Granule state monitoring method, device, apparatus, and storage medium
By acquiring and analyzing particle parameters in the wafer cleaning environment and generating variation curves, the problem of not being able to monitor the particle status of the cleaning environment in a timely manner in existing technologies is solved. This enables rapid and effective particle status monitoring, improving the efficiency of anomaly detection and the yield rate of the cleaning machine.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- SHANGHAI JINGMENG SILICON CORP
- Filing Date
- 2023-04-13
- Publication Date
- 2026-08-04
AI Technical Summary
Existing wafer cleaning machines cannot effectively monitor the particle status of the cleaning environment, resulting in problems being discovered only when they occur and thus unable to be dealt with in a timely manner.
By acquiring particle parameters of the wafer cleaning environment through environmental particle detection equipment, calculating the range and sum of particle parameters by dividing time periods, extracting sample particle parameters, and generating particle parameter change curves, the particle status of the cleaning environment can be monitored.
Rapidly generate particle parameter variation curves to effectively monitor the wafer cleaning environment, promptly detect and handle anomalies, improve the efficiency of anomaly detection in cleaning equipment, and enhance process capabilities and yield.
Smart Images
Figure CN116399772B_ABST
Abstract
Description
Technical Field
[0001] This disclosure relates to the field of semiconductor technology, and in particular to a method, apparatus, device, and storage medium for monitoring particle state. Background Technology
[0002] In the semiconductor technology field, wafers must be clean before entering the processing stage, thus requiring wafer cleaning. Current cleaning machines cannot effectively monitor the particle status of the wafer cleaning environment; particle abnormalities are typically only detected when problems arise or become severe. Therefore, how to effectively monitor the particle status of the wafer cleaning environment has become a pressing technical problem to be solved. Summary of the Invention
[0003] Embodiments of this disclosure provide a particle state monitoring method, apparatus, device, and storage medium.
[0004] In a first aspect, embodiments of this disclosure provide a particle state monitoring method, the method comprising: Obtain particle parameters of the wafer cleaning environment as detected by environmental particle detection equipment; The detection duration is divided into multiple time periods according to the preset time step; Based on the maximum and minimum particle parameters detected in each time period, calculate the range of particle parameters for each time period, and then calculate the sum of the particle parameter ranges based on the range of particle parameters for each time period. Based on the ratio of the range of particle parameters in each time period to the sum of the ranges of particle parameters, and the number of particle parameters detected in each time period, calculate the sampling quantity of particle parameters in each time period, and extract sample particle parameters from the particle parameters detected in each time period according to the sampling quantity of particle parameters in each time period. Based on the maximum and minimum particle parameters detected in each time period, as well as the sample particle parameters corresponding to each time period, sub-particle parameter change curves corresponding to each time period are generated, and the sub-particle parameter change curves are spliced together in chronological order to obtain the particle parameter change curve. Based on the particle parameter variation curve, the particle status of the wafer cleaning environment is monitored.
[0005] In some possible implementations of the first aspect, the environmental particle detection equipment detects particle parameters in the wafer cleaning environment, including: The laser module of the environmental particle detection equipment irradiates the air in the wafer cleaning environment; The imaging module of the environmental particle detection equipment captures images of particles in the irradiated air to obtain particle images; The processing module of the environmental particle detection equipment calculates particle parameters of the wafer cleaning environment based on particle images.
[0006] In some possible implementations of the first aspect, the processing module of the environmental particle detection equipment calculates particle parameters of the wafer cleaning environment based on the particle image, including: Count the number of light spots in the particle image; The particle parameters of the wafer cleaning environment are calculated based on the number of light spots in the particle image and the area of the corresponding shooting region.
[0007] In some possible implementations of the first aspect, the environmental particulate detection equipment is deployed inside the cleaning machine.
[0008] In some possible implementations of the first aspect, the deployment location of the environmental particulate detection equipment within the cleaning machine is determined by the following steps: The internal space of the cleaning machine is modeled to obtain the internal space model of the cleaning machine. Select multiple location points from the space model of the cleaning machine platform; Obtain particle parameters of the wafer cleaning environment detected by environmental particle detection equipment deployed at multiple locations; Generate a spatial distribution map of particle parameters based on the particle parameters corresponding to each location point; Based on the spatial distribution map of particle parameters, determine the deployment location of the environmental particle detection equipment within the cleaning machine.
