Semiconductor manufacturing equipment management system and method thereof

Abstract

To provide a semiconductor manufacturing apparatus management system and a method therefor for determining a recipe depending on aging of a semiconductor manufacturing apparatus.SOLUTION: A semiconductor manufacturing management system for determining a recipe for deposition processing in a semiconductor manufacturing apparatus for depositing an SiC film on an SiC substrate using epitaxial growth comprises: an input device for monitoring a thickness of a by-product on a susceptor; one or more storage devices for storing history information on past deposition processes of a semiconductor manufacturing apparatus (for example, a chamber and an SiC epitaxial growth device); and one or more processors that acquire a target value of a characteristic of a deposited film in the next deposition process by the semiconductor manufacturing apparatus, decide a recipe for the next deposition process of the semiconductor manufacturing apparatus using one or more functions including an estimation model on the basis of the history information and the target value, and determine necessity of maintenance of the semiconductor manufacturing apparatus on the basis of a result of comparing a value of an aging function with a specified value.SELECTED DRAWING: Figure 8

Description

[Technical field]

[0001] The present invention relates to a management system for semiconductor manufacturing equipment and a method thereof. [Background technology]

[0002] For example, semiconductor power elements are required to have high breakdown voltage, low on-resistance, and low switching loss, but silicon (Si) power elements, which are currently the mainstream, are approaching their theoretical performance limits. Silicon carbide (SiC) has a dielectric breakdown field strength approximately one order of magnitude higher than that of Si. Therefore, by making the drift layer that maintains the breakdown voltage approximately one-tenth as thin and increasing the impurity concentration by approximately 100 times, it is theoretically possible to reduce the element resistance by three orders of magnitude or more. In addition, SiC has a band gap approximately three times larger than that of Si, which allows it to operate at high temperatures. SiC semiconductor elements are expected to exceed the performance of Si semiconductor elements, and the development of semiconductor manufacturing equipment for SiC is underway.

[0003] One of the semiconductor manufacturing equipment for SiC is the SiC epitaxial growth equipment. Epitaxial growth of SiC is a technology to form a SiC film on an off-cut SiC substrate. Generally, SiC substrates have a high donor concentration, so the donor concentration and film thickness must be adjusted according to the withstand voltage used, and epitaxial growth is performed to manufacture SiC elements. The requirements for epitaxial growth technology are diverse, such as larger diameter epitaxial growth in response to larger diameter substrates, ensuring uniformity of donor concentration, ensuring uniformity of epitaxial film thickness, high-speed growth, and low crystal defects.

[0004] To meet all of these requirements with precision, equipment with many control parameters (input parameters) is required. Accordingly, to fully utilize the performance of semiconductor manufacturing equipment, it is necessary to determine several to several dozens of control parameters. Therefore, as equipment performance improves, the equipment structure becomes more complex, making it increasingly difficult to determine the combination of control parameters that will produce the desired film formation results. This lengthens device development time and causes development costs to increase.

[0005] Furthermore, in SiC epitaxial growth equipment, by-products derived from the material gases adhere firmly to components other than the SiC substrate (inner walls and susceptor) during SiC epitaxial growth. These by-products are exposed to high temperatures during epitaxial growth and evaporate, causing deterioration of the epitaxial results over time. In other CVD equipment, by-products can be easily removed by gas cleaning, but no effective gas cleaning method has been established for SiC at present. For this reason, frequent maintenance such as opening the chamber to remove the by-products is required, which increases development costs.

[0006] Therefore, in order to reduce development costs, there is a demand for a function and equipment that can semi-automatically search for optimal control parameters taking into account changes over time, easily bring out the best performance of the equipment, and further notify the timing of maintenance. Methods for modifying a process recipe taking into account changes over time are described in Patent Documents 1 and 2.

[0007] Patent Document 1 is an example of a document disclosing a means for correcting deviations in film formation results due to changes over time. Patent Document 1 discloses a method for correcting film thickness deviations. Specifically, it discloses the following: "A control device controls the operation of a substrate processing apparatus that forms a film on a substrate by atomic layer deposition, and includes a recipe storage unit that stores film formation conditions according to the type of film, a model storage unit that stores a process model that represents the effect of the film formation conditions on the characteristics of the film, a log storage unit that stores actual measured values ​​of the film formation conditions during film formation, and a control unit that calculates film formation conditions that satisfy the target characteristics of the film based on measurement results of the characteristics of the film formed under the film formation conditions stored in the recipe storage unit, the process model stored in the model storage unit, and the actual measured values ​​of the film formation conditions stored in the log storage unit" (abstract).

[0008] For example, Patent Document 2 is an example of a document that discloses a means for correcting differences due to assembly and dimensional variations between equipment and chambers, i.e., machine differences, and changes over time. Patent Document 2 discloses the following: "In a semiconductor manufacturing apparatus that includes a plurality of semiconductor manufacturing equipment and a control device that controls each of these semiconductor manufacturing equipment, and controls the plurality of semiconductor manufacturing equipment according to one supplied recipe to manufacture a common semiconductor device, a recipe correction amount is calculated by referring to recipe correction data stored in advance based on difference data between the performance of the equipment for which the recipe is used when the recipe is used and the equipment performance obtained by using a semiconductor manufacturing equipment to be used in the future among the plurality of semiconductor manufacturing equipment, and the supplied recipe is corrected based on the calculated recipe correction amount and supplied to the semiconductor manufacturing equipment to be used in the future." (Summary) [Prior art documents] [Patent documents]

[0009] [Patent Document 1] JP 2017-174983 A [Patent Document 2] JP 2013-135044 A Summary of the Invention [Problem to be solved by the invention]

[0010] However, in the method of Patent Document 1, the recipe is corrected when the deposition exceeds the tolerance, and depending on the amount of deviation, the next process cannot be proceeded. In other words, there is a risk of failure, which may cause an increase in development costs. In addition, when increasing the number of items to be corrected, it is necessary to create a new correlation model corresponding to the number of input parameters of the device, and creating the correlation model requires a lot of man-hours.

[0011] The method of Patent Document 2 also requires the preparation of correlation data of output results for each input parameter in advance as a means of correcting for machine differences and changes over time. When increasing the number of items to be corrected, a correlation model corresponding to the number of input parameters of the device must be newly created, and creating the correlation model requires a lot of man-hours. In addition, in order to perform the correction, processing must be performed in advance after maintenance using a recipe for constructing performance history data, which can lead to increased development costs. [Means for solving the problem]

[0012] Among the inventions disclosed in this application, a brief summary of representative inventions is as follows. One aspect of the present invention is a management system that determines a recipe for a semiconductor manufacturing equipment, the management system including one or more storage devices and one or more processors, wherein the one or more storage devices store history information of past processing of the semiconductor manufacturing equipment, the one or more processors acquire a target value of a specific object in a next processing by the semiconductor manufacturing equipment, and determine a recipe for the next processing of the semiconductor manufacturing equipment using one or more functions including an estimation model based on the history information and the target value, the input of the estimation model includes recipe candidates for the next processing of the semiconductor manufacturing equipment, and the output of the estimation model includes an estimated value of the specific object. Effect of the Invention

[0013] According to one aspect of the present invention, a recipe can be determined according to changes over time in a semiconductor manufacturing device.

