Process equipment experimental parameter management system and method for micro-nano processing laboratory

Abstract

According to the experimental parameter management system and method for the process equipment in the micro-nano processing laboratory provided by the invention, the data acquisition module can determine the corresponding acquisition mode according to the equipment characteristics of the process equipment when an experimental user uses the process equipment in the micro-nano processing laboratory to carry out a micro-nano processing experiment on a sample; the experiment parameters generated by the experiment user in the experiment process are noninductively acquired according to the acquisition mode, so that the data acquisition efficiency can be improved, and the error rate can be reduced; the data processing module can organize the collected experimental parameters into a process chain formed after the same sample is processed in different process equipment according to a process time axis, so that multi-modal data can be associated according to the process time axis, and after multi-dimensional visual analysis is carried out through the data analysis module, the data can be stored in the process chain. And generating a comprehensive analysis report and displaying the comprehensive analysis report, so that an experiment user traces the experiment process according to the comprehensive analysis report and optimizes the technological process.

Description

Technical Field

[0001] This application relates to the technical field of laboratory data management, and particularly to an experimental parameter management system and method for process equipment in a micro-nano processing laboratory. Background Art

[0002] A micro-nano processing laboratory involves a large number of high-precision process equipment (such as lithography equipment, coating equipment, etching equipment, measurement equipment, etc.). Among them, the process of real-time collection and efficient management of the process parameters of the process equipment is an important link in experimental traceability, process optimization, and quality control.

[0003] Currently, when managing experimental parameters of process equipment in a micro-nano processing laboratory, it is mainly carried out by manual recording. Since there are many types of process equipment in the micro-nano processing laboratory and the data formats and communication protocols of different equipment are different, the manual recording method has problems such as low efficiency, easy error, and difficulty in associating multi-modal data, which seriously affects the analysis and utilization of experimental data by researchers. Summary of the Invention

[0004] The purpose of this application aims to solve at least one of the above technical defects, especially the technical defect that when managing experimental parameters of process equipment in a micro-nano processing laboratory in the prior art, the manual recording method has low efficiency, easy error, and difficulty in associating multi-modal data.

[0005] This application provides an experimental parameter management system for process equipment in a micro-nano processing laboratory. The system includes:

[0006] A data acquisition module, configured to determine a corresponding acquisition method according to the equipment characteristics of the process equipment when an experimental user uses the process equipment in the micro-nano processing laboratory to perform a micro-nano processing experiment on a sample, and acquire the experimental parameters generated by the experimental user during the experiment according to the acquisition method;

[0007] A data processing module, configured to organize the experimental parameters into a process chain formed after processing the same sample in different process equipment according to the process time axis;

[0008] A data analysis module, configured to generate a comprehensive analysis report and display it after visually analyzing the experimental parameters generated when the same sample is processed in different process equipment and the process chain corresponding to each sample.

[0009] Optionally, the process of determining the corresponding acquisition method according to the equipment characteristics of the process equipment includes:

[0010] Determine the equipment type of the process equipment according to the equipment characteristics of the process equipment, where the same process equipment corresponds to at least one equipment type;

[0011] Determine the corresponding acquisition method based on the device type of the process equipment, where each device type corresponds to one acquisition method.

[0012] Optionally, the process of determining the corresponding acquisition method based on the device type of the process equipment includes:

[0013] When the device type of the process equipment is a Windows device, the acquisition method of the process equipment is to use UIA technology for data acquisition;

[0014] When the device type of the process equipment is a special device, the acquisition method of the process equipment is to use OCR technology for data acquisition;

[0015] When the device type of the process equipment is a log device, the acquisition method of the process equipment is to use file parsing for data acquisition;

[0016] When the device type of the process equipment is a database device, the acquisition method of the process equipment is to use SQLite reading for data acquisition.

[0017] Optionally, the process of sorting the experimental parameters into a process chain formed after the same sample is processed by different process equipment according to the process time axis includes:

[0018] Determine the sample ID, process flow, process duration of each process, and process parameters of each process when the experimental user conducts a micro-nano processing experiment on the sample according to the experimental parameters;

[0019] Sort the experimental parameters into a process chain formed after the same sample is processed by different process equipment according to the sample ID, the process flow, the process duration of each process, and the process parameters of each process.

[0020] Optionally, the process of determining the sample ID, process flow, process duration of each process, and process parameters of each process when the experimental user conducts a micro-nano processing experiment on the sample according to the experimental parameters includes:

[0021] Determine the process equipment used by the experimental user when conducting a micro-nano processing experiment on the sample according to the experimental parameters, and the sample ID of the sample read by the process equipment;

[0022] Determine the process type, process sequence, process parameters of each process, and the start time and end time of each process corresponding to the process equipment used by the experimental user when conducting a micro-nano processing experiment on samples with the same sample ID according to the experimental parameters;

[0023] Determine the process flow of the experimental user when performing micro-nano processing experiments on samples with the same sample ID according to the process type and the process sequence;

[0024] Determine the process duration of each process when the experimental user performs micro-nano processing experiments on samples with the same sample ID according to the start time and end time of each process.

[0025] Optionally, the process of organizing the experimental parameters into a process chain formed after processing the same sample in different process devices according to the sample ID, the process flow, the process duration of each process, and the process parameters of each process includes:

[0026] Extract the process flow, the process duration of each process, and the process parameters of the sample corresponding to the same sample ID from the experimental parameters according to the sample ID, the process flow, the process duration of each process, and the process parameters of each process;

[0027] Generate a process chain formed after processing the same sample in different process devices according to the process flow, the process duration of each process, and the process parameters of the sample corresponding to the same sample ID.

[0028] Optionally, the process of generating and displaying a comprehensive analysis report after performing visual analysis on the experimental parameters generated when processing the same sample in different process devices and the process chain corresponding to each sample includes:

[0029] Perform parameter comparison and trend analysis on the experimental parameters generated when processing the same sample in different process devices and the process chain corresponding to each sample in different dimensions to obtain comparison and analysis results in different dimensions;

[0030] Generate and display a comprehensive analysis report according to the comparison and analysis results in different dimensions.

[0031] Optionally, the process of performing parameter comparison and trend analysis on the experimental parameters generated when processing the same sample in different process devices and the process chain corresponding to each sample in different dimensions to obtain comparison and analysis results in different dimensions includes:

[0032] Perform horizontal comparison, vertical analysis, parameter correlation analysis, and anomaly detection on the experimental parameters generated when processing the same sample in different process devices and the process chain corresponding to each sample to obtain comparison and analysis results in different dimensions.

