Wafer yield control method, device, equipment and medium based on component data

Abstract

The present application provides a wafer yield control method, device, equipment and medium based on component data. The method includes: obtaining basic component data of the wafer currently being manufactured; wherein the basic component data includes component name, component parameters and the manufacturing link where the component is located; based on the predetermined key component data associated with the wafer yield, the basic component data is screened; the key component data is input into the machine learning model to determine whether the estimated yield of the wafer currently being manufactured reaches a preset threshold; if the preset threshold is not reached, an instruction to adjust the key component data is generated. This technical solution can improve the wafer yield by controlling the component data, avoiding the problem of low yield in wafer manufacturing and the inability to improve the wafer yield through effective means.

Description

Technical Field

[0001] The present application relates to the field of semiconductor manufacturing technology, and in particular to a wafer yield control method, device, equipment and medium based on component data. Background Art

[0002] In recent years, with the rapid development of science and technology, the semiconductor manufacturing industry has also developed rapidly. As the basic material for making silicon semiconductor chips, the manufacturing technology of wafers has become one of the foundations and core competitiveness that restrict the development of semiconductors.

[0003] The wafer manufacturing process is very complicated, requiring tedious manufacturing processes such as thin film deposition, photoresist coating, photolithography development, etching, measurement, cleaning, ion implantation, etc. More importantly, there are many types of parts on the equipment and production lines, and different materials or slight size differences may lead to changes in wafer yield. Based on this, the analysis of the impact of component data on yield becomes very important.

[0004] However, there is no analysis of the impact of component data on yield. For example, different parameters or materials of a component may lead to different wafer yields. Therefore, it is necessary to find an intelligent method to mine the attribution of component data to yield, which is a technical problem that technicians in this field need to solve urgently. Summary of the invention

[0005] The purpose of this application is to provide a wafer yield control method, device, equipment and medium based on component data, at least to solve the problem of analyzing whether component data has an impact on wafer yield. The purpose of this application is to provide a new wafer yield control method based on component data. This method can mine key component data related to wafer yield from conventional component data, and predict whether the yield of the currently manufactured wafers meets the standards based on the key component data, so as to improve the wafer yield by controlling the component data, and avoid the problem of low yield in wafer manufacturing and inability to improve the wafer yield through effective means.

[0006] To achieve the above objectives, some embodiments of the present application provide the following aspects:

[0007] In a first aspect, some embodiments of the present application further provide a wafer yield control method based on component data, the method comprising:

[0008] Obtaining basic component data of the wafer currently being manufactured; wherein the basic component data includes component name, component parameters, and the manufacturing link where the component is located;

[0009] Based on predetermined key component data associated with wafer yield, the basic component data is screened;

[0010] Inputting the key component data into the machine learning model to determine whether the estimated yield of the currently manufactured wafers reaches a preset threshold;

[0011] If the preset threshold is not reached, an instruction to adjust the key component data is generated.

[0012] In a second aspect, some embodiments of the present application further provide a wafer yield control device based on component data, the device comprising:

[0013] A basic component data acquisition module is used to acquire basic component data of the wafer currently being manufactured; wherein the basic component data includes component name, component parameters, and the manufacturing link where the component is located;

[0014] A key component data determination module, used to screen the basic component data based on predetermined key component data associated with wafer yield;

[0015] A wafer yield prediction module, used to input the key component data into the machine learning model to determine whether the estimated yield of the currently manufactured wafers reaches a preset threshold;

[0016] The component data adjustment module is used to generate an instruction to adjust the key component data if the preset threshold is not reached.

[0017] In a third aspect, some embodiments of the present application further provide a computer device, the device comprising:

[0018] one or more processors; and

[0019] A memory storing computer program instructions, wherein when the computer program instructions are executed, the processor executes the wafer yield control method based on component data as described above.

[0020] In a fourth aspect, some embodiments of the present application further provide a computer-readable medium having computer program instructions stored thereon, wherein the computer program instructions can be executed by a processor to implement the wafer yield control method based on component data as described above.

