Wafer thickness detection method, device, equipment, medium and product

Abstract

This application provides a method, apparatus, device, medium, and product for wafer thickness detection. By collecting multiple thickness data of the wafer and environmental parameters of the detection environment, and performing filtering processing on the multiple thickness data at least once to obtain the filtered mean value, the filter mean value is then corrected based on the environmental parameters by calling a dynamic error correction model to obtain the corrected thickness value of the wafer. This improves the accuracy of the detected wafer thickness and ensures the stability and effectiveness of subsequent processing based on the wafer thickness value.

Description

Technical Field

[0001] This application relates to the field of semiconductor technology, and in particular to a method, apparatus, device, medium and product for measuring the thickness of a wafer. Background Technology

[0002] In the wafer manufacturing process, the thinning process is a key step that uses grinding, polishing and other methods to process the wafer thickness to the target value. Its precision directly affects the mechanical strength, electrical properties and yield of the wafer and subsequent packaging processes.

[0003] In existing technologies, when detecting the thickness of wafers, filtering algorithms that remove outliers are typically used. However, these algorithms cannot effectively separate random errors from systematic errors, and the measurement results deviate due to environmental fluctuations. Ultimately, this results in low accuracy of the detected wafer thickness, affecting subsequent processing based on the wafer thickness and making it difficult to meet the wafer processing requirements of high-precision equipment. Summary of the Invention

[0004] This application provides a wafer thickness detection method, apparatus, equipment, medium, and product. By using a multi-layer filtering architecture and a dynamic error correction model, it solves the shortcomings of existing technologies in terms of environmental interference compensation, error separation, and measurement stability, and provides a high-precision and robust online detection solution for wafer thinning processes.

[0005] The first aspect of this application provides a method for detecting the thickness of a wafer, comprising: collecting multiple thickness data of the wafer and environmental parameters of the detection environment; performing filtering processing on the multiple thickness data at least once to obtain a filtered mean value of the multiple thickness data; and, based on the environmental parameters, calling a dynamic error correction model to correct the filtered mean value to obtain a corrected thickness value of the wafer.

[0006] A second aspect of this application provides a wafer thickness detection device, comprising: a data acquisition module for acquiring multiple thickness data of the wafer and environmental parameters of the detection environment; a filtering module for performing at least one filtering process on the multiple thickness data to obtain a filtered mean value of the multiple thickness data; and a correction module for correcting the filtered mean value of the wafer based on the environmental parameters by calling a dynamic error correction model to obtain a corrected thickness value of the wafer.

[0007] A third aspect of this application provides an electronic device, comprising: a processor, and a memory communicatively connected to the processor; the memory storing computer-executable instructions; and the processor executing the computer-executable instructions stored in the memory to implement the method described in the first aspect of this application.

[0008] A fourth aspect of this application provides a computer-readable storage medium storing computer-executable instructions that, when executed by a processor, are used to implement the method described in the first aspect of this application.

[0009] The fifth aspect of this application provides a computer program product, including a computer program that, when executed by a processor, implements the method described in the first aspect of this application.

[0010] In summary, the wafer thickness detection method, apparatus, equipment, medium, and product provided in this application collect multiple thickness data points of the wafer and environmental parameters of the detection environment. After filtering the multiple thickness data points at least once to obtain a filtered mean, a dynamic error correction model is then invoked based on the environmental parameters to correct the filtered mean, resulting in a corrected wafer thickness value. Through the synergistic effect of the filtering architecture and the dynamic error correction model, staged suppression and compensation of measurement errors are achieved, enabling high-precision and high-stability measurement of wafer thickness under different operating conditions. Compared with existing wafer thickness detection methods, filtering reduces random and systematic errors in the multiple thickness data points measured by the thickness sensor, and the correction by the dynamic error correction model reduces deviations in measurement results caused by environmental fluctuations. This improves the accuracy of the detected wafer thickness, reducing the detection precision to the micrometer level and ensuring the stability and effectiveness of subsequent processing based on the wafer thickness value. Attached Figure Description

[0011] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0012] Figure 1 This is a schematic diagram illustrating the application scenario of this application;

[0013] Figure 2 A schematic diagram of an embodiment of the wafer thickness detection device provided in this application;

[0014] Figure 3 A schematic flowchart of an embodiment of the wafer thickness detection method provided in this application;

[0015] Figure 4 A schematic diagram illustrating the processing of thickness data and environmental parameters provided in this application;

[0016] Figure 5A schematic diagram of the thickness values ​​detected by the wafer thickness detection method provided in this application;

