Wafer washing effect detection method, device, equipment and storage medium

Abstract

The embodiments of the present disclosure provide a method, apparatus, device, and storage medium for detecting the effect of wafer rinsing, which are applied to the field of semiconductor technology. The method includes: obtaining resistivity data of pure water in a target water tank when the wafer is located in the target water tank, wherein the target water tank is the last water tank before the drying tank; generating a pure water resistivity change curve based on the resistivity data of the pure water in the target water tank; and detecting the rinsing effect of the wafer based on the pure water resistivity change curve. In this way, the wafer rinsing effect can be detected to determine whether there are residual chemical reagents on the wafer surface, thereby preventing wafers with poor rinsing effects from being transferred to the next process.

Description

Technical Field

[0001] The present disclosure relates to the field of semiconductor technology, and in particular to a method, device, equipment, and storage medium for detecting wafer washing effects. Background Art

[0002] In the semiconductor industry, wafers must be clean before entering the processing stage, necessitating wafer cleaning. Currently, commonly used wet tank cleaning processes utilize a variety of chemical reagents, such as HF, NH3.H2O, and H2O2. If these chemicals are not rinsed thoroughly with DIW (pure water) before entering the drying tank, residues can easily remain on the wafer surface, causing surface fogging, localized chemical corrosion, and other issues. Therefore, measuring wafer rinsing effectiveness has become a pressing technical challenge. Summary of the Invention

[0003] The present disclosure provides a wafer washing effect detection method, device, equipment and storage medium.

[0004] In a first aspect, an embodiment of the present disclosure provides a method for detecting wafer washing effect, the method comprising:

[0005] Obtaining resistivity data of pure water in a target water tank when the wafer is in the target water tank, where the target water tank is the last water tank before the drying tank;

[0006] Generate a pure water resistivity change curve based on the resistivity data of pure water in the target water tank;

[0007] The wafer rinsing effect is tested based on the pure water resistivity change curve.

[0008] In some implementations of the first aspect, obtaining resistivity data of pure water in a target water tank when the wafer is located in the target water tank includes:

[0009] The resistivity measuring device deployed in the target water tank measures the resistivity of the pure water in the target water tank in real time to obtain the resistivity data of the pure water at each moment;

[0010] The resistivity data of pure water when the wafer is located in the target water tank is extracted from the resistivity data of pure water at each time point.

[0011] In some implementations of the first aspect, the deployment position of the resistivity measurement device in the target water tank is determined by the following steps:

[0012] Inputting attribute data of a target water tank and a resistivity measurement device into a pre-trained location determination model to obtain a deployment location of the resistivity measurement device within the target water tank, wherein the attribute data of the target water tank includes size, model, and overflow rate, and the attribute data of the resistivity measurement device includes size and model;

[0013] The location determination model is obtained by training a preset neural network using a first training data set, wherein the samples in the first training data set use the attribute data of the target water tank and the resistivity measurement equipment inside it as sample features, and use the deployment location of the resistivity measurement equipment in the target water tank as the sample label.

[0014] In some implementations of the first aspect, the deployment position of the resistivity measuring device in the target water tank is an optimal deployment position, and the optimal deployment position is determined by the following steps:

[0015] acquiring resistivity data of pure water measured by a plurality of resistivity measurement devices deployed in a target water tank within a target time period;

[0016] The multiple resistivity measurement devices are deployed at different locations, and the target time period is the time period from when the unrinsed wafer enters the target water tank until it leaves the target water tank;

[0017] Calculate the resistivity range corresponding to each resistivity measuring device based on the resistivity data of pure water measured by each resistivity measuring device within the target time period;

[0018] Clustering each resistivity measuring device according to the resistivity range corresponding to each resistivity measuring device to obtain multiple device sets;

[0019] According to the resistivity range corresponding to each resistivity measuring device in each device set, the average resistivity range corresponding to each device set is calculated;

[0020] Select the device set with the largest average resistivity range from multiple device sets as the target device set;

[0021] Building a spatial model according to the deployment position of each resistivity measurement device in the target device set;

[0022] The center point of the spatial model is determined as the optimal deployment location for the resistivity measurement equipment.

