SPUTTERING APPARATUS, METHOD, PROGRAM, AND SYSTEM FOR PREDICTING FILM QUALITY

Abstract

To provide a sputtering device capable of determining a sputtering material, and sputtering conditions and a substrate temperature for obtaining target film quality, and a prediction method, program and prediction system for film quality.SOLUTION: There is disclosed a sputtering device 1 capable of generating a prediction model for film quality. The sputtering device 1 comprises: a sputtering part 10 which forms a film on a substrate with sputtered particles emitted from a target T based upon sputtering conditions and substrate temperature including a plurality of parameters; a processor 21 which determines sputtering conditions and substrate temperature for generating a prediction model for predicting film quality of the film formed by the sputtering part 10, or forming the film on the substrate S to predetermined film quality based upon the prediction model; and a storage 23 which stores the prediction model generated by the processor 21 or a prediction model acquired from another device.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present invention relates to a sputtering apparatus, a method, a program, and a system for predicting film quality. [Background technology]

[0002] 2. Description of the Related Art As an apparatus used for forming a thin film, a sputtering apparatus utilizing the sputtering phenomenon in which ions or the like are collided with a target to knock out sputter particles is known.

[0003] In order to form a film with the desired film quality (electrical conductivity, reflectivity, surface roughness, surface hardness, residual stress, etc., excluding film thickness distribution) in a sputtering device, it is necessary to determine the conditions by adjusting various parameters such as the device specifications (chamber and target, substrate position and shape, discharge voltage, magnetic field distribution, etc.), sputtering material (sputtering target material), film formation conditions (inert gas pressure, discharge voltage, etc.), and substrate temperature. In the following explanation, the device specifications, sputtering material, and film formation conditions are collectively referred to as sputtering conditions. Substrate temperature is not included in the sputtering conditions.

[0004] In order to determine the sputtering conditions and the substrate temperature, it is necessary for an experienced operator to evaluate the film that has actually been formed and to repeatedly adjust various parameters by trial and error, and it takes time and effort to determine the sputtering conditions and the substrate temperature. Here, a method of predicting the film thickness distribution by simulation to determine the sputtering conditions is disclosed in, for example, Patent Document 1 and Patent Document 2. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] Japanese Patent Application Publication No. 6-280010 [Patent Document 1] JP 2000-1777 A Summary of the Invention [Problem to be solved by the invention]

[0006] However, in Patent Document 1, although the film thickness distribution can be simulated and analyzed using the results of the simulation analysis of the target erosion distribution, this cannot be used to determine the sputtering conditions and substrate temperature for obtaining the desired film quality. Also, in Patent Document 2, the film thickness t(q) can be obtained by integrating the product of the sputter particle generation frequency coefficient er(p), the arrival amount f(p,q), and the arrival rate Pm over the erosion region s, but this cannot be used to determine the sputtering conditions and substrate temperature for obtaining the desired film quality.

[0007] Therefore, an object of the present disclosure is to provide a sputtering apparatus, a film quality prediction method, a program, and a prediction system that are capable of determining sputtering conditions and substrate temperature for obtaining a desired film quality. [Means for solving the problem]

[0008] A sputtering apparatus according to an aspect of the present disclosure is a sputtering apparatus capable of generating a prediction model of film quality. Specifications of sputtering equipment, sputtering materials, and deposition conditions A prediction model that predicts the sputtering section that forms a film on a substrate with sputtered particles emitted from a target, and the quality of the film formed by the sputtering section, based on sputtering conditions including multiple parameters and the substrate temperature. of Generate death The present invention includes a calculation unit that determines sputtering conditions and a substrate temperature when a film is formed on a substrate with a predetermined film quality based on the parameters of the sputtering conditions or the prediction model, and a storage unit that stores the prediction model generated by the calculation unit or the prediction model acquired from another device. and the substrate temperature When a film is formed on a substrate by changing the parameters, the average energy of sputtered particles incident on the substrate is calculated for each parameter. The average energy is normalized by dividing it by the cohesive energy of the atoms of the sputtered material. A prediction model is generated by interpolating, through a predetermined calculation, each of the average energies obtained, the substrate temperature during film formation for each parameter, and an evaluation value of the film quality of the film formed with each parameter.

[0009] A prediction system according to an aspect of the present disclosure is a prediction system including a plurality of sputtering apparatuses and a storage device that stores a prediction model of the film quality of a film generated by each of the plurality of sputtering apparatuses. Specifications of sputtering equipment, sputtering materials, and deposition conditions A prediction model that predicts the sputtering section that forms a film on a substrate with sputtered particles emitted from a target, and the quality of the film formed by the sputtering section, based on sputtering conditions including multiple parameters and the substrate temperature. of Generate death The present invention includes a calculation unit that determines sputtering conditions and a substrate temperature when a film is formed on a substrate with a predetermined film quality based on the parameters of the sputtering conditions or the prediction model, a storage unit that stores the prediction model generated by the calculation unit or a prediction model acquired from another device, an input unit that receives an input of the prediction model from the storage unit that stores the prediction model or from another device, and an output unit that outputs the prediction model generated by the calculation unit to the storage unit or to another device. and the substrate temperature When a film is formed on a substrate by changing the parameters, the average energy of sputtered particles incident on the substrate is calculated for each parameter. The average energy is normalized by dividing it by the cohesive energy of the atoms of the sputtered material. A prediction model is generated by interpolating, through a predetermined calculation, each of the average energies obtained, the substrate temperature during film formation for each parameter, and an evaluation value of the film quality of the film formed with each parameter.

[0010] A method for predicting film quality according to an aspect of the present disclosure is a method for predicting the quality of a film formed on a substrate using a prediction model in a sputtering apparatus that forms a film on the substrate with sputtered particles emitted from a target. The prediction method includes: Specifications of sputtering equipment, sputtering materials, and deposition conditions Each parameter of the sputtering conditions including multiple parameters and the substrate temperature A step of calculating an average energy of sputtered particles incident on the substrate for each parameter when a film is formed on the substrate by changing the parameters; The average energy is normalized by dividing it by the cohesive energy of the atoms of the sputtered material. and generating a prediction model of the film quality by interpolating, by a predetermined calculation, each of the average energies obtained, the substrate temperature during film formation for each parameter, and an evaluation value of the film quality of the film formed with each parameter.

