SILICON CARBIDE SINGLE CRYSTAL MANUFACTURING APPARATUS, COMPUTING DEVICE, AND MANUFACTURING METHOD OF SILICON CARBIDE SINGLE CRYSTAL

A silicon carbide single crystal manufacturing apparatus includes a pressure sensor and a computing device. The pressure sensor is configured to measure a gas pressure of a supply gas containing a silicon carbide raw material gas and introduced into a crucible through a gas introducing pipe. The computing device is configured to perform a prediction of a clogging time, which is a time until the gas introduction pipe is clogged with a solid deposit, based on the gas pressure measured by the pressure sensor a learning model created by machine learning using data calculated from results of simulations of growing the silicon carbide single crystal and results of experiments of growing the silicon carbide single crystal.

CROSS REFERENCE TO RELATED APPLICATION

The present application claims the benefit of priority from Japanese Patent Application No. 2023-151432 filed on Sep. 19, 2023. The entire disclosure of the above application is incorporated herein by reference.

TECHNICAL FIELD

The present disclosure relates to a silicon carbide (hereinafter referred to as SiC) single crystal manufacturing apparatus, a computing device for a SiC single crystal manufacturing apparatus, and a manufacturing method of a SiC single crystal.

BACKGROUND

One method for growing a SiC single crystal is a gas growth method in which a SiC source gas is introduced into a SiC single crystal manufacturing apparatus (hereinafter referred to as a crystal manufacturing apparatus) to glow a SiC single crystal on a surface of a seed crystal. In the gas growth method, since the SiC source gas can be continuously supplied for a long period of time, it is expected to glow a long SiC single crystal. However, if a solid deposit accumulates inside the crystal manufacturing apparatus and clogs the gas flow passages, the supply of the SiC source gas is interrupted and the growth conditions change, making it impossible to grow a long SiC single crystal. Thus, it is desirable to predict and respond to clogging of the gas flow passage in the crystal manufacturing apparatus.

SUMMARY

The present disclosure provides a SiC single crystal manufacturing apparatus, a computing device for a SiC single crystal manufacturing apparatus, and a manufacturing method of a SiC single crystal in which a clogging time, which is a time until a gas introduction pipe for introducing a supply gas containing a SiC raw material gas into a crucible is clogged with a solid deposit, based on a gas pressure measured by a pressure sensor and a learning model created by machine learning using data calculated from results of simulations of growing the SiC single crystal and results of experiments of growing the silicon carbide single crystal.

DETAILED DESCRIPTION

Next, a relevant technology is described only for understanding the following embodiments. In order to predict clogging of a gas flow passage in a crystallization equipment, it is necessary to carry out repeated experiments to monitor a clogging state. For example, in a motor drive device, in order to predict a failure of a fan motor, a rotation speed of the fan motor may be observed and a reward may be calculated from a time when an alarm is output and a time when the fan motor actually fails, and the reward may be stored as past data. Then, the artificial intelligence may predict a failure of the fan motor based on the observation result of the rotation speed of the fan motor and the calculation result of the reward, and may output the alarm if a failure is predicted.

As described above, in a case of the motor drive device, the rotation speed of the fan motor can be directly observed. However, in a case of a crystal manufacturing apparatus, it is difficult to directly observe a volume of a solid deposit that clogs the gas flow passage or powder that is generated in the air and causes the solid deposit.

A silicon carbide single crystal manufacturing apparatus according to a first aspect of the present disclosure includes a crucible, a pedestal, a gas introduction pipe, a heating device, a pressure sensor, and a computing device. The crucible constitutes a reaction chamber and having a cylindrical shape with a hollow portion. The pedestal is disposed in the hollow portion of the crucible and having one surface on which a seed crystal for growing a silicon carbide single crystal is to be disposed. The gas introduction pipe has a gas outlet through which a supply gas containing a silicon carbide raw material gas for growing the silicon carbide single crystal on a surface of the seed crystal is introduced into the crucible from below the pedestal. The heating device is configured to heat and decompose the silicon carbide raw material gas. The pressure sensor is configured to measure a gas pressure of the supply gas. The computing device is configured to perform a prediction of a clogging time, which is a time until the gas introduction pipe is clogged with a solid deposit, based on the gas pressure measured by the pressure sensor and a learning model created by machine learning using data calculated from results of simulations of growing the silicon carbide single crystal and results of experiments of growing the silicon carbide single crystal.

The silicon carbide single crystal manufacturing apparatus described above includes the pressure sensor configured to measure the gas pressure of the supply gas. In addition, based on a fact that there is a correlation between predicted clogging times based on a learning model obtained by combining machine learning through a large number of simulations with experimental results and actual clogging times obtained by experiments, the learning model obtained by machine learning through simulations is created. The clogging time is then predicted based on the gas pressure measured by the pressure sensor and the learning model. Accordingly, it is possible to predict a clogging state based on the measured result of the gas pressure that is a directly observable parameter.

