A method, device, storage medium and system for predicting thermal errors of a die bonder

Abstract

The present invention discloses a method, device, storage medium, and system for predicting thermal errors of a die bonder, belonging to the technical field of thermal error prediction for a die bonder. The method comprises obtaining thermal errors at multiple thermal error measurement points on the die bonder's X-axis, the temperatures at multiple temperature measurement points on the die bonder measured when measuring the thermal errors, and the distance between the position to be predicted on the die bonder and the starting point of the X-axis; based on the correlation between the temperatures and the thermal errors, screening multiple candidate temperature-sensitive points corresponding to each thermal error measurement point from all temperature measurement points, and finally screening multiple temperature-sensitive points from all candidate temperature-sensitive points; inputting the distances and the temperatures at the multiple temperature-sensitive points into a linear regression model to calculate the thermal error of the position to be predicted. The present invention achieves accurate prediction of the thermal error of the die bonder by simultaneously considering temperature and position factors, and improves prediction accuracy through screening of temperature-sensitive points.

Description

Technical Field

[0001] The present invention relates to a method, device, storage medium and system for predicting thermal errors of a die bonder, and belongs to the technical field of thermal error prediction of a die bonder. Background Art

[0002] As an indispensable core piece of equipment in semiconductor chip and light-emitting diode chip packaging and testing production lines, die bonders have attracted significant attention for their high speed and precision. They not only represent the forefront of technological innovation but also serve as a powerful engine driving high-quality development and sustained progress in the manufacturing industry. However, the accuracy of die bonders is constrained by a variety of complex factors, such as geometric, thermal, and vibration errors. While numerous research teams have conducted in-depth studies on these factors, it is noteworthy that there are relatively few dedicated experimental studies on die bonder thermal errors and corresponding compensation technologies.

[0003] Currently, the prediction of thermal errors is basically focused on the thermal error prediction of CNC machine tools, while there is almost no research on the thermal error of die bonders. Summary of the Invention

[0004] The object of the present invention is to provide a method, device, storage medium and system for predicting thermal errors of a die bonder, so as to achieve accurate prediction of thermal errors of a die bonder.

[0005] To achieve the above objectives, the present invention is implemented by adopting the following technical solutions:

[0006] In a first aspect, the present invention provides a method for predicting thermal errors of a die bonder, comprising:

[0007] Obtaining thermal errors at multiple thermal error measurement points on the die bonder's X-axis, temperatures at multiple temperature measurement points on the die bonder measured when measuring the thermal errors, and a distance between a position to be predicted on the die bonder and a starting point on the X-axis;

[0008] Based on the correlation between the temperature and the thermal error, screening out multiple candidate temperature sensitive points corresponding to each thermal error measurement point from all temperature measurement points, and screening out multiple final temperature sensitive points from all candidate temperature sensitive points;

[0009] The distance and the temperatures at the multiple temperature-sensitive points are input into a linear regression model to calculate the thermal error of the position to be predicted; wherein the linear regression model is constructed based on the relationship between the distance between the position to be predicted and the starting point of the X-axis and the temperature at the temperature-sensitive points.

[0010] Furthermore, the step of selecting a plurality of candidate temperature sensitive points corresponding to each thermal error measurement point from all temperature measurement points includes:

[0011] The candidate temperature sensitive point corresponding to the i-th thermal error measurement point is calculated by the following method:

[0012] All temperature measurement points are divided into multiple groups. In each group of temperature measurement points, the correlation between the temperature at each temperature measurement point and the thermal error at the i-th thermal error measurement point is calculated using the fuzzy cluster analysis method. The temperature measurement point corresponding to the maximum correlation value is selected as the candidate temperature sensitive point in the group.

[0013] The candidate temperature sensitive points of each of the multiple groups of temperature measurement points are calculated to obtain multiple candidate temperature sensitive points corresponding to the i-th thermal error measurement point.

[0014] Furthermore, the step of selecting a plurality of candidate temperature sensitive points corresponding to each thermal error measurement point from all temperature measurement points includes:

[0015] The candidate temperature sensitive point corresponding to the i-th thermal error measurement point is calculated by the following method:

[0016] The correlation between the temperatures at all temperature measurement points and the thermal errors at the i-th thermal error measurement point is calculated by the fuzzy cluster analysis method, the correlations are sorted from large to small, and the temperature measurement points corresponding to the first preset number of correlations are used as the alternative temperature sensitive points corresponding to the i-th thermal error measurement point.