[0011] A program according to an aspect of the present disclosure includes a computer program for predicting, using a prediction model, the quality of a film formed on a substrate in a sputtering apparatus that forms a film on a substrate with sputter particles emitted from a target. In addition, the specifications of the sputtering equipment, sputtering materials, and deposition conditions Each parameter of the sputtering conditions including multiple parameters and the substrate temperature A step of calculating an average energy of sputtered particles incident on the substrate for each parameter when a film is formed on the substrate by changing the parameters; The average energy is normalized by dividing it by the cohesive energy of the atoms of the sputtered material. and generating a prediction model of the film quality by interpolating, by a predetermined calculation, each of the average energies obtained by the above calculation, the substrate temperature during the formation of the film for each parameter, and an evaluation value of the film quality of the film formed with each parameter. Run it . Effect of the Invention

[0012] According to the present disclosure, by using a prediction model, it is possible to easily determine sputtering conditions and substrate temperature for obtaining a desired film quality. [Brief description of the drawings]

[0013] [Figure 1] 1 is a schematic diagram showing a schematic configuration of a sputtering apparatus according to an embodiment of the present invention. [Diagram 2] 1 is a flowchart showing a method for creating a predictive model. [Diagram 3] FIG. 2 is a block diagram showing an example of a software configuration that functions to create a predictive model. [Figure 4] FIG. 1 is a diagram showing an example of the results of plotting the measured values ​​of the specific conductivity (conductivity of film / conductivity of target material bulk) of a titanium film formed at a substrate temperature of 80° C. [Diagram 5] FIG. 13 is a diagram showing an example of the results of plotting the measured values ​​of the specific conductivity of a titanium film formed at a substrate temperature of 200° C. [Figure 6] FIG. 13 is a diagram showing an example of the results of plotting the measured values ​​of the specific conductivity of a titanium film formed at a substrate temperature of 400° C. [Figure 7]FIG. 1 shows a predictive model for the specific conductivity of titanium. [Figure 8] FIG. 1 is a diagram showing an example of the results of plotting measured values ​​of reflectance (wavelength 400 nm) of a titanium film formed at a substrate temperature of 80° C. [Figure 9] FIG. 1 is a diagram showing an example of the results of plotting measured values ​​of reflectance (wavelength 400 nm) of a titanium film formed at a substrate temperature of 200° C. [Figure 10] FIG. 1 is a diagram showing an example of the results of plotting measured values ​​of reflectance (wavelength 400 nm) of a titanium film formed at a substrate temperature of 400° C. [Figure 11] FIG. 13 is a diagram showing a prediction model for the reflectance of titanium (wavelength 400 nm). [Figure 12] FIG. 1 is a diagram showing a prediction model for the reflectance of titanium (wavelength 600 nm). [Figure 13] FIG. 13 is a diagram showing a prediction model for the reflectance of titanium (wavelength 800 nm). [Figure 14] FIG. 13 is a diagram showing a prediction model for the reflectance of titanium (wavelength 900 nm). [Figure 15] 1 is a flowchart showing a method for predicting a film quality. [Figure 16] FIG. 13 is a block diagram showing functions of a simulation unit according to a modified example. [Figure 17] FIG. 13 is a schematic diagram showing a schematic configuration of a prediction system including a sputtering apparatus according to a modified example. [Figure 18] FIG. 10 is a diagram illustrating an application scene of a prediction system according to a modified example. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0014] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the accompanying drawings. In the following preferred embodiments, the same or common parts are designated by the same reference characters in the drawings, and the description thereof will not be repeated.

[0015] [Overall configuration of sputtering equipment] 1 is a schematic diagram showing a schematic configuration of a sputtering apparatus according to the present embodiment. The sputtering apparatus 1 includes a sputtering section 10 and an information processing section 20.

[0016] The sputtering unit 10 forms a film on the substrate S with sputtered particles (hereinafter also referred to as "target particles") emitted from a target T based on sputtering conditions and a substrate temperature. The sputtering unit 10 includes, as an example, a controller 11, a chamber 12, an exhaust mechanism 13, a gas supply mechanism 14, a target holder 15, a substrate holder 16, and a power supply 17. The sputtering unit 10 (more specifically, the controller 11) is connected to an information processor 20 so as to be able to communicate with the information processor 20.

[0017] The controller 11 controls the entire sputtering unit 10. Typically, the controller 11 includes one or more processors, a main memory that temporarily stores various data, a storage that non-temporarily stores programs and the like, an input / output interface, and the like.

[0018] An exhaust mechanism 13 and a gas supply mechanism 14 are connected to the chamber 12. In addition, a target holder 15 and a substrate holder 16 are arranged inside the chamber 12.

[0019] The inside of the chamber 12 is depressurized to a vacuum by the exhaust mechanism 13. The exhaust mechanism 13 drives a pump in accordance with instructions from the controller 11 to depressurize the inside of the chamber 12. Also, an inert gas (e.g., argon gas) for generating plasma is supplied into the chamber 12 by a gas supply mechanism 14. The gas supply mechanism 14 includes a gas cylinder 14a and a mass flow controller 14b, and supplies an amount of gas (e.g., flow rate or pressure) instructed by the controller 11 into the chamber 12.

[0020] The target holding unit 15 holds a target T of a sputtering material for forming a film on the substrate S. The substrate holding unit 16 holds the substrate S. The power supply 17 applies a voltage between the target holding unit 15 and the substrate holding unit 16 in accordance with an instruction from the controller 11. The power supply 17 may be an AC power supply or a DC power supply. In the present embodiment, as an example, the power supply 17 will be described as being a DC power supply.

[0021] When forming a film on the substrate S, for example, the controller 11 controls each part of the sputtering unit 10 as follows. First, the controller 11 controls the exhaust mechanism 13 to evacuate the chamber 12. Then, the controller 11 controls the power supply 17 to apply a voltage between the target holder 15 and the substrate holder 16, and controls the gas supply mechanism 14 to supply an inert gas into the chamber 12. This converts the inert gas into plasma, and ions collide with the target T, causing target particles to be emitted from the target T due to the collision. The emitted target particles adhere to the substrate S, forming a film of the components of the target T on the substrate S.

[0022] The information processing unit 20 creates a prediction model for predicting film quality based on sputtering conditions and substrate temperature. The created prediction model is used, for example, to determine sputtering conditions and substrate temperature for obtaining a desired film quality. The information processing unit 20 typically includes a processor 21, a main memory 22, a storage 23, a communication interface (I / F) 24, an input unit 25, and a display unit 26. These components are connected via a bus 27.

[0023] The processor 21 is composed of a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), etc., and reads out a program stored in the storage 23, and deploys and executes the program in the main memory 22. By executing the program, the processor 21 creates a prediction model for predicting film quality based on film formation conditions, or outputs information used for determining sputtering conditions and a substrate temperature for obtaining a desired film quality based on the prediction model.

[0024] The main memory 22 is configured with a volatile storage device such as a random access memory (RAM) or a dynamic RAM (DRAM). The storage 23 (storage unit) is configured with a non-volatile storage device such as a hard disk drive (HDD) or a solid state drive (SSD). For example, the storage 23 stores a prediction model created by the processor 21 or a prediction model acquired from another sputtering device. In this embodiment, the storage 23 stores the prediction model created by the processor 21.

[0025] The communication I / F 24 exchanges signals with the controller 11 of the sputtering unit 10 by wired or wireless communication. The communication I / F 24 also functions as an output unit that outputs the prediction model generated by the processor 21 (arithmetic unit) to an external storage device or another sputtering device. The communication I / F 24 also functions as an input unit that accepts input of the prediction model from an external storage device that stores the prediction model or from another sputtering device.

[0026] The input unit 25 receives user operations and is typically configured with a touch panel, a keyboard, a mouse, etc. The display unit 26 is an example of an output destination of information used to determine the sputtering conditions and the substrate temperature for obtaining the desired film quality, and is configured with a liquid crystal panel capable of displaying images, etc. In the information processing unit 20, for example, when the sputtering material and the desired film quality are inputted into the input unit 25, the necessary sputtering conditions and the substrate temperature can be determined, and the conditions are displayed on the display unit 26.