[0014] Other objects, configurations and advantages than those mentioned above will become apparent from the following description of the embodiments. [Brief description of the drawings]

[0015] [Figure 1] In the first embodiment, an estimation of the film thickness of the Nth film forming process according to a comparative example is shown. [Diagram 2] In the first embodiment, the film thickness of the Nth film forming process is estimated. [Diagram 3]In the first embodiment, an example of training data used to construct an estimation model after predicting an input that results in an output of X for the Nth time is shown. [Figure 4] In the first embodiment, the relationship between the processing order and the output (film thickness) in the training data is shown. [Diagram 5] In the first embodiment, the relationship between the processing order in the training data and one of the input parameters (gas flow rate) is shown. [Figure 6] In the first embodiment, a method for estimating suitable inputs (recipe) using an estimation model is illustrated. [Figure 7] In the first embodiment, it is shown that the combination of input values ​​and output values ​​of the estimation model can change for each process. [Figure 8] In the first embodiment, a configuration example of a semiconductor manufacturing management system is shown typically. [Figure 9] 1 illustrates a schematic diagram of an example of a logical configuration of a semiconductor manufacturing management system in a first embodiment. [Figure 10] 2A and 2B are schematic diagrams illustrating another example of a logical configuration of the semiconductor manufacturing control system in the first embodiment. [Figure 11] 1 shows an example of training data for updating an estimation model, which is stored in a training data database in the first embodiment. [Figure 12] In the first embodiment, an example of a target value for the next film forming process will be shown. [Figure 13] 2 shows an example of optimal input values ​​(recipe) determined by the recipe search unit in the first embodiment. [Figure 14] FIG. 2 is a logical configuration diagram for explaining the processing of the semiconductor manufacturing control system in the first embodiment. [Figure 15] 2 shows a flowchart of a process executed by a semiconductor manufacturing control system in the first embodiment. [Figure 16] In the first embodiment, an example of the configuration of a cluster device is shown. [Figure 17] In the first embodiment, a configuration example of a SiC epi-cluster apparatus is shown. [Figure 18A]11 is a graph showing an example of a change over time in output by an estimation model in the second embodiment. [Figure 18B] 11 is a graph showing an example of a change over time in output by an estimation model in the second embodiment. [Figure 18C] 11 is a graph showing an example of a change over time in output by an estimation model in the second embodiment. [Figure 19] FIG. 11 is a conceptual diagram of training data in the second embodiment. [Figure 20] In the second embodiment, a more specific example of the training data will be given. [Figure 21] In the second embodiment, an example of a target value for the next film forming process will be shown. [Figure 22] In the second embodiment, an example of optimal input values ​​(recipe) determined by the recipe search unit is shown. [Figure 23] FIG. 11 is a logical configuration diagram for explaining the processing of the semiconductor manufacturing control system of the present embodiment in the second embodiment. [Figure 24] 11 shows a flowchart of a process executed by a semiconductor manufacturing control system in the second embodiment. [Diagram 25] 13 shows a flowchart of a process executed by a semiconductor manufacturing control system in the third embodiment. [Figure 26] 13 shows an example of a recipe search function setting window in the third embodiment. [Figure 27] 13 shows an example of a target value setting input window in the third embodiment. [Figure 28] 13 shows an example of a board information input window in the third embodiment. [Figure 29] 13 shows an example of an evaluation result input window in the third embodiment. [Diagram 30] In the third embodiment, an example of an optimal recipe output window is shown. [Diagram 31] 13 shows an example of a maintenance notification message box in embodiment 3. [Diagram 32] 13 shows an example of a maintenance evaluation result output window in the third embodiment. [Diagram 33]13 shows an example of a history information output window in the third embodiment. [Diagram 34] In the fourth embodiment, an example of input data for training data including factors thought to affect changes over time is shown below. [Diagram 35] In the fifth embodiment, an example of input data for training data including factors thought to affect changes over time is shown below. [Diagram 36] In the sixth embodiment, an example of a graph of the function g_a(t) is shown. [Figure 37] 13 shows an example of a graph of a time-dependent change function of recipe a in the sixth embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0016] Hereinafter, embodiments of the present invention will be described with reference to the drawings. Note that in the following embodiments, when necessary for convenience, the description will be divided into a plurality of sections or embodiments, but unless otherwise specified, they are not unrelated to each other, and one is related to the other in terms of partial or full modification, details, supplementary explanation, etc.

[0017] In addition, in the following embodiments, when the number of elements (including the number, numerical value, amount, range, etc.) is mentioned, it is not limited to the specific number, and may be more than or less than the specific number, unless otherwise specified or clearly limited in principle to a specific number. Furthermore, in the following embodiments, the components (including element steps, etc.) are not necessarily essential, unless otherwise specified or clearly considered essential in principle. <Summary>

[0018] In this embodiment, the derivation of an appropriate recipe taking into account the aging of a semiconductor manufacturing device will be described. The semiconductor manufacturing management system autonomously estimates changes over time in the chamber from historical information on past semiconductor manufacturing processes, and derives an appropriate recipe (processing conditions for semiconductor devices) suited to the equipment status.

[0019] In this embodiment, since the change over time is estimated from the processing history information, there is no need to form a film to check the change over time, and the number of steps can be reduced. In addition, in this embodiment, since a machine learning model is configured based on the processing history information, there is no need to prepare correlation data for each parameter that configures the recipe. Furthermore, in this embodiment, since an appropriate recipe that takes into account the change over time can be derived, the number of maintenance steps can be reduced.

[0020] As described above, the semiconductor manufacturing control system of this embodiment determines an appropriate recipe for the next process by using a machine learning model (estimation model). The input of the estimation model includes a recipe, and the output includes an estimate of a value obtained as a result of processing by the semiconductor manufacturing equipment (estimate of a specific target).

[0021] An example of a film formation process will be described below. An example of a film formation process is, for example, epitaxial film formation of Si or SiC. The input in the film formation process (estimation model) includes a recipe, and the output includes an estimate of the characteristics of the film to be formed. The film characteristics include, for example, a film thickness or an impurity concentration profile. In the example described below, the film thickness is taken as the output. Note that the features of this embodiment can be applied to semiconductor manufacturing processes other than the film formation process.

[0022] <Embodiment 1> For the sake of simplicity, an example will be described in which the recipe for the film formation process that is repeatedly executed is the same. FIG. 1 shows an estimation of the film thickness of the Nth film formation process by a comparative example. The estimation model of the comparative example estimates the film thickness from the recipe without referring to the process history information. In other words, the input of the estimation model is only the recipe for each film formation process. If there is a change over time in the film formation device, the film thickness (output) will vary even if the same recipe A (input A) is used. Therefore, the estimation model of the comparative example cannot accurately estimate the film thickness due to the Nth process. In other words, the comparative example cannot estimate the input A that becomes the target A (film thickness A).

[0023] 2 shows the estimation of the film thickness of the Nth film forming process according to this embodiment. First, processing history information is added to training data, and an estimation model is constructed using machine learning suitable for time series analysis (e.g., RNN (Recurrent Neural Network) or LSTM (Long-Short Term Memory)). Furthermore, an optimal solution search method is used to predict an input (input A) that causes the estimation model to output a value close to target A of the Nth process. Here, processing history information is, for example, the number of processed wafers or the number of times of processing.

[0024] Next, consider an example where the output (film thickness) remains constant despite changes over time. The input (recipe) required to obtain a constant output (film thickness) changes due to the effects of changes over time. If we consider the amount of output fluctuation due to changes over time to be a function of the input, we believe that accurate predictions cannot be made even if the number of processed wafers or number of times processed is used as processing history information.

[0025] Therefore, by using recipe history as processing history information, it becomes possible to autonomously learn the feature amount of the change over time. Fig. 3 shows an example of training data 31 used to construct an estimation model after predicting an input that will result in an output of X at the Nth time. Fig. 4 shows the relationship between the processing order and the output (film thickness) in the training data 31, and Fig. 5 shows the relationship between the processing order and one of the input parameters (gas flow rate) in the training data 31.

[0026] The training data used to construct an estimation model for predicting an input that will result in an output of X at the Nth time is the data obtained by removing the Nth record from the training data 31 shown in FIG. 3. The training data 31 indicates the semiconductor manufacturing recipe that is the input of the estimation model, the film thickness and concentration that are the output, their respective in-plane distributions, and recipe record information. The recipe history information of each record indicates the time series of the recipe for all past processing (after chamber maintenance). Note that all past processing may also mean all processing since chamber maintenance. The recipe history information "-" of the first record (No. 1 in the table) indicates that there is no processing (recipe) shown as history before the processing of that record.

[0027] 6 is a schematic diagram showing a method for estimating an appropriate input (recipe) using an estimation model. The estimation model 23 outputs an output value 42 for an input value 41. The input value 41 and the output value 42 are represented, for example, by a vector. This method searches for an input value 41 that causes an error between a target value (also simply called a target value) 35 of a specific target in a semiconductor manufacturing device and an output value (estimated value) 42 to be within an allowable range.