[0021] Compared with the prior art, the solution provided in the embodiment of the present application obtains the basic component data of the wafer currently being manufactured; wherein the basic component data includes the component name, component parameters, and the manufacturing link where the component is located; based on the predetermined key component data associated with the wafer yield, the basic component data is screened; the key component data is input into the machine learning model to determine whether the estimated yield of the wafer currently being manufactured reaches a preset threshold; if the preset threshold is not reached, an instruction to adjust the key component data is generated. This solution can improve the wafer yield by controlling the component data, avoiding the problem of low yield in wafer manufacturing and the inability to improve the wafer yield through effective means. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 It is a flow chart of a wafer yield control method based on component data provided in the first embodiment of the present application;

[0023] Figure 2 It is a flowchart of the training process of the machine learning model provided in Example 2 of the present application;

[0024] Figure 3 It is a flow chart of a wafer yield control method based on component data provided in Embodiment 3 of the present application;

[0025] Figure 4 It is a structural schematic diagram of a wafer yield control device based on component data provided in Embodiment 4 of the present application;

[0026] Figure 5 It is a structural diagram of the computer device provided in Example 5 of the present application. DETAILED DESCRIPTION

[0027] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0028] The technical solution provided by the embodiment of the present application is described in detail below through specific embodiments and their application scenarios in conjunction with the accompanying drawings.

[0029] Embodiment 1

[0030] Figure 1 1 is a flow chart of a wafer yield control method based on component data provided in the first embodiment of the present application. Figure 1As shown, the specific steps include: The process includes:

[0031] Step S101, obtaining basic component data of the wafer currently being manufactured; wherein the basic component data includes component name, component parameters and the manufacturing link where the component is located.

[0032] Wafer refers to the silicon wafer used to make silicon semiconductor circuits. Its raw material is silicon. High-purity polysilicon is dissolved and doped with silicon crystal seeds, and then slowly pulled out to form cylindrical single crystal silicon. After grinding, polishing and slicing, the silicon crystal rod is formed into a silicon wafer, that is, a wafer. Currently, the wafer production lines on the market are mainly 8 inches and 12 inches. The main processing methods for wafers are sheet processing and batch processing, that is, processing one or more wafers at the same time.

[0033] During the wafer manufacturing process, various basic component data are involved, and each basic component data is regulated to ensure the quality of each manufacturing process. Good machine parameter data is conducive to controlling the production of excellent wafer products.

[0034] In this solution, the basic component data includes the component name, component parameters, and the manufacturing link where the component is located. Among them, the component names may include O-Ring seals, precision bearings, metal parts, Value valves, silicon / SiC parts, Robots, quartz parts (capacitor quartz, electrolytic quartz), filters, RF power supplies, ceramic parts, ESC electrostatic chucks, pressure gauges, pumps, MFC flow meters, stepper motors, etc. Specifically, each component may correspond to one or more manufacturing links, for example, O-Ring seals are used for CVD, PVD, CMP, etc.; precision bearings are used for ion implantation, PVD, RTP, WET, etc.; metal parts are used for PVD, Value valves are used for CVD, PVD, RTP, photolithography, ion implantation, etc.; silicon / SiC parts are used for plasma etching equipment, Robots are used for photolithography, WET, TF, ETCH, DIEF, etc.; quartz parts (capacitor quartz, electrolytic quartz) are used for etching, furnaces tubes, etc.; Filters are used for photolithography, RTP, WET, etc.; RF power supplies are used for etching, ion implantation, CVD, PVD, etc.; ceramic parts are used for etching, ion implantation, CVD, PVD, etc.; ESC electrostatic chucks are used for CVD, PVD, ALD, plasma etching equipment, wet etching equipment, etc.; pressure gauges are used for ion implantation, WET, etc.; pumps are used for ion implantation, WET, PVD, etc.; MFC flow meters are used for ion implantation, CVD, RTP, etc.; stepper motors are used for CVD, PVD, ETCH, etc.

[0035] In this solution, the name of the component and the manufacturing link or manufacturing equipment where it is located can be connected with a hyphen to serve as the component data.

[0036] Step S102, screening the basic component data based on predetermined key component data associated with wafer yield.

