Method and system for classifying wafer products

Abstract

The invention provides a wafer product classification method and system, and the method comprises the steps: carrying out the primary classification of wafer products according to the external features of the wafer products, and enabling the external features to comprise one or more of a wafer size, a technology node, and an application platform of the wafer products; obtaining shipment quantity and process characteristic data of the wafer products with the same external characteristics, wherein the shipment quantity and process characteristic data comprises weight vectors of the shipment quantity and the process characteristics and actual values of the shipment quantity and the process characteristics; according to the weight vectors of the shipment quantity and the process characteristics and the actual values of the shipment quantity and the process characteristics, the comprehensive relative weight of each wafer product with the same external characteristics is obtained; and performing secondary classification on the wafer products with the same external characteristics according to the comprehensive relative weight and the actual productivity bottleneck of the processing equipment. The method simplifies product combination and accurately predicts factory productivity.

Description

Technical Field

[0001] The present application relates to the field of semiconductor technology, and in particular to a method and system for classifying wafer products. Background Art

[0002] With the rapid development of science and technology and the emergence of artificial intelligence, the demand for chips from various advanced technologies has gradually diversified. Therefore, the demand for wafers has gradually increased, and the product types of wafers have become more and more complex. For semiconductor wafer foundries with huge production scales, the high complexity of products will make it more difficult and less accurate to predict the factory's capacity. How to select representative products under complex product demands and classify them according to the factory's actual capacity bottlenecks, simplify the product portfolio, and accurately predict the factory's future capacity has become an important issue. Summary of the invention

[0003] The technical problem to be solved by this application is: in view of the complexity of semiconductor factory products, the difficulty and low accuracy in predicting the factory's future production capacity, a method and system for classifying wafer products are proposed to simplify the product portfolio and accurately predict the factory's production capacity.

[0004] In order to solve the above technical problems, the present application provides a wafer product classification method, comprising: classifying the wafer products according to external features of the wafer products, wherein the external features include one or more of wafer size, process technology node and application platform of the wafer products;

[0005] Obtaining shipment volume and process characteristic data of wafer products having the same external characteristics, wherein the shipment volume and process characteristic data include weight vectors of the shipment volume and process characteristic, and actual values ​​of the shipment volume and process characteristic;

[0006] Obtaining a comprehensive relative weight of each wafer product having the same external characteristics according to the weight vectors of the shipment volume and the process characteristics and the actual values ​​of the shipment volume and the process characteristics;

[0007] The wafer products with the same external characteristics are secondary classified according to the comprehensive relative weight and the actual production capacity bottleneck of the processing equipment.

[0008] In some embodiments of the present application, the process characteristics include one or more of the number of mask layers, the number of metal layers, the number of ion implantation layers, and the number of bonding layers.

[0009] In some embodiments of the present application, the method for obtaining the weight vector of the shipment volume and process characteristics includes: constructing a judgment matrix based on the shipment volume and process characteristics of the wafer product, each matrix element in the judgment matrix represents the relative importance between any two values ​​in the shipment volume and process characteristics; normalizing the judgment matrix; and obtaining the weight vector of the shipment volume and process characteristics of the wafer product based on the normalized judgment matrix, each vector element in the weight vector represents the relative weight of each value in the shipment volume and process characteristics.

[0010] In some embodiments of the present application, the weight vector of the shipment volume and process characteristics of the wafer product is obtained based on the judgment matrix after normalization, including: adding the matrix elements of each row in the judgment matrix after normalization to generate the corresponding initial weight vector; normalizing the initial weight vector to obtain the weight vector of the shipment volume and process characteristics of the wafer product.

[0011] In some embodiments of the present application, it also includes: performing consistency check on the relative weights of various values ​​in the shipment volume and process characteristics of the wafer product.

