CN112599438B - High-precision detection system and detection method for MiniLED wafer defects - Google Patents
High-precision detection system and detection method for MiniLED wafer defects Download PDFInfo
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Abstract
The invention discloses a high-precision optical detection system and a detection method for Mini LED wafer defects, relates to the technical field of micro device defect detection, and solves the problem that in the prior art, the optical detection precision and efficiency of a wafer cannot be considered, so that the wafer cannot be detected completely. And generating a wafer defect signal and sending the wafer defect signal to the cloud detection platform when the defect exists, marking the wafer as a defect wafer, generating a matching signal and sending the matching signal and the defect wafer to the detection matching unit. The invention improves the accuracy of defect detection and improves the working efficiency.
Description
Technical Field
The invention relates to the technical field of defect detection, in particular to a high-precision detection system and a detection method for a MiniLED wafer defect.
Background
With the continuous innovative development of the display technology of the intelligent equipment, the requirement on the resolution of the screen is higher and higher, and the MiniLED array device comes along with the development. Compared with the conventional LED, the size of the MiniLED is further reduced, and the manufacturing process is more complex, so the detection requirement and difficulty of the MiniLED are further improved. Because the size of the Mini LED is generally 100-200 μm, and the size of the conventional LED is generally more than 1000 μm, the optical resolution of the detection of the present automatic detection system for the appearance defects of the conventional LED cannot meet the detection requirements of the Mini LED; at present, the manual detection mode of a microscope is mostly adopted for detecting the appearance defects of the MiniLED, and the defects that the detection efficiency is low, the capacity requirement cannot be met, the manual detection is easy to fatigue, the detection result is uneven, the standard is not uniform due to human errors and the like generally exist.
However, in the prior art, the optical detection precision and efficiency of the wafer cannot be simultaneously considered, so that the technical problem of incapability of full detection is solved.
Disclosure of Invention
The invention aims to provide a high-precision detection system and a detection method for the defects of a MiniLED wafer, wherein the defect data of the wafer is analyzed through a defect analysis unit, so that the defects are detected, the defect data of the wafer is obtained, the defect analysis coefficient Xi of the wafer is obtained through a formula, if the defect analysis coefficient Xi of the wafer is more than or equal to the defect analysis coefficient threshold value, the defect of the corresponding wafer is judged, a wafer defect signal is generated and sent to a cloud detection platform, after the cloud detection platform receives the wafer defect signal, the wafer is marked as a defective wafer, and then a matching signal is generated and the matching signal and the defective wafer are sent to a detection matching unit; if the defect analysis coefficient Xi of the wafer is smaller than the defect analysis coefficient threshold value, judging that no defect exists in the corresponding wafer, generating a wafer normal signal and sending the wafer normal signal to the cloud detection platform, and after receiving the wafer normal signal, the cloud detection platform generates a non-detection signal and sends the non-detection signal to a mobile phone terminal of a manager; and the wafer is subjected to defect analysis, so that the accuracy of defect detection is improved, and the working efficiency is improved.
The purpose of the invention can be realized by the following technical scheme:
the high-precision detection system for the defects of the MiniLED wafer comprises a defect analysis unit, a detection matching unit, a flatness analysis unit, a lens analysis unit, a cloud detection platform, a registration login unit and a database;
the defect analysis unit is used for analyzing defect data of a wafer so as to detect defects, the defect data of the wafer comprises type data, quantity data and size data, the type data is the type number of the defect types existing in the wafer, the quantity data is the defect number of the wafer corresponding to the defect types, the size data is the average size of the defects existing in the wafer, the wafer is marked as i, i =1, 2, … …, n, n is a positive integer, and the specific analysis and detection process is as follows:
step S1: acquiring the type number of the defect types existing in the wafer, and marking the type number of the defect types existing in the wafer as Si;
step S2: acquiring the defect number of the wafer with the corresponding defect type, and marking the defect number of the wafer with the corresponding defect type as Qi;
step S3: acquiring the average size of the wafer with defects, and marking the average size of the wafer with the defects as Ci;
step S4: by the formula
Acquiring a defect analysis coefficient Xi of the wafer, wherein a1, a2 and a3 are all proportionality coefficients, and a1 is greater than a2 is greater than a3 is greater than 0;
step S5: comparing the defect analysis coefficient Xi of the wafer with a defect analysis coefficient threshold:
if the defect analysis coefficient Xi of the wafer is larger than or equal to the defect analysis coefficient threshold value, judging that the corresponding wafer has defects, generating a wafer defect signal and sending the wafer defect signal to a cloud detection platform, marking the wafer as a defective wafer after the cloud detection platform receives the wafer defect signal, then generating a matching signal and sending the matching signal and the defective wafer to a detection matching unit;
if the defect analysis coefficient Xi of the wafer is smaller than the defect analysis coefficient threshold value, judging that no defect exists in the corresponding wafer, generating a wafer normal signal and sending the wafer normal signal to the cloud detection platform, and after receiving the wafer normal signal, the cloud detection platform generates a non-detection signal and sends the non-detection signal to a mobile phone terminal of a manager.