[0009] In some possible implementations of the first aspect, the sample size of particle parameters for each time period is calculated based on the ratio of the particle parameter range to the sum of particle parameter ranges for each time period, and the number of particle parameters detected within each time period, including: For any given time period, the ratio of the particle parameter range corresponding to that time period to the sum of the particle parameter ranges is multiplied by the number of particle parameters detected within that time period to obtain the particle parameter sampling quantity for that time period.
[0010] In some possible implementations of the first aspect, the particle status of the wafer cleaning environment is monitored based on the particle parameter variation curve, including: If the particle parameter change curve is detected to be higher than the highest control line for particle parameters, then the particle state of the wafer cleaning environment is determined to be abnormal. If no particle parameter change curve is detected above the maximum control line for particle parameters, then the particle status of the wafer cleaning environment is determined to be normal.
[0011] Secondly, embodiments of this disclosure provide a particle state monitoring device, the device comprising: The acquisition module is used to acquire particle parameters of the wafer cleaning environment detected by the environmental particle detection equipment; The segmentation module is used to divide the detection duration into multiple time periods according to a preset time step. The calculation module is used to calculate the range of particle parameters for each time period based on the maximum and minimum particle parameters detected in each time period, and to calculate the sum of the particle parameter ranges based on the range of particle parameters for each time period. The extraction module is used to calculate the number of particle parameters to be sampled in each time period based on the ratio of the range of particle parameters in each time period to the sum of the ranges of particle parameters, and the number of particle parameters detected in each time period. Based on the number of particle parameters to be sampled in each time period, the module extracts sample particle parameters from the particle parameters detected in each time period. The generation module is used to generate sub-particle parameter change curves for each time period based on the maximum and minimum particle parameters detected in each time period, as well as the sample particle parameters corresponding to each time period. The sub-particle parameter change curves are then spliced together in chronological order to obtain the particle parameter change curve. The monitoring module is used to monitor the particle status in the wafer cleaning environment based on the particle parameter change curve.
[0012] Thirdly, embodiments of this disclosure provide an electronic device comprising: at least one processor; and a memory communicatively connected to the at least one processor; the memory storing instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the method described above.
[0013] Fourthly, embodiments of this disclosure provide a non-transitory computer-readable storage medium storing computer instructions for causing a computer to perform the methods described above.
[0014] In the embodiments of this disclosure, particle parameters of the wafer cleaning environment detected by an environmental particle detection device can be obtained. The detection time is divided into multiple time periods according to a preset time step. The sum of particle parameter ranges is calculated based on the particle parameter ranges corresponding to each time period. The sampling quantity of particle parameters corresponding to each time period is calculated based on the ratio of the particle parameter ranges corresponding to each time period to the sum of particle parameter ranges, and the number of particle parameters detected in each time period. Based on this, sample particle parameters are extracted from the particle parameters detected in each time period. Based on the maximum and minimum particle parameters detected in each time period, and the sample particle parameters corresponding to each time period, a particle parameter change curve is quickly generated, thereby effectively monitoring the particle status of the wafer cleaning environment.
[0015] It should be understood that the description in the Summary of the Invention section is not intended to limit the key or essential features of the embodiments of this disclosure, nor is it intended to restrict the scope of this disclosure. Other features of this disclosure will become readily apparent from the following description. Attached Figure Description
[0016] The above and other features, advantages, and aspects of the embodiments of this disclosure will become more apparent from the accompanying drawings and the following detailed description. The drawings are provided for a better understanding of the invention and are not intended to limit the scope of this disclosure. In the drawings, the same or similar reference numerals denote the same or similar elements, wherein: Figure 1 A flowchart of a particle state monitoring method provided by an embodiment of this disclosure is shown; Figure 2 A structural diagram of a particle state monitoring device provided in an embodiment of this disclosure is shown; Figure 3 A structural diagram of an exemplary electronic device capable of implementing embodiments of the present disclosure is shown. Detailed Implementation
[0017] To make the objectives, technical solutions, and advantages of the embodiments of this disclosure clearer, the technical solutions of the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this disclosure, and not all embodiments. Based on the embodiments of this disclosure, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this disclosure.
