Integrated circuit manufacturing equipment state monitoring system and method

Abstract

The invention belongs to the technical field of integrated circuit manufacturing, and provides an integrated circuit manufacturing equipment state monitoring system and method. The state evaluation module is connected with the data acquisition module; the early warning module is connected with the state evaluation module; the self-adaptive adjustment module is connected with the state evaluation module; by integrating the data acquisition module, the state evaluation module, the early warning module and the self-adaptive adjustment module, operation parameters and environment parameters of equipment can be acquired in real time, data processing and state evaluation are performed based on a preset algorithm model, and comprehensive monitoring of the equipment state is realized; the system not only improves the accuracy and the real-time performance of monitoring, but also effectively reduces the cost and the difficulty of manual inspection, meanwhile, technical innovation is performed in the aspects of data acquisition, state evaluation, early warning, adaptive adjustment and the like, and the stability and the reliability of equipment are further improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of integrated circuit manufacturing, and specifically relates to an integrated circuit manufacturing equipment status monitoring system and method. Background Art

[0002] In the field of integrated circuit manufacturing, the stability and reliability of equipment are crucial for production efficiency and product quality. However, there are often many deficiencies in the existing means of monitoring the status of integrated circuit manufacturing equipment. Most traditional equipment monitoring methods rely on manual inspections and regular maintenance. This method is not only inefficient but also difficult to detect potential equipment failures in a timely manner. In addition, although some monitoring systems based on simple sensors can collect the operating data of equipment in real time, they often lack advanced data processing and analysis capabilities and are difficult to accurately evaluate the operating status of equipment. Therefore, how to achieve comprehensive, real-time, and accurate monitoring of integrated circuit manufacturing equipment, and timely discover and warn of potential failures has become an urgent technical problem to be solved.

[0003] For this reason, those skilled in the art have proposed an integrated circuit manufacturing equipment status monitoring system and method to solve the problems raised in the background art. Summary of the Invention

[0004] To solve the above technical problems, the present invention provides an integrated circuit manufacturing equipment status monitoring system and method to solve the problems that most existing equipment monitoring methods rely on manual inspections and regular maintenance, which is not only inefficient but also difficult to detect potential equipment failures in a timely manner. In addition, although some monitoring systems based on simple sensors can collect the operating data of equipment in real time, they often lack advanced data processing and analysis capabilities and are difficult to accurately evaluate the operating status of equipment.

[0005] An integrated circuit manufacturing equipment status monitoring system includes:

[0006] A data acquisition module for real-time acquisition of the operating parameters and environmental parameters of integrated circuit manufacturing equipment;

[0007] A status evaluation module connected to the data acquisition module for processing the collected data based on a preset algorithm model to evaluate the operating status of integrated circuit manufacturing equipment;

[0008] An early warning module connected to the status evaluation module for sending out an early warning signal when the evaluation result shows that the equipment status is abnormal;

[0009] An adaptive adjustment module connected to the status evaluation module for adaptively adjusting the operating parameters of integrated circuit manufacturing equipment according to the evaluation result to maintain the stable operating state of the equipment.

[0010] Preferably, the data acquisition module includes multiple sensors, which are respectively used to collect the operating parameters of the equipment such as temperature, pressure, vibration, current, voltage, etc. and the environmental parameters such as temperature, humidity, cleanliness, etc. in the workshop. When collecting data in the data acquisition module, the wavelet transform algorithm is used to analyze non-stationary signals and extract time-frequency features.

[0011] Preferably, the state evaluation module includes a machine learning algorithm model, which can accurately identify the normal state and abnormal state of the equipment by training a large amount of historical data.

[0012] Preferably, the state evaluation module further includes introducing a convolutional neural network (CNN) to process more complex data structures, including time series data or image data.

[0013] Preferably, the warning module includes a sound alarm device, a light alarm device, and a communication interface, which are used to send warning signals to the operator or the remote monitoring system.