A computing device for a silicon carbide single crystal manufacturing apparatus according to a second aspect of the present disclosure includes a processor and a memory. The memory stores instructions configured to, when executed by the processor and when growing a silicon carbide single crystal using the silicon carbide single crystal manufacturing apparatus in such a manner that a seed crystal is disposed on one surface of a pedestal arranged in a hollow portion of a crucible having a cylindrical shape and constituting a reaction chamber, a supply gas containing a silicon carbide raw material gas is introduced into the crucible from a gas outlet of a gas introduction pipe below the pedestal, and the silicon carbide raw material gas is thermally decomposed and supplied to a surface of the seed crystal to grow the silicon carbide single crystal, cause the processor to acquire a gas pressure of the supply gas measured by a pressure sensor, and perform a prediction of a clogging time, which is a time until the gas introduction pipe is clogged with a solid deposit, based on the gas pressure measured by the pressure sensor and a learning model created by machine learning using data calculated from results of simulations of growing the silicon carbide single crystal and results of experiments of growing the silicon carbide single crystal.

In this manner, the gas pressure of the supply gas is measured by the pressure sensor provided in the crystal manufacturing apparatus. In addition, based on a fact that there is a correlation between predicted clogging times based on a learning model obtained by machine learning through a large number of simulations and actual clogging times obtained by experiments, the learning model obtained by machine learning through simulations is created. The clogging time is then predicted based on the gas pressure measured by the pressure sensor and the learning model. Accordingly, the computing device can predict a clogging state based on the measured result of the gas pressure that is a directly observable parameter.

A manufacturing method of a silicon carbide single crystal according to a third aspect of the present disclosure, includes growing a silicon carbide single crystal using a silicon carbide single crystal manufacturing apparatus by disposing a seed crystal on one surface of a pedestal arranged in a hollow portion of a crucible having a cylindrical shape and constituting a reaction chamber, introducing a supply gas containing a silicon carbide raw material gas into the crucible from a gas outlet of a gas introduction pipe below the pedestal, and thermally decomposing and supplying the silicon carbide raw material gas to a surface of the seed crystal, measuring a gas pressure of the supply gas by a pressure sensor, and perform a prediction of a clogging time, which is a time until the gas introduction pipe is clogged with a solid deposit, based on the gas pressure measured by the pressure sensor and a learning model created by machine learning using data calculated from results of simulations of growing the silicon carbide single crystal and results of experiments of growing the silicon carbide single crystal.

As described above, the gas pressure of the supply gas is measured by the pressure sensor provided in the crystal manufacturing apparatus. In addition, based on a fact that there is a correlation between predicted clogging times based on a learning model obtained by machine learning through a large number of simulations and actual clogging times obtained by experiments, the learning model obtained by machine learning through simulations is created. The clogging time is then predicted based on the gas pressure measured by the pressure sensor and the learning model. Accordingly, it is possible to manufacture the SiC single crystal while predicting a clogging state based on the measured result of the gas pressure that is a directly observable parameter.

In the following embodiments including other embodiments to be described below, the same or equivalent components will be described with the same reference numerals.

First Embodiment

A crystal manufacturing apparatus1shown inFIG.1is used for manufacturing a SiC single crystal ingot by long length growth. The crystal manufacturing apparatus1corresponds to a silicon carbide single crystal manufacturing apparatus. The crystal manufacturing apparatus1has a function of predicting a clogging time of a gas flow passage, and grows a SiC single crystal3on a surface of a seed crystal2while predicting the clogging time. In the present disclosure, the term “clogging time” refers to a period from a time at which the crystal manufacturing apparatus1predicts clogging of the gas flow passage to a time at which the gas flow passage is clogged. The “clogging time” may be a period from when the gas flow passage of the crystal manufacturing apparatus1is not clogged at all to when the gas flow passage is clogged, or may be a period from when the clogging of the gas flow passage is progressing to when the gas flow passage is clogged.

Configuration of Crystal Manufacturing Apparatus1

The crystal manufacturing apparatus1is installed such that the vertical direction ofFIG.1is oriented in the vertical direction. The crystal manufacturing apparatus1supplies a SiC raw material gas to the surface of the seed crystal2composed of a SiC single crystal substrate to grow the SiC single crystal3on the surface of the seed crystal2. The crystal manufacturing apparatus1includes a gas introduction pipe4, a gas introduction source5, a gas exhaust port6, a vacuum chamber7, a thermal insulator8, a heating chamber9, a pedestal10, a rotary pulling mechanism11, first and second heating devices12and13, a pressure sensor14and a controller15.

