Searching apparatus, plasma processing apparatus, and searching method

Abstract

In order to reduce the number of searches for parameter values ​​to be set in the plasma processing apparatus, it is desirable to further improve the prediction accuracy of the learning model. [Solution] A search device 3 searches for a value of a first parameter to be set in a plasma processing device 1 that processes a processing object into a target object using a process gas. A control unit 39 selects a plurality of first combinations consisting of possible values ​​of the first parameter. For each of the first combinations, the control unit 39 obtains a value of a second parameter indicating the state of the process gas converted into plasma and a value of a third parameter indicating an evaluation of the processing object after processing. The control unit 39 generates a learning model 82 in which the first parameter and the second parameter are explanatory variables and the third parameter is an objective variable. The control unit 39 uses the learning model 82 to predict the value of the first parameter such that each value of the third parameter becomes a predetermined value.

Description

[Technical field]

[0001] The present invention relates to a searching apparatus, a plasma processing apparatus, and a searching method. [Background technology]

[0002] As disclosed in Patent Document 1 (JP 2019-165123 A), there is a technology that generates a learning model in which parameters to be set in a plasma processing device are used as explanatory variables and parameters indicating the evaluation of the processing object after processing are used as objective variables, and uses the learning model to search for values ​​of parameters to be set in the plasma processing device in order to process the processing object into a target object. Summary of the Invention [Problem to be solved by the invention]

[0003] In order to reduce the number of searches for parameter values ​​to be set in the plasma processing apparatus, it is desirable to further improve the prediction accuracy of the learning model. [Means for solving the problem]

[0004] A searching device according to a first aspect searches for values ​​of one or more first parameters to be set in a plasma processing device that processes a processing object into a target object using a process gas. The searching device includes a control unit and a storage unit. The control unit selects a plurality of first combinations consisting of possible values ​​of the first parameters. The control unit acquires, for each of the first combinations, values ​​of one or more second parameters indicating a state of the process gas that has been converted into plasma and values ​​of one or more third parameters indicating an evaluation of the processing object after processing, by setting each of the first combinations in the plasma processing device. The control unit stores learning data in the storage unit, which associates the first combination with each of the acquired values ​​of the second parameters and each of the acquired values ​​of the third parameters. The control unit generates a learning model using the learning data. The learning model has the first parameter and the second parameter as explanatory variables and the third parameter as a target variable. The control unit predicts, using the learning model, values ​​of the first parameters such that the values ​​of the third parameters are predetermined values.

[0005] In the searching device of the first aspect, the learning model has one or more second parameters indicating the state of the plasma-converted process gas as explanatory variables in addition to one or more first parameters set in the plasma processing device. As a result, the searching device can further improve the prediction accuracy of the learning model.

[0006] A search device of a second aspect is a search device of the first aspect, in which the control unit selects, by an experimental design method, from a plurality of second combinations composed of values ​​that each of the first parameters can take, a first combination having a smaller number of combinations than the second combination.

[0007] As a result, the searching device can efficiently search for parameter values ​​to be set in the plasma processing device.

[0008] The searching apparatus of a third aspect is the searching apparatus of the first or second aspect, wherein the first parameter includes at least one of a plasma generation power, a bias power, a process gas pressure, a process gas flow rate, and a type of process gas.

[0009] A searching device of a fourth aspect is any one of the searching devices of the first aspect to the third aspect, wherein the second parameter includes a parameter that directly or indirectly represents at least one of electron temperature, electron density, radical species, radical density, ion species, ion density, and ion energy.

[0010] The searching device of a fifth aspect is the searching device of the fourth aspect, wherein the parameter indirectly representing the radical species and the parameter indirectly representing the radical density include at least one of the emission wavelength of the plasma, the emission intensity of the emission wavelength of the plasma and the ratio of the emission intensities of each of a plurality of emission wavelengths of the plasma, the mass of radicals in the plasma, the observed intensity of the mass of radicals in the plasma and the ratio of the observed intensities of each of a plurality of masses of radicals in the plasma, or a value obtained by calculation using at least one of them.