[0028] The input values ​​41 include input values ​​(recipe candidates) for the semiconductor manufacturing equipment of the next process. By varying the recipe candidates in the input values ​​41, a recipe (input values ​​41) that obtains an output value 42 within an acceptable range is searched for. The method further adds the input values ​​41 to the training data 31 along with recipe history information. The method further updates the estimation model 23 using the updated training data 31.

[0029] In actual semiconductor manufacturing, the same target is not always set every time. Therefore, as shown in Fig. 7, the combination of input and output values ​​of the estimation model may change for each process. By applying recipe history information as processing history information, it becomes possible to autonomously capture feature quantities associated with changes over time and build an accurate estimation model that includes changes over time.

[0030] A more specific configuration example of this embodiment will be described below. Fig. 8 shows a schematic configuration example of a semiconductor manufacturing management system. In the example of Fig. 8, the semiconductor manufacturing management system 100 is composed of one computer. The semiconductor manufacturing management system 100 includes a processor 110, a memory 120, an auxiliary storage device 130, a network (NW) interface 140, an I / O interface 145, an input device 151, and an output device 152. The above components are connected to each other by a bus. The memory 120, the auxiliary storage device 130, or a combination thereof is a storage device.

[0031] The memory 120 is composed of, for example, a semiconductor memory, and is mainly used to temporarily store programs and data. Programs stored in the memory 120 include an operating system (not shown), an integrated management program 121, an apparatus control program 122, an estimation model program 123, a recipe search program 124, and an analysis and evaluation program 125.

[0032] The integrated management program 121 manages the other programs and mediates communication between them. The equipment control program 122 controls semiconductor manufacturing equipment (e.g., a chamber). The estimation model program 123 is a model that takes a semiconductor manufacturing recipe as input and outputs an estimated value of a target object in semiconductor manufacturing (e.g., film characteristics in film formation), and any appropriate model such as a neural network, a support vector machine, or a kernel is used. The recipe search program 124 searches for an appropriate recipe for achieving a target value of the target object in semiconductor manufacturing using the estimation model program 123. The analysis and evaluation program 125 analyzes and evaluates the actual measurement value of the target object in semiconductor manufacturing.

[0033] The processor 110 executes various processes according to the programs stored in the memory 120. The processor 110 operates according to the programs to realize various functional units. For example, the processor 110 functions as an integrated management unit, an apparatus control unit, an estimation model, a recipe search unit, and an analysis and evaluation unit according to each of the above programs.

[0034] The auxiliary storage device 130 stores a training data database 131. The training data database 131 stores data for training an estimation model. The auxiliary storage device 130 is configured from a large-capacity storage device such as a hard disk drive or a solid-state drive, and is used to hold programs and data for a long period of time.

[0035] For ease of explanation, programs 121-125 are stored in memory 120, and training data database 131 is stored in auxiliary storage device 130, but the storage location of data in semiconductor manufacturing control system 100 is not limited. For example, the programs and data stored in auxiliary storage device 130 are loaded into memory 120 at startup or when necessary, and various processes of semiconductor manufacturing control system 100 are performed by processor 110 executing the programs. Therefore, in the following, processes executed by the functional units are processes by processor 110 or semiconductor manufacturing control system 100 in accordance with the programs.

[0036] The network interface 140 is an interface for connection to a network. The semiconductor manufacturing control system 100 communicates with other devices in the system or devices related to the system via the network interface 140. The input device 151 is a hardware device that allows a user to input instructions, information, and the like, and includes, for example, a keyboard and a pointing device. The output device 152 is a hardware device that displays various images for input and output, and is, for example, a display device.

[0037] The semiconductor manufacturing control system 100 includes one or more processors and one or more computing devices. Each processor may include single or multiple computing units or processing cores. A processor may be implemented as, for example, a central processing unit, a microprocessor, a microcomputer, a microcontroller, a digital signal processor, a state machine, a logic circuit, a graphic processing unit, a system on a chip, and / or any device that manipulates signals based on control instructions.

[0038] The functions of the semiconductor manufacturing control system 100 may be implemented by distributed processing using a computer system including multiple computers. The multiple computers communicate with each other via a network to execute processing in a coordinated manner. Figures 9 and 10 show schematic diagrams of other logical configurations of the semiconductor manufacturing control system.

[0039] 9, the integrated management unit 21, the device control unit 22, the estimation model 23, the recipe search unit 24, and the analysis and evaluation unit 25 communicate with each other via a network (NW) 200. For example, the integrated management unit 21, the device control unit 22, the estimation model 23, the recipe search unit 24, and the analysis and evaluation unit 25 are realized by programs installed in different computers. Distributed processing by multiple computers realizes rapid processing.

[0040] 10, the integrated management unit 21, the equipment control unit 22, and the analysis and evaluation unit 25 are implemented in one computer. The estimation model 23 and the recipe search unit 24 are implemented on the cloud. As will be described later, the processing by the recipe search unit 24 imposes the highest load, so by implementing the recipe search unit 24 on the cloud, rapid processing can be achieved. In addition, the recipe search unit 24 can be shared among multiple semiconductor manufacturing management systems.

[0041] 11 shows an example of training data 31 for updating the estimation model 23, which is stored in the training data database 131. The training data 31 includes input data 311 to be input to the estimation model 23, and output data 312 which is target data to be output from the estimation model 23.

[0042] In the example of FIG. 11, each record of the input data 311 indicates input values ​​for one film formation process that are input to the estimation model 23 in training the estimation model 23. The input values ​​are composed of multiple variable values ​​(variable parameter values) that constitute a film formation recipe. In the example of FIG. 11, the input values ​​are composed of a process time, a process temperature, a process pressure, gas flow rates of three different types of gas, and a carrier gas flow rate, and are represented as vectors. Each input value is a recipe for an actually performed film formation.

[0043] 11 illustrates three different types of gases, SiH4, C3H8, and N2, but other source gases containing Si, other source gases containing C, and other gases containing dopants (impurities) can be used. Other examples of source gases containing Si are SiH4 and SiClH3. The carrier gas is, for example, H2. In addition to these gases, the input value may include an assist gas (for example, HCl).

[0044] Each record of the output data 312 indicates a target value for an output value output from the estimation model 23 for one film formation process in training of the estimation model 23. The output value is composed of multiple variable values ​​indicating the characteristics of the formed film, and is expressed as a vector. In the example of FIG. 11, the output value is composed of the film thickness, the impurity concentration, and the crystal defect density. Each output value is an actual measurement value of the result of an actually performed film formation process.

[0045] Records of the same process number in the input data 311 and the output data 312 are associated with each other. In the example of Fig. 11, history information is not explicitly shown. The recipe history information of each record is the recipes of past processes (records) arranged in the processing order.

[0046] 12 shows an example of a target value 35 for the next film formation process. The target value 35 indicates a target value for the next process. In this example, it indicates a target value of the characteristics of the film obtained by the next film formation process. The elements of the target value 35 are the same as the elements of the output value of the estimation model 23. The recipe search unit 24 searches for an input value that will give an output value of the estimation model 23 close to the target value 35.

[0047] 13 shows an example of optimal input values ​​(recipe) 36 determined by the recipe search unit 24. The recipe search unit 24 sequentially inputs input values ​​to the estimation model 23, and searches for input values ​​for which the error between the output value and the target value 35 is within an allowable range, as the optimal input values ​​(recipe) 36.

[0048] The processing of the semiconductor manufacturing control system 100 will be described with reference to Figs. 14 and 15. Fig. 14 is a logical configuration diagram for explaining the processing of the semiconductor manufacturing control system 100. The recipe search unit 24 includes a model configuration unit 241, a recipe estimation unit 242, and a convergence determination unit 243. In order to obtain an optimal recipe for the next processing, the user includes a target value and an error allowable range in the input data 51 from the input device 151. When the user analyzes the processing target obtained by the actual processing, the user further inputs the analysis evaluation result as the input data 51 from the input device 151. In the example of the film formation processing, the analysis evaluation result is the output data of the training data 31 described with reference to Fig. 11. The analysis evaluation result is stored in the training data database 131.