The gas introduction pipe4is provided at a lower position of the crystal manufacturing apparatus1and constitutes a piping for introducing a supply gas20containing various SiC raw material gases such as silane and propane from the gas introduction source5into the crystal manufacturing apparatus1. The gas introduction pipe4extends from below the heating chamber9toward the pedestal10, and a part of the gas introduction pipe4is positioned inside the heating chamber9. The gas introduction pipe4introduces the supply gas20into the heating chamber9from the below the heating chamber9. In the example shown inFIG.1, the gas introduction pipe4is configured to protrude from a bottom surface of the vacuum chamber7toward the pedestal10. However, the gas introduction pipe4may also be configured not to protrude from the bottom surface of the vacuum chamber7. The gas introduction pipe4introduces the supply gas20from the gas introduction source5into the crystal manufacturing apparatus1, either in a state of individual gases or as a mixed gas. The supply gas20may contain gases other than the source gases, for example, a carrier gas such as H2(hydrogen), an etching gas such as chlorosilane, and a dopant gas such as N2(nitrogen). Thus, the gas introduction pipe4may have a piping structure in which the gas species are separately introduced into the crystal manufacturing apparatus1, or the gas introduction pipe4may have a piping structure in which some or all of the gas species are mixed into a mixed gas and then the mixed gas is introduced into the crystal manufacturing apparatus1.

In the present embodiment, the gas introduction pipe4includes a first introduction pipe41arranged concentrically and a second introduction pipe42arranged at a distance from the first introduction pipe41on its outer periphery, and the supply gas20is introduced through the first introduction pipe41. The second introduction pipe42may be omitted. In the present embodiment, the supply gas20is not introduced through the second introduction pipe42. The supply gas20containing the same SiC raw material gas may be introduced from the first introduction pipe41and the second introduction pipe42, or types of gases introduced through the first introduction pipe41and the second introduction pipe32may be different from each other. For example, the SiC raw material gas may be introduced from the first introduction pipe41and the etching gas may be introduced from the second introduction pipe42.

An outlet of the gas introduction pipe4at a portion through which the SiC raw material gas is introduced is referred to as a gas outlet4a.In the present embodiment, even when a downstream end of the second introduction pipe42in a flow direction of the supply gas20protrudes closer to the pedestal10than a downstream end of the first introduction pipe41, an outlet of the first introduction pipe41that introduces the SiC raw material gas is defined as the gas outlet4a.

The gas introduction source5supplies the supply gas20containing the SiC raw material gas into the crystal manufacturing apparatus1. In the present embodiment, the gas introduction source5includes the SiC raw material gas source such as silane or propane. In a case where a carrier gas, an etching gas, or a dopant gas is used, the gas introduction source5further includes a source of gas to be used.

Although not shown, the gas introduction source5is equipped with a heating device for controlling a temperature of the supply gas20and a flow rate control device for controlling a flow rate, and the temperature and the flow rate of each gas can be controlled according to a growth state of the SiC single crystal3. When other gas species are used as the supply gas20in addition to the SiC raw material gas, a heating device and a flow rate control device are provided for each gas species.

The gas exhaust port6exhausts a waste portion of the supply gas20after the supply gas20is supplied to the seed crystal2, that is, an unreacted gas of the SiC raw material gas, the carrier gas, the dopant gas, and the like to the outside of the crystal manufacturing apparatus1as exhaust gas.

The vacuum chamber7is made of quartz glass or the like and has a bottomed tube shape providing a hollow portion. In the present embodiment, the vacuum chamber7has a bottomed cylindrical shape. The vacuum chamber7is configured so that the supply gas20can be introduced and exhausted. The vacuum chamber7accommodates other components of the crystal manufacturing apparatus1. The vacuum chamber7has a structure capable of reducing a pressure in an accommodated internal space by vacuum drawing. As described above, the gas introduction pipe4and the gas introduction source5for supplying the supply gas20are provided at the bottom of the vacuum chamber7. The vacuum chamber7has a side wall that is penetrated at a middle position in a height direction, and the gas exhaust port6is disposed at a penetrated portion of the side wall.

The thermal insulator8has a tube shape providing a hollow portion, in the present embodiment, a cylindrical shape, and is disposed coaxially with the vacuum chamber7. Specifically, the thermal insulator8is includes a bottom portion8aand a side wall portion8b.The bottom portion8ahas a cylindrical shape providing a hollow portion into which the gas introduction pipe4is fitted. The side wall portion8bhas a cylindrical shape and extends toward the pedestal10along an outer peripheral surface of the bottom portion8a.The bottom portion8aand the side wall portion8bhave a smaller diameter than the vacuum chamber7and are positioned inside the vacuum chamber7. Accordingly, thermal insulator8restricts heat transfer from a growth space inside the thermal insulator8toward the vacuum chamber7. The thermal insulator8is made of, for example, graphite. A surface of the thermal insulator8may be coated with a high-melting point metal carbide such as tantalum carbide (TaC) or niobium carbide (NbC) so as to be less likely to be thermally etched. The thermal insulator8is penetrated at a middle position in a height direction, and the gas exhaust port6is disposed at a penetrated portion.

The heating chamber9configures a crucible serving as a reaction chamber, and has a tube shape providing a hollow portion, in the present embodiment, a cylindrical shape. The hollow portion of the heating chamber9forms the growth space for the SiC single crystal3on the surface of the seed crystal2. The heating chamber9is made of, for example, graphite. A surface of the heating chamber9may be coated with a high-melting point metal carbide such as TaC or NbC so as to be less likely to be thermally etched.