Further, the detection matching unit is configured to analyze a defect wafer parameter and a microscope parameter, so as to reasonably match the microscope to the defect wafer, the defect wafer parameter includes a size of the defect wafer and a number of particles of the defect wafer, the microscope parameter includes a display multiple of the microscope and a field area of the microscope, the microscope is marked as o, o =1, 2, … …, m, m is a positive integer, and a specific analysis matching process is as follows:
step SS 1: acquiring the size of a defective wafer and the number of particles of the defective wafer, and marking the size of the defective wafer and the number of particles of the defective wafer as CC and SL respectively; and by the formula
Obtaining a grade coefficient QX of the defect wafer, wherein b1 and b2 are proportional coefficients, b1 is greater than b2 is greater than 0, and e is a natural constant;
step SS 2: comparing the grade coefficient QX of the defective wafer with the grade coefficients L1 and L2, wherein both the grade coefficients L1 and L2 are the grade coefficient threshold values of the defective wafer, and L1 is more than L2;
if the grade coefficient QX of the defective wafer is larger than or equal to L1, marking the corresponding defective wafer as a first-grade defective wafer;
if the grade coefficient L2 of the defective wafer is more than QX and less than L1, marking the corresponding defective wafer as a second grade defective wafer;
if the grade coefficient QX of the defective wafer is less than L2, marking the corresponding defective wafer as a third grade defective wafer;
step SS 3: acquiring the display multiple of the microscope and the field area of the microscope, and respectively marking the display multiple of the microscope and the field area of the microscope as XSo and MJo; and by the formula
Obtaining a grade coefficient DJo of the microscope, wherein b3 and b4 are proportional coefficients, b3 is greater than b4 is greater than 0, and e is a natural constant;
step SS 4: comparing the microscope's scale factor DJo to L3 and L4, both L3 and L4 being microscope's scale factor thresholds, and L3 > L4;
if the grade coefficient DJo of the microscope is more than or equal to L3, marking the corresponding microscope as a first grade microscope;
if the grade coefficient of the microscope is L4 < DJo < L3, marking the corresponding microscope as a second grade microscope;
if the grade coefficient DJo of the microscope is less than or equal to L4, marking the corresponding microscope as a third grade microscope;
step SS 5: and matching the defective wafer and the microscope according to grades, and sending the matched defective wafer and the matched microscope to a mobile phone terminal of a manager.
Further, the flatness analysis unit is configured to analyze wafer parameter data to detect a wafer, where the wafer parameter data includes flatness data and height data, the flatness data is a flatness value of an outer surface of a wafer chip, and the height data is a maximum height difference value of core grains in the wafer chip, and the specific analysis process is as follows:
step T1: acquiring a flatness value of the outer surface of the wafer chip, and marking the flatness value of the outer surface of the wafer chip as SZ;
step T2: acquiring the maximum height difference of the chip grains in the wafer chip, and marking the maximum height difference of the chip grains in the wafer chip as CZ;
step T3: by the formula
Acquiring a flatness analysis coefficient PZD of a wafer chip, wherein v1 and v2 are proportional coefficients, v1 is greater than v2 is greater than 0, and beta is an error correction factor and takes the value of 2.36512;
step T4: comparing the flatness analysis coefficient PZD of the wafer chip with a flatness analysis coefficient threshold value:
if the flatness analysis coefficient PZD of the wafer chip is larger than or equal to the flatness analysis coefficient threshold value, judging that the flatness of the corresponding wafer chip is normal, generating a normal flatness signal and sending the normal flatness signal to a mobile phone terminal of a manager;
and if the flatness analysis coefficient PZD of the wafer chip is smaller than the flatness analysis coefficient threshold value, judging that the flatness of the corresponding wafer chip is abnormal, generating a flatness abnormal signal and sending the flatness abnormal signal to a mobile phone terminal of a manager.
