Semiconductor silicon wafer surface warping appearance judgment method and system

Abstract

The embodiment of the invention relates to the technical field of semiconductor integrated circuit manufacturing, and discloses a semiconductor silicon wafer surface warping appearance judgment method and system. Comprising the following steps: placing a knife edge ruler on a processed and sliced silicon wafer, irradiating the front end of a contact surface of the knife edge ruler and the silicon wafer with a red light source with specific intensity, and acquiring a red light intensity picture; gray processing is carried out on the red light intensity picture, gray difference edges are obtained through extraction, and gray blocks in different shapes are formed; performing contrast difference judgment on adjacent gray blocks based on a local contrast measurement method, and calculating to obtain a contrast difference value; and judging the warping degree of the silicon wafer based on the calculated contrast difference value to obtain a detection result of the warping degree of the silicon wafer. The technical problem that the warping of the silicon wafer cannot be accurately judged by the existing silicon wafer manufacturing method can be at least solved.

Description

Technical Field

[0001] The present disclosure relates to the field of semiconductor integrated circuit manufacturing technology, and particularly to a method and system for determining the warped shape of the surface of a semiconductor wafer. Background Art

[0002] Wafers are warped due to many non-lithography process factors, such as the effects of processes like spin coating, baking, exposure, etching, etc. After warping, the shapes of the wafers are various, typically bowl-shaped, umbrella-shaped, saddle-shaped, etc. The warping degrees of different wafers are also different, and the warping degree is generally in the range of dozens to hundreds of micrometers. When the wafer is warped, the actual position of the alignment marks on the wafer deviates from the theoretical position, making it difficult to accurately align the wafer and the mask. Therefore, the alignment accuracy is affected by the warping degree of the wafer. A large alignment deviation makes it difficult to achieve the overlay accuracy, thus affecting the lithography effect and the yield of the device. Therefore, improving wafer warping is of great significance for fine alignment.

[0003] Therefore, there is an urgent need for a design solution that can effectively judge the warped shape of a semiconductor wafer. Summary of the Invention

[0004] An object of the present application is to provide a method and system for determining the warped shape of the surface of a semiconductor wafer. By combining a straight edge with the wafer and using high-intensity red light for warped shape judgment, it solves at least the technical problem that the existing wafer manufacturing methods cannot accurately judge the warping of the wafer.

[0005] To achieve the above object, some embodiments of the present application provide the following aspects:

[0006] In a first aspect, some embodiments of the present application provide a method for determining the warped shape of the surface of a semiconductor wafer, including the following steps:

[0007] Place a straight edge on the processed sliced wafer, and irradiate the front end of the contact surface between the straight edge and the wafer with a red light source of a specific intensity, and collect a red light intensity image;

[0008] Perform gray-scale processing on the red light intensity image, and extract the gray-scale difference edges to form gray-scale blocks of different shapes;

[0009] Based on the local contrast measurement method, judge the contrast difference between adjacent gray-scale blocks, and calculate the contrast difference value;

[0010] Based on the calculated contrast difference value, judge the warping degree of the wafer to obtain the wafer warping degree detection result.

[0011] In a second aspect, some embodiments of the present application also provide a system for judging the warped shape of the surface of a semiconductor silicon wafer, including: a red light intensity image acquisition module, a gray block construction module, a contrast difference value calculation module, and a silicon wafer warping degree judgment module;

[0012] The red light intensity image acquisition module is used to place a straight edge ruler on the silicon wafer after processing and slicing, and irradiate the front end of the contact surface between the straight edge ruler and the silicon wafer with a red light source of a specific intensity, and acquire a red light intensity image;

[0013] The gray block construction module is used to perform gray processing on the red light intensity image, extract the gray difference edge, and form gray blocks of different shapes;

[0014] The contrast difference value calculation module is used to judge the contrast difference between adjacent gray blocks based on the local contrast measurement method, and calculate the contrast difference value;

[0015] The silicon wafer warping degree judgment module is used to judge the warping degree of the silicon wafer based on the calculated contrast difference value, and obtain the detection result of the silicon wafer warping degree.