[0012] In some embodiments of the present application, the consistency check of the relative weights of the values ​​in the shipment volume and process characteristics of the wafer product includes:

[0013] Obtaining the maximum eigenvalue of the judgment matrix, and determining the consistency test index of the relative weight according to the maximum eigenvalue;

[0014] When the inspection index meets the preset threshold, it is determined that the relative weights of the corresponding values ​​in the shipment volume and process characteristics of the wafer product meet the consistency requirements.

[0015] In some embodiments of the present application, the method for obtaining the comprehensive relative weight of each wafer product having the same external characteristics includes:

[0016] in, is the weight vector of the shipment volume and process characteristics; Pk is the comprehensive relative weight of each wafer product, and k is different for different wafer products. These are actual values ​​for shipment volume and process characteristics of different wafer products.

[0017] In some embodiments of the present application, the wafer products with the same external characteristics are secondary classified according to the comprehensive relative weight and the actual production capacity bottleneck of the processing equipment, including: sorting according to the comprehensive relative weight to obtain the representativeness of each wafer product with the same external characteristics; sorting the representativeness of each wafer product with the same external characteristics; and secondary classifying the wafer products with the same external characteristics based on the sorting result after sorting the representativeness and the actual production capacity bottleneck of the processing equipment.

[0018] In some embodiments of the present application, the wafer sizes include 6 inches, 8 inches, and 12 inches.

[0019] In some embodiments of the present application, the process technology nodes include at least 180nm, 150nm, 130nm, 110nm, 90nm, 70nm, 65nm, 55nm, 48nm, 40nm, 38nm, 28nm, 24nm, 22nm, 16nm, 14nm, 12nm, 7nm, 6nm, 5nm, 4nm and 3nm.

[0020] In some embodiments of the present application, the application platforms of the wafer products include logic device process platform, high voltage device process platform, aluminum wire process platform, bipolar CMOS-DMOS device process platform, flash memory process platform, ultra-low power consumption process platform, high dielectric constant process platform and CMOS image sensor process platform.

[0021] In some embodiments of the present application, the shipment volume of the wafer product is greater than or equal to 500 pieces.

[0022] In another aspect of the present application, a system for classifying wafer products is provided, comprising a processor, wherein the processor executes any one of the above methods for classifying wafer products.

[0023] Compared with the prior art, the wafer product classification method of the technical solution of the present application has the following beneficial effects:

[0024] The method for classifying wafer products described in the embodiment of the present application selects and classifies representative products based on the comprehensive relative weights of the wafer products, simplifies the combination of wafer products, and can accurately predict factory production capacity.

[0025] The method described in the embodiment of the present application first classifies the wafer products according to their external characteristics, and then obtains the comprehensive relative weights of the series of products of the wafer products with the same external characteristics in the same wafer size, the same process technology node, and the same application platform according to the shipment volume and process characteristics of the wafer products, and sorts them to obtain representativeness, selects highly representative products and classifies them according to the actual production capacity bottleneck of the factory to simplify the product portfolio, accurately predict the factory production capacity, and ensure the maximum utilization of the factory production capacity and on-time delivery of products. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] The following drawings describe in detail the exemplary embodiments disclosed in this application. The same reference numerals represent similar structures in several views of the drawings. Those skilled in the art will understand that these embodiments are non-limiting, exemplary embodiments, and the drawings are only for the purpose of illustration and description, and are not intended to limit the scope of this application. Other embodiments may also accomplish the inventive intent in this application. It should be understood that the drawings are not drawn to scale. Among them:

[0027] Figure 1 A process flow chart of a method for classifying wafer products according to an embodiment of the present application;

[0028] Figure 2 A process flow chart of a method for obtaining a weight vector of the shipment volume and process characteristics of the wafer product in an embodiment. DETAILED DESCRIPTION

[0029] The following description provides specific application scenarios and requirements of the present application, with the purpose of enabling those skilled in the art to make and use the content in the present application. It will be apparent to those skilled in the art that various local modifications to the disclosed embodiments are apparent, and the general principles defined herein may be applied to other embodiments and applications without departing from the spirit and scope of the present application. Therefore, the present application is not limited to the embodiments shown, but to the widest scope consistent with the claims.