Further, the lens analysis unit is used for analyzing lens parameters so as to detect the lens, the lens parameters are definition data and distortion data, the definition data is definition of a picture when the lens performs optical imaging, the distortion data is distortion percentage of the picture when the lens performs optical imaging, and the specific analysis and detection process is as follows:
step TT 1: acquiring the definition of a picture when the lens performs optical imaging, and marking the definition of the picture when the lens performs optical imaging as QXD;
step TT 2: acquiring the distortion percentage of a picture when the lens performs optical imaging, and marking the distortion percentage of the picture when the lens performs optical imaging as JBB;
step TT 3: by the formula
Obtaining an analysis detection coefficient JT of a lens, wherein v3 and v4 are both proportional coefficients, v3 is greater than v4 is greater than 0, and alpha is an error correction factor and takes the value of 2.365412;
step TT 4: comparing the JT of the shot with the JT of the shot threshold:
if the analysis detection coefficient JT of the lens is not less than the analysis detection coefficient threshold of the lens, judging that the corresponding lens is normal, generating a normal lens signal and sending the normal lens signal to a mobile phone terminal of a maintainer;
and if the analysis detection coefficient JT of the lens is less than the analysis detection coefficient threshold of the lens, judging that the corresponding lens is abnormal, generating a lens abnormal signal and sending the lens abnormal signal to a mobile phone terminal of a maintainer.
Further, the registration login unit is used for the manager and the maintainer to submit the manager information and the maintainer information for registration through the mobile phone terminal, and the manager information and the maintainer information which are successfully registered are sent to the database for storage, the manager information comprises the name, the age, the time of entry and the mobile phone number of the personal real name authentication, and the maintainer information comprises the name, the age, the time of entry and the mobile phone number of the personal real name authentication.
Furthermore, the MiniLED wafer appearance defect detection system further includes a vision device, an automatic focusing device, a sample stage device and a computer system; the vision device comprises an industrial camera, an industrial lens and an illumination light source; the automatic focusing device comprises a distance measuring module and a displacement module; the industrial camera is an industrial camera with a high frame rate and a large target surface, and the industrial lens is an industrial lens with high magnification and a large visual field; the sample stage device comprises a sample stage and a fixing module; the computer system comprises an industrial control computer, system software, an image acquisition card and a motion control card.
Compared with the prior art, the invention has the beneficial effects that:
1. in the invention, defect data of a wafer is analyzed through a defect analysis unit so as to detect defects, the defect data of the wafer is obtained, a defect analysis coefficient Xi of the wafer is obtained through a formula, if the defect analysis coefficient Xi of the wafer is more than or equal to a defect analysis coefficient threshold value, the wafer is judged to have defects corresponding to the wafer, a wafer defect signal is generated and sent to a cloud detection platform, the cloud detection platform marks the wafer as a defective wafer after receiving the wafer defect signal, and then a matching signal is generated and the matching signal and the defective wafer are sent to a detection matching unit; if the defect analysis coefficient Xi of the wafer is smaller than the defect analysis coefficient threshold value, judging that no defect exists in the corresponding wafer, generating a wafer normal signal and sending the wafer normal signal to the cloud detection platform, and after receiving the wafer normal signal, the cloud detection platform generates a non-detection signal and sends the non-detection signal to a mobile phone terminal of a manager; the wafer is subjected to defect analysis, so that the accuracy of defect detection is improved, and the working efficiency is improved;
2. in the invention, the parameters of the defect wafer and the parameters of the microscope are analyzed by the detection matching unit, so that the microscope is reasonably matched with the defect wafer through a formula
Obtaining the grade coefficient QX of the defective wafer and passing through a formula
Obtaining a grade coefficient DJo of the microscope, and matching the defect wafer with the microscopeMatching the levels, and sending the matched defect wafer and the microscope to a mobile phone terminal of a manager; the wafer and the microscope are reasonably matched, so that the working efficiency of wafer detection is improved, and meanwhile, the production cost is reduced.