[0017] Furthermore, the final plurality of temperature sensitive points are screened out from all candidate temperature sensitive points, including:

[0018] All temperatures measured at the multiple candidate temperature sensitive points are sorted from largest to smallest according to the number of repetitions, and the candidate temperature sensitive points corresponding to the first second preset number of temperatures are used as the final temperature sensitive points.

[0019] Furthermore, the temperature at the measuring point is: a temperature obtained by a single measurement at the measuring point or a temperature obtained by taking an average value after multiple measurements at the measuring point.

[0020] Furthermore, if there are three temperature sensitive points, the linear regression model is expressed as follows:

[0021] (6);

[0022] (7);

[0023] (8);

[0024] (9);

[0025] (10);

[0026] Among them, E x is the thermal error at a distance of x mm from the starting point of the X axis of the die bonder, a x is the constant coefficient corresponding to the position to be predicted that is x mm away from the starting point of the X axis of the die bonder, b x is the T corresponding to the predicted position with a distance of x mm from the starting point of the X axis of the die bonder *1 The coefficient, c x is the T corresponding to the predicted position with a distance of x mm from the starting point of the X axis of the die bonder *2 The coefficient of d x is the T corresponding to the predicted position with a distance of x mm from the starting point of the X axis of the die bonder *3 The coefficient of T *1 is the temperature at the first temperature sensitive point, T *2 is the temperature at the second temperature sensitive point, T *3 is the temperature at the third temperature sensitive point, x is the distance between the position to be predicted and the starting point of the X axis of the die bonder, in millimeters, is used to calculate a x The slope of the univariate linear regression model is is used to calculate a x The constant term of the univariate linear regression model is is used to calculate b x The slope of the univariate linear regression model is is used to calculate b x The constant term of the univariate linear regression model is is used to calculate c x The slope of the univariate linear regression model is is used to calculate c x The constant term of the univariate linear regression model is is used to calculate d x The slope of the univariate linear regression model is is used to calculate d x The constant term in the univariate linear regression model.

[0027] In a second aspect, the present invention provides a device for predicting thermal errors of a die bonder, comprising:

[0028] a data acquisition module configured to: acquire thermal errors at a plurality of thermal error measurement points on an X-axis of the die bonder, temperatures at a plurality of temperature measurement points on the die bonder measured when measuring the thermal errors, and a distance between a position to be predicted on the die bonder and a starting point of the X-axis;

[0029] a temperature sensitive point screening module configured to: screen out multiple candidate temperature sensitive points corresponding to each thermal error measurement point from all temperature measurement points based on the correlation between the temperature and the thermal error, and screen out multiple final temperature sensitive points from all candidate temperature sensitive points;

[0030] The thermal error prediction module is configured to: input the distance and the temperatures at multiple temperature-sensitive points into a linear regression model to calculate the thermal error of the position to be predicted; wherein the linear regression model is constructed based on the relationship between the distance between the position to be predicted and the starting point of the X-axis and the temperature at the temperature-sensitive points.

[0031] In a third aspect, the present invention provides a computer-readable storage medium having a computer program / instruction stored thereon. When the computer program / instruction is executed by a processor, the steps of the method for predicting thermal errors of a die bonder described in any one of the first aspects are implemented.

[0032] In a fourth aspect, the present invention provides a computer system comprising:

[0033] Memory, used to store computer programs / instructions;

[0034] A processor is configured to execute the computer program / instructions to implement the steps of the method for predicting thermal errors of a die bonder according to any one of the first aspects.

[0035] Compared with the prior art, the present invention has the following beneficial effects:

[0036] The present invention provides a method, device, storage medium and system for predicting the thermal error of a die bonder. By simultaneously considering both temperature and position when constructing a linear regression model, the thermal error of the die bonder is a quantity related to both temperature and stroke position. This allows the model to be used for predicting the thermal error of the die bonder with higher accuracy and is more adaptable to situations where the temperature change caused by the high-speed reciprocating motion of the die bonder patch and the corresponding thermal error change are very drastic. Based on the correlation between the temperature at the temperature measurement point and the thermal error at the thermal error measurement point, temperature-sensitive points with temperature-sensitive characteristics are screened out, and the thermal error is predicted on this basis to improve the prediction accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 This is a flow chart of a method for predicting thermal errors of a die bonder provided in this embodiment;

[0038] Figure 2 is an evaluation curve diagram of the linear regression model of the X-axis starting point provided in this embodiment;

[0039] Figure 3 is a deviation curve diagram of the thermal error measurement value and the true value of the thermal error at the starting point of the X-axis provided in this embodiment;