[0033] Optionally, the system further includes:

[0034] An exception handling module, configured to, when detecting an abnormal event in the comprehensive analysis report, perform an abnormal reminder according to the event type of the abnormal event, and save the abnormal data segment of the abnormal event.

[0035] This application also provides a method for managing experimental parameters of process equipment in a micro-nano processing laboratory, the method including:

[0036] When an experimental user uses process equipment in a micro-nano processing laboratory to perform a micro-nano processing experiment on a sample, determining a corresponding acquisition method according to the equipment characteristics of the process equipment, and acquiring the experimental parameters generated by the experimental user during the experiment according to the acquisition method;

[0037] According to the process timeline, organizing the experimental parameters into a process chain formed after the same sample is processed by different process equipment;

[0038] After performing visual analysis on the experimental parameters generated when the same sample is processed by different process equipment and the process chain corresponding to each sample, generating and displaying a comprehensive analysis report.

[0039] It can be seen from the above technical solutions that the embodiments of this application have the following advantages:

[0040] A system and method for managing experimental parameters of process equipment in a micro-nano processing laboratory provided by this application, the system including a data acquisition module, a data processing module, and a data analysis module. Among them, the data acquisition module can, when an experimental user uses process equipment in a micro-nano processing laboratory to perform a micro-nano processing experiment on a sample, determine a corresponding acquisition method according to the equipment characteristics of the process equipment, and senselessly acquire the experimental parameters generated by the experimental user during the experiment according to the acquisition method, which can not only improve the data acquisition efficiency but also reduce the error rate; the data processing module can, according to the process timeline, organize the acquired experimental parameters into a process chain formed after the same sample is processed by different process equipment, so that multi-modal data can be associated according to the process timeline, and through the data analysis module, after performing visual analysis on the experimental parameters generated when the same sample is processed by different process equipment and the process chain corresponding to each sample, generating and displaying a comprehensive analysis report, so that the experimental user can trace the experimental process according to the comprehensive analysis report, and after optimizing the process flow, summarize high-quality and effective processes, thereby reducing the R & D cycle and accelerating the acquisition of R & D results. Description of the Drawings

[0041] To more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the accompanying drawings required for the description of the embodiments or the prior art. Obviously, the accompanying drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other accompanying drawings can be obtained based on these drawings.

[0042] Figure 1 It is a schematic structural diagram of a process equipment experimental parameter management system for a micro-nano processing laboratory provided by an embodiment of the present application;

[0043] Figure 2 It is a schematic diagram of the experimental data recording process when processing different samples of different experimental users provided by an embodiment of the present application;

[0044] Figure 3 It is a display diagram of the process recipe associated with depositing metal on the surface of sample 0001 using a vacuum interconnected pvd500 device provided by an embodiment of the present application;

[0045] Figure 4 It is a display diagram of the monitoring data for depositing metal on the surface of sample 0001 using a vacuum interconnected pvd500 device provided by an embodiment of the present application;

[0046] Figure 5 It is a display diagram of the UI interface of the Windows device used during compound etching provided by an embodiment of the present application;

[0047] Figure 6 It is a display diagram of the visualization interface for depositing metal on the surface of sample 0001 using a vacuum interconnected pvd500 device provided by an embodiment of the present application;

[0048] Figure 7 It is a display diagram of the visualization interface for conducting micro-nano processing experiments using this system provided by an embodiment of the present application;

[0049] Figure 8 It is a schematic flow diagram of a process equipment experimental parameter management method for a micro-nano processing laboratory provided by an embodiment of the present application. Detailed implementation manners

[0050] The following will clearly and completely describe the technical solutions in the embodiments of the present application in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, rather than all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present application.

[0051] In one embodiment, as Figure 1 shownFigure 1 Schematic diagram of the structure of an experimental parameter management system for a micro-nano processing laboratory provided by an embodiment of the present application; the present application provides an experimental parameter management system for a micro-nano processing laboratory, and the system may include:

[0052] A data acquisition module 110, configured to determine a corresponding acquisition method according to the device characteristics of the process device when an experimental user uses the process device in the micro-nano processing laboratory to perform a micro-nano processing experiment on a sample, and acquire the experimental parameters generated by the experimental user during the experiment according to the acquisition method.

[0053] A data processing module 120, configured to organize the experimental parameters into a process chain formed after a same sample is processed by different process devices according to a process time axis.

[0054] A data analysis module 130, configured to generate and display a comprehensive analysis report after visually analyzing the experimental parameters generated when a same sample is processed by different process devices and the process chain corresponding to each sample.

[0055] In this embodiment, the management system may include a data acquisition module 110, a data processing module 120, and a data analysis module 130. In the present application, when an experimental user uses the process device in the micro-nano processing laboratory to perform a micro-nano processing experiment on a sample, the data acquisition module 110 may, during the experiment, determine a corresponding acquisition method according to the device characteristics of the process device used by the experimental user, and acquire the experimental parameters generated by the experimental user during the experiment in a non-intrusive manner according to the acquisition method. In this way, the experimental user only needs to focus on the specific experimental operation process, and there is no need to separately record the experimental parameters generated during the experiment, and the experimental parameters generated during the experiment can be acquired in a non-intrusive manner through this system, thereby effectively improving the acquisition accuracy and acquisition efficiency of experimental data and effectively reducing the error rate.

[0056] Moreover, since the present application sets different acquisition methods according to the device characteristics of different process devices when acquiring experimental parameters, and acquires experimental parameters according to the corresponding acquisition methods, this can not only cope with the situation of a large variety of devices, different operating systems, and inconsistent data interfaces in the micro-nano processing laboratory, but also trace the data by recording the experimental parameters generated when the experimental user uses different process devices to process the sample, so as to review the experimental process and summarize high-quality and effective processes.

[0057] Among them, the device characteristics of different process devices in the present application may be one or multiple. Therefore, the acquisition method corresponding to the same process device in the present application may be one or multiple, and can be specifically set according to the actual situation, which is not limited here. Schematically, as Figure 2 shownFigure 2 Schematic diagram of the experimental data recording process when processing different samples of different experimental users provided by this application embodiment; Figure 2 It can be seen that this application can record the experimental parameters generated when processing different samples provided by the same experimental user in the corresponding process equipment. For example, for sample 001 provided by experimental user USER1, this application can use a variety of process equipment to conduct micro-nano processing experiments on it, such as process equipment 1, process equipment 2, and process equipment 3. Then, this application can senselessly collect the experimental parameters generated when sample 001 is processed in different process equipment through data acquisition module 110. The specific collection methods include but are not limited to operations such as screen recording, screenshot taking, and page parameter acquisition of the process equipment, which are not limited here. Similarly, this application can also perform the above experimental parameter recording operations on sample 002 and sample 003 provided by experimental user USER1, or on sample 004, sample 005, sample 006, etc. provided by experimental user USER2, so as to obtain the experimental parameters generated when different samples provided by different experimental data are processed in the same or different process equipment.