[0030] There are currently many ways to classify wafer products: size, technology node, application platform, number of mask layers, etc., but any one of them is one-sided and cannot truly and accurately reflect production capacity requirements.

[0031] Therefore, the technical solution of the present application processes the external characteristics, shipment volume and process characteristics of the wafer products to obtain the comprehensive relative weight of each wafer product under the same wafer size, the same process technology node and the same application platform, and sorts them to obtain the representativeness of each wafer product. Then, the highly representative wafer products are selected and classified according to the actual production capacity bottleneck of the factory to simplify the product portfolio, accurately predict the factory production capacity, and ensure the maximum utilization of the factory production capacity and on-time delivery of products.

[0032] The wafer product classification method of the present application is not only applicable to wafer fabs, but can also be applied to other production fields with high product complexity. The purpose is to select representative products based on weight analysis and classify products under complex product demands, simplify product portfolios, and accurately predict factory capacity demand and capacity load.

[0033] The following is a more detailed description of the wafer product classification method of the present application in conjunction with the accompanying drawings and embodiments.

[0034] This application provides a method for classifying wafer products. Figure 1 ,include:

[0035] Step S1: classifying the wafer products according to their external features, where the external features include one or more of wafer size, process technology node, and application platform of the wafer products;

[0036] Step S2: Obtaining shipment volume and process characteristic data of wafer products having the same external characteristics, wherein the shipment volume and process characteristic data include weight vectors of the shipment volume and process characteristic, and actual values ​​of the shipment volume and process characteristic;

[0037] Step S3: obtaining a comprehensive relative weight of each wafer product having the same external characteristics according to the weight vectors of the shipment volume and the process characteristics and the actual values ​​of the shipment volume and the process characteristics;

[0038] Step S4: performing secondary classification on the wafer products having the same external characteristics according to the comprehensive relative weight and the actual production capacity bottleneck of the processing equipment.

[0039] In combination with step S1 and Table 1, the wafer products are classified according to their external features, wherein the external features include one or more of wafer size, process technology node, and application platform of the wafer products. Optionally, the external features include wafer size, process technology node, and application platform of the wafer products at the same time.

[0040] In the current semiconductor field, wafer products that have achieved breakthroughs in production technology are first classified into 6-inch, 8-inch, and 12-inch wafer sizes. If there are larger-sized mass-produced wafer products in the future, the larger-sized wafer products will also be classified.

[0041] The embodiment of the present application also classifies the wafer products according to process technology nodes. For advanced 12-inch wafers, the process technology nodes include at least 180nm, 150nm, 130nm, 110nm, 90nm, 70nm, 65nm, 55nm, 48nm, 40nm, 38nm, 28nm, 24nm, 22nm, 16nm, 14nm, 12nm, 7nm, 6nm, 5nm, 4nm, 3nm, etc. The process technology node refers to the critical size of the semiconductor device, and may be other process technology node classifications depending on the different or changed process technology node classification methods in the industry.

[0042] The embodiments of the present application are also classified according to the application platforms of wafer products. The application platforms of wafer products mainly include logic device process platform (LG), high voltage device process platform (HV), aluminum line process platform (Al line), bipolar CMOS-DMOS device process platform (BCD), flash memory process platform (Flash), ultra-low power process platform (ULP), high dielectric constant process platform (HK), CMOS image sensor process platform (CIS), etc. The process platform is classified according to the main devices and application scenarios that the process platform can provide.

[0043] Execute step S2 to obtain shipment volume and process characteristic data of wafer products with the same external characteristics, wherein the shipment volume and process characteristic data include weight vectors of the shipment volume and process characteristics, and actual values ​​of the shipment volume and process characteristics.

[0044] In some embodiments of the present application, wafer products of the same wafer size, the same process technology node, and the same wafer product application platform are differentiated according to differences in shipment volume and process characteristics.