[0040] Figure 4 : is an evaluation curve diagram of the linear regression model provided in this embodiment at a position 40 mm from the starting point of the X axis;

[0041] Figure 5 This is a deviation curve diagram of the thermal error measurement value at a position 40 mm from the X-axis starting point and the actual thermal error value provided by this embodiment;

[0042] Figure 6 : is an evaluation curve diagram of the linear regression model provided in this embodiment at a position 80 mm from the starting point of the X-axis;

[0043] Figure 7 This is a deviation curve diagram of the thermal error measurement value at a position 80 mm from the X-axis starting point and the actual thermal error value provided by this embodiment;

[0044] Figure 8 : is an evaluation curve diagram of the linear regression model provided in this embodiment at a position 120 mm from the starting point of the X-axis;

[0045] Figure 9 This is a deviation curve diagram of the thermal error measurement value at a position 120 mm from the X-axis starting point and the actual thermal error value provided by this embodiment;

[0046] Figure 10 : is an evaluation curve diagram of the linear regression model provided in this embodiment at a position 160 mm from the starting point of the X-axis;

[0047] Figure 11 This is a deviation curve diagram of the thermal error measurement value at a position 160 mm from the X-axis starting point and the true value of the thermal error provided by this embodiment. DETAILED DESCRIPTION

[0048] The present invention will be further described below in conjunction with the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solutions of the present invention and are not intended to limit the scope of protection of the present invention.

[0049] Example 1

[0050] like Figure 1 As shown, the present invention provides a method for predicting thermal errors of a die bonder, comprising:

[0051] Obtaining thermal errors at multiple thermal error measurement points on the die bonder's X-axis, temperatures at multiple temperature measurement points on the die bonder measured when measuring the thermal errors, and a distance between a position to be predicted on the die bonder and a starting point on the X-axis;

[0052] Based on the temperature and the thermal error, screening out multiple candidate temperature sensitive points corresponding to each thermal error measurement point from all temperature measurement points, and screening out multiple final temperature sensitive points from all candidate temperature sensitive points;

[0053] The distance and the temperature at multiple temperature-sensitive points are input into a constructed linear regression model to calculate the thermal error of the position to be predicted; wherein the linear regression model is constructed based on the relationship between the distance between the position to be predicted and the starting point of the X-axis and the temperature at the temperature-sensitive point.

[0054] The present invention takes both temperature and position factors into consideration when constructing a linear regression model. The thermal error of the die bonder is a quantity that is related to both temperature and stroke position. This makes the model more accurate when used to predict the thermal error of the die bonder, and more adaptable to situations where the temperature change caused by the high-speed reciprocating motion of the die bonder patch and the corresponding thermal error change are very drastic. Based on the correlation between the temperature at the temperature measurement point and the thermal error at the thermal error measurement point, temperature-sensitive points with temperature-sensitive characteristics are screened out, and the thermal error is predicted on this basis to improve the prediction accuracy.

[0055] Example 2

[0056] This embodiment provides a method for predicting thermal errors of a die bonder to overcome fluctuations in temperature-sensitive points, including:

[0057] Step 1: Obtain the thermal error of each thermal error measurement point on the X-axis of the die bonder and the temperature of each temperature measurement point on the die bonder.

[0058] The temperature measurement points on the X-axis of the die bonder have the following requirements: they must be located near the heat source on the die bonder, and the number of temperature sensors at all temperature measurement points must be greater than the number of heat sources on the die bonder, and a temperature sensor must be installed at each temperature measurement point.

[0059] Temperature through T ij To express, T ij represents the temperature measured by the temperature sensor at the jth temperature measurement point when measuring the thermal error of the i-th thermal error measurement point. The number of thermal error measurement points is m, that is, the maximum value of i is m. The number of temperature measurement points is n, that is, the maximum value of j is n. In this embodiment, the value of m is 5 and the value of n is 12.

[0060] Thermal error is determined by E x To express, E x Indicates the thermal error at a distance of x mm from the starting point of the X-axis of the die bonder.

[0061] In this embodiment, five thermal error measurement points are used as an example. The distance between any two adjacent thermal error measurement points is equal. The five thermal error measurement points are located at: the die bonder's X-axis starting point (denoted by S0, i.e., the first thermal error measurement point, with a distance of 0 mm from the die bonder's X-axis starting point); 40 mm from the starting point on the die bonder's X-axis (denoted by S40, i.e., the second thermal error measurement point); 80 mm from the starting point on the die bonder's X-axis (denoted by S80, i.e., the third thermal error measurement point); 120 mm from the starting point on the die bonder's X-axis (denoted by S120, i.e., the fourth thermal error measurement point); and 160 mm from the starting point on the die bonder's X-axis (denoted by S160, i.e., the fifth thermal error measurement point). The thermal error measurement points can be represented by the general formula Sx, where Sx represents a thermal error measurement point that is x mm away from the die bonder's X-axis starting point.

