Method for automatically distinguishing photomask particles

Abstract

The invention provides a method for automatically distinguishing photomask particles. The method comprises the following steps: S1, performing photomask particle detection on a photomask to obtain a photomask detection result; s2, providing a photomask management system, automatically generating a control rule in the photomask management system, and generating a control table according to the detection data and the control rule; s3, uploading the photomask detection result to the photomask management system and comparing the photomask detection result with the management and control table; s4, if the photomask detection result accords with the content in the management and control table, the photomask management system sends an instruction to the machine table for automatic judgment passing; and if the photomask detection result does not conform to the content in the control table, the photomask management system sends an instruction to the machine table to automatically judge that the judgment is not passed. The photomask detection results are integrated in the photomask management system in a unified manner and are compared with the management and control table in the photomask management system, the photomask management system performs automatic judgment, and the particle machine which does not affect exposure does not give an alarm repeatedly, so that the purposes of saving manpower and maintenance cost are achieved.

Description

Technical Field

[0001] The invention belongs to the technical field of semiconductors, and in particular relates to a method for automatically distinguishing photomask particles. Background Art

[0002] Existing mask particle detection is divided into two methods: online detection and offline detection. Online detection refers to the detection of the mask surface before and after each exposure, which is usually completed by the built-in detection unit in the lithography machine, and the control rules are set in the machine parameters; offline detection refers to regular defect detection of the mask. The detection time interval can be set in the mask management system, and the decision on whether to perform detection can also be made based on the number of times it is used. The control rules are set in the machine menu.

[0003] For both online and offline detection, the detection range and card control rules need to be set. The card control rules for online and offline detection are set in the machine or machine menu respectively. The card control rules and detection results are stored independently in their own systems and need to be checked on different machines. If there are particles that do not affect exposure, the machine will repeatedly alarm when detected, and NG requires manual processing. For the above problems, a method for automatically distinguishing mask particles is proposed, which can reduce labor costs and improve work efficiency. Summary of the invention

[0004] The present invention is to solve all or part of the problems of the above-mentioned prior art, and provides a method for automatically identifying mask particles, and provides a mask management system. The mask management system automatically generates control rules, generates a control table based on the control rules, and uploads the mask detection results to the mask management system for comparison with the control table. After the mask detection results are identified, a corresponding instruction is issued to the machine to automatically determine whether it passes or fails. The mask management system can automatically generate control rules, generate a control table based on the control rules, and the mask detection results are integrated into the mask management system, and automatically determined by comparing with the control table. The machine will not repeatedly alarm for particles that do not affect exposure, thereby achieving the purpose of saving manpower and maintenance costs.

[0005] The present invention provides a method for automatically distinguishing photomask particles, comprising the following steps: S1: performing photomask particle detection on a photomask to obtain a photomask detection result; S2: providing a photomask management system, automatically generating control rules in the photomask management system, and generating a control table based on the detection data and the control rules; S3: uploading the photomask detection result to the photomask management system and comparing it with the control table; S4: if the photomask detection result conforms to the content in the control table, the photomask management system gives an instruction to the machine to automatically determine that it has passed; if the photomask detection result does not conform to the content in the control table, the photomask management system gives an instruction to the machine to automatically determine that it has failed. The photomask detection result is integrated into the photomask management system, and automatically determined by comparing it with the control table. The machine will not repeatedly alarm for particles that do not affect exposure, thereby saving manpower and maintenance costs.

[0006] The contents in the control table include the mask ID and the particle detection judgment standard. The particle detection judgment standard includes the particle position judgment standard and the particle size judgment standard. The particle position and particle size have an important influence on the photolithography process and need to be strictly controlled. The control table may include multiple mask IDs and the particle detection judgment standard corresponding to each mask.

[0007] The particle detection judgment standard is generated by more than three detection data, and the detection data must all meet the control rules to ensure that the particle detection judgment standard is set reasonably.