[0016] Compared with the related art, the present application has the following beneficial effects:

[0017] In the solution provided by the present application, by combining a straight edge ruler with the silicon wafer and using high-intensity red light for warped shape judgment, it is possible to more accurately capture the minute warping changes on the surface of the silicon wafer. Through gray processing, gray block construction, local contrast measurement, and the introduction of a quantitative evaluation model, the quantitative evaluation of the warping degree of the silicon wafer is realized, improving the accuracy and reliability of the determination. By quickly acquiring the red light intensity image and performing gray processing and contrast calculation, the rapid judgment of the warping degree of the silicon wafer is realized. Compared with the traditional manual measurement or complex laser interferometry, the method of the present application is more efficient and suitable for the rapid detection requirements of large-scale production lines. The quantitative evaluation model adopted obtains the weight coefficient through experimental data fitting, making the detection result have higher repeatability. Through the standardized detection process and parameter settings, the consistency of the detection results between different batches and different devices is ensured. The detection resolution of the present application is as high as 1μm, meeting the strict requirements for the warping degree of the silicon wafer in processes below 28nm. By accurately detecting the warping degree of the silicon wafer, it helps to improve the lithography alignment accuracy and the device yield. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] One or more embodiments are illustrated by way of example in the accompanying drawings, and these illustrative descriptions do not limit the embodiments. Elements with the same reference numerals in the drawings are represented as similar elements, unless otherwise stated, and the drawings in the figures do not constitute a proportional limitation.

[0019] Figure 1 Schematic flowchart of a method for determining the warped shape of the surface of a semiconductor silicon wafer according to the first embodiment of the present application;

[0020] Figure 2 Schematic diagram of the positional relationship between a straightedge and a silicon wafer in a method for determining the warped shape of the surface of a semiconductor silicon wafer according to the first embodiment of the present application;

[0021] Figure 3 Schematic diagram of the position of a red light source in a method for determining the warped shape of the surface of a semiconductor silicon wafer according to the first embodiment of the present application;

[0022] Figure 4 Schematic diagram of the system modules of a system for determining the warped shape of the surface of a semiconductor silicon wafer according to the first embodiment of the present application;

[0023] Figure 5 Schematic diagram of the structure of a system for determining the warped shape of the surface of a semiconductor silicon wafer according to the second embodiment of the present application;

[0024] Figure 6 Exemplary structural diagram of an electronic device according to an embodiment of the present application. Detailed implementation manners

[0025] To make the objectives, technical solutions, and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. Apparently, the described embodiments are some, but not all, of the embodiments of the present application. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present application without creative efforts shall fall within the scope of protection of the present application.

[0026] First embodiment

[0027] The first embodiment of the present application relates to a method for determining the warped shape of the surface of a semiconductor silicon wafer. As Figure 1 shown, the method may include the following steps:

[0028] S101. Place a straightedge on the silicon wafer after processing and slicing, and irradiate the front end of the contact surface between the straightedge and the silicon wafer with a red light source of a specific intensity to collect a red light intensity picture.

[0029] As Figure 2 、 Figure 3 shown, in this embodiment, red visible light with a wavelength of 630 nanometers to 780 nanometers is irradiated on the front end of the contact surface between the straightedge and the silicon wafer through a light-emitting source, and the red visible light with an illuminance > 800 lumens and the strongest penetration passes through the contact surface gap between the two, and then a light intensity picture is collected.

[0030] The knife-edge straightedge contact detection technology of the present invention uses a precision knife-edge straightedge (the radius of curvature of the cutting edge ≤ 0.002 mm) that complies with the ISO 7532 standard to form a nanoscale contact interface with the silicon wafer to be measured. The three-dimensional warping deformation is converted into a two-dimensional contact gap, and the warping characteristics are reflected through the contact state between the cutting edge and the silicon wafer. When there is a bowl-shaped warp, an annular gap band is formed, and when there is a saddle-shaped warp, an X-shaped gap distribution appears, realizing the encoding of the morphological characteristics. Compared with the traditional laser interference method, it reduces the equipment complexity while improving the sensitivity to minute deformations.