[0014] Preferably, the T2 statistic algorithm is introduced for anomaly detection in the warning module.

[0015] Preferably, the adaptive adjustment module includes a controller, which is used to adjust the process parameters, equipment speed, feed rate, etc. of the equipment according to the evaluation results. When making adjustments, the particle swarm optimization (PSO) algorithm is introduced to find the optimal equipment parameter settings.

[0016] An integrated circuit manufacturing equipment status monitoring method, using the above integrated circuit manufacturing equipment status monitoring system, includes:

[0017] S1. Real-time collect the operating parameters and environmental parameters of the integrated circuit manufacturing equipment;

[0018] S2. Process the collected data based on a preset algorithm model to evaluate the operating status of the integrated circuit manufacturing equipment;

[0019] S3. When the evaluation result shows that the equipment status is abnormal, send a warning signal;

[0020] S4. Adaptively adjust the operating parameters of the integrated circuit manufacturing equipment according to the evaluation results to maintain the stable operating state of the equipment.

[0021] A processor configured to execute the above integrated circuit manufacturing equipment status monitoring system.

[0022] A computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the above integrated circuit manufacturing equipment status monitoring system is implemented.

[0023] Compared with the prior art, the present invention has the following beneficial effects:

[0024] 1. By integrating a data acquisition module, a status evaluation module, an early warning module, and an adaptive adjustment module, the present invention constructs a complete integrated circuit manufacturing equipment status monitoring system. This system can collect the operating parameters and environmental parameters of the equipment in real time, and perform data processing and status evaluation based on a preset algorithm model, thereby achieving comprehensive monitoring of the equipment status. It not only improves the accuracy and real-time performance of monitoring, but also effectively reduces the cost and difficulty of manual inspection.

[0025] 2. The present invention adopts the wavelet transform algorithm in the data acquisition module to analyze non-stationary signals and extract time-frequency features, enabling the system to capture abnormal signals of the equipment more accurately and improving the sensitivity of fault detection. At the same time, by introducing machine learning algorithm models and convolutional neural networks (CNNs) to process more complex data structures, the accuracy and reliability of status evaluation are further improved.

[0026] 3. The present invention sets up a sound alarm device, a light alarm device, and / or a communication interface in the early warning module, which can send early warning signals to operators or remote monitoring systems in a timely manner. It not only ensures the timely transmission of early warning information, but also provides multiple alarm methods, facilitating operators to make quick responses according to the actual situation.

[0027] 4. The present invention introduces the particle swarm optimization (PSO) algorithm in the adaptive adjustment module to find the optimal equipment parameter settings. It not only improves the accuracy and efficiency of equipment adjustment, but also helps to maintain the stable operating state of the equipment and extend the service life of the equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 is a framework diagram of the integrated circuit manufacturing equipment status monitoring system of the present invention;

[0029] Figure 2 is a flowchart of the integrated circuit manufacturing equipment status monitoring method of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0030] The following further describes the embodiments of the present invention in detail with reference to the drawings and examples. The following examples are used to illustrate the present invention, but cannot be used to limit the scope of the present invention.

[0031] Example: The present invention provides an integrated circuit manufacturing equipment status monitoring system, as Figure 1 shown, including:

[0032] A data acquisition module for collecting the operating parameters and environmental parameters of the integrated circuit manufacturing equipment in real time;

[0033] A status evaluation module, connected to the data acquisition module, is used to process the collected data based on a preset algorithm model to evaluate the operating status of the integrated circuit manufacturing equipment;

[0034] An early warning module, connected to the status evaluation module, is used to send out an early warning signal when the evaluation result shows that the equipment status is abnormal;

[0035] An adaptive adjustment module, connected to the status evaluation module, is used to adaptively adjust the operating parameters of the integrated circuit manufacturing equipment according to the evaluation result to maintain the stable operating state of the equipment.

