Method and system for visualizing inspection data of photomask particles

Abstract

The application provides a photomask particle inspection data visualization analysis method and system. The method comprises the following steps: obtaining a photomask particle inspection report and exposure information between different machines; analyzing the distribution and change trend of the particles on the photomask based on the report and the information; and generating a visualization analysis interface according to the analysis result. The application realizes the mapping of the particle coordinates and the graphical exposure range through an automatic algorithm, and generates a particle change trend chart and a machine difference comparison table by using color to distinguish size grades. The application realizes one-key data collection and analysis, shortens the photomask analysis time to about 0.03 seconds, significantly improves the production efficiency and judgment accuracy, effectively reduces the risk of manual misjudgment, and guarantees the semiconductor production yield. The application also provides related systems, electronic devices and storage media.

Description

Technical Field

[0001] This invention relates to the field of integrated circuit manufacturing, and in particular to a method and system for visual analysis of photomask particle inspection data. Background Technology

[0002] In the semiconductor integrated circuit manufacturing process, photolithography is a crucial step in determining chip feature sizes, and the cleanliness of the photomask, as the master for pattern transfer, directly affects product yield. To ensure production quality, particle inspection of the photomasks used is typically performed after batch exposure is completed.

[0003] In existing technical solutions, when particle inspection results are deemed non-compliant, process engineers need to conduct a risk assessment for the batch of products. The traditional assessment process typically includes: first, reading particle inspection reports individually from different inspection machines; second, manually retrieving the previous inspection report for the photomask and visually comparing the differences between the two reports; and finally, manually consulting the tooling form to determine the exposure area and then assessing whether the particles fall within the critical pattern area.

[0004] However, the above-mentioned traditional practices have the following drawbacks:

[0005] High data dispersion: Particle inspection data is distributed across reports from different machines and at different times, lacking unified integrated management, which makes information retrieval inconvenient.

[0006] Low analysis efficiency: Relying on manual reading and comparison of reports, the judgment of a single photomask usually takes about 15 minutes, which is difficult to meet the needs of large-scale mass production.

[0007] Limited accuracy of judgment: Manual comparison of coordinates and dimensions is prone to visual fatigue and subjective bias, which may lead to misjudgment and may result in unqualified products flowing to the next process or unnecessary downtime for photomask cleaning.

[0008] Therefore, how to achieve efficient integration and visualization analysis of photomask particle inspection data has become a technical problem that urgently needs to be solved in the semiconductor manufacturing field. Summary of the Invention

[0009] The problem this invention aims to solve is the current technical situation where photomask particle inspection data is scattered across different machines and relies on manual comparison, resulting in low analysis efficiency and a high risk of errors. The invention provides a visual analysis solution for photomask particle inspection data that enables one-click data collection, analysis, summarization, and graphing, thereby improving the accuracy of photomask condition assessment and work efficiency.

[0010] Visualization and analysis methods for photomask particle inspection data include:

[0011] Step 1: Obtain particle inspection reports and exposure information of the photomask from different machines;

[0012] Step 2: Based on the particle inspection report and exposure information, analyze the distribution and changing trend of particles on the photomask;

[0013] Step 3: Generate a visual analysis interface based on the analysis results.

[0014] Preferably, in step one, the particle inspection report includes the inspection history of the photomask generated on all inspection machines within a preset time period.

[0015] Preferably, in step one, the exposure information includes the graphic exposure range coordinates of the photomask.

[0016] Preferably, in step two, analyzing the distribution of particles on the photomask includes: extracting the particle coordinates from the particle inspection report and mapping the particle coordinates to the exposure information to determine whether the particles are within the pattern exposure range.

[0017] Preferably, in step two, analyzing the changing trend of particles includes: extracting particle size information from the particle inspection report and statistically analyzing the change in particle quantity over the inspection time.

[0018] Preferably, in step three, the visualization analysis interface includes a particle change trend graph.

