Planar prescription lens mass production defect grading report output method and system

Abstract

The invention discloses a planar prescription lens mass production defect grading report output method and a planar prescription lens mass production defect grading report output system applied to the field of optical lens detection and quality control, and aims at mass production appearance and optical quality control of microstructure / diffraction prescription lenses. Defect detection, type classification and feature quantification are automatically completed, a severity score Q is calculated based on a defect type-size-position-optical influence function, and hard threshold gating and a regression / rework / degradation judgment strategy are further implemented through a QC rule engine. And outputting a standardized report containing a reason code, a threshold version, a tracing field, an original data hash abstract and a digital signature, realizing cross-station consistent acceptance and traceable auditing, and supporting process recharge and a CAPA closed loop.

Description

Technical Field

[0001] This invention relates to the field of optical lens inspection and quality control, and in particular to a method and system for outputting defect grading reports for mass production of planar prescription lenses. Background Technology

[0002] Planar prescription lenses (including prescription lenses with microstructures / diffraction structures) may develop various types of defects during mass production, such as particulate contamination, scratches, splicing marks, mold release residue, and coating defects. These defects not only affect the appearance but may also introduce scattering / haze, wavefront error, and image quality degradation.

[0003] Current mass production QC typically relies on subjective human judgment or single-device indicators, resulting in inconsistent rejection rules, inconsistent standards across workstations, and a lack of reason codes and auditable traceability fields. This makes yield optimization and CAPA (Capability, Performance, and Accounting) loop closure difficult. Therefore, a solution is needed that can fuse multi-source inspection data, form a unified defect semantics and severity score, and output a standardized report. Summary of the Invention

[0004] The core of this invention lies in utilizing a QC rule engine to implement hard threshold gating and rejection / rework / degradation strategies, outputting standardized reports with reason codes, threshold versions, hash digests, and digital signatures. Furthermore, by issuing control modules, conclusions are only allowed to be sent to manufacturing equipment or the MES system when hash and signature verification pass, ensuring data consistency and audit reliability.

[0005] To solve the above problems, the present invention adopts the following technical solution.

[0006] The method for generating a defect grading report for mass production of planar prescription lenses includes the following steps: S1. Obtain lens batch information, prescription / structure version number, and QC threshold and rule set, and access multi-source test data of the lens to be tested; S2. Preprocess and align the multi-source detection data to construct a unified lens coordinate system Σ L The following data representation; S3. Perform defect detection on a unified data representation to obtain a defect set {dj}, and for each defect dj... j Output defect type c j and characteristic quantity {a j ,l j ,(x j ,y j )}, where d j For the j-th defect; c j Let a be the type of the j-th defect. j Let l be the area of ​​the j-th defect / the equivalent area. j Let x be the length of the j-th defect, (x)j ,y j () represents the location of the j-th defect; S4, Based on defect type c j The severity score Q is calculated based on the defect size characteristics and defect location, and the impact ΔM of the defect on optical performance is also calculated. S5. Input the severity score Q and impact ΔM into the QC rule engine, and output the conclusion (pass / rework / downgrade / scrap) and reason code according to the rejection rules; S6. Generate a standardized quality report and traceability data package. The traceability data package shall include at least the threshold version, algorithm version, original data hash digest, and digital signature fields.

[0007] Furthermore, in step S2, coordinate alignment includes mapping the appearance image coordinates, contour measurement coordinates, and optical measurement coordinates to a unified lens coordinate system Σ_L using reference marks, edge contours, or fixture references.

[0008] Furthermore, in step S3, defect type c j It includes at least one or more of the following: particles / contamination, scratches, splicing marks, mold release residue, and coating defects.