[0011] A searching device of a sixth aspect is the searching device of the fourth aspect, wherein the parameter indirectly representing the ion species and the parameter indirectly representing the ion density include at least one of the following: the emission wavelength of the plasma, the emission intensity of the emission wavelength of the plasma and the ratio of the emission intensities of each of a plurality of emission wavelengths of the plasma, the mass of the ion, the observed intensity of the mass of the ion and the ratio of the observed intensities of each of a plurality of masses of ions, or a value obtained by calculation using at least one of them.

[0012] A searching apparatus according to a seventh aspect is the searching apparatus according to any one of the first aspect to the sixth aspect, in which the third parameter includes at least one of film thickness, processing shape, processing speed, sputter resistance, and deposited film composition.

[0013] The searching device of an eighth aspect is the searching device of any one of the first aspect to the seventh aspect, in which the control unit acquires the respective values ​​of the second parameter and the respective values ​​of the third parameter for the predicted value of the first parameter by setting each predicted value of the first parameter in the plasma processing apparatus. The control unit updates the learning data by adding data associating each predicted value of the first parameter with each acquired value of the second parameter and each acquired value of the third parameter to the learning data. The control unit updates the learning model using the updated learning data.

[0014] With such a configuration, the search device of the eighth aspect can further improve the prediction accuracy of the learning model.

[0015] A plasma processing apparatus according to a ninth aspect processes a processing object into a target object using a process gas. The plasma processing apparatus includes a processing chamber, a plasma generating device, and a searching device. The processing object is placed in the processing chamber. The plasma generating device generates a plasma-converted process gas in the processing chamber according to the setting of each value of one or more first parameters, and performs plasma processing on the processing object. The searching device searches for each value of the first parameters to be set in the plasma generating device. The searching device includes a control unit and a storage unit. The control unit selects a plurality of first combinations constituted by values ​​that each of the first parameters can take. The control unit acquires, for each of the first combinations, a value of one or more second parameters indicating a state of the plasma-converted process gas and a value of one or more third parameters indicating an evaluation of the processing object after processing, by setting each of the first combinations in the plasma generating device. The control unit stores learning data in the storage unit, which associates the combinations of the first combinations with each of the acquired values ​​of the second parameters and each of the acquired values ​​of the third parameters. The control unit generates a learning model using the learning data. The learning model has the first parameter and the second parameter as explanatory variables and the third parameter as a target variable. The control unit uses the learning model to predict values ​​of the first parameter such that the values ​​of the third parameter are predetermined values.

[0016] In the plasma processing apparatus of the ninth aspect, the learning model has one or more second parameters indicating the state of the process gas turned into plasma as explanatory variables in addition to one or more first parameters set in the plasma generating apparatus. As a result, the plasma processing apparatus can further improve the prediction accuracy of the learning model.

[0017] A search method according to a tenth aspect searches for values ​​of one or more first parameters to be set in a plasma processing apparatus that processes a processing object into a target object using a process gas. The search method includes a selection step, an acquisition step, a generation step, and a prediction step. The selection step selects a plurality of first combinations consisting of possible values ​​of the first parameters. The acquisition step acquires, for each of the first combinations, values ​​of one or more second parameters indicating a state of the plasma-converted process gas and values ​​of one or more third parameters indicating an evaluation of the processing object after processing, by setting each of the first combinations in the plasma processing apparatus. The generation step stores learning data in a storage unit, which associates the first combinations with each of the acquired values ​​of the second parameters and each of the acquired values ​​of the third parameters. The generation step generates a learning model using the learning data. The learning model uses the first parameter and the second parameter as explanatory variables and the third parameter as a target variable. The prediction step predicts, using the learning model, values ​​of the first parameters such that the values ​​of the third parameters are predetermined values.