[0058] Furthermore, after this application collects the experimental parameters generated by the experimental user during the experiment, due to the large differences in the data formats generated by different process equipment (such as videos, screenshots, text parameters, etc.), therefore, this application can organize the obtained experimental parameters into a process chain formed after processing the same sample in different process equipment in the manner of a process time axis, so as to construct a complete process data chain. This process data chain is very important for both scientific researchers, process equipment, and laboratory managers. This application organizes a large amount of experimental data of different experimental users into the form of a process chain through data processing module 120, and optimizes and summarizes high-quality and effective processes through the process chain, thereby greatly accelerating the R & D cycle and obtaining R & D results to a great extent.

[0059] It can be understood that in semiconductor manufacturing, the process time axis can represent both the sequence of the process flow (i.e., the sequence relationship of steps) and the actual time consumption of each step (i.e., the time resource allocation). This application organizes the process chain formed after processing the same sample in different process equipment through the process time axis. In this way, it can not only analyze the logical correctness of the process flow and the parameter correlation of the process design through the process chain, but also optimize the equipment utilization rate and experimental work order scheduling through the process chain, etc., thereby effectively improving the process accuracy and laboratory production capacity.

[0060] Furthermore, after the data processing module 120 of the present application organizes the experimental parameters into a process chain formed after the same sample is processed in different process equipment, the data analysis module 130 can also perform visual analysis on the experimental parameters generated when the same sample is processed in different process equipment and the process chain corresponding to each sample, and then generate and display a comprehensive analysis report. In this way, it is possible to monitor the experimental parameters in real time and give early warnings of abnormalities, so as to promptly detect abnormal process parameters and ensure the smooth progress of the experiment and the reliability of the experimental results. At the same time, it is also possible to perform in-depth analysis on the experimental parameters, such as parameter correlation analysis and trend prediction, and summarize experience based on the analysis results, so as to systematically optimize the process, improve the equipment utilization rate, and provide data support for the intelligent laboratory.

[0061] For example, the data analysis module 130 of the present application can display the experimental parameters generated when the same sample is processed in different process equipment in the form of charts or curves, so that the experimental user can intuitively monitor the change trend and correlation of the experimental parameters. Schematically, as Figure 3 、 Figure 4 shown, Figure 3 is a process recipe display diagram related to depositing metal on the surface of sample 0001 using the vacuum interconnected pvd500 equipment provided by the embodiment of the present application, Figure 4 is a monitoring data display diagram of depositing metal on the surface of sample 0001 using the vacuum interconnected pvd500 equipment provided by the embodiment of the present application; from Figure 3 、 Figure 4 it can be seen that before depositing metal on the surface of sample 0001 using the vacuum interconnected pvd (Physical Vapor Deposition) 500 equipment of the present application, a pre-configured process recipe can be associated first. This process recipe is used to control the key variables in the pvd process to ensure that the performance of the film, such as thickness, uniformity, adhesion, and crystallization quality, meets the requirements. Among them, Figure 3 the process recipe shows the parameter requirements of photoresist, thickness, spin coating, pre-baking, Bias (deviation), exposure dose, and beam current. When the present application processes sample 0001 according to these parameter requirements, relevant experimental parameters can be collected through the acquisition method corresponding to the vacuum interconnected pvd500 equipment and displayed by different colors and corresponding broken lines, as Figure 4As shown, the experimental parameters monitored in this application include but are not limited to the current current, current thickness, and current increase rate during the control of evaporation boat 1, the current current, current thickness, and current increase rate during the control of evaporation boat 2, the current angle during the rotation control, the baking power and chamber vacuum degree of the evaporation chamber, and the current position of the sample stage, etc. The specific monitoring range can be set according to the actual situation and is not limited here. This application shows the variation of multiple process parameters over time in the form of a line chart, so that experimental users can more quickly identify outliers and potential problems in the process parameters, and thus take corresponding measures for optimization.

[0062] In addition, the data analysis module 130 of this application can also perform visual analysis on the process chain corresponding to each sample. When performing visual analysis on the process chain, it can be systematically disassembled and evaluated from multiple dimensions, and the final analysis results are displayed. For example, this application can analyze from the time dimension, such as the time intervals of each process, the process duration of each process, and the duration of the entire process chain, etc., or from the process parameter dimension, such as the stability of key parameters, parameter coupling relationships, and process windows, etc., or from the equipment and resource dimension, such as equipment utilization rate, equipment dependency relationships, maintenance cycles, etc., or from the material and energy efficiency dimension, such as material flow, energy consumption, waste / by-products, etc., and from the quality and yield dimension, flexibility and fault tolerance dimension, data and informatization dimension, cost dimension, etc., and then obtain the corresponding analysis results.

[0063] Therefore, the comprehensive analysis report of this application can be an analysis report obtained by performing visual analysis on the experimental parameters generated when the same sample is processed in different process equipment, or an analysis report obtained by performing visual analysis on the process chain corresponding to each sample, or an analysis report obtained by performing visual analysis on the experimental parameters generated when the same sample is processed in different process equipment and the process chain corresponding to each sample. The specific report form and report content can be selected according to the actual situation and are not limited here.

[0064] In the above embodiments, the system includes a data acquisition module, a data processing module, and a data analysis module. Among them, when an experimental user uses a process device in a micro-nano processing laboratory to perform a micro-nano processing experiment on a sample, the data acquisition module can determine the corresponding acquisition method according to the device characteristics of the process device, and acquire the experimental parameters generated by the experimental user during the experiment in a non-intrusive manner according to this acquisition method. This can not only improve the data acquisition efficiency but also reduce the error rate. The data processing module can organize the acquired experimental parameters into a process chain formed after the same sample is processed by different process devices according to the process timeline, so that multi-modal data can be correlated according to the process timeline. After the data analysis module visually analyzes the experimental parameters generated when the same sample is processed by different process devices and the process chain corresponding to each sample, a comprehensive analysis report is generated and displayed, so that the experimental user can trace the experimental process according to the comprehensive analysis report, optimize the process flow, summarize high-quality and effective processes, thereby reducing the R & D cycle and accelerating the acquisition of R & D results.