[0008] The first detection data that meets the control rules is used as the benchmark data, and the subsequent detection data are compared with the benchmark data. At least two groups of the detection data are selected based on the benchmark data, and the particle detection judgment standard is generated with the benchmark data to ensure that the particle detection judgment standard is accurately set and the error is small.

[0009] The comparison content between the reference data and the detection data includes the particle position and particle size. Based on the reference data, the detection data used to generate the particle detection judgment standard is selected in combination with the measurement stability and error of the detection machine.

[0010] The particle X-direction position determination standard in the particle position determination standard takes the range from the minimum value to the maximum value of the X-direction position in the three groups of the detection data, and the Y-direction position determination standard takes the range from the minimum value to the maximum value of the Y-direction position in the three groups of the detection data, and determines the particle position determination standard for the determination of the mask detection result.

[0011] The particle size determination standard is determined by taking the range of the minimum particle size minus 1 μm to the maximum particle size plus 1 μm in the three groups of detection data, while not exceeding the critical value that affects exposure. The particle size determination standard is determined so that the particle size does not affect the photolithography process and maintains high-precision pattern transfer.

[0012] After the control table is generated, the mask management system retains the detection data to facilitate subsequent verification and management.

[0013] The control table can be maintained manually to adapt to actual production conditions.

[0014] The photomask management system can be used to automatically determine the photomask detection result of online detection or offline detection, and is suitable for determining online detection results and offline detection results.

[0015] Compared with the prior art, the beneficial effects of the present invention mainly include the following: providing a mask management system that can automatically generate control rules, generating a control table through the control rules and detection data, integrating the mask detection results into the mask management system and comparing them with the control table, particles in the mask that do not affect exposure will not be detected and trigger an alarm, eliminating repeated operations on the machine side and reducing manual maintenance costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0017] Figure 1 The present invention provides a flow chart of a method for automatically distinguishing mask particles. DETAILED DESCRIPTION

[0018] The following description and accompanying drawings fully demonstrate specific embodiments of the present invention so that those skilled in the art can practice them. Other embodiments may include structural, logical, electrical, process and other changes. The embodiments represent possible variations only. Unless clearly required, separate components and functions are optional, and the order of operation may vary. The parts and features of some embodiments may be included in or replace the parts and features of other embodiments.

[0019] This embodiment provides a method for automatically identifying mask particles. The specific steps are as follows: Figure 1 shown.

[0020] S1: Performing mask particle detection on the mask to obtain a mask detection result. The mask particle detection is divided into two types: online detection and offline detection. The mask detection results obtained by the two detection methods can both be automatically determined.

[0021] S2: Provide a mask management system, which automatically generates control rules in the mask management system. The control rules include the location and size of particles. The generation of control rules includes the existence of measurement errors and different machine errors. The measurement error is the detection error of the same machine, and the machine error is the detection error between different machines. Generate a control table based on the detection data and control rules. The content in the control table includes the mask ID and the particle detection judgment standard. The particle detection judgment standard includes the particle location judgment standard and the particle size judgment standard. The particle location and particle size have an important impact on the photolithography process and need to be strictly controlled. The control table may contain multiple mask IDs and the particle detection judgment standards corresponding to the masks. The particle detection judgment standard is generated by more than three detection data. According to the generated particle location judgment standard, when particles that do not affect exposure are detected, the machine will not repeatedly alarm. The first detection data that meets the control rules is used as the benchmark data, and the subsequent detection data is compared with the benchmark data. At least two groups of detection data are selected based on the benchmark data, and the particle detection judgment standard is generated with the benchmark data to ensure that the particle detection judgment standard is set accurately and the error is small. The comparison between the benchmark data and the test data includes the particle position and particle size. Based on the benchmark data, the test data used to generate the particle detection judgment standard is selected in combination with the measurement stability and error of the detection machine. The selection of test data is related to the machine measurement stability and machine error. For a single model, the test data is generally taken when the difference between the X-direction and Y-direction position and the benchmark data is within the range of ±0.5mm, and the difference between the particle size and the benchmark data is within the range of ±1μm; for multiple models, the range will be larger.