[0027] In order to form a film with the desired film quality in the sputtering apparatus 1, it is necessary to determine the conditions by adjusting various parameters such as the specifications of the apparatus, the sputtering material, the film formation conditions, the substrate temperature, etc. Therefore, in order to determine the sputtering conditions and the substrate temperature for obtaining the desired film quality using a new apparatus or a new sputtering material, it is necessary to repeatedly perform the work of forming the film and the evaluation test of the obtained film, which places a large burden on the user.

[0028] The sputtering apparatus 1 according to this embodiment can create a prediction model for predicting the necessary sputtering conditions and substrate temperature by inputting the sputtering material and the desired film quality, or can use the prediction model to determine the sputtering conditions and substrate temperature for obtaining the desired film quality, thereby reducing the burden on the user. A method for creating a prediction model and a method for using the created prediction model will be described below.

[0029] [How to create a forecast model] FIG. 2 is a flowchart showing a method for creating a prediction model. FIG. 3 is a block diagram showing an example of a software configuration that functions to create a prediction model. Each function shown in FIG. 3 can be realized by the processor 21 reading and executing a program (simulation program) stored in the storage 23. A prediction model is created for each evaluation item of the sputtering material of the target T and the film quality. Of course, a prediction model may be created for each sputtering material of the target T or for each evaluation item of the film quality. The evaluation items of the film quality include, for example, evaluation items evaluated from the viewpoint of optics such as reflectance / transmittance / absorption rate, smoothness, hardness, conductivity, residual stress, etc.

[0030] As shown in Fig. 2, the method of creating a prediction model is mainly divided into data collection (S1) and creation of a prediction model (S2). The data collection (S1) includes a film forming step (S10), a step (S12) of inputting or acquiring by communication measurements of the film formed in S10 using another measuring device, and a film evaluation step (S14) based on the measurements acquired in S12. The film forming step and evaluation step are repeated by changing the sputtering conditions and substrate temperature (S18) until film formation under all predetermined sputtering conditions and substrate temperatures is completed (until YES is determined in S16).

[0031] Creating the predictive model includes a step (S22) of calculating the incident energy (E), a step (S24) of plotting the value obtained there, the substrate temperature when the film was created in S10, and the data obtained in S12, and a step (S26) of creating the predictive model.

[0032] As shown in Fig. 3, the information processing unit 20 includes a simulation unit 210, a plot unit 220, and a model creation unit 230. The simulation unit 210 has a function for realizing the step (S22) of calculating the incident energy (E) shown in Fig. 2. The plot unit 220 has a function for realizing the step (S24) of plotting shown in Fig. 2. The model creation unit 230 has a function for realizing the step (S26) of creating a prediction model shown in Fig. 2.

[0033] (Simulation Division 210) The simulation unit 210 calculates the incident energy (E). The incident energy (E) is the energy (kinetic energy) when the target particles are incident on the substrate S, and is calculated by simulation based on the sputtering conditions in the film forming step (S10). The sputtering conditions include the shape of the sputtering unit 10 (the distance between the substrate S and the target T, and the shape and dimensions of the chamber 12, etc.), the shapes of the substrate S and the target T, the material of the target T, the pressure of the inert gas, and the voltage of the power source 17, etc.

[0034] The simulation unit 210 includes an ejection condition determination unit 212 , a flight path determination unit 214 , and an incident energy calculation unit 216 .

[0035] The ejection condition determination unit 212 determines where on the target T the target particles are ejected from (ejection position of the target particles), where the target particles are ejected towards (ejection direction of the target particles), and how fast the target particles are ejected (initial velocity of the target particles).

[0036] The injection condition determination unit 212 determines the injection position of the target particles according to the Monte Carlo method, using the erosion pattern of the target T as the injection distribution of the target particles. The erosion pattern of the target may be a measured value obtained using the sputtering unit 10, or an estimated value from a measured value measured under conditions similar to the sputtering conditions. The erosion pattern depends mainly on the specifications of the sputtering unit 10. The erosion pattern corresponding to the type (e.g., model number) of the sputtering unit 10 connected to the information processing unit 20 is stored in advance in the storage 23, for example. In this case, the injection condition determination unit 212 acquires the erosion pattern corresponding to the type of the sputtering unit 10 from the storage 23. The information processing unit 20 may also be configured to be able to communicate with a storage device in which the erosion pattern for each type of sputtering unit 10 is stored. In this case, the injection condition determination unit 212 acquires the erosion pattern from the storage device based on the type of the sputtering unit 10 connected to the information processing unit 20.

[0037] The injection position is set at an inert gas ion (e.g., Ar + ) to the target T, the target particles are emitted from the target T with the same distribution as the incidence frequency distribution of the inert gas ions (e.g., Ar + The incidence frequency distribution of the electrons 11a, 11b, 11c, 11d, 11e, 11f, 11g, 11h, 11i, 11j, 11m, 11n, 11m, 11n, 11n, 11m, 11n, 11n, 11m, 11n, 11n, 11b, 11c, 11m, 11n, 11b, 11n ...

[0038] The emission condition determination unit 212 determines the emission direction of the target particle by assuming that the target particle is emitted in any direction within a range of 2π for the plane orientation and that the target particle is emitted according to, for example, the cosine law for the normal orientation.

[0039] The ejection condition determination unit 212 determines the initial velocity of the target particles on the assumption that the velocity distribution of the ejected target particles follows Thompson's formula, which are equations (1) to (3).

[0040]

number

[0041]

number

[0042]

number

[0043] M 1 is the atomic weight of the inert gas ion incident on the target T, M is the atomic weight of the target particle, E b is the cohesive energy of the target molecule, E 1 is the energy of the inert gas ion incident on the target T (E for Ar) 1 =1.6×10 -19 V 1 ), V 1 is the voltage between the two poles, E i is the kinetic energy of the ejected particle, m is the mass of the target particle, and v is the initial velocity of the target particle.

[0044] M 1 , M., E. b , E 1 , m are characteristic values ​​such as physical properties. For example, the injection condition determination unit 212 determines the target T and the type of inert gas input from the input unit 25 or the sputtering unit 10, and the interelectrode voltage V 1 Based on M 1, M., E. b , E 1 , m. V 1 is input from the input unit 25 or the sputtering unit 10, for example.

[0045] The ejection condition determination unit 212 sends the obtained ejection position, ejection direction, and initial velocity of the target particle to the flight path determination unit 214 .

[0046] The flight path determination unit 214 determines the flight path of the target particle (ejected particle) ejected from the target T. Specifically, the flight path determination unit 214 determines the position where the ejected particle collides with the inert gas atom in the chamber 12, and the flight speed and flight direction of the ejected particle after the collision. The flight path determination unit 214 determines the flight path of the ejected particle by repeating the calculation of the collision position, and the flight speed and flight direction after the collision, using the ejection position, ejection direction, and initial speed of the target particle sent from the ejection condition determination unit 212 as initial values, until the ejected particle reaches the wall surface. As an example, in the chamber 12, the collision position, the flight speed after the collision, and the flight direction after the collision are determined on the assumption that the inert gas in the chamber 12 is distributed according to the Maxwell method. It is assumed that the ejected particle that reaches the wall surface adheres to the arrival position.