Drawings
In order to facilitate understanding for those skilled in the art, the present invention will be further described with reference to the accompanying drawings.
Fig. 1 is a schematic block diagram of the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the following embodiments, and it should be understood that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1, the system for high-precision detecting the defect of the MiniLED wafer of the present invention includes a defect analyzing unit, a detecting and matching unit, a flatness analyzing unit, a lens analyzing unit, a cloud detecting platform, a registration unit and a database;
the registration login unit is used for the manager and the maintainer to submit manager information and maintainer information for registration through the mobile phone terminal, and sending the manager information and the maintainer information which are successfully registered to the database for storage, wherein the manager information comprises the name, the age, the time of entry and the mobile phone number of real name authentication of the manager, and the maintainer information comprises the name, the age, the time of entry and the mobile phone number of real name authentication of the maintainer;
the defect analysis unit is used for analyzing defect data of the wafer so as to detect defects, the defect data of the wafer comprises type data, quantity data and size data, the type data is the type number of the defect types existing in the wafer, the quantity data is the defect number of the defect types corresponding to the defect types existing in the wafer, the size data is the average size of the defects existing in the wafer, the wafer is marked as i, i =1, 2, … …, n, n is a positive integer, and the specific analysis and detection process is as follows:
step S1: acquiring the type number of the defect types existing in the wafer, and marking the type number of the defect types existing in the wafer as Si;
step S2: acquiring the defect number of the wafer with the corresponding defect type, and marking the defect number of the wafer with the corresponding defect type as Qi;
step S3: acquiring the average size of the wafer with defects, and marking the average size of the wafer with the defects as Ci;
step S4: by the formula
Acquiring a defect analysis coefficient Xi of the wafer, wherein a1, a2 and a3 are all proportionality coefficients, and a1 is greater than a2 is greater than a3 is greater than 0;
step S5: comparing the defect analysis coefficient Xi of the wafer with a defect analysis coefficient threshold:
if the defect analysis coefficient Xi of the wafer is larger than or equal to the defect analysis coefficient threshold value, judging that the corresponding wafer has defects, generating a wafer defect signal and sending the wafer defect signal to a cloud detection platform, marking the wafer as a defective wafer after the cloud detection platform receives the wafer defect signal, then generating a matching signal and sending the matching signal and the defective wafer to a detection matching unit;
if the defect analysis coefficient Xi of the wafer is smaller than the defect analysis coefficient threshold value, judging that no defect exists in the corresponding wafer, generating a wafer normal signal and sending the wafer normal signal to the cloud detection platform, and after receiving the wafer normal signal, the cloud detection platform generates a non-detection signal and sends the non-detection signal to a mobile phone terminal of a manager;
the detection matching unit is used for analyzing parameters of the defect wafer and parameters of the microscope, so that the microscope is reasonably matched with the defect wafer, the parameters of the defect wafer comprise the size of the defect wafer and the number of particles of the defect wafer, the parameters of the microscope comprise the display multiple of the microscope and the visual field area of the microscope, the microscope is marked as o, o =1, 2, … …, m and m are positive integers, and the specific analysis matching process is as follows:
step SS 1: acquiring the size of a defective wafer and the number of particles of the defective wafer, and marking the size of the defective wafer and the number of particles of the defective wafer as CC and SL respectively; and by the formula
Obtaining a grade coefficient QX of the defect wafer, wherein b1 and b2 are proportional coefficients, b1 is greater than b2 is greater than 0, and e is a natural constant;
step SS 2: comparing the grade coefficient QX of the defective wafer with the grade coefficients L1 and L2, wherein both the grade coefficients L1 and L2 are the grade coefficient threshold values of the defective wafer, and L1 is more than L2;
if the grade coefficient QX of the defective wafer is larger than or equal to L1, marking the corresponding defective wafer as a first-grade defective wafer;
if the grade coefficient L2 of the defective wafer is more than QX and less than L1, marking the corresponding defective wafer as a second grade defective wafer;
if the grade coefficient QX of the defective wafer is less than L2, marking the corresponding defective wafer as a third grade defective wafer;