[0062] The temperature measurement point can be expressed using the general formula F j To express, F j represents the jth temperature measurement point.

[0063] In this embodiment, the measurement order of the five thermal error measurement points is: S0, S40, S80, S120 and S160; when the guide rail on the die bonder moves to S0 (the first thermal error measurement point), the thermal error E0 at S0 is measured, and the temperatures of all temperature measurement points are measured synchronously at this time to obtain T 11 To T 1n When the guide rail on the die bonder moves to S40 (the second thermal error measurement point), measure the thermal error E at S40. 40 At this time, the temperature of all temperature measurement points is measured synchronously to obtain T 21 To T 2n When the guide rail on the die bonder moves to S80 (the third thermal error measurement point), measure the thermal error E at S80. 80 At this time, the temperature of all temperature measurement points is measured synchronously to obtain T 31 To T 3n When the guide rail on the die bonder moves to S120 (the fourth thermal error measurement point), measure the thermal error E at S120. 120 At this time, the temperature of all temperature measurement points is measured synchronously to obtain T 41 To T 4n When the guide rail on the die bonder moves to S160 (the fifth thermal error measurement point), measure the thermal error E at S160. 160 At this time, the temperature of all temperature measurement points is measured synchronously to obtain T 51 To T 5n .

[0064] Step 2: Based on the temperature and thermal error obtained in step 1, filter out temperature-sensitive points from multiple temperature measurement points.

[0065] Temperature sensitive points are temperature measurement points with temperature sensitive characteristics. In this embodiment, the temperature sensitive points are screened out by grouping and combining to find the mode. The specific screening method is as follows:

[0066] The candidate temperature sensitive points corresponding to the first thermal error measurement point are first calculated by the following method: all temperature measurement points are divided into three groups. In each group of temperature measurement points, the correlation between the temperature of each temperature measurement point and the thermal error E0 at the first thermal error measurement point is calculated by the fuzzy cluster analysis method. The temperature measurement point corresponding to the maximum correlation value is taken as the candidate temperature sensitive point in the group. The candidate temperature sensitive points of each of the three groups of temperature measurement points are calculated by the above method, and the three candidate temperature sensitive points corresponding to the first thermal error measurement point are obtained.

[0067] The alternative temperature sensitive points corresponding to other thermal error measurement points are calculated using the same method as that for calculating the alternative temperature sensitive points corresponding to the first thermal error measurement point. Finally, all the alternative temperature sensitive points corresponding to the five thermal error measurement points are obtained. Each thermal error measurement point corresponds to three alternative temperature sensitive points, for a total of 15 alternative temperature sensitive points.

[0068] All temperatures measured at the 15 candidate temperature-sensitive points are listed one by one. Taking each candidate temperature-sensitive point as an example where only one measurement is performed, a total of 75 temperatures are measured (when measuring the thermal error at each thermal error measurement point, the temperatures of all temperature-sensitive points are measured simultaneously. There are a total of 5 thermal error measurement points, so 5×15=75 temperatures). The candidate temperature-sensitive points corresponding to the top three temperatures with the highest number of repetitions are taken as the final temperature-sensitive points.

[0069] Step 3: Based on the temperature at the temperature-sensitive point selected in step 2, a linear regression model is constructed to predict the thermal error within the entire range of the die bonder stroke.

[0070] The temperatures at the three selected temperature sensitive points are numbered as T *1 、T *2 and T *3 .

[0071] A polynomial fitting model is constructed for the measured thermal error and temperature. The obtained polynomial fitting model is as follows:

[0072] E0=a0+b0T *1 + c0T *2 + d0T *3 (1);

[0073] E 40 =a 40 +b 40 T *1 + c 40 T*2 + d 40 T *3 (2);

[0074] E 80 =a 80 +b 80 T *1 + c 80 T *2 + d 80 T *3 (3);

[0075] E 120 =a 120 +b 120 T *1 + c 120 T *2 + d 120 T *3 (4);

[0076] E 160 =a 160 +b 160 T *1 + c 160 T *2 + d 160 T *3 (5);