[0036] As can be seen from the above, by integrating the data acquisition module, the status evaluation module, the early warning module and the adaptive adjustment module, this system can collect the operating parameters and environmental parameters of the equipment in real time, and perform data processing and status evaluation based on a preset algorithm model, realizing comprehensive monitoring of the equipment status. This system not only improves the accuracy and real-time performance of monitoring, but also effectively reduces the cost and difficulty of manual inspection. At the same time, technical innovations have been carried out in aspects such as data acquisition, status evaluation, early warning and adaptive adjustment, further enhancing the stability and reliability of the equipment and extending the service life of the equipment.

[0037] Further, the data acquisition module includes multiple sensors, which are respectively used to collect operating parameters such as the temperature, pressure, vibration, current, and voltage of the equipment and environmental parameters such as the temperature, humidity, and cleanliness in the workshop. When data is collected in the data acquisition module, the wavelet transform algorithm is used to analyze non-stationary signals and extract time-frequency features. The formula of the wavelet transform algorithm includes:

[0038]

[0039] Among them, W f (a, b) is the wavelet transform coefficient, f(t) is the original signal, ψ is the wavelet basis function, a is the scale parameter, and b is the displacement parameter.

[0040] As can be seen from the above, this data acquisition module uses multiple sensors to comprehensively collect key parameters of the equipment and the environment, and uses the wavelet transform algorithm to process data, which can accurately analyze non-stationary signals and extract time-frequency features. It significantly improves the accuracy and efficiency of data acquisition, enables the system to capture abnormal signals of the equipment more sensitively, provides a more reliable data basis for subsequent status evaluation and early warning, and thus enhances the performance and reliability of the entire monitoring system.

[0041] Further, the status evaluation module includes a machine learning algorithm model, which can accurately identify the normal state and abnormal state of the equipment by training a large amount of historical data. The formula of the machine learning algorithm model includes:

[0042]

[0043] Among them, α i is the Lagrange multiplier, y i is the sample label, K(x i ,x) is the kernel function and b is the bias term.

[0044] As can be seen from the above, the state assessment module adopts a machine learning algorithm model, which can accurately identify the normal and abnormal states of the equipment through training of a large amount of historical data. It not only improves the accuracy and reliability of state assessment, but also provides strong decision support for early warning and adaptive adjustment. Through machine learning algorithms, the system can continuously learn and optimize to better adapt to changes in equipment status, thereby improving the intelligence level and adaptability of the entire monitoring system.

[0045] Furthermore, the state assessment module also includes introducing a convolutional neural network (CNN) to process more complex data structures, including time series data or image data, and the convolution operation formula of the convolutional neural network includes:

[0046]

[0047] Among them, S is the feature map, I is the input image, and K is the convolution kernel.

[0048] As can be seen above, the state assessment module introduces convolutional neural networks (CNN) to process more complex data structures, such as time series data and image data. It further improves the accuracy and comprehensiveness of state assessment, enabling the system to more effectively process and analyze various complex data, thereby more accurately judging the operating status of the equipment. Through the deep learning and feature extraction capabilities of convolutional neural networks, the system can mine more useful information from the data, providing a more accurate basis for equipment fault warning and adaptive adjustment.

[0049] Furthermore, the warning module includes a sound alarm device, a light alarm device and / or a communication interface, which is used to send a warning signal to an operator or a remote monitoring system.

[0050] As can be seen from the above, the early warning module integrates sound alarm devices, light alarm devices and / or communication interfaces, and can send early warning signals to operators or remote monitoring systems in a timely and diverse manner. It ensures the rapid transmission and effective reception of early warning information, allowing operators to respond quickly and take measures, thereby effectively avoiding or reducing the losses that may be caused by equipment failure. At the same time, the combination of multiple alarm methods also improves the flexibility and adaptability of the system, meeting the early warning needs in different scenarios.

[0051] Further, when performing anomaly detection in the warning module, the T2 statistic algorithm is introduced, and the formula of the T2 statistic algorithm includes:

[0052]

[0053] Wherein, is the sample mean vector, μ 0 is the assumed population mean vector, and S is the sample covariance matrix.