[0023] In some implementations of the first aspect, detecting the rinsing effect of the wafer according to the pure water resistivity change curve includes:

[0024] If the pure water resistivity change curve is detected to be higher than the pure water resistivity minimum control line, the wafer rinsing effect is determined to be qualified;

[0025] If it is detected that the pure water resistivity change curve is lower than the pure water resistivity minimum control line, it is determined that the rinsing effect of the wafer is unqualified.

[0026] In some implementations of the first aspect, detecting the rinsing effect of the wafer according to the pure water resistivity change curve includes:

[0027] Compare the resistivity change curve of pure water with that of standard pure water;

[0028] If the comparison is passed, the rinsing effect of the wafer is determined to be qualified;

[0029] If the comparison fails, the rinsing effect of the wafer is determined to be unqualified.

[0030] In some implementations of the first aspect, detecting the rinsing effect of the wafer according to the pure water resistivity change curve includes:

[0031] The pure water resistivity change curve is input into a pre-trained flushing effect detection model to obtain the flushing effect; the flushing effect detection model is obtained by training a preset neural network using a second training data set, wherein the samples in the second training data set use the pure water resistivity change curve as the sample feature, and the flushing effect corresponding to the pure water resistivity change curve as the sample label.

[0032] In a second aspect, an embodiment of the present disclosure provides a wafer washing effect detection device, the device comprising:

[0033] an acquisition module, configured to acquire resistivity data of pure water in a target water tank when the wafer is located in the target water tank, wherein the target water tank is the last water tank before the drying tank;

[0034] A generation module, used for generating a pure water resistivity change curve based on the resistivity data of pure water in the target water tank;

[0035] The detection module is used to detect the rinsing effect of the wafer according to the pure water resistivity change curve.

[0036] In a third aspect, an embodiment of the present disclosure provides an electronic device comprising: at least one processor; and a memory communicatively connected to the at least one processor; the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the method described above.

[0037] In a fourth aspect, an embodiment of the present disclosure provides a non-transitory computer-readable storage medium storing computer instructions, where the computer instructions are used to cause a computer to execute the method described above.

[0038] In the embodiments of the present disclosure, the principle that chemical reagents entering pure water will cause the resistivity of pure water to decrease can be referred to. According to the corresponding pure water resistivity data when the wafer is placed in the last water tank before the drying tank (that is, the last water tank in the wet tank cleaning process), a pure water resistivity change curve is generated, and the wafer rinsing effect is effectively detected based on the pure water resistivity change curve to determine whether there are residual chemical reagents on the wafer surface, so as to avoid wafers with poor rinsing effect from flowing to the next process.

[0039] It should be understood that the contents described in the Summary of the Invention section are not intended to limit the key or important features of the embodiments of the present disclosure, nor are they intended to limit the scope of the present disclosure. Other features of the present disclosure will become readily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] The above and other features, advantages and aspects of the embodiments of the present disclosure will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. The accompanying drawings are provided for a better understanding of the present disclosure and do not constitute a limitation of the present disclosure. In the accompanying drawings, the same or similar reference numerals represent the same or similar elements, among which:

[0041] Figure 1 A flowchart of a wafer washing effect detection method provided by an embodiment of the present disclosure is shown;

[0042] Figure 2 A schematic diagram of a pure water resistivity change curve provided by an embodiment of the present disclosure is shown, where the horizontal axis represents time and the vertical axis represents resistivity value in MΩ;

[0043] Figure 3 A structural diagram of a wafer washing effect detection device provided by an embodiment of the present disclosure is shown;

[0044] Figure 4 A structural diagram of an exemplary electronic device capable of implementing an embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0045] To make the objectives, technical solutions, and advantages of the embodiments of the present disclosure more clear, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the drawings in the embodiments of the present disclosure. Obviously, the described embodiments are part of the embodiments of the present disclosure, not all of the embodiments. Based on the embodiments of the present disclosure, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present disclosure.

[0046] In this document, the term "and / or" simply describes a relationship between related objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A exists alone, A and B exist simultaneously, or B exists alone. Furthermore, the character " / " in this document generally indicates that the related objects are in an "or" relationship.

[0047] In response to the problems encountered in the background art, embodiments of the present disclosure provide a method, apparatus, device, and storage medium for detecting wafer rinsing effectiveness. Specifically, resistivity data of pure water in a target water tank (the last water tank before the drying tank) is acquired while the wafer is in the target water tank. A pure water resistivity curve is generated based on the resistivity data of the pure water in the target water tank. Based on the pure water resistivity curve, the wafer rinsing effectiveness is effectively detected to determine whether chemical reagents remain on the wafer surface, thereby preventing wafers with poor rinsing effects from being passed to the next process.