[0017] Figure 6 A schematic diagram of an embodiment of the wafer thickness detection device provided in this application;

[0018] Figure 7 A schematic diagram of the structure of an embodiment of the electronic device provided in this application. Detailed Implementation

[0019] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0020] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a particular order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented, for example, in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0021] This invention relates to high-precision thickness measurement in semiconductor wafer manufacturing, and is particularly applicable to the online thickness measurement stage in wafer thinning processes. In wafer manufacturing, the thinning process is a crucial step that processes the wafer thickness to a target value through methods such as grinding and polishing. Its accuracy directly affects the wafer's mechanical strength, electrical properties, and the yield of subsequent packaging processes.

[0022] For example, Figure 1 This is a schematic diagram illustrating the application scenario of this application, such as... Figure 1 As shown, to measure the thickness of the wafer, different detection areas, denoted as the inner and outer sides, are divided on the wafer. Multiple measurement points are set within these areas to acquire thickness-related data. The data collected by the detection points in the left annular area is processed and analyzed to ultimately determine the actual thickness value of the wafer.

[0023] During wafer thinning, existing technologies cannot achieve the required processing precision, resulting in significant errors in the final wafer thickness after thinning. Current wafer manufacturing equipment typically employs filtering algorithms to remove outliers for thickness measurement, leading to large measurement errors that fail to meet the processing requirements of high-precision equipment.

[0024] It can be seen that the existing wafer thickness detection methods can only eliminate instantaneous noise and cannot effectively separate random errors from systematic errors, resulting in unstable measurement baselines. Furthermore, they do not consider the systematic influence of environmental parameters such as temperature, humidity, noise, and wind speed on sensor measurements, causing measurement results to deviate with environmental fluctuations. Ultimately, this leads to low accuracy in the detected wafer thickness, affecting subsequent processing based on wafer thickness.

[0025] Based on the shortcomings of the existing technology, this application solves the deficiencies of the existing technology in terms of environmental interference compensation, error separation and measurement stability by adopting a multi-layer filtering architecture and dynamic error correction model, and provides a high-precision and robust online detection solution for wafer thinning process.

[0026] The technical solutions of this application will be described in detail below with specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments.

[0027] Figure 2 A schematic diagram of an embodiment of the wafer thickness detection device provided in this application is shown below. Figure 2 The wafer thickness measurement device 10 shown can be applied to wafer thinning or polishing machines to achieve, for example... Figure 1 The scenario shown involves measuring the thickness of a wafer to determine the thickness of the wafer to be processed.

[0028] Specifically, such as Figure 2 The wafer thickness detection device 10 shown includes a thickness sensor 101, an environmental sensor 102, and a control unit.

[0029] The thickness sensor 101 is used to collect thickness data of the wafer 20. The thickness sensor 101 can be a high-precision non-contact sensor, such as a laser interferometer or a capacitive sensor.

[0030] The environmental sensor 102 is used to detect environmental parameters of the wafer thickness detection device 10. In one embodiment, the environmental parameter can be at least one of temperature, humidity, noise, or wind speed. Therefore, the environmental sensor 102 can include at least one of a temperature sensor, humidity sensor, noise sensor, or wind speed sensor.

[0031] The control unit 100 can be used to acquire the wafer thickness data collected by the thickness sensor 101 and the environmental parameters detected by the environmental sensor 102, determine the thickness value of the wafer 20, and use the control unit 100 or other units or devices for subsequent calculations.

[0032] In one embodiment, the control unit 100 may be an industrial computer or other device with relevant data processing capabilities.

[0033] In one embodiment, such as Figure 2 The wafer thickness detection device 10 shown also includes a display device, and the control unit 100 can be used to send the calculated thickness value to the display device for display.

[0034] Figure 3 This is a schematic flowchart of an embodiment of the wafer thickness detection method provided in this application, as shown below. Figure 3 The wafer thickness measurement method shown can be applied to, for example... Figure 2 The wafer thickness detection device shown is specifically executed by the control unit 100. Specifically, as... Figure 3 The wafer thickness measurement method shown includes:

[0035] S101: Collects multiple thickness data of the wafer, as well as environmental parameters of the detection environment.

[0036] Specifically, the control unit 100 can be used to acquire multiple thickness data of the same wafer collected by the thickness sensor 101, forming a thickness data sequence of the wafer. Taking n thickness data as an example, the multiple thickness data can be denoted as... .