[0009] Furthermore, in step S4, the severity score Q is calculated as follows: ; Where Nd is the total number of defects detected in the lens under inspection, j j For defect indexing, w(c j ) represents the weight associated with the defect type, a j The area is the defect area or equivalent area, γ is the scale index, and r is the area of ​​the defect or equivalent area. j g(r) is the radial distance from the defect to the optically sensitive region. j Let be the location influence function, the summation term be the severe sharing of each defect locality, h(ΔM) be the optical influence mapping function, and β be the coefficient. This represents the current overall optical performance degradation of the entire lens.

[0010] Furthermore, the optical influence quantity ΔM includes at least one of the following: haze / scattering increment, wavefront RMS increment, or MTF decrease at a preset spatial frequency.

[0011] Furthermore, in step S5, the QC rule engine includes hard threshold gating and policy gating. When any hard threshold is triggered, the system will directly reject the decision and output the corresponding reason code. When no hard threshold is triggered, the system will output a pass, rework, or downgrade conclusion based on the severity score Q and the grading threshold range.

[0012] Furthermore, the standardized quality report in step S6 includes at least batch information, version number, Top-N list of defects, severity score Q, conclusion, cause code, threshold version ID, testing device ID, timestamp, and raw data hash digest.

[0013] Furthermore, the traceability data package includes the defect detection model version, scoring parameter version, and log chain index field for cross-workstation consistency verification.

[0014] Furthermore, standardized quality reports and traceability data packages will feed back cause code distribution, severity score Q distribution, and critical defect type statistics into process window maintenance and CAPA closed-loop. Furthermore, once the hash verification and digital signature verification of the traceability data packet pass, the conclusion and handling recommendations for the corresponding lens are sent to the manufacturing equipment or MES system. A mass production defect grading and reporting system for planar prescription lenses includes a data access module, a coordinate alignment module, a defect detection and classification module, a severity assessment module, a QC rule engine module, a report output module, a traceability database module, and a signature and verification module.

[0015] Furthermore, the data access module is used to access appearance images, contour / surface data, and scattering / haze or wavefront data, and writes a device ID and timestamp for each data source.

[0016] Furthermore, the defect detection and classification module includes a traditional image processing unit and a machine learning classifier, used to output the defect type c. j Defect location and characteristic quantity {a j ,l j ,(x j ,y j Any one of the following: )}

[0017] Furthermore, the severity assessment module is used to determine the severity based on the defect type weight w(c). j ), position influence function g(r) j The severity score Q is calculated using the optical influence mapping function h(ΔM).

[0018] Furthermore, the QC rule engine module includes a reason code mapping table and a threshold version management unit, which are used to output conclusions and reason codes and record threshold version IDs.

[0019] Compared with the prior art, the advantages of this invention are: This solution provides a method and system for grading and reporting defects in the mass production of planar prescription lenses. This solution operates within a unified lens coordinate system Σ. LThe system integrates appearance, contour, and optical inspection data to achieve defect detection, classification, and feature quantification. It calculates the severity score Q using defect type weights, location influence functions, and optical influence mapping functions. A QC rule engine implements hard threshold gating and rejection / rework / degradation strategies, outputting a standardized report with reason code, threshold version, hash digest, and digital signature. Furthermore, a control module ensures that conclusions are only sent to manufacturing equipment or the MES system after hash and signature verification, guaranteeing data consistency and audit reliability. Attached Figure Description

[0020] Figure 1 This is a schematic diagram of the defect classification and reporting output system of the present invention; Figure 2 This is a schematic cross-sectional view of the planar prescription lens / microstructure stack of the present invention; Figure 3 This is a schematic diagram of the defect classification and QC rejection process of the present invention; Figure 4 This is a schematic diagram of the radial profile of the phase reset / step height of the present invention; Figure 5 This is a schematic diagram of the closed-loop link of design-manufacturing-measurement-defect classification in this invention; Figure 6 This is a schematic diagram illustrating the error convergence of the closed-loop iteration of the present invention; Figure 7 This is a schematic diagram of the traceable data packet and version binding structure of the present invention; Figure 8 This is a schematic diagram illustrating typical defects and roughness / scattering risks of the present invention; Figure 9 This is a schematic diagram of the system alternative / optional modules of the present invention; Figure 10 This is a schematic diagram of the standardized defect / QC report output of the present invention. Detailed Implementation

[0021] The technical solutions will now be clearly and completely described with reference to the accompanying drawings in the embodiments of the present invention.