[0018] In the search method of the tenth aspect, the learning model has one or more second parameters indicating the state of the process gas plasmatized as explanatory variables in addition to one or more first parameters set in the plasma processing apparatus. As a result, the search method can further improve the prediction accuracy of the learning model. [Brief description of the drawings]

[0019] [Figure 1] FIG. 2 is a functional block diagram of the plasma processing apparatus. [Figure 2A] 1 is a flowchart for explaining an example of a process of a plasma processing apparatus. [Figure 2B] 1 is a flowchart for explaining an example of a process of a plasma processing apparatus. [Figure 2C] 1 is a flowchart for explaining an example of a process of a plasma processing apparatus. [Diagram 3] FIG. 13 is a diagram illustrating a comparison of the accuracy of learning models. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0020] (1) Overall structure The plasma processing apparatus 1 processes an object to be processed into a target object using a process gas. The plasma processing apparatus 1 is, for example, a film forming apparatus, a pattern processing apparatus, an ion implantation apparatus, etc. The film forming apparatus is, for example, a CVD (Chemical Vapor Deposition) apparatus, a PVD (Physical Vapor Deposition) apparatus, etc. The pattern processing apparatus is, for example, a dry etching apparatus, etc. The ion implantation apparatus is, for example, a plasma doping apparatus, etc.

[0021] Fig. 1 is a functional block diagram of a plasma processing apparatus 1. As shown in Fig. 1, the plasma processing apparatus 1 mainly includes a processing chamber 70, a plasma generation apparatus 2, and a searching apparatus 3. The plasma generation apparatus 2 and the searching apparatus 3 are communicatively connected via a communication line 90.

[0022] (2) Detailed configuration (2-1) Processing room In the processing chamber 70, an object to be processed is placed.

[0023] Various measuring devices for measuring the state of the plasmatized process gas and the state of the processing target after processing are installed in the processing chamber 70. The measuring devices include, for example, a mass spectrometer, an optical emission spectrometer, a film thickness gauge, a SEM (Scanning Electron Microscope), an XPS (X-ray Photoelectron Spectroscopy), a temperature sensor, a pressure sensor, and a flow rate sensor.

[0024] (2-2) Plasma generation device The plasma generating apparatus 2 generates a plasmatized process gas in the processing chamber 70 according to the setting of each value of one or more first parameters, and performs plasma processing on the object to be processed. Hereinafter, one combination of each value of one or more first parameters may be referred to as a recipe. As shown in Fig. 1, the plasma generating apparatus 2 mainly has a storage unit 21, a communication unit 22, and a control unit 29. The control unit 29, the storage unit 21, and the communication unit 22 are communicatively connected via internal wiring.

[0025] (2-2-1) Storage section The storage unit 21 is a storage device such as a RAM, a ROM, and a HDD (hard disk drive), etc. The storage unit 21 stores programs executed by the control unit 29, data necessary for executing the programs, and the like.

[0026] (2-2-2) Communications Department The communication unit 22 is a communication interface device for communicating with the searching device 3 via a communication line 90 .

[0027] (2-2-3) Control Unit The control unit 29 is a processor such as a CPU or a GPU. The control unit 29 reads and executes a program stored in the storage unit 21 to realize various functions of the plasma generating device 2. The control unit 29 can write a calculation result to the storage unit 21 and read information stored in the storage unit 21 according to the program.

[0028] As shown in FIG. 1, the control unit 29 mainly includes an acquisition unit 291 and a plasma processing unit 292 as functional blocks.

[0029] (2-2-3-1) Acquisition section The acquiring unit 291 acquires the values ​​of one or more first parameters from the searching device 3. The acquiring unit 291 sets the values ​​of the first parameters acquired from the searching device 3 in the plasma processing unit 292.

[0030] The first parameter includes at least one of a plasma generation power, a bias power, a process gas pressure, a process gas flow rate, and a type of process gas. The plasma generation power is, for example, an ICP (Inductively Coupled Plasma) power, a CCP (Capacitively Coupled Plasma) power, and an ECR (Electron Cyclotron Resonance) power. The value of the type of process gas is, for example, a fluorine-based gas such as CF4 or CHF3, or a mixed gas of a fluorine-based gas such as CF4+H2 and hydrogen. The first parameter may further include a ratio of hydrogen contained in the process gas.