[0037] In this solution, basic component data can be screened based on predetermined key component data. For example, if there are 4 key component data and 10 basic component data, and three component data A, B and C among them correspond, then the three component data A, B and C among the 10 basic component data can be screened out, and the fourth key component data can be considered not involved in the current wafer manufacturing process and can be ignored.

[0038] In addition, a pre-trained machine learning model can be used to screen basic component data to obtain key component data.

[0039] Understandably, machine learning models can better mine the impact of various basic component data on product yield. For example, if a certain machine production equipment uses precision bearings of different sizes, the wafer yield may be different, so it is necessary to find an intelligent method to mine the attribution of component data to yield.

[0040] Among them, machine learning simulation includes but is not limited to regression tree models. Machine learning simulation can be a model obtained by learning various basic component data and wafer yield related data collected historically, and each group of related data is learned. The model can obtain the current wafer yield after inputting the basic component data of the current wafer manufacturing. In this solution, a part of the algorithm in the machine learning model can be used to filter out key component data from the basic component data. Among them, the key component data is the basic component data that has a direct impact on the wafer yield.

[0041] Step S103, inputting the key component data into the machine learning model to determine whether the estimated yield of the currently manufactured wafers reaches a preset threshold.

[0042] Among them, after determining the key component data, the values ​​of the key component data of the currently manufactured wafer can be input into the machine learning model to determine the estimated yield of the currently manufactured wafer according to the output results of the machine learning model. It can be understood that the estimated wafer yield here is estimated based on the key component data currently input. In the process of wafer production, an assembly line operation is adopted. Generally, other data will not change in the continuous manufacturing process of multiple wafers, so the estimation based on the key component data will have a very high accuracy.

[0043] In this solution, the preset threshold value may be a value set according to demand, for example, the yield rate needs to be above 95% so that the manufacturing of the current batch of wafers can meet the production demand. Then the preset threshold value here may be set to 95%.

[0044] After obtaining the estimated yield of the currently manufactured wafer, it can be identified whether the obtained estimated yield reaches or exceeds 95%. For example, if the estimated yield reaches 97% or exceeds 95%, it can be determined that the preset threshold is reached.

[0045] Step S104: if the preset threshold is not reached, an instruction to adjust the key component data is generated.

[0046] Here, if the estimated yield of the currently manufactured wafers output by the machine learning model does not reach the preset threshold, for example, the estimated yield of the currently manufactured wafers is 92%, which is less than the preset threshold of 95%, then an instruction to adjust the key component data can be generated.

[0047] For example, if the estimated wafer yield obtained based on the key component data of the current wafer manufacturing is lower than the preset threshold, the current key component data can be adjusted. The adjustment instruction here can be for one key component data or for multiple key component data, and can be adjusted from the original value to the target value. For example, the diameter of the precision bearing is adjusted from 2.03 mm to 2.04 mm.

[0048] This technical solution provides a new wafer yield control method based on component data. The method can mine key component data related to wafer yield from conventional component data, and predict whether the yield of the currently manufactured wafers meets the standard based on the key component data, so as to improve the wafer yield by controlling the component data, and avoid the problem of low yield in wafer manufacturing and inability to improve the wafer yield through effective means.

[0049] In another embodiment, after generating an instruction to adjust the key component data if the preset threshold is not reached, the method further includes:

[0050] After adjusting the key component data, inputting the adjusted updated component data into the machine learning model;

[0051] Identify whether the predicted yield of the output of the machine learning model reaches a preset threshold;

[0052] If the predicted yield does not reach a preset threshold, an alarm message is generated.

[0053] It is understandable that after the adjustment instruction is generated, the key component data can be adjusted to obtain updated component data. The updated component data is input into the machine learning model again. At this time, if the predicted yield still does not reach the preset threshold, an alarm message is generated. Generally, the yield of the current wafer manufacturing may have been affected by other factors, and no matter how the key component data is adjusted, the preset threshold cannot be reached.

[0054] This arrangement of the technical solution can improve the yield of wafer manufacturing. And in the process of improving the yield, if it is found that the preset threshold is still not met, an alarm message can be generated to ensure the yield of the current wafer manufacturing through manual intervention. In addition, the user can also be informed of which key component data adjustments have been made based on the alarm, so that the staff can check whether the above adjustments are reasonable. For example, if the staff cancels the adjustment of a key component data, it means that there is a certain irrationality in the adjustment of the key component data, and the machine learning model needs to be updated based on this to ensure the accuracy of the output results of the machine learning model.