[0054] As can be seen from the above, when performing anomaly detection, the warning module introduces the T2 statistic algorithm. By calculating the statistical distance between the sample mean vector and the assumed population mean vector and combining the sample covariance matrix for comprehensive analysis, it can more accurately identify the abnormal state of the device. It improves the sensitivity and accuracy of the warning module, enabling the system to issue a warning when there is a slight change in the device state, providing more sufficient response time for the operator, and thus further ensuring the stable operation of the device and the continuity of production.

[0055] Further, the adaptive adjustment module includes a controller for adjusting the process parameters, device speed, feed rate, etc. of the device according to the evaluation results. When making adjustments, the particle swarm optimization (PSO) algorithm is introduced to find the optimal device parameter settings. The formula of the particle swarm optimization algorithm includes:

[0056]

[0057] Wherein, is the velocity of particle i in the d-th dimension, w is the inertia weight, c 1 and c 2 are learning factors, r 1 and r 2 are random numbers, is the historical best position of particle i, is the global best position.

[0058] As can be seen from the above, the adaptive adjustment module can intelligently adjust the process parameters, speed, feed rate, etc. of the device according to the state evaluation results by introducing the particle swarm optimization (PSO) algorithm to find the optimal device parameter settings. It not only improves the accuracy and efficiency of device adjustment, but also helps the device maintain the best operating state under complex and changeable working conditions, thereby extending the service life of the device, improving production efficiency and product quality. The introduction of the particle swarm optimization algorithm enables the system to simulate the optimization process of group behavior in nature and find the global optimal solution through continuous iteration and update, providing strong technical support for the adaptive adjustment of the device.

[0059] Furthermore, the state monitoring system of the integrated circuit manufacturing equipment in the embodiment is compared with the current traditional equipment monitoring method (comparative example), and the following table is obtained:

[0060]

[0061]

[0062] As can be seen from the above table, the state monitoring system of the integrated circuit manufacturing equipment in this embodiment is superior to the traditional equipment monitoring method in terms of data acquisition comprehensiveness, data processing and analysis capabilities, state evaluation accuracy, early warning timeliness and accuracy, adaptive adjustment capabilities, monitoring efficiency and real-time performance, and system intelligence level. Although the initial investment may be relatively high, the long-term maintenance cost is low, and it can significantly improve the monitoring efficiency and accuracy, providing a strong guarantee for the stable operation of the integrated circuit manufacturing equipment.

[0063] A method for monitoring the state of an integrated circuit manufacturing equipment, as Figure 2 shown, uses the above-mentioned state monitoring system of the integrated circuit manufacturing equipment, and includes:

[0064] S1. Real-time collect the operating parameters and environmental parameters of the integrated circuit manufacturing equipment;

[0065] S2. Process the collected data based on a preset algorithm model to evaluate the operating state of the integrated circuit manufacturing equipment;

[0066] S3. When the evaluation result shows that the equipment state is abnormal, issue a warning signal;

[0067] S4. Adaptively adjust the operating parameters of the integrated circuit manufacturing equipment according to the evaluation result to maintain the stable operating state of the equipment.

[0068] An embodiment of the present application provides an electronic device, which is applicable to the above-mentioned state monitoring system of the integrated circuit manufacturing equipment, and includes:

[0069] A memory, used to protect computer programs and data;

[0070] A processor, used to run the system program.

[0071] An embodiment of the present application provides a computer storage medium, which is applicable to the above-mentioned state monitoring system of the integrated circuit manufacturing equipment, and performs hierarchical confidentiality management on the above-mentioned system and data according to the confidentiality management requirements.

[0072] Those skilled in the art should understand that the embodiments of the present application can be provided as a system or a computer program product. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0073] The present application is described with reference to the flowcharts and / or block diagrams of devices (systems) and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, and the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate a device for implementing the functions specified in one process Figure 1 one process or multiple processes and / or blocks Figure 1 or multiple blocks.