[0049] The semiconductor manufacturing control system 100 outputs the optimum recipe for the next process as output data 52 from the output device 152. Furthermore, when the semiconductor manufacturing control system 100 analyzes the processing target obtained by the actual processing, the analysis and evaluation result is output from the output device 152 as output data 52.

[0050] 15 shows a flowchart of the process executed by the semiconductor manufacturing control system 100. First, the integrated management unit 21 acquires a target value and an allowable error range as input data 51 from the input device 151 (S101). The integrated management unit 21 passes the target value and the allowable error range to the recipe estimation unit 242.

[0051] The recipe estimation unit 242 uses the estimation model 23 to derive a recipe that realizes a value close to the target value, and estimates the processing result (S102). For example, the recipe estimation unit 242 uses a random search method or a simulated annealing method to determine an input value that includes a recipe that is closest to the target value. The output value of the estimation model 23 for the input value is the estimated processing result.

[0052] The input values ​​include the recipe for the current process and past recipe history. The recipe estimation unit 242 searches for input values ​​that have a common recipe history but a different recipe for the current process, and that have an output value within an allowable range. Each parameter value of the recipe has constraints (upper and lower limits) specific to the semiconductor manufacturing equipment. Candidate recipes are selected from within the range of the constraints. For example, upper and lower limits of the processing temperature and the pressure. Alternatively, a condition that clearly does not allow the target film formation may be set as the lower limit. For example, the processing temperature may be set to a temperature at which epitaxial growth of SiC occurs, such as 1000°C or higher.

[0053] Next, the convergence determination unit 243 acquires the estimated processing result from the recipe estimation unit 242, and determines whether the error between the estimated processing result and the target value is within the error tolerance range specified by the user (S103). If the estimated processing result is not within the error tolerance range from the target value (S103: NO), the convergence determination unit 243 prompts the user to re-input the target value and the error tolerance range on the output device 152 via the integrated management unit 21. The flow returns to step S101. Note that the user can carry out the processing even if the optimal recipe does not satisfy the tolerance range. This is similar to the other embodiments.

[0054] If the estimated processing result is within the allowable error range from the target value (S103: YES), the device control unit 22 causes the process execution unit 27 of the processing chamber to execute processing (in this example, a film formation process) using the determined optimal recipe, and further stores the optimal recipe in the training data database 131 (S104).

[0055] For example, the device control unit 22 acquires the optimum recipe determined by the recipe estimation unit 242 via the integrated management unit 21. The device control unit 22 specifies the optimum recipe and instructs the process execution unit 27 to execute processing, and also adds the optimum recipe to the input data 311 of the training data 31 via the integrated management unit 21. The process execution unit 27 executes processing according to the specified optimum recipe.

[0056] The analysis and evaluation unit 25 analyzes and evaluates the processing results by the process execution unit 27 (S105). For example, the analysis and evaluation unit 25 controls an analysis and evaluation device (analysis and evaluation chamber) that contains and analyzes the processing object (a substrate on which a film has been formed in this example) to perform analysis and evaluation of the processing object. In this example, the analysis and evaluation unit 25 analyzes and evaluates the characteristics of the formed film. The analysis and evaluation unit 25 stores the analysis and evaluation results in the training data database 131 via the integrated management unit 21 in association with the corresponding optimal recipe (S106).

[0057] The integrated management unit 21 may obtain the analysis and evaluation results from the user via the input device 151. The integrated management unit 21 stores the analysis and evaluation results obtained from the user in association with the corresponding optimal recipe in the training data database 131. The model configuration unit 241 updates the estimation model 23 using the updated training data database 131 (S107).

[0058] The analysis and evaluation unit 25 compares the analysis and evaluation result with the target value considering the allowable error (S108). If the analysis and evaluation result is within the allowable error range from the target value (S108: YES), the analysis and evaluation unit 25 presents the comparison result to the user via the integrated management unit 21 and ends this flow. If the analysis and evaluation result is not within the allowable error range from the target value (S108: NO), the analysis and evaluation unit 25 presents the comparison result to the user via the integrated management unit 21 and asks the user whether or not re-deposition is necessary. If re-deposition is necessary, the integrated management unit 21 instructs the recipe estimation unit 242 to execute step S102. If re-deposition is not necessary, this flow ends.

[0059] As described above, the semiconductor manufacturing in this embodiment uses a semiconductor manufacturing apparatus and an analysis and evaluation apparatus. These apparatuses may be clustered. This allows efficient semiconductor manufacturing. FIG. 16 shows an example of the configuration of a cluster apparatus 400. The cluster apparatus 400 includes a plurality of chambers. Specifically, the cluster apparatus 400 includes a substrate analysis chamber 401, a cleaning chamber 402, an analysis and evaluation chamber 403, a processing chamber 404, a regeneration chamber 405, and a load lock chamber and a transfer chamber 406.

[0060] The substrate analysis chamber 401 is a chamber for analyzing and evaluating a substrate. It can be used to obtain substrate information described in the third embodiment. The substrate analysis chamber 401 can be used to measure substrate shapes such as warpage, edge shapes, and plate thickness, or to evaluate dislocations, stacking faults, surface roughness (Ra, etc.), etc., using X-rays, PL light, laser light, a microscope, etc. The analysis and evaluation chamber 403 is a chamber for analyzing and evaluating a substrate processed in the processing chamber 404. The regeneration chamber 405 is a chamber for regenerating a substrate processed in the processing chamber 404. In the process described with reference to FIGS. 14 and 15, the process execution unit 27 is, for example, the processing chamber 404. The analysis and evaluation unit 25 or a user performs analysis and evaluation of the substrate processed by the analysis and evaluation chamber 403.

[0061] 17 shows an example of the configuration of a SiC epi-cluster apparatus 450. The SiC epi-cluster apparatus 450 includes a substrate analysis chamber 451, an epi-analysis chamber 452, a defect analysis chamber 453, an epi-growth chamber 454, a CMP (Chemical Mechanical Polishing) chamber 455, and a load lock chamber and a transfer chamber 456.

[0062] The epi growth chamber 454 is an example of a processing chamber, and is used to epitaxially grow a SiC film on a substrate. The epi analysis chamber 452 is an example of an analysis and evaluation chamber, and is used to analyze and evaluate the film thickness and impurity concentration (dopant concentration) of the epi film (i.e., SiC film) formed in the epi growth chamber 454. The defect analysis chamber 453 is an example of an analysis and evaluation chamber, and is used to analyze and evaluate defects in the epi film formed in the epi growth chamber 454 using X-rays, PL light, laser light, and a microscope. The CMP chamber 455 is an example of a regeneration chamber 405, and is used to reuse a substrate when the formed epi film does not have the desired characteristics.

[0063] 14 and 15, the process execution unit 27 is, for example, the epitaxial growth chamber 454. The analysis and evaluation unit 25 or a user performs analysis and evaluation of the SiC film formed by the epitaxial analysis chamber 452 and the defect analysis chamber 453 to obtain values ​​of the film thickness, impurity concentration, and crystal defect density. If the SiC film does not have the desired characteristics, for example, the device control unit 22 or a user removes the SiC film from the substrate in the CMP chamber 455 and epitaxially grows a SiC film on the substrate again.

[0064] The determination of an appropriate recipe according to this embodiment can be applied to various semiconductor processes (apparatuses). For example, it can be applied to lithography apparatuses, film formation apparatuses, pattern processing apparatuses, ion implantation apparatuses, cleaning apparatuses, etc. Lithography apparatuses include, for example, exposure apparatuses, electron beam lithography apparatuses, and X-ray lithography apparatuses. Film formation apparatuses include, for example, CVD (Chemical Vapor Deposition) apparatuses, PVD (Physical Vapor Deposition) apparatuses, evaporation apparatuses, sputtering apparatuses, and thermal oxidation apparatuses.

[0065] The pattern processing device includes, for example, a wet etching device, a dry etching device, an electron beam processing device, and a laser processing device. The ion implantation device includes, for example, a plasma doping device and an ion beam doping device. The cleaning device includes, for example, a liquid cleaning device and an ultrasonic cleaning device.