[0031] The acquiring unit 291 acquires the values ​​of one or more second parameters indicating the state of the plasmatized process gas and the values ​​of one or more third parameters indicating an evaluation of the processing target after processing, using various measuring devices installed in the processing chamber 70. The acquiring unit 291 transmits the acquired values ​​of the second parameters and the acquired values ​​of the third parameters to the searching device 3.

[0032] The second parameter includes a parameter directly representing at least one of the electron temperature, the electron density, the radical species, the radical density, the ion species, the ion density, and the ion energy. The acquisition unit 291 acquires the electron temperature, the electron density, the radical species, the radical density, the ion species, the ion density, and the ion energy using, for example, a mass analyzer, an emission spectrometer, or the like.

[0033] The second parameter may include a parameter indirectly representing at least one of the electron temperature, electron density, radical species, radical density, ion species, ion density, and ion energy. For example, the parameter indirectly representing the radical species and the parameter indirectly representing the radical density include at least one of the following: the emission wavelength of the plasma, the emission intensity of the emission wavelength of the plasma, and the ratio of the emission intensity of each of the plurality of emission wavelengths of the plasma, the mass of radicals in the plasma, and the observed intensity of the mass of radicals in the plasma, and the ratio of the observed intensity of each of the plurality of masses of radicals in the plasma, or a value obtained by calculation using at least one of them. For example, the parameter indirectly representing the ion species and the parameter indirectly representing the ion density include at least one of the following: the emission wavelength of the plasma, the emission intensity of the emission wavelength of the plasma, and the ratio of the emission intensity of each of the plurality of emission wavelengths of the plasma, the mass of an ion, the observed intensity of the mass of an ion, and the ratio of the observed intensity of each of the plurality of masses of an ion, or a value obtained by calculation using at least one of them. The acquisition unit 291 acquires the emission wavelength of the plasma and the emission intensity of the emission wavelength of the plasma using, for example, an emission spectrometer, etc. The acquisition unit 291 acquires the mass of radicals in the plasma, the observed intensity of the mass of radicals in the plasma, the mass of ions, and the observed intensity of the mass of ions using, for example, a mass spectrometer, etc.

[0034] The third parameter includes at least one of a film thickness, a processing shape, a processing speed, a sputtering resistance, and a deposition film composition. The acquisition unit 291 acquires the film thickness value using, for example, a film thickness meter, an SEM, or the like. The processing speed is, for example, an etching rate, a film formation rate, or the like. The acquisition unit 291 acquires the processing speed value using, for example, a flow rate sensor, or the like. The processing shape is, for example, a processing depth, a selection ratio, a maximum diameter, a bottom diameter, and a blockage, or the like. The acquisition unit 291 acquires the processing shape value using, for example, an SEM, or the like. The acquisition unit 291 acquires the sputtering resistance and the deposition film composition values ​​using, for example, an XPS, or the like.

[0035] (2-2-3-2) Plasma treatment section The plasma processing unit 292 generates a plasma of a process gas in the processing chamber 70 in accordance with the recipe set by the acquisition unit 291, and performs plasma processing on the object to be processed.

[0036] (2-3) Search device The searching device 3 searches for a recipe to be set in the plasma generating device 2. As shown in Fig. 1, the searching device 3 mainly has a storage unit 31, a communication unit 32, an input unit 33, a display unit 34, and a control unit 39. The control unit 39, the storage unit 31, the communication unit 32, the input unit 33, and the display unit 34 are communicatively connected via internal wiring.

[0037] (2-3-1) Storage section The storage unit 31 is a storage device such as a RAM, a ROM, and a HDD (hard disk drive), etc. The storage unit 31 stores programs executed by the control unit 39, data necessary for executing the programs, and the like.

[0038] The storage unit 31 stores learning data 81 and a learning model 82, which will be described later. The storage unit 31 also stores, for example, commercially available software for implementing experimental design and machine learning.

