Electronic device manufacturing system

Abstract

Provided is a manufacturing system, particularly a manufacturing system suitable for manufacturing an electronic device, comprising: a machine for providing historical parameter data and real-time parameter data; the modeling calculation unit is used for receiving the historical parameter data and establishing a control sequence table according to the historical parameter data; and the analysis and calculation unit is used for calculating an action value according to the control sequence table and the real-time parameter data.

Description

Technical Field

[0001] The present invention relates to a manufacturing system, and particularly to a system for manufacturing electronic devices. Background Art

[0002] In traditional process control, measurement stations are inserted into the manufacturing process, and the product quality characteristics of in-process products are sampled and measured to confirm whether they meet the product specifications. In addition, control charts can be used to monitor whether process parameters deviate abnormally. However, in the highly automated semiconductor and panel manufacturing industries, when the equipment at a process station malfunctions and causes process parameter deviations, when the measurement station samples and measures the first abnormal product, a large number of defective products may have been produced, resulting in significant losses.

[0003] Advanced Process Control monitors the process parameters of equipment with a Fault Detection and Classification system, advancing process monitoring to the process station where abnormalities occur to reduce the cost losses of a large number of defective products. However, the number of equipment process parameters is large and their correlation is high, making it difficult to effectively and correctly detect abnormalities, and the existing Fault Detection and Classification system is difficult to effectively and correctly detect whether the correlation of parameters deviates.

[0004] Therefore, a manufacturing system is needed to efficiently and correctly detect the deviation of parameter correlation to correct parameters that violate the correlation. Summary of the Invention

[0005] To solve the above problems, embodiments of the present disclosure provide a manufacturing system for an electronic device, including: a machine platform for providing historical parameter data and real-time parameter data; a modeling calculation unit for receiving the historical parameter data and establishing a control sequence table based thereon; and an analysis calculation unit for calculating an action value based on the control sequence table and the real-time parameter data. Description of the Drawings

[0006] The present disclosure can be more fully understood by reading the following detailed description and examples with reference to the accompanying drawings, in which:

[0007] Figure 1 is a schematic distribution diagram of parameters of a process according to some embodiments of the present disclosure.

[0008] Figure 2 is a reasonable distribution range observed from perspective A1 or perspective A2 of Figure 1 according to some embodiments of the present disclosure.

[0009] Figure 3 is a reasonable distribution range observed from the direction of the third principal component according to some embodiments of the present disclosure.

[0010] Figure 4 is a reasonable distribution range observed from the direction of the second principal component according to some embodiments of the present disclosure.

[0011] Figure 5 is a method for establishing a manufacturing control sequence list of a manufacturing method according to some embodiments of the present disclosure.

[0012] Figure 6 is a manufacturing method according to some embodiments of the present disclosure.

[0013] Figure 7 is a manufacturing method according to some embodiments of the present disclosure.

[0014] Figure 8 is a positive and negative value table of numerical values according to some embodiments of the present disclosure.

[0015] Figure 9 is a schematic diagram of the action values of different parameter groups according to some embodiments of the present disclosure.

[0016] Figure 10 is a schematic diagram of a manufacturing system according to some embodiments of the present disclosure.

[0017]

Symbol Explanation

[0018] 100: Method

[0019] 110: Step

[0020] 120: Step

[0021] 130: Step

[0022] 200: Manufacturing method

[0023] 210: Step

[0024] 220: Step

[0025] 222: Step

[0026] 224: Step

[0027] 300: Manufacturing method

[0028] 310: Step

[0029] 320: Step

[0030] 321: Step

[0031] 322: Step

[0032] 323: Step

[0033] 324: Step

[0034] 325: Step

[0035] 326: Step

[0036] 1010: Modeling and Calculation Unit

[0037] 1020: Data Database

[0038] 1025: Analysis Database

[0039] 1030: Analysis and Calculation Unit

[0040] 1040: Machine

[0041] 1099: User

[0042] 2241: Step

[0043] 2242: Step

[0044] 2243: Step

[0045] 3261: Step

[0046] 3262: Step

[0047] 3263: Step

[0048] A1: Perspective

[0049] A2: Perspective

[0050] E1: Control Boundary

[0051] E2: Control Boundary

[0052] O: Process Center

[0053] PC1: First Principal Component

[0054] PC2: Second Principal Component

[0055] PC3: Third Principal Component

[0056] R: Reasonable Distribution Range

[0057] S1: Sample

[0058] S2: Sample

[0059] S3: Sample

[0060] S4: Sample

[0061] X1: First Parameter

[0062] X2: Second Parameter

[0063] X3: Third Parameter Detailed Implementation Manner

[0064] The present disclosure can be more clearly understood by referring to the following detailed description and in conjunction with the accompanying drawings. It should be noted that, for the convenience of the reader's understanding and the simplicity of the drawings, only a part of the display device is shown in the multiple drawings of the present disclosure, and the specific components in the drawings are not drawn to actual scale. In addition, the number and size of each component in the drawings are only for illustration and are not used to limit the scope of the present disclosure. In addition, similar and / or corresponding reference numerals may be used in different embodiments, only for simply and clearly describing some embodiments, and do not represent any association between the different embodiments and / or structures discussed.

[0065] Throughout the specification of the present disclosure and the appended claims, certain terms are used to refer to specific components. Those skilled in the art should understand that electronic device manufacturers may refer to the same component by different names. This document does not intend to distinguish components that have the same function but different names. In the following specification and claims, words such as "comprising", "including", "having" are open-ended words, and thus should be interpreted as meaning "including but not limited to...". Therefore, when the description of the present disclosure uses the terms "comprising", "including" and / or "having", it specifies the existence of the corresponding features, regions, steps, operations and / or components, but does not exclude the existence of one or more corresponding features, regions, steps, operations and / or components.