[0066] In optimizing the opening dimensions in an exposure tool, examples of input elements of the estimation model are the exposure dose, resist thickness, resist type, etc., and examples of output elements are the opening dimensions, deviation from the design dimensions, resist collapse, etc. In optimizing the film formation process of a CVD tool, examples of input elements are the gas flow rate, process temperature, applied bias, pressure, etc., and examples of output elements are the film stress, density, particles in the furnace, etc.

[0067] In optimizing the gate insulating film process for MOS and IGBT in CVD and thermal oxidation equipment, examples of input elements are gas flow rate, process temperature, furnace pressure, etc., and examples of output elements are interface state density, dielectric breakdown characteristics, channel mobility, PBTI & NBTI characteristics, etc. In optimizing the concentration profile in ion implantation equipment, examples of input elements are implantation energy, dose amount, etc., and examples of output elements are peak concentration position, tail shape in the depth direction, etc.

[0068] In optimizing the etching shape in a pattern processing device, examples of input elements are gas flow rate, applied bias, etc., and examples of output elements are trench shape (taper angle, sub-trench, roughness of trench bottom), etc. In optimizing the cleaning process in a cleaning device, examples of input elements are chemical type, chemical concentration, processing temperature, etc., and examples of output elements are particles, metal contamination, etching rate, actual concentration, etc. In optimizing the contact resistance in a laser annealing device, examples of input elements are wavelength, laser intensity, step, etc., and an example of an output element is resistance value.

[0069] As described above, in this embodiment, a recipe for the next process of the semiconductor manufacturing equipment is determined based on history information of past processes of the semiconductor manufacturing equipment and a specified target value, using one or more functions including the estimation model 23. The one or more functions in this embodiment are configured by the estimation model 23. The input of the estimation model 23 further includes the recipe history of the semiconductor manufacturing equipment.

[0070] As described above, in this embodiment, by using a machine learning technique using past history information of a semiconductor manufacturing device, it is possible to autonomously capture feature quantities associated with changes over time and build an accurate estimation model including changes over time. In addition, since an optimal recipe that takes changes over time into consideration can be derived, the number of maintenance operations can be reduced. In addition, since a machine learning model is formed based on history information, it is not necessary to prepare correlation data.

[0071] <Embodiment 2> In the second embodiment, the semiconductor manufacturing equipment determines whether maintenance is required. This reduces the number of maintenance operations. For example, the semiconductor manufacturing management system 100 determines that maintenance is required when the estimated result of the optimal recipe found by the search is not within an allowable range from the target value.

[0072] 18A, 18B, and 18C are graphs showing an example of the change over time in output by an estimation model. In each graph, the horizontal axis indicates the gas flow rate as an input to the estimation model, and the vertical axis indicates the concentration distribution as an output of the estimation model. In each graph, a solid function curve 501 indicates the relationship between the input value and the output value of the estimation model. The semiconductor manufacturing equipment has a settable range for the input value. Also, an allowable range is set based on a target value for the output value. In each graph, a dashed rectangle 502 indicates a region that satisfies both the settable range and the allowable range.

[0073] FIG. 18A shows a graph in a state where the change over time is small. The appropriate gas flow rate that achieves an output within an allowable range is within the settable range. In this way, when the effect of the change over time is small, an optimal value for the input that satisfies the target value exists, and the optimal recipe can be searched for. FIG. 18B and FIG. 18C show graphs of an estimated model that has been affected by the change over time. The function curve 501 of the estimated model is expected to shift in the up, down, left, and right directions.

[0074] As shown in Fig. 18B, when function curve 501 deviates significantly upward, even the extreme value falls outside the allowable range. As shown in Fig. 18C, when function curve 501 deviates significantly to the right, the input value corresponding to the output value within the allowable range falls outside the settable range. In other words, if a recipe in which the output value falls within the allowable range based on the target value cannot be found even after searching for an optimal recipe, it should be determined that maintenance of the semiconductor manufacturing equipment is necessary.

[0075] In this embodiment, an estimation model is constructed by including maintenance information in the training data. The estimation model makes it possible to determine whether maintenance is necessary and to derive an optimal recipe. Fig. 19 is a conceptual diagram of training data 61. The difference from the training data of the first embodiment is that the input value includes information on performed maintenance, and further, the history information includes maintenance history in addition to recipe history.

[0076] For example, suppose there are three types of maintenance with different contents and effects (maintenance 1, maintenance 2, maintenance 3). The input value of the training data 61 includes information on the maintenance performed before processing. If maintenance has not been performed, the value indicating maintenance is 0. If maintenance has been performed, a numerical value corresponding to the type of maintenance is included in the input value. The value indicating maintenance is, for example, 1 for maintenance 1, 2 for maintenance 2, and 3 for maintenance 3. Note that the maintenance identifier is arbitrary. By adding the details of maintenance to the input and history, the semiconductor manufacturing control system 100 can autonomously determine what maintenance is required.

[0077] A specific example of this embodiment for determining whether maintenance is required will be described below. Differences from the first embodiment will be mainly described. Fig. 20 shows a more specific example of training data 61. In addition to the training data 31 in the first embodiment, the input data 611 has a pre-film-deposition maintenance field. The pre-film-deposition maintenance field indicates an identifier of maintenance performed before the film-deposition process, and "0" indicates that no maintenance was performed.

[0078] Fig. 21 shows an example of a target value 65 for the next film formation process. Fig. 22 shows an example of an optimal input value (recipe) 66 determined by the recipe search unit 24. The optimal input value (recipe) 66 has a cell for pre-film formation maintenance, corresponding to the input data 611. The example of Fig. 22 shows that maintenance 2 should be performed before performing the film formation process indicated by the optimal input value (recipe) 66.

[0079] The processing of the semiconductor manufacturing control system 100 will be described with reference to Figures 23 and 24. Figure 23 is a logical configuration diagram for explaining the processing of the semiconductor manufacturing control system 100 of this embodiment. The recipe search unit 24 includes a maintenance effect evaluation unit 245 in addition to the components of the first embodiment.

[0080] The user includes an identifier of the actually performed maintenance in addition to the information of the first embodiment in the input data 51 from the input device 151. The maintenance identifier is stored in the training data database 131. The semiconductor manufacturing control system 100 outputs a required maintenance notification and a maintenance evaluation result in addition to the information of the first embodiment as output data 52 from the output device 152.

[0081] Fig. 24 shows a flowchart of the process executed by the semiconductor manufacturing control system 100 of this embodiment. Steps S121 to S128 correspond to steps S101 to S108 in the flowchart of Fig. 15 of embodiment 1. The flowchart of Fig. 24 differs from embodiment 1 in the step that is executed when the determination result in step S123 is NO.

[0082] In step S123, if the estimated processing result is not within the error tolerance from the target value (S123: NO), the recipe estimation unit 242 uses the estimation model 23 to derive a maintenance and recipe that realizes a value close to the target value, and estimates the processing result (S129). For example, the recipe estimation unit 242 uses a random search method or a simulated annealing method to determine an input value that is closest to the target value. The input value indicates any maintenance to be performed, and the value of the element corresponding to the maintenance is a value other than 0. The output value of the estimation model 23 for that input value is the estimated processing result.

[0083] Recipe estimation unit 242 notifies the user of the required maintenance on output device 152 via integrated management unit 21. For example, recipe estimation unit 242 holds a message list associated with a maintenance identifier, and presents a message corresponding to the determined maintenance identifier on output device 152. The user performs maintenance on the semiconductor manufacturing equipment in accordance with the maintenance notification, and inputs information about the performed maintenance from input device 151. Note that semiconductor manufacturing management system 100 may automatically perform the maintenance.

[0084] When the maintenance effect evaluation unit 245 receives information about maintenance via the integrated management unit 21, it evaluates the effect of maintenance on the semiconductor manufacturing equipment. For example, the maintenance effect evaluation unit 245 uses a sensor mounted in the chamber to evaluate the thickness of deposits on the sidewall or the amount of reduction thereof. The maintenance effect evaluation unit 245 presents the evaluation results on the output device 152 via the integrated management unit 21. The user can check whether appropriate maintenance has been performed by referring to the evaluation results. Note that the user may perform the maintenance evaluation and omit the maintenance effect evaluation unit 245.