[0037] In one embodiment, the thickness sensor 101 can be used to collect multiple thickness data of the wafer at a preset time interval or frequency and send them to the control unit 100; or, the control unit 100 can also send a collection command to the thickness sensor 101, so that the thickness sensor 101 collects multiple thickness data of the wafer according to the received collection command and sends them to the control unit 100.

[0038] Specifically, the control unit 100 can be used to acquire environmental parameters detected by the environmental sensor 102, denoted as synchronously acquired temperature (T), humidity (H), noise (N), and wind speed (W).

[0039] In one embodiment, the environmental sensor 102 can be used to acquire environmental parameters at preset time intervals or frequencies and send them to the control unit 100; or, the control unit 100 can also send an acquisition command to the environmental sensor 102, so that the environmental sensor 102 can acquire environmental parameters according to the received acquisition command and send them to the control unit 100.

[0040] In one embodiment, after acquiring the aforementioned multiple thickness data and environmental parameters, the control unit 100 can also store the data in a cache area, awaiting processing in subsequent steps.

[0041] S102: Perform at least one filtering process on the multiple thickness data obtained in S101 to obtain the filtered mean of the multiple thickness data.

[0042] In one embodiment, Figure 4 This is a schematic diagram of the processing of thickness data and environmental parameters provided in this application. Specifically, the control unit 100 performs a first-layer filtering process on the multiple thickness data obtained in S101 to generate an intermediate value of the multiple thickness data. Then, it performs a second-layer filtering process on the intermediate value of the multiple thickness data to obtain the filtered mean value of the multiple thickness data.

[0043] In one embodiment, the first layer of filtering includes median filtering. Median filtering, by sorting and taking the median value, effectively eliminates noise corresponding to pulse-like coarse errors caused by transient electromagnetic interference, particulate contamination, or minute vibrations, thereby reducing random errors in multiple thickness data.

[0044] In one embodiment, the second layer of filtering includes an arithmetic mean. The arithmetic mean calculates the average of the medians over multiple periods to further smooth out random errors caused by slight fluctuations or periodic disturbances in the sensor itself, thereby obtaining a more stable preliminary thickness estimate, denoted as the filtered mean, to improve the stability of the baseline value.

[0045] For example, the control unit 100 specifically processes the multiple thickness data acquired in S101. Median filtering is performed to remove impulse noise from multiple thickness data points, generating the median value of the thickness data, denoted as . .

[0046] Subsequently, the control unit 100 sets the intermediate values ​​of multiple thickness data. Arithmetic averaging smoothing is performed to eliminate sensor fluctuations, for example, by continuously acquiring the median value over K calculation cycles. , recorded as The arithmetic mean of the data is calculated to obtain the filtered mean of multiple thickness data, denoted as . .

[0047] S103: Based on the environmental parameters obtained in S101, the dynamic error correction model is called to correct the filtered mean value calculated in S102, and the corrected thickness value of the wafer is obtained.

[0048] Specifically, the dynamic error correction model establishes a mapping relationship between temperature, humidity, noise, wind speed and system error through experimental data, calculates the error components under the current environment in real time, and compensates for them from the baseline value, ultimately reducing the error of the overall measured wafer thickness value.

[0049] In one embodiment, the dynamic error correction model is generated based on the mapping relationship between environmental parameters and measurement errors. For example, in the specific implementation process, an error function Δ=f(T, H, N, W) can be established based on experimental data, and the function form can be determined through regression analysis or machine learning model, and then the function can be encapsulated into a dynamic error correction model.

[0050] The machine learning model can be a neural network, random forest, etc., and the regression analysis can be a multiple regression model. This application does not limit the specific model.

[0051] Record the current environmental parameters as In S103, the control unit 100 specifically processes the environmental parameters. Substituting these values ​​into the dynamic error model Δ=f(T, H, N, W), we obtain the current measurement error corresponding to the environmental parameters. .

[0052] Combination Figure 4 As shown, the control unit 100 calculates the current measurement error. Filtered mean of multiple thickness data The correction process is performed to obtain the corrected thickness value C of the wafer.

[0053] The specific correction method can be based on the current measurement error. Filtered mean of multiple thickness data The relative relationships between them can be determined, for example, the control unit 100 can be determined by the formula C= - The correction process is performed to obtain the corrected thickness value C of the wafer, or the control unit 100 can use the formula C= + The correction process is performed to obtain the corrected thickness value C of the wafer.