[0047] The flight path determination unit 214 determines the collision position by calculating the flight distance of the emitted particle until it collides with the gas in the chamber 12 from the mean free path of the flying particle in the neutral gas. The mean free path of the flying particle in the neutral gas is calculated based on, for example, the physical property value of the inert gas and the environment in the chamber 12 (for example, the supply amount of the inert gas (i.e., pressure P), temperature, etc.). The environment in the chamber 12 is input from the input unit 25 or the sputtering unit 10, for example.

[0048] The flight path determination unit 214 determines the flight speed and flight direction after the collision by assuming that the ejected particle undergoes a perfect elastic collision with an inert gas atom and that the collision potential U is, for example, of the Born-Mayer type shown in equation (4).

[0049]

number

[0050] ρ is the interparticle distance, and A and B are constants determined by the emitted particles and the inert gas atoms. The constants A and B are predetermined according to the type of the target T and the inert gas, and are stored in the storage 23 for each combination of the target T and the inert gas. The flight path determination unit 214 acquires the constants A and B from the storage 23 based on the type of the target T and the inert gas input from the input unit 25 or the sputtering unit 10, for example.

[0051] The flight path determination unit 214 determines the flight path of the emitted particles, thereby determining the number of emitted particles that reach the surface of the substrate S and the speed of each emitted particle when it reaches the surface of the substrate S. The flight path determination unit 214 sends the number of emitted particles that reach the surface of the substrate S and the speed of each emitted particle when it reaches the surface of the substrate S to the incident energy calculation unit 216.

[0052] The incident energy calculation unit 216 calculates the incident energy (E). Specifically, the incident energy calculation unit 216 calculates the kinetic energy of each emitted particle from the speed of each emitted particle when it arrives at the substrate S surface sent from the flight path determination unit 214, and tallies up the calculated kinetic energy of each emitted particle. The incident energy calculation unit 216 calculates the incident energy (E) by dividing the tallied kinetic energy by the number of emitted particles sent from the flight path determination unit 214 that arrive at the substrate S surface.

[0053] The incident energy calculation unit 216 sends the determined incident energy (E) to the plot unit 220 .

[0054] The simulation unit 210 calculates the incident energy (E) corresponding to each of the plurality of sputtering conditions set in the data collection step (S1).

[0055] (Plot section 220) The plot unit 220 plots data consisting of the incident energy (E), substrate temperature (Ts), and film quality evaluation result (Q) sent from the incident energy calculation unit 216 in a three-dimensional space (E-Ts-Q space).

[0056] The substrate temperature (Ts) is an actual value measured in the film forming step (S10). The film quality evaluation result (Q) is an actual value obtained in the film evaluation step (S14). The substrate temperature (Ts) and the film quality evaluation result (Q) are stored in storage 23 in association with the sputtering conditions.

[0057] As an example, the plot unit 220 acquires the sputtering conditions, the substrate temperature (Ts), and the evaluation result (Q) of the film quality from the storage 23. The plot unit 220 plots the acquired substrate temperature (Ts) and the evaluation result (Q) of the film quality, as well as the incident energy (E) corresponding to the sputtering conditions sent from the simulation unit 210, in a three-dimensional space (ETQ space).

[0058] The plot unit 220 plots all incident energies (E) sent from the simulation unit 210 in a three-dimensional space (E-Ts-Q space).

[0059] In addition, when multiple evaluation items are evaluated in the film evaluation step (S14), each evaluation item is plotted. For example, when reflectance and conductivity are evaluated in the film evaluation step (S14), the evaluation result of reflectance (Q1) is plotted in E-Ts-Q1 space, and the evaluation result of conductivity (Q2) is plotted in E-Ts-Q2 space.

[0060] In addition, the plot unit 220 may plot the incident energy (E) normalized by the cohesive energy (Eb) of the atoms constituting the target T, or may plot the substrate temperature (Ts) normalized by the melting point of the sputtering material of the target T. It is known that the micro cross-sectional structure (film structure) of the film is strongly correlated with the two parameters of the incident energy and the substrate temperature, but it has been found that the relationship between the film structure and the film quality differs for each evaluation item of the sputtering material and the film quality. Therefore, in this disclosure, through many film formation experiments, film quality evaluations and simulation calculations, a film quality prediction model using the above two parameters for each evaluation item of the sputtering material and the film quality is created, and it has been found that the film quality of the sputtering material can be uniquely determined by specifying the two parameters of the incident energy (E) and the substrate temperature (Ts) on the prediction model.

[0061] The plotting unit 220 sends the plot results and information indicating the evaluation axes (evaluation items of the film quality) to the model creating unit 230 in association with each other.

[0062] The model creation unit 230 estimates a range where the film quality is the same by connecting plots having a common film quality evaluation result (Q) according to the plot results, and creates a prediction model. In other words, the model creation unit 230 draws contour lines in a three-dimensional space by connecting plots having a common film quality evaluation result (Q) through interpolation.

[0063] The process of connecting the plots may be performed by a user. For example, the model creation unit 230 may output a scatter diagram obtained by plotting to the display unit 26 and instruct the user to connect the plots via the input unit 25. The user may connect the plots via the input unit 25.

[0064] The model creation unit 230 stores the created prediction model in the storage 23. Note that the model creation unit 230 may store the prediction model in the storage 23 by associating the film quality evaluation item of the prediction model with the material of the target T of the prediction model.

[0065] It has been described that the plot unit 220 and the model creation unit 230 create a prediction model by plotting the acquired substrate temperature (Ts) and film quality evaluation result (Q) and the incident energy (E) sent from the simulation unit 210 in a three-dimensional space (E-Ts-Q space) and connecting the plots, but the creation method is not limited to this. As long as the information processing unit 20 can generate a prediction model by performing "interpolation" that smoothly connects a finite number of actual measured values ​​with a predetermined calculation using a table, function, graph, etc., any method may be used to create the prediction model.

[0066] [Example 1 of creating a predictive model] Next, a specific example 1 of creating a prediction model will be described. First, in a chamber with a diameter of 275 mm and a height of 300 mm, a film is formed by sputtering on a Si substrate with a diameter of 100 mm using titanium as a sputtering material with a diameter of 100 mm. The conductivity (electrical conductivity, electrical conductivity) of the formed film is actually measured, and the results of plotting the relationship with incident energy are shown in the figure. FIG. 4 is a diagram showing an example of the plot of the actual measured values ​​of the conductivity of a titanium film formed at a substrate temperature of 80° C. FIG. 5 is a diagram showing an example of the plot of the actual measured values ​​of the conductivity of a titanium film formed at a substrate temperature of 200° C. FIG. 6 is a diagram showing an example of the plot of the actual measured values ​​of the conductivity of a titanium film formed at a substrate temperature of 400° C.

[0067] In the graphs shown in Figures 4 to 6, the horizontal axis represents incident energy (E*) and the vertical axis represents specific conductivity, and the measured values ​​are plotted. ( Average energy per incident particle divided by the number of incident particles ) is a dimensionless quantity normalized by dividing the measured conductivity by the bulk conductivity of the sputtered material (Eb).