step SS 3: acquiring the display multiple of the microscope and the field area of the microscope, and respectively marking the display multiple of the microscope and the field area of the microscope as XSo and MJo; and by the formula
Obtaining a grade coefficient DJo of the microscope, wherein b3 and b4 are proportional coefficients, b3 is greater than b4 is greater than 0, and e is a natural constant;
step SS 4: comparing the microscope's scale factor DJo to L3 and L4, both L3 and L4 being microscope's scale factor thresholds, and L3 > L4;
if the grade coefficient DJo of the microscope is more than or equal to L3, marking the corresponding microscope as a first grade microscope;
if the grade coefficient of the microscope is L4 < DJo < L3, marking the corresponding microscope as a second grade microscope;
if the grade coefficient DJo of the microscope is less than or equal to L4, marking the corresponding microscope as a third grade microscope;
step SS 5: matching the defective wafer and the microscope according to grades, and sending the matched defective wafer and the microscope to a mobile phone terminal of a manager;
the flatness analysis unit is used for analyzing the wafer parameter data so as to detect the wafer, the wafer parameter data comprise flatness data and height data, the flatness data are flatness values of the outer surface of the wafer chip, the height data are the maximum height difference values of the chip grains in the wafer chip, and the specific analysis process comprises the following steps:
step T1: acquiring a flatness value of the outer surface of the wafer chip, and marking the flatness value of the outer surface of the wafer chip as SZ;
step T2: acquiring the maximum height difference of the chip grains in the wafer chip, and marking the maximum height difference of the chip grains in the wafer chip as CZ;
step T3: by the formula
Acquiring a flatness analysis coefficient PZD of a wafer chip, wherein v1 and v2 are proportional coefficients, v1 is greater than v2 is greater than 0, and beta is an error correction factor and takes the value of 2.36512;
step T4: comparing the flatness analysis coefficient PZD of the wafer chip with a flatness analysis coefficient threshold value:
if the flatness analysis coefficient PZD of the wafer chip is larger than or equal to the flatness analysis coefficient threshold value, judging that the flatness of the corresponding wafer chip is normal, generating a normal flatness signal and sending the normal flatness signal to a mobile phone terminal of a manager;
if the flatness analysis coefficient PZD of the wafer chip is smaller than the flatness analysis coefficient threshold value, judging that the flatness of the corresponding wafer chip is abnormal, generating a flatness abnormal signal and sending the flatness abnormal signal to a mobile phone terminal of a manager;
the lens analysis unit is used for analyzing lens parameters, so that the lens is detected, the lens parameters are definition data and distortion data, the definition data is definition of a picture when the lens performs optical imaging, the distortion data is distortion percentage of the picture when the lens performs optical imaging, and the specific analysis and detection process is as follows:
step TT 1: acquiring the definition of a picture when the lens performs optical imaging, and marking the definition of the picture when the lens performs optical imaging as QXD;
step TT 2: acquiring the distortion percentage of a picture when the lens performs optical imaging, and marking the distortion percentage of the picture when the lens performs optical imaging as JBB;
step TT 3: by the formula
Obtaining an analysis detection coefficient JT of a lens, wherein v3 and v4 are both proportional coefficients, v3 is greater than v4 is greater than 0, and alpha is an error correction factor and takes the value of 2.365412;
step TT 4: comparing the JT of the shot with the JT of the shot threshold:
if the analysis detection coefficient JT of the lens is not less than the analysis detection coefficient threshold of the lens, judging that the corresponding lens is normal, generating a normal lens signal and sending the normal lens signal to a mobile phone terminal of a maintainer;
if the analysis detection coefficient JT of the lens is less than the analysis detection coefficient threshold of the lens, judging that the corresponding lens is abnormal, generating a lens abnormal signal and sending the lens abnormal signal to a mobile phone terminal of a maintainer;
the MiniLED wafer defect detection method comprises the following specific steps:
the method comprises the steps that firstly, defect analysis is carried out, defect data of a wafer are analyzed through a defect analysis unit, so that defects are detected, if the defect analysis coefficient Xi of the wafer is larger than or equal to a defect analysis coefficient threshold value, the wafer is judged to have defects, a wafer defect signal is generated and sent to a cloud detection platform, the cloud detection platform marks the wafer as a defective wafer after receiving the wafer defect signal, and then a matching signal is generated and sent to a detection matching unit;
step two, detecting and matching, namely analyzing parameters of the defect wafer and parameters of the microscope through a detecting and matching unit, so as to reasonably match the microscope with the defect wafer;