[0048] The wafer washing effect detection method, device, equipment and storage medium provided by the embodiments of the present disclosure are described in detail below with reference to the accompanying drawings through specific embodiments.

[0049] Figure 1 FIG. 1 shows a flow chart of a wafer washing effect detection method provided by an embodiment of the present disclosure, as shown in FIG. Figure 1 As shown, the wafer washing effect detection method 100 may include the following steps:

[0050] S110 , obtaining resistivity data of pure water in the target water tank when the wafer is located in the target water tank.

[0051] The target water tank is the last water tank before the drying tank, i.e., the last water tank in the wet tank cleaning process. For example, if the wet tank cleaning process follows the following flow: O3 - HF (HF / DIW) - DIW - SC1 (NH3.H2O / H2O2 / DIW) - DIW - SC2 (HCl / H2O2 / DIW) - DIW - O3 - DIW - drying, the target water tank is the last DIW tank.

[0052] As an example, the resistivity of pure water in the target water tank can be measured in real time by deploying a resistivity measuring device, such as a resistivity meter, in the target water tank to obtain the resistivity data of pure water at each moment, and the resistivity data of pure water when the wafer is in the target water tank can be quickly extracted from the resistivity data of pure water at each moment.

[0053] It is worth noting that the deployment position of the resistivity measurement equipment in the target water tank can be accurately determined by the following steps:

[0054] The attribute data of the target water tank and the resistivity measurement device are input into a pre-trained location determination model, and the location determination model performs calculations to quickly obtain the deployment position of the resistivity measurement device in the target water tank.

[0055] Among them, the attribute data of the target water tank may include dimensions (such as length, width, height), model, overflow rate, etc.; the attribute data of the resistivity measuring device may include dimensions (such as length, width, height), model, etc.; the position determination model can be obtained by training a preset neural network (such as a convolutional neural network, a recurrent neural network, a long short-term memory neural network, etc.) using a first training data set. The samples in the first training data set use the attribute data of the target water tank and the resistivity measuring device therein as sample features, and use the deployment position of the resistivity measuring device in the target water tank as the sample label. The target water tank here can be any target water tank and is not restricted here.

[0056] In addition, the deployment position of the resistivity measurement equipment in the target water tank is the optimal deployment position, which can be determined by the following steps:

[0057] First, pure water resistivity data is obtained from multiple resistivity measurement devices deployed in a target water tank during a target time period. The multiple resistivity measurement devices are deployed at different locations. The target time period is the time from when an unrinsed wafer (with a large amount of chemical reagents on its surface) enters the target water tank until it leaves.

[0058] Next, based on the resistivity data of pure water measured by each resistivity measuring device during the target time period, the resistivity range corresponding to each resistivity measuring device is calculated. For example, the resistivity range corresponding to each resistivity measuring device can be calculated by subtracting the minimum value from the maximum value in the resistivity data of pure water measured by the resistivity measuring device during the target time period.

[0059] Then, each resistivity measurement device is clustered according to the resistivity range corresponding to each resistivity measurement device to obtain multiple device sets. The clustering algorithm can be a K-Means algorithm or a community discovery algorithm, which is not limited here.

[0060] Then, based on the resistivity range corresponding to each resistivity measuring device in each device set, the average resistivity range corresponding to each device set is calculated. The device set with the largest average resistivity range difference is selected from multiple device sets as the target device set. A spatial model is constructed according to the deployment position of each resistivity measuring device in the target device set, and the center point of the spatial model is determined as the optimal deployment position of the resistivity measuring device.

[0061] In this way, the diffusion of chemical reagents at different positions in the target water tank can be taken into consideration, so that the position with the most prominent diffusion situation can be selected as the deployment position, thereby facilitating the improvement of the detection efficiency of the wafer cleaning effect.

[0062] S120 , generating a pure water resistivity change curve based on the resistivity data of the pure water in the target water tank.

[0063] In some embodiments, the resistivity data at each moment may be arranged in chronological order and connected in sequence to obtain a pure water resistivity change curve.

[0064] S130 , detecting the rinsing effect of the wafer according to the pure water resistivity change curve.