[0039] (2-3-2) Communications Department The communication unit 32 is a communication interface device for communicating with the plasma generating device 2 via a communication line 90.

[0040] (2-3-3) Input section The input unit 33 is, for example, a keyboard and a mouse. A user can input various commands and various information to the search device 3 using the input unit 33.

[0041] The user uses the input unit 33 to input possible values ​​for each of the first parameters (domains of each of the first parameters).

[0042] (2-3-4) Display section The display unit 34 is, for example, a monitor. A screen for inputting various information, various information stored in the storage unit 31, etc. are displayed on the display unit 34.

[0043] On the display unit 34, for example, the finally selected recipe is displayed.

[0044] (2-3-5) Control Unit The control unit 39 is a processor such as a CPU or a GPU. The control unit 39 reads and executes programs stored in the storage unit 31 to realize various functions of the search device 3. The control unit 39 can write calculation results to the storage unit 31 and read information stored in the storage unit 31 according to the programs.

[0045] As shown in FIG. 1, the control unit 39 mainly has an acquisition unit 391, a selection unit 392, a generation unit 393, and a prediction unit 394 as functional blocks.

[0046] (2-3-5-1) Acquisition department The acquiring unit 391 acquires possible values ​​for each of the first parameters from the user via the input unit 33. The acquiring unit 291 transmits the possible values ​​for each of the first parameters to the selecting unit 392.

[0047] When the acquiring unit 391 receives the first recipe group from the selecting unit 392, it transmits the first recipe group to the plasma generating apparatus 2. The acquiring unit 391 acquires each value of one or more second parameters and each value of one or more third parameters for each recipe of the first recipe group by setting each recipe of the first recipe group in the plasma generating apparatus 2. The acquiring unit 391 transmits the first recipe group received from the selecting unit 392 and each value of the second parameter and each value of the third parameter acquired from the plasma generating apparatus 2 to the selecting unit 392.

[0048] When the acquiring unit 391 receives the first predicted recipe or the second predicted recipe from the prediction unit 394, it transmits the first predicted recipe or the second predicted recipe to the plasma generation apparatus 2. The acquiring unit 391 acquires the values ​​of the second parameter and the values ​​of the third parameter for the first predicted recipe or the second predicted recipe by setting the first predicted recipe or the second predicted recipe in the plasma generation apparatus 2. The acquiring unit 391 transmits the first predicted recipe or the second predicted recipe received from the prediction unit 394 and the values ​​of the second parameter and the values ​​of the third parameter acquired from the plasma generation apparatus 2 to the selecting unit 392.

[0049] (2-3-5-2) Selection department When the selection unit 392 receives the possible values ​​of each of the first parameters from the acquisition unit 391, the selection unit 392 selects a first recipe group (a plurality of first combinations) constituted by the possible values ​​of each of the first parameters.

[0050] Specifically, first, the selection unit 392 defines a second recipe group (a plurality of second combinations) consisting of values ​​that each of the first parameters can take. For example, the second recipe group is a collection of all combinations consisting of values ​​that each of the first parameters can take.

[0051] Next, the selection unit 392 selects a first recipe group having a smaller number of recipes than the second recipe group from the second recipe group by using an experimental design. The experimental design is, for example, a D-optimal design, an orthogonal array, a central composite design, etc. The selection unit 392 executes the experimental design using, for example, commercially available software for implementing the experimental design.

[0052] The selection unit 392 transmits the selected first recipe group to the acquisition unit 391.

[0053] When the selection unit 392 receives the first recipe group, the values ​​of the second parameters, and the values ​​of the third parameters from the acquisition unit 391, the selection unit 392 determines whether or not there is a recipe in the first recipe group in which the values ​​of the third parameters are the first predetermined value. The first predetermined value is the value of the third parameters of the target object. If there is, the selection unit 392 selects the recipe. In other words, the recipe is a recipe for processing the processing target into the target object. If there is not, the selection unit 392 transmits the first recipe group, the values ​​of the second parameters, and the values ​​of the third parameters received from the acquisition unit 391 to the generation unit 393.