[0055] In another embodiment, after inputting the key component data into the machine learning model to determine whether the estimated yield of the wafer currently being manufactured reaches a preset threshold, the method further includes:

[0056] If the preset threshold is reached, the wafers are manufactured according to the current key component data, and the yield of the currently manufactured wafers is counted as updated sample data;

[0057] The machine learning model is iteratively updated according to the updated sample data.

[0058] In one case, if the estimated yield of the currently manufactured wafers reaches the preset threshold, the wafer manufacturing can continue according to the current component data, and the yield of this batch of wafers can be calculated. The yield of this batch of wafers and the current key component data are used as updated sample data, and the machine learning model is further iterated and updated, which can make the output results of the machine learning model more accurate.

[0059] Through such a setting, this solution can gradually iterate and update the machine learning model over time. Not only does the sample data used not need to be manually produced and has higher accuracy, but it can also better reflect the wafer manufacturing status of the current equipment. After iteration, the estimated results output by the machine learning model will be more accurate and in line with actual production needs.

[0060] In another embodiment, the machine learning model is an Xgboost regression algorithm tree model.

[0061] Among them, Xgboost is an open source machine learning project that efficiently implements the GBDT algorithm and makes many improvements in algorithms and engineering. It is widely used in Kaggle competitions and many other machine learning competitions and has achieved good results. The full name of Xgboost is (eXtreme Gradient Boosting). It is a tool for large-scale parallel boosted trees. It is currently the fastest and best open source boosted tree toolkit. The algorithm used by Xgboost is an improvement on GBDT (gradient boosting decision tree), which can be used for both classification and regression problems. The biggest difference from GBDT is that Xgboost calculates the leaf node weights of the tree to be fitted in the next step by performing a second-order Taylor expansion on the objective function (the structure of the tree needs to be determined first), and then calculates the size of the loss reduction of each split node according to the loss function, so as to select the appropriate attribute for splitting according to the split loss.

[0062] The loss function formula obtained by the second-order expansion is closely related to the process of splitting nodes. First, traverse all the attributes of all nodes for splitting. Assuming that a value of the a attribute is selected as the splitting node, the weights of each leaf node in the tree structure can be calculated according to the formula obtained by Taylor expansion, so as to calculate the degree of loss reduction, and then comprehensively select the feature that makes the loss reduce the most as the splitting attribute of the current node. And so on, until the termination condition is met.

[0063] By using the Xgboost regression algorithm tree model, this solution can more accurately predict the wafer yield based on component data and improve the control accuracy of component data during the wafer manufacturing process.

[0064] Embodiment 2

[0065] Figure 2 Schematic diagram of the training process of the machine learning model provided in Example 2 of the present application. Figure 2 As shown, the specific steps include:

[0066] Step S201, obtaining historical component data of historically manufactured wafers and historical yield of wafer manufacturing.

[0067] The historical component data may be component data collected in the past wafer manufacturing process. The historical yield of wafer manufacturing may be the yield data of each batch of wafers in the historical manufacturing process. It can be known that the yield data may be the ratio of the number of qualified wafers to the number of all wafers produced in the current batch.

[0068] Step S202, performing data preprocessing and feature engineering processing on the historical component data to obtain sample data; and using the historical yield as a label for the sample data.

[0069] Among them, data preprocessing is used to improve the quality of sample data and make the sample data more suitable for analysis and model training. Common data preprocessing methods include data cleaning, data conversion, data integration, data reduction, etc. For example, duplicate data, abnormal data, and missing data in sample data can be processed to clean the sample data.

[0070] Feature engineering is used to extract features from sample data through data mining technology. Common feature engineering processing methods include standardization, interval scaling, normalization, quantitative feature binarization, qualitative feature dummy coding, missing value processing, and data conversion, etc.

[0071] The advantage of this arrangement of the technical solution is that it improves the standardization of sample data, accelerates the convergence speed of model training, improves training efficiency, and makes the training effect of the model faster and more accurate.