[0074] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured article including an instruction device, and the instruction device implements the functions specified in one process Figure 1 one process or multiple processes and / or blocks Figure 1 or multiple blocks.

[0075] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process. Thus, the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one process Figure 1 one process or multiple processes and / or blocks Figure 1 or multiple blocks.

[0076] In a typical configuration, a computing device includes one or more processors (CPUs), an input / output interface, a network interface, and a memory.

[0077] The memory may include non-permanent memory in a computer-readable medium, in the form of random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0078] Computer readable media include permanent and non-permanent, removable and non-removable media, and can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include transitory media such as modulated data signals and carrier waves.

[0079] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, commodity or device including the elements.

[0080] The embodiments of the present invention are provided for the purpose of illustration and description. Although the embodiments of the present invention have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limitations of the present invention. Ordinary technicians in this field can change, modify, replace and modify the above embodiments within the scope of the present invention.

Claims

1. An integrated circuit manufacturing equipment status monitoring system, characterized in that: include: A data acquisition module, used to collect operating parameters and environmental parameters of integrated circuit manufacturing equipment in real time; A state evaluation module, connected to the data acquisition module, for processing the collected data based on a preset algorithm model; An early warning module, connected to the status evaluation module, for issuing an early warning signal when the evaluation result shows that the device status is abnormal; The adaptive adjustment module is connected to the state evaluation module and is used to adaptively adjust the operating parameters of the integrated circuit manufacturing equipment according to the evaluation results.

2. The integrated circuit manufacturing equipment status monitoring system as claimed in claim 1, characterized in that: The data acquisition module includes multiple sensors, which are used to collect temperature, pressure, vibration, current, voltage parameters of the equipment and temperature, humidity, cleanliness parameters in the workshop. Wavelet transform algorithm is used when collecting data in the data acquisition module.

3. The integrated circuit manufacturing equipment status monitoring system as claimed in claim 1, characterized in that: The state assessment module includes a machine learning algorithm model, which can accurately identify the normal state and abnormal state of the equipment by training a large amount of historical data.

4. The integrated circuit manufacturing equipment status monitoring system as claimed in claim 3, characterized in that: The state assessment module also includes introducing a convolutional neural network to process more complex data structures, including time series data or image data.

5. The integrated circuit manufacturing equipment status monitoring system as claimed in claim 1, characterized in that: The early warning module includes a sound alarm device, a light alarm device, and a communication interface, which are used to send an early warning signal to an operator or a remote monitoring system.

6. The integrated circuit manufacturing equipment status monitoring system as claimed in claim 5, characterized in that: The T2 statistic algorithm is introduced when performing anomaly detection in the early warning module.

7. The integrated circuit manufacturing equipment status monitoring system as claimed in claim 1, characterized in that: The adaptive adjustment module includes a controller for adjusting the process parameters, equipment rotation speed, and feed rate of the equipment according to the evaluation results, and the adjustment is made by introducing a particle swarm optimization algorithm.

8. A method for monitoring the state of integrated circuit manufacturing equipment, characterized in that: The integrated circuit manufacturing equipment status monitoring system according to any one of claims 1 to 7 comprises: S1. Real-time collection of operating parameters and environmental parameters of integrated circuit manufacturing equipment; S2. Processing the collected data based on a preset algorithm model to evaluate the operating status of the integrated circuit manufacturing equipment; S3. When the evaluation results show that the equipment status is abnormal, an early warning signal is issued; S4. Adaptively adjust the operating parameters of the integrated circuit manufacturing equipment based on the evaluation results to maintain a stable operating state of the equipment.

9. A processor, characterized in that: The integrated circuit manufacturing equipment state monitoring system is configured to execute the integrated circuit manufacturing equipment state monitoring system according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and when the computer program is executed by a processor, the integrated circuit manufacturing equipment status monitoring system according to any one of claims 1 to 7 is implemented.

Images