[0054] Figure 5 A schematic diagram of the thickness values ​​detected by the wafer thickness detection method provided in this application, as shown below. Figure 5 As shown, the black dots represent the thickness value before correction, and the orange curve represents the corrected thickness value C. It can be seen that, compared with the disordered changes of the black dots, the corrected thickness value C can more stably reflect the thickness of the wafer.

[0055] In one embodiment, after the control unit 100 calculates the corrected thickness value C, it can output the corrected thickness value C to the host computer software interface for visualization display, and / or, the control unit 100 can also transmit the corrected thickness value C to the Manufacturing Execution System (MES) for quality judgment, data traceability or feedback control to complete a complete inspection cycle.

[0056] In summary, the wafer thickness detection method provided in this application collects multiple thickness data of the wafer and environmental parameters of the detection environment. After filtering the multiple thickness data at least once to obtain the filtered mean, the dynamic error correction model is called based on the environmental parameters to correct the filtered mean, thereby obtaining the corrected thickness value of the wafer. Thus, through the synergistic effect of the filtering architecture and the dynamic error correction model, the measurement error is suppressed and compensated in stages, enabling high-precision and high-stability measurement of wafer thickness under different operating conditions.

[0057] Compared with existing wafer thickness detection methods, this method reduces random and systematic errors in multiple thickness data obtained by thickness sensors through filtering, and reduces deviations caused by environmental fluctuations in measurement results through dynamic error correction model correction, thereby improving the accuracy of the detected wafer thickness. The detection precision of wafer thickness values ​​is improved to the micrometer level, ensuring the stability and effectiveness of subsequent processing based on wafer thickness values.

[0058] More specifically, this application also provides a specific implementation of a dynamic error correction model, wherein the dynamic error correction model can be composed of the following sub-functions: Δ = Δ_t(T) + Δ_h(H) + Δ_n(N) + Δ_w(W). Wherein, Δ_t(T), Δ_h(H), Δ_n(N), and Δ_w(W) are error component functions caused by temperature, humidity, noise, and wind speed, respectively.

[0059] The temperature error component function Δ_t(T) can be used to compensate for thermal drift using a linear model. Its functional form can be Δ_t(T) = α*(T- Where α is the temperature coefficient (μm / °C), which can be determined through calibration experiments, representing the system measurement deviation caused by each degree of temperature deviation from the reference value. T is the real-time acquired temperature value (°C). The reference temperature (°C) is typically the temperature at which the system is calibrated under standard laboratory conditions (e.g., 22°C). This linear function reflects the measurement system error caused by the thermal expansion and contraction or thermal drift characteristics of the sensor and its electronic components, and its magnitude is proportional to the deviation of the current temperature from the reference temperature.

[0060] The humidity error component function Δ_h(H) can be used to model nonlinear effects using a quadratic function. Its functional form can be Δ_h(H) = β*(H- )+γ*(H- )². Where β and γ are humidity coefficients (μm / %RH, μm / %RH²), which can be determined through multiple regression analysis of experimental data. H is the real-time collected humidity value (%RH). The reference humidity is %RH. This quadratic function is used to model the complex nonlinear effects of humidity on the refractive index of the optical sensor path, as well as effects such as condensation that may occur in high-humidity environments. The linear term captures the main trend, while the quadratic term captures higher-order nonlinear responses.

[0061] The noise error component function Δ_n(N) can be used to simulate periodic interference using harmonic functions. Its functional form is Δ_n(N) = k*sin(2π*ω*N), where k is the noise influence coefficient (μm), representing the maximum error amplitude caused by vibration noise. ω is the frequency coefficient (1 / dB), used to map the noise intensity to vibration at a specific frequency. N is the real-time acquired noise value (dB). This harmonic function simulates the periodic interference error caused by environmental noise (especially mechanical vibration at a specific frequency) transmitted to the sensor platform, affecting high-precision measurements; its amplitude is related to the noise intensity.

[0062] The wind speed error component function Δ_w(W) can be used to describe airflow disturbances using an exponentially decaying function, with the functional form Δ_w(W) = λ*(1-e^(-W / W_s)). Here, λ is the maximum wind speed error coefficient (μm), representing the maximum error that may be caused by extremely high wind speeds. W_s is the wind speed scaling factor (m / s), determining the rate of increase of the error with increasing wind speed. W is the real-time acquired wind speed value (m / s). This exponentially decaying function simulates the disturbance error caused by airflow to sensor readings, whose influence increases rapidly with increasing wind speed and gradually approaches saturation. This effectively describes the impact of airflow-induced cooling effects or physical jitter on system stability.