[0045] In some embodiments of the present application, the shipment volume of the selected wafer product is greater than or equal to 500 wafers; the process characteristics of the wafer product include but are not limited to: the number of mask layers, the number of metal layers, the number of ion implantation layers, the number of bonding layers, etc. That is, in some embodiments of the present application, the process characteristics include one or more of the number of mask layers, the number of metal layers, the number of ion implantation layers, and the number of bonding layers.

[0046] The selection of the process characteristics of the wafer product is determined based on the number of times the wafer product process is executed in semiconductor manufacturing projects, the general applicability of the wafer product process in semiconductor manufacturing projects, and the number of equipment used in the wafer product process.

[0047] For example, process characteristics such as the number of mask layers, metal layers, ion implantation layers, and bonding layers are executed many times in the semiconductor manufacturing process (the same product process uses mask multiple times, metal layers are made, ion implantation processes, etc.), and all application platforms and the manufacture of all semiconductor products include the process characteristics (the process has a wide range of applicability), and the number of times the above process equipment is used in semiconductor manufacturing is also relatively large (the equipment load is relatively heavy). Therefore, the selection of the process characteristics of the wafer product can better solve the problem of product allocation and capacity bottlenecks in the semiconductor manufacturing process from a practical perspective.

[0048] After wafer products with the same external characteristics are differentiated according to differences in shipment volume and process characteristics, weight vectors of the shipment volume and process characteristics are obtained.

[0049] Reference Figure 2 As shown, the method for obtaining the weight vector of the shipment volume and process characteristics of the wafer product includes:

[0050] Step S31: constructing a judgment matrix according to the shipment volume and process characteristics of the wafer product, wherein each matrix element in the judgment matrix represents the relative importance between any two values ​​in the shipment volume and the process characteristics;

[0051] In some embodiments of the present application, after constructing the judgment matrix, the relative importance of any two values ​​in the judgment matrix is ​​obtained, and the relative importance is marked as d ij , where i represents the number of columns of the judgment matrix, and j represents the number of rows of the judgment matrix.

[0052] The judgment matrix can be constructed according to the shipment volume and process characteristics of the wafer products according to the importance, and the relative importance of each pair in the matrix can be judged.

[0053] In one embodiment of the present application, the importance includes: setting a relative importance scale according to the importance of different factors, and the relative importance scale is, for example, 1, 2, 3, 4, 5, 6, 7, 8, 9, etc. For example: if two factors have the same importance, the scale is 1; if the former is slightly more important than the latter, the scale is 3; if the former is obviously more important than the latter, the scale is 5; if the former is strongly more important than the latter, the scale is 7; if the former is extremely more important than the latter, the scale is 9; if the importance of the former and the latter is between the above scales, the scales are 2 / 4 / 6 / 8 respectively.

[0054] Table 2 Ratio scale of relative importance

[0055]

[0056] In one embodiment of the present application, a judgment matrix A constructed according to the shipment volume and process characteristics of wafer products according to importance is shown in Table 3.

[0057] Table 3 Judgment matrix A constructed according to the shipment volume and process characteristics of wafer products according to importance

[0058]

[0059] That is, the relative importance of the number of mask layers, metal layers, ion implantation layers, and bonding layers to the shipment volume ij They are: d 12 =3,d 13 =2,d 14 =2,d 15 =3; and the relative importance of the number of mask layers and the number of bonding layers is d 52 =1, the relative importance of the number of ion implantation layers and the number of metal layers is d 34 =1 / 2, the relative importance of the number of bonding layers and the number of metal layers is d 35 =5. In some embodiments of the present application, the relative importance d in the judgment matrix A ij The numerical values ​​are empirical values ​​obtained under multiple project and process conditions in semiconductor production.

[0060] Execute step S32: perform normalization processing on the judgment matrix to obtain a normalized judgment matrix;

[0061] In some embodiments of the present application, the normalization process includes: according to the relative importance d ij Get the relative weight H ij , where the relative weight Wherein, the i is the column of the judgment matrix, and the j is the row of the judgment matrix.