[0066] In addition, relative terms may be used in the embodiments, such as "below" or "bottom" and "above" or "top", to describe the relative relationship of one component of the drawing to another component. It can be understood that if the device in the drawing is flipped upside down, the component described on the "below" side will become the component on the "above" side.

[0067] When a corresponding member (such as a component, a film layer or a region) is referred to as "on another member" or "connected to another member", it can be directly on another member or directly connected to another member, or there may be other members between the two. On the other hand, when a member is referred to as "directly on another member" or "directly connected to another member", there are no members between the two. In addition, when a member is referred to as "on another member", there is an up-and-down relationship between the two in the top-down direction, and this member can be above or below another member, and this up-and-down relationship depends on the orientation of the device.

[0068] The terms "about", "substantially" or "approximately" are generally interpreted as within 10% of the given value or range, or within 5%, 3%, 2%, 1% or 0.5% of the given value or range.

[0069] In the present disclosure, when it is mentioned that component A overlaps component B, it is intended to include the case of at least partial overlap.

[0070] It is understood that although terms such as "first", "second", etc. may be used herein to describe various components, layers, and / or parts, these components, layers, and / or parts should not be limited by these terms, and these terms are only used to distinguish different components, layers, and / or parts. Therefore, a first component, layer, and / or part discussed below may be referred to as a second component, layer, and / or part without departing from the inspiration of some embodiments of the present disclosure. Additionally, for the sake of brevity, the terms "first", "second", etc. may not be used in the specification to distinguish different components. Without violating the scope defined by the appended claims, the first component and / or the second component recited in the claims may be interpreted as any component that conforms to the description in the specification.

[0071] It should be noted that the technical solutions provided in different embodiments below can be mutually replaced, combined, or mixed for use to form another embodiment without violating the spirit of the present disclosure.

[0072] Unless otherwise defined, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by those skilled in the art to which this disclosure pertains. It is understood that these terms, such as those defined in a commonly used dictionary, should be interpreted as having a meaning consistent with the relevant technology and the background or context of the present disclosure, and should not be interpreted in an idealized or overly formal manner unless specifically defined herein.

[0073] The electronic device may include a display device, a backlight device, an antenna device, a sensing device, a vehicle device or a splicing device, but is not limited thereto. The electronic device may be a bendable or flexible electronic device. The display device may be a non-self-luminous display device or a self-luminous display device. The antenna device may be a liquid crystal antenna device or a non-liquid crystal antenna device, and the sensing device may be a sensing device for sensing capacitance, light, heat or ultrasound, but is not limited thereto. The electronic components may include passive components and active components, such as capacitors, resistors, inductors, diodes, transistors, etc. The diode may include a light emitting diode or a photodiode. The light emitting diode may, for example, include an organic light emitting diode (OLED), a sub-millimeter light emitting diode (mini LED), a micro light emitting diode (micro LED) or a quantum dot light emitting diode (quantum dot LED), but is not limited thereto. The splicing device may, for example, be a display device splicing device or an antenna splicing device, but is not limited thereto. It should be noted that the electronic device may be any combination of the foregoing, but is not limited thereto. The present disclosure will be described below using a method for controlling process parameters for manufacturing an electronic device, but the present disclosure is not limited thereto.

[0074] See also Figure 1 , Figure 1 is a schematic distribution diagram of process parameters for manufacturing an electronic device according to some embodiments of the present disclosure. Figure 1 The first parameter X1, the second parameter X2, and the third parameter X3 of the process are shown. It should be noted that Figure 1 The three parameters shown (the first parameter X1 , the second parameter X2 , and the third parameter X3 ) are only an example, and the present disclosure is not limited to this number.

[0075] In fact, manufacturing electronic devices requires at least one machine to perform the manufacturing process. Controlling the manufacturing process of a machine requires at least one parameter, such as process temperature, flow rate of the incoming gas, pressure, concentration of the incoming fluid, pH, etc. Therefore, the process can have more or fewer parameters than 3 parameters. For example, a process can have more than 10 parameters, and Figure 1 The parameters shown are only 3 of them, which does not mean that the process only includes 3 parameters.

[0076] Please go back Figure 1 , the reasonable distribution range R of the parameter can be presented as a pancake-like shape, and the thickness in the center can be greater than the thickness at the edge. When the parameter is within the reasonable distribution range R (within the pancake-like shape), the machine can operate normally, and the manufactured product can also meet the predetermined specifications. On the contrary, when the parameter is outside the reasonable distribution range R (not within the pancake-like shape), the machine will not operate normally, and the manufactured electronic device will not meet the predetermined specifications.

[0077] Among these numerous parameters, some parameters have a relatively high correlation with each other, while some have a relatively low correlation. For example, the flow rate of the fluid introduced may cause a change in the fluid concentration, and the two have a high correlation, but have a relatively low correlation or no correlation with the process temperature. Therefore, in order to facilitate observing the correlation between parameters, the parameters can be subjected to principal component transformation. Performing principal component transformation on the parameters is based on the correlation of the parameters. Parameters with a high correlation can have weight values with similar magnitudes, while parameters with a low correlation can have weight values with magnitudes that are less similar compared to those with a high correlation, and they are combined with each other to form a principal component. Additionally, the remaining weight values can be allocated to the remaining principal components.

[0078] According to a first exemplary embodiment of the present disclosure, the first parameter X1, the second parameter X2, and the third parameter X3 are subjected to principal component transformation to facilitate observing the correlation between the first parameter X1, the second parameter X2, and the third parameter X3.

[0079] For example, the first parameter X1 and the second parameter X2 have a high correlation, while the third parameter X3 can be independent of the first parameter X1 and the second parameter X2 and have no correlation with them.

[0080] The results of subjecting the first parameter X1, the second parameter X2, and the third parameter X3 to principal component transformation can be as shown in Table 1:

[0081]

[0082] Table 1

[0083] Among them, the numbers in the table represent the loadings of the first parameter X1, the second parameter X2, and the third parameter X3 on the first principal component PC1, the second principal component PC2, and the third principal component PC3, that is, the weight values used to calculate the principal components.