[0031] Among them, the red light source with a specific intensity uses a red LED array light source in the 630 - 780 nm band (complies with the CIE 1931 chromaticity standard), and a Fresnel lens is configured to achieve parallel light output. The key technical parameters include: light intensity > 800 lumens (complies with the ANSI FL1 standard); color purity Δλ < 5 nm; incident angle 45 ± 0.5°. The penetration depth of long-wavelength red light in silicon material reaches 300 μm (3 times that of blue light), which can effectively penetrate the surface oxide layer; bright and dark Fraunhofer diffraction fringes are generated in the contact surface gap, and the relationship between the warping degree and the fringe spacing satisfies d = λL / (2s) (d is the gap, s is the fringe spacing). Compared with white light interference, monochromatic light can avoid dispersion interference and improve the image signal-to-noise ratio by more than 15 dB.

[0032] In this application, by placing the knife-edge straightedge on the silicon wafer and irradiating the contact surface with a red light source of a specific intensity, using the penetration power and contrast enhancement characteristics of red light, the clarity and accuracy of image acquisition are improved, and the minute warping changes on the silicon wafer surface can be captured more precisely, providing high-quality image data for subsequent processing.

[0033] S102. Perform gray-scale processing on the red light intensity picture, extract the gray-scale difference edges, and form gray-scale blocks of different shapes.

[0034] In this embodiment, the gray-scale values can show obvious gray-scale differences for different types of red light intensities. By analyzing the gray-scale values, different red light intensity regions can be accurately identified, and thus the gray-scale blocks representing different light intensities can be determined.

[0035] Specifically, in this embodiment, first perform gray-scale processing on the red light intensity picture, calculate the gray-scale values of the image, and obtain the gray-scale image, specifically:

[0036] D(x,y) = Td(x,y) (1)

[0037] Among them, D(x,y) is the gray-scale image, T is the gray-scale conversion operator, and d(x,y) is the original red light intensity image.

[0038] Subsequently, calculate the upper limit, lower limit, and initial threshold of the gray-scale values in the image, specifically:

[0039]

[0040] Among them, S is the initial threshold, Dmax is the upper limit of the gray value, and Dmin is the lower limit of the gray value.

[0041] According to the initial threshold S, the segmentation of the selected partial area of the image is completed to form a segmented part and the remaining part, specifically:

[0042]

[0043] Among them, D f is the gray value of the segmented part, D q is the gray value of the remaining part, D(i,j) is the gray value N i,j is the number of pixel points.

[0044] After calculating the segmented part, the division of the image gray blocks is completed based on the segmented part and the regional part, and the division of the gray blocks of the entire gray image is completed by iteratively updating equations (3) and (4), and several image blocks with different shapes are obtained.

[0045] This application simplifies the image processing process, improves the processing efficiency, and retains the important information in the image by performing gray processing on the collected red light intensity pictures, extracting the edges of gray differences, and forming gray blocks with different shapes, providing a basis for subsequent contrast calculation and warpage judgment.

[0046] S103. Based on the local contrast measurement method, judge the contrast difference between adjacent gray blocks and calculate the contrast difference value.

[0047] After determining different gray blocks, the local contrast measurement method is used to judge the contrast difference between adjacent gray blocks. The specific process is as follows:

[0048] First, based on the gray difference ratio between the segmented part and the remaining part, the difference value is calculated by the local contrast measurement method, specifically:

[0049]

[0050] Among them, R is the contrast difference value between different gray blocks, I′ is the average value of the gray values of the central gray block, Imax is the maximum value of the gray values of 8 sub-pixel blocks around the central pixel of the gray block, and Imin is the minimum value of the gray values of 8 sub-pixel blocks around the central pixel of the gray block.

[0051] Among them, I′ is specifically:

[0052]

[0053] wherein, L is the length of the sub-pixel, is the gray value of the central gray block.