[0062] In some embodiments of the present application, Table 3 is normalized by column to obtain a judgment matrix after column normalization as shown in Table 4 below, wherein each element in the judgment matrix after normalization is the relative weight H ij , where i represents the number of columns of the judgment matrix, and j represents the number of rows of the judgment matrix.

[0063] Table 4 Judgment matrix after column normalization

[0064]

[0065] Execute step S33 to obtain a weight vector of the shipment volume and process characteristics of the wafer product based on the normalized judgment matrix, wherein each vector element in the weight vector represents a relative weight of each value in the shipment volume and process characteristics.

[0066] In some embodiments of the present application, the weight vector of the shipment volume and process characteristics of the wafer product is obtained based on the judgment matrix after normalization, including: adding the matrix elements of each row in the judgment matrix after normalization to generate the corresponding initial weight vector; normalizing the initial weight vector to obtain the weight vector of the shipment volume and process characteristics of the wafer product.

[0067] Specifically, the matrix elements (relative weights H ij ) Add up the rows to get the h of each column i The value is used as the initial weight vector, for the h i The values ​​are normalized to obtain the weight vector of the shipment volume and process characteristics of the wafer product. in,

[0068] Continuing with the judgment matrix normalized by columns in Table 4 as an example, the judgment matrix is ​​summed by rows, as shown in Table 5, to obtain h i The values ​​are:

[0069] Table 5

[0070] <![CDATA[h1]]> 0.42 <![CDATA[h2]]> 1.66 <![CDATA[h3]]> 0.66 <![CDATA[h4]]> 0.49 <![CDATA[h5]]> 1.70

[0071] Continue to follow the formula for Table 5 After normalization, the weight vector of the shipment volume and process characteristics of the wafer product is obtained: As shown in Table 6:

[0072] Table 6

[0073]

[0074] In some embodiments of the present application, step S34 is further included, in which the consistency check is performed on the relative weights of the values ​​in the shipment volume and process characteristics of the wafer product, wherein the consistency check on the relative weights of the values ​​in the shipment volume and process characteristics of the wafer product includes:

[0075] Step S341, obtaining the maximum characteristic root λmax of the judgment matrix, and determining the consistency test index of the relative weight according to the maximum characteristic root; wherein, Among them, (AH) i :For each row of the judgment matrix A, respectively, and the weight vector The result of multiplying and summing .

[0076] For example

[0077] In some embodiments of the present application, the method for obtaining the maximum eigenvalue λmax of the judgment matrix includes:

[0078] Compare each row of the judgment matrix A with the weight vector The values ​​of the corresponding columns of are multiplied respectively, and then the products are summed up, as shown in Table 7, to obtain AH1 to AH5, and AH1 to AH5 are arranged in a column:

[0079] Table 7

[0080] <![CDATA[AH1]]> 0.43 <![CDATA[AH2]]> 1.73 <![CDATA[AH3]]> 0.68 <![CDATA[AH4]]> 0.49 <![CDATA[AH5]]> 1.89

[0081] Then according to The maximum characteristic root λmax is obtained. In the embodiment of the present application, i is 5. According to Table 7 and Table 5, the maximum characteristic root λmax can be obtained as 5.15.

[0082] Then, according to the maximum characteristic root, the consistency test index C of the relative weight is obtained. R , where C R =C I / R I , C I =(λmax-n) / (n-1), R I represents the average random consistency, n is the number of rows of the judgment matrix; when n=5, it can be seen from the table that C I is 1.12, then we can get the C I =(λmax-n) / (n-1)=0.04, then the C R Equal to 0.03.

[0083] Execute step S343, when the inspection index meets the preset threshold, determine whether the relative weights of the corresponding values ​​in the shipment volume and process characteristics of the wafer product meet the consistency requirements.