[0084] That is to say, the first principal component PC1, the second principal component PC2, and the third principal component PC3 and the first parameter X1, the second parameter X2, and the third parameter X3 can be represented by the following formulas 1, 2, and 3:

[0085] PC1 = 0.8×X1 + 0.8×X2 + 0.1×X3 (Formula 1)

[0086] PC2 = 0.1×X1 + 0.1×X2 + 0.8×X3 (Formula 2)

[0087] PC3 = 0.1×X1 + 0.1×X2 + 0.1×X3 (Formula 3)

[0088] Since the first parameter X1 and the second parameter X2 are highly correlated, in the first principal component PC1 and the second principal component PC2, the weight values of the first parameter X1 and the second parameter X2 can be close to each other.

[0089] Moreover, in the first exemplary embodiment, the first principal component PC1 is strongly correlated with the first parameter X1 and the second parameter X2, and the second principal component PC2 is strongly correlated with the third parameter X3, such that the first principal component PC1 can explain the first parameter X1 and the second parameter X2, and the second principal component PC2 can explain the third parameter X3, so that the first principal component PC1 and the second principal component PC2 can explain most of the variations of the first parameter X1, the second parameter X2, and the third parameter X3 (in this first exemplary embodiment, 90%).

[0090] More specifically, the variations of the first parameter X1 and the second parameter X2 can be integrated into the first principal component PC1, the variations of the third parameter X3 can be integrated into the second principal component PC2, and the remaining variations (residuals) can be integrated into the third principal component PC3.

[0091] In other words, for the first parameter X1, its first parameter loading sequence is [0.8, 0.1, 0.1], for the second parameter X2, its second parameter loading sequence is [0.8, 0.1, 0.1], and for the third parameter X3, its third parameter loading sequence is [0.1, 0.8, 0.1].

[0092] Please refer to Figure 2 and Figure 3 . Figure 2 is the reasonable distribution range R observed from the perspective A1 or perspective A2 according to some embodiments of the present disclosure; Figure 1 is the reasonable distribution range R observed from the direction of the third principal component PC3 according to some embodiments of the present disclosure. As Figure 3 shown, the reasonable distribution range R observed from the direction of the perspective A1 or perspective A2 can be approximately elliptical, while Figure 2 shown, the reasonable distribution range R observed from the direction of the third principal component PC3 can be approximately circular. Figure 3 shown, the reasonable distribution range R observed from the direction of the third principal component PC3 can be approximately circular.

[0093] Figure 3 It is possible to observe the first T-squared control chart established by the Hotelling's T-squared distribution method based on the first principal component PC1 and the second principal component PC2. Moreover, the first T-squared control chart further has a control boundary E1, and the control boundary E1 surrounds the reasonable distribution range R.

[0094] As Figure 3As shown, when samples S1 and S2 are within the control boundary E1, it can be considered that samples S1 and S2 meet the specifications of the first principal component PC1 and the second principal component PC2; when samples S3 and S4 are outside the range of the control boundary E1, it can be considered that samples S3 and S4 do not meet the specifications of the first principal component PC1 and the second principal component PC2.

[0095] Please continue to refer to Figure 3 , since sample S3 exceeds the control boundary E1 in the direction of the first principal component PC1, therefore, the problem of sample S3 mainly occurs when the first parameter X1 and / or the second parameter X2 deviate from the process center O, while the third parameter X3 has low variation (closer to the process center O), resulting in the first parameter X1 and / or the second parameter X2 affecting the yield of the electronic device; similarly, sample S4 exceeds the control boundary E1 in the direction of the second principal component PC2, so the problem of sample S4 mainly occurs when the third parameter X3 deviates from the process center O, while the first parameter X1 and / or the second parameter X2 do not deviate from the process center O, resulting in the third parameter X3 affecting the yield of the electronic device.

[0096] Please refer to Figure 4 , Figure 4 is the reasonable distribution range R observed from the direction of the second principal component PC2 according to some embodiments of the present disclosure. Figure 4 The second T-squared control chart established with the first principal component PC1 and the third principal component PC3 can be observed. And, the second T-squared control chart also has a control boundary E2, and the control boundary E2 defines the acceptable variation range of the third principal component PC3 and is located near the reasonable distribution range R and parallel to the first principal component PC1.

[0097] As Figure 4 shown, since both samples S1 and S3 are within the control boundary E2, it can be considered that samples S1 and S3 meet the specifications of the third principal component PC3; while samples S2 and S4 are outside the control boundary E2, it can be considered that samples S2 and S4 do not meet the specifications of the third principal component PC3.

[0098] It should be understood that since the reasonable distribution range R of this embodiment can be a three-dimensional shape, when observing the first T-squared control chart and the second T-squared control chart from a two-dimensional perspective, it may cause sample S2 to be within the control boundary E1 but outside the control boundary E2. Similarly, the same concept can be used to explain samples S1, S3, and S4.

[0099] In addition, it should be noted that in other embodiments, when the number of parameters increases, the reasonable distribution range R can have other dimensions, and similar results can be produced for the corresponding samples.

[0100] When the sample position is within the range of the control boundary E2, it can be determined that the correlation of the parameters has not deviated; when the sample position is outside the range of the control boundary E2, it can be determined that the correlation of the parameters has deviated. Therefore, the correlations of the first parameter X1 and the second parameter X2 of the sample S1 and the sample S3 have not deviated; however, the correlations of the first parameter X1 and the second parameter X2 of the sample S2 and the sample S4 have deviated.

[0101] According to the foregoing description, the samples of the first exemplary embodiment can have 4 cases as shown in Table 2.

[0102] First T-Squared Control Chart Second T-Squared Control Chart Sample 1 Conform Conform Sample 2 Conform Non-conform Sample 3 Non-conform Conform Sample 4 Non-conform Non-conform

[0103] Table 2

[0104] Therefore, using the second T-square control chart can find that the correlation of the parameters of sample 2 has deviated. And this is a situation that cannot be found only by using the first square control chart.

[0105] Please refer to Figure 5 and Figure 10 , Figure 5 is a method 100 for establishing a manufacturing control sequence table of a manufacturing method according to some embodiments of the present disclosure. Figure 10 The modeling calculation unit 1010 in can establish a manufacturing control sequence table according to the method 100.

[0106] According to some embodiments of the present disclosure, the method 100 for establishing a manufacturing control sequence table of a manufacturing method can include step 110, step 120, and step 130.

[0107] According to some embodiments of the present disclosure, the modeling calculation unit 1010 first uses historical parameter data to establish a manufacturing control sequence table between different parameters, and then hands it over to the analysis calculation unit 1030 to monitor whether the parameters (real-time parameter data) of the machine tool in the current process conform to the manufacturing control sequence table according to the control sequence table. If the parameters of the machine tool in the current process do not conform to the manufacturing control sequence table, the machine tool management personnel can be notified for confirmation to avoid affecting the quality of the manufactured electronic device due to the parameters of the machine tool in the current process. The historical parameter data includes the values of all process parameters when manufacturing each electronic device. For example, the historical parameters can include all process parameters of manufacturing the first electronic device, all process parameters of the second electronic device... all process parameters of the Nth electronic device, and there are no problems with product quality from the first electronic device to the Nth electronic device.

[0108] In step 110, the modeling calculation unit 1010 first receives the historical parameter data values of the first parameter and the historical parameter data values of the second parameter. The first parameter and the second parameter can form a parameter group. After step 110, it can proceed to step 120.

[0109] In step 120, the modeling calculation unit 1010 calculates the average value and the standard deviation value of the historical parameter data values of the first parameter, and calculates the average value and the standard deviation value of the historical parameter data values of the second parameter, and calculates the correlation sequence table between the first parameter and the second parameter.

[0110] After step 120, it can proceed to step 130. In step 130, the modeling calculation unit 1010 consolidates the correlation sequence tables of different parameter groups, the average values and the standard deviation values of the historical parameter data values of each parameter to establish a manufacturing control sequence table of the historical parameter data. In the second exemplary embodiment, the correlation sequence table calculated based on the historical parameter data of this parameter group can be the covariance matrix table of this parameter group. In the third exemplary embodiment, the correlation sequence table calculated based on the historical parameter data of this parameter group can be the load sequence table of this parameter group.

[0111] The covariance matrix table of this parameter group can be obtained from the historical parameter data of this parameter group. And the covariance matrix table can be defined as in Formula 4:

[0112]

[0113] S XX represents the variance of the first parameter itself, that is, the square value of the standard deviation of the historical parameter data values of the first parameter; represents the variance of the second parameter itself, that is, the square value of the standard deviation of the historical parameter data values of the second parameter; s′ xX represents the covariance of the second parameter corresponding to the first parameter; s xX represents the covariance of the first parameter corresponding to the second parameter. s′ xX and s xX The values are the same.

[0114] Taking the second exemplary embodiment as an example, this parameter group includes the first parameter and the second parameter, and the average values of the historical parameter data of this parameter group can be [5.7 17.889] respectively, and the correlation sequence table (i.e., the covariance matrix table) that can be calculated from the historical parameter data of this parameter group by Formula 4 can be

[0115] Taking the second exemplary embodiment as an example, this parameter group is exemplified as [5.687 18.508].

[0116] Taking the third exemplary embodiment as an example, a load sequence list can be generated in the manner of Table 1 of the first exemplary embodiment as an associated sequence list, which will be described in detail later.

[0117] Please refer to Figure 6 and Figure 10 , Figure 6 which is a manufacturing method 200 according to some embodiments of the present disclosure. Figure 10 The analysis and calculation unit 1030 in Figure 6 can implement the manufacturing method 200. As

[0118] shown, the manufacturing method 200 may include step 210 and step 220.

[0119] According to some embodiments of the present disclosure, before step 210, the modeling and calculation unit 1010 may execute method 100, such as executing step 110, step 120, and step 130, to model (a control sequence list) using historical parameter data, the similarities of which will not be elaborated here.

[0120] Taking the first exemplary embodiment as an example, in step 210, the analysis and calculation unit 1030 may receive the values of the first parameter X1 and the second parameter X2.

[0121] According to some embodiments of the present disclosure, after step 210, step 220 may be proceeded to.

[0122] According to some embodiments of the present disclosure, in step 220, the analysis and calculation unit 1030 calculates a contribution value of this parameter group. For the sake of understanding, a second exemplary embodiment will be used to illustrate here.

[0123] According to some embodiments of the present disclosure, step 220 may include step 222 and step 224.

[0124] According to some embodiments of the present disclosure, in step 222, the analysis and calculation unit 1030 may first calculate an intermediate value. The calculation of the intermediate value can be as described below.

[0125] First, in this embodiment, calculating the change of the second parameter with the first parameter as a reference violates the degree of correlation between the two parameters. Calculate a first index value. The first index value can be an s 2 value. The s 2 value can be defined as in Formula 5:

[0126]

[0127] Taking the second exemplary embodiment as an illustration, the s 2 value calculated via Formula 5 is: s 2 = 1.108 - (-0.086)(0.07) -1 (-0.086) = 1.002.

[0128] Next, calculate a second index value. The second index value can be a value. It can be defined as in Formula 6:

[0129]

[0130] where the b2 value can be defined as in Formula 7:

[0131]

[0132] Taking the second exemplary embodiment as an illustration, the b2 value calculated via Formula 7 is: b2 = (0.07) -1 (-0.086) = -1.229.

[0133] Taking the second exemplary embodiment as an illustration, the value calculated via Formula 6 is:

[0134] Next, the intermediate value can be calculated. The intermediate value can be defined as in Formula 8:

[0135]

[0136] Taking the second exemplary embodiment as an illustration, the intermediate value calculated via Formula 8 is:

[0137] The present disclosure further illustrates step 220 with another exemplary embodiment. In another exemplary embodiment, the average value of the historical parameter data of this parameter group can be [5.687 17.005], and the correlation sequence table (covariance matrix) of the historical parameter data of this parameter group can be

[0138] Illustrated by another exemplary embodiment, s calculated via Equation 5 2 has a value of: s 2 = 1.108 - (-0.086)(0.07) -1 (-0.086) = 1.002.

[0139] Illustrated by another exemplary embodiment, the value calculated via Equation 6 is:

[0140] Illustrated by another exemplary embodiment, b calculated via Equation 7 p has a value of: b2 = (0.07) -1 (-0.086) = -1.229.

[0141] Illustrated by another exemplary embodiment, the intermediate value calculated via Equation 8 is:

[0142] According to some embodiments of the present disclosure, after step 222, step 224 may be performed.

[0143] According to some embodiments of the present disclosure, in step 224, the analysis and calculation unit 1030 determines the action value. According to some embodiments of the present disclosure, step 224 may include step 2241, step 2242, and step 2243.

[0144] According to some embodiments of the present disclosure, in step 2241, the analysis and calculation unit 1030 determines whether the intermediate value is less than 0. When the intermediate value is less than 0, proceed to step 2242; when the intermediate value is not less than 0, proceed to step 2243.

[0145] According to some embodiments of the present disclosure, in step 2242, the analysis and calculation unit 1030 determines the action value to be the absolute value of the intermediate value.

[0146] According to some embodiments of the present disclosure, in step 2243, the analysis and calculation unit 1030 determines the action value to be 0.

[0147] Illustrating step 224 with a second exemplary embodiment, the intermediate value of the second exemplary embodiment is 0.6024, and thus the action value of the second exemplary embodiment is determined to be 0.

[0148] Illustrating step 152 with a third exemplary embodiment, the intermediate value of another exemplary embodiment is -0.899, and thus the action value of the third exemplary embodiment is determined to be the absolute value of the intermediate value, i.e., 0.899.

[0149] According to some embodiments of the present disclosure, the greater the action value, the higher the likelihood of deviation in the correlation of the parameter group. Therefore, the machine operator needs to first confirm whether the correlation of the parameter group with a larger action value has deviated, which may affect the manufacturing yield of the electronic device, and then arrange for machine repair or maintenance to maintain the yield of manufacturing the electronic device.

[0150] It should be noted that since the action value has been standardized, the action value is not an absolute value but a relative value. When the action value of a parameter group is greater than that of another parameter group, the degree of deviation of the correlation of the parameter group with the larger action value is greater than that of the parameter group with the smaller action value.

[0151] Therefore, the correlation of the parameter group with the largest (or larger) action value should be confirmed first.

[0152] According to some embodiments of the present disclosure, the parameter groups with action values greater than 0 can be gradually confirmed to maintain the manufacturing yield of the electronic device.

[0153] Please refer to Figure 7 and Figure 10 , Figure 7 which is a manufacturing method 300 according to some embodiments of the present disclosure. Figure 10 The analysis and calculation unit 1030 in can implement the manufacturing method 300. The manufacturing method 300 can be applied to an operating process or equipment.

[0154] According to some embodiments of the present disclosure, the manufacturing method 300 may include step 310 and step 320.

[0155] According to some embodiments of the present disclosure, step 310 may be similar to step 210, and the similarities will not be elaborated here.

[0156] According to some embodiments of the present disclosure, before step 310, the modeling calculation unit 1010 may execute method 100, such as executing step 110, step 120, and step 130, to model (control sequence list) using historical parameter data, and the similarities will not be elaborated here.

[0157] According to some embodiments of the present disclosure, in step 320, the analysis and calculation unit 1030 calculates an action value of this parameter group. For the sake of easy understanding, a fourth exemplary embodiment will be used here to illustrate. In this embodiment, the parameter group includes a first parameter and a second parameter, but is not limited thereto. The control sequence list may include a first parameter load sequence list established from the historical parameter data of the first parameter, a second parameter load sequence list established from the historical parameter data of the second parameter, the average value and standard deviation established from the historical parameter data of the first parameter, and the average value and standard deviation established from the historical parameter data of the second parameter.

[0158] According to some embodiments of the present disclosure, step 320 may include step 321, step 322, step 323, step 324, step 325, and step 326.

[0159] According to some embodiments of the present disclosure, in step 321, the analysis and calculation unit 1030 normalizes the first parameter of this parameter group to obtain a first parameter normalization value as the first index value.

[0160] According to some embodiments of the present disclosure, the first parameter normalization value may subtract the average value of the historical parameter data of the first parameter from the first parameter, and then divide by the standard deviation of the historical parameter data of the first parameter. The first parameter normalization value may be defined as in Formula 9:

[0161]

[0162] According to some other embodiments of the present disclosure, the first parameter normalization value may subtract the average value of the historical parameter data of the first parameter from the first parameter, and then divide by the range of the historical parameter data of the first parameter. The first parameter normalization value may be defined as in Formula 10:

[0163]

[0164] Taking the fourth exemplary embodiment as an example, the first parameter normalization value obtained through Formula 9 may be: -3.696.

[0165] Now discuss step 322. According to some embodiments of the present disclosure, in step 322, the analysis and calculation unit 1030 normalizes the second parameter of this parameter group to obtain a second parameter normalization value as the second index value. According to some embodiments of the present disclosure.

[0166] According to some embodiments of the present disclosure, the second parameter normalization value may refer to the calculation method of Formula 9, and replace the first parameter, the average value of the historical parameter data of the first parameter, and the standard deviation of the historical parameter data of the first parameter in Formula 9 with the second parameter, the average value of the historical parameter data of the second parameter, and the standard deviation of the historical parameter data of the second parameter. Similarly, similar processing can also be referred to Formula 10. This will not be repeated here.

[0167] Taking the fourth exemplary embodiment as an example, the second parameter normalization value obtained through referring to Formula 9 may be: 0.869.

[0168] According to some embodiments of the present disclosure, the sequence of step 321 and step 322 may be interchanged, or they may be performed simultaneously. The present disclosure is not limited thereto.

[0169] In step 323, the analysis and calculation unit 1030 may calculate the first index sequence table and the second index sequence table from the first parameter load sequence table and the second parameter load sequence table obtained by matching the first index value with the second index value and the historical parameter data.

[0170] Taking the fourth exemplary embodiment as an example, the principal component sequence table may be:

[0171]

[0172] It should be noted that each column in the principal component sequence table represents a principal component. And the closer the parameter load values of the same principal component are, the higher the correlation. The same sign indicates a positive correlation, otherwise it indicates a negative correlation.

[0173] Taking an example, by transposing Table 1 in the specification of this case, the principal component sequence table of the first parameter X1, the second parameter X2, and the third parameter X3 and the first principal component PC1, the second principal component PC2, and the third principal component PC3 can be obtained.

[0174] Please return to the fourth exemplary embodiment.

[0175] In the principal component sequence, the first parameter load sequence table may be the first row of the principal component sequence. Therefore, the first parameter load sequence table may be:

[0176] [0.189775 0.128483 -0.098062 0.331953 0.091517 -0.015495……-0.029899 -0.028827 0.007917 -0.000221 -0.031173]

[0177] In the principal component sequence, the second parameter load sequence table may be the second row of the principal component sequence. Therefore, the second parameter load sequence table may be:

[0178] [0.207535 0.115781 -0.123197 0.271138 0.047849 -0.005243……-0.045011 -0.04445 0.030515 0.004801 -0.00552]

[0179] Taking the fourth exemplary embodiment as an example, the first index sequence table obtained in step 323 may be:

[0180] [-0.70145 -0.4749 0.362459 -1.22698 -0.33827 0.057274……0.110514 0.106552 -0.02926 0.000817 0.115223]

[0181] Illustrated by the fourth exemplary embodiment, the second index sequence table may be:

[0182] [0.18035 0.100615 -0.10706 0.235622 0.041582 -0.00456……-0.03912 -0.03863 0.026518 0.004172 -0.0048]

[0183] According to some embodiments of the present disclosure, after step 323, step 324 may be performed.

[0184] According to some embodiments of the present disclosure, in step 324, the analysis and calculation unit 1030 multiplies each of the multiple first index sequence values of the first pointer sequence table with each of the multiple second index sequence values of the second index sequence table in sequence to obtain a product sequence table. For example, each index sequence value in the index sequence table of the first parameter is multiplied with each index sequence value in the index sequence table of the second parameter in sequence to obtain the product sequence table.

[0185] Please refer to Figure 8 , Figure 8 which is the positive and negative value table according to some embodiments of the present disclosure.

[0186] As Figure 8 shown, when the first parameter normalization value is positive (+), the first parameter load value is positive (+), the second parameter normalization value is positive (+), and the second parameter load value is positive (+), the product value is positive (+).

[0187] When the first parameter normalization value is positive (+), the first parameter load value is positive (+), the second parameter normalization value is negative (-), and the second parameter load value is negative (-), the product value of its product sequence table is positive (+).

[0188] When the first parameter normalization value is positive (+), the first parameter load value is positive (+), the second parameter normalization value is negative (-), and the second parameter load value is positive (+), the product value of its product sequence table is negative (-).

[0189] When the first parameter normalization value is positive (+), the first parameter load value is positive (+), the second parameter normalization value is positive (+), and the second parameter load value is negative (-), the product value of its product sequence table is negative (-).

[0190] When the first parameter normalization value is negative (-), the first parameter load value is negative (-), the second parameter normalization value is positive (+), and the second parameter load value is positive (+), the product value of its product sequence table is positive (+).

[0191] When the standardized value of the first parameter is negative (-), the load value of the first parameter is negative (-), the standardized value of the second parameter is negative (-), and the load value of the second parameter is negative (-), the product value in the product sequence table is positive (+).

[0192] When the standardized value of the first parameter is negative (-), the load value of the first parameter is negative (-), the standardized value of the second parameter is negative (-), and the load value of the second parameter is positive (+), the product value in the product sequence table is negative (-).

[0193] When the standardized value of the first parameter is negative (-), the load value of the first parameter is negative (-), the standardized value of the second parameter is negative (+), and the load value of the second parameter is positive (-), the product value in the product sequence table is negative (-).

[0194] When the standardized value of the first parameter is negative (-), the load value of the first parameter is positive (+), the standardized value of the second parameter is positive (+), and the load value of the second parameter is positive (+), the product value in the product sequence table is negative (-).

[0195] When the standardized value of the first parameter is negative (-), the load value of the first parameter is positive (+), the standardized value of the second parameter is negative (-), and the load value of the second parameter is negative (-), the product value in the product sequence table is negative (-).

[0196] When the standardized value of the first parameter is negative (-), the load value of the first parameter is positive (+), the standardized value of the second parameter is negative (-), and the load value of the second parameter is positive (+), the product value in the product sequence table is positive (+).

[0197] When the standardized value of the first parameter is negative (-), the load value of the first parameter is positive (+), the standardized value of the second parameter is positive (+), and the load value of the second parameter is negative (-), the product value in the product sequence table is positive (+).

[0198] When the standardized value of the first parameter is positive (+), the load value of the first parameter is negative (-), the standardized value of the second parameter is positive (+), and the load value of the second parameter is positive (+), the product value in the product sequence table is negative (-).

[0199] When the standardized value of the first parameter is positive (+), the load value of the first parameter is negative (-), the standardized value of the second parameter is negative (-), and the load value of the second parameter is negative (-), the product value in the product sequence table is negative (-).

[0200] When the normalized value of the first parameter is positive (+), the load value of the first parameter is negative (-), the normalized value of the second parameter is negative (-), and the load value of the second parameter is positive (+), the product value in the product sequence table is positive (+).

[0201] When the normalized value of the first parameter is positive (+), the load value of the first parameter is negative (-), the normalized value of the second parameter is positive (+), and the load value of the second parameter is negative (-), the product value in the product sequence table is positive (+).

[0202] According to some embodiments of the present disclosure, the positive or negative value of the product value in the product sequence table can be used to determine whether the change directions of two parameters (parameter groups) are consistent with the change direction of the parameter load value.

[0203] When the product value is positive (+), the change directions of the two parameters (parameter groups) are consistent with the change direction of the parameter load value; and when the product value is negative (-), the change directions of the two parameters (parameter groups) are inconsistent with the change direction of the parameter load value (i.e., the correlation of the two parameters (parameter groups) deviates).

[0204] Taking the fourth exemplary embodiment as an example, the product sequence table obtained in step 324 may be:[[]]

[0205] [-0.12651 -0.04778 -0.0388 -0.2891 -0.01407 -0.00026……-0.00432 -0.00412 -0.00078 3.41E-06 -0.00055]

[0206] Therefore, it can be obtained that the change direction of the parameter group in the fourth exemplary embodiment is inconsistent with the change directions of multiple parameter load values (i.e., the multiple correlations of the two parameters (parameter groups) deviate).

[0207] According to some embodiments of the present disclosure, after step 324, it may proceed to step 325.

[0208] According to some embodiments of the present disclosure, in step 325, the analysis and calculation unit 1030 sums up the multiple product values in the product sequence table to obtain an intermediate value.

[0209] Taking the fourth exemplary embodiment as an example, the intermediate value obtained in step 325 may be: -0.5263.

[0210] According to some embodiments of the present disclosure, after step 325, it may proceed to step 326.

[0211] According to some embodiments of the present disclosure, in step 326, the analysis and calculation unit 1030 determines the action value. According to some embodiments of the present disclosure, step 326 may include step 3261, step 3262, and step 3263.

[0212] According to some embodiments of the present disclosure, in step 3261, the analysis and calculation unit 1030 determines whether the intermediate value is less than 0. When the intermediate value is less than 0, proceed to step 3262; when the intermediate value is not less than 0, proceed to step 3263.

[0213] According to some embodiments of the present disclosure, in step 3262, the analysis and calculation unit 1030 determines that the action value is the absolute value of the intermediate value.

[0214] According to some embodiments of the present disclosure, in step 3263, the analysis and calculation unit 1030 determines that the action value is 0.

[0215] Taking the fourth exemplary embodiment as an example to illustrate step 326, the intermediate value of the fourth exemplary embodiment is -0.5263 (less than 0), so it is determined that the action value of the fourth exemplary embodiment is the absolute value of the intermediate value, that is, 0.5263.

[0216] According to some embodiments of the present disclosure, the larger the value of the action value, the higher the degree of deviation of the correlation of the parameter group. Therefore, it is necessary to preferentially confirm the correlation of the parameter group with a larger action value.

[0217] Please refer to Figure 9 , Figure 9 which is a schematic diagram of the action values of different parameter groups according to some embodiments of the present disclosure.

[0218] As Figure 9 shown, the parameter groups with larger action values can be highlighted in bright colors to emphasize the higher degree of deviation of the correlation of this parameter group.

[0219] It should be noted that since the action value has been normalized (via manufacturing method 200 or manufacturing method 300), the action value is not an absolute value but a relative value. When the action value of a parameter group is greater than that of another parameter group, the degree of deviation of the correlation of the parameter group with the larger action value is greater than that of the parameter group with the smaller action value.

[0220] Therefore, the correlation of the parameter group with the largest (or larger) action value should be preferentially confirmed.

[0221] Returning to Figure 9 , in this embodiment, the correlation of the parameter group with a larger action value is preferentially confirmed. Therefore, the correlations of the parameter groups with action values of 0.05787, 0.023916, and 0.021683 are preferentially confirmed.

[0222] After confirming and correcting the correlation of the parameter sets with corresponding effect values of 0.05787, 0.023916, and 0.021683, the correlation of these parameter sets can be re-examined to confirm that the correlation of these parameter sets meets the requirements.

[0223] After that, the correlation of the parameter set with the maximum (or larger) effect value can be corrected.

[0224] Generally speaking, the manufacturing method of the embodiments of the present disclosure can effectively detect the deviation of the correlation of the parameter sets in the manufacturing process, enabling the machine operators to quickly confirm and / or correct the correlation of the parameter sets. Moreover, the manufacturing method of the embodiments of the present disclosure can detect the deviation of the correlation of the parameter sets when the parameters conform to the known control chart, thereby avoiding excessive defective products in the manufacturing process. Furthermore, the manufacturing method of the embodiments of the present disclosure can judge the degree of deviation of the parameter sets and correct the correlation of the parameter sets according to the degree of deviation of the parameter sets, so that the manufacturing process conforms to the operating conditions.

[0225] Please refer to Figure 10 , Figure 10 which is a schematic diagram of a manufacturing system 1000 according to some embodiments of the present disclosure.

[0226] The manufacturing system 1000 may include a modeling calculation unit 1010, a data database 1020, an analysis calculation unit 1030, and a machine tool 1040. The machine tool 1040 can store the parameters required for the manufacturing process (including historical parameter data and real-time parameter data) in the data database 1020. The modeling calculation unit 1010 can use the historical parameter data to establish a control sequence list (such as the aforementioned control sequence list). The data database 1020 may include an analysis database 1025, and the analysis database 1025 can store the data required for the analysis calculation unit 1030 (for example, real-time parameter data and / or the control sequence list established by the modeling calculation unit 1010, but not limited thereto). The analysis calculation unit 1030 can calculate the effect value based on the control sequence list and / or the real-time parameter data of the manufacturing process of the machine tool 1040 for the user 1099 to confirm.

[0227] According to some embodiments of the present disclosure, the machine tool 1040 can be located within the factory scope. According to some embodiments of the present disclosure, the data database can be entirely within the factory scope, or partially within the factory scope and partially set outside the factory scope, such as cloud space, but not limited thereto.

[0228] According to some embodiments of the present disclosure, the modeling calculation unit 1010 and the analysis calculation unit 1030 may all be outside the factory scope (e.g., cloud space) or all within the factory scope.

[0229] An embodiment of the present application further provides a computer-readable storage medium, on which a computer program is stored, and the computer program can be used to cause a computer to execute the operation method of any one of the above-described embodiments.

[0230] An embodiment of the present application further provides a non-volatile computer-readable storage medium, in which one or more program modules are stored. When the one or more program modules are applied to a device, the device can be caused to execute the instructions of any one of the steps included in the above-described embodiments.

[0231] The above-mentioned computer-readable storage medium may be, for example, (but not limited to) an electrical, magnetic, optical, electromagnetic, infrared or semiconductor device or component, or any combination of the above. More specific examples of the computer-readable storage medium may include, but are not limited to, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), an optical fiber, a CD-ROM, an optical storage device, a magnetic storage device, or a suitable combination of any of the above.

[0232] Although the embodiments and advantages of the present disclosure have been described above, it should be understood that those skilled in the art can make various changes, substitutions and alterations to the present disclosure without departing from the spirit and scope of the present disclosure. It should be noted that different embodiments can be arbitrarily combined into other embodiments as long as the combination conforms to the spirit of the present disclosure. In addition, the scope of the present disclosure is not limited to the processes, machines, manufactures, compositions, devices, methods and steps in the specific embodiments described in the specification. Those skilled in the art can understand the existing or developing processes, machines, manufactures, compositions, devices, methods and steps from some embodiments of the present disclosure. Therefore, the scope of the present disclosure includes the foregoing processes, machines, manufactures, compositions, devices, methods and steps. In addition, each item in the appended patent claims constitutes a separate embodiment, and the scope of the present disclosure further includes each combination of the appended claims and embodiments.

Claims

1. A manufacturing system for an electronic device, characterized in that, Comprising: A machine platform for providing a historical parameter data and a real-time parameter data; A modeling calculation unit for receiving the historical parameter data and establishing a control sequence table based thereon; and An analysis calculation unit for calculating an action value based on the control sequence table and the real-time parameter data.

2. The manufacturing system according to claim 1, characterized in that, The real-time parameter data includes a value of a first parameter and a value of a second parameter. The analysis calculation unit calculates a first index value and a second index value respectively by matching the value of the first parameter and the value of the second parameter with the control sequence table to calculate the action value.

3. The manufacturing system according to claim 2, wherein The control sequence table includes a covariance matrix table of the historical parameter data, an average value of the historical parameter data of the first parameter, a standard deviation value of the historical parameter data of the first parameter, an average value of the historical parameter data of the second parameter, and a standard deviation value of the historical parameter data of the second parameter. wherein the first index value is an s 2 value wherein the second index value is a value.

4. The manufacturing system according to claim 3, wherein The covariance matrix table is defined as: where the s 2 value is defined as: wherein the value is defined as:

5. The manufacturing system according to claim 3, characterized in that The analysis and calculation unit calculates an intermediate value based on the s 2 value and the value. When the intermediate value is less than 0, the analysis and calculation unit sets the action value to the absolute value of the intermediate value. When the intermediate value is not less than 0, the analysis and calculation unit sets the action value to 0.

6. The manufacturing system according to claim 2, wherein The control sequence value includes a first parameter load sequence table of the historical parameter data, a second parameter load sequence table, an average value of the historical parameter data of the first parameter, a standard deviation value of the historical parameter data of the first parameter, an average value of the historical parameter data of the second parameter, and a standard deviation value of the historical parameter data of the second parameter. The first index value is a first parameter standardization value, and the second index value is a second parameter standardization value.

7. The manufacturing system according to claim 6, wherein The standardization of the first parameter includes: Subtracting the average value of the historical parameter data of the first parameter from the first parameter, and then dividing by the standard deviation value of the historical parameter data of the first parameter, and The standardization of the second parameter includes: Subtracting the average value of the historical parameter data of the second parameter from the second parameter, and then dividing by the standard deviation value of the historical parameter data of the second parameter.

8. The manufacturing system according to claim 6, wherein The standardization of the first parameter includes: Subtracting the average value of the historical parameter data of the first parameter from the first parameter, and then dividing by a range of the historical parameter data of the first parameter, and The standardization of the second parameter includes: Subtracting the average value of the historical parameter data of the second parameter from the second parameter, and then dividing by a range of the historical parameter data of the second parameter.

9. The manufacturing system according to claim 6, characterized in that, The analysis calculation unit multiplies the first pointer value by the first parameter load sequence table to obtain a first index sequence table, and multiplies the second index value by the second parameter load sequence table to obtain a second index sequence table.

10. The manufacturing system according to claim 9, wherein, The analysis calculation unit multiplies the first pointer sequence table by the second index sequence table to obtain a plurality of product values, and sums up the plurality of product values to obtain an intermediate value.

11. The manufacturing system according to claim 10, characterized in that, When the intermediate value is less than 0, the analysis calculation unit sets the action value as the absolute value of the intermediate value. When the intermediate value is not less than 0, the analysis calculation unit sets the action value as 0.

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