[0018] Furthermore, the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this article generally indicates that the preceding and following related objects have an "or" relationship.
[0019] To address the problems in the background art, embodiments of this disclosure provide a particle state monitoring method, apparatus, device, and storage medium. Specifically, it acquires particle parameters of the wafer cleaning environment detected by an environmental particle detection device, divides the detection time into multiple time periods according to a preset time step, calculates the sum of particle parameter ranges based on the particle parameter ranges corresponding to each time period, calculates the particle parameter sampling quantity for each time period based on the ratio of the particle parameter ranges corresponding to each time period to the sum of particle parameter ranges, and the number of particle parameters detected in each time period, and extracts sample particle parameters from the particle parameters detected in each time period. Based on the maximum and minimum particle parameters detected in each time period, and the sample particle parameters corresponding to each time period, it quickly generates a particle parameter change curve, thereby effectively monitoring the particle state of the wafer cleaning environment.
[0020] In this way, particle parameter variation curves can be quickly generated with a small number of particle parameters, thereby enabling rapid and effective monitoring of particle status in the wafer cleaning environment.
[0021] The particle state monitoring method, apparatus, device, and storage medium provided in this disclosure will be described in detail below with reference to the accompanying drawings and specific embodiments.
[0022] Figure 1 A flowchart of a particle state monitoring method provided by an embodiment of this disclosure is shown, such as... Figure 1 As shown, the particle state monitoring method 100 may include the following steps: S110, acquire particle parameters of the wafer cleaning environment detected by the environmental particle detection equipment.
[0023] In some embodiments, the environmental particle detection equipment can be deployed inside the cleaning machine, and correspondingly, the wafer cleaning environment is the environment inside the cleaning machine.
[0024] It is worth noting that the deployment location of the environmental particle detection equipment within the cleaning machine can be precisely determined through the following steps: The internal space of the cleaning machine is modeled to obtain the internal space model of the cleaning machine.
[0025] Multiple locations are selected from the space model of the cleaning machine to obtain the particle parameters of the wafer cleaning environment detected by environmental particle detection equipment deployed at multiple locations.
[0026] Based on the particle parameters corresponding to each location point, a spatial distribution map of particle parameters is generated.
[0027] Based on the spatial distribution map of particle parameters, determine the deployment location of the environmental particle detection equipment within the cleaning machine.
[0028] For example, the spatial distribution map of particle parameters can be input into a pre-trained location determination model, which can then perform calculations to quickly determine the deployment location of the environmental particle detection equipment within the cleaning machine.
[0029] The location determination model can be obtained by training a preset neural network using a sample set. The sample set uses the spatial distribution map of particle parameters as the main sample and the deployment location of the environmental particle detection equipment in the cleaning machine as the sample label.
[0030] For example, an environmental particle detection device may include a laser module, an imaging module, and a processing module, which can effectively detect particle parameters in a wafer cleaning environment through the following steps: The laser module irradiates the air in the wafer cleaning environment, causing light scattering from the air particles.
[0031] The imaging module captures images of particles in the illuminated air, obtaining particle images. Specifically, it captures images of the light spots scattered by the particles.
[0032] The processing module calculates the particle parameters of the wafer cleaning environment based on the particle image.
[0033] For example, the processing module can count the number of light spots in the particle image, and calculate the particle parameters (such as PM2.5 parameters and PM10 parameters) of the wafer cleaning environment simply and conveniently based on the number of light spots in the particle image and the area of the shooting area corresponding to the particle image.
[0034] S120 divides the detection time into multiple time periods according to the preset time step.
[0035] The preset time step can be flexibly adjusted. The length of the last time segment among multiple time segments is less than or equal to the preset time step, while the lengths of the other time segments are equal to the preset time step.
[0036] S130, calculate the particle parameter range corresponding to each time period based on the maximum and minimum particle parameters detected in each time period, and calculate the sum of particle parameter ranges based on the particle parameter ranges corresponding to each time period.