[0077] Among them, E x is the thermal error at a distance of x mm from the starting point of the X axis of the die bonder, that is, E 0、 E 40、 E 80、 E 120、 E 160 are the thermal errors at distances of 0 mm, 40 mm, 80 mm, 120 mm, and 160 mm from the starting point of the X-axis of the die bonder; T *1 is the temperature at the first temperature sensitive point, T *2 is the temperature at the second temperature sensitive point, T *3 is the temperature at the third temperature sensitive point, a0 is the constant coefficient in the polynomial fitting model corresponding to E0, a 40 It's E 40 The constant coefficient in the corresponding polynomial fitting model, a 80 It's E 80 The constant coefficient in the corresponding polynomial fitting model, a 120 It's E 120 The constant coefficient in the corresponding polynomial fitting model, a 160 It's E 160 The constant coefficient in the polynomial fitting model corresponding to E0, b0 is the constant coefficient in the polynomial fitting model corresponding to E0 *1The coefficient of b 40 It's E 40 The corresponding polynomial fitting model T *1 The coefficient of b 80 It's E 80 The corresponding polynomial fitting model T *1 The coefficient of b 120 It's E 120 The corresponding polynomial fitting model T *1 The coefficient of b 160 It's E 160 The corresponding polynomial fitting model T *1 The coefficient of E0, c0 is the polynomial fitting model corresponding to T *2 The coefficient, c 40 It's E 40 The corresponding polynomial fitting model T *2 The coefficient, c 80 It's E 80 The corresponding polynomial fitting model T *2 The coefficient, c 120 It's E 120 The corresponding polynomial fitting model T *2 The coefficient, c 160 It's E 160 The corresponding polynomial fitting model T *2 The coefficient of d0 is the polynomial fitting model corresponding to E0. *3 The coefficient of d 40 It's E 40 The corresponding polynomial fitting model T *3 The coefficient of d 80 It's E 80 The corresponding polynomial fitting model T *3 The coefficient of d 120 It's E 120 The corresponding polynomial fitting model T *3 The coefficient of d 160 It's E 160 The corresponding polynomial fitting model T *3 All the above coefficients are constants determined by linear fitting.

[0078] The linear regression model can be obtained from formulas (1), (2), (3), (4) and (5), that is, formula (6):

[0079] E x =a x +b x T *1 + c x T *2 + d x T*3 (6);

[0080] Among them, E x is the thermal error of the position to be predicted at a distance of x mm from the starting point of the X axis of the die bonder, a x is the constant coefficient corresponding to the position to be predicted that is x mm away from the starting point of the X axis of the die bonder, b x is the T corresponding to the predicted position with a distance of x mm from the starting point of the X axis of the die bonder *1 The coefficient, c x is the T corresponding to the predicted position with a distance of x mm from the starting point of the X axis of the die bonder *2 The coefficient of d x is the T corresponding to the predicted position with a distance of x mm from the starting point of the X axis of the die bonder *3 The coefficient of T *1 is the temperature at the first temperature sensitive point, T *2 is the temperature at the second temperature sensitive point, T *3 is the temperature at the third temperature sensitive point.

[0081] Formula (6) is the general formula for calculating thermal error. The coefficients for each position are different. x 、b x 、c x and d x It is determined by linear fitting, specifically by constructing the following univariate linear regression model:

[0082] (7);

[0083] (8);

[0084] (9);

[0085] (10);

[0086] Where x is the distance between the position to be predicted and the starting point of the X axis of the die bonder, in millimeters. is used to calculate a x The slope of the univariate linear regression model is is used to calculate a x The constant term of the univariate linear regression model is is used to calculate b x The slope of the univariate linear regression model is is used to calculate b x The constant term of the univariate linear regression model is is used to calculate c x The slope of the univariate linear regression model is is used to calculate cx The constant term of the univariate linear regression model is is used to calculate d x The slope of the univariate linear regression model is is used to calculate d x The constant term in the univariate linear regression model.

[0087] 、 、 、 、 、 、 and The values of are determined by linear fitting.

[0088] The thermal error of any position can be predicted by formula (6). It can be calculated by substituting the distance from the position to the starting point of the X-axis of the die bonder into formula (6).

[0089] Step 4: Verify the linear regression model obtained in step 3 by obtaining some historical thermal errors and corresponding historical temperatures.