[0065] In one embodiment, the process of determining the corresponding acquisition method according to the device characteristics of the process device may include:

[0066] According to the device characteristics of the process device, determine the device type of the process device, where the same process device corresponds to at least one device type.

[0067] Based on the device type of the process device, determine the corresponding acquisition method, where each device type corresponds to one acquisition method.

[0068] In this embodiment, when the data acquisition module 110 determines the corresponding acquisition method according to the device characteristics of the process device, it can first determine the device type of the process device according to the device characteristics of the process device, and the same process device corresponds to one or more device types, such as Windows devices, that is, hardware devices running the Microsoft Windows operating system, special devices, that is, hardware devices that do not support data acquisition using UIA (User Interface Automation), and log devices, such as spin coater developers, dicing machines, devices that need to monitor the consumption of consumables, etc.

[0069] After determining the device type of the process device, the present application can determine the corresponding acquisition method according to the device type of the process device, and each device type corresponds to one acquisition method. For example, Windows devices correspond to one data acquisition method, special devices correspond to one data acquisition method, log devices correspond to one data acquisition method, and database devices correspond to one data acquisition method. This data acquisition method can be set according to the actual situation or updated irregularly, and is not limited here.

[0070] This application sets different acquisition methods according to the device characteristics of different process devices, and acquires experimental parameters invisibly according to the corresponding acquisition methods. This can not only cope with the situation of a wide variety of devices, different operating systems, and inconsistent data interfaces in a micro-nano processing laboratory, but also trace the data by recording the experimental parameters generated when experimental users process samples using different process devices, so as to review the experimental process and summarize high-quality and effective processes.

[0071] In one embodiment, the process of determining the corresponding acquisition method based on the device type of the process device may include:

[0072] When the device type of the process device is a Windows device, the acquisition method of the process device is to use UIA technology for data acquisition.

[0073] When the device type of the process device is a special device, the acquisition method of the process device is to use OCR technology for data acquisition.

[0074] When the device type of the process device is a log device, the acquisition method of the process device is to use file parsing for data acquisition.

[0075] When the device type of the process device is a database device, the acquisition method of the process device is to use SQLite reading for data acquisition.

[0076] In this embodiment, when determining the corresponding acquisition method according to the device type of the process device, due to the wide variety of devices in the micro-nano processing laboratory, this application can classify the process devices in the micro-nano processing laboratory into several device types such as Windows devices, special devices, log devices, and database devices. The same process device can correspond to one or more device types. For example, the same process device can be both a Windows device and a log device, both a special device and a database device, both a log device and a database device. Specifically, it can be determined according to the device characteristics of the process device, which will not be elaborated here.

[0077] Further, when the device type of the process device of this application is a Windows device, the acquisition method of this process device is to use UIA technology for data acquisition. It can be understood that each UI element in a Windows device has unique attributes, such as name, type, location, visibility, etc. Schematically, as Figure 5 shown Figure 5This is a UI interface display diagram of a Windows device used for compound etching in the embodiments of the present application. Multiple process attributes are shown in this interface, including but not limited to temperature, time, upper electrode, lower electrode, etc. The UIA accurately locates target elements by identifying these attributes. For example, in the device software of a micro-nano processing laboratory, the UIA can, according to the text attributes of parameter names, locate the input box or display area of the corresponding parameter, such as locating the temperature parameter input box corresponding to the text "temperature setting", or the parameter input box corresponding to other texts, etc., so as to obtain or modify the parameter value subsequently. Among them, before using the UIA to capture elements in the present application, a unified policy configuration is used to pick up and capture the elements to be collected in advance once. After obtaining the characteristics of the elements, the data is then grabbed in full volume for matching. Here, picking up means that when using interface automation technology, the software identifies and locates elements on the interface (such as buttons, text boxes, etc.) to perform subsequent operations, such as obtaining text and recording the operation of a button being clicked.

[0078] In addition, the UIA of the present application can also work based on an event-driven mechanism. For example, when the client monitors a click event of the experimental software, it can call the server interface to obtain the element attributes to be collected corresponding to the click, and then collect the specific attribute values on the page according to these attributes. If the "save parameters" button is clicked, the UIA will obtain the operation attributes related to this button and automatically collect the experimental parameter data to be saved on the current page. After the operation is executed, the UIA can also obtain the result feedback by reading the attribute changes of the UI elements. For example, when the "start experiment" operation is executed, the UIA can read the text attributes of the device status display area to check whether the experiment is successfully started. If the status display changes from "not started" to "running", it indicates that the operation has been successfully executed.

[0079] When the device type of the process device in the present application is a special device, that is, a hardware device that does not support data collection, the data collection method for this type of process device can be to use OCR technology for data collection. For example, when the present application needs to collect elements of a Web program, parse Web elements, collect Java program elements, parse Java elements, collect C++ program elements, or parse C++ elements, since there are devices in the laboratory that do not support the UIA framework, such as some measurement devices developed using the Java framework, some older versions of Oxford devices, and some devices where some elements do not support UIA picking up, it is impossible to use UIA technology for data collection. At this time, the present application can use OCR technology for data collection.

[0080] In a specific implementation manner, when the present application uses OCR technology for data collection, its working process mainly includes several key links such as image preprocessing, character segmentation, feature extraction, character recognition, and post-processing, which are specifically as follows:

[0081] Image preprocessing: In the micro-nano processing laboratory, when OCR technology is used to process the experimental software page data, the relevant pages can be photographed and saved first, and then uploaded to the CDA server. The relevant pages here include but are not limited to key user operation interfaces, such as recipe addition, saving operations after modification, and modification and saving operations of parameters that affect equipment safety. Different process equipment has different pages to be photographed and saved, which can be set according to actual conditions. When the CDA server receives the image, a series of preprocessing operations can be performed. For example, grayscale conversion, that is, converting color images into grayscale images, can simplify subsequent processing steps and improve processing efficiency; noise reduction processing, removing noise interference in the image, and avoiding the influence of noise on character recognition; binarization operation, converting the image into a black and white binary image, so that the contrast between the text and the background is clearer, which is convenient for subsequent processing; tilt correction, if the image is tilted, it is corrected by an algorithm to ensure that the text is neatly arranged. When this application performs OCR processing on screenshots of the experimental equipment software interface, these preprocessing operations can make subsequent character recognition more accurate.

[0082] Character segmentation: After preprocessing, the text is usually presented in the form of continuous strings or words. The OCR system can segment these characters into individual characters so that they can be recognized one by one. The system can determine the segmentation boundaries based on features such as the blank area between characters and the connection of strokes. For example, in images related to experimental data, for text such as "temperature: 25℃", the OCR system can accurately segment characters such as "temperature", "degree", ":", "2", "5", "℃", and prepare for subsequent character recognition.

[0083] Feature extraction: For each segmented character, the OCR system can extract its unique features, which are the key basis for character recognition. Taking the number "8" as an example, the OCR system can extract its closed circular structure and the intersection of strokes and other features, and match them with the predefined character templates or feature libraries in the system. In the micro-nano processing experimental data, different parameter values, equipment names and other characters have their own unique features, and the OCR system recognizes them by accurately extracting these features.

[0084] Character recognition: Comparing the extracted character features with the character template library is the core recognition step of OCR technology. This application can find the character that best matches the current character features through pattern recognition algorithms, such as neural networks, support vector machines, etc., to determine the content of the characters in the image. When processing the experimental software page image, the OCR system can compare the extracted character features with the characters in the character template library one by one, calculate the similarity, and finally determine the specific character corresponding to the character.

[0085] Post - processing: After the recognition is completed, in order to improve the accuracy and readability of the text, the OCR system can also perform post - processing operations. These post - processing operations can use methods such as correcting recognition errors, context analysis, and dictionary matching to correct possible recognition errors. For example, in the experimental data, if the recognition result "2023 - 01 - 01" is misrecognized as "2023 - 01 - 07", then through context information, such as the time logic of the experiment or the association with other relevant data, the above - mentioned error can be discovered and corrected; the post - processing operation also includes adding punctuation marks. This application can add appropriate punctuation marks to the recognized text according to the grammar and semantic rules of the text; it can also include adjusting the text format to make the typesetting of the text more standardized and in line with people's reading habits. After the post - processing operation of this application, the text data recognized from the experimental software page image can be better utilized and analyzed.

[0086] Furthermore, when the device type of the process device in this application is a log device, the data acquisition method of this type of process device can be to use file parsing for data acquisition. For example, this application can collect log data through timed polling or file system event listening, and after selecting the corresponding parsing method according to the log format of the log data, use this parsing method for parsing and perform data cleaning and storage on the parsed log data.

[0087] When the device type of the process device in this application is a database device, the data acquisition method of this type of process device can be to use the SQLite (embedded database management system) reading method for data acquisition. The core technology adopted by SQLite when reading data combines file I / O optimization, cache management, index algorithms, and transaction control mechanisms to achieve efficient and stable data access.

[0088] In one embodiment, the process of sorting the experimental parameters into a process chain formed after the same sample is processed in different process devices according to the process time axis may include:

[0089] Determine the sample ID, process flow, process duration of each process, and process parameters of each process when the experimental user performs micro - nano processing experiments on the sample according to the experimental parameters.

[0090] Sort the experimental parameters into a process chain formed after the same sample is processed in different process devices according to the sample ID, the process flow, the process duration of each process, and the process parameters of each process.

[0091] In this embodiment, when the data processing module 120 arranges the experimental parameters into a process chain formed after a same sample is processed by different process devices according to the process timeline, since the process timeline can represent both the sequence of the process flow and the actual time consumption of each step, therefore, this application can determine the sample ID, the process flow, the process time consumption of each process, and the process parameters of each process when the experimental user conducts a micro-nano processing experiment on the sample based on the experimental parameters generated during the experiment. In this way, the experimental parameters can be arranged into a process chain formed after a same sample is processed by different process devices according to the sample ID, the process flow, the process time consumption of each process, and the process parameters of each process.

[0092] It can be understood that when the experimental user conducts a micro-nano processing experiment using the process devices in the micro-nano processing laboratory in this application, the user can first receive a sample and register the ID of the received sample. In this way, during the experiment, each process device can scan the sample to read the sample ID of the sample, record the current timestamp, and then conduct the micro-nano processing experiment. All the relevant data generated during this process can be collected by the data acquisition module 110. Therefore, the experimental parameters obtained in this application include both the sample ID read by the process device, the process parameters of each process device, the process time consumption, and the process flow of the entire experiment. After extracting these data from the experimental parameters, this application can arrange them into a process chain formed after a same sample is processed by different process devices according to the process timeline. In this way, the process chain can be used to optimize and summarize high-quality and effective processes, thereby greatly accelerating the R & D cycle and obtaining R & D results.

[0093] In one embodiment, the process of determining the sample ID, the process flow, the process time consumption of each process, and the process parameters of each process when the experimental user conducts a micro-nano processing experiment on the sample according to the experimental parameters may include:

[0094] Determine the process device used by the experimental user when conducting a micro-nano processing experiment on the sample according to the experimental parameters, and the sample ID of the sample read by the process device.

[0095] Determine the process type, the process sequence, the process parameters of each process, and the start time and end time of each process corresponding to the process device used by the experimental user when conducting a micro-nano processing experiment on a sample with the same sample ID according to the experimental parameters.

[0096] Determine the process flow of the experimental user when conducting a micro-nano processing experiment on a sample with the same sample ID according to the process type and the process sequence.

[0097] When determining the process duration of each process during the micro-nano processing experiment of the experimental user on samples with the same sample ID, based on the start time and end time of each process.

[0098] In this embodiment, when extracting the sample ID, process flow, process duration of each process, and process parameters of each process from the experimental parameters, the present application can first determine the process equipment used by the experimental user during the micro-nano processing experiment on the sample according to the experimental parameters, as well as the sample ID corresponding to the sample read by the process equipment. In this way, the process equipment used during the micro-nano processing experiment on the sample with the same sample ID can be determined according to the sample ID, as well as the process type, process sequence, process parameters of each process, start time and end time of each process corresponding to the process equipment. Then, the corresponding process flow can be determined according to the process type and process sequence, and the process duration of each process can be determined according to the start time and end time of each process.

[0099] In a simple example, the process time axis of the present application can be expressed as: oxidation (4 hours) → coating (1 hour) → etching (0.5 hours). Therefore, when the present application determines the process flow, process duration of each process, and process parameters of each process corresponding to the sample with the same sample ID according to the experimental parameters, a process chain formed after processing the same sample in different process equipment can be constructed.

[0100] In one embodiment, the process of organizing the experimental parameters into a process chain formed after processing the same sample in different process equipment according to the sample ID, the process flow, the process duration of each process, and the process parameters of each process may include:

[0101] Extract the process flow, process duration of each process, and process parameters of each process corresponding to the sample with the same sample ID from the experimental parameters according to the sample ID, the process flow, the process duration of each process, and the process parameters of each process.

[0102] Generate a process chain formed after processing the same sample in different process equipment according to the process flow, process duration of each process, and process parameters of each process corresponding to the sample with the same sample ID.

[0103] In this embodiment, after determining the sample ID, process flow, process duration of each process, and process parameters of each process in the experimental parameters, the present application can extract the process flow, process duration of each process, and process parameters of each process corresponding to the sample with the same sample ID from the experimental parameters. In this way, a complete process chain corresponding to the sample can be constructed according to the process flow, process duration of each process, and process parameters of each process corresponding to the sample with the same sample ID.

[0104] In a specific implementation, the sample information of the present application may be: Wafer ID: NFF 12345 (identified by digital code), process route: oxidation → coating → etching. The implementation process is as follows:

[0105] 1 Oxidation process (oxidation furnace):

[0106] The barcode reader captures the wafer ID and associates with the process recipe;

[0107] Real-time collect parameters: temperature (set value 1050°C ± 5°C), oxygen flow rate (10 SLM ± 0.5);

[0108] Generate timestamp: 2025-04-20 09:15:32.456.

[0109] 2 Coating process (PECVD):

[0110] The barcode reader captures the wafer ID and automatically loads the PECVD recipe;

[0111] Collect from the SQLite database: RF power (300W ± 10), deposition rate (15 nm / min ± 1);

[0112] Time synchronization calibration: clock error < 1 ms.

[0113] 3 Etching process (dry etching equipment):

[0114] The barcode reader captures the wafer ID and matches the etching parameters;

[0115] UIA collect: etching rate (100 nm / min ± 5), endpoint detection signal;

[0116] Abnormal event: RF impedance abnormality detected at 09:32:15 (triggering a secondary alarm).

[0117] As can be seen from the above process, after the present application constructs a process chain formed by processing the same sample in different process equipment, the logical correctness of the process flow and the parameter correlation of the process design can be analyzed according to the process chain, and the equipment utilization rate and experimental work order scheduling can also be optimized through the process chain, thereby effectively improving the process accuracy and laboratory production capacity.

[0118] In one embodiment, the process of generating and presenting a comprehensive analysis report after visually analyzing the experimental parameters generated when processing the same sample in different process equipment and the process chain corresponding to each sample may include:

[0119] Compare the experimental parameters generated when the same sample is processed in different process equipment and the process chain corresponding to each sample in different dimensions, and perform trend analysis to obtain the comparison and analysis results in different dimensions.

[0120] Generate a comprehensive analysis report based on the comparison and analysis results in different dimensions and display it.

[0121] In this embodiment, when performing visual analysis on the experimental parameters generated when the same sample is processed in different process equipment and the process chain corresponding to each sample, it is possible to compare the experimental parameters generated when the same sample is processed in different process equipment and the process chain corresponding to each sample in different dimensions, and then perform trend analysis to obtain the comparison and analysis results in different dimensions. Then, the present application can generate a comprehensive analysis report based on the comparison and analysis results in different dimensions and display it.

[0122] Specifically, the data analysis module 130 of the present application can support parameter comparison and trend analysis in multiple dimensions. For example, the present application can monitor the experimental parameters generated when the same sample is processed in different process equipment in real time, and analyze the rationality and correlation of each parameter, etc.; it can also perform comparative analysis on the process chain according to different dimensions such as process flow, process time consumption, process parameters, etc., to reveal the correlation between different process steps and the impact of parameter changes on experimental results; it can also perform visual analysis on the process chains formed by different samples of different experimental users. At the same time, the present application can also provide an intuitive visual interface to display the analysis results in the form of charts, curves, etc., so that experimental users or system managers can more intuitively understand the experimental results, and thus more effectively optimize the process and manage the equipment. Schematically, as Figure 6 、 7 shown, Figure 6 is a visual interface display diagram for depositing metal on the surface of sample 0001 using the vacuum interconnection pvd500 equipment provided by the embodiment of the present application, Figure 7 is a visual interface display diagram for performing micro-nano processing experiments using the system provided by the embodiment of the present application; Figure 6 displays the process recipe, RPA acquisition process, monitoring process, alarm, measurement results, experimental time, and results and explanations when the experimental user deposits metal on the surface of sample 0001 using the vacuum interconnection pvd500 equipment. The experimental user can Figure 6 intuitively understand the current experimental situation through the visual interface shown. Figure 7 displays the trend statistics of the user activity in the current micro-nano processing laboratory and the relevant data of the system. The system manager can optimize the management of the laboratory according to the data displayed on this visual interface.

[0123] In addition, the comprehensive analysis report of the present application not only includes the measurement results and result descriptions of the same sample processed in different process equipment, but also includes the detailed analysis results of the process chain of each sample. Therefore, the present application can also provide targeted improvement suggestions and predictive analysis according to the needs of experimental users. For example, for a specific process step, if the analysis results show that the process parameters of this step fluctuate greatly, resulting in unstable experimental results, the present application can put forward suggestions for optimizing the process parameters in the comprehensive analysis report, or recommend replacing with more stable process equipment. At the same time, the present application can also use machine learning algorithms to deeply mine and analyze experimental data to predict the trends and possible problems of future experimental results, providing more comprehensive decision-making support for experimental users.

[0124] It should be noted that the experimental parameter management system of the present application can not only be applied to micro-nano processing laboratories, but also be extended to other fields that require precise control of process parameters, such as semiconductor manufacturing, biomedicine, etc. By introducing the experimental parameter management system and method of the present application, enterprises and research institutions in these fields can more efficiently manage experimental data, optimize process flows, improve experimental accuracy and production capacity, thereby accelerating the R & D process and promoting technological innovation.

[0125] In one embodiment, the parameter comparison and trend analysis of the experimental parameters generated when the same sample is processed in different process equipment and the process chain corresponding to each sample in different dimensions to obtain the comparison analysis results in different dimensions may include:

[0126] Performing horizontal comparison, vertical analysis, parameter correlation analysis, and anomaly detection on the experimental parameters generated when the same sample is processed in different process equipment and the process chain corresponding to each sample to obtain the comparison analysis results in different dimensions.