[0054] When the selection unit 392 receives the first predicted recipe or the second predicted recipe, the values ​​of the second parameter, and the values ​​of the third parameter from the acquisition unit 391, the selection unit 392 determines whether or not there exists a first predicted recipe or a second predicted recipe in which the values ​​of the third parameter are the first predetermined value. If there exists, the selection unit 392 selects the recipe. In other words, the recipe is a recipe for processing the processing object into a target object. If there does not exist, the selection unit 392 transmits the first predicted recipe or the second predicted recipe, the values ​​of the second parameter, and the values ​​of the third parameter received from the acquisition unit 391 to the generation unit 393.

[0055] (2-3-5-3) Generation part When the generating unit 393 receives the first recipe group, the values ​​of the second parameter, and the values ​​of the third parameter from the selecting unit 392, the generating unit 393 stores the learning data 81 in which the recipes of the first recipe group, the values ​​of the second parameter, and the values ​​of the third parameter are associated with each other in the storage unit 31. The generating unit 393 uses the learning data 81 to generate a learning model 82. The learning model 82 uses the first parameter and the second parameter as explanatory variables and the third parameter as a target variable. The learning model 82 is, for example, Gaussian process regression, linear regression, regression tree, or the like. The generating unit 393 generates the learning model 82 using, for example, commercially available software for realizing machine learning.

[0056] When the generation unit 393 receives the first predicted recipe or the second predicted recipe from the selection unit 392, the generation unit 393 updates the learning data 81 by adding data associating each of the first predicted recipe or the second predicted recipe with each value of the second parameter and each value of the third parameter to the learning data 81. The generation unit 393 updates the learning model 82 using the updated learning data 81.

[0057] (2-3-5-4) Prediction section When the generation unit 393 generates or updates the learning model 82, the prediction unit 394 uses the learning model 82 to predict a recipe in which each value of the third parameter becomes the first predetermined value.

[0058] Specifically, the prediction unit 394 defines a third recipe group having a greater number of recipes than the second recipe group, and inputs each recipe of the third recipe group into the learning model 82. For example, the third recipe group is a set of all combinations consisting of possible values ​​of each first parameter after increasing the possible values ​​of each first parameter. At this time, each value of the second parameter to be input into the learning model 82 is predicted, for example, from each recipe of the third recipe group. For example, the generation unit 393 generates a learning model in which the first parameter is an explanatory variable and the second parameter is an objective variable, using learning data that associates the recipes of the first recipe group with each value of the second parameter. The prediction unit 394 inputs each recipe of the third recipe group into the learning model, thereby predicting each value of the second parameter to be input into the learning model 82.

[0059] Each value of the second parameter input to the learning model 82 may be, for example, a predefined value. For example, when the second parameter is ion energy, five levels of values, (100, 150, 200, 250, 300) (unit: eV), are predefined as the ion energy. The prediction unit 394 inputs five types (five levels) of ion energy values ​​to the learning model 82 for each recipe in the third recipe group.

[0060] The prediction unit 394 determines whether or not a recipe exists in which the values ​​of the third parameters output from the learning model 82 are the first predetermined value. If a recipe exists, the prediction unit 394 transmits to the acquisition unit 391 a recipe (first predicted recipe) in which the values ​​of the third parameters are the first predetermined value. If a recipe does not exist, the prediction unit 394 extracts one or more recipes (second predicted recipes) from a set excluding recipes included in the current learning data 81 from the third recipe group, and transmits the second predicted recipes to the acquisition unit 391. For example, if the learning model 82 is Gaussian process regression, the prediction unit 394 extracts the second predicted recipe using Bayesian optimization.

[0061] (3) Processing An example of processing performed by the plasma processing apparatus 1 will be described with reference to the flow charts of FIGS. 2A to 2C.

[0062] As shown in step S1, the user inputs to the searching device 3 the domain of each first parameter.

[0063] After completing step S1, as shown in step S2, the searching device 3 defines a second recipe group, and selects a first recipe group from the second recipe group by an experimental design method.