[0085] The device control unit 22 acquires the post-maintenance optimum recipe determined by the recipe estimation unit 242 via the integrated management unit 21. The device control unit 22 specifies the optimum recipe and instructs the process execution unit 27 to execute processing, and adds information on the maintenance and the optimum recipe to the input data 611 of the training data 61 via the integrated management unit 21. The process execution unit 27 executes processing according to the specified optimum recipe (S130).

[0086] The analysis and evaluation unit 25 analyzes and evaluates the processing result by the process execution unit 27 (S131). The analysis and evaluation unit 25 stores the analysis and evaluation result in the training data database 131 via the integrated management unit 21 in association with the corresponding optimal recipe (S132). The analysis and evaluation result may be input by the user. The analysis and evaluation unit 25 further presents the analysis and evaluation to the user on the output device 152 via the integrated management unit 21. The model construction unit 241 updates the estimation model 23 using the updated training data database 131 (S133). The flow then proceeds to step S128.

[0087] As described above, in this embodiment, the past process history information of the semiconductor manufacturing equipment includes information about maintenance of the semiconductor manufacturing equipment. Furthermore, the input to the estimation model 23 includes maintenance to be performed before the next process. By adding the details of the maintenance to the process history information, it is possible to autonomously estimate the change over time of the semiconductor manufacturing equipment from the process history information and determine whether maintenance is required.

[0088] Specifically, this embodiment can derive an optimal recipe suited to the equipment status when maintenance is not required, and can present a maintenance method when maintenance is required. By presenting the required maintenance, the user can perform appropriate maintenance at the appropriate time. According to this embodiment, film formation to check the aging of the semiconductor manufacturing equipment is not required, and the number of steps can be reduced.

[0089] <Embodiment 3> In the third embodiment, it is determined whether or not the substrate to be processed needs to be changed. This allows a substrate that does not satisfy the required characteristics to be appropriately changed to a new substrate, thereby reducing the number of unnecessary processes. For example, when the estimated result of the optimal recipe found by the search is not within the allowable range from the target value, the semiconductor manufacturing control system 100 determines that the substrate needs to be changed.

[0090] For example, SiC substrates have more defects such as dislocations than Si substrates, and the dislocation density affects the defect density after epitaxial growth. Therefore, when the output of the estimation model 23 includes defect density, it is necessary to take into account defect information on the substrate. Furthermore, warping of the substrate also affects the epitaxial growth results. Therefore, by taking into account substrate information, it is possible to derive an optimal recipe with high accuracy.

[0091] The input of the estimation model 23 in this embodiment includes substrate information in addition to the input of embodiment 2. Therefore, the input data of the training data includes the substrate information. When deriving an optimal recipe (input values ​​that give an output value within an error tolerance range from a target value), the input substrate information needs to satisfy a pre-specified substrate specification. If the substrate has many defects, there is a possibility that the optimal recipe will not fall within a specified value (a pre-determined value).

[0092] For this reason, this embodiment uses the estimation model 23 to calculate how many defects are necessary to derive an optimal recipe. For example, there is a method of gradually reducing the number of substrate defects in the substrate information input to the estimation model 23, calculating whether an optimal recipe can be derived (whether it falls within an acceptable range) under each condition of the number of defects, and notifying the defect density at which the optimal recipe can be derived. After that, the user is asked to decide whether to change the substrate, and if the substrate is not changed, a decision is made as to whether maintenance is necessary.

[0093] The processing of the semiconductor manufacturing control system 100 will be described with reference to Fig. 25. The following mainly describes the differences from the second embodiment. A logical configuration diagram for explaining the processing of the semiconductor manufacturing control system 100 of this embodiment is substantially similar to Fig. 23 in the second embodiment. One difference from the second embodiment is that the input data 51 includes information on the substrate to be processed. As described above, the input data of the estimation model 23 and the training data includes substrate information indicating the substrate specifications.

[0094] In the flowchart of Fig. 25, steps S141, and S143 to S149 correspond to steps S121 to S128 in the flowchart of Fig. 24. In step S142, the integrated management unit 21 acquires the board information input by the user from the input device 151, and passes it to the recipe search unit 24.

[0095] In step S144, if the estimation result by the estimation model 23 is outside the allowable error range from the target value (S144: NO), the recipe estimation unit 242 uses the estimation model 23 to derive board specifications necessary for deriving a recipe that realizes a value close to the target value (S150). Specifically, the recipe estimation unit 242 maintains the recipe and maintenance information in the input values ​​to the estimation model 23, and changes only the board information. As described in the first embodiment, the recipe estimation unit 242 searches for board information in which the error between the output value of the estimation model 23 and the target value is within the allowable error range. The board specifications to be searched for are selected from a predetermined range.

[0096] When board information that can obtain an output value within the allowable range is found, the recipe estimation unit 242 determines to change the board (S151: YES). The recipe estimation unit 242 notifies the user of the board change and the specifications required for the new board on the output device 152 via the integrated management unit 21. The flow returns to step S142.

[0097] If the substrate information capable of obtaining an output value within the allowable range is not found, the recipe estimation unit 242 determines not to change the substrate (S151: NO). After that, the recipe estimation unit 242 determines whether maintenance is required. Steps S152 to S156 correspond to steps S129 to S133 in the flowchart of FIG. 24.

[0098] In the above example, the substrate information is acquired from the user via the input device 151. In another example, the semiconductor manufacturing control system 100 may acquire the substrate information using a substrate evaluation device. The semiconductor manufacturing control system 100 performs an evaluation of the substrate placed in the chamber before performing the process of the flowchart in Fig. 25. The maintenance process of this embodiment may be omitted.

[0099] Below, examples of GUI images displayed on the output device 152 that can be used in some embodiments are described. The images described below are merely examples, and any GUI image may be used as long as it is capable of inputting and outputting required information. First, an example of a GUI image for a user to input data will be described. Information entered in an input window is stored in the memory 120 or the auxiliary storage device 130 by the integrated management unit 21.

[0100] 26 shows an example of a recipe exploration function setting window 531. A user can enable or disable the recipe exploration function in the recipe exploration function setting window 531. When the recipe exploration function is set to be enabled, each of the processes of the multiple embodiments in this specification is executed.

[0101] Fig. 27 shows an example of a target value setting input window 532. The target value setting input window 532 accepts input of a target value and an allowable range of the estimation model 23 from a user. In the example of Fig. 27, the target value and the allowable range are set for each parameter (element of the output value). Furthermore, the upper and lower limits of the allowable range are set individually.

[0102] Fig. 28 shows an example of a board information input window 533. The board information input window 533 accepts input of information about a board from a user. In the example of Fig. 28, the board information input window 533 allows input of information about multiple boards, and the number of the slot into which the board is inserted, the board ID, and values ​​of multiple attributes of the board are input. The input values ​​to the estimation model 23 include attribute values ​​of the board information.

[0103] 29 shows an example of the evaluation result input window 534. The evaluation result input window 534 is a window for the user to input the evaluation results of an object processed by semiconductor manufacturing equipment. The evaluation result input window 534 has cells for inputting a process number for identifying the process and an evaluation value of the process result. In the above example of film formation, at least the values ​​of film thickness, impurity concentration, and crystal defect density are input. When the semiconductor manufacturing control system 100 automatically evaluates the processed object, this window is not used.

[0104] Next, an example of a GUI image for presenting information to the user will be described. Fig. 30 shows an example of an optimum recipe output window 535. The optimum recipe output window 535 is displayed on the output device 152 via the integrated management unit 21. It includes recipe contents and an estimation result obtained by the recipe. For example, the recipe estimation unit 242 presents to the user, via the optimum recipe output window 535, a recipe that is estimated to obtain an estimation result within an allowable range specified by the user, and the estimation result.