[0054] Subsequently, a method of calculating the local standard deviation by using a sliding window is adopted to calculate the image contrast difference value. Let the window size be k×k, specifically:

[0055]

[0056] wherein, C local (x, y) is the contrast difference value, I(x + i, y + i) is the gray value of the gray block of the gray image, and μ x,y is the average gray value within the window, specifically:

[0057]

[0058] wherein, q is the gray value (0 - 255), Nq is the number of pixels under the gray value, and n is the total number of pixels of the gray image.

[0059] This application can more accurately reflect the warping degree of the silicon wafer surface and improve the accuracy and reliability of warping degree judgment by judging the contrast difference of adjacent gray blocks and calculating the contrast difference value.

[0060] S104. Judge the warping degree of the silicon wafer based on the calculated contrast difference value to obtain the silicon wafer warping degree detection result.

[0061] In this embodiment, the specific judgment process is as follows:

[0062] Set a difference threshold. If the contrast difference value of the gray image is greater than the threshold, it is judged that the contact and fitting degree between the straightedge and the silicon wafer at the corresponding position is sparse; otherwise, it is judged that the contact and fitting degree between the straightedge and the silicon wafer is tight. In this embodiment, the obtained difference threshold is 125.

[0063] Furthermore, in some embodiments, the method further includes:

[0064] To further improve the detection accuracy, after obtaining the contrast difference value, a warping degree quantization model based on multi-dimensional feature fusion is established, that is, a warping degree - contrast mapping function is established:

[0065]

[0066] Among them, Warp represents the warpage of the silicon wafer, which is a quantitative index of the warpage degree of the silicon wafer to be finally evaluated. Its numerical value directly reflects the severity of the silicon wafer warpage; α, β, and γ are weight coefficients. Preferably, α = 0.73, β = 0.18, and γ = 0.09, which are weight coefficients obtained by fitting 2000 groups of experimental data; C max and C min respectively represent the maximum and minimum values of the contrast difference value in the entire analysis image area, reflecting the local maximum deformation amplitude. By participating in the calculation of the warpage through the difference between the two, it can reflect the influence of the range of the overall contrast difference of the image on the warpage. C(x, y) represents the contrast value at the position (x, y) in the image, which is a function that changes with position. ∫∫ Ω C(x, y)dxdy represents the integral of the contrast value of the entire image (unit: gray level·mm 2 ), reflecting the distribution of the overall contrast of the image. represents the L2 norm of the contrast gradient field (unit: gray level / mm), capturing the characteristics of deformation mutations.

[0067] In the step S103, all gray blocks are traversed, and the maximum / minimum contrast difference values are recorded:

[0068] C max = max(C local (x, y)), C min = max(C local (x, y))

[0069] The Gaussian numerical integration method is used to calculate the global integral:

[0070]

[0071] Among them, the weight coefficient w pq takes values according to the 4th-order Gaussian integration table. P represents the number of integration nodes divided in the x direction (horizontal direction), and Q represents the number of integration nodes divided in the y direction (vertical direction). These two parameters jointly define the discretization grid density of the two-dimensional integration region Ω. For example, if P = 4 and Q = 4, the entire region is divided into 4×4 = 16 integration points. The weight w pq of each node is determined by the product of the weight w p in the x direction and the weight w q in the y direction, that is, w pq = w p ·w q .

[0072] The gradient field is calculated by the Sobel operator:

[0073]

[0074] Among them, the spatial derivative is calculated by the central difference method.

[0075] Calibration of model parameters:

[0076] Experimental design: Prepare 200 groups of standard samples (including bowl-shaped / umbrella-shaped / saddle-shaped warping), and use a white light interferometer (Zygo NewView 9000) to measure the true warping degree.

[0077] Data acquisition: Obtain 10 groups of red light image data for each sample, a total of 2000 groups of training data.

[0078] Regression analysis: Use Ridge Regression to fit the weight coefficients, with the regularization parameter λ = 0.15, and obtain α = 0.73 ± 0.02, β = 0.18 ± 0.01, γ = 0.09 ± 0.005 (95% confidence interval).