[0084] In some embodiments of the present application, the preset threshold is the inspection index C R <0.1, that is, the test index C R <0.1, the relative weights of the corresponding values ​​in the shipment volume and process characteristics of the wafer product meet the consistency requirements. Therefore, the relative weights of the corresponding values ​​in the shipment volume and process characteristics of the wafer product in the embodiment of the present application meet the consistency requirements.

[0085] The embodiment of the present application performs consistency check on the relative weights of the corresponding values ​​in the shipment volume and process characteristics of the wafer product to determine whether the relative weights constructed in the judgment matrix A are correct. If the inspection result obtained after the inspection is that the consistency requirement is not met, it is necessary to re-construct the judgment matrix A according to the importance based on the shipment volume and process characteristics of the wafer product, and perform steps S31 to S34 again.

[0086] Next, step S3 is executed: according to the weight vector of the shipment volume and process characteristics of the wafer product, and the actual values ​​of the shipment volume and process characteristics of the wafer product, the comprehensive relative weight (Pk) of each wafer product is obtained.

[0087] In an embodiment of the present application, after obtaining the weight vector of the shipment volume and process characteristics of the wafer product, the comprehensive relative weight (Pk) of each wafer product with the same external characteristics can be obtained based on the weight vector of the shipment volume and process characteristics of the wafer product and the actual values ​​of the shipment volume and process characteristics of the wafer product.

[0088] In some embodiments of the present application, the weight vector shown in Table 6 is substituted into the actual values ​​of the shipment volume and process characteristics of the wafer product (such as shown in Table 7), and the comprehensive relative weight (Pk) of each wafer product is obtained, where is the weight vector of the shipment volume and process characteristics; is the actual value of the shipment volume and process characteristics of different wafer products, and k is different wafer products, e.g. These are the actual values ​​of the shipment volume and process characteristics of product 1.

[0089] Table 7 Actual values ​​of shipments and process characteristics of different wafer products

[0090]

[0091] In the present application example, the comprehensive relative weights of different products are shown in Table 8:

[0092]

[0093] Step S4: performing secondary classification on the wafer products having the same external characteristics according to the comprehensive relative weight and the actual production capacity bottleneck of the processing equipment.

[0094] In some embodiments of the present application, the wafer products with the same external characteristics are secondary classified according to the comprehensive relative weights and the actual production capacity bottleneck of the processing equipment, including: sorting the comprehensive relative weights to obtain the representativeness of each wafer product with the same external characteristics; sorting the representativeness of each wafer product with the same external characteristics; and secondary classifying the wafer products with the same external characteristics based on the sorting results after sorting the representativeness and the actual production capacity bottleneck of the processing equipment.

[0095] For example, the representative ranking of the products 1 to 7 is shown in Table 9:

[0096]

[0097] In the embodiment of the present application, on the basis of ranking the representativeness of each wafer product, the wafer products are classified according to the actual capacity bottleneck of the factory's processing equipment. For example, assuming that the photomask process is the biggest capacity bottleneck of the factory at present, among products 1 to 7, the number of mask layers is mainly 33 layers and 35 layers, then the number of mask layers can be prioritized according to the first two representative products.

[0098] The capacity bottleneck refers to the process with the largest difference between the actual production capacity and the target production capacity among the different processes of the semiconductor factory. For example, the photomask process is the biggest capacity bottleneck of the current factory. Among the 7 products in Table 7, the number of mask layers is mainly 33 and 35. The number of mask layers can be prioritized according to the first two representative products (products 2 and 3). In other words, the representative ranking of product 2 is 1, and the number of mask layers is 33. Product 2 is taken as the first representative product, and product 4 with the same number of mask layers is also classified into the first representative product; the representative ranking of product 3 is 2, and the number of mask layers is 35. Product 3 is taken as the second representative product, and product 05 with the same number of mask layers is also classified into the second representative product. If the number of mask layers of product 6 and product 7 is greater than 35, they are classified into the closest second representative product. The number of mask layers of product 1 is 32, and it is classified into the closest first representative product.