[0019] Preferably, in step three, the visualization analysis interface includes a photomask diagram, which uses different colors to distinguish the size grades of particles.

[0020] Preferably, in step three, the particles are marked with a preset color in the photomask diagram according to the relationship between the particle size and the preset size threshold.

[0021] Preferably, in step three, the visualization analysis interface includes a machine-side difference comparison table generated with one click, used to display the differences in inspection results of the same photomask under different machines.

[0022] The photomask particle inspection data visualization and analysis system includes:

[0023] The data acquisition module is used to acquire particle inspection reports of the photomask across different machines and the exposure information of the photomask;

[0024] The data analysis module is used to analyze the distribution and changing trends of particles on the photomask based on particle inspection reports and exposure information;

[0025] The visualization output module is used to generate a visual analysis interface based on the analysis results.

[0026] As described above, the method and system for visualizing and analyzing photomask particle inspection data of the present invention have the following beneficial effects:

[0027] This invention automates the process of one-click data collection, analysis, summarization, and visualization, significantly reducing the analysis time for a single photomask from approximately 15 minutes to about 0.03 seconds, greatly improving production efficiency. By precisely mapping particle coordinates to the graphic exposure range and using color to distinguish particle size levels, engineers can intuitively and accurately assess the risk of particle impact on yield, effectively avoiding subjective errors from manual comparison. Furthermore, trend charts and machine difference comparison tables enable real-time monitoring of long-term particle increase / decrease trends and machine status, providing scientific data support for photomask cleaning cycles and machine maintenance, ensuring stable operation of the semiconductor production line and high product yield. Attached Figure Description

[0028] Figure 1 The diagram shows a flowchart of a method for efficient visualization analysis of photomask particle inspection data according to the present invention.

[0029] Figure 2 This diagram illustrates the mapping relationship between particle coordinates and graphic exposure range for efficient visualization analysis of photomask particle inspection data according to the present invention.

[0030] Figure 3 The diagram shows a trend of particle quantity changing over time or batch, which is a method for efficient visualization analysis of photomask particle inspection data according to the present invention.

[0031] Figure 4 The diagram shown is a schematic block diagram of a system structure for efficient visualization analysis of photomask particle inspection data according to the present invention.

[0032] Figure 5 The diagram shows a schematic of the hardware structure of an electronic device for efficient visualization analysis of photomask particle inspection data according to the present invention. Detailed Implementation

[0033] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention.

[0034] Figure 1 A schematic diagram illustrating the overall process of a method for visualizing and analyzing photomask particle inspection data according to some embodiments of the present invention is shown.

[0035] refer to Figure 1 The methods for visualizing and analyzing particle inspection data from photomasks include:

[0036] Step 1: Obtain particle inspection reports and exposure information of the photomask from different machines.

[0037] In some embodiments, the photomask may be selected from at least one of binary masks, phase-shifting masks, attenuation phase-shifting masks, alternating phase-shifting masks, and chromium-free phase-shifting masks. The substrate material of the photomask may include quartz, calcium fluoride, silicon carbide, or low-thermal-expansion glass, and the photomask surface may be covered with a molybdenum / silicon multilayer film, a chromium layer, a molybdenum silicide layer, or a tantalum nitride layer. Particle inspection reports may be generated by an IRIS (In-Situ Photomask Inspection System) inspection machine, laser scanning inspection equipment, electron beam inspection equipment, or high-resolution optical microscopy imaging equipment. These reports may be obtained in real-time via a standard interface of a factory automation system, or by extracting structured or unstructured data files, such as XML, CSV, JSON, or TXT format files, from a specified network shared path, relational database server, or non-relational database. Exposure information may be stored in a manufacturing execution system, a lithography job schedule, a processing information database, or a tooling form, and its content includes the coordinate alignment parameters of the photomask within the lithography machine, exposure energy settings, pattern distribution definitions, and mask layout boundary definitions.