[0072] Step S203: input the labeled sample data into the initial model, train the initial model, and obtain a machine learning model.

[0073] The training method can be supervised training or unsupervised training.

[0074] Specifically, the labeled sample data can be arranged in the order of time nodes and input into the initial model, the initial model can be trained, and the parameters of the initial model can be adjusted in each training round until the training is completed to obtain the machine learning model.

[0075] During the training process, the training end condition may be that the prediction accuracy of the machine learning model is high enough, for example, the prediction accuracy exceeds 80%. The training end condition may also be that the number of training times reaches a preset number. For example, the preset number of times is 20,000 times. Those skilled in the art may set a preset threshold or preset number of times according to actual conditions so that the machine learning model will not overfit while learning the factors affecting the wafer yield of the component data.

[0076] The advantage of this technical solution is that by providing a training process for the machine learning model, an accurate machine learning model can be obtained. The control of the number and authenticity of samples can improve the training speed and accuracy of the machine learning model.

[0077] Embodiment 3

[0078] Figure 3: is a flow chart of a wafer yield control method based on component data provided in the third embodiment of the present application. Figure 3 As shown, the specific steps include:

[0079] Step S301, obtaining basic component data of the wafer currently being manufactured; wherein the basic component data includes component name, component parameters, and the manufacturing link where the component is located;

[0080] Step S302, determining the importance index of each basic component data and the wafer yield based on the first algorithm included in the machine learning model;

[0081] In this solution, the machine learning model may include a portion of an algorithm for filtering out importance indicators related to wafer yield from basic component data. It can be understood that the importance indicator can be used to characterize the importance of the impact of the component data on the wafer yield.

[0082] Step S303, determining a correlation index between each basic component data and the wafer yield based on a second algorithm included in the machine learning model;

[0083] In this solution, the machine learning model may include another part of the algorithm for filtering out correlation indicators related to wafer yield from basic component data. It can be understood that the correlation indicator can be used to characterize the correlation of the impact of the component data on the wafer yield. For example, an increase or decrease in the value will have a positive or negative correlation with the wafer yield.

[0084] Step S304: determining key component data from basic component data according to the importance index and / or the relevance index.

[0085] In this solution, whether the basic component data is key component data can be determined based on the importance index and / or the correlation index. For example, if the importance index is high enough, it can be determined as key component data, or if the correlation index shows a positive correlation or negative correlation, it can be determined as key component data, or if both the importance index is high enough and the correlation index shows a positive correlation, it can be determined as key component data.

[0086] Step S305, inputting the key component data into the machine learning model to determine whether the estimated yield of the currently manufactured wafers reaches a preset threshold;

[0087] Step S306: If the preset threshold is not reached, an instruction to adjust the key component data is generated.

[0088] Based on the above technical solutions, this technical solution provides a specific method for screening out key component data from basic component data based on a machine learning model. Through such a setting, interference with component data that has little or no impact on wafer yield can be avoided. Through key component data, accurate data can be better provided for wafer yield estimation and component data adjustment.

[0089] In one embodiment, determining the key component data from the basic component data according to the importance index and / or the relevance index includes:

[0090] If the importance index of the current basic component data exceeds the importance threshold, the current basic component data is determined to be key component data;

[0091] or,

[0092] If the correlation index of the current basic component data exceeds the positive correlation threshold, the current basic component data is determined to be key component data, and is positively correlated; if the correlation index of the current basic component data is less than the negative correlation threshold, the current basic component data is determined to be key component data, and is negatively correlated;

[0093] or,

[0094] If the importance index of the current basic component data exceeds the importance threshold, and the correlation index of the current basic component data exceeds the positive correlation threshold, the current basic component data is determined to be key component data; or, if the importance index of the current basic component data exceeds the importance threshold, and the correlation index of the current basic component data is less than the negative correlation threshold, the current basic component data is determined to be key component data.

[0095] The correlation may include being greater than a positive correlation threshold or being less than a negative correlation threshold, both of which may reflect the degree of correlation between the current component data and the wafer yield.