[0079] Verification index: Coefficient of determination R of the model 2 = 0.963, Mean Absolute Error (MAE) = 3.2 μm

[0080] Warping degree determination:

[0081] Compare the calculated Warp value with the process threshold:

[0082] Warp < 50 μm: Qualified (the contact surface fits tightly).

[0083] 50 μm ≤ Warp ≤ 150 μm: Warning (process adjustment is required).

[0084] Warp > 150 μm: Unqualified (there are significant gaps on the contact surface).

[0085] According to the above embodiments of the present application, by introducing a quantitative evaluation model, a mapping relationship between the warping degree and the contrast is established, realizing the quantitative evaluation of the warping degree, providing a scientific basis for the judgment of the warping degree of silicon wafers, improving the accuracy and objectivity of the judgment, being able to upgrade the traditional qualitative judgment to quantitative detection, realizing the accurate judgment and quantitative evaluation of the warping degree of the semiconductor silicon wafer surface, with a resolution of 1 μm (the original scheme is 25 μm), meeting the process requirements below 28 nm, and providing strong technical support for the semiconductor integrated circuit manufacturing field.

[0086] The step division of the above various methods is only for clear description. When implemented, they can be combined into one step or some steps can be split into multiple steps. As long as the same logical relationship is included, they are all within the protection scope of this patent; adding insignificant modifications to the algorithm or process or introducing insignificant designs, but not changing the core design of its algorithm and process, are all within the protection scope of this patent.

[0087] Second Embodiment

[0088] As Figure 4 shown, the second embodiment of the present application relates to a system for determining the warping profile of the surface of a semiconductor silicon wafer, including: a red light intensity image acquisition module 1, a gray block construction module 2, a contrast difference value calculation module 3, and a silicon wafer warping degree judgment module 4;

[0089] The red light intensity image acquisition module 1 is used to place a knife-edge straightedge on the processed sliced silicon wafer, and irradiate the front end of the contact surface between the knife-edge straightedge and the silicon wafer with a red light source of a specific intensity, and acquire a red light intensity image;

[0090] The gray block construction module 2 is used to perform gray processing on the red light intensity image, extract the gray difference edge, and form gray blocks of different shapes;

[0091] The contrast difference value calculation module 3 is used to judge the contrast difference between adjacent gray blocks based on the local contrast measurement method, and calculate the contrast difference value;

[0092] The silicon wafer warping degree judgment module 4 is used to judge the warping degree of the silicon wafer based on the calculated contrast difference value, and obtain the detection result of the silicon wafer warping degree.

[0093] In this embodiment, the system is set on a detection table. Specifically, the detection table is as Figure 5 shown. The detection table must be made of non-reflective black material on all sides, and provide a strip-shaped red light-emitting light source with a light source intensity > 800 lumens, a red light wavelength of 630 nm - 780 nm. The light illumination requirements provided by the surrounding environment of the detection table are a brightness of < 50 lux, an illuminance uniformity > 0.7, a color rendering index (Ra) > 80, and the illumination should have no stroboscopic or extremely low stroboscopic.

[0094] It is not difficult to find that this embodiment is a system embodiment corresponding to the first embodiment, and this embodiment can be implemented in cooperation with the first embodiment. The relevant technical details mentioned in the first embodiment are still valid in this embodiment. To avoid repetition, they will not be elaborated here. Correspondingly, the relevant technical details mentioned in this embodiment can also be applied to the first embodiment.

[0095] It is worth mentioning that each module involved in this embodiment is a logical module. In practical applications, a logical unit can be a physical unit, a part of a physical unit, or a combination of multiple physical units. In addition, to highlight the innovative part of the present application, units not closely related to solving the technical problems proposed in the present application are not introduced in this embodiment, but this does not mean that there are no other units in this embodiment.

[0096] In addition, some embodiments of the present application further provide an electronic device. The electronic device may be various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and so on. The electronic device may also be various forms of mobile devices, such as personal digital processors, cellular phones, smart phones, wearable devices, and other similar computing devices.