[0065] In some embodiments, if the pure water resistivity change curve is detected to be higher than the pure water resistivity minimum control line, it can be determined that the rinsing effect of the wafer is qualified; if the pure water resistivity change curve is detected to be lower than the pure water resistivity minimum control line, it can be determined that the rinsing effect of the wafer is unqualified.

[0066] Among them, the minimum control line of pure water resistivity can be flexibly adjusted according to needs and is not restricted here.

[0067] In other embodiments, the pure water resistivity change curve can be compared with the standard pure water resistivity change curve. If the comparison is passed, the wafer rinsing effect is determined to be qualified; if the comparison is not passed, the wafer rinsing effect is determined to be unqualified.

[0068] For example, the similarity between the pure water resistivity change curve and the standard pure water resistivity change curve can be calculated. If the similarity is greater than a preset threshold, it is determined that the comparison is passed, and then the wafer rinsing effect is determined to be qualified. If the similarity is less than or equal to the preset threshold, it is determined that the comparison fails, and then the wafer rinsing effect is determined to be unqualified.

[0069] The standard pure water resistivity change curve may be obtained by fitting the pure water resistivity change curves corresponding to a plurality of wafers with qualified rinsing effects, or may be independently set by the user based on experience, and is not limited here.

[0070] In some other embodiments, the pure water resistivity change curve may be input into a pre-trained flushing effect detection model, and the flushing effect detection model may be used for calculation to quickly obtain the flushing effect.

[0071] Among them, the flushing effect detection model is obtained by training a preset neural network (such as a convolutional neural network, a recurrent neural network, a long short-term memory neural network, etc.) using the second training data set. The samples in the second training data set use the pure water resistivity change curve as the sample feature, and the flushing effect corresponding to the pure water resistivity change curve as the sample label. The pure water resistivity change curve here can be any pure water resistivity change curve, and there is no restriction here.

[0072] In the embodiments of the present disclosure, the principle that chemical reagents entering pure water will cause the resistivity of pure water to decrease can be referred to. According to the corresponding pure water resistivity data when the wafer is placed in the last water tank before the drying tank (that is, the last water tank in the wet tank cleaning process), a pure water resistivity change curve is generated, and the wafer rinsing effect is effectively detected based on the pure water resistivity change curve to determine whether there are residual chemical reagents on the wafer surface, so as to avoid wafers with poor rinsing effect from flowing to the next process.

[0073] The wafer washing effect detection method 100 provided by the embodiment of the present disclosure is described in detail below with reference to a specific embodiment, as follows:

[0074] (1) Pre-deploy multiple resistivity measuring instruments at different locations in a target water tank, and obtain resistivity data of pure water measured by the multiple resistivity measuring instruments within the target water tank within a target time period. The target time period is the time period from when an unrinsed wafer enters the target water tank until it leaves the target water tank.

[0075] (2) Based on the resistivity data of pure water measured by each resistivity meter during the target time period, the resistivity range corresponding to each resistivity meter is calculated. The resistivity meters are clustered according to the resistivity range corresponding to each resistivity meter to obtain multiple device sets.

[0076] (3) According to the resistivity range corresponding to each resistivity meter in each equipment set, the average resistivity range corresponding to each equipment set is calculated, and the equipment set with the largest average resistivity range is selected as the target equipment set from multiple equipment sets.

[0077] (4) A spatial model is constructed based on the deployment positions of each resistivity meter in the target equipment set, and the center point of the spatial model is determined as the optimal deployment position of the resistivity meter. Then, multiple existing resistivity meters are dismantled and a resistivity meter is deployed at the optimal deployment position.

[0078] (5) The fault detection and classification system (FDC) obtains the resistivity data of pure water when the wafer is in the target water tank through a resistivity meter deployed in the target water tank.

[0079] (6) FDC generates a pure water resistivity change curve based on the pure water resistivity data.

[0080] (7) FDC detects the rinsing effect of the wafer based on the pure water resistivity change curve.

[0081] Specific tests can be Figure 2 As shown in the figure, the curve represents the pure water resistivity change curve, and the dotted line represents the pure water resistivity minimum control line. If the pure water resistivity change curve is detected to be higher than the pure water resistivity minimum control line, the wafer rinsing effect can be determined to be qualified; if the pure water resistivity change curve is detected to be lower than the pure water resistivity minimum control line, the wafer rinsing effect can be determined to be unqualified.