The heating chamber9is disposed so as to surround the pedestal10, and is penetrated at a lower position, in which the gas exhaust port6is disposed. Although there is a small gap between an inner peripheral surface of the heating chamber9and the pedestal10, the gap is narrow. Thus, the exhaust gas is bent back downward by the pedestal10. Therefore, as shown by the arrows inFIG.1, the supply gas20introduced from the gas introduction pipe4is supplied to the seed crystal2and a growth surface of the SiC single crystal3, then turned back by the pedestal10, passes between the heating chamber9and the gas introduction pipe4, and is then exhausted to the outside from the gas exhaust port6. The SiC raw material gas contained in the supply gas20is decomposed by the heating chamber9heated by the first and second heating devices12and13before the supply gas20from the gas introduction pipe4is introduced to the seed crystal2.

The pedestal10is a member for placing the seed crystal2. The pedestal10has, for example, a disk shape. The seed crystal2having a circular plate shape is placed on one surface of the pedestal10facing the growth space, and the SiC single crystal3is grown on the surface of the seed crystal2. For example, a central axis of the pedestal10is arranged coaxially with a central axis of the heating chamber9and a central axis of a shaft11aof the rotary pulling mechanism11. The pedestal10is made of, for example, graphite. A surface of the pedestal10may be coated with a high-melting point metal carbide such as TaC or NbC to be less likely to be thermally etched. The pedestal10is connected to the shaft11ain a surface opposite to the surface on which the seed crystal2is disposed. The pedestal10is rotated with the rotation of the shaft11a,and can be pulled upward while the shaft11ais pulled up.

The rotary pulling mechanism11rotates and pulls up the pedestal10through the shaft11aformed of a pipe member or the like. In the present embodiment, the shaft11ais formed in a straight line extending up and down. One end of the shaft11ais connected to the surface of the pedestal10opposite to the surface on which the seed crystal2is attached, and the other end of the shaft11ais connected to a main body of the rotary pulling mechanism11. The shaft11ais also made of, for example, graphite. A surface of the shaft11amay be coated with a high-melting point metal carbide such as TaC or NbC to be less likely to be thermally etched. With the above configuration, the pedestal10, the seed crystal2, and the SiC single crystal3can be rotated and pulled up, so that the growth surface of the SiC single crystal3can have a desired temperature distribution, and a temperature of the growth surface can be adjusted to a temperature suitable for growth along with the growth of the SiC single crystal3.

The first and second heating devices12and13are formed of a heating coil such as an induction heating coil or a direct heating coil, and are disposed so as to surround a periphery of the vacuum chamber7. In the case of the present embodiment, the first and second heating devices12and13are configured by induction heating coils. The first heating device12and the second heating device13are configured to be capable of independently controlling the temperature of a target location. The first heating device12is disposed at a position corresponding to a lower position of the heating chamber9, and the second heating device13is disposed at a position corresponding to the pedestal10. Therefore, the temperature of the lower portion of the heating chamber9can be controlled by the first heating device12to heat and decompose the SiC raw material gas. In addition, the temperature around the pedestal10, the seed crystal2, and the SiC single crystal3can be controlled to a temperature suitable for the growth of the SiC single crystal3by the second heating device13.

The pressure sensor14measures a pressure of the supply gas20containing the SiC source gas. It is preferable that the pressure sensor14is positioned inside the gas introduction pipe4compared to the gas outlet4a,that is, at a position not extending beyond the gas outlet4a,to measure the pressure of the supply gas20containing the SiC raw material gas within the gas introduction pipe4. It is more preferable that the pressure sensor14is disposed below a surface of the bottom portion8aof the thermal insulator8that faces the pedestal10.

The pressure sensor14may be disposed at any position where the pressure sensor14can measure the pressure of the supply gas20containing the SiC raw material gas, However, a position that is most likely to be clogged in the gas flow passage is the supply passage of the SiC raw material gas, which is the first introduction pipe41in the present embodiment. Thus, the pressure sensor14is disposed at a position inside the first introduction pipe41located upstream of the gas outlet4ain the flow direction of the supply gas20(hereinafter simply referred to as upstream). More specifically, the pressure sensor14is disposed at a position in the first introduction pipe41located below that surface of the bottom portion8athat faces the pedestal10, where heat transfer is hindered by the thermal insulator8.

In the present embodiment, the gas introduction pipe4has a piping structure in which the first introduction pipe41is surrounded by the second introduction pipe42at a position located downstream of the gas outlet4ain the flow direction of the supply gas20(hereinafter simply referred to as downstream). Therefore, various gases from the first introduction pipe41and the second introduction pipe42can join at this position. In the above-described piping structure, the pressure sensor14may be disposed at a position in the second introduction pipe42located downstream of the gas outlet4a.However, when the pressure sensor14is disposed upstream of the gas outlet4awhere clogging is predicted, it is easier to measure the gas pressure that changes depending on the clogging state. Thus, it is preferable to dispose the pressure sensor14in the first introduction pipe41, which is more likely to be clogged than the second introduction pipe42.