[0105] 31 shows an example of the maintenance notification message box 536. The maintenance notification message box 536 presents the user with information on the maintenance required to obtain a desired processing result. As described above, information indicating the relationship between the maintenance and the message is set in advance, and the recipe estimation unit 242 refers to the information, acquires a message corresponding to the maintenance determined using the estimation model 23, and displays it in the maintenance notification message box 536.

[0106] 32 shows an example of the maintenance evaluation result output window 537. The maintenance evaluation result output window 537 shows the maintenance content (identifier) ​​and the evaluation result of that maintenance. When the maintenance effect evaluation unit 245 evaluates the maintenance result, this window 537 is used. When the user evaluates the maintenance, this window 537 is not used.

[0107] 33 shows an example of a history information output window 538. The history information output window 538 shows basic information about the process, the process recipe, and the evaluation results of the processed object. The basic information about the process includes, for example, the process date and time and an identifier of the substrate to be processed. For example, the integrated management unit 21 displays the history information output window 538 in response to a request from a user. The basic information about the process is stored, for example, in the memory 120 or the auxiliary storage device 130, and is updated by the integrated management unit 21. The process recipe and the evaluation results of the processed object correspond to the input data and output data of the training data database 131.

[0108] <Embodiment 4> The methods of the first, second and third embodiments use recipe history information, which requires a huge amount of calculation, and may result in a long calculation time and insufficient computer processing power. One possible solution to this problem is to reduce the number of variables (recipe elements), but this narrows the search range and may affect the derivation of an optimal solution. Therefore, the fourth, fifth and sixth embodiments explain a method for reducing the amount of calculation.

[0109] In order to obtain an optimal recipe (a recipe within an acceptable range) by calculation, it is preferable to autonomously acquire feature quantities of changes over time from history information as described in the first, second and third embodiments. However, as described above, the problem is that the amount of calculation increases. Therefore, in this embodiment, in order to reduce the amount of calculation, factors that are thought to affect the changes over time are added to the input values ​​of the estimation model.

[0110] 34 shows an example of input data 711 of training data including factors thought to affect the change over time. In addition to the input data 311 of the training data 31 of the first embodiment, the input data 711 has columns for an integrated processing time, an integrated film thickness, and an integrated flow rate (SiH4). These indicate integrated values ​​(cumulative values) from the last maintenance to the immediately preceding processing. The integrated values ​​are an example of history information of past processing, similar to the recipe history. The output data of the training data of this embodiment is similar to the output data 312 of the first embodiment. Note that the integrated values ​​input to the estimation model 23 together with the recipe may include target values ​​of the recipe and processing.

[0111] FIG. 34 shows an example of a film formation process by epitaxial growth of SiC, where the integrated processing time, integrated film thickness, and integrated flow rate (SiH4) respectively indicate the integrated time of epitaxial growth time, the integrated value of epitaxial growth film thickness, and the integrated value of the flow rate of gas (SiH4) since the last maintenance. The integrated processing time and the integrated flow rate are the integrated values ​​(cumulative values) of recipe elements of past processes by the semiconductor manufacturing equipment, and the integrated value of growth film thickness is the integrated value (cumulative value) of the process product. The process product is the product targeted by the film formation process, and does not include by-products. In this embodiment, the input value of the estimation model 23 includes the integrated value as history information instead of the recipe history described in the first, second, and third embodiments. This makes it possible to greatly reduce the variables of the input value compared to the input value including the recipe history.

[0112] The recipe estimation unit 242 uses the estimation model 23 to search for a recipe (optimum recipe) that gives an output value within an allowable range from the target value, as described in the other embodiments above. There are constraints of the semiconductor manufacturing equipment for each integrated value. For example, if the target film thickness is 10 μm, the integrated film thickness is the current value + 10 μm. The integrated processing time is the current value + processing time. Other processing temperatures and processing pressures also have upper limits determined by the equipment structure. The optimum recipe is searched for within the range of these constraints.

[0113] <Embodiment 5> In the fifth embodiment, in order to reduce the amount of calculation, factors that are thought to affect the change over time are added to the input values ​​of the estimation model. Fig. 35 shows an example of input data 811 of training data including factors that are thought to affect the change over time. The input data 811 has a column for by-product film thickness in addition to the input data 311 of the training data 31 of the first embodiment.

[0114] In the input data 811, the by-product film thickness column indicates the by-product film thickness value before processing according to the recipe of the same record is executed. The by-product film thickness indicates the film thickness of a by-product deposited in a processing chamber, for example, the film thickness of a by-product deposited on the inner wall of the chamber or the film thickness of a by-product on a wafer susceptor. A by-product film is a film of any material that is not originally intended to be generated by processing. The by-product film thickness can be measured by the user or by a sensor in the chamber.

[0115] In this embodiment, the input values ​​of the estimation model 23 include a by-product film thickness value before processing as historical information, instead of the recipe history described in the first, second, and third embodiments. The by-product film thickness changes depending on past processing, and therefore indicates the processing history. By using the by-product film thickness as historical information, the variables of the input values ​​can be significantly reduced compared to input values ​​that include recipe history. In addition, the by-product film thickness more directly indicates the change over time of the semiconductor manufacturing equipment (chamber), and therefore the optimum recipe can be more appropriately estimated.

[0116] <Embodiment 6> In the sixth embodiment, an estimation model (function) and a time-varying function are used to determine a recipe for the next process of a semiconductor manufacturing equipment. In this embodiment, the actual process result (training data output) is corrected by the time-varying function. The correction using the time-varying function removes components caused by the time-varying of the semiconductor manufacturing equipment. In other words, the corrected process result is a process result that assumes that there is no time-varying of the semiconductor manufacturing equipment.

[0117] The input values ​​to the estimation model 23 in this embodiment include recipe candidates for the next process, and do not include history information as in the other embodiments. The output data of the training data is a processing result assuming that there is no change over time in the semiconductor manufacturing equipment, and is a value obtained by correcting the actual processing result with a time-varying function. In comparing the output value (estimated value) of the estimation model 23 with the target value, the estimated value and / or the target value are compared after being corrected with the time-varying function. In this way, the estimation model 23 outputs an estimated value of the processing result for a recipe for the semiconductor manufacturing equipment assuming no change over time.

[0118] As described above, since the estimation model 23 does not include history information in the input values, it is possible to reduce the processing load for searching for an optimal recipe using the estimation model 23. The history information of the semiconductor manufacturing equipment is used to create a time-varying function. Below, a method for deriving the time-varying function will be explained using an example. The time-varying function is derived by regression analysis using the history information.

[0119] As an example, the defect density D, which is one of the film formation results in a certain chamber state, is described by the following function g: The variable t of the function g represents the by-product film thickness in the chamber. D = g(t)

[0120] When recipe a is executed when the by-product film thickness in the chamber is t1 or t2, the defect densities D_t1 and D_t2 are expressed as follows, respectively. D_t1 = g_a(t1) D_t2 = g_a(t2)

[0121] Here, since the function g_a(t) is unknown, regression analysis is performed to derive the function g_a(t). Figure 36 shows an example of a graph of the function g_a(t). The black dots in the graph are the measured values, and the solid curve is a curve fitted to the measured values, which corresponds to the function g_a(t).

[0122] Next, consider the function g_a(t). The function g_a(t) can be considered to be the sum of factors due to recipe a alone and factors due to changes over time. Therefore, the function g_a(t) can be rewritten as follows: g_a(t)=D_a+f_a(t) The function D_a is a constant that represents the defect density caused by recipe a regardless of aging. The function f_a(t) represents the defect density caused by aging in the processing of recipe a. The function f_a(t) is a aging function.

[0123] Next, consider the relationship between f(t) when recipe a and recipe b are executed. When the recipe is changed, the effect of change over time may increase or decrease, but the physical mechanism by which the change over time occurs does not change, so the following relationship holds: f_b(t)=cf_a(t) That is, gb(t) is g_b(t)=D_b+f_b(t)=D_b+cf_a(t) Note that D_b is a constant representing the defect density caused by recipe b.

[0124] Since there are two unknowns, D_b and c, if there are two processing results according to recipe b, the time-varying function for recipe b can be derived. As described above, the model constructing unit 241 derives the time-varying function for each recipe, and further configures the estimation model 23 that is not affected by changes over time. The input of the estimation model 23 does not include processing history information.