[0082] (8) If the FDC detects that the wafer rinsing effect is qualified, no processing is performed.

[0083] (9) If the FDC detects that the rinsing effect of the wafer is unqualified, it will issue an alarm and notify the Manufacturing Execution System (MES) to hold the machine to prevent the wafer with poor rinsing effect from flowing to the next process and waiting for further processing.

[0084] It should be noted that for the aforementioned method embodiments, for simplicity of description, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present disclosure is not limited by the order of the actions described, because according to the present disclosure, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily required by the present disclosure.

[0085] The above is an introduction to the method embodiment. The following is a further explanation of the solution disclosed in the present disclosure through an apparatus embodiment.

[0086] Figure 3 FIG. 1 shows a structural diagram of a wafer washing effect detection device provided by an embodiment of the present disclosure, such as Figure 3 As shown, the wafer washing effect detection device 300 may include:

[0087] The acquisition module 310 is used to acquire resistivity data of pure water in a target water tank when the wafer is located in the target water tank, wherein the target water tank is the last water tank before the drying tank.

[0088] The generating module 320 is configured to generate a pure water resistivity variation curve according to the resistivity data of the pure water in the target water tank.

[0089] The detection module 330 is used to detect the rinsing effect of the wafer according to the pure water resistivity change curve.

[0090] In some embodiments, the acquisition module 310 is specifically configured to:

[0091] The resistivity measuring device deployed in the target water tank measures the resistivity of the pure water in the target water tank in real time to obtain the resistivity data of the pure water at each moment;

[0092] The resistivity data of pure water when the wafer is located in the target water tank is extracted from the resistivity data of pure water at each time point.

[0093] In some embodiments, the deployment location of the resistivity measurement device in the target water tank is determined by the following steps:

[0094] Inputting attribute data of a target water tank and a resistivity measurement device into a pre-trained location determination model to obtain a deployment location of the resistivity measurement device within the target water tank, wherein the attribute data of the target water tank includes size, model, and overflow rate, and the attribute data of the resistivity measurement device includes size and model;

[0095] The location determination model is obtained by training a preset neural network using a first training data set, wherein the samples in the first training data set use the attribute data of the target water tank and the resistivity measurement equipment inside it as sample features, and use the deployment location of the resistivity measurement equipment in the target water tank as the sample label.

[0096] In some embodiments, the deployment position of the resistivity measurement device in the target water tank is an optimal deployment position, and the optimal deployment position is determined by the following steps:

[0097] acquiring resistivity data of pure water measured by a plurality of resistivity measurement devices deployed in a target water tank within a target time period;

[0098] The multiple resistivity measurement devices are deployed at different locations, and the target time period is the time period from when the unrinsed wafer enters the target water tank until it leaves the target water tank;

[0099] Calculate the resistivity range corresponding to each resistivity measuring device based on the resistivity data of pure water measured by each resistivity measuring device within the target time period;

[0100] Clustering each resistivity measuring device according to the resistivity range corresponding to each resistivity measuring device to obtain multiple device sets;

[0101] According to the resistivity range corresponding to each resistivity measuring device in each device set, the average resistivity range corresponding to each device set is calculated;

[0102] Select the device set with the largest average resistivity range from multiple device sets as the target device set;

[0103] Building a spatial model according to the deployment position of each resistivity measurement device in the target device set;

[0104] The center point of the spatial model is determined as the optimal deployment location for the resistivity measurement equipment.

[0105] In some embodiments, the detection module 330 is specifically configured to:

[0106] If the pure water resistivity change curve is detected to be higher than the pure water resistivity minimum control line, the wafer rinsing effect is determined to be qualified;

[0107] If it is detected that the pure water resistivity change curve is lower than the pure water resistivity minimum control line, it is determined that the rinsing effect of the wafer is unqualified.

[0108] In some embodiments, the detection module 330 is specifically configured to:

[0109] Compare the resistivity change curve of pure water with that of standard pure water;

[0110] If the comparison is passed, the rinsing effect of the wafer is determined to be qualified;

[0111] If the comparison fails, the rinsing effect of the wafer is determined to be unqualified.