thirdly, flatness detection, namely analyzing wafer parameter data through a flatness analysis unit so as to detect a wafer, judging that the flatness of the corresponding wafer chip is abnormal if the flatness analysis coefficient PZD of the wafer chip is smaller than the flatness analysis coefficient threshold value, generating a flatness abnormal signal and sending the flatness abnormal signal to a mobile phone terminal of a manager, and replacing a high-flatness objective table after the manager receives the flatness abnormal signal, wherein the high-flatness objective table comprises high-flatness glass and a vacuum negative pressure system, a plastic blue film loaded with the LED wafer chip is adsorbed onto the high-flatness glass with the height tolerance within 3 mu m through the vacuum negative pressure system, and the blue film is tightly attached to the surface of the glass under the action of the vacuum negative pressure, so that the surface height difference of the LED wafer chip to be detected is kept within 5 mu m;
step four, lens detection, namely analyzing lens parameters through a lens analysis unit so as to detect the lens, judging that the corresponding lens is abnormal if an analysis detection coefficient JT of the lens is less than an analysis detection coefficient threshold of the lens, generating a lens abnormal signal and sending the lens abnormal signal to a mobile phone terminal of a maintainer; after the maintainer receives the abnormal signal of the lens, the maintainer reasonably matches the lens with the camera, and adopts a full-width target surface camera for the 3-time lens; for a 5-fold lens, a 2inch target surface camera was used.
Example 1
The high-precision detection system for the defects of the MiniLED wafer comprises a high-power high-numerical-aperture lens, a large-target-surface high-resolution camera, a light source, a displacement objective table and a computer image processing system; the large-target-surface high-resolution camera is matched with the high-power high-numerical-aperture lens, the high-power high-numerical-aperture lens is arranged below the large-target-surface high-resolution camera, and the displacement objective table is arranged right below the high-power high-numerical-aperture lens during detection; the lens is a high-power high-numerical aperture lens, the multiplying power is 3 times or more, the defects of 0.5-200 mu m on the surface of the Mini-LED can be clearly imaged, and products with appearance defects can be effectively distinguished and screened; the large target surface high-resolution camera is a large target surface high-resolution camera with the depth of more than 4/3 inch; the resolution of the Mini-LED wafer appearance defect optical detection system can be smaller than 1.6 mu m; the light source is a stroboscopic or normally bright light source; the computer image processing system receives the detection data of the large target surface high-resolution camera and the high-power high-numerical aperture lens, and the optical detection system adopts a machine vision mode to replace manual visual inspection to automatically judge and classify the defects, so that large-batch automatic measurement can be realized; during detection, the Mini-LED wafer is placed on a displacement objective table; the resolution of the Mini-LED wafer appearance defect optical detection method can be less than 1 mu m.
The working principle of the invention is as follows:
during working, analyzing the defects, analyzing the defect data of the wafer through a defect analyzing unit so as to detect the defects, detecting and matching, and analyzing the parameters of the defective wafer and the parameters of a microscope through a detecting and matching unit so as to reasonably match the microscope with the defective wafer; the flatness detection is carried out, the wafer parameter data are analyzed through a flatness analysis unit, so that the wafer is detected, a manager receives a flatness abnormal signal and then replaces a high-flatness objective table, the high-flatness objective table comprises high-flatness glass and a vacuum negative pressure system, a plastic blue membrane loaded with an LED wafer chip is adsorbed onto the high-flatness glass with the height tolerance within 3 mu m through the vacuum negative pressure system, and the blue membrane is tightly attached to the surface of the glass under the action of vacuum negative pressure, so that the surface height difference of the LED wafer chip to be detected is kept within 5 mu m; lens detection, namely analyzing lens parameters through a lens analysis unit so as to detect the lens, reasonably matching the lens and a camera by a maintainer after the maintainer receives a lens abnormal signal, and adopting a full-width target surface camera for a 3-time lens; for a 5-fold lens, a 2inch target surface camera was used.
The above formulas are all calculated by taking the numerical value of the dimension, the formula is a formula which obtains the latest real situation by acquiring a large amount of data and performing software simulation, and the preset parameters in the formula are set by the technical personnel in the field according to the actual situation.