[0063] Based on the above functional forms, a specific model implementation can be obtained by combining the above sub-functions. A specific and implementable comprehensive error model implementation is as follows: As can be seen, this embodiment uses component function modeling to quantify the impact of environmental interference on measurement results into a computable mathematical model and achieve real-time compensation. Specifically, through the combination of the above component functions, the dynamic correction model can calculate the total error under the current environment in real time and subtract it from the baseline value, ultimately reducing environmentally related measurement errors and significantly improving the environmental adaptability of the detection results.

[0064] In one embodiment, when the control unit 100 collects multiple thickness data of the wafer in S101, it can also obtain multiple sensor data of the wafer collected by multiple sensors through multi-sensor fusion technology, and obtain one thickness data by weighted fusion of the multiple sensor data.

[0065] This application does not limit the specific implementation of multiple sensors, such as laser sensors, capacitive sensors, ultrasonic sensors, etc. Specifically, the control unit 100 can obtain thickness data by weighted fusion of multiple sensor data collected by multiple sensors through a weighted fusion algorithm. Through sensor redundancy design, the failure risk of a single sensor is eliminated, thereby significantly improving measurement reliability and adapting to a wider range of material and environmental requirements.

[0066] In one embodiment, the control unit 100 can also update the dynamic error correction model after executing the wafer thickness detection method. For example, the control unit 100 can obtain the correction result of the filtered mean and update the dynamic error correction model based on environmental parameters and the correction result. Exemplarily, the control unit 100 updates the dynamic error correction model using real-time collected environmental parameters. The error function parameters are dynamically updated based on the corrected thickness value (C). For example, when the sensor's sensitivity decreases due to aging, the model automatically adjusts the α value to ensure temperature correction accuracy. Through an online learning mechanism, the dynamically corrected model possesses adaptive capabilities, maintaining high correction accuracy over the long term. It is better suited to equipment aging and long-term environmental changes, eliminating the need for manual calibration, extending the model's lifespan, and reducing the rate of correction accuracy decay.

[0067] In the foregoing embodiments of this application, a wafer thickness detection method provided by the embodiments of this application has been described. In order to realize the functions of the methods provided by the embodiments of this application, the wafer thickness detection device, as the execution subject, can implement the above functions through hardware structure and / or software modules. Whether a certain function is executed in the form of hardware structure, software module, or hardware structure plus software module depends on the specific application and design constraints of the technical solution.

[0068] For example, a wafer thickness measurement device can be used as... Figure 2 The structure shown. For example, Figure 6 A schematic diagram of an embodiment of the wafer thickness detection device provided in this application is shown below. Figure 6The wafer thickness detection device 1000 shown includes: an acquisition module 1001, a filtering module 1002, and a correction module 1003. The acquisition module 1001 acquires multiple thickness data points of the wafer and environmental parameters of the detection environment. The filtering module 1002 performs at least one filtering process on the multiple thickness data points to obtain a filtered mean value. The correction module 1003, based on the environmental parameters, calls a dynamic error correction model to correct the filtered mean value, obtaining a corrected thickness value for the wafer.

[0069] like Figure 6 The specific implementation and principle of the wafer thickness detection device 1000 shown can be referred to the aforementioned wafer thickness detection method, which has the same implementation and principle, and will not be repeated here.

[0070] It should be understood that the division of the various modules in the above device is merely a logical functional division. In actual implementation, they can be fully or partially integrated into a single physical entity, or they can be physically separated. Furthermore, these modules can be implemented entirely in software via processing element calls; they can be fully implemented in hardware; or some modules can be implemented by processing element calls to software, while others are implemented in hardware. For example, a module can be a separately established processing element, or it can be integrated into a chip within the above device. Alternatively, it can be stored as program code in the memory of the above device, and its functions can be called and executed by a processing element of the device. The implementation of other modules is similar. Moreover, these modules can be fully or partially integrated together, or they can be implemented independently. The processing element here can be an integrated circuit with signal processing capabilities. In the implementation process, each step of the above method or each of the above modules can be completed through integrated logic circuits in the hardware of the processor element or through software instructions.

[0071] For example, these modules can be one or more integrated circuits configured to implement the above methods, such as one or more application-specific integrated circuits (ASICs), one or more digital signal processors (DSPs), or one or more field-programmable gate arrays (FPGAs). As another example, when a module is implemented using processing element scheduler code, the processing element can be a general-purpose processor, such as a central processing unit (CPU) or other processor capable of calling program code. Furthermore, these modules can be integrated together to implement a system-on-a-chip (SOC).