In the present embodiment, various gases introduced from the first introduction pipe41and the second introduction pipe42join together. However, the gas introduction pipe4may also have a piping structure in which multiple introduction pipes remain separate and various gases do not join together. In that case, it is preferable that the pressure sensor14is disposed in an introduction pipe through which the SiC source gas is introduced.

The controller15corresponds to a computing device. The controller15controls the flow rate and the temperature of the gases supplied from the gas introduction source5, controls the temperature at the lower position of the heating chamber9and the temperature at the growth surface position of the SiC single crystal3by the first and second heating devices12,13, and controls the pulling amount by the rotary pulling mechanism11. The controller15also stores a learning model based on experiments and simulations, and predicts the clogging time based on the learning model. The learning model stored in the controller15and the prediction of the clogging time will be described later.

The crystal manufacturing apparatus1is configured as described above. Next, a manufacturing method of the SiC single crystal3using the crystal manufacturing apparatus1configured as above will be described.

Manufacturing Method of SiC Single Crystal3

First, the seed crystal2is attached to the one surface of the pedestal10. The seed crystal2is, for example, an off substrate in which the growth surface of the SiC single crystal3, that is, one surface opposite to the pedestal10has a predetermined off-angle, such as 4° or 8°, with respect to a (0001) C-plane. Subsequently, the pedestal10and the seed crystal2are disposed in the heating chamber9. Then, the first heating device12and the second heating device13are controlled to provide a desired temperature distribution. In other words, the temperature distribution is controlled such that the SiC raw material gas contained in the supply gas20is heated and decomposed to be supplied to the surface of the seed crystal2, and the SiC raw material gas is recrystallized on the surface of the seed crystal2, while a sublimation rate is higher than a recrystallization rate in the heating chamber9. Accordingly, the temperature of the bottom portion of the heating chamber9can be set to a high temperature of 2000° C. or higher, for example, 2500° C., and the temperature of the surface of the seed crystal2can be set to a temperature lower than that of the bottom portion of the heating chamber9and suitable for recrystallization of the SiC single crystal3, for example, about 2200° C.

In addition, while the vacuum chamber7is maintained at a desired pressure, the supply gas20containing the SiC raw material gas is introduced through the gas introduction pipe4. As a result, the supply gas20is supplied to the seed crystal2as shown by the arrow inFIG.1and the SiC single crystal3is grown on the surface of the seed crystal2. Then, the rotary pulling mechanism11pulls up the pedestal10and the seed crystals2and the SiC single crystal3in accordance with the growth rate of the SiC single crystal3while rotating them through the shaft11a.As a result, a height of the growth surface of the SiC single crystal3is kept substantially constant, and the temperature distribution of the growth surface temperature can be controlled with high controllability.

When growing the SiC single crystal3in this manner, the clogging time of the gas introduction pipe4is predicted based on the learning model stored in the controller15. The following describes the learning model stored in the controller15and the clogging time prediction.

Learning Model and Clogging Time Prediction

When creating a learning model, it is conceivable that a large number of experiments are carried out to accumulate data and the learning model is created based on the accumulated data. However, a reinforcement learning, in which modeling is performed after accumulating data through multiple experiments, is not suitable for growing the SiC single crystal3because of the following reasons. A growth process of the SiC single crystal3includes increasing the temperature from room temperature to a high temperature exceeding 2000° C., growing the SiC single crystal3, and then decreasing the temperature of the crystal manufacturing apparatus1that has been further heated. The temperature increase process, the growth process, and the temperature decrease process all take a long time, and each manufacturing process of the SiC single crystal3requires high costs in terms of time and money. Thus, it is difficult to conduct multiple experiments, and reinforcement learning that involves multiple experiments is not suitable for predicting clogging in the crystal manufacturing apparatus1. In addition, graphite parts such the thermal insulator8and heating chamber9are used within the crystal manufacturing apparatus1, but the deterioration state of these parts is not constant. Thus, in terms of reproducibility, reinforcement learning based on multiple experiments is not suitable for predicting the clogging time of the crystal manufacturing equipment1.

Therefore, as a method for creating the learning model without conducting multiple experiments, the present inventor carried out a verification whether it is possible to model the results of a large number of simulations and perform fitting based on the results of a small number of experiments. When conducting a large number of experiments and modeling the results, the SiC single crystal3is actually grown in the crystal manufacturing apparatus1, and the time until the gas introduction pipe4becomes clogged with a solid deposit is directly observed. Therefore, when the results of the large number of experiments are modeled, there is a correlation between the actual clogging time until the gas introduction pipe4becomes clogged when the SiC single crystal3is actually grown using the crystal manufacturing apparatus1and the clogging time predicted based on the learning model.

However, when the results of the large number of simulations are modeled, if there is no correlation with the experimental results, the modeling based on the simulation results is meaningless. Therefore, the present inventor carried out the verification. As a result, the present inventor found a correlation between the experimental results and the simulation results, and confirmed that it is possible to model the results of a large number of simulations and perform fitting based on the results of a small number of experiments.

Specifically, a furnace structure of the crystal manufacturing apparatus1shown inFIG.1, types of gases introduced during the growth of the SiC single crystal3, the gas flow rate, the temperature, and other conditions (hereinafter referred to as growth conditions) were determined, and the clogging time from the start of growth until the vicinity of the gas outlet4aof the gas introduction pipe4was clogged with a solid deposit was measured. The furnace structure refers to the structure of the crystal manufacturing apparatus1including the dimensions, thicknesses, and the like of the vacuum chamber7, the thermal insulator8, the heating chamber9, and the like. In addition, a simulation model having the same structure as the crystal manufacturing apparatus1used in the experiment was adopted to obtain simulation values for the temperature, the gas flow rate, and the solid generation amount at each coordinate point. When the solid generation amount reaches the radius of the gas outlet4aof the gas introduction pipe4, it is assumed that the gas outlet4ais clogged with a solid deposit generated on the inner wall surface on both sides of the central axis of the gas introduction pipe4. Therefore, the time from the start of growth until the solid deposit reaches the radius of the gas outlet4awas defined as the clogging time.

As a result, the results shown inFIG.2were obtained. At each point inFIG.2, a value on the vertical axis indicates an output value of the clogging time obtained as a result of an experiment and a value on the horizontal axis indicates an output value of the clogging time predicted as a result of a simulation under the same growth condition as the experiment. The growth conditions for each plot were different from each other. As can be seen by analyzingFIG.2, when comparing plots of different conditions, there is a tendency that the clogging time in the simulation increases with increase in the clogging time in the experiment. Thus, it can be said that the clogging time in the experiment and the clogging time in the simulation have a relationship with a certain degree of regularity, and the clogging time in the experiment and the clogging time in the simulation are correlated.

Therefore, by performing machine learning using the data obtained by performing a large number of simulations as training data and then fitting the learning model obtained by the machine learning to experimental values, it is possible to predict the clogging time with high accuracy even if only a small number of experiments are performed.

The present inventor further examined various parameters as parameters to be used for predicting the clogging time, and confirmed that there is a particular correlation with the clogging time when a gas pressure measured at a position upstream in the flow direction of the supply gas20of the location where a solid deposit has adhered is used as a parameter.

Specifically, as shown inFIG.3, a simulation model was created in which a pseudo-annular solid deposit30was attached to the inner peripheral wall of the gas introduction pipe4, and the relationship between the size of the solid deposit30and the gas pressure Pobs at a position upstream of the solid deposit30was examined by analyzing simulation data. A dimension of the solid deposit30in a radial direction of the gas introduction pipe4is defined as L(t), and the dimension L(t) is changed to obtain the corresponding gas pressure Pobs. A dimension of the solid deposit30in the central axial direction of the gas introduction pipe4was set to 23.5 mm, and an inner dimension of the gas introduction pipe4was set to a radius of 15 mm. The inner dimension of the gas introduction pipe4is any value determined according to the dimensions of each part of the crystal manufacturing apparatus1. The dimension of the solid deposit30was empirically determined based on experiments.

As a result of the simulation, when the dimension L(t)=0 mm, 5 mm, 10 mm, and 15 mm, the gas pressure Pobs was 1.28145 [Pa], 1.34166 [Pa], 2.14737 [Pa], and 1423.02 [Pa], respectively. The results of this simulation are plotted as shown inFIG.4, and it can be seen that there is a relationship in which the gas pressure Pobs increases with increase in the dimension L(t). In other words, it is recognized that there is a correlation between the dimension L(t) and the gas pressure Pobs.

On the other hand, if the dimension L(t) could be directly observed, the clogging time of the gas introduction pipe4could be confirmed, but it is difficult to directly observe the inside of the crystal manufacturing apparatus1during the growth of the SiC single crystal3. However, if the gas pressure Pobs can be measured, it is possible to estimate the dimension L(t) of the solid deposit, which is correlated with the gas pressure Pobs, and the clogging time can be predicted from the estimated dimension L(t). Specifically, since it is assumed that the gas introduction pipe4is more clogged with the solid deposit30with increase in the gas pressure Pobs, the clogging time shortens with increase in the gas pressure Pobs. Thus, there is a correlation in which the clogging time shortens linearly or nonlinearly with increase in the gas pressure Pobs. For example, as shown inFIG.5, a correlation in which the clogging time shortens linearly with increase in the gas pressure Pobs is assumed.

Therefore, if the simulation results satisfy this correlation, it can be said that the clogging time prediction using the simulation model is valid. In addition, since the simulation result shows that the gas pressure Pobs increases with increase in the dimension L(t) as shown inFIG.4, it can be said that the validity of the clogging time prediction using the simulation model is proven. Therefore, when the pressure sensor14for measuring the gas pressure Pobs is disposed at a location where the gas pressure Pobs varies depending on the clogging state, and the gas pressure Pobs is measured by the pressure sensor14, the size of the solid deposit30can be estimated based on the gas pressure Pobs, and the clogging time can be predicted. For example, it is preferable to dispose the pressure sensor14upstream of a position in the gas introduction pipe4that is assumed to be particularly susceptible to clogging by a solid deposit30.

Based on the above verification, it was confirmed that it is possible to create a learning model for predicting the clogging time by modeling the results of a large number of simulations and fitting them based on the results of a small number of experiments. Therefore, the inventor performed a large number of simulations and applied machine learning to data showing the results of the simulations to create a learning model.

Any method such as neural network or deep learning can be used for machine learning. When using a neural network, a learning model can be created, for example, as follows.

Specifically, a large number of simulations, for example 1,000 to 10,000 simulations, are performed, and data relating to various conditions for growing the SiC single crystal3used in the simulations are input as the input values xi. For example, data on the configuration of the crystal manufacturing apparatus1, data on the types of gases used, data on gas flow rates, data on the temperature of each part in the crystal manufacturing apparatus1, and the like are input as data for the input values xi in an input layer. Then, using the input simulation data as teacher data, learning is carried out by a neural network to create a learning model as shown inFIG.6. For example, a learning model is created in which an output value f(xi) in an output layer L3is obtained for an input value xi of each node in an input layer L1via an intermediate layer L2. The input value xi varies depending on the various conditions under which the simulation is performed, and various conditions corresponding to each of the simulations are input.

In this case, the learning model may be a model having the input layer L1, the intermediate layer L2, and the output layer L3, which are surrounded by a dashed line inFIG.6. However, it is preferable to also incorporate a correction layer L4that takes into account an offset between the value of the output layer L3of the neural network and the output values of the simulations. The value of the correction layer L4can be a function expressed as y=a×f(xi)+b based on the output value f(xi), for example.

Then, data on various conditions obtained by performing experiments fewer times than the number of simulations, for example 30 times, is input, and fitting is performed using data on the actual clogging time. In this example, an experiment is conducted in which various sensors are disposed to measure the state of each part of the crystal manufacturing apparatus1as shown inFIG.7, and the temperature inside the crystal manufacturing apparatus1is raised from 20° C. to 2500° C. to grow the SiC single crystal3. The various sensors include the pressure sensor14located upstream of gas outlet4ain gas introduction pipe4, pressure sensors16aand16b,flow rate sensors17aand17b,a pyrometer18, and the like. The pressure sensor16ais disposed at a position downstream of a clogging surface31(surrounded by a dashed line) that is assumed to be clogged by the solid deposit30in the first introduction pipe41. The pressure sensor16bis disposed in the growth space. The flow rate sensor17ais disposed at a position where the flow rate of the supply gas20at the location where the pressure sensor14is disposed is measured. The flow rate sensor17bis disposed at a position to measure the gas flow rate in the growth space. The pyrometer18is disposed at a position to measure the temperature of a rear surface of the pedestal10. Then, various conditions obtained from the experimental conditions are input as the input values x to the learning model shown inFIG.6, and the actual clogging time data obtained during the experiment is input to the correction layer L4so as to fine-tune a and b in the function y=a×f(x)+b of the correction layer L4. Accordingly, it is possible to fit the learning model obtained by the simulations to the experimental results, and improve the learning model so as to predict the clogging time with higher accuracy.

Once the learning model in which the experimental results are fitted to the simulation data has been obtained in this manner, data on various conditions are then input as input values xi of each node in the input layer L1when actually manufacturing the SiC single crystal3. For example, data on the configuration of the crystal manufacturing apparatus1, data on the types of gases used, data on gas flow rates, data on the temperature of each part in the crystal manufacturing apparatus1, and the like are input as the input values xi. Accordingly, it is possible to obtain the clogging time predicted under those conditions as the values of the output layer L3and the correction layer L4of the learning model.

When various different conditions are input, the clogging time is predicted according to those conditions, and the relationship between the gas pressure Pobs and the clogging time can be obtained based on the predicted clogging time, for example, as shown inFIG.8.FIG.8shows cases in which different clogging times are obtained when various different conditions are input as conditions A, B, and C, and the relationship between the gas pressure Pobs and the clogging time according to each condition is obtained. Then, once the relationship between the gas pressure Pobs and the clogging time has been determined in this manner, the clogging time from a time point to clogging can be predicted based on that relationship and the gas pressure Pobs indicated by the measurement value of the pressure sensor14.

As described above, the crystal manufacturing apparatus1includes the pressure sensor14configured to measure the gas pressure Pobs of the supply gas20. In addition, based on a fact that there is the correlation between the predicted clogging time based on the learning model obtained by the machine learning through the large number of simulations and the actual clogging time obtained by the experiments, the learning model is created by combining the machine learning through the simulations with the experimental results. Then, the clogging time is predicted based on the gas pressure Pobs measured by the pressure sensor14and the learning model. Accordingly, it is possible to predict the clogging state based on the measured result of the gas pressure Pobs that is a directly observable parameter.

Furthermore, the crystal manufacturing apparatus1of the present embodiment can provide the following effects.

In the present embodiment, the pressure sensor14is disposed upstream of the gas outlet4aof the gas introduction pipe4in the crystal manufacturing apparatus1, so that the gas pressure Pobs depending on the clogged state can be measured. Accordingly, it is possible to predict the clogging state with high accuracy.

Since the learning model can be created through the simulations, it is possible to create the learning model without conducting a large number of experiments. Therefore, it is possible to reduce the experiment cost.

When the learning model is fine-tuned by being combined with the results of a small number of experiments, it is possible to predict the clogging time with higher accuracy.

Since the clogging time changes depending on various conditions, the controller15can also use the learning model to calculate and output an optimal process that optimizes the growth conditions, the configuration of the crystal manufacturing apparatus1, and the like within a feasible range, using the clogging time as an objective variable. For example, the controller15can calculate the clogging time using the learning model and derive the growth conditions and the configuration of the crystal manufacturing apparatus1that will lengthen the clogging time, so as to lengthen the clogging time.

The controller15can also feedback the predicted clogging time, calculate the growth conditions for the SiC single crystal3that can lengthen the clogging time, and output those growth conditions, for example, by making adjustments to achieve those growth conditions. For example, the gas pressure Pobs is measured during the growth of the SiC single crystal3to predict the clogging time, and various conditions including the predicted clogging time and the gas pressure Pobs are fed back. In addition, the controller15determine the growth conditions, such as at least one of various conditions including the types of gases, the gas flow rates, and the temperature of the reaction chamber, that will lengthen the clogging time using the learning model. By adjusting the growth conditions to achieve the desired growth conditions, the clogging time can be further lengthened.

If the change over time in the gas pressure Pobs does not coincide with the predicted change shown inFIG.8, deterioration of the heating chamber9or the thermal insulator8may have occurred. These are not constant, unpredictable changes and are difficult to predict through simulation. Even in such a case, by feeding back various conditions including the gas pressure Pobs to find conditions that are less likely to cause clogging, it is possible to obtain various conditions that take into account changes in conditions that cannot be predicted by simulation, and further lengthen the clogging time.

Other Embodiments

While the present disclosure has been described in accordance with the embodiment described above, the present disclosure is not limited to the embodiment and includes various modifications and equivalent modifications. In addition, various combinations and configurations, as well as other combinations and configurations that include only one element, more, or less, fall within the scope and spirit of the present disclosure.

For example, in the above embodiment, as shown inFIG.6, an example has be shown in which the number of nodes in the input layer L1of the learning model is five, and the number of layers in the intermediate layer L2is three. However, the numbers of nodes in the input layer L1and the intermediate layer L2, and the number of layers in the intermediate layer L2can be set optionally.

The learning model can be made more accurate by providing the correction layer L4that takes into account the offset or by performing fitting based on experimental results. However, the learning model may also be created based on a large number of simulations. The number of simulations and experiments to be performed when creating the learning model is also any number. However, the learning model can be more accurate when the number of simulations or the number of experiments is increased.

In addition, in the above embodiment, an example has been described in which the neural network is used as machine learning. However, other learning methods such as deep learning, support vector machines, random forests, and k-nearest neighbor methods may also be applied.

In the above embodiment, the crystal manufacturing apparatus1is described as being equipped with the controller15that stores the learning model. However, the present disclosure can also be such that the controller15is a computing device that performs clogging time prediction and is configured separately from the crystal manufacturing apparatus1. In this case, the computing device stores the learning model, acquires data indicating the measurement results from the pressure sensor14, and predicts the clogging time based on the measurement results and the learning model. Similarly, the present disclosure can be understood as a method for predicting the clogging time.

In addition, in the above embodiment, the crystal manufacturing apparatus1employs a side-flow system in which the supply gas20is supplied to the growth surface of the SiC single crystal3and then discharged in the outer peripheral direction of the heating chamber9. This is just one example. For example, the crystal manufacturing apparatus1may employ an up-flow system, in which the supply gas20passes over the outer peripheral surface of the SiC single crystal3and beside the pedestal10and is discharged further upward. In another example, the crystal manufacturing apparatus1may employ a return flow system in which the supply gas20is supplied to the growth surface of the SiC single crystal3and then returned in the same direction as the supply direction.

The controller15, that is, the computing device and the method thereof described in the present disclosure may be realized by a dedicated computer provided by configuring a processor and a memory programmed to execute one or more functions embodied by a computer program. Alternatively, the controller and the method described in the present disclosure may be implemented by a special purpose computer configured as a processor with one or more special purpose hardware logic circuits. Alternatively, the controller and the method described in the present disclosure may be implemented by one or more special purpose computer, which is configured as a combination of a processor and a memory, which are programmed to perform one or more functions, and a processor which is configured with one or more hardware logic circuits. The computer program may be stored, as instructions to be executed by a computer, in a tangible non-transitory computer-readable medium.