[0096] Here, the importance value range can be 0 to 1, for example, the importance threshold can be 0.8, and the correlation can be positive or negative, and the value range can be -1 to 1, where -1 to 0 is negative correlation and 0 to 1 is positive correlation. The two can be combined to find the key component data. The two indicators can be output based on one model. For example, the correlation indicator can generally be output with a small algorithm in the data processing and feature engineering stage, and the importance indicator is generally output in the model training stage.

[0097] This arrangement of the scheme can better reflect the impact of component data on wafer yield, and can more accurately determine key component data, providing a precise control range for subsequent wafer yield prediction and component data adjustment.

[0098] This scheme also provides a preferred implementation scheme, which is as follows:

[0099] The first step is to collect data:

[0100] Feature data:

[0101] Contains product data of major components that are widely used in equipment and production lines. Component attribute data includes component size, material, etc. Historical target data: can be defective product count or yield data.

[0102] The second step is to perform feature engineering on the collected data, convert the raw data into feature representations that can be used by machine learning algorithms, and perform mining extraction and feature engineering, such as text data encoding, feature normalization or standardization operations. Set thresholds for the collected historical wafer yield data, and issue an alarm if the threshold range is exceeded.

[0103] The third step is to build a regression algorithm tree model such as Xgboost, perform Bayesian optimization on the hyperparameters, find the optimal model hyperparameters for training, and save the trained model.

[0104] The fourth step is to output the importance ranking of component features and yield based on the regression algorithm tree model. Through correlation analysis, output the correlation coefficient ranking of component features and yield. Perform attribution analysis based on importance index and correlation index, explain the correlation between specific component factors and yield, determine which component factors play a key role in improving or reducing the yield, and find out the key component factors that affect the yield.

[0105] The fifth step is to adjust the component factors for the wafers to be predicted in production based on the key yield factors output in the previous step. Similarly, process the relevant component data features, call the trained model, and output the wafer yield regression prediction value.

[0106] The sixth step is to determine the yield threshold based on the output wafer yield. If it is lower than the threshold, the machine engineer will be notified.

[0107] The seventh step is to iterate and optimize the model regularly based on the newly added historical task data to continuously improve the model's effectiveness.

[0108] This technical solution, 1. Outputs the component feature importance index and correlation index through a machine learning model, which can help determine which component factors have a greater impact on the yield. Combined with the determination of the machine and time window for yield analysis, cross-analyze components and yield-related factors, and attribute and define possible related factors for abnormal processing, and accumulate abnormal data features. Through the prediction model, based on different component data, the future wafer yield situation can be simulated, and predictions and intervention adjustments can be made in advance. Through the prediction model, for real-time wafer production, combined with current component data, yield threshold judgments can be made and timely alarms can be issued. Closely synchronizing component data with the prediction model system can play an important role in improving production efficiency and yield.

[0109] Embodiment 4

[0110] Figure 4 Schematic diagram of the structure of the wafer yield control device based on component data provided in the fourth embodiment of the present application. Figure 4 As shown, specifically including the following:

[0111] The basic component data acquisition module 410 is used to acquire the basic component data of the wafer currently being manufactured; wherein the basic component data includes the component name, component parameters, and the manufacturing link where the component is located;

[0112] A key component data determination module 420, for screening the basic component data based on predetermined key component data associated with wafer yield;

[0113] The wafer yield prediction module 430 is used to input the key component data into the machine learning model to determine whether the estimated yield of the wafer currently manufactured reaches a preset threshold;

[0114] The component data adjustment module 440 is used to generate an instruction to adjust the key component data if the preset threshold is not reached.

[0115] The wafer yield control device based on component data in the embodiment of the present application can be a device, or a component, integrated circuit, or chip in a terminal. The device can be a mobile electronic device or a non-mobile electronic device. Exemplarily, the mobile electronic device can be a mobile phone, a tablet computer, a laptop computer, a PDA, an in-vehicle electronic device, a wearable device, an ultra-mobile personal computer (ultra-mobile personal computer, UMPC), a netbook or a personal digital assistant (personal digital assistant, PDA), etc., and the non-mobile electronic device can be a server, a network attached storage (Network Attached Storage, NAS), a personal computer (personal computer, PC), a television (television, TV), a teller machine or a self-service machine, etc., which is not specifically limited in the embodiment of the present application.