[0090] Figure 2 、 Figure 4 、 Figure 6 、 Figure 8 and Figure 10 The evaluation curves of the linear regression model obtained by linear fitting at different positions represent the trend of thermal error changing with the number of samplings. There are two curves in the evaluation curve, namely the thermal error true value change curve and the thermal error measurement value change curve. The vertical axis is the thermal error in mm, and the horizontal axis is the number of samplings, which is a pure value. Figure 2 、 Figure 4 、 Figure 6 、 Figure 8 and Figure 10 It can be seen that the fitting result of the linear regression model at the first position (the starting point of the X-axis) is very good, while the fitting results at the other four positions are slightly worse. Figure 2 、 Figure 4 、 Figure 6 、 Figure 8 and Figure 10 In the linear regression model, the true value change curve of thermal error is obtained, that is, Figure 2 、 Figure 4 、 Figure 6 、 Figure 8 and Figure 10 The curve formed by connecting the symbols 0 in the graph is plotted using the measurement results of the thermal error to obtain the thermal error measurement value change curve, which is obtained by Figure 2 、 Figure 4 、 Figure 6 、 Figure 8 and Figure 10 The curves connected by the * symbol in the figure show good agreement between the true and measured thermal error values. The fourth position, 120 mm from the X-axis starting point, exhibits the largest fitting error, with a root mean square error of 0.00121 mm and an error band of 0.00176 mm. This error band is less than 3 μm, meeting the requirements. The above analysis demonstrates that the proposed linear regression model exhibits excellent fitting performance.

[0091] Figure 3 、 Figure 5 、 Figure 7 、 Figure 9 and Figure 11 These are the deviation curves of the thermal error measurement value and the true value of the thermal error at different positions. Figure 3 、 Figure 5 、 Figure 7 、 Figure 9 and Figure 11 The units of the horizontal coordinate and the total coordinate are both mm, which represent the deviation between the measured thermal error and the true thermal error value. Figure 3 、 Figure 5 、 Figure 7 、 Figure 9 and Figure 11 When the deviation curve completely coincides with the straight line with a slope of 1 and an intercept of 0, there is no deviation between the measured thermal error value and the true thermal error value, and the estimated error is 0. The further the deviation curve moves away from the straight line with a slope of 1 and an intercept of 0, the lower the estimated accuracy. Figure 3 、 Figure 5 、 Figure 7 、 Figure 9 and Figure 11 It can be seen that the estimation accuracy is the highest at the first position, but the estimation accuracy of the other four positions is also relatively high.

[0092] In this embodiment, the number of temperature sensitive points is selected to be three, but is not limited to three. As long as there are two or more temperature sensitive points, the thermal error prediction method of the die bonding machine provided in this embodiment can be implemented. The linear regression models corresponding to different numbers of temperature sensitive points can be obtained by referring to the construction process of the linear regression model corresponding to three temperature sensitive points.

[0093] Example 3

[0094] Based on the same technical concept as Example 2, this embodiment provides a method for predicting thermal errors of a die bonder that overcomes fluctuations in temperature sensitive points. The difference between this embodiment and Example 2 is that in step 2, the candidate temperature sensitive point corresponding to the first thermal error measurement point is first calculated by the following method:

[0095] The candidate temperature sensitive points corresponding to the first thermal error measurement point are first calculated by the following method: the correlation between the temperatures at all temperature measurement points and the thermal error E1 at the first thermal error measurement point is calculated by the fuzzy cluster analysis method, the correlations are sorted from large to small, and the temperature measurement points corresponding to the top three correlations are taken as the three candidate temperature sensitive points corresponding to the first thermal error measurement point.

[0096] Example 4

[0097] Based on the same technical concept as Example 1, this embodiment provides a device for predicting thermal errors of a die bonder, including:

[0098] a data acquisition module configured to: acquire thermal errors at a plurality of thermal error measurement points on an X-axis of the die bonder, temperatures at a plurality of temperature measurement points on the die bonder measured when measuring the thermal errors, and a distance between a position to be predicted on the die bonder and a starting point of the X-axis;

[0099] a temperature sensitive point screening module configured to: screen out multiple candidate temperature sensitive points corresponding to each thermal error measurement point from all temperature measurement points based on the correlation between the temperature and the thermal error, and screen out multiple final temperature sensitive points from all candidate temperature sensitive points;

[0100] The thermal error prediction module is configured to: input the distance and the temperatures at multiple temperature-sensitive points into a linear regression model to calculate the thermal error of the position to be predicted; wherein the linear regression model is constructed based on the relationship between the distance between the position to be predicted and the starting point of the X-axis and the temperature at the temperature-sensitive points.