[0097] The electronic device includes: one or more processors; and a memory storing computer program instructions, which when executed cause the processors to perform the steps of the method provided in any one or more of the above embodiments. Figure 6 An exemplary structural diagram of the electronic device is disclosed. As Figure 6 shown, the electronic device includes: one or more processors 1101, a memory 1102, and interfaces for connecting various components, including a high-speed interface and a low-speed interface. Each component is interconnected using different buses and may be mounted on a common motherboard or otherwise installed as needed. The processor can process instructions executed within the electronic device, including instructions stored in the memory or on the memory to display graphical information of a GUI on an external input / output device (such as a display device coupled to the interface). In some other embodiments, if necessary, multiple processors and / or multiple buses may be used together with multiple memories and multiple memories. Similarly, multiple electronic devices may be connected, and each device provides some necessary operations (for example, as a server array, a set of blade servers, or a multi-processor system). Among them, the components shown herein, their connections and relationships, and their functions are only examples and are not intended to limit the implementation of the present application described and / or claimed herein.

[0098] The electronic device may further include: an input device 1103 and an output device 1104. The processor 1101, the memory 1102, the input device 1103, and the output device 1104 may be connected by a bus or other means, Figure 6 taking connection by bus as an example.

[0099] The input device 1103 can receive input digital or character information and generate key signal inputs related to the user settings and function controls of the electronic device, such as input devices like touchscreens, keypads, mice, trackpads, touchpads, pointing sticks, one or more mouse buttons, trackballs, joysticks, etc. The output device 1104 can include display devices, auxiliary lighting devices (e.g., LEDs), and haptic feedback devices (e.g., vibration motors), etc. The display device can include, but is not limited to, liquid crystal displays (LCDs), light-emitting diode (LED) displays, and plasma displays. In some embodiments, the display device can be a touchscreen.

[0100] To provide interaction with the user, the electronic device can be a computer. The computer has: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and a pointing device (e.g., a mouse or a trackball), through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or haptic feedback); and the input from the user can be received in any form (including acoustic input, voice input, or haptic input).

[0101] In the embodiments of the present application, a computer program / instructions is stored on a computer-readable medium. When the computer program / instructions are executed by a processor, the steps of the method provided in any one or more of the above embodiments are implemented. The computer-readable medium can be included in the electronic device described in the above embodiments; or it can exist separately without being assembled into the device. The above computer-readable medium carries one or more computer-readable instructions.

[0102] The memory 1102 can be used as a non-transitory computer-readable storage medium, which can be used to store non-transitory software programs, non-transitory computer-executable programs, and modules. By running the non-transitory software programs, instructions, and modules stored in the memory 1102, the processor 1101 executes various functional applications and data processing of the server to implement the program instructions / modules corresponding to the method provided in any one or more of the above embodiments of the present application.

[0103] The memory 1102 may include a program storage area and a data storage area. The program storage area may store an operating system and application programs required for at least one function. The data storage area may store data created according to the use of the electronic device and the like. In addition, the memory 1102 may include a high-speed random access memory and may also include a non-transitory memory, such as at least one magnetic disk storage device, a flash memory device, or other non-transitory solid-state storage devices. In some embodiments, the memory 1102 may optionally include a memory remotely provided with respect to the processor 1101, and these remote memories may be connected to the electronic device through a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0104] It should be noted that the computer-readable medium described in this application may be a computer-readable signal medium, a computer-readable storage medium, or any combination of the two. The computer-readable medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples of the computer-readable storage medium may include, but are not limited to, an electrical connection having one or more wires, 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 above. In this application, the computer-readable medium may be any tangible medium that contains or stores a program, and the program may be used by or in combination with an instruction execution system, apparatus, or device.

[0105] The computer-readable medium includes permanent and non-permanent, removable and non-removable media, and information storage can be implemented by any method or technology. The information may be computer-readable instructions, data structures, program modules, or other data. Examples of the computer's storage medium 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 technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD), or other optical storage, magnetic cassette tapes, magnetic tape disk storage, or other magnetic storage devices, or any other non-transmission medium that can be used to store information accessible by a computing device.