[0022] The particle position determination standard of the particle X-direction position in the particle position determination standard takes the range from the minimum value to the maximum value of the X-direction position in the three sets of test data, and the Y-direction position determination standard takes the range from the minimum value to the maximum value of the Y-direction position in the three sets of test data; the particle size determination standard takes the range from the minimum value minus 1 to the maximum value plus 1 of the particle size in the three sets of test data, and does not exceed the critical value that affects exposure. After the control table is generated, the mask management system retains the test data for subsequent verification and management.

[0023] S3: Upload the mask detection result to the mask management system and compare it with the control table; the control table can be manually maintained to adjust the particle detection judgment standard to adapt to the actual production situation.

[0024] S4: If the mask inspection result is consistent with the content in the control table, the mask management system will issue an instruction to the machine to automatically determine whether it has passed; if the mask inspection result does not meet the content in the control table, the mask management system will issue an instruction to the machine to automatically determine whether it has failed, thereby realizing automated judgment of the mask inspection result and saving manpower.

[0025] It should be understood that some commonly used English nouns or letters used in this application for the sake of clarity are only used for exemplary references rather than restrictive interpretations or specific usages, and the protection scope of this application should not be limited by their possible Chinese translations or specific letters. It should also be noted that, in this article, relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations.

Claims

1. A method for automatically identifying mask particles, characterized in that: The following steps are involved: S1: Performing a mask particle detection on the mask to obtain a mask detection result; S2: Providing a mask management system, wherein the mask management system automatically generates control rules, and generates a control table according to the detection data and the control rules; S3: uploading the mask detection result to the mask management system and comparing it with the control table; S4: If the mask detection result meets the content in the control table, the mask management system issues an instruction to the machine to automatically determine that it has passed; If the mask detection result does not conform to the content in the control table, the mask management system will issue an instruction to the machine to automatically determine that it has failed.

2. The method for automatically identifying mask particles according to claim 1, characterized in that: The content in the control table includes the mask ID and the particle detection judgment standard, and the particle detection judgment standard includes the particle position judgment standard and the particle size judgment standard.

3. The method for automatically identifying mask particles according to claim 2, characterized in that: The particle detection judgment standard is generated by more than three detection data, and the detection data must all meet the control rules.

4. The method for automatically distinguishing mask particles according to claim 3, characterized in that: The first detection data that meets the control rule is used as the benchmark data, and the subsequent detection data are compared with the benchmark data. At least two groups of the detection data are selected based on the benchmark data, and the particle detection judgment standard is generated with the benchmark data.

5. The method for automatically distinguishing mask particles according to claim 4, characterized in that: The comparison content between the reference data and the detection data includes the particle position and the particle size.

6. The method for automatically identifying mask particles according to claim 2, characterized in that: The particle X-direction position determination standard in the particle position determination standard takes the range from the minimum value to the maximum value of the X-direction position in the three sets of the detection data, and the Y-direction position determination standard takes the range from the minimum value to the maximum value of the Y-direction position in the three sets of the detection data.

7. The method for automatically identifying mask particles according to claim 2, characterized in that: The particle size determination standard is a range from the minimum value minus 1 μm to the maximum value plus 1 μm of the particle size in the three groups of detection data, while not exceeding the critical value that affects exposure.

8. The method for automatically identifying mask particles according to claim 1, characterized in that: After the control table is generated, the mask management system retains the detection data.

9. The method for automatically identifying mask particles according to claim 1, characterized in that: The control table can be maintained manually.

10. The method for automatically identifying mask particles according to claim 1, characterized in that: The photomask management system can be used to automatically determine the photomask detection result of online detection or offline detection.

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