[0101] Example 5

[0102] Based on the same technical concept as Example 1, this embodiment provides a computer-readable storage medium having a computer program / instruction stored thereon. When the computer program / instruction is executed by a processor, the steps of the method for predicting thermal errors of a die bonder provided in Example 1 are implemented:

[0103] Obtaining thermal errors at multiple thermal error measurement points on the die bonder's X-axis, temperatures at multiple temperature measurement points on the die bonder measured when measuring the thermal errors, and a distance between a position to be predicted on the die bonder and a starting point on the X-axis;

[0104] Based on the correlation between the temperature and the thermal error, screening out multiple candidate temperature sensitive points corresponding to each thermal error measurement point from all temperature measurement points, and screening out multiple final temperature sensitive points from all candidate temperature sensitive points;

[0105] The distance and the temperatures at the multiple temperature-sensitive points are input into a linear regression model to calculate the thermal error of the position to be predicted; wherein the linear regression model is constructed based on the distance between the position to be predicted and the starting point of the X-axis and the temperature at the temperature-sensitive points.

[0106] Example 6

[0107] Based on the same technical concept as Example 1, this embodiment provides a computer system, including:

[0108] Memory, used to store computer programs / instructions;

[0109] A processor is configured to execute the computer program / instructions to implement the steps of the method for predicting thermal errors of a die bonder provided in Example 1:

[0110] Obtaining thermal errors at multiple thermal error measurement points on the die bonder's X-axis, temperatures at multiple temperature measurement points on the die bonder measured when measuring the thermal errors, and a distance between a position to be predicted on the die bonder and a starting point on the X-axis;

[0111] Based on the correlation between the temperature and the thermal error, screening out multiple candidate temperature sensitive points corresponding to each thermal error measurement point from all temperature measurement points, and screening out multiple final temperature sensitive points from all candidate temperature sensitive points;

[0112] The distance and the temperatures at the multiple temperature-sensitive points are input into a linear regression model to calculate the thermal error of the position to be predicted; wherein the linear regression model is constructed based on the relationship between the distance between the position to be predicted and the starting point of the X-axis and the temperature at the temperature-sensitive points.

[0113] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0114] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0115] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0116] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0117] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for predicting thermal errors of a die bonder, characterized in that: include: Obtaining thermal errors at multiple thermal error measurement points on the die bonder's X-axis, temperatures at multiple temperature measurement points on the die bonder measured when measuring the thermal errors, and a distance between a position to be predicted on the die bonder and a starting point on the X-axis; Based on the correlation between the temperature and the thermal error, screening out multiple candidate temperature sensitive points corresponding to each thermal error measurement point from all temperature measurement points, and screening out multiple final temperature sensitive points from all candidate temperature sensitive points; Inputting the distance and the temperatures at the plurality of temperature-sensitive points into a linear regression model to calculate the thermal error of the position to be predicted; wherein the linear regression model is constructed based on the relationship between the distance between the position to be predicted and the starting point of the X-axis and the temperature at the temperature-sensitive points; If there are three temperature sensitive points, the linear regression model is expressed as: （6）； （7）； （8）； （9）； （10）； Among them, E x is the thermal error at a distance of x mm from the starting point of the X axis of the die bonder, a x is the constant coefficient corresponding to the position to be predicted that is x mm away from the starting point of the X axis of the die bonder, b x is the T corresponding to the predicted position with a distance of x mm from the starting point of the X axis of the die bonder *1 The coefficient, c x is the T corresponding to the predicted position with a distance of x mm from the starting point of the X axis of the die bonder *2 The coefficient of d x is the T corresponding to the predicted position with a distance of x mm from the starting point of the X axis of the die bonder *3 The coefficient of T *1 is the temperature at the first temperature sensitive point, T *2 is the temperature at the second temperature sensitive point, T *3 is the temperature at the third temperature sensitive point, x is the distance between the position to be predicted and the starting point of the X axis of the die bonder, in millimeters, is used to calculate a x The slope of the univariate linear regression model is is used to calculate a x The constant term of the univariate linear regression model is is used to calculate b x The slope of the univariate linear regression model is is used to calculate b x The constant term of the univariate linear regression model is is used to calculate c x The slope of the univariate linear regression model is is used to calculate c x The constant term of the univariate linear regression model is is used to calculate d x The slope of the univariate linear regression model is is used to calculate d x The constant term in the univariate linear regression model.