[0106] Computer program code for performing the operations of this application can be written in one or more programming languages or combinations thereof. The programming languages include object-oriented programming languages such as Java, Smalltalk, C++, and also include conventional procedural programming languages such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, executed as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computer (for example, by using an Internet service provider to connect through the Internet).

[0107] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. For example, an application-specific integrated circuit (ASIC), a general-purpose computer, or any other similar hardware device can be used. In some embodiments, the software program of this application can be executed by a processor to implement the above steps or functions. Similarly, the software program of this application (including related data structures) can be stored in a computer-readable recording medium, for example, a RAM memory, a magnetic or optical drive, or a floppy disk and similar devices. In addition, some steps or functions of this application can be implemented by hardware, for example, as a circuit that cooperates with a processor to execute each step or function.

[0108] The computer program product provided by the embodiments of this application includes one or more computer programs / instructions. When the computer programs / instructions are executed by a processor, they wholly or partially generate the processes or functions described in the embodiments of this application. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by wire (such as coaxial cable, optical fiber, digital subscriber line (DSL)) or wirelessly (such as infrared, wireless, microwave, etc.). 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 includes one or more integrated available media. The available media can be magnetic media (for example, floppy disks, hard disks, magnetic tapes), optical media (for example, DVDs), or semiconductor media (for example, solid state disks (SSDs)), etc.

[0109] The flowcharts or block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of devices, methods, and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagram may represent a module, a segment of a program, or a portion of code that contains one or more executable instructions for implementing the specified logical function. It should also be noted that, in some alternative implementations, the functions noted in the blocks may occur in a different order than that noted in the accompanying drawings. For example, two consecutive blocks shown may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, and combinations of blocks in the block diagram and / or flowchart, may be implemented by a dedicated hardware-based system that performs the specified functions or operations, or may be implemented by a combination of dedicated hardware and computer instructions.

[0110] The scope of the present application is defined by the appended claims rather than the above description. Therefore, all changes that fall within the meaning and scope of the equivalent elements of the claims are intended to be encompassed within the present application. Any reference signs in the claims should not be construed as limiting the claims concerned. In addition, it is obvious that the term "comprising" does not exclude other elements or steps, and the singular does not exclude the plural. The multiple elements or devices recited in the apparatus claims may also be implemented by one element or device through software or hardware. The terms "first", "second", etc. are only used for descriptive distinction and do not represent any particular order, nor can they be construed as indicating or implying relative importance..

[0111] As described above, these are only specific embodiments of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed in the present application can easily conceive of changes or substitutions, which should all be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims, and the above embodiments should be regarded as exemplary and non-limiting.

Claims

1. A method for determining the warping profile of the surface of a semiconductor silicon wafer, characterized in that, It includes the following steps: Place the knife-edge straightedge on the processed sliced silicon wafer, and irradiate the front end of the contact surface between the knife-edge straightedge and the silicon wafer with a red light source of a specific intensity to collect a red light intensity picture; Perform gray-scale processing on the red light intensity picture, and extract the gray-scale difference edges to form gray-scale blocks of different shapes; Based on the local contrast measurement method, judge the contrast difference between adjacent gray-scale blocks, and calculate the contrast difference value; Based on the calculated contrast difference value, judge the warping degree of the silicon wafer to obtain the detection result of the silicon wafer warping degree.

2. The method for determining the surface warping profile of a semiconductor silicon wafer according to claim 1, wherein The brightness of the red light source is greater than 800 lumens, where the wavelength of the red light is 630 nanometers - 780 nanometers.

3. The method for determining the warping profile of the surface of a semiconductor silicon wafer according to claim 1, wherein The process of performing gray-scale processing on the red light intensity picture and extracting the gray-scale difference edges to form gray-scale blocks of different shapes is as follows: Perform gray-scale processing on the red light intensity image to obtain a gray-scale image, calculate the gray-scale value of the image, determine the upper and lower limits of the gray-scale value in the gray-scale image, and calculate the initial threshold; Based on the initial threshold, segment the gray-scale image to form the gray-scale values of the segmented part and the gray-scale values of the remaining part, and obtain gray-scale blocks of different light intensities based on the gray-scale values of the segmented part and the gray-scale values of the remaining part; Traverse and iterate all gray-scale images to extract several gray-scale blocks of different shapes.