[0068] In the graphs shown in Figures 4 to 6, films were formed at three different substrate temperatures (80°C, 200°C, 400°C), with the distance between the substrate S and the target T (TS distance) changed from 50 mm, 75 mm, and 100 mm, and the Ar pressure changed from 0.3 Pa to 16 Pa, and the results of measuring the specific conductivity of the films are plotted. Note that for a 100 mm diameter Si substrate, the plots for a 50 mm TS distance are shown with white circles, those for a 75 mm TS distance are shown with white triangles, and those for a 100 mm TS distance are shown with white squares. Note that Figures 4 and 5 also plot the results of measurements made on a 150 mm diameter substrate (TS distance 125 mm) using a different device with black squares.

[0069] In the plot section 220 and the model creation section 230, the actual measured values ​​shown in FIG. 4 to FIG. 6 are plotted on the horizontal axis as the incident energy (E * ) and the vertical axis is the substrate temperature (T * ) and then, by interpolating and connecting the plots with the same specific conductivity among the individual plots, contour lines are drawn to create a prediction model. Figure 7 shows a prediction model for the specific conductivity of titanium. Note that the substrate temperature (T * ) is a dimensionless quantity normalized by dividing the substrate temperature (Ts) during the formation of the sputtered film by the melting point of the sputtered material.

[0070] As can be seen from the contours shown in Fig. 7, the incident energy (E * ) and substrate temperature (T * The film quality (specific conductivity) of the formed film is uniquely determined from the coordinates of the white circle (E * ,T * )=(0.55,0.20), the specific conductivity of the resulting film can be predicted to be 0.255. The prediction model shown in Fig. 7 is called the membrane property diagram (MPD) for specific conductivity.

[0071] [Example 2 of creating a predictive model] Next, a specific example 2 of creating a prediction model will be described. First, in a chamber with a diameter of 275 mm and a height of 300 mm, a film is formed by sputtering on a Si substrate with a diameter of 100 mm using titanium as a sputtering material with a diameter of 100 mm. The reflectance of the formed film at a wavelength of 400 nm (hereinafter referred to as reflectance (wavelength 400 nm)) is actually measured, and the result of plotting the relationship with the incident energy is shown in the figure. FIG. 8 is a diagram showing an example of the result of plotting the actual measured values ​​of the reflectance (wavelength 400 nm) of a titanium film formed at a substrate temperature of 80° C. FIG. 9 is a diagram showing an example of the result of plotting the actual measured values ​​of the reflectance (wavelength 400 nm) of a titanium film formed at a substrate temperature of 200° C. FIG. 10 is a diagram showing an example of the result of plotting the actual measured values ​​of the reflectance (wavelength 400 nm) of a titanium film formed at a substrate temperature of 400° C.

[0072] In the graphs shown in Figs. 8 to 10, the horizontal axis is the incident energy (E * ) and the vertical axis is the reflectance. The reflectance (wavelength 400 nm) is the value obtained by dividing the intensity of the light reflected by the film when the formed film is irradiated with light of wavelength 400 nm by the intensity of the irradiated light.

[0073] In the graphs shown in Figs. 8 to 10, the results of measuring the reflectance (wavelength 400 nm) of a film formed at each of three substrate temperatures (80°C, 200°C, 400°C), with the distance between the substrate S and the target T (TS distance) changed from 50 mm, 75 mm, and 100 mm, and the Ar pressure changed from 0.3 Pa to 16 Pa, are plotted. Note that for a Si substrate with a diameter of 100 mm and a TS distance of 50 mm, the plots with a TS distance of 75 mm are shown with white circles, the plots with a TS distance of 75 mm, and the plots with a TS distance of 100 mm are shown with white squares. Note that Figs. 8 and 9 also plot the results of a measurement using a substrate with a diameter of 150 mm (TS distance of 125 mm) from a different device, using black squares.

[0074] In the plot section 220 and the model creation section 230, the actual measured values ​​shown in FIG. 8 to FIG. 10 are plotted on the horizontal axis as the incident energy (E * ) and the vertical axis is the substrate temperature (T *) and a prediction model is created by drawing contour lines by interpolating and connecting the plots with the same reflectance (wavelength 400 nm). Figure 11 is a diagram showing a prediction model for the reflectance of titanium (wavelength 400 nm).

[0075] As can be seen from FIG. 11, the incident energy (E * ) and substrate temperature (T * The reflectance of the formed film (wavelength 400 nm) is uniquely determined from the coordinates of the white circle in Figure 11 (E * ,T * )=(0.55,0.20), the reflectance (wavelength 400 nm) of the obtained film can be predicted to be 14.1%. The prediction model shown in Fig. 11 is called MPD for reflectance (wavelength 400 nm).

[0076] Prediction models can be created in a similar manner for the film reflectance (wavelength 600 nm), the film reflectance (wavelength 800 nm), and the film reflectance (wavelength 900 nm). FIG. 12 is a diagram showing a prediction model for titanium reflectance (wavelength 600 nm). FIG. 13 is a diagram showing a prediction model for titanium reflectance (wavelength 800 nm). FIG. 14 is a diagram showing a prediction model for titanium reflectance (wavelength 900 nm).

[0077] [How to use the prediction model] The following describes how to use the prediction model created by the information processing unit 20. The prediction model can be used, for example, to determine sputtering conditions and substrate temperature for obtaining a desired film quality. As an example, when the evaluation items of the sputtering material and the desired film quality are input via the input unit 25, the information processing unit 20 selects a prediction model corresponding to the evaluation items of the sputtering material and the film quality, and determines the incident energy and substrate temperature for obtaining the desired film quality based on the selected prediction model. Furthermore, the information processing unit 20 determines various parameters that can realize the incident energy determined by the prediction model, taking into account the specifications of the device. In this way, the information processing unit 20 can not only determine the sputtering conditions and substrate temperature for obtaining the desired film quality based on the prediction model, but also determine the specifications of the device for obtaining the desired film quality while taking into account various parameters.

[0078] Therefore, the prediction model obtained from a certain sputtering device (for example, a sputtering device for a prototype) can be used to determine the specifications, sputtering conditions, and substrate temperature of another sputtering device (for example, a sputtering device for planned mass production). Specifically, the information processing unit 20 predicts the film quality of a film to be formed on the substrate S using the prediction model from, for example, the specifications, sputtering material, film formation conditions, and substrate temperature of the mass production sputtering device. If the desired film quality differs from the predicted film quality, the information processing unit 20 recalculates the prediction of the film quality using the prediction model by changing the specifications, sputtering conditions, and substrate temperature of the device. If the desired film quality matches the predicted film quality, these are adopted as the specifications, sputtering conditions, and substrate temperature of the mass production sputtering device. Note that, if the information processing unit 20 does not have a prediction model corresponding to the evaluation items of the sputtering material and film quality in the storage unit, it can acquire the corresponding prediction model from an external storage device or acquire the corresponding prediction model by performing an experiment to form a film in the sputtering unit 10.

[0079] A method for predicting the film quality of a film formed on a substrate S using a prediction model will be described in more detail with reference to a flowchart. Fig. 15 is a flowchart showing the film quality prediction method. The film quality prediction method includes a step (S42) of receiving input of sputtering conditions, substrate temperature, and evaluation items of film quality, a step (S44) of calculating incident energy (E) based on the sputtering conditions including the specifications of the apparatus, and a step (S46) of predicting the film quality based on a prediction model.