2. The method for predicting thermal errors of a die bonder according to claim 1, wherein: The step of selecting a plurality of candidate temperature sensitive points corresponding to each thermal error measurement point from all temperature measurement points includes: The candidate temperature sensitive point corresponding to the i-th thermal error measurement point is calculated by the following method: All temperature measurement points are divided into multiple groups. In each group of temperature measurement points, the correlation between the temperature at each temperature measurement point and the thermal error at the i-th thermal error measurement point is calculated using the fuzzy cluster analysis method. The temperature measurement point corresponding to the maximum correlation value is selected as the candidate temperature sensitive point in the group. The candidate temperature sensitive points of each of the multiple groups of temperature measurement points are calculated to obtain multiple candidate temperature sensitive points corresponding to the i-th thermal error measurement point.

3. The method for predicting thermal errors of a die bonder according to claim 1, wherein: The step of selecting a plurality of candidate temperature sensitive points corresponding to each thermal error measurement point from all temperature measurement points includes: The candidate temperature sensitive point corresponding to the i-th thermal error measurement point is calculated by the following method: The correlation between the temperatures at all temperature measurement points and the thermal errors at the i-th thermal error measurement point is calculated by the fuzzy cluster analysis method, the correlations are sorted from large to small, and the temperature measurement points corresponding to the first preset number of correlations are used as the alternative temperature sensitive points corresponding to the i-th thermal error measurement point.

4. The method for predicting thermal errors of a die bonder according to claim 1, wherein: The final plurality of temperature sensitive points are screened out from all candidate temperature sensitive points, including: All temperatures measured at the multiple candidate temperature sensitive points are sorted from largest to smallest according to the number of repetitions, and the candidate temperature sensitive points corresponding to the first second preset number of temperatures are used as the final temperature sensitive points.

5. The method for predicting thermal error of a die bonder according to claim 1, wherein: The temperature at the measuring point is: a temperature obtained by a single measurement at the measuring point or a temperature obtained by taking an average value after multiple measurements at the measuring point.

6. A device for predicting thermal errors of a die bonder, characterized in that: include: a data acquisition module configured to: acquire thermal errors at a plurality of thermal error measurement points on an X-axis of the die bonder, temperatures at a plurality of temperature measurement points on the die bonder measured when measuring the thermal errors, and a distance between a position to be predicted on the die bonder and a starting point of the X-axis; a temperature sensitive point screening module configured to: screen out multiple candidate temperature sensitive points corresponding to each thermal error measurement point from all temperature measurement points based on the correlation between the temperature and the thermal error, and screen out multiple final temperature sensitive points from all candidate temperature sensitive points; a thermal error prediction module configured to: input the distance and the temperatures at the plurality of temperature-sensitive points into a linear regression model to calculate the thermal error of the position to be predicted; wherein the linear regression model is constructed based on the relationship between the distance between the position to be predicted and the starting point of the X-axis and the temperatures at the temperature-sensitive points; If there are three temperature sensitive points, the linear regression model is expressed as follows: （6）； （7）； （8）； （9）； （10）； Among them, E x is the thermal error at a distance of x mm from the starting point of the X axis of the die bonder, a x is the constant coefficient corresponding to the position to be predicted that is x mm away from the starting point of the X axis of the die bonder, b x is the T corresponding to the predicted position with a distance of x mm from the starting point of the X axis of the die bonder *1 The coefficient, c x is the T corresponding to the predicted position with a distance of x mm from the starting point of the X axis of the die bonder *2 The coefficient of d x is the T corresponding to the predicted position with a distance of x mm from the starting point of the X axis of the die bonder *3 The coefficient of T *1 is the temperature at the first temperature sensitive point, T *2 is the temperature at the second temperature sensitive point, T *3 is the temperature at the third temperature sensitive point, x is the distance between the position to be predicted and the starting point of the X axis of the die bonder, in millimeters, is used to calculate a x The slope of the univariate linear regression model is is used to calculate a x The constant term of the univariate linear regression model is is used to calculate b x The slope of the univariate linear regression model is is used to calculate b x The constant term of the univariate linear regression model is is used to calculate c x The slope of the univariate linear regression model is is used to calculate c x The constant term of the univariate linear regression model is is used to calculate d x The slope of the univariate linear regression model is is used to calculate d x The constant term in the univariate linear regression model.

7. A computer-readable storage medium having a computer program / instruction stored thereon, characterized in that: When the computer program / instruction is executed by a processor, the steps of the method for predicting thermal errors of a die bonder according to any one of claims 1 to 5 are implemented.

8. A computer system, characterized in that: include: Memory, used to store computer programs / instructions; A processor is configured to execute the computer program / instructions to implement the steps of the method for predicting thermal errors of a die bonder according to any one of claims 1 to 5.

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