[0116] The wafer yield control device based on component data in the embodiment of the present application can be a device with an operating system. The operating system can be an Android operating system, an iOS operating system, or other possible operating systems, which are not specifically limited in the embodiment of the present application.

[0117] The wafer yield control device based on component data provided in the embodiment of the present application can implement each process implemented in the above method embodiment. To avoid repetition, it will not be repeated here.

[0118] Embodiment 5

[0119] In addition, the embodiment of the present application also provides a computer device, Figure 5 Schematic diagram of the structure of the computer device provided in Example 5 of the present application. Figure 5 As shown, the device includes a memory 51 for storing computer-readable instructions and a processor 52 for executing the computer-readable instructions, wherein when the computer-readable instructions are executed by the processor, the processor is triggered to execute the method described.

[0120] The methods and / or embodiments in the embodiments of the present application may be implemented as computer software programs. For example, the embodiments of the present disclosure include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes program code for executing the method shown in the flowchart. When the computer program is executed by the processing unit, the above functions defined in the method of the present application are executed.

[0121] It should be noted that the computer-readable medium described in the present application may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with 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 the present application, a computer-readable medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, device or device.

[0122] In the present application, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, device, or device. The program code contained on the computer-readable medium may be transmitted using any suitable medium, including but not limited to: wireless, wire, optical cable, RF, etc., or any suitable combination of the above.

[0123] Computer program code for performing the operations of the present application may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate 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 may 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 may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0124] The flow chart or block diagram in the accompanying drawings shows the possible architecture, function and operation of the equipment, method and computer program product according to various embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some implementations as replacements, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated system for hardware that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0125] As another aspect, the embodiments of the present application further provide a computer-readable medium, which may be included in the device described in the above embodiments; or may exist independently without being assembled into the device. The above computer-readable medium carries one or more computer-readable instructions, which may be executed by a processor to implement the steps of the methods and / or technical solutions of the above multiple embodiments of the present application.

[0126] In a typical configuration of the present application, the terminal and the equipment of the service network each include one or more processors (CPU), input / output interface, network interface and memory.

[0127] The memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0128] Computer readable media include permanent and non-permanent, removable and non-removable media, and can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, modules of programs or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, read-only compact disk (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices or any other non-transmission medium that can be used to store information that can be accessed by a computing device.

[0129] In addition, an embodiment of the present application further provides a computer program, which is stored in a computer device, so that the computer device executes the method for controlling code execution.

[0130] It should be noted that the present application can be implemented in software and / or a combination of software and hardware, for example, can be implemented using an application specific integrated circuit (ASIC), a general purpose computer or any other similar hardware device. In certain embodiments, the software program of the present application can be executed by a processor to implement the above steps or functions. Similarly, the software program of the present 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 the present application can be implemented using hardware, for example, as a circuit that cooperates with a processor to perform each step or function.

[0131] It is obvious to those skilled in the art that the present application is not limited to the details of the above exemplary embodiments, and that the present application can be implemented in other specific forms without departing from the spirit or basic features of the present application. Therefore, from any point of view, the embodiments should be regarded as exemplary and non-restrictive, and the scope of the present application is limited by the attached claims rather than the above description, so it is intended to include all changes that fall within the meaning and scope of the equivalent elements of the claims in the present application. Any figure mark in the claims should not be regarded as limiting the claims involved. In addition, it is obvious that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or devices stated in the device claim can also be implemented by one unit or device through software or hardware. The words first, second, etc. are used to indicate names, and do not indicate any particular order.