[0080] In the step (S42) of receiving input of the sputtering conditions and the evaluation items of the substrate temperature and the film quality, information required for calculating the incident energy (E) is input as the sputtering conditions. Although the "information required for calculating the incident energy (E)" varies depending on the calculation method of the incident energy (E), in the present embodiment, as an example, it includes the shape of the apparatus, the type of the target T and the inert gas, the voltage between the electrodes, and the supply amount of the inert gas (i.e., the pressure in the chamber 12).

[0081] The step (S44) of calculating the incident energy (E) is common to the step (S22) of calculating the incident energy (E) included in the method of creating a prediction model, and is realized by the simulation unit 210 shown in FIG.

[0082] At the time of prediction, the injection condition determination unit 212 acquires an erosion pattern based on the shape of the device received in S42. The injection condition determination unit 212 acquires an erosion pattern based on the type of target T and inert gas and the interelectrode voltage V 1 From M 1 , M., E. b , E 1 , m, and other property values. The flight path determination unit 214 determines the constants A and B from the target T and the type of inert gas received in S42.

[0083] In the step (S46) of predicting the film quality, a prediction model corresponding to the sputtering material of the target T and the evaluation items of the film quality received in S42 is referenced, and an evaluation result (Q) is obtained from the calculated incident energy (E) and the substrate temperature (Ts) received in S42. The obtained evaluation result is output to the display unit 26, for example. For example, the point (E * =0.55,T * =0.2) is 0.255.

[0084] The user determines whether the desired film quality matches the evaluation result (Q) output on the display unit 26, and if they do not match, returns to S42 and re-inputs the changeable sputtering conditions and substrate temperature. The user repeatedly compares the evaluation result (Q) obtained under the re-input sputtering conditions and substrate temperature with the desired film quality, thereby determining the sputtering conditions and substrate temperature for obtaining the desired film quality.

[0085] The change of the sputtering conditions and the substrate temperature, and the comparison and judgment of the target film quality and the evaluation result (Q) may be executed by a program. In this case, for example, the evaluation value indicated by the target film quality, the changeable sputtering conditions and the substrate temperature, and the changeable range of these are inputted via the input unit 25. Then, as one example, the information processing unit 20 changes the changeable sputtering conditions and the substrate temperature within a possible range and repeats the comparison with the evaluation result (Q) until the difference between the obtained evaluation result and the input evaluation value becomes equal to or less than a predetermined threshold, thereby determining the sputtering conditions and the substrate temperature for obtaining the target film quality.

[0086] When the prediction models shown in FIGS. 7 and 11 to 14 are used, the substrate temperature (T * ) The incident energy (E) was calculated in the process of S44, and the coordinates (E * ,T * ) = (0.55, 0.20), the predicted values ​​of the film quality can be obtained from each prediction model. Specifically, the coordinates (E * ,T *) = (0.55, 0.20), the predicted value of the specific conductivity of the film is 0.255. Similarly, the coordinates (E * ,T * ) = (0.55, 0.20), the predicted value of the titanium reflectance (wavelength 400 nm) of the film is 14.1%. The coordinates of the white circle (E * ,T * ) = (0.55, 0.20), the predicted value of the titanium reflectance (wavelength 600 nm) of the film is 14.7%. The coordinates (E * ,T * ) = (0.55, 0.20), the predicted value of the titanium reflectance (wavelength 800 nm) of the film is 25.4%. The coordinates of the white circle (E * ,T * ) = (0.55, 0.20), we obtain a predicted value of 27.1% for the titanium reflectance of the film (wavelength 900 nm).

[0087] The above-mentioned method of using the prediction model is an example, and various other methods of use are possible. For example, by inputting data on the film quality of the required film into the prediction model, the incident energy (E * ) and substrate temperature (T * ) is uniquely determined, so this incident energy (E * ) and substrate temperature (T * By setting sputtering conditions that achieve the above for a plurality of types of sputtering apparatus, it becomes possible to produce films of the same quality using a plurality of types of sputtering apparatus.

[0088] [Variation 1] In the above embodiment, the information processing section 20 has a function for predicting the film quality. The information processing section 20 may have a function for predicting the film thickness distribution in addition to the film quality.

[0089] More specifically, the simulation unit may further include a function for predicting the film thickness distribution. Fig. 16 is a block diagram showing the functions of the simulation unit according to the modified example. The simulation unit 210a differs from the simulation unit 210 according to the above embodiment in that it further includes a film thickness distribution calculation unit 218.

[0090] As described above, the flight path determination unit 214 determines the flight path assuming that the emitted particles that reach the wall surface will adhere at the arrival position. In other words, by determining the flight path, the arrival position of the target particle is also determined. The flight path determination unit 214 sends the arrival position to the film thickness distribution calculation unit 218.

[0091] The film thickness distribution calculation unit 218 calculates the film thickness distribution of the film formed on the substrate S surface from the arrival positions of the target particles sent from the flight path determination unit 214, assuming that the emitted particles that reach the wall surface adhere at the arrival positions. The incident energy distribution of the film can be calculated by sending the arrival positions to the incident energy calculation unit 216. When a distribution of film quality occurs on a large-area substrate, it becomes possible to predict the distribution. In this case, the average energy is calculated per unit area at each position on the substrate.

[0092] [Variation 2] In the above embodiment, the sputtering apparatus creates a prediction model using evaluation results of a film formed by a sputtering unit included in the sputtering apparatus. Note that the information processing unit included in the sputtering apparatus may collect evaluation results of films formed by other sputtering apparatuses, and create and update a prediction model by further using the collected evaluation results.

[0093] 17 is a schematic diagram showing a schematic configuration of a prediction system including a sputtering apparatus according to a modified example. The prediction system SYS includes a plurality of sputtering apparatuses 1A, 1B, and 1C. The sputtering apparatuses 1A, 1B, and 1C include sputtering units 10A, 10B, and 10C, respectively. The sputtering apparatuses 1A, 1B, and 1C also include information processing units 20A, 20B, and 20C, respectively.

[0094] The sputtering units 10A, 10B, and 10C are common to the sputtering unit 10 according to the above embodiment. The sputtering units 10A, 10B, and 10C may have chambers with different shapes.

[0095] The information processing units 20A, 20B, and 20C differ from the information processing unit 20 according to the above embodiment in that they are communicably connected to each other via a network NW, but are the same as the information processing unit 20 in other respects.

[0096] Each of the information processors 20B and 20C associates the plot results with the evaluation items of the film quality and transmits them to the information processor 20A. The information processor 20A creates a prediction model using the plot results and the evaluation items of the film quality obtained based on the evaluation results of the film formed by the sputtering unit 10A, as well as the information sent from each of the information processors 20B and 20C.

[0097] Each of the information processors 20B and 20C may or may not have a function for creating a prediction model. Also, each of the information processors 20B and 20C may not have a function for obtaining plot results. In this case, each of the information processors 20B and 20C may transmit information (such as sputtering conditions) required for the simulation unit 210 to calculate the incident energy (E) and evaluation results including evaluation items of film quality to the information processor 20A. The information processor 20A obtains plot results based on the transmitted information. Furthermore, the prediction models created by each of the information processors 20A to 20C may be stored in an external storage device (for example, a server) connected via the network NW. In the external storage device, the prediction models created for each evaluation item of the sputtering material and film quality of the target T may be accumulated as a library.