[0064] After completing step S2, the searching device 3 transmits the first recipe group to the plasma generating device 2 as shown in step S3.

[0065] After completing step S3, as shown in step S4, the plasma generating apparatus 2 sets the first recipe group in the plasma processing unit 292 and performs plasma processing. The plasma generating apparatus 2 transmits to the searching device 3 the values ​​of the second parameters and the values ​​of the third parameters acquired by the plasma processing.

[0066] After completing step S4, as shown in step S5, the searching device 3 judges whether or not there is a recipe in the first recipe group in which each value of the third parameter is the first predetermined value. If there is, the process proceeds to step S14. If there is not, the process proceeds to step S6.

[0067] Proceeding from step S5 to step S6, the search device 3 stores learning data 81 in which recipes in the first recipe group are associated with each value of the second parameter and each value of the third parameter in the storage unit 31. The search device 3 uses the learning data 81 to generate a learning model 82.

[0068] After completing step S6, the search device 3 defines a third recipe group and inputs each recipe in the third recipe group into the learning model 82, as shown in step S7.

[0069] After completing step S7, as shown in step S8, the search device 3 determines whether or not there is a recipe in which each value of the third parameter output from the learning model 82 is the first predetermined value. If there is, the process proceeds to step S9. If there is not, the process proceeds to step S10.

[0070] When proceeding from step S8 to step S9, the searching device 3 transmits the first predicted recipe to the plasma generating device 2.

[0071] When proceeding from step S8 to step S10, the searching device 3 transmits the second predicted recipe to the plasma generating device 2.

[0072] After completing step S9 or step S10, as shown in step S11, the plasma generating apparatus 2 sets the first predicted recipe or the second predicted recipe in the plasma processing unit 292 and performs plasma processing. The plasma generating apparatus 2 transmits to the searching device 3 the values ​​of the second parameters and the values ​​of the third parameters acquired by the plasma processing.

[0073] After completing step S11, as shown in step S12, the searching device 3 judges whether or not there exists a first predicted recipe or a second predicted recipe in which each value of the third parameter is the first predetermined value. If there exists a recipe, the process proceeds to step S14. If there does not exist a recipe, the process proceeds to step S13.

[0074] Proceeding from step S12 to step S13, the search device 3 updates the learning data 81 by adding data associating each of the first predicted recipe or the second predicted recipe with each value of the second parameter and each value of the third parameter to the learning data 81. The generation unit 393 updates the learning model 82 using the updated learning data 81.

[0075] After completing step S13, the process returns to step S7, and the search device 3 inputs each recipe in the third recipe group into the learning model 82.

[0076] When proceeding from step S5 or step S12 to step S14, the searching device 3 selects a recipe in which the value of the third parameter becomes the first predetermined value. In other words, the searching device 3 selects a recipe for processing the processing object into a target object.

[0077] (4) Verification (4-1) Premise In this verification, the number of times the plasma processing is performed by the plasma processing apparatus 1 of this embodiment (the number of recipes set in the plasma generating apparatus 2) before a recipe for processing the processing object into a target object is selected was verified.

[0078] In this verification, the plasma processing apparatus 1 is a dry etching apparatus. In other words, the plasma processing apparatus 1 processes an object to be processed into a target object by plasma processing using an etching gas (process gas).

[0079] The source power and bottom power supplied to the processing chamber 70 are a high-frequency power supply of 13.56 MHz.

[0080] The object to be processed is SiO2 (TEOS).

[0081] The first parameters are the ICP power, the bias power, the pressure of the etching gas, the flow rate of the etching gas, the ratio of hydrogen contained in the etching gas, and the type of the etching gas.

[0082] As the domain of the first parameter, the ICP power has four levels of values, namely (400, 600, 800, 1000) (unit: W). The bias power has three levels of values, namely (100, 200, 300) (unit: W). The etching gas pressure has four levels of values, namely (10, 20, 30, 40) (unit: mTorr). The etching gas flow rate has three levels of values, namely (50, 75, 100) