JP6012311B2 - Film yield prediction system and method - Google Patents

Description

  The present invention relates to a film yield prediction system and method for detecting defects that occur during the manufacture of optical films and thereby calculating the expected yield of products.

  The optical film is a film used for manufacturing a liquid crystal display (LCD), and is a term that comprehensively refers to a polarizing film, a diffusion film, a reflective film, a prism film, and the like. Such an optical film is generally manufactured in a form wound on a roll, and then the film wound on the roll is cut and supplied to a user's desired size. At this time, if a defect occurs on the film, the corresponding part is discarded in the inspection process, and the number and location of such defects are important factors that determine the yield of the optical film.

  In order to confirm such defects in optical products, in-line automatic optical inspection systems are generally used in the industry for manufacturing optical films. The in-line automatic optical inspection machine marks the defect occurrence position with ink or barcode so that the marked part in the subsequent process can be discarded or an additional inspection can be performed on it. To do.

  However, facilities such as the conventional in-line automatic optical inspection system only determine the presence and position of defects, and it is impossible to predict the yield of the polarizing film. Accordingly, a system for predicting the yield based on the product size or the cutting position is required before the optical film raw material is commercialized.

  It is an object of the present invention to provide a film yield prediction system and method for detecting defects that occur during the production of optical films and thereby calculating the expected yield of products.

  1. A first inspection unit that detects a defect on the optical film during the specific stage during the manufacturing process of the optical film and generates first defect data including the position of the detected defect, and is distinguished from the specific stage A second inspection unit that detects a defect on the optical film during another stage of the manufacturing process and generates second defect data including a position of the detected defect; The data merging unit for merging the defect data and the second defect data, and the defect data merged in the data merging unit, the predicted cutting position of the optical film and the expected yield of the optical film according to the cutting size are obtained. An optical film yield prediction system, comprising: a yield prediction unit for calculating.

  2. In 1 above, the first inspection unit and the second inspection unit capture an image of the optical film from the upper surface of the optical film, and generate the defect data from the captured image. .

  3. In the above 1, the data merging unit compares the position coordinates of the first defect data and the second defect data to calculate the correction coordinates of the first defect data, and uses the calculated correction coordinates. Of the first defect data whose position has been corrected, the remaining first defect data excluding defects present at the same position as the second defect data is combined with the second defect data. The yield prediction system for merging the first defect data and the second defect data.

  4). In the above item 3, the data merging unit is configured so that the number of overlapping defects among the defects included in the first defect data and the defects included in the second defect data is maximized. Yield prediction system that calculates correction coordinates for defect data.

  5. 2. The yield prediction system according to 1, further comprising a seam defect exclusion unit that eliminates a seam defect from the first defect data and the second defect data.

  6). 5. In the above item 5, the seam defect exclusion unit includes defects equal to or more than a preset number in the direction perpendicular to the longitudinal direction of the optical film among the defects included in the first defect data and the second defect data. Yield prediction system that recognizes and eliminates the corresponding defect as a seam defect when the is recognized.

  7). In 1 above, the yield predicting unit determines the presence or absence of defects in each of the films cut according to the predicted cutting position of the optical film and the size of the cut film, and the number and cutting of the cut films. A yield prediction system for calculating an expected yield of the optical film from the number of films having defects among the obtained films.

  8). In the above 1, the specific stage in the manufacturing process of the optical film is a stage before applying a pressure-sensitive adhesive or an adhesive to the optical film, and is different from the specific stage in the manufacturing process. These are the yield prediction systems which are the stage after apply | coating the said adhesive or adhesive agent to the said optical film.

  9. In the above 1, the yield prediction system, wherein the first defect data and the second defect data further include brightness and size of defects detected independently.

10. Detecting a defect on the optical film that is performing a specific step during the manufacturing process of the optical film, and generating first defect data including a position of the detected defect;
Detecting defects on the optical film during other steps in the manufacturing process that are distinguished from the specific steps, and generating second defect data including positions of the detected defects;
Merging the first defect data and the second defect data;
Calculating a predicted yield of the optical film according to a predicted cutting position and a cutting size of the optical film based on the defect data merged in the merging step.

  11. In the above 10, the generation step of the first defect data and the generation step of the second defect data are performed by taking an image of the optical film from an upper surface of the optical film and obtaining the defect data from the taken image. Yield prediction method to generate.

  12 10. In the above 10, the merging step includes the step of calculating the correction coordinates of the first defect data by comparing the position coordinates of the first defect data and the second defect data, and the calculated correction coordinates. Correcting the position of the first defect data according to the above, and the remaining first defect excluding the defect present at the same position as the second defect data among the position-corrected first defect data Combining the data with the second defect data.

  13. 13. The yield prediction method according to 12, wherein the first defect data and the second defect data further include brightness and size of defects detected independently.

14 In the above 13, the calculation step of the correction coordinates of the first defect data includes
A first step of selecting a defect having brightness and size equal to or larger than a preset value from the first defect data as a representative defect;
A second stage of selecting one of the representative defects;
A third step of selecting, from the second defect data, a defect having the same brightness and size as the defect selected in the second step;
A fourth step of calculating a difference value between the positions of the defect selected in the second step and the defect selected in the third step and correcting the position of the representative defect by the calculated difference value of the position. When,
A fifth step of calculating the number of defects that overlap with the second defect data among the representative defects corrected in the fourth step;
For each of the representative defects selected in the first stage, the second stage to the fifth stage are repeatedly performed, and the position of the representative defect having the largest number of defects duplicated in the fifth stage. And a sixth stage of selecting a difference value of the correction coordinates as the correction coordinates.

  15. 10. The yield prediction method according to claim 10, further comprising the step of eliminating joint defects from the first defect data and the second defect data before performing the merging step.

  16. 15. In the step 15, the seam defect elimination step includes a defect equal to or more than a preset number in the direction perpendicular to the longitudinal direction of the optical film among the defects included in the first defect data and the second defect data. Yield prediction method that recognizes a corresponding defect as a seam defect and eliminates it.

  17. 10. In the above 10, the specific stage in the manufacturing process of the optical film is a stage before applying a pressure-sensitive adhesive or an adhesive to the optical film, and is different from the specific stage in the manufacturing process. These are the yield prediction methods which are the stage after apply | coating the said adhesive or adhesive agent to the said optical film.

  18. 18. A computer-readable recording medium having recorded thereon a program for executing the method according to any one of 10 to 17 on a computer.

  According to the present invention, it is possible to accurately determine the expected yield due to the cutting position and size of the optical film before performing the post-process of the optical film manufactured in the original form. In addition, since the sheet film can be manufactured by cutting the optical film at the optimum cutting position and size, there is an advantage that the manufacturing cost of the optical film can be reduced.

It is a figure which shows the laminated structure of the optical film by one Embodiment of this invention. It is a block diagram for demonstrating the yield prediction system of the optical film by one Embodiment of this invention. It is a figure for demonstrating the process of eliminating the seam defect in the seam defect exclusion part by one Embodiment of this invention. It is a flowchart which shows the calculation method of the correction coordinate in the data merge part by one Embodiment of this invention. It is a figure which illustrates the data merged in the 1st defect data, the 2nd defect data, and the data merge part. It is a figure for demonstrating the yield prediction simulation by the position of the cutting in a yield prediction part. It is a figure for demonstrating the yield prediction simulation by the position of the cutting in a yield prediction part. It is a figure for demonstrating the yield prediction simulation by the position of the cutting in a yield prediction part. It is a figure for demonstrating the yield prediction simulation by the size of the cutting in a yield prediction part. It is a figure for demonstrating the yield prediction simulation by the size of the cutting in a yield prediction part. It is a figure which shows the yield prediction system by one Embodiment of this invention.

  Hereinafter, specific embodiments of the present invention will be described with reference to the drawings. However, this is merely an example, and the present invention is not limited to this.

  In describing the present invention, when it is determined that a specific description of a known technique according to the present invention may depart from the gist of the present invention, a detailed description thereof will be omitted. Terms and the like to be described later are terms that are defined in consideration of the functions of the present invention, and this may vary depending on the user's, operator's intention or customs. For this reason, it should be defined based on the contents throughout this specification.

  The technical idea of the present invention is determined by the scope of the claims, and the following embodiments are for specifically explaining the technical idea of the present invention to those who have ordinary knowledge in the technical field to which the present invention belongs. It's just one way.

  Before describing embodiments of the present invention and the like, first, the structure and manufacturing process of a general optical film will be briefly described.

  FIG. 1 is a view showing a laminated structure of an optical film according to an embodiment of the present invention. As shown in FIG. 1, an optical film according to an embodiment of the present invention includes a polarizer, a polarizer protective layer laminated on both sides of the polarizer, a separator laminated on the upper surface of the upper polarizer protective layer, and a lower side. The protective film laminated | stacked on the lower surface of a polarizer protective layer is included.

  The manufacturing process of the optical film having such a laminated structure will be described as follows. First, a polyvinyl alcohol (PVA) film that has been dyed, crosslinked, and stretched is dried to obtain a polarizer. Next, an adhesive is used on both sides of the produced polarizer, and a triacetyl cellulose (TAC) film is attached to produce a polarizing plate. Thereafter, a separator (SP Film) is attached to one surface of the manufactured polarizing plate using an adhesive, and a protective film (PF Film) is attached to the opposite surface.

  As described above, each optical film member or the like for manufacturing an optical film, that is, a PVA film, a TAC film, a separator, a protective film, and the like is a belt-like sheet-like product, and is wound around a roll. The produced optical film is also wound on a roll and provided to a subsequent process.

  Thereafter, in the post-process, the optical sheet wound in a roll shape is pulled out and cut into a sheet product of a predetermined size, and the cut sheet is shipped excluding the defective sheet. become.

  FIG. 2 is a block diagram for explaining an optical film yield prediction system 200 according to an embodiment of the present invention. The yield prediction system 200 according to an embodiment of the present invention detects a defect (or a defect) of the optical film being manufactured in the manufacturing process of the optical film described above, and uses the positional information of the detected defect to perform a post process. By calculating the expected yield according to the cutting position and size of the optical film manufactured before performing the process, it is configured so that the cutting position and size that can obtain the highest yield can be calculated. It is what.

  As shown in FIG. 2, a yield prediction system 200 according to an embodiment of the present invention includes a first inspection unit 202, a second inspection unit 204, a seam defect exclusion unit 206, a data merging unit 208, and a yield prediction unit 210. Prepare.

  The first inspection unit 202 and the second inspection unit 204 detect defects on the optical film being manufactured, and generate defect data including information on the position, brightness, and size of the detected defects. For distinction, the defect data generated from the first inspection unit 202 is referred to as first defect data, and the defect data generated from the second inspection unit 204 is referred to as second defect data.

  The first inspection unit 202 and the second inspection unit 204 can be arranged so as to be located at different stages, respectively, during the manufacturing process of the optical film. For example, as in the embodiment shown in FIG. 1, the first inspection unit 202 collects defect data from the optical film before the adhesive is applied for adhesion of the separator / protective film after bonding the TAC film. The second inspection unit 204 can collect defect data from the optical film after application of the adhesive (coating inspection machine). However, this is exemplary, and the first inspection unit 202 and the second inspection unit 204 are in positions suitable for identifying defects in the optical film that occur during the optical film manufacturing process. Note that it can exist anywhere in the process.

  As described above, the reason for configuring the first inspection unit 202 and the second inspection unit 204 to be positioned at different process stages is to improve the recognition rate of defects existing in the optical film. For example, a defect that can be easily recognized before adhesion of the separator and the protective film may not be recognized well after adhesion of the separator and the protective film, and conversely, a defect newly generated in the adhesion process of the separator and the protective film. May also exist. Therefore, when only one of the first inspection unit 202 and the second inspection unit 204 is provided, the probability of detecting a defect present in the optical film is relatively lowered. As a result, in the present invention, the first inspection unit 202 and the second inspection unit 204 are used, and defects on the optical film are recognized independently for each manufacturing process. By combining the recognized defects, the defect existing in the optical film is recognized without omission.

  The first inspection unit 202 and the second inspection unit 204 may include a plurality of camera modules disposed on the upper surface of the optical film, and the optical film is photographed using the camera module. Can be configured to detect defects on the film from the captured image. For this reason, light sources 202a and 204a can be provided on the opposite side of the surface on which the camera module is located with respect to the optical film, and the camera module is emitted from the light sources 202a and 204a and passes through the optical film. It can be configured to shoot light to do. In this case, when there is a defect in the optical film, the corresponding portion has low light transmittance, so that the defect on the optical film can be easily identified.

  The seam defect exclusion unit 206 excludes seam defects from the first defect data and the second defect data. When any of the TAC film, separator, and protective film is exhausted during the optical film production process, the work of pasting the end of the existing film and the start of the newly supplied film to each other To progress. In this case, the seam portion of each film is recognized as a defect by the first inspection unit 202 and the second inspection unit 204, and specifically, as shown in the left drawing of FIG. It is recognized that a defect is continuously generated in a direction perpendicular to the Z direction in the drawing (portion indicated by A in the drawing).

  Thus, when a seam defect occurs, there is a very high possibility that an error will occur when the first defect data and the second defect data are merged in the data merger 208 described later. The data merging unit 208 performs data merging using the coordinates of the defect commonly shown in the first defect data and the second defect data, but when a seam defect occurs, different seam parts are arranged in the same region. This is because it can be mistakenly determined. For this reason, the seam defect elimination unit 206 prevents the possibility of a merge error occurring in the data merge unit 208 by eliminating the seam defect using the first defect data and the second defect data. Specifically, the seam defect exclusion unit 206 is preset in a direction perpendicular to the longitudinal direction (Z direction) of the optical film among the defects included in the first defect data and the second defect data. When more than a certain number of defects are recognized, the defect is recognized as a seam defect and eliminated. FIG. 3 is a diagram for explaining a seam defect elimination process in such a seam defect elimination unit 206. Before the seam defect (A portion) is eliminated on the left side, the seam defect is eliminated on the right side. Each is shown below. Further, when the first defect data and the second defect data do not have a seam portion, the seam defect elimination unit 206 can be omitted.

  The data merging unit 208 merges the first defect data and the second defect data. Defects occurring in the optical film are recorded in common with the first defect data and the second defect data, the defect recorded only in the first defect data, the defect recorded only in the second defect data, and the like. Can be divided into defects. The data merging unit 208 eliminates duplicate data from the data recorded in the first defect data and the second defect data, and integrates the two data into one, so that all data recognized from the optical film can be obtained. One defect data including the defect data is generated.

  Specifically, the data merging unit 208 compares the position coordinates of the first defect data and the second defect data to calculate the correction coordinates of the first defect data, and the calculated correction coordinates Of the first defect data whose position is corrected by the above, the remaining first defect data excluding the defects existing at the same position as the second defect data are combined with the second defect data. Thus, the first defect data and the second defect data are merged. That is, even if the defect is the same, the first inspection unit 202 and the second inspection unit 204 have different positions depending on the relative positions of the first inspection unit 202, the second inspection unit 204, and the optical film. Can be determined. Therefore, the data merging unit 208 calculates the correction coordinates using the difference between the defect positions recorded in common with the first defect data and the second defect data, and thereby the first defect data. Is corrected so that the same defect exists at the same position on the first defect data and the second defect data. In the embodiment, it is described that the position of the first defect data is corrected. However, this is an example, and conversely, the position of the second defect data is corrected and the first defect data is corrected. Obviously, it is possible to match one defect data.

  Hereinafter, a method for calculating the correction coordinates will be described.

  FIG. 4 is a flowchart illustrating a correction coordinate calculation method 400 in the data merging unit 208 according to an embodiment of the present invention. In the present invention, the correction coordinates are calculated so that the number of overlapping defects among the defects included in the first defect data and the defects included in the second defect data is maximized.

  First, a predetermined number of representative defects are selected from the first defect data (or second defect data) (402). At this time, the representative defect may be a defect having a brightness and a size equal to or larger than a preset value among defects recorded in the first defect data (second defect data). Since such defects and the like are relatively easy to recognize, there is a high possibility that they are included in all of the first defect data and the second defect data.

  Next, one of the selected representative defects is selected (404), and a defect corresponding to the selected defect, i.e., a defect having the same brightness and size as the selected defect, is selected. The second defect data (first defect data) is selected (406). At this time, the defect having the same brightness and size as the selected defect is not only a defect having the same brightness and size as the selected defect but also within a predetermined error range. Including defects. That is, even if it is the same defect, its size or brightness can be recognized differently due to an error of the device or the like, and therefore the corresponding defect is selected in view of such a point.

  Next, a difference value between the position of the defect selected at step 404 and the position of the defect selected at step 406 is calculated (408), and the position of a defect or the like included in the representative defect is calculated by the calculated position difference value. (410), among the representative defects corrected in step 410, the number of defects that overlap with the second defect data (first defect data) is calculated (412).

  Thereafter, the steps 404 to 412 are repeatedly performed for each of the representative defects selected in the step 402, and the representative defect having the largest number of defects overlapping with the second defect data (first defect data). The position difference value is selected as the correction coordinate (416).

  FIG. 5 illustrates the first defect data, the second defect data, and the data merged by the data merging unit 208.

  The yield predicting unit 210 calculates the expected yield of the optical film according to the predicted cutting position and the cutting size of the optical film based on the defect data merged by the data merging unit 208.

  Specifically, the yield prediction unit 210 performs a cutting simulation of the optical film according to the preset cutting position and cutting size, and among the films in which the defects included in the merged defect data are cut, The presence or absence of which film is included is calculated.

  FIG. 6 is a diagram for explaining a yield prediction simulation based on a cutting position in the yield predicting unit 210. FIG. 6A is a diagram illustrating a rectangular sheet form in which the film is inclined leftward with respect to the film traveling direction as a reference. 6B shows an example in which the film is cut with reference to the center of the film, and FIG. 6C shows an example in which the film is cut in a right direction. A portion indicated by a dot in the drawing indicates a defect of the film.

  As can be seen from FIG. 6A, in the case of FIG. 6A, there are 8 sheets having defects, 9 in the case of FIG. 6B, and 6 in the case of FIG. 6C. Thus, the respective yields are calculated to be 10/18 = 55% in the case of FIG. 6A, 9/18 = 50% in the case of FIG. 6B, and 12/18 = 66% in the case of FIG. 6C. Therefore, when defects are distributed in the form as shown in FIG. 6, it can be seen that it is advantageous in terms of yield to cut the film as shown in FIG. 6C.

  FIG. 7 is a diagram for explaining a yield prediction simulation based on the cutting size in the yield predicting unit 210. FIG. 7A shows an example in which the film is cut with a small size and FIG. 7B shows a large size. When the yield in each case is predicted by the same method as in FIG. 6, 9/18 = 50% in the case of FIG. 7A and 4/10 = 40% in the case of FIG. 7B. Therefore, in this case, it can be seen that cutting with a size as shown in FIG. 7A is advantageous in terms of yield.

  The yield predicting unit 210 performs a yield prediction simulation based on the preset film cutting position and cutting size in this way, and the calculated expected yield in each case is displayed to the user. Then, the user confirms the calculated expected yield, selects the cutting position and size that can obtain the highest yield, and performs the post-process.

  FIG. 8 is a flowchart illustrating a yield prediction method (800) according to an embodiment of the present invention.

  First, during the manufacturing process of the optical film, a defect on the optical film that is performing a specific step is detected, and first defect data including the position, brightness, and size of the detected defect is generated (802).

  Next, a defect on the optical film that is performing another stage in the manufacturing process that is distinguished from the specific stage is detected, and second defect data including a position, brightness, and size of the detected defect Is generated (804).

  As described above, the steps 802 and 804 may take an image of the optical film from the upper surface of the optical film, and generate the defect data from the taken image.

  Next, when there is a seam defect in the first defect data and the second defect data, the seam defect is eliminated (806), and the first defect data and the second defect are eliminated. The defect data is merged (808).

  At this time, in step 806, among the defects included in the first defect data and the second defect data, more than a predetermined number of defects are recognized in a direction perpendicular to the longitudinal direction of the optical film. In this case, the corresponding defect can be recognized as a seam defect and eliminated.

  In step 808, the correction coordinates of the first defect data are calculated by comparing with the position coordinates of the first defect data and the second defect data, and the first correction data is used to calculate the first defect data. After correcting the position of the defect data of the first defect data, the first defect data excluding the defects present at the same position as the second defect data among the first defect data subjected to the position correction is used as the second defect data. It can be configured to combine with defect data.

  Thereafter, the expected yield of the optical film according to the predicted cutting position and the cutting size of the optical film is calculated from the merged defect data (810).

  On the other hand, the embodiment of the present invention can include a computer-readable recording medium including a program for performing the method described in this specification on a computer. The computer-readable recording medium may include program instructions, local data files, local data structures, etc. alone or in combination. Such media may be specially designed and constructed for the present invention, or may be known and usable by those having ordinary knowledge in the computer software field. Examples of computer-readable recording media include magnetic media such as hard disks, floppy (registered trademark) disks and magnetic tapes, optical recording media such as CD-ROMs and DVDs, magnetic-optical media such as floppy disks, and ROMs. A hardware device specially configured to store and execute program instructions, such as RAM, flash memory, is included. Examples of program instructions can include not only machine language code generated by a compiler but also high-level language code that can be executed by a computer using an interpreter or the like.

  As described above, the present invention has been described in detail by using the representative embodiments. However, those having ordinary knowledge in the technical field to which the present invention belongs can be variously modified without departing from the scope of the present invention. Understand that various modifications are possible.

  Accordingly, the scope of rights of the present invention should not be defined only by the embodiments described, but should be defined not only by the claims described later, but also by the equivalents of the claims.

DESCRIPTION OF SYMBOLS 100: Yield prediction system 102: 1st test | inspection part 104: 2nd test | inspection part 106: Seam defect exclusion part 108: Data merge part 110: Yield prediction part

Claims (15)

A first inspection unit that detects a defect on the optical film during a specific stage during the manufacturing process of the optical film and generates first defect data including a position of the detected defect;
A second inspection unit that detects a defect on the optical film that is performing another stage in the manufacturing process that is distinguished from the specific stage, and generates second defect data including a position of the detected defect. When,
The position coordinates of the first defect data and the second defect data are compared, and the defect included in the first defect data and the defect included in the second defect data are duplicated. The correction coordinates of the first defect data are calculated so that the number becomes maximum, and the same as the second defect data among the first defect data whose position is corrected by the calculated correction coordinates. A data merging unit for merging the first defect data and the second defect data by combining the remaining first defect data excluding the defects present at the position with the second defect data;
A yield predicting system for an optical film, comprising: a yield predicting unit that calculates an expected yield of the optical film according to a predicted cutting position and a cutting size of the optical film based on defect data merged by the data merging unit.   2. The yield according to claim 1, wherein the first inspection unit and the second inspection unit capture an image of the optical film from an upper surface of the optical film, and generate the defect data from the captured image. Prediction system.   The yield prediction system according to claim 1, further comprising a seam defect exclusion unit that excludes seam defects from the first defect data and the second defect data. The seam defect removal unit has recognized more than a predetermined number of defects in the direction perpendicular to the longitudinal direction of the optical film among the defects included in the first defect data and the second defect data. The yield prediction system according to claim 3 , wherein the corresponding defect is recognized as a seam defect and eliminated.   The yield prediction unit determines the presence or absence of defects in each of the films cut according to the predicted cutting position of the optical film and the size of the cut film, and determines the number of cut films and the number of cut films. The yield prediction system according to claim 1, wherein an expected yield of the optical film is calculated from the number of films having defects.   The specific stage in the manufacturing process of the optical film is a stage before applying an adhesive or an adhesive to the optical film, and the other stage in the manufacturing process that is distinguished from the specific stage is the optical stage. The yield prediction system according to claim 1, which is a stage after the adhesive or adhesive is applied to a film.   The yield prediction system according to claim 1, wherein the first defect data and the second defect data further include brightness and size of defects detected independently. Detecting a defect on the optical film that is performing a specific step during the manufacturing process of the optical film, and generating first defect data including a position of the detected defect;
Detecting defects on the optical film during other steps in the manufacturing process that are distinguished from the specific steps, and generating second defect data including positions of the detected defects;
The position coordinates of the first defect data and the second defect data are compared, and the defect included in the first defect data and the defect included in the second defect data are duplicated. Calculating correction coordinates of the first defect data so that the number is maximized;
Of the first defect data whose position has been corrected by the calculated correction coordinates, the remaining first defect data excluding defects present at the same position as the second defect data is the second defect data. Merging the first defect data and the second defect data by combining with the defect data of
And calculating a predicted yield of the optical film according to a predicted cutting position and a cutting size of the optical film based on the defect data merged in the merging step. The generating step of the first defect data and the generating step of the second defect data take an image of the optical film from an upper surface of the optical film, and generate the defect data from the taken image. Item 9. The yield prediction method according to Item 8 . The yield prediction method according to claim 8 , wherein the first defect data and the second defect data further include brightness and size of defects detected independently. The step of calculating the correction coordinates of the first defect data includes:
A first step of selecting a defect having brightness and size equal to or larger than a preset value from the first defect data as a representative defect;
A second stage of selecting one of the representative defects;
A third step of selecting, from the second defect data, a defect having the same brightness and size as the defect selected in the second step;
A fourth step of calculating a difference value between the positions of the defect selected in the second step and the defect selected in the third step and correcting the position of the representative defect by the calculated difference value of the position. When,
A fifth step of calculating the number of defects that overlap with the second defect data among the representative defects corrected in the fourth step;
For each representative defect selected in the first step, the to second stage without performing repeatedly the fifth stage, the number of defects that are duplicated in the fifth stage of the most common representative defects The yield prediction method according to claim 10 , further comprising: a sixth step of selecting a position difference value as the correction coordinate. The yield prediction method according to claim 8 , further comprising a step of eliminating a seam defect from the first defect data and the second defect data before performing the merging step. In the seam defect elimination step, the defects included in the first defect data and the second defect data are recognized by a predetermined number or more in a direction perpendicular to the longitudinal direction of the optical film. The yield prediction method according to claim 12 , wherein the corresponding defect is recognized as a seam defect and eliminated. The specific stage in the manufacturing process of the optical film is a stage before applying an adhesive or an adhesive to the optical film, and the other stage in the manufacturing process that is distinguished from the specific stage is the optical stage. The yield prediction method according to claim 8 , which is a stage after applying the pressure-sensitive adhesive or adhesive to a film. A computer-readable recording medium having recorded thereon a program for executing the method according to any one of claims 8 to 14 on a computer.