[0098] [Variation 3] In the above embodiment, the sputtering apparatus is provided with an information processing unit having a function for predicting film quality. Note that a data center provided outside the sputtering apparatus may have the function for predicting film quality.

[0099] 18 is a diagram showing a schematic diagram of an application scene of a prediction system according to a modified example. The prediction system SYSa according to the modified example includes factories (factory A, factory B, etc.) in which sputtering equipment and evaluation equipment for evaluating a film formed by the sputtering equipment are installed, and a data center 200 configured to be able to communicate with each factory.

[0100] The data center 200 includes a simulation unit 210, a plot unit 220, and a model creation unit 230 that are included in the information processing unit 20 according to the above embodiment. That is, the data center 200 includes a function for creating a prediction model.

[0101] The data center 200 acquires information for creating a prediction model for a simulation from each factory. For example, the data center 200 acquires plot results and film quality evaluation items from factory A, and acquires sputtering conditions, substrate temperatures, and film quality evaluation results (including film quality evaluation items) from factory B.

[0102] The data center 200 creates a new prediction model based on the acquired information, or updates an already-created prediction model. An already-created prediction model is updated, for example, by adding a newly acquired plot result to the plot result included in the already-created prediction model and reconnecting the plots.

[0103] In this way, the information obtained at each factory is aggregated in the data center, and the plot results are accumulated in the data center 200. Therefore, in the prediction system SYSa according to the modified example, a highly accurate prediction model can be created.

[0104] Moreover, by collecting information obtained at each factory in the data center, plot results related to evaluation items of various sputtering materials and various film qualities can be obtained, and the data center 200 can create various prediction models. The data center 200 may further have a function of providing the prediction models.

[0105] For example, when a new factory C is constructed for mass production of a film, the user (information processing device) inquires of the data center 200 by specifying the sputtering material to be used and the evaluation items of the film quality according to the intended use of the film. In this case, the data center 200 sends the prediction model corresponding to the specified sputtering material and the evaluation items of the film quality from among the prediction models created, to the user (information processing device). This allows the user (information processing device) to determine the sputtering conditions and the substrate temperature by using the sent prediction model (for example, prediction model A-1). This makes it unnecessary to carry out trial and error using a test sputtering device (repeating the work of adjusting various parameters, the work of actually forming a film, and the work of evaluating the film actually formed and obtained by trial and error), and reduces the time and labor required to determine the sputtering conditions and the substrate temperature for obtaining the desired film quality.

[0106] The data center 200 may further include a function of predicting film quality provided in the information processing unit 20 according to the above embodiment. In this case, the user (information processing device) sends to the data center 200 the sputtering material to be used, evaluation items of film quality according to the intended use of the film, evaluation values ​​indicated by the desired film quality, changeable sputtering conditions and substrate temperature, and the changeable ranges thereof. The data center 200 may determine sputtering conditions and substrate temperature for obtaining a film that satisfies the specified evaluation value based on the sent information, and return the determined sputtering conditions and substrate temperature to the user (information processing device).

[0107] [Variation 4] In the above embodiment, the sputtering device 1 predicts the film quality on the premise of sputtering by the two-pole sputtering method. The prediction of the film quality may be made by using a prediction model obtained by plotting the average energy of the sputter particles incident on the substrate, the substrate temperature during film formation, and the evaluation result of the film quality in a three-dimensional space. In other words, as long as the average energy of the sputter particles incident on the substrate can be calculated by simulation, the prediction of the film quality is not limited to the two-pole sputtering method, but can also be applied to sputtering devices or deposition devices that use, for example, magnetron sputtering, ion beam sputtering, reactive sputtering, and sputtering techniques that combine these.

[0108] [Major disclosures] As described above, the present embodiment includes the following disclosure.

[0109] [Configuration 1] A sputtering apparatus capable of generating a predictive model of film quality comprises: a sputtering unit that forms a film on a substrate with sputter particles emitted from a target based on sputtering conditions including a plurality of parameters and a substrate temperature; a calculation unit that generates a predictive model that predicts the quality of a film formed by the sputtering unit, or determines the sputtering conditions and substrate temperature when a film is formed on a substrate with a predetermined film quality based on the predictive model; and a memory unit that stores the predictive model generated by the calculation unit or a predictive model obtained from another sputtering apparatus. When a film is formed on a substrate by changing each parameter of the sputtering conditions, the calculation unit calculates the average energy of sputter particles incident on the substrate for each parameter, and generates a predictive model by interpolating each calculated average energy, the substrate temperature at the time of film formation for each parameter, and an evaluation value of the film quality of the film formed with each parameter using a predetermined calculation.

[0110] This enables the sputtering apparatus to easily determine sputtering conditions and substrate temperatures to obtain new film qualities by utilizing a predictive model generated from the average energy, the substrate temperature during film formation for each parameter, and values ​​evaluating the film quality of the film formed with each parameter.

[0111] [Configuration 2] The calculation unit calculates the flight path and speed of the sputtered particles released from the target based on the sputtering conditions, calculates the average energy from the calculated flight path and speed, and calculates the film thickness distribution of the film formed on the substrate from the calculated flight path.

[0112] [Configuration 3] The film quality includes multiple evaluation items, and the calculation unit generates a prediction model for each evaluation item of the film quality. This makes it possible to easily determine the sputtering conditions and the substrate temperature by using the prediction model for each evaluation item of the film quality.

[0113] [Configuration 4] The sputtering unit can select one of several types of sputtering materials to use as a target, and the calculation unit generates a prediction model for each type of sputtering material. This makes it easy to determine the sputtering conditions and substrate temperature using the prediction model for each type of sputtering material.

[0114] [Configuration 5] The apparatus further includes an output unit that outputs the prediction model generated by the calculation unit to an external storage device or to another sputtering apparatus, thereby making it possible to use the generated prediction model in another sputtering apparatus.

[0115] [Configuration 6] The apparatus further includes an external storage device that stores the prediction model, or an input unit that receives input of the prediction model from another sputtering apparatus, thereby making it possible to use a prediction model generated by another sputtering apparatus.

[0116] [Configuration 7] A prediction system including a plurality of sputtering devices and a storage device that stores a prediction model of a film quality generated by each of the plurality of sputtering devices, each of the plurality of sputtering devices having a sputtering unit that forms a film on a substrate with sputter particles emitted from a target based on sputtering conditions including a plurality of parameters and a substrate temperature, a calculation unit that generates a prediction model that predicts the film quality of a film formed by the sputtering unit, or determines sputtering conditions and a substrate temperature when a film is formed on a substrate with a predetermined film quality based on the prediction model, and a storage device that stores the prediction model generated by the calculation unit or a prediction model acquired from another sputtering device. The apparatus includes a memory unit that stores a prediction model, an input unit that accepts input of the prediction model from a memory unit that stores the prediction model or another sputtering apparatus, and an output unit that outputs the prediction model generated by the calculation unit to the memory unit or another sputtering apparatus.When a film is formed on a substrate by changing each parameter of the sputtering conditions, the calculation unit calculates the average energy of sputter particles incident on the substrate for each parameter, and generates a prediction model by interpolating each of the calculated average energies, the substrate temperature during film formation for each parameter, and an evaluation value of the film quality of the film formed with each parameter using a predetermined calculation.