The foregoing is merely exemplary and illustrative of the present invention and various modifications, additions and substitutions may be made by those skilled in the art to the specific embodiments described without departing from the scope of the invention as defined in the following claims.
Claims (5)
- The high-precision optical detection system for the defects of the Mini LED wafer is characterized by comprising a defect analysis unit, a detection matching unit, a flatness analysis unit, a lens analysis unit, a cloud detection platform, a registration unit and a database;the defect analysis unit is used for analyzing defect data of a wafer so as to detect defects, the defect data of the wafer comprises type data, quantity data and size data, the type data is the type number of the defect types existing in the wafer, the quantity data is the defect number of the wafer corresponding to the defect types, the size data is the average size of the defects existing in the wafer, the wafer is marked as i, i =1, 2, … …, n, n is a positive integer, and the specific analysis and detection process is as follows:step S1: acquiring the type number of the defect types existing in the wafer, and marking the type number of the defect types existing in the wafer as Si;step S2: acquiring the defect number of the wafer with the corresponding defect type, and marking the defect number of the wafer with the corresponding defect type as Qi;step S3: acquiring the average size of the wafer with defects, and marking the average size of the wafer with the defects as Ci;step S4: by the formulaAcquiring a defect analysis coefficient Xi of the wafer, wherein a1, a2 and a3 are all proportionality coefficients, and a1 is greater than a2 is greater than a3 is greater than 0;
step S5: comparing the defect analysis coefficient Xi of the wafer with a defect analysis coefficient threshold:if the defect analysis coefficient Xi of the wafer is larger than or equal to the defect analysis coefficient threshold value, judging that the corresponding wafer has defects, generating a wafer defect signal and sending the wafer defect signal to a cloud detection platform, marking the wafer as a defective wafer after the cloud detection platform receives the wafer defect signal, then generating a matching signal and sending the matching signal and the defective wafer to a detection matching unit;if the defect analysis coefficient Xi of the wafer is smaller than the defect analysis coefficient threshold value, judging that no defect exists in the corresponding wafer, generating a wafer normal signal and sending the wafer normal signal to the cloud detection platform, and after receiving the wafer normal signal, the cloud detection platform generates a non-detection signal and sends the non-detection signal to a mobile phone terminal of a manager;the detection matching unit is used for analyzing parameters of the defect wafer and parameters of the microscope, so that the microscope is reasonably matched with the defect wafer, the parameters of the defect wafer comprise the size of the defect wafer and the number of particles of the defect wafer, the parameters of the microscope comprise the display multiple of the microscope and the visual field area of the microscope, the microscope is marked as o, o =1, 2, … …, m and m are positive integers, and the specific analysis matching process is as follows:step SS 1: acquiring the size of a defective wafer and the number of particles of the defective wafer, and marking the size of the defective wafer and the number of particles of the defective wafer as CC and SL respectively; and by the formulaObtaining a grade coefficient QX of the defect wafer, wherein b1 and b2 are proportional coefficients, b1 is greater than b2 is greater than 0, and e is a natural constant;
step SS 2: comparing the grade coefficient QX of the defective wafer with the grade coefficients L1 and L2, wherein both the grade coefficients L1 and L2 are the grade coefficient threshold values of the defective wafer, and L1 is more than L2;if the grade coefficient QX of the defective wafer is larger than or equal to L1, marking the corresponding defective wafer as a first-grade defective wafer;if the grade coefficient L2 of the defective wafer is more than QX and less than L1, marking the corresponding defective wafer as a second grade defective wafer;if the grade coefficient QX of the defective wafer is less than L2, marking the corresponding defective wafer as a third grade defective wafer;step SS 3: acquiring the display multiple of the microscope and the field area of the microscope, and respectively marking the display multiple of the microscope and the field area of the microscope as XSo and MJo; and by the formulaObtaining a grade coefficient DJo of the microscope, wherein b3 and b4 are proportional coefficients, b3 is greater than b4 is greater than 0, and e is a natural constant;