[0022] First implementation method: Please see Figure 1 - Figure 10 The method for outputting a mass production defect grading report for planar prescription lenses includes the following steps: S1. Obtain lens batch information, prescription / structure version number, and QC threshold and rule set, and access multi-source test data of the lens to be tested; S2. Preprocess and align the multi-source detection data to construct a unified lens coordinate system Σ LThe data below indicates that coordinate alignment includes mapping the appearance image coordinates, contour measurement coordinates, and optical measurement coordinates to a unified lens coordinate system Σ using reference marks, edge contours, or fixture references. L ; S3. Perform defect detection on a unified data representation to obtain a defect set {d}. j}, and for each defect d j Output defect type c j and characteristic quantity {a j ,l j ,(x j ,y j )}, Defect type c j It includes at least one or more of the following: particles / contamination, scratches, seam marks, mold release residue, and coating defects, wherein d j For the j-th defect; c j Let a be the type of the j-th defect. j Let l be the area of ​​the j-th defect / the equivalent area. j Let x be the length of the j-th defect, (x) j ,y j () represents the location of the j-th defect; S4, Based on defect type c j The severity score Q is calculated based on the defect size characteristics and defect location, and the impact of the defect on optical performance ΔM is also calculated. The severity score Q is calculated as follows: ; Where Nd is the total number of defects detected in the lens under inspection, j j For defect indexing, w(c j ) represents the weight associated with the defect type, a j The area is the defect area or equivalent area, γ is the scale index, and r is the area of ​​the defect or equivalent area. j g(r) is the radial distance from the defect to the optically sensitive region. j Let be the location influence function, the summation term be the severe sharing of each defect locality, h(ΔM) be the optical influence mapping function, and β be the coefficient. This represents the current overall optical performance degradation of the entire lens. The optical influence quantity ΔM includes at least one of the following: haze / scattering increment, wavefront RMS increment, or MTF decrease at a preset spatial frequency. S5. Input the severity score Q and impact ΔM into the QC rule engine. Based on the rejection rules, output the conclusion (pass / rework / downgrade / scrap) and reason code. The QC rule engine includes hard threshold gating and policy gating. When any hard threshold is triggered, it directly rejects the application and outputs the corresponding reason code. When no hard threshold is triggered, it outputs the pass, rework, or downgrade conclusion based on the severity score Q and the grading threshold range. S6. Generate a standardized quality report and traceability data package. The traceability data package shall include at least the threshold version, algorithm version, original data hash digest, and digital signature fields.

[0023] The standardized quality report in step S6 shall include at least batch information, version number, Top-N list of defects, severity score Q, conclusion, cause code, threshold version ID, testing equipment ID, timestamp, and raw data hash digest.

[0024] Additionally, the traceability data packet includes the defect detection model version, scoring parameter version, and log chain index field for cross-workstation consistency verification.

[0025] Standardized quality reports and traceability data packages feed back cause code distribution, severity score Q distribution, and critical defect type statistics into process window maintenance and CAPA closed loop.

[0026] Once the hash verification and digital signature verification of the traceability data packet pass, the conclusion and disposal recommendations for the corresponding lens are sent to the manufacturing equipment or MES system. A mass production defect grading and reporting system for planar prescription lenses includes a data access module, a coordinate alignment module, a defect detection and classification module, a severity assessment module, a QC rule engine module, a report output module, a traceability database module, and a signature and verification module.