[0037] Specifically, the maximum particle parameter detected within a time period can be subtracted from the minimum particle parameter detected to obtain the particle parameter range corresponding to that time period. Then, the particle parameter ranges corresponding to each time period are summed to obtain the total particle parameter range.
[0038] S140. Based on the ratio of the range of particle parameters corresponding to each time period to the sum of the ranges of particle parameters, and the number of particle parameters detected in each time period, calculate the sampling quantity of particle parameters corresponding to each time period, and extract sample particle parameters from the particle parameters detected in each time period according to the sampling quantity of particle parameters corresponding to each time period.
[0039] For any given time period, the ratio of the particle parameter range corresponding to that time period to the sum of the particle parameter ranges can be multiplied by the number of particle parameters detected within that time period to obtain the particle parameter sampling number for that time period (i.e., how many particle parameters to select).
[0040] Based on the number of particle parameters sampled for each time period, sample particle parameters are randomly selected from the particle parameters detected within each time period. Alternatively, based on the number of particle parameters sampled for each time period, sample particle parameters can be extracted from the particle parameters detected within each time period in a preset order and at intervals.
[0041] Understandably, the maximum and minimum particle parameters are no longer extracted when extracting sample particle parameters.
[0042] S150: Based on the maximum and minimum particle parameters detected in each time period, as well as the sample particle parameters corresponding to each time period, generate sub-particle parameter change curves for each time period, and then stitch together the sub-particle parameter change curves in chronological order to obtain the particle parameter change curve.
[0043] For any given time period, the maximum particle parameter, minimum particle parameter, and particle parameters of each sample can be arranged in chronological order and connected sequentially to obtain the particle parameter variation curve. Then, the variation curves of each sub-particle parameter can be spliced together in chronological order to obtain the particle parameter variation curve.
[0044] S160 monitors the particle status of the wafer cleaning environment based on the particle parameter variation curve.
[0045] In some embodiments, the particle parameter change curve can be detected. If the particle parameter change curve is detected to be higher than the maximum control line for particle parameters, the particle state of the wafer cleaning environment is determined to be abnormal; if the particle parameter change curve is not detected to be higher than the maximum control line for particle parameters, the particle state of the wafer cleaning environment is determined to be normal.
[0046] In other embodiments, the number of peaks and troughs in the particle parameter change curve that are above the highest control line of particle parameters can be counted. The counted number of peaks and troughs is accumulated, and the ratio of the accumulated sum to the total number of peaks and troughs is calculated. If the ratio is greater than or equal to a preset threshold, the particle state of the wafer cleaning environment is determined to be abnormal; if the ratio is less than the preset threshold, the particle state of the wafer cleaning environment is determined to be normal.
[0047] Furthermore, if the particle condition in the wafer cleaning environment is determined to be abnormal, an alarm can be triggered, the machine can be held, and further processing can be initiated.
[0048] In the embodiments of this disclosure, particle parameter variation curves can be quickly generated with a small number of particle parameters, thereby enabling rapid and effective monitoring of particle status in the wafer cleaning environment.
[0049] The particle state monitoring method provided in this disclosure will be described in detail below with reference to a specific embodiment: (1) An environmental particle detection device deployed inside the cleaning machine detects the particle parameters of the wafer cleaning environment and transmits them to the Fault Detection and Classification (FDC) system.
[0050] (2) The FDC system divides the detection time into multiple time periods according to the preset time step.
[0051] (3) The FDC system calculates the particle parameter range corresponding to each time period based on the maximum and minimum particle parameters detected in each time period, and calculates the sum of particle parameter ranges based on the particle parameter ranges corresponding to each time period.
[0052] (4) The FDC system calculates the number of particle parameters to be sampled in each time period based on the ratio of the range of particle parameters to the sum of the ranges of particle parameters in each time period and the number of particle parameters detected in each time period. Based on the number of particle parameters to be sampled in each time period, the system extracts sample particle parameters from the particle parameters detected in each time period.
[0053] (5) The FDC system generates sub-particle parameter change curves for each time period based on the maximum and minimum particle parameters detected in each time period and the sample particle parameters corresponding to each time period. The sub-particle parameter change curves are then spliced together in chronological order to obtain the particle parameter change curve.