[0099] The method for classifying wafer products described in the embodiment of the present application selects and classifies representative products based on the comprehensive relative weights of the wafer products, simplifies the combination of wafer products, and can accurately predict factory production capacity.

[0100] The method described in the embodiment of the present application first classifies the wafer products according to their external characteristics, and then obtains the comprehensive relative weights of the series of products of the wafer products with the same external characteristics in the same wafer size, the same process technology node, and the same application platform according to the shipment volume and process characteristics of the wafer products, and sorts them to obtain representativeness, selects highly representative products and classifies them according to the actual capacity bottleneck of the factory processing equipment to simplify the product portfolio, accurately predict the factory capacity, and ensure the maximum utilization of the factory capacity and on-time delivery of products.

[0101] Another aspect of the present application provides a system for classifying wafer products, including a processor, the processor performing any one of the methods for classifying wafer products described in the embodiments of the present application. The processor can be in the form of one or more processors, and in some embodiments, the processor can include one or more hardware processors, such as a microcontroller, a microprocessor, an application-specific integrated circuit (ASIC), an application-specific instruction set processor (ASIP), a central processing unit (CPU), a physical processing unit (PPU), a microcontroller unit, a digital signal processor (DSP), a field programmable gate array (FPGA), an advanced RISC machine (ARM), a programmable logic device (PLD), any circuit or processor capable of performing one or more functions, etc., or any combination thereof.

[0102] The processor can be connected to different device components and different forms of memory, such as a disk (non-temporary memory) and a read-only memory (ROM) or a random access memory (RAM), etc., for storing various data files to be processed and / or transmitted. The memory can be a local storage medium or a shared storage medium obtained from a network using a communication port, such as a cloud platform.

[0103] In summary, after reading the contents of this application, those skilled in the art will appreciate that the aforementioned application contents may be presented only in an exemplary manner and may not be restrictive. Although not explicitly stated herein, those skilled in the art will appreciate that this application is intended to encompass various reasonable changes, improvements and modifications to the embodiments. These changes, improvements and modifications are within the spirit and scope of the exemplary embodiments of this application.

[0104] It should be understood that the term "and / or" used in this embodiment includes any or all combinations of one or more of the associated listed items. It should be understood that when an element such as a layer, region, or substrate is referred to as being "on" another element, it can be directly on the other element, or there can be an intermediate element. In contrast, the term "directly" means that there are no intermediate elements. It should also be understood that the terms "comprising," "containing," "including," or "comprising," when used in this application document, indicate the presence of the recorded features, wholes, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components, and / or groups thereof.

[0105] It should also be understood that although the terms first, second, third, etc. can be used to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another element. Therefore, without departing from the teachings of the present application, the first element in some embodiments can be referred to as the second element in other embodiments. The same reference numerals or the same reference signs represent the same elements throughout the specification.

[0106] In addition, the present specification describes exemplary embodiments by reference to idealized exemplary cross-sectional views and / or plan views and / or stereograms. Therefore, differences from the illustrated shapes due to, for example, manufacturing techniques and / or tolerances are foreseeable. Therefore, the exemplary embodiments should not be interpreted as being limited to the shapes of the regions shown herein, but should include deviations in shapes caused by, for example, manufacturing. For example, an etched region shown as a rectangle will typically have circular or curved features. Therefore, the region shown in the figure is schematic in nature, and its shape is not intended to illustrate the actual shape of the region of the device nor to limit the scope of the exemplary embodiments.

Claims

1. A method for classifying wafer products, characterized in that: include: Classifying the wafer products according to external features of the wafer products, wherein the external features include one or more of wafer size, process technology node, and application platform of the wafer products; Obtaining shipment volume and process characteristic data of wafer products having the same external characteristics, wherein the shipment volume and process characteristic data include weight vectors of the shipment volume and process characteristic, and actual values ​​of the shipment volume and process characteristic; Obtaining a comprehensive relative weight of each wafer product having the same external characteristics according to the weight vectors of the shipment volume and the process characteristics and the actual values ​​of the shipment volume and the process characteristics; The wafer products with the same external characteristics are secondary classified according to the comprehensive relative weight and the actual production capacity bottleneck of the processing equipment.