[0027] The data access module is used to access appearance images, contour / surface data, and scattering / haze or wavefront data, and writes a device ID and timestamp for each data source. The defect detection and classification module includes a traditional image processing unit and a machine learning classifier to output the defect type c. j Defect location and characteristic quantity {a j ,l j ,(x j ,y j Any of the following, the severity assessment module is used to determine the severity based on the defect type weight w(c) j ), position influence function g(r) j The severity score Q is calculated using the optical influence mapping function h(ΔM).

[0028] The QC rules engine module includes a reason code mapping table and a threshold version management unit. Reason codes identify the reasons triggering rejection, rework, or downgrade, and are used to output conclusions and reason codes, as well as record the threshold version ID. The QC rules engine module includes hard threshold gating and policy gating: hard threshold gating is used for direct rejection (e.g., fog H > τ). H Wavefront RMS > τ W The area of ​​the critical defect is aj>τ aetc.), and output a reason code; Policy gating is used to output pass / rework / downgrade for samples that do not trigger the hard threshold according to the interval threshold of Q. For example: Q ≤ τ pass Determined to pass; τ pass <Q ≤ τ rework Determined to rework; Q > τ scrap Determined to scrap.

[0029] The system performs aggregation analysis on the distribution of reason codes, defect type statistics, and Q distribution, outputs process window maintenance suggestions and CAPA items, and backfills the results to the maintenance of manufacturing parameters and threshold versions to achieve closed-loop optimization and error convergence of "design-manufacturing-metrology-QC".

[0030] For a certain batch of lenses, three types of defects are detected: a particle a1 = 0.02 mm² is located in the sensitive area r1 = 2 mm; a scratch with a length l2 = 3 mm and an equivalent area a2 = 0.05 mm² is located in r2 = 8 mm; a splicing mark a3 = 0.04 mm² is located in r3 = 12 mm. Set w(particle) = 1.0, w(scratch) = 0.8, w(splicing mark) = 0.6, γ = 0.7, and g(r) takes 1.0 when r ≤ 5 mm, 0.6 when 5 < r ≤ 10 mm, and 0.3 when r > 10 mm. If the measured haze increment ΔH triggers h(ΔM) = 0.2 at the same time, then Q can be obtained and the conclusion of "rework" can be output according to the threshold interval, and the reason code and recommended disposal are given in the report.

[0031] The above is only a preferred specific implementation manner of the present invention; it includes all the protection scopes of the present invention. Any person skilled in the art within the technical scope disclosed by the present invention, according to the technical solution of the present invention and its improvement concept, makes equivalent replacements or changes, and should be covered by the protection scope of the present invention.

Claims

1. A method for outputting a mass production defect grading report for planar prescription lenses, characterized in that: Includes the following steps: S1. Obtain lens batch information, prescription / structure version number, and QC threshold and rule set, and access multi-source test data of the lens to be tested; S2. Preprocess and align the multi-source detection data to construct a unified lens coordinate system Σ. L The following data representation; S3. Perform defect detection on the unified data representation to obtain the defect set {d}. j }, and for each defect d j Output defect type c j and characteristic quantity {a j ,l j ,(x j ,y j )}, where d j For the j-th defect; c j For the first j The type of defect, a j Let l be the area of ​​the j-th defect / the equivalent area. j Let x be the length of the j-th defect, (x) j ,y j () represents the location of the j-th defect; S4, Based on defect type c j The severity score Q is calculated based on the defect size characteristics and defect location, and the impact ΔM of the defect on optical performance is also calculated. S5. Input the severity score Q and the impact amount ΔM into the QC rule engine, and output the conclusion (pass / rework / downgrade / scrap) and reason code according to the rejection rules; S6. Generate a standardized quality report and traceability data package, wherein the traceability data package includes at least a threshold version, an algorithm version, a hash digest of the original data, and a digital signature field.

2. The method for outputting a mass production defect grading report for planar prescription lenses according to claim 1, characterized in that: In step S2, coordinate alignment includes mapping the appearance image coordinates, contour measurement coordinates, and optical measurement coordinates to a unified lens coordinate system Σ using reference marks, edge contours, or fixture references. L .

