CN113791084A - Mini LED wafer appearance defect detection system and method - Google Patents
Mini LED wafer appearance defect detection system and method Download PDFInfo
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Abstract
The invention discloses a MiniLED wafer appearance defect detection system and a method, relates to the technical field of defect detection, and solves the technical problem that the optical detection precision and efficiency of a wafer cannot be considered in the prior art, so that the wafer cannot be detected completely; and the wafer is subjected to defect analysis, so that the accuracy of defect detection is improved, and the working efficiency is improved.
Description
Technical Field
The invention relates to the technical field of defect detection, in particular to a system and a method for detecting appearance defects of a MiniLED wafer.
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 miniLED wafer appearance defect detection system and a miniLED wafer appearance defect detection method, wherein defect data of a wafer are analyzed through a defect analysis unit, so that defects are detected, the defect data of the wafer are obtained, a defect analysis coefficient Xi of the wafer is obtained through a formula, 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, 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:
a MiniLED wafer appearance defect detection system 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 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 quantity 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 is 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 with 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 is 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 QX ═ CC × b1+ SL × b2) eb1+b2Obtaining 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 is represented by the formula DJo ═ (XSo × b3+ MJo × b4) eb3+b4Obtaining 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.
Further, a method for detecting appearance defects of a MiniLED wafer comprises the following specific steps:
step A: carrying out feeding operation, and adsorbing and fixing the wafer sample to be detected on a sample carrying platform by using a fixing module;
and B: adopting an n2 partition mode for the wafer, wherein n is a positive integer;
and C: accurately moving the wafer sample to a distance measurement area by using an X/Y axis displacement module, sequentially measuring the distance of the representative chip in each partition according to a preset partition track, and calculating the distance measurement value in each partition to obtain the distance measurement deviation mean value of the partition
Step D: after the partition distance measurement operation is finished, the wafer sample is accurately moved to an imaging area by using the X/Y axis displacement module, each partition chip is imaged by using the visual device according to a preset partition track in sequence, and when the industrial lens field of the visual device enters a certain partition for the first time, the absolute value of the distance measurement deviation mean value of the partition is firstly used
Comparing DOF of depth of field of the industrial lens
Focusing operation is carried out, the Z-axis displacement module is utilized to quickly adjust the sample carrying platform, so that the distance between the sample and the lens is equal to the nominal working distance of the lens, and clear visual imaging is ensured; when in use
When the camera is used, the vision device is used for imaging directly without focusing operation;
step E: and after the imaging of each partition is finished, carrying out blanking operation to finish the detection of the wafer.
Further, in step D, it is preferable that
Focusing operation is carried out, the Z-axis displacement module is utilized to quickly adjust the sample carrying platform, so that the distance between the sample and the lens is equal to the nominal working distance of the lens, and clear visual imaging is ensured; when in use
The image is directly formed by the vision device without focusing operation.
And further, in the step D, the industrial lens and the distance measuring module are quickly and synchronously adjusted by utilizing the Z-axis displacement module, so that the distance between the sample and the lens is equal to the nominal working distance of the lens, and the clear visual imaging is ensured.
Further, in the step C, a distance measuring module is used for measuring distance, and the mean value of the distance measuring deviation is
The calculation method comprises the following steps: firstly, the distance measurement value of the distance measurement module is reset to zero, the distance between the corresponding distance measurement module and the wafer at the position under the corresponding distance measurement module is selected as the reset zero position when the distance between the lens and the wafer at the position under the lens is the nominal working distance of the lens, the distance measurement is carried out on the representative chips in each partition, and the average value of the deviation value of each distance measurement is calculatedIs the mean value of the range deviation
Further, the working distance in the step D is determined according to the distance measurement deviation mean value, if the distance measurement deviation mean value X is negative, the sample carrying platform is moved upwards, and if the distance measurement deviation mean value X is negative, the sample carrying platform is moved upwards
The sample stage was adjusted downward for regular. During ranging, the central axis of the ranging device coincides with the central axis of the sample carrier, and during imaging, the central axis of the industrial lens coincides with the central axis of the sample carrier.
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 defect wafer parameters and microscope parameters are analyzed by the detection matching unit, so that the microscope is reasonably matched with the defect wafer, the grade coefficient QX of the defect wafer is obtained by a formula, the grade coefficient DJo of the microscope is obtained by the formula, the defect wafer and the microscope are matched with each other according to the grade, and the matched defect wafer and microscope are sent 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.
3. The invention provides a quick detection device and a quick detection method for a Mini-LED chip with a zone focusing function, which fully consider the practical flatness of a semiconductor chip wafer, fully utilize the field depth range of a high-power industrial lens, and innovatively adopt an effective zone focusing mode, particularly an n-type zone focusing mode in the automatic detection process of the semiconductor chip, particularly the Mini-LED chip on the premise of ensuring clear imaging2The sectional focusing mode effectively reduces the focusing operation times and the focusing time by properly dividing the Mini-LED chip wafer and carrying out selective and accurate focusing operation, not only solves the problem that the partial region of the wafer cannot be clearly imaged due to virtual focus in the automatic detection of the conventional semiconductor chip, particularly the Mini-LED chip, but also solves the problem that the full detection cannot be carried out due to the excessively low detection efficiency caused by the increase of an automatic focusing device. In a word, the system effectively improves the yield of the semiconductor chips, guarantees the sustainable and healthy development of the semiconductor industry and has obvious economic benefit.