step SS 4: comparing the microscope's scale factor DJo to L3 and L4, both L3 and L4 being microscope's scale factor thresholds, and L3 > L4;if the grade coefficient DJo of the microscope is more than or equal to L3, marking the corresponding microscope as a first grade microscope;if the grade coefficient of the microscope is L4 < DJo < L3, marking the corresponding microscope as a second grade microscope;if the grade coefficient DJo of the microscope is less than or equal to L4, marking the corresponding microscope as a third grade microscope;step SS 5: and matching the defective wafer and the microscope according to grades, and sending the matched defective wafer and the matched microscope to a mobile phone terminal of a manager. - 2. The system of claim 1, wherein the flatness analysis unit is configured to analyze wafer parameter data to detect the wafer, the wafer parameter data includes flatness data and height data, the flatness data is a flatness value of an outer surface of the wafer chip, the height data is a maximum height difference of the chips of the wafer chip, and the specific analysis process includes:step T1: acquiring a flatness value of the outer surface of the wafer chip, and marking the flatness value of the outer surface of the wafer chip as SZ;step T2: acquiring the maximum height difference of the chip grains in the wafer chip, and marking the maximum height difference of the chip grains in the wafer chip as CZ;step T3: by the formulaAcquiring a flatness analysis coefficient PZD of a wafer chip, wherein v1 and v2 are proportional coefficients, v1 is greater than v2 is greater than 0, and beta is an error correction factor and takes the value of 2.36512;
step T4: comparing the flatness analysis coefficient PZD of the wafer chip with a flatness analysis coefficient threshold value:if the flatness analysis coefficient PZD of the wafer chip is larger than or equal to the flatness analysis coefficient threshold value, judging that the flatness of the corresponding wafer chip is normal, generating a normal flatness signal and sending the normal flatness signal to a mobile phone terminal of a manager;and if the flatness analysis coefficient PZD of the wafer chip is smaller than the flatness analysis coefficient threshold value, judging that the flatness of the corresponding wafer chip is abnormal, generating a flatness abnormal signal and sending the flatness abnormal signal to a mobile phone terminal of a manager. - 3. The system of claim 1, wherein the lens analysis unit is configured to analyze lens parameters to detect the lens, the lens parameters are sharpness data and distortion data, the sharpness data is sharpness of a picture when the lens performs optical imaging, the distortion data is distortion percentage of the picture when the lens performs optical imaging, and the specific analysis and detection process includes:step TT 1: acquiring the definition of a picture when the lens performs optical imaging, and marking the definition of the picture when the lens performs optical imaging as QXD;step TT 2: acquiring the distortion percentage of a picture when the lens performs optical imaging, and marking the distortion percentage of the picture when the lens performs optical imaging as JBB;step TT 3: by the formulaObtaining an analysis detection coefficient JT of a lens, wherein v3 and v4 are both proportional coefficients, v3 is greater than v4 is greater than 0, and alpha is an error correction factor and takes the value of 2.365412;
step TT 4: comparing the JT of the shot with the JT of the shot threshold:if the analysis detection coefficient JT of the lens is not less than the analysis detection coefficient threshold of the lens, judging that the corresponding lens is normal, generating a normal lens signal and sending the normal lens signal to a mobile phone terminal of a maintainer;and if the analysis detection coefficient JT of the lens is less than the analysis detection coefficient threshold of the lens, judging that the corresponding lens is abnormal, generating a lens abnormal signal and sending the lens abnormal signal to a mobile phone terminal of a maintainer. - 4. The Mini LED wafer defect high-precision optical detection system of claim 1, wherein the registration login unit is used for the manager and the maintainer to submit the manager information and the maintainer information through the mobile phone terminal for registration, and to send the manager information and the maintainer information which are successfully registered to the database for storage, the manager information comprises the name, the age, the time of entry and the mobile phone number for real name authentication of the manager, and the maintainer information comprises the name, the age, the time of entry and the mobile phone number for real name authentication of the maintainer.
- 5. The high-precision optical detection system for the defects of the Mini LED wafer according to claim 1, wherein the detection system for the appearance defects of the Mini LED wafer further comprises a vision device, an automatic focusing device, a sample stage device and a computer system; the vision device comprises an industrial camera, an industrial lens and an illumination light source; the automatic focusing device comprises a distance measuring module and a displacement module; the industrial camera is an industrial camera with a high frame rate and a large target surface, and the industrial lens is an industrial lens with high magnification and a large visual field; the sample stage device comprises a sample stage and a fixing module; the computer system comprises an industrial control computer, system software, an image acquisition card and a motion control card.
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