[0112] In some embodiments, the detection module 330 is specifically configured to:

[0113] The pure water resistivity change curve is input into a pre-trained flushing effect detection model to obtain the flushing effect; the flushing effect detection model is obtained by training a preset neural network using a second training data set, wherein the samples in the second training data set use the pure water resistivity change curve as the sample feature, and the flushing effect corresponding to the pure water resistivity change curve as the sample label.

[0114] It is understandable that Figure 3 Each module / unit in the wafer washing effect detection device 300 shown has the function of realizing Figure 1 The functions of the various steps in the wafer washing effect detection method 100 shown and their ability to achieve corresponding technical effects are not described here for the sake of brevity.

[0115] Figure 4A block diagram of an exemplary electronic device capable of implementing embodiments of the present disclosure is shown. Electronic device 400 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 400 may also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are provided as examples only and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0116] like Figure 4 As shown, the electronic device 400 may include a computing unit 401, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 402 or a computer program loaded from a storage unit 408 into a random access memory (RAM) 403. Various programs and data required for the operation of the electronic device 400 may also be stored in the RAM 403. The computing unit 401, the ROM 402, and the RAM 403 are connected to each other via a bus 404. An input / output (I / O) interface 405 is also connected to the bus 404.

[0117] Multiple components in the electronic device 400 are connected to the I / O interface 405, including an input unit 406, such as a keyboard, a mouse, etc.; an output unit 407, such as various types of displays, speakers, etc.; a storage unit 408, such as a magnetic disk, an optical disk, etc.; and a communication unit 409, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 409 allows the electronic device 400 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0118] The computing unit 401 may be a variety of general-purpose and / or specialized processing components with processing and computing capabilities. Some examples of the computing unit 401 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units that run machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The computing unit 401 performs the various methods and processes described above, such as method 100. For example, in some embodiments, the method 100 may be implemented as a computer program product, including a computer program tangibly embodied in a computer-readable medium, such as a storage unit 408. In some embodiments, part or all of the computer program may be loaded and / or installed onto the device 400 via the ROM 402 and / or the communication unit 409. When the computer program is loaded into the RAM 403 and executed by the computing unit 401, one or more steps of the method 100 described above may be performed. Alternatively, in other embodiments, the computing unit 401 may be configured to perform the method 100 in any other appropriate manner (e.g., by means of firmware).

[0119] 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), systems on chip (SOCs), programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system comprising at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0120] The program code for implementing the method of the present disclosure can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device so that when the program code is executed by the processor or controller, the functions / operations specified in the flow chart and / or block diagram are implemented. The program code can be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0121] In the context of the present disclosure, a computer-readable medium can be a tangible medium that can contain or store a program for use by an instruction execution system, device or equipment or used in combination with an instruction execution system, device or equipment. A computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. A computer-readable medium can include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or equipment, or any suitable combination of the foregoing. A more specific example of a computer-readable storage medium can include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0122] It should be noted that the present disclosure also provides a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to enable a computer to execute method 100 and achieve the corresponding technical effects achieved by executing the method in the embodiment of the present disclosure. For the sake of brevity, they will not be repeated here.

[0123] In addition, the present disclosure also provides a computer program product, which includes a computer program. When the computer program is executed by a processor, the method 100 is implemented.

[0124] To provide interaction with a user, the embodiments described above may 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 can provide input to the computer. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0125] The embodiments described above can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with embodiments of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), and the Internet.

[0126] A computer system may include a client and a server. The client and server are generally remote from each other and typically interact through a communication network. The client-server relationship arises through computer programs running on the respective computers and having a client-server relationship with each other. The server may be a cloud server, a server in a distributed system, or a server integrated with a blockchain.

[0127] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in this disclosure can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved. This is not limited herein.

[0128] The above specific embodiments do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure shall be included within the scope of protection of this disclosure.