[0054] (6) The FDC system detects the particle parameter change curve. If the particle parameter change curve is detected to be higher than the maximum control line of particle parameters, the particle status of the wafer cleaning environment is determined to be abnormal. At this time, an alarm is triggered, and the Manufacturing Execution System (MES) is notified to hold the machine and wait for further processing. If the particle parameter change curve is not detected to be higher than the maximum control line of particle parameters, the particle status of the wafer cleaning environment is determined to be normal and no processing is required.
[0055] In this way, by monitoring the particle state of the wafer cleaning environment, it is possible to effectively determine whether the wafer cleaning environment meets the requirements. Abnormalities in the wafer cleaning environment can be detected and addressed promptly. Furthermore, interference from the wafer cleaning environment can be quickly eliminated when abnormalities occur, greatly improving the efficiency of troubleshooting cleaning machine malfunctions. In short, this significantly improves the process capability and yield of wafer cleaning, while greatly reducing the risk of wafers becoming abnormal and flowing to the next process.
[0056] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this disclosure is not limited to the described order of actions, because according to this disclosure, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily essential to this disclosure.
[0057] The above is an introduction to the method embodiments. The following describes the solution described in this disclosure further through device embodiments.
[0058] Figure 2 A structural diagram of a particle state monitoring device provided in an embodiment of this disclosure is shown, as follows: Figure 2 As shown, the particle state monitoring device 200 may include: The acquisition module 210 is used to acquire particle parameters of the wafer cleaning environment detected by the environmental particle detection equipment.
[0059] The segmentation module 220 is used to divide the detection duration into multiple time periods according to a preset time step.
[0060] The calculation module 230 is used to calculate the particle parameter range corresponding to each time period based on the maximum and minimum particle parameters detected in each time period, and to calculate the sum of particle parameter ranges based on the particle parameter ranges corresponding to each time period.
[0061] The extraction module 240 is used to calculate the sampling quantity of particle parameters for each time period based on the ratio of the range of particle parameters for each time period to the sum of the ranges of particle parameters, and the number of particle parameters detected in each time period. Based on the sampling quantity of particle parameters for each time period, the module extracts sample particle parameters from the particle parameters detected in each time period.
[0062] The generation module 250 is used to generate sub-particle parameter change curves for each time period based on the maximum and minimum particle parameters detected in each time period and the sample particle parameters corresponding to each time period, and to splice the sub-particle parameter change curves in chronological order to obtain the particle parameter change curve.
[0063] The monitoring module 260 is used to monitor the particle status of the wafer cleaning environment based on the particle parameter change curve.
[0064] In some embodiments, the environmental particle detection equipment detects particle parameters in the wafer cleaning environment, including: The laser module of the environmental particle detection equipment irradiates the air in the wafer cleaning environment; The imaging module of the environmental particle detection equipment captures images of particles in the irradiated air to obtain particle images; The processing module of the environmental particle detection equipment calculates particle parameters of the wafer cleaning environment based on particle images.
[0065] In some embodiments, the processing module of the environmental particle detection equipment calculates particle parameters of the wafer cleaning environment based on particle images, including: Count the number of light spots in the particle image; The particle parameters of the wafer cleaning environment are calculated based on the number of light spots in the particle image and the area of the corresponding shooting region.
[0066] In some embodiments, the environmental particulate detection equipment is deployed inside the cleaning machine.
[0067] In some embodiments, the deployment location of the environmental particulate detection equipment within the cleaning machine is determined by the following steps: The internal space of the cleaning machine is modeled to obtain the internal space model of the cleaning machine. Select multiple location points from the space model of the cleaning machine platform; Obtain particle parameters of the wafer cleaning environment detected by environmental particle detection equipment deployed at multiple locations; Generate a spatial distribution map of particle parameters based on the particle parameters corresponding to each location point; Based on the spatial distribution map of particle parameters, determine the deployment location of the environmental particle detection equipment within the cleaning machine.