Claims

1. A wafer yield control method based on component data, characterized in that: The method comprises: Obtaining basic component data of the wafer currently being manufactured; wherein the basic component data includes component name, component parameters, and the manufacturing link where the component is located; Based on predetermined key component data associated with wafer yield, the basic component data is screened; Inputting the key component data into a machine learning model to determine whether the estimated yield of the currently manufactured wafers reaches a preset threshold; If the preset threshold is not reached, an instruction to adjust the key component data is generated; The basic component data is screened based on predetermined key component data associated with wafer yield, including: Determine the importance index of each basic component data and the wafer yield based on a first algorithm included in the machine learning model; Determine a correlation index between each basic component data and the wafer yield based on a second algorithm included in the machine learning model; Determining key component data from basic component data according to the importance index and / or the relevance index; Wherein, determining the key component data from the basic component data according to the importance index and / or the relevance index includes: If the importance index of the current basic component data exceeds the importance threshold, the current basic component data is determined to be key component data; or, If the correlation index of the current basic component data exceeds the positive correlation threshold, the current basic component data is determined to be key component data, and is positively correlated; if the correlation index of the current basic component data is less than the negative correlation threshold, the current basic component data is determined to be key component data, and is negatively correlated; or, If the importance index of the current basic component data exceeds the importance threshold, and the correlation index of the current basic component data exceeds the positive correlation threshold, the current basic component data is determined to be key component data; or, if the importance index of the current basic component data exceeds the importance threshold, and the correlation index of the current basic component data is less than the negative correlation threshold, the current basic component data is determined to be key component data.

2. The method according to claim 1, characterized in that The training process of the machine learning model includes: Obtain historical component data for historically manufactured wafers, as well as historical yields of wafer manufacturing; Performing data preprocessing and feature engineering processing on the historical component data to obtain sample data; and using the historical yield as a label for the sample data; The labeled sample data is input into the initial model, and the initial model is trained to obtain a machine learning model.

3. The method according to claim 1, characterized in that After generating an instruction to adjust the key component data if the preset threshold is not reached, the method further includes: After adjusting the key component data, inputting the adjusted updated component data into the machine learning model; Identify whether the predicted yield of the output of the machine learning model reaches a preset threshold; If the predicted yield does not reach a preset threshold, an alarm message is generated.

4. The method according to claim 1, characterized in that After inputting the key component data into the machine learning model to determine whether the estimated yield of the currently manufactured wafers reaches a preset threshold, the method further includes: If the preset threshold is reached, the wafers are manufactured according to the current key component data, and the yield of the currently manufactured wafers is counted as updated sample data; The machine learning model is iteratively updated according to the updated sample data.

5. The method according to any one of claims 1 to 4, characterized in that The machine learning model is an Xgboost regression algorithm tree model.

6. A wafer yield control device based on component data, characterized in that: The device comprises: A basic component data acquisition module is used to acquire basic component data of the wafer currently being manufactured; wherein the basic component data includes component name, component parameters, and the manufacturing link where the component is located; A key component data determination module, used to screen the basic component data based on predetermined key component data associated with wafer yield; A wafer yield prediction module, used to input the key component data into a machine learning model to determine whether the estimated yield of the currently manufactured wafers reaches a preset threshold; A component data adjustment module, used for generating an instruction to adjust the key component data if the preset threshold is not reached; Wherein, the key component data determination module is specifically used for: Determine the importance index of each basic component data and the wafer yield based on a first algorithm included in the machine learning model; Determine a correlation index between each basic component data and the wafer yield based on a second algorithm included in the machine learning model; Determining key component data from basic component data according to the importance index and / or the relevance index; The key component data determination module is also specifically used for: If the importance index of the current basic component data exceeds the importance threshold, the current basic component data is determined to be key component data; or, If the correlation index of the current basic component data exceeds the positive correlation threshold, the current basic component data is determined to be key component data, and is positively correlated; if the correlation index of the current basic component data is less than the negative correlation threshold, the current basic component data is determined to be key component data, and is negatively correlated; or, If the importance index of the current basic component data exceeds the importance threshold, and the correlation index of the current basic component data exceeds the positive correlation threshold, the current basic component data is determined to be key component data; or, if the importance index of the current basic component data exceeds the importance threshold, and the correlation index of the current basic component data is less than the negative correlation threshold, the current basic component data is determined to be key component data.

7. A computer device, characterized in that: The device comprises: One or more processors; and a memory storing computer program instructions, wherein when the computer program instructions are executed, the processor executes the wafer yield control method based on component data as described in any one of claims 1-5.

8. A computer-readable medium, characterized in that Computer program instructions are stored thereon, and the computer program instructions can be executed by a processor to implement the wafer yield control method based on component data as described in any one of claims 1-5.

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