Claims

1. A wafer washing effect detection method, characterized in that: The method comprises: Obtaining resistivity data of pure water in a target water tank when the wafer is located in the target water tank, wherein the target water tank is the last water tank before the drying tank; generating a pure water resistivity change curve according to the resistivity data of the pure water in the target water tank; Performing a rinse effect test on the wafer according to the pure water resistivity change curve; The step of obtaining resistivity data of pure water in the target water tank when the wafer is located in the target water tank includes: The resistivity measuring device deployed in the target water tank measures the resistivity of the pure water in the target water tank in real time to obtain the resistivity data of the pure water at each moment; Extracting the resistivity data of pure water when the wafer is located in the target water tank from the resistivity data of pure water at each moment; The deployment position of the resistivity measuring device in the target water tank is an optimal deployment position, and the optimal deployment position is determined by the following steps: acquiring resistivity data of pure water measured by a plurality of resistivity measuring devices deployed in the target water tank within a target time period; The multiple resistivity measurement devices are deployed at different locations, and the target time period is the time period from when the unrinsed wafer enters the target water tank to when it leaves the target water tank; Calculate the resistivity range corresponding to each resistivity measuring device based on the resistivity data of pure water measured by each resistivity measuring device within the target time period; Clustering each resistivity measuring device according to the resistivity range corresponding to each resistivity measuring device to obtain multiple device sets; According to the resistivity range corresponding to each resistivity measuring device in each device set, the average resistivity range corresponding to each device set is calculated; Select the device set with the largest average resistivity range from multiple device sets as the target device set; Building a spatial model according to the deployment position of each resistivity measurement device in the target device set; The position of the center point of the spatial model is determined as the optimal deployment position of the resistivity measurement equipment.

2. The method according to claim 1, characterized in that The step of detecting the rinsing effect of the wafer according to the pure water resistivity change curve includes: If it is detected that the pure water resistivity change curve is higher than the pure water resistivity minimum control line, it is determined that the rinsing effect of the wafer is qualified; If it is detected that the pure water resistivity change curve is lower than the pure water resistivity minimum control line, it is determined that the rinsing effect of the wafer is unqualified.

3. The method according to claim 1, characterized in that The step of detecting the rinsing effect of the wafer according to the pure water resistivity change curve includes: Comparing the pure water resistivity change curve with a standard pure water resistivity change curve; If the comparison passes, it is determined that the rinsing effect of the wafer is qualified; If the comparison fails, it is determined that the rinsing effect of the wafer is unqualified.

4. The method according to claim 1, wherein The step of detecting the rinsing effect of the wafer according to the pure water resistivity change curve includes: The pure water resistivity change curve is input into a pre-trained flushing effect detection model to obtain the flushing effect; the flushing effect detection model is obtained by training a preset neural network using a second training data set, wherein the samples in the second training data set use the pure water resistivity change curve as the sample feature, and the flushing effect corresponding to the pure water resistivity change curve is the sample label.

5. A wafer washing effect detection device, characterized in that: The device comprises: an acquisition module, configured to acquire resistivity data of pure water in a target water tank when the wafer is located in the target water tank, wherein the target water tank is the last water tank before the drying tank; A generating module, configured to generate a pure water resistivity change curve based on the resistivity data of the pure water in the target water tank; A detection module, configured to detect the rinsing effect of the wafer according to the pure water resistivity change curve; The acquisition module is specifically used for: The resistivity measuring device deployed in the target water tank measures the resistivity of the pure water in the target water tank in real time to obtain the resistivity data of the pure water at each moment; Extracting the resistivity data of pure water when the wafer is located in the target water tank from the resistivity data of pure water at each moment; The deployment position of the resistivity measuring device in the target water tank is an optimal deployment position, and the optimal deployment position is determined by the following steps: acquiring resistivity data of pure water measured by a plurality of resistivity measuring devices deployed in the target water tank within a target time period; The multiple resistivity measurement devices are deployed at different locations, and the target time period is the time period from when the unrinsed wafer enters the target water tank to when it leaves the target water tank; Calculate the resistivity range corresponding to each resistivity measuring device based on the resistivity data of pure water measured by each resistivity measuring device within the target time period; Clustering each resistivity measuring device according to the resistivity range corresponding to each resistivity measuring device to obtain multiple device sets; According to the resistivity range corresponding to each resistivity measuring device in each device set, the average resistivity range corresponding to each device set is calculated; Select the device set with the largest average resistivity range from multiple device sets as the target device set; Building a spatial model according to the deployment position of each resistivity measurement device in the target device set; The position of the center point of the spatial model is determined as the optimal deployment position of the resistivity measurement equipment.

6. An electronic device, characterized in that: The electronic device comprises: 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, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 4.

7. A non-transitory computer-readable storage medium storing computer instructions, characterized in that: The computer instructions are used to enable a computer to execute the method according to any one of claims 1 to 4.

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