4. The method for determining the surface warpage profile of a semiconductor silicon wafer according to claim 3, wherein, The gray-scale values of the segmented part and the gray-scale values of the remaining part are respectively: Among them, D f is the gray value of the segmented part, D q is the gray value of the remaining part, D(i, j) is the gray value N i,j is the number of pixel points, and S is the initial threshold.

5. The method for determining the warpage profile of the surface of a semiconductor silicon wafer according to claim 1, characterized in that, The process of judging the contrast difference between adjacent gray-scale blocks based on the local contrast measurement method and calculating the contrast difference value is as follows: Select the central gray-scale block of the gray-scale image, determine the gray-scale value and sub-pixel length of the central gray-scale block, and calculate the average value of the pixel gray-scale values of the central gray-scale block of the gray-scale image; Select different adjacent gray-scale blocks, and calculate the extreme values of the gray-scale values of 8 sub-pixel blocks around the central pixel of the gray-scale block respectively; Adopt the local contrast measurement method to calculate the local contrast difference value based on the extreme values and the average value of the pixel gray-scale values of the central gray-scale block; Based on the local contrast difference value, traverse the gray-scale image by using the method of calculating the local standard deviation with a sliding window to obtain the significant contrast value of the gray-scale image and obtain the contrast difference value.

6. The method for determining the surface warping profile of a semiconductor silicon wafer according to claim 5, wherein The specific local contrast difference value is: Among them, R is the local contrast difference value between different gray blocks, I′ is the average value of the gray values of the central gray block, and I max is the maximum value of the gray values of 8 sub-pixel blocks around the central pixel of the gray block, and I min is the minimum value of the gray values of 8 sub-pixel blocks around the central pixel of the gray block.

7. The method for determining the warping profile of the surface of a semiconductor silicon wafer according to claim 5, characterized in that The specific contrast difference value is: Among them, C local (x, y) is the contrast difference value, and I(x + i, y + i) is the gray value of the gray block of the grayscale image, and μ x,y is the average gray value within the window.

8. The method for determining the surface warpage profile of a semiconductor silicon wafer according to claim 7, wherein The process of judging the warping degree of the silicon wafer based on the calculated contrast difference value is as follows: Set a difference threshold. If the contrast difference value of the gray-scale image is greater than the difference threshold, it is judged that the contact and fitting degree between the knife-edge straightedge and the silicon wafer at the corresponding position is sparse, otherwise, it is judged that the contact and fitting degree between the knife-edge straightedge and the silicon wafer is tight.

9. The method for determining the surface warping profile of a semiconductor silicon wafer according to claim 7, wherein The method further includes: After obtaining the contrast difference value, establish a warping degree quantization model based on multi-dimensional feature fusion; According to the warping degree quantization model and the contrast difference value, judge the warping degree of the silicon wafer to obtain the detection result of the silicon wafer warping degree.

10. A semiconductor silicon wafer surface warpage profile determination system for implementing the semiconductor silicon wafer surface warpage profile determination method according to any one of claims 1-9, characterized in that, It includes: A red light intensity picture acquisition module (1), a gray-scale block construction module (2), a contrast difference value calculation module (3), and a silicon wafer warping degree judgment module (4); The red light intensity image acquisition module (1) is used to place a straight edge on the processed and sliced silicon wafer, and irradiate the front end of the contact surface between the straight edge and the silicon wafer with a red light source of a specific intensity to acquire a red light intensity image; The gray block construction module (2) is used to perform gray processing on the red light intensity image, extract the gray difference edges, and form gray blocks of different shapes; The contrast difference value calculation module (3) is used to judge the contrast difference between adjacent gray blocks based on the local contrast measurement method and calculate the contrast difference value; The silicon wafer warping degree judgment module (4) is used to judge the warping degree of the silicon wafer based on the calculated contrast difference value to obtain the detection result of the silicon wafer warping degree.

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