[0072] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product. A computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the flow or function according to the embodiments of this application is generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid-state disk (SSD)).

[0073] For example, Figure 7 A schematic diagram of the structure of an embodiment of the electronic device provided in this application is shown below. Figure 7 The electronic device 2000 shown can be used to perform the wafer thickness detection method provided in any embodiment of this application.

[0074] In one embodiment, such as Figure 7The control device 2000 shown includes one or more processors 2001 and a memory 2002. The memory 2002 stores computer-executable instructions, and the processor 2001 can execute the computer-executable instructions stored in the memory 2002. When the computer-executable instructions are executed by the processor 2001, the processor 2001 implements the wafer thickness detection method provided in any of the foregoing embodiments of this application.

[0075] In one embodiment, such as Figure 7 The control device 2000 shown also includes a communication interface 2003, through which the processor 2001 can communicate with other devices, such as sending and receiving data through the communication interface 2003.

[0076] In the above embodiments, it should be understood that the processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor.

[0077] The memory may include random access memory (RAM) and may also include non-volatile memory (NVM), such as at least one disk storage device.

[0078] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of illustration, the buses shown in the accompanying drawings are not limited to a single bus or a single type of bus.

[0079] This application also provides a chip for executing instructions, which is used to execute the wafer thickness detection method provided in any of the foregoing embodiments of this application.

[0080] This application also provides a computer program product, including a computer program that, when executed, implements the wafer thickness detection method provided in any of the foregoing embodiments of this application.

[0081] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed, can be used to implement the wafer thickness detection method provided in any of the foregoing embodiments of this application.

[0082] The aforementioned readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The readable storage medium can be any available medium accessible to a general-purpose or special-purpose computer.

[0083] An exemplary readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can reside in an Application Specific Integrated Circuit (ASIC). Alternatively, the processor and the readable storage medium can exist as discrete components in the device.

[0084] The division of units is merely a logical functional division; in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.

[0085] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0086] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0087] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0088] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0089] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

Claims

1. A method for measuring the thickness of a wafer, characterized in that, include: Collect multiple thickness data of the wafer, as well as environmental parameters of the detection environment; The multiple thickness data are filtered at least once to obtain the filtered mean of the multiple thickness data; Based on the environmental parameters, a dynamic error correction model is invoked to correct the filtered mean value, thereby obtaining the corrected thickness value of the wafer.

2. The method according to claim 1, characterized in that, The step of performing at least one filtering process on the plurality of thickness data to obtain the filtered mean of the plurality of thickness data includes: The multiple thickness data are subjected to a first-level filtering process to generate an intermediate value of the multiple thickness data. A second layer of filtering is performed on the median value of the multiple thickness data to obtain the filtered mean value of the multiple thickness data.

3. The method according to claim 2, characterized in that, The first layer of filtering includes median filtering. The second layer of filtering includes arithmetic averaging.

4. The method according to any one of claims 1-3, characterized in that, The step of invoking a dynamic error correction model based on the environmental parameters to correct the filtered mean and obtain the corrected thickness value of the wafer includes: The environmental parameters are substituted into the dynamic error correction model to determine the current measurement error corresponding to the environmental parameters; wherein the dynamic error correction model is generated based on the mapping relationship between the environmental parameters and the measurement error; The filtered mean is corrected based on the current measurement error to obtain the corrected thickness value of the wafer.

5. The method according to claim 4, characterized in that, The environmental parameters include at least one of temperature, humidity, noise, or wind speed.

6. The method according to claim 1, characterized in that, The acquisition of multiple thickness data of the wafer includes: Acquire multiple sensor data of the wafer collected by multiple sensors; The thickness data is obtained by weighted fusion of the data from the multiple sensors.

7. The method according to claim 1, characterized in that, Also includes: Obtain the correction result of the filtered mean; The dynamic error correction model is updated based on the environmental parameters and the correction results.

8. A wafer thickness detection device, characterized in that, include: The acquisition module is used to acquire multiple thickness data of the wafer, as well as environmental parameters of the detection environment; A filtering module is used to perform filtering processing on the multiple thickness data at least once to obtain the filtered mean value of the multiple thickness data; The correction module is used to call the dynamic error correction model based on the environmental parameters to correct the filtered mean value and obtain the corrected thickness value of the wafer.

9. An electronic device, characterized in that, include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory to implement the method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the method as described in any one of claims 1-7.

11. A computer program product, characterized in that, Includes a computer program that, when executed by a processor, implements the method as described in any one of claims 1-7.

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