[0068] In some embodiments, the extraction module 240 is specifically used for: For any given time period, the ratio of the particle parameter range corresponding to that time period to the sum of the particle parameter ranges is multiplied by the number of particle parameters detected within that time period to obtain the particle parameter sampling quantity for that time period.
[0069] In some embodiments, the monitoring module 260 is specifically used for: If the particle parameter change curve is detected to be higher than the highest control line for particle parameters, then the particle state of the wafer cleaning environment is determined to be abnormal. If no particle parameter change curve is detected above the maximum control line for particle parameters, then the particle status of the wafer cleaning environment is determined to be normal.
[0070] Understandable Figure 2 Each module / unit in the particle state monitoring device 200 shown has the ability to implement Figure 1 The functions of each step in the particle state monitoring method 100 shown, and the corresponding technical effects they achieve, will not be elaborated here for the sake of brevity.
[0071] Figure 3 A structural diagram of an exemplary electronic device capable of implementing embodiments of the present disclosure is shown. Electronic device 300 is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. Electronic device 300 may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present disclosure described and / or claimed herein.
[0072] like Figure 3 As shown, the electronic device 300 may include a computing unit 301, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 302 or a computer program loaded from a storage unit 308 into a random access memory (RAM) 303. The RAM 303 may also store various programs and data required for the operation of the electronic device 300. The computing unit 301, ROM 302, and RAM 303 are interconnected via a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.
[0073] Multiple components in electronic device 300 are connected to I / O interface 305, including: input unit 306, such as keyboard, mouse, etc.; output unit 307, such as various types of displays, speakers, etc.; storage unit 308, such as disk, optical disk, etc.; and communication unit 309, such as network card, modem, wireless transceiver, etc. Communication unit 309 allows electronic device 300 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.
[0074] The computing unit 301 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 301 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 301 performs the various methods and processes described above, such as method 100. For example, in some embodiments, method 100 may be implemented as a computer program product, including a computer program tangibly contained in a computer-readable medium, such as storage unit 308. In some embodiments, part or all of the computer program may be loaded and / or installed on device 300 via ROM 302 and / or communication unit 309. When the computer program is loaded into RAM 303 and executed by the computing unit 301, one or more steps of method 100 described above may be performed. Alternatively, in other embodiments, the computing unit 301 may be configured to perform method 100 by any other suitable means (e.g., by means of firmware).
[0075] The various embodiments described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), system-on-a-chip (SoCs), payload programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.
[0076] The program code used to implement the methods of this disclosure may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.
[0077] In the context of this disclosure, a computer-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. A computer-readable medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of computer-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.
[0078] It should be noted that this disclosure also provides a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to cause a computer to execute method 100 and achieve the corresponding technical effects achieved by the embodiments of this disclosure in executing the method. For the sake of brevity, these will not be elaborated here.
[0079] In addition, this disclosure also provides a computer program product including a computer program that implements method 100 when executed by a processor.
[0080] To provide interaction with a user, the embodiments described above can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).
[0081] The embodiments described above can be implemented in computing systems that include backend components (e.g., as a data server), or computing systems that include middleware components (e.g., an application server), or computing systems that include frontend components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with the implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., a communication network). Examples of communication networks include local area networks (LANs), wide area networks (WANs), and the Internet.
[0082] Computer systems can include clients and servers. Clients and servers are generally located far apart and typically interact via communication networks. Client-server relationships are created by computer programs running on the respective computers and having a client-server relationship with each other. Servers can be cloud servers, servers in distributed systems, or servers incorporating blockchain technology.
[0083] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this disclosure can be achieved, and this is not limited herein.
[0084] The specific embodiments described above do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure should be included within the scope of protection of this disclosure.