2. The method for classifying wafer products according to claim 1, characterized in that: The process characteristics include one or more of the number of mask layers, the number of metal layers, the number of ion implantation layers and the number of bonding layers.

3. The method for classifying wafer products according to claim 1, characterized in that: The method for obtaining the weight vector of the shipment volume and process characteristics includes: According to the shipment volume and process characteristics of the wafer product, a judgment matrix is ​​constructed, wherein each matrix element in the judgment matrix represents the relative importance between any two values ​​in the shipment volume and the process characteristics; Normalizing the judgment matrix; Based on the normalized judgment matrix, a weight vector of the shipment volume and process characteristics of the wafer product is obtained, and each vector element in the weight vector represents the relative weight of each value in the shipment volume and process characteristics.

4. The method for classifying wafer products according to claim 3, characterized in that: The judgment matrix after normalization is used to obtain the weight vector of the shipment volume and process characteristics of the wafer product, including: Adding the matrix elements of each row in the normalized judgment matrix to generate the corresponding initial weight vector; The initial weight vector is normalized to obtain a weight vector of the shipment volume and process characteristics of the wafer product.

5. The method for classifying wafer products according to claim 3, characterized in that: Also includes: The relative weights of the various values ​​in the shipment volume and process characteristics of the wafer products are checked for consistency.

6. The method for classifying wafer products according to claim 5, characterized in that: The consistency check of the relative weights of the values ​​in the shipment volume and process characteristics of the wafer product includes: Obtaining the maximum eigenvalue of the judgment matrix, and determining the consistency test index of the relative weight according to the maximum eigenvalue; When the inspection index meets the preset threshold, it is determined that the relative weights of the corresponding values ​​in the shipment volume and process characteristics of the wafer product meet the consistency requirements.

7. The method for classifying wafer products according to claim 1, characterized in that: The method for obtaining the comprehensive relative weight of each wafer product having the same external characteristics comprises: in, 为 The weight vector of the shipment volume and process characteristics ； Pk is the comprehensive relative weight of each wafer product, and k is different for different wafer products. These are actual values ​​for shipment volume and process characteristics of different wafer products.

8. The method for classifying wafer products according to claim 1, characterized in that: The secondary classification of the wafer products having the same external characteristics according to the comprehensive relative weight and the actual production capacity bottleneck of the processing equipment includes: Sorting the comprehensive relative weights to obtain representativeness of each wafer product having the same external characteristics; Ranking the representativeness of each wafer product with the same external characteristics; Based on the sorting results after the representativeness is sorted and the actual production capacity bottleneck of the processing equipment, the wafer products with the same external characteristics are secondary classified.

9. The method for classifying wafer products according to claim 1, characterized in that: The wafer sizes include 6 inches, 8 inches and 12 inches.

10. The method for classifying wafer products according to claim 1, characterized in that: The process technology nodes include at least 180nm, 150nm, 130nm, 110nm, 90nm, 70nm, 65nm, 55nm, 48nm, 40nm, 38nm, 28nm, 24nm, 22nm, 16nm, 14nm, 12nm, 7nm, 6nm, 5nm, 4nm and 3nm.

11. The method for classifying wafer products according to claim 1, characterized in that: The application platforms of the wafer products include logic device process platform, high voltage device process platform, aluminum wire process platform, bipolar CMOS-DMOS device process platform, flash memory process platform, ultra-low power consumption process platform, high dielectric constant process platform and CMOS image sensor process platform.

12. The method for classifying wafer products according to claim 1, characterized in that: The shipment volume of the wafer product is greater than or equal to 500 pieces.

13. A system for classifying wafer products, comprising a processor, wherein the processor executes any one of the methods for classifying wafer products according to claims 1 to 12.

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