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, a MiniLED wafer appearance defect detection system includes 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 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 the defects, the defect data of the wafer comprises type data, quantity data and size data, the type data is the type and the quantity of the defect types existing in the wafer, the quantity data is the defect quantity 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 is 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 the parameters of the defect wafer and the 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 particle number 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 is 1, 2, … …, m and m are positive integers, and the specific analysis matching process comprises the following steps:
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 QX ═ CC × b1+ SL × b2) eb1+b2Obtaining 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 visual field area of the microscope, and comparing the display multiple of the microscope and the visual field area of the microscopeLabeled XSo and MJo, respectively; and is represented by the formula DJo ═ (XSo × b3+ MJo × b4) eb3+b4Obtaining 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;
a MiniLED wafer appearance 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
A MiniLED wafer appearance defect detection system 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.
Example 2:
carrying out feeding operation, and adsorbing and fixing the wafer sample to be detected on a sample carrying platform by using a fixing module; adopting an n2 partition mode for the wafer, wherein n is a positive integer; accurately moving the wafer sample to a distance measurement area by using an X/Y axis displacement module, sequentially measuring the distance of the representative chip in each partition according to a preset partition track, and calculating the distance measurement value in each partition to obtain the distance measurement deviation mean value of the partition
(ii) a After the partition distance measurement operation is finished, the wafer sample is accurately moved to an imaging area by using an X/Y axis displacement module, and the wafer sample is installed by using visionImaging each partition chip according to a preset partition track in sequence, and when the industrial lens visual field of the visual device firstly enters a certain partition, firstly utilizing the absolute value of the range deviation mean value of the partition
Comparing DOF of depth of field of the industrial lens
Focusing operation is carried out, the Z-axis displacement module is utilized to quickly adjust the sample carrying platform, so that the distance between the sample and the lens is equal to the nominal working distance of the lens, and clear visual imaging is ensured; when in use
When the camera is used, the vision device is used for imaging directly without focusing operation; and after the imaging of each partition is finished, carrying out blanking operation to finish the detection of the wafer.
The working principle of the invention is as follows:
a MiniLED wafer appearance defect detection system and method, while working, the defect analysis, analyze the defect data of the wafer through the defect analysis unit, thus detect the defect, detect and match, analyze the defect wafer parameter and microscope parameter through detecting the matching unit, thus match the microscope to the defect wafer rationally; 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 (7)
1. A MiniLED wafer appearance defect detection system 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 login unit and a database;
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 quantity 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 is 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.
2. The MiniLED wafer appearance defect inspection system of claim 1, wherein the inspection matching unit is configured to analyze a defect wafer parameter and a microscope parameter, so as to reasonably match the microscope for 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 is 1, 2, … …, m, m is a positive integer, 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 QX ═ CC × b1+ SL × b2) eb1+b2Obtaining 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 is represented by the formula DJo ═ (XSo × b3+ MJo × b4) eb3+b4Obtaining 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.
3. The MiniLED wafer appearance defect detection system of claim 1, wherein the flatness analysis unit is configured to analyze wafer parameter data to detect a wafer, 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, the height data is a maximum height difference value of a chip 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.
4. The miniLED wafer appearance defect detection system of claim 1, wherein the lens analysis unit is configured to analyze lens parameters to detect a 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 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.
5. The MiniLED wafer appearance defect detection system of claim 1, wherein the registration login unit is configured to allow a manager and a maintainer to submit manager information and maintainer information via a mobile phone terminal for registration, and send the manager information and the maintainer information that are successfully registered to the database for storage, the manager information includes a name, an age, an enrollment time of the manager and a mobile phone number for personal real name authentication, and the maintainer information includes a name, an age, an enrollment time of the maintainer and a mobile phone number for personal real name authentication.
6. The system of claim 1, wherein the system further comprises a vision device, an auto-focus 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.
7. A MiniLED wafer appearance defect detection method is characterized by comprising the following steps:
step A: carrying out feeding operation, and adsorbing and fixing the wafer sample to be detected on a sample carrying platform by using a fixing module;
and B: using n for the wafer2A partition mode, wherein n is a positive integer;
and C: accurately moving the wafer sample to a distance measurement area by using an X/Y axis displacement module, sequentially measuring the distance of the representative chip in each partition according to a preset partition track, and calculating the distance measurement value in each partition to obtain the distance measurement deviation mean value of the partition
Step D: after the partition distance measurement operation is finished, moving the wafer sample to an imaging area by using an X/Y axis displacement module, and imaging each partition chip in sequence by using a vision device according to a preset partition track;
when the field of view of the industrial lens of the vision device firstly enters a certain partition, the absolute value of the range deviation mean value of the partition is firstly utilized
Comparing the depth of field DOF of the industrial lens,
when in use
And then, carrying out focusing operation, and quickly adjusting the sample carrying platform by using the Z-axis displacement module to enable the distance between the sample and the lens to be equal to the nominal working distance of the lensSeparating and ensuring the clear visual imaging;
when in use
When the camera is used, the vision device is used for imaging directly without focusing operation;
step E: and after the imaging of each partition is finished, carrying out blanking operation to finish the detection of the wafer.
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