[0127] In this embodiment, when performing parameter comparison and trend analysis of the experimental parameters generated when the same sample is processed in different process equipment and the process chain corresponding to each sample in different dimensions, horizontal comparison, vertical analysis, parameter correlation analysis, and anomaly detection can be performed on the experimental parameters generated when the same sample is processed in different process equipment and the process chain corresponding to each sample to obtain the comparison analysis results in different dimensions, and the comparison analysis results can reveal equipment performance, process stability, and optimization directions.

[0128] Specifically, when this application conducts horizontal comparison (comparison between devices), it can perform cross-device comparison with the same parameters. For example, under the same process conditions, it can compare the output parameters of different devices (such as etching rate, film uniformity), and can also conduct equipment stability analysis, such as calculating the standard deviation / CPK value of each equipment parameter to identify devices with large fluctuations. When this application conducts vertical analysis (time trend), it can analyze the historical trend of a single device, such as analyzing the change of the same equipment parameter over time, and can also analyze the differences between batches, such as comparing the consistency of key parameters of different experimental batches, and can further analyze the time interval of the process time axis, such as the oxidation-to-etching interval exceeding the standard (standard < 2h, actual 2h 15min). Further, when this application conducts parameter correlation analysis, it can analyze the correlation between process parameters and results, such as establishing a regression model to analyze the relationship between input parameters such as temperature and pressure and the output result (such as yield), and the correlation coefficient between coating rate and etching uniformity, and can also analyze the multi-parameter synergistic effect, such as using PCA (Principal Component Analysis) for dimensionality reduction to find the parameter combination with the greatest impact. When this application conducts anomaly detection, it can identify outliers, such as marking abnormal experimental data through IQR (Interquartile Range) or machine learning models (such as Isolation Forest), and can also conduct equipment failure early warning, such as training an LSTM network based on historical data to predict parameter anomalies, etc.

[0129] When this application conducts parameter comparison and trend analysis in different dimensions, it can use a variety of analysis tools and methods, including but not limited to statistical analysis methods, visualization tools, machine learning models, etc. Through the above methods, the process can be systematically optimized, equipment utilization rate can be improved, and data support can be provided for the intelligent laboratory.

[0130] In one embodiment, the system may further include:

[0131] An anomaly handling module, configured to, when detecting an anomaly event in the comprehensive analysis report, give an anomaly reminder according to the event type of the anomaly event, and save the anomaly data segment of the anomaly event.

[0132] In this embodiment, the experimental parameter management system may further include an anomaly handling module. When detecting an anomaly event in the comprehensive analysis report, the anomaly handling module can give an anomaly reminder according to the event type of the anomaly event and save the anomaly data segment of the anomaly event.

[0133] For example, the anomaly handling module of this application can specially mark the anomaly value, such as coloring it red. When key parameters or alarms occur, it can also use methods such as email, phone, DingTalk, etc. for notification to interface with other systems for anomaly handling. Specifically, it can be set according to the event type of the anomaly event, and no limitation is made here.

[0134] In a specific implementation manner, when the abnormal scenario of the present application is that the temperature curve of the RTP rapid annealing furnace is abnormal, the abnormal handling process of the present application may include the following:

[0135] It is found through real-time monitoring that the slope of the heating section decreases by 15% (exceeding the threshold of 10%);

[0136] The system automatically retrieves historical data for comparison (the last 20 process curves);

[0137] It is found through correlation check that the resistance value of the heater deviates by 8%, and the fluctuation of the protective gas flow increases;

[0138] A maintenance work order is triggered: it is recommended to replace the heater component and calibrate the mass flowmeter;

[0139] The abnormal data segment is stored (including the complete context of the first 5 minutes / last 5 minutes).

[0140] It can be understood that the existence of the abnormal handling module in the present application can ensure that experimental users obtain abnormal information in a timely manner and take corresponding measures for processing, thereby avoiding adverse effects of abnormal events on experimental results. At the same time, the stored abnormal data segment can also provide strong data support for subsequent abnormal analysis and processing.

[0141] In another specific implementation manner, if the present application detects an RF impedance abnormality during the etching process, the abnormal handling module can immediately trigger a secondary alarm and notify the experimental user by means of email, phone, etc. The experimental user can, according to the abnormal reminder, timely check the status of the etching equipment, find out the cause of the abnormality, and take corresponding measures for repair. At the same time, the abnormal handling module can also store the abnormal data segment of the abnormal event, including information such as the time of the abnormality occurrence, the abnormal value, and relevant parameters, to provide data support for subsequent analysis and processing.

[0142] In addition, the experimental parameter management system of the present application can also be integrated with other management systems, such as the equipment management system, the production management system, etc., to achieve data sharing and interaction. Through integration with these systems, the present application can more comprehensively master information such as the operating status of experimental equipment and the production progress, and provide more comprehensive data support for the optimization and management of experiments.

[0143] In one embodiment, as Figure 8 shown, Figure 8 is a schematic flow chart of a method for managing experimental parameters of process equipment in a micro-nano processing laboratory provided by an embodiment of the present application; the present application also provides a method for managing experimental parameters of process equipment in a micro-nano processing laboratory, and the method may include:

[0144] S210: When an experimental user conducts a micro-nano processing experiment on a sample using the processing equipment in the micro-nano processing laboratory, determine the corresponding acquisition method according to the equipment characteristics of the processing equipment, and acquire the experimental parameters generated by the experimental user during the experiment according to the acquisition method.

[0145] S220: According to the process timeline, organize the experimental parameters into a process chain formed after the same sample is processed by different processing equipment.

[0146] S230: After performing visual analysis on the experimental parameters generated when the same sample is processed by different processing equipment and the process chain corresponding to each sample, generate a comprehensive analysis report and display it.

[0147] In the above embodiment, this method is applied to the process equipment experimental parameter management system of the micro-nano processing laboratory. This system includes a data acquisition module, a data processing module, and a data analysis module. Among them, the data acquisition module can, when an experimental user conducts a micro-nano processing experiment on a sample using the processing equipment in the micro-nano processing laboratory, determine the corresponding acquisition method according to the equipment characteristics of the processing equipment, and acquire the experimental parameters generated by the experimental user during the experiment in a non-intrusive manner according to this acquisition method. This can not only improve the data acquisition efficiency but also reduce the error rate. The data processing module can, according to the process timeline, organize the acquired experimental parameters into a process chain formed after the same sample is processed by different processing equipment. In this way, multi-modal data can be associated according to the process timeline, and through the data analysis module, after performing visual analysis on the experimental parameters generated when the same sample is processed by different processing equipment and the process chain corresponding to each sample, generate a comprehensive analysis report and display it, so that the experimental user can trace the experimental process according to the comprehensive analysis report, and after optimizing the process flow, summarize high-quality and effective processes, thereby reducing the R & D cycle and accelerating the acquisition of R & D results.

[0148] Finally, 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 actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements but also includes other elements not expressly listed, or also includes elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "including a..." does not exclude the existence of additional identical elements in the process, method, article or device including the said element.

[0149] The various embodiments in this specification are described in a progressive manner. Each embodiment focuses on the differences from other embodiments. The various embodiments can be combined as needed, and the same or similar parts can be referred to each other.

[0150] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be obvious to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but rather to the broadest scope consistent with the principles and novel features disclosed herein.

Claims

1. A process equipment experimental parameter management system for a micro-nano processing laboratory, characterized in that, The system includes: A data acquisition module, which is used to determine a corresponding acquisition method according to the device characteristics of the process device when an experimental user conducts a micro-nano processing experiment on a sample using the process device in a micro-nano processing laboratory, and acquire the experimental parameters generated by the experimental user during the experiment according to the acquisition method; A data processing module, which is used to organize the experimental parameters into a process chain formed after the same sample is processed by different process devices according to the process time axis; A data analysis module, which is used to generate and display a comprehensive analysis report after visually analyzing the experimental parameters generated when the same sample is processed by different process devices and the process chain corresponding to each sample.

2. The process equipment experimental parameter management system for a micro-nano processing laboratory according to claim 1, characterized in that, The process of determining a corresponding acquisition method according to the device characteristics of the process device includes: Determine the device type of the process device according to the device characteristics of the process device, where at least one device type corresponds to the same process device; Determine a corresponding acquisition method based on the device type of the process device, where one acquisition method corresponds to each device type.

3. The process equipment experimental parameter management system of a micro-nano processing laboratory according to claim 2, wherein, The process of determining a corresponding acquisition method based on the device type of the process device includes: When the device type of the process device is a Windows device, the acquisition method of the process device is to use UIA technology for data acquisition; When the device type of the process device is a special device, the acquisition method of the process device is to use OCR technology for data acquisition; When the device type of the process device is a log device, the acquisition method of the process device is to use file parsing for data acquisition; When the device type of the process device is a database device, the acquisition method of the process device is to use SQLite reading for data acquisition.

4. The process equipment experimental parameter management system for a micro-nano processing laboratory according to claim 1, wherein, The process of organizing the experimental parameters into a process chain formed after the same sample is processed by different process devices according to the process time axis includes: Determine the sample ID, process flow, process duration of each process, and process parameters of each process when the experimental user conducts a micro-nano processing experiment on the sample according to the experimental parameters; Organize the experimental parameters into a process chain formed after the same sample is processed by different process devices according to the sample ID, the process flow, the process duration of each process, and the process parameters of each process.

5. The process equipment experimental parameter management system of a micro-nano processing laboratory according to claim 4, characterized in that, The process of determining the sample ID, process flow, process duration of each process, and process parameters of each process when the experimental user conducts a micro-nano processing experiment on the sample according to the experimental parameters includes: Determine the process device used by the experimental user when conducting a micro-nano processing experiment on the sample according to the experimental parameters, and the sample ID of the sample read by the process device; Determine the process type, process sequence, process parameters of each process, and the start time and end time of each process corresponding to the process device used by the experimental user when conducting a micro-nano processing experiment on a sample with the same sample ID according to the experimental parameters; Determine the process flow of the experimental user when performing micro-nano processing experiments on samples with the same sample ID according to the process type and the process sequence; Determine the process duration of each process when the experimental user performs micro-nano processing experiments on samples with the same sample ID according to the start time and end time of each process.

6. The process equipment experimental parameter management system of a micro-nano processing laboratory according to claim 4, characterized in that, The process of organizing the experimental parameters into a process chain formed after processing the same sample in different process equipment according to the sample ID, the process flow, the process duration of each process, and the process parameters of each process includes: Extract the process flow corresponding to the sample with the same sample ID, the process duration of each process, and the process parameters of each process from the experimental parameters according to the sample ID, the process flow, the process duration of each process, and the process parameters of each process; Generate a process chain formed after processing the same sample in different process equipment according to the process flow corresponding to the sample with the same sample ID, the process duration of each process, and the process parameters of each process.

7. A process equipment experimental parameter management system for a micro-nano processing laboratory according to any one of claims 1-6, characterized in that, The process of generating and displaying a comprehensive analysis report after visual analysis of the experimental parameters generated when processing the same sample in different process equipment and the process chain corresponding to each sample includes: Perform parameter comparison and trend analysis on the experimental parameters generated when processing the same sample in different process equipment and the process chain corresponding to each sample in different dimensions to obtain the comparison and analysis results in different dimensions; Generate and display a comprehensive analysis report according to the comparison and analysis results in different dimensions.

8. The process equipment experimental parameter management system for a micro-nano processing laboratory according to claim 7, characterized in that, The process of performing parameter comparison and trend analysis on the experimental parameters generated when processing the same sample in different process equipment and the process chain corresponding to each sample in different dimensions to obtain the comparison and analysis results in different dimensions includes: Perform horizontal comparison, vertical analysis, parameter correlation analysis, and anomaly detection on the experimental parameters generated when processing the same sample in different process equipment and the process chain corresponding to each sample to obtain the comparison and analysis results in different dimensions.

9. The process equipment experimental parameter management system of a micro-nano processing laboratory according to claim 1 or 8, characterized in that, The system further includes: An anomaly handling module for, when detecting an anomaly event in the comprehensive analysis report, performing anomaly reminder according to the event type of the anomaly event and saving the anomaly data segment of the anomaly event.

10. A method for managing experimental parameters of process equipment in a micro-nano processing laboratory, characterized in that, The method includes: When the experimental user uses the process equipment in the micro-nano processing laboratory to perform micro-nano processing experiments on samples, determine the corresponding acquisition method according to the equipment characteristics of the process equipment, and acquire the experimental parameters generated by the experimental user during the experiment according to the acquisition method; Organize the experimental parameters into a process chain formed after processing the same sample in different process equipment according to the process time axis; Generate and display a comprehensive analysis report after visual analysis of the experimental parameters generated when processing the same sample in different process equipment and the process chain corresponding to each sample.

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