Claims
1. A method for monitoring particle state, characterized in that, The method includes: Obtain particle parameters of the wafer cleaning environment as detected by environmental particle detection equipment; The detection duration is divided into multiple time periods according to the preset time step; Based on the maximum and minimum particle parameters detected in each time period, calculate the range of particle parameters for each time period, and then calculate the sum of the particle parameter ranges based on the range of particle parameters for each time period. Based on the ratio of the range of particle parameters in each time period to the sum of the ranges of particle parameters, and the number of particle parameters detected in each time period, calculate the sampling quantity of particle parameters in each time period, and extract sample particle parameters from the particle parameters detected in each time period according to the sampling quantity of particle parameters in each time period. Based on the maximum and minimum particle parameters detected in each time period, as well as the sample particle parameters corresponding to each time period, sub-particle parameter change curves corresponding to each time period are generated, and the sub-particle parameter change curves are spliced together in chronological order to obtain the particle parameter change curve. The particle status of the wafer cleaning environment is monitored based on the particle parameter variation curve. The step of calculating the particle parameter sampling quantity for each time period based on the ratio of the particle parameter range to the sum of particle parameter ranges for each time period, and the number of particle parameters detected within each time period, includes: For any given time period, the ratio of the particle parameter range corresponding to that time period to the sum of the particle parameter ranges is multiplied by the number of particle parameters detected within that time period to obtain the particle parameter sampling quantity corresponding to that time period.
2. The method according to claim 1, characterized in that, The environmental particle detection equipment detects particle parameters in the wafer cleaning environment, including: The laser module of the environmental particle detection equipment irradiates the air in the wafer cleaning environment; The imaging module of the environmental particle detection device captures images of particles in the irradiated air to obtain particle images. The processing module of the environmental particle detection equipment calculates the particle parameters of the wafer cleaning environment based on the particle image.
3. The method according to claim 2, characterized in that, The processing module of the environmental particle detection equipment calculates particle parameters of the wafer cleaning environment based on the particle image, including: Count the number of light spots in the particle image; The particle parameters of the wafer cleaning environment are calculated based on the number of light spots in the particle image and the area of the shooting region corresponding to the particle image.
4. The method according to any one of claims 1-3, characterized in that, The environmental particulate detection equipment is deployed inside the cleaning machine.
5. The method according to claim 4, characterized in that, The deployment location of the environmental particulate detection equipment within the cleaning machine is determined through the following steps: The internal space of the cleaning machine is modeled to obtain the internal space model of the cleaning machine. Select multiple location points from the internal space model of the cleaning machine; Obtain particle parameters of the wafer cleaning environment detected by environmental particle detection equipment deployed at multiple locations; Generate a spatial distribution map of particle parameters based on the particle parameters corresponding to each location point; Based on the spatial distribution map of particle parameters, determine the deployment location of the environmental particle detection equipment within the cleaning machine.
6. The method according to claim 1, characterized in that, The step of monitoring the particle status of the wafer cleaning environment based on the particle parameter change curve includes: If the particle parameter change curve is detected to be higher than the highest control line for particle parameters, then the particle state of the wafer cleaning environment is determined to be abnormal. If the particle parameter change curve is not detected to be higher than the maximum control line for particle parameters, then the particle state of the wafer cleaning environment is determined to be normal.
7. A particle state monitoring device, characterized in that, The apparatus is used to perform the method according to any one of claims 1-6, comprising: The acquisition module is used to acquire particle parameters of the wafer cleaning environment detected by the environmental particle detection equipment; The segmentation module is used to divide the detection duration into multiple time periods according to a preset time step. The calculation module is used to calculate the range of particle parameters for each time period based on the maximum and minimum particle parameters detected in each time period, and to calculate the sum of the particle parameter ranges based on the range of particle parameters for each time period. The extraction module is used to calculate the number of particle parameters to be sampled in each time period based on the ratio of the range of particle parameters in each time period to the sum of the ranges of particle parameters, and the number of particle parameters detected in each time period. Based on the number of particle parameters to be sampled in each time period, the module extracts sample particle parameters from the particle parameters detected in each time period. The generation module is used to generate sub-particle parameter change curves for each time period based on the maximum and minimum particle parameters detected in each time period, as well as the sample particle parameters corresponding to each time period. The sub-particle parameter change curves are then spliced together in chronological order to obtain the particle parameter change curve. The monitoring module is used to monitor the particle status of the wafer cleaning environment based on the particle parameter change curve.
8. An electronic device, characterized in that, The electronic device includes: At least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-6.
9. A non-transitory computer-readable storage medium storing computer instructions, characterized in that, The computer instructions are used to cause the computer to perform the method according to any one of claims 1-6.