[0125] As described above, it is possible to remove the influence of changes over time from the output data of the training data by using the time-varying function, and it becomes easy to construct an estimation model 23 that is not affected by changes over time. In the above explanation, the defect density is given as an example of the output value, but the output value may be, for example, the concentration, average concentration, or in-plane concentration variation of an impurity at a certain coordinate on the wafer surface, the film thickness, average film thickness, or in-plane film thickness variation at a certain coordinate on the wafer surface, downfall defects, stacking faults, basal plane dislocations, etc.

[0126] Furthermore, although the by-product thickness on the chamber wall has been given as an example of an input value, the input value may also be, for example, the by-product thickness on the susceptor, fluctuations in inner diameter caused by clogging of the injector, etc. Although it is necessary to monitor the by-product thickness and inner diameter, for convenience, instead of these, the input value may be, for example, the integrated film thickness, the integrated flow rate of the material gas, the integrated amount of heat given to the chamber, etc.

[0127] The time-varying function can be used to determine whether or not maintenance is required. The recipe estimation unit 242 may determine whether or not maintenance is required based on the result of comparing the output of the time-varying function with a threshold value. FIG. 37 shows an example of a graph of the time-varying function of recipe a. For example, when the value of the time-varying function of a recipe determined to be an optimal recipe exceeds a specified value (a value determined in advance), the recipe estimation unit 242 may determine that maintenance is required.

[0128] The present invention is not limited to the above-mentioned embodiment, but includes various modified examples. For example, the above-mentioned embodiment has been described in detail to clearly explain the present invention, and is not necessarily limited to those including all of the configurations described. It is also possible to replace a part of the configuration of one embodiment with the configuration of another embodiment, and it is also possible to add the configuration of another embodiment to the configuration of one embodiment. It is also possible to add, delete, or replace a part of the configuration of each embodiment with another configuration.

[0129] In addition, the above-mentioned configurations, functions, processing units, etc. may be realized in part or in whole by hardware, for example, by designing them as integrated circuits. In addition, the above-mentioned configurations, functions, etc. may be realized in software by a processor interpreting and executing a program that realizes each function. Information such as the program, table, file, etc. that realizes each function can be stored in a memory, a recording device such as a hard disk or SSD (Solid State Drive), or a recording medium such as an IC card or SD card.

[0130] In addition, the control lines and information lines shown are those that are considered necessary for the explanation, and not all control lines and information lines in the product are necessarily shown. In reality, it can be considered that almost all components are connected to each other. [Explanation of symbols]

[0131] 21 Integrated management unit, 22 Equipment control unit, 23 Estimation model, 24 Recipe search unit, 25 Analysis and evaluation unit, 27 Process execution unit, 31 Training data, 35 Target value, 41 Input value, 42 Output value, 51 Input data at input device, 52 Output data at output device, 61 Training data, 65 Target value, 66 Optimal input value, 100 Semiconductor manufacturing management system, 110 Processor, 120 Memory, 121 Integrated management program, 122 Equipment control program, 123 Estimation model program, 124 Recipe search program, 125 Analysis and evaluation program, 130 Auxiliary storage device, 131 Training data database, 140 Network interface, 145 I / O interface, 151 Input device, 152 Output device, 241 Model construction unit, 242 Recipe estimation unit, 243 Convergence judgment unit, 245 Maintenance effect evaluation unit, 311, 611, 711, 811 Input data of training data, 312, 612 Output data of training data, 400 Cluster device, 401 Substrate analysis chamber, 402 Cleaning chamber, 403 Analysis evaluation chamber, 404 Processing chamber, 405 Regeneration chamber, 406 Load lock chamber and transfer chamber, 450 SiC epi cluster device, 451 Substrate analysis chamber, 452 Epi analysis chamber, 453 Defect analysis chamber, 454 Epi growth chamber, 455 CMP chamber, 456 Load lock chamber and transfer chamber, 501 Function curve, 502 Dashed rectangle, 531 Recipe search function setting window, 532 Target value setting input window, 533 Substrate information input window, 534 Evaluation result input window, 535 Optimal recipe output window, 536 Maintenance notification message box, 537 Maintenance evaluation result output window, 538 History information output window

Claims

1. A management system that determines a recipe for a film formation process in a semiconductor manufacturing apparatus that forms a SiC film on a SiC substrate by epitaxial growth, comprising: one or more storage devices; one or more processors; The semiconductor manufacturing equipment has a means for monitoring a thickness of a by-product on a susceptor, the one or more storage devices store history information of past film formation processes of the semiconductor manufacturing equipment; The one or more processors acquire target values ​​of characteristics of a film to be formed in a next film formation process by the semiconductor manufacturing equipment; determining a recipe for the next film formation process of the semiconductor manufacturing equipment using one or more functions including an estimation model based on the history information and the target value; the one or more functions include a time-varying function indicating a time-varying change of the semiconductor manufacturing equipment, The time-varying function is configured based on the history information; The one or more processors determine whether or not maintenance of the semiconductor manufacturing equipment is required based on a comparison result between the value of the time-varying function and a specified value.

2. The management system according to claim 1, an input of the estimation model includes a recipe candidate for the next film formation process of the semiconductor manufacturing equipment; The output of the estimation model includes an estimate of a property of the produced film.

3. The management system according to claim 1, A management system that determines the maintenance as maintenance to be performed before a recipe for the film formation process.

4. The management system according to claim 1, The management system, wherein the history information includes a cumulative value of the thickness of the SiC film in past film formation processes performed by the semiconductor manufacturing equipment.

5. The management system according to claim 1, A management system in which the history information includes a cumulative value of thickness of the by-product during past maintenance performed by the semiconductor manufacturing equipment.

6. The management system according to claim 2, the history information includes a recipe history of the semiconductor manufacturing equipment and information about maintenance performed on the semiconductor manufacturing equipment; the input of the estimation model includes maintenance to be performed on the semiconductor manufacturing equipment before the next deposition process; When there is maintenance to be performed before the next film deposition process, the one or more processors output a message indicating the maintenance to be performed before the next film deposition process to an output device.

7. The management system according to claim 6, The one or more processors determine a recipe candidate for which the output of the estimation model is within an allowable range from the target value as a recipe for the next film formation process.

8. The management system according to claim 2, A management system wherein the input of the estimation model further includes information about a substrate that is a target of the next film formation process.

9. The management system according to claim 2, A management system in which the one or more processors compare the target value and the estimated value after correcting the target value and / or the estimated value using the time-varying function, and determine the recipe based on the result of the comparison.

10. A method for determining a recipe for a film formation process in a semiconductor manufacturing apparatus for forming a SiC film on a SiC substrate by epitaxial growth, comprising: Monitoring the thickness of the by-product on the susceptor; The management system stores history information of past film formation processes of the semiconductor manufacturing equipment, The method comprises: The management system acquires target values ​​of characteristics of a film to be formed in a next film formation process by the semiconductor manufacturing equipment, the management system determines a recipe for the next film formation process of the semiconductor manufacturing equipment using one or more functions including an estimation model based on the history information and the target value; the one or more functions include a time-varying function indicating a time-varying change of the semiconductor manufacturing equipment, The time-varying function is configured based on the history information; The method further comprises the step of: determining whether or not maintenance of the semiconductor manufacturing equipment is required based on a result of comparing the value of the time-varying function with a specified value.

11. 11. The method of claim 10, an input of the estimation model includes a recipe candidate for the next film formation process of the semiconductor manufacturing equipment; The method, wherein the output of the estimation model includes an estimate of a property of the resultant film.

12. 12. The method of claim 11, the history information includes a recipe history of the semiconductor manufacturing equipment and information about maintenance performed on the semiconductor manufacturing equipment; the input of the estimation model includes maintenance to be performed on the semiconductor manufacturing equipment before the next deposition process; In the method, when there is maintenance that needs to be performed before the next film deposition process, the management system outputs a message indicating the maintenance that needs to be performed before the next film deposition process to an output device.

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