[0038] In some embodiments, in step one, the particle inspection report includes the inspection history of the photomask generated by all inspection machines within a preset time period. The preset time period can be flexibly set according to the scheduling requirements of the production line, such as the past 24 hours, 48 ​​hours, one week, one month, or a complete production cycle. The inspection history includes not only the results of a single inspection, but also consistency data from multiple repeated inspections, machine number, inspection timestamp, and environmental parameters at the time of inspection. By aggregating the historical records of all inspection machines, the system can construct a cleanliness evolution profile of the photomask throughout its entire lifecycle. This cross-machine data integration can eliminate detection blind spots, sensor drift, or system errors that may exist in a single machine, providing a complete and continuous data dimension for subsequent trend analysis, thereby achieving comprehensive monitoring of the photomask's health status.

[0039] In some embodiments, in step one, the exposure information includes the patterned exposure range coordinates of the photomask. The patterned exposure range coordinates define the effective area on the photomask actually used for semiconductor circuit pattern transfer, commonly referred to as the main pattern area or exposure field of view. In addition to the main pattern area, the exposure information can be further refined into coordinates for the dicing area, alignment mark area, optical proximity correction area, and frame area. These coordinates can exist in GDSII format, OASIS format, or a custom text format. These coordinates can be obtained by parsing the photomask's design layout file or by extracting the actual exposure field of view parameters from the lithography machine's job log. By obtaining these precise geometric definitions, the system can establish a virtual coordinate system that perfectly corresponds to the physical photomask.

[0040] Step 2: Based on the particle inspection report and exposure information, analyze the distribution and changing trend of particles on the photomask.

[0041] In some embodiments, the analysis process is executed by a computing engine deployed on a central server, cloud computing platform, or edge computing node. This computing engine can run on frameworks developed using Python, Java, C++, or R, and utilizes multithreading, parallel computing, or distributed computing technologies to process large-scale datasets. The analysis algorithm may include coordinate transformation algorithms to uniformly transform the raw particle coordinates collected from different instruments to the standard physical coordinate system of the photomask, eliminating coordinate offsets caused by loading deviations or differences in instrument calibration. Furthermore, the analysis process may incorporate clustering analysis algorithms to identify clustered particles, thereby determining whether the pollution source originates from specific mechanical friction or chemical reactions.

[0042] In some embodiments, step two, analyzing the distribution of particles on the photomask, includes: extracting particle coordinates from the particle inspection report and mapping the particle coordinates to exposure information to determine whether the particles are within the pattern exposure range. (Reference) Figure 2 The figure shows the mapping relationship between particle coordinates and the exposure range of the image. Figure 2The blue rectangle in the diagram represents the pattern exposure area, and the small dots scattered inside and outside the rectangle represent detected particles. During the mapping process, the processor performs spatial geometry calculations to determine whether the coordinates of each particle fall within the closed polygon defined by the pattern exposure area. To improve fault tolerance, the system can also set a buffer of a preset width at the edge of the pattern exposure area. If a particle is within the pattern exposure area, the system automatically marks it as a high-risk particle because these particles are highly likely to be projected onto the wafer during exposure, causing short circuits, open circuits, or pattern distortion. If the particle is located in the non-exposure area, it is marked as low-risk. This automated mapping analysis replaces the tedious process of manually consulting the tooling form and layout, significantly reducing the risk of missed detections and improving the scientific rigor of risk assessment.

[0043] In some embodiments, step two, analyzing the changing trend of particles, includes: extracting particle size information from the particle inspection report and statistically analyzing the change in particle quantity over inspection time. (Reference) Figure 3 This figure illustrates the trend of particle count over time or batch. Trend analysis can employ time series analysis methods to calculate the growth rate, fluctuation range, or average size evolution of the particle count. The system compares the particle distribution maps of the same photomask in multiple consecutive inspections, using image recognition or coordinate matching algorithms to identify which particles are newly generated, which are existing, and whether the size of existing particles has increased. Through this dynamic tracking, the system can identify potentially growing particles. By monitoring the change in particle count over time, data support can be provided for the photomask cleaning cycle, avoiding photomask damage caused by over-cleaning, and also preventing yield loss due to untimely cleaning.

[0044] Step 3: Generate a visual analysis interface based on the analysis results. The visual analysis interface can be displayed using a web interface based on HTML5, CSS3, and JavaScript, or as a desktop application developed using frameworks like React, Vue, or Angular. The interface design follows the principle of intuitiveness, transforming complex coordinate and dimensional data into an easy-to-understand graphical language. The interface can integrate multiple functional windows, including a real-time monitoring window, a historical tracking window, a machine comparison window, and a statistical report window. Through interactive design, users can hover the mouse over or click on any particle icon on the interface to display detailed parameters of that particle, such as precise coordinates, pixel area, scattering intensity, and its size class.

[0045] In some embodiments, in step three, the visualization analysis interface includes a particle change trend graph. (Reference) Figure 3Particle size variation trend charts can take the form of line charts, stacked bar charts, scatter plots, or heatmaps. In a line chart, the horizontal axis represents the inspection time, batch number, or Lot ID, and the vertical axis represents the total number of particles or the number of particles of a specific size grade. Figure 3 Different colored curves in the graph represent particle trends across different size ranges. Through the trend graph, engineers can visually observe fluctuations in mask cleanliness. If the curve shows an abnormal rise, the system automatically triggers an alert mechanism, notifying engineers via email, SMS, or instant messaging to check the production environment, the cleanliness of the lithography machine's interior, or the status of the inspection equipment. This visual display helps to quickly locate anomalies in the production process, shortening troubleshooting time.

[0046] In some embodiments, in step three, the visualization analysis interface includes a photomask diagram, which uses different colors to distinguish particle size levels. (Reference) Figure 2 The photomask diagram can be a proportionally scaled vector graphic, realistically reproducing the shape and outline of the photomask, including the main graphic area and the non-exposed area. Particles are superimposed on the diagram as icons such as circles, squares, or triangles. Color assignment can be based on a preset color lookup table to ensure that particles of different risk levels are visually distinct. In addition to color differentiation, the system can also represent the actual physical size of the particles through icon size, thus providing a two-dimensional visual feedback.

[0047] In some embodiments, in step three, the particles are marked with a preset color in the photomask diagram based on the relationship between the particle size and a preset size threshold. For example, the system can set multiple size thresholds, such as 0.1 micrometers, 0.3 micrometers, and 0.5 micrometers. (Reference) Figure 2 If the particle size is larger than 0.5 micrometers, it is marked in red in the photomask diagram, representing a severe defect; if the particle size is between 0.3 and 0.5 micrometers, it is marked in yellow or orange, representing a significant defect; and if the particle size is between 0.1 and 0.3 micrometers, it is marked in green or gray, representing a minor defect. Through this multi-level color management, engineers can quickly identify large particles that pose the greatest threat to yield, thus prioritizing high-risk areas and optimizing the photomask processing priority.

[0048] In some embodiments, step three includes a visualization analysis interface that generates a machine-level difference comparison table with a single click, displaying the differences in inspection results for the same photomask on different machines. The difference comparison table can be in matrix or radar chart form, with horizontal rows representing different inspection machines and vertical columns representing particle counts, average size, or coordinate repeatability indicators. The system automatically calculates the detection deviations between different machines, such as coordinate offsets or consistency in size measurements. If the inspection results of a particular machine consistently deviate from the production line average, the system will indicate that the machine may have issues with light source aging, lens contamination, or stage calibration. This comparison function not only helps monitor photomask quality but also serves as an important reference tool for the health management and preventative maintenance of inspection machines.

[0049] The method provided in this invention achieves one-click data collection, analysis, summarization, and graphing through an automated process. In a real production environment, the system can directly interface with the raw output files of the IRIS machine, completing data cleaning and format conversion without manual intervention. The system analyzes a single photomask in approximately 0.03 seconds, a tens of thousands-fold improvement in efficiency compared to the 15 minutes required for traditional manual judgment. This extremely rapid response capability enables real-time risk assessment on the production line, significantly reducing the time lot spends in the inspection process. Simultaneously, the system eliminates misjudgments caused by visual fatigue, lack of experience, or inconsistent subjective judgment standards during manual comparison through precise coordinate mapping and color grading. This high-precision analysis ensures that every photomask put into production meets stringent quality standards, thereby effectively improving the final yield of semiconductor products and reducing wafer scrap costs due to photomask defects.

[0050] Figure 4 A structural block diagram of a photomask particle inspection data visualization and analysis system according to some embodiments of the present invention is shown. (Reference) Figure 4The photomask particle inspection data visualization and analysis system includes a data acquisition module, a data analysis module, and a visualization output module. The data acquisition module obtains particle inspection reports and exposure information for photomasks across different testing machines. This module can include multiple data adapters to ensure compatibility with data formats from different manufacturers and models of testing machines, and supports data acquisition via FTP, HTTP, or message queue protocols. The data analysis module analyzes the distribution and trends of particles on the photomask based on the particle inspection reports and exposure information. This module integrates a high-performance geometric operation library, a spatial index database, and a statistical analysis engine, supporting parallel processing of analysis requests for multiple photomasks and automatically identifying the characteristics of specific photomask models. The visualization output module generates a visual analysis interface based on the analysis results. This module can include a graphics rendering engine, dynamic chart components, and a report generation engine, supporting the export of analysis results to PDF, Excel, or high-resolution image formats, facilitating offline analysis or production reporting for engineers.

[0051] Figure 5 A schematic diagram of the hardware structure of an electronic device according to an embodiment of this application is shown. (Reference) Figure 5 The electronic device may include a processor 1501, a communication interface 1502, a memory 1503, and a communication bus 1504. The processor 1501, the communication interface 1502, and the memory 1503 communicate with each other through the communication bus 1504. In some embodiments, the electronic device may serve as a core computing node in a semiconductor manufacturing execution system, responsible for processing particle inspection data from multiple lithography processes in real time.

[0052] The communication bus 1504 can be a standard bus for interconnecting peripheral components or an extended industry standard structure bus, etc. The communication bus 1504 can be divided into an address bus, a data bus, and a control bus, etc. For ease of representation, Figure 5 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus. The high-speed bus architecture ensures low-latency transmission of large-scale granular coordinate data between the processor 1501 and the memory 1503, thereby achieving millisecond-level analysis response.

[0053] The communication interface 1502 is used for communication between the electronic device and other devices. In some embodiments, the communication interface 1502 can be connected to the output port of the IRIS inspection machine, the factory database server, or the engineer's terminal device, supporting multiple communication protocols such as Ethernet, Fibre Channel, or wireless LAN to achieve automatic retrieval of inspection reports.

[0054] Memory 1503 is used to store computer programs. Memory 1503 may include random access memory or non-volatile memory, such as at least one disk storage device. Optionally, memory 1503 may also be at least one storage device located remotely from processor 1501. Memory 1503 may pre-store a database of exposure area coordinates for different types of photomasks and a preset size threshold table for rapid retrieval and comparison during analysis.

[0055] When processor 1501 executes the computer program stored in memory 1503, it implements the photomask particle inspection data visualization and analysis method described in the above embodiments. Processor 1501 can be a general-purpose processor, including a central processing unit, network processor, etc.; it can also be a digital signal processor, application-specific integrated circuit, field-programmable gate array, or other programmable logic device, discrete gate or transistor logic device, or discrete hardware component. By executing coordinate mapping algorithms and trend statistical logic in parallel, processor 1501 can process inspection reports of multiple photomasks simultaneously, thereby controlling the analysis time for a single photomask to approximately 0.03 seconds.

[0056] This application also provides a computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform any of the photomask particle inspection data visualization and analysis methods described in the above embodiments. The computer-readable storage medium can be any usable medium accessible to a computer or a data storage device such as a server or data center that integrates one or more usable media. The usable medium can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media (e.g., solid-state drives).

[0057] This application also provides a computer program product containing instructions that, when run on a computer, cause the computer to execute any of the photomask particle inspection data visualization and analysis methods described in the above embodiments. The computer program product can be implemented wholly or partially through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented wholly or partially as a computer program product. Computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line) or wireless (e.g., infrared, wireless, microwave, etc.) means. This flexible deployment method ensures the stable operation of the visualization and analysis system in complex semiconductor manufacturing network environments, enabling real-time monitoring and early warning of photomask quality.

[0058] It should be noted that the illustrations provided in this embodiment are only schematic representations of the basic concept of the present invention. Therefore, the drawings only show the components related to the present invention and are not drawn according to the actual number, shape and size of the components in the actual implementation. In the actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0059] The above embodiments are merely illustrative of the principles and effects of the present invention and are not intended to limit the invention. Any person skilled in the art can modify or alter the above embodiments without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or alterations made by those skilled in the art without departing from the spirit and technical concept disclosed in the present invention should still be covered by the claims of the present invention.

Claims

1. A method for visual analysis of particle inspection data from photomasks, characterized in that, At least including: Step 1: Obtain particle inspection reports of the photomask across different machines and the exposure information of the photomask; Step 2: Based on the particle inspection report and the exposure information, analyze the distribution and changing trend of particles on the photomask; Step 3: Generate a visual analysis interface based on the analysis results.

2. The method for visualizing and analyzing photomask particle inspection data according to claim 1, characterized in that: In step one, the particle inspection report includes the inspection history of the photomask generated on all inspection machines within a preset time period.

3. The method for visualizing and analyzing photomask particle inspection data according to claim 1, characterized in that: In step one, the exposure information includes the graphic exposure range coordinates of the photomask.

4. The method for visualizing and analyzing photomask particle inspection data according to claim 1, characterized in that: In step two, analyzing the distribution of the particles on the photomask includes: extracting the particle coordinates from the particle inspection report and mapping the particle coordinates to the exposure information to determine whether the particles are within the pattern exposure range.

5. The method for visualizing and analyzing photomask particle inspection data according to claim 1, characterized in that: In step two, analyzing the changing trend of the particles includes: extracting particle size information from the particle inspection report and statistically analyzing the change in particle quantity over the inspection time.

6. The method for visualizing and analyzing photomask particle inspection data according to claim 1, characterized in that: In step three, the visualization analysis interface includes a particle change trend graph.

7. The method for visualizing and analyzing photomask particle inspection data according to claim 1, characterized in that: In step three, the visualization analysis interface includes a photomask diagram, which uses different colors to distinguish the size grades of the particles.

8. The method for visualizing and analyzing photomask particle inspection data according to claim 7, characterized in that: In step three, based on the relationship between the particle size and the preset size threshold, the particles are marked with a preset color in the photomask diagram.

9. The method for visualizing and analyzing photomask particle inspection data according to claim 1, characterized in that: In step three, the visualization analysis interface includes a machine-side difference comparison table generated with one click, which is used to display the differences in inspection results of the same photomask under different machines.

10. A data visualization and analysis system for photomask particle inspection, characterized in that, include: The data acquisition module is used to acquire particle inspection reports of the photomask across different machines and the exposure information of the photomask; The data analysis module is used to analyze the distribution and changing trend of particles on the photomask based on the particle inspection report and the exposure information. The visualization output module is used to generate a visual analysis interface based on the analysis results.

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