3. The method for outputting a mass production defect grading report for planar prescription lenses according to claim 1, characterized in that: In step S3, the defect type c j It includes at least one or more of the following: particles / contamination, scratches, splicing marks, mold release residue, and coating defects.

4. The method for outputting a mass production defect grading report for planar prescription lenses according to claim 1, characterized in that: In step S4, the severity score Q is calculated as follows: ； Where Nd is the total number of defects detected in the lens under inspection, j j For defect indexing, w(c j ) represents the weight associated with the defect type, a j The area is the defect area or equivalent area, γ is the scale index, and r is the area of ​​the defect or equivalent area. j g(r) is the radial distance from the defect to the optically sensitive region. j Let be the location influence function, the summation term be the severe sharing of each defect locality, h(ΔM) be the optical influence mapping function, and β be the coefficient. This represents the current overall optical performance degradation of the entire lens.

5. The method for outputting a mass production defect grading report for planar prescription lenses according to claim 4, characterized in that: The optical influence quantity ΔM includes at least one of the following: haze / scattering increment, wavefront RMS increment, or MTF decrease at a preset spatial frequency.

6. The method for outputting a mass production defect grading report for planar prescription lenses according to claim 1, characterized in that: In step S5, the QC rule engine includes hard threshold gating and policy gating. When any hard threshold is triggered, the system will directly reject the decision and output the corresponding reason code. When no hard threshold is triggered, the system will output a pass, rework, or downgrade conclusion based on the severity score Q and the grading threshold range.

7. The method for outputting a mass production defect grading report for planar prescription lenses according to claim 1, characterized in that: The standardized quality report in step S6 includes at least batch information, version number, Top-N list of defects, severity score Q, conclusion, cause code, threshold version ID, testing device ID, timestamp, and raw data hash digest.

8. The method for outputting a mass production defect grading report for planar prescription lenses according to claim 1, characterized in that: The traceability data package further includes the defect detection model version, scoring parameter version, and log chain index field for cross-workstation consistency verification.

9. The method for outputting a mass production defect grading report for planar prescription lenses according to claim 1, characterized in that: The standardized quality report and traceability data package will feed back the cause code distribution, severity score Q distribution, and critical defect type statistics into the process window maintenance and CAPA closed loop.

10. The method for outputting a mass production defect grading report for planar prescription lenses according to claim 1, characterized in that: When the hash verification and digital signature verification of the traceability data packet pass, the conclusion and disposal suggestions for the corresponding lens are sent to the manufacturing equipment or MES system.

11. A mass production defect grading and reporting output system for planar prescription lenses, characterized in that: It includes a data access module, a coordinate alignment module, a defect detection and classification module, a severity assessment module, a QC rule engine module, a report output module, a traceability database module, and a signature and verification module.

12. The planar prescription lens mass production defect grading and reporting output system according to claim 11, characterized in that: The data access module is used to access appearance images, contour / surface data, and scattering / haze or wavefront data, and writes a device ID and timestamp for each data source.

13. The planar prescription lens mass production defect grading and reporting output system according to claim 11, characterized in that: The defect detection and classification module includes a traditional image processing unit and a machine learning classifier, used to output the defect type cj, defect location, and feature quantity {a}. j ,l j ,(x j ,y j Any one of the following: )} 14. The planar prescription lens mass production defect grading and reporting output system according to claim 11, characterized in that: The severity assessment module is used to assess the severity based on the defect type weight w(c). j ), position influence function g(r) j The severity score Q is calculated using the optical influence mapping function h(ΔM).

15. A mass production defect grading and reporting output system for planar prescription lenses according to claim 11, characterized in that: The QC rule engine module includes a reason code mapping table and a threshold version management unit, which are used to output conclusions and reason codes and record threshold version IDs.

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