when it is determined that the recommended error value does not converge. The error value and the first recommended optical parameter set are calculated to obtain a second recommended optical parameter set; an automatic setting module is configured to perform setting of the automatic optical inspection system according to the second recommended optical parameter set; wherein, When it is judged that the recommended error value converges, the error calculation module is further used to determine that the first recommended optical parameter set is one of the best optical parameter sets of the automatic optical inspection system. The automatic optical inspection system as described in Claim 9, wherein the camera module is further configured to obtain a plurality of initial images of the object under test under a plurality of initial optical parameter sets; the error calculation module The group is further configured to perform calculations on the standard image of the UUT and the initial images of the UUT according to the optimized error function, so as to obtain the relationship between the initial images of the UUT and the standard image of the UUT. a plurality of initial error values; and the optical parameter recommendation module is further configured to perform calculations based on the initial error values and the initial optical parameter sets to obtain the first recommended optical parameter set. The automatic optical inspection system as described in claim 10, wherein the optical parameter recommendation module is further configured to execute: when it is judged that the recommended error value has not converged, perform calculations based on the initial error values and the initial optical parameter sets , to obtain the second recommended optical parameter set. The automatic optical inspection system as described in claim 10, wherein the optical parameter recommendation module is further configured to perform: obtaining a plurality of expected values of a plurality of candidate optical parameter sets compared with the initial optical parameter sets; and determining the The maximum of the expected values is the first recommended optical parameter set. The automatic optical inspection system as claimed in claim 12, wherein the optical parameter recommendation module is further configured to perform: the optical parameter recommendation module obtains the difference between each of the candidate optical parameter sets compared with each of the initial optical parameter sets a plurality of gaps; and according to the gaps and the initial error values, the expected values are obtained through operation. The automatic optical inspection system as described in Claim 9 further includes a light source module, and the automatic setting module is configured to execute according to the optimal optical parameters, and set the camera module and/or the light source module. The automatic optical inspection system as described in Claim 9 further includes: an optimization module configured to perform calculations on a training standard image, an expert standard image and a non-expert standard image to obtain the optimized error function. The automatic optical inspection system as recited in claim 15, wherein the optimization module is further configured to perform: to minimize the error between the training standard image and the expert standard image and to minimize the error between the training standard image and the non-expert The error between the standard images is the target, and a plurality of optimization weights of the optimization error function are obtained through operation.

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