[0117] This allows the sputtering apparatus to easily determine the sputtering conditions and substrate temperature to obtain the desired film quality for new sputtering material by utilizing the predictive model stored in the memory device, and also enables the predictive model stored in the memory device to be updated.

[0118] [Configuration 8] A method for predicting the quality of a film formed on a substrate in a sputtering apparatus that forms a film on a substrate with sputtered particles emitted from a target, using a prediction model, includes the steps of: calculating an average energy of sputtered particles incident on the substrate for each parameter when a film is formed on the substrate by changing each parameter of sputtering conditions including a plurality of parameters; and generating a prediction model by interpolating, by a predetermined calculation, each of the calculated average energies, the substrate temperature during film formation for each parameter, and an evaluation value of the film quality of the film formed with each parameter.

[0119] Thus, the film quality prediction method can predict the film quality of a film formed on a substrate by utilizing the generated prediction model.

[0120] [Configuration 9] A program executed by a computer predicts the quality of a film formed on a substrate using a prediction model in a sputtering apparatus that forms a film on a substrate with sputtered particles emitted from a target, the program including the steps of: calculating an average energy of sputtered particles incident on the substrate for each parameter when a film is formed on the substrate by changing each parameter of sputtering conditions including a plurality of parameters in a sputtering apparatus that forms a film on a substrate with sputtered particles emitted from a target; and generating a prediction model by interpolating, by a predetermined calculation, each of the calculated average energies, the substrate temperature during film formation for each parameter, and an evaluation value of the film quality of the film formed with each parameter.

[0121] This allows the program to predict the film quality of the film formed on the substrate by utilizing the generated prediction model.

[0122] The embodiments disclosed herein should be considered to be illustrative and not restrictive in all respects. The scope of the present invention is defined by the claims, not the above description, and is intended to include all modifications within the scope and meaning equivalent to the claims. [Explanation of symbols]

[0123] 1, 1A, 1B, 1C sputtering apparatus, 10, 10A, 10B, 10C sputtering section, 11 controller, 12 chamber, 13 exhaust mechanism, 14 gas supply mechanism, 14a gas cylinder, 14b mass flow controller, 15 target holder, 16 substrate holder, 17 power supply, 20, 20A, 20B, 20C information processing section, 21 processor, 22 main memory, 23 storage, 24 communication I / F, 25 input section, 26 display section, 27 bus, 200 data center, 210, 210a simulation section, 212 injection condition determination section, 214 flight path determination section, 216 incident energy calculation section, 218 film thickness distribution calculation section, 220 plot section, 230 model creation section, S substrate, SYS, SYSa prediction system, T target.

Claims

1. A sputtering apparatus capable of generating a prediction model of a film quality, comprising: a sputtering unit that forms a film on a substrate with sputter particles emitted from a target based on sputtering conditions including a plurality of parameters of the sputtering device, a sputtering material, and film formation conditions, and a substrate temperature; a calculation unit that generates the prediction model for predicting the quality of a film to be formed by the sputtering unit, or determines the sputtering conditions and the substrate temperature when a film having a predetermined film quality is formed on the substrate based on the prediction model; A storage unit that stores the prediction model generated by the calculation unit or the prediction model acquired from another device, The calculation unit is When a film is formed on the substrate by changing each parameter of the sputtering conditions and a substrate temperature, an average energy of the sputtered particles incident on the substrate is calculated for each parameter; Normalizing the average energy by dividing it by the cohesive energy of the atoms of the sputtered material; A sputtering apparatus that generates the prediction model by interpolating, through a predetermined calculation, each of the normalized average energies, the substrate temperature during film formation for each parameter, and an evaluation value of the film quality of the film formed with each parameter.

2. Film quality includes multiple evaluation items, The sputtering apparatus according to claim 1 , wherein the calculation unit generates the prediction model for each evaluation item of film quality.

3. The sputtering unit can select one of a plurality of types of sputtering materials to be used as the target, The sputtering apparatus according to claim 1 , wherein the calculation unit generates the prediction model for each type of the sputtering material.

4. 4. The sputtering apparatus according to claim 1, further comprising an output section that outputs the prediction model generated by the calculation section to an external storage device or another device.

5. 5. The sputtering apparatus according to claim 1, further comprising an input unit that receives an input of the prediction model from an external storage device that stores the prediction model or from another device.

6. The calculation unit is Calculating a flight path and a velocity of the sputtered particles emitted from the target based on the sputtering conditions; Calculating the average energy from the calculated flight path and speed; 2. The sputtering apparatus according to claim 1, further comprising: a thickness distribution of the film formed on the substrate being calculated from the calculated flight path.

7. A prediction system comprising: a plurality of sputtering apparatuses; and a storage device that stores a prediction model of a film quality of a film generated by each of the plurality of sputtering apparatuses, Each of the plurality of sputtering apparatuses includes: a sputtering unit that forms a film on a substrate with sputter particles emitted from a target based on sputtering conditions including a plurality of parameters of the sputtering device, a sputtering material, and film formation conditions, and a substrate temperature; a calculation unit that generates the prediction model for predicting the quality of a film to be formed by the sputtering unit, or determines the sputtering conditions and the substrate temperature when a film having a predetermined film quality is formed on the substrate based on the prediction model; a storage unit that stores the prediction model generated by the calculation unit or the prediction model acquired from another device; an input unit that receives an input of the prediction model from the storage device that stores the prediction model or another device; an output unit that outputs the prediction model generated by the calculation unit to the storage device or another device, The calculation unit is When a film is formed on the substrate by changing each parameter of the sputtering conditions and a substrate temperature, an average energy of the sputtered particles incident on the substrate is calculated for each parameter; Normalizing the average energy by dividing it by the cohesive energy of the atoms of the sputtered material; A prediction system that generates the prediction model by interpolating, through a predetermined calculation, each of the normalized average energies, the substrate temperature during film formation for each parameter, and an evaluation value of the film quality of the film formed with each parameter.

8. A method for predicting film quality in a sputtering apparatus that forms a film on a substrate with sputter particles emitted from a target, the method comprising the steps of: a step of calculating an average energy of the sputtered particles incident on the substrate for each parameter when a film is formed on the substrate by changing each parameter of sputtering conditions including a plurality of parameters of the sputtering apparatus, the sputtering material, and the film formation conditions, and a substrate temperature; normalizing the average energy by dividing it by the cohesive energy of the atoms of the sputtered material; a step of generating the prediction model of film quality by interpolating, by a predetermined calculation, each of the normalized average energies, the substrate temperature during film formation for each parameter, and an evaluation value of the film quality of the film formed with each parameter.

9. In a sputtering apparatus for forming a film on a substrate by sputtering particles emitted from a target, a computer is provided for predicting the quality of a film formed on the substrate using a prediction model, a step of calculating an average energy of the sputtered particles incident on the substrate for each parameter when a film is formed on the substrate by changing each parameter of sputtering conditions including a plurality of parameters of the sputtering apparatus, the sputtering material, and the film formation conditions, and a substrate temperature; normalizing the average energy by dividing it by the cohesive energy of the atoms of the sputtered material; and generating the prediction model of film quality by interpolating, by a predetermined calculation, each of the normalized average energies, the substrate temperature during film formation for each parameter, and an evaluation value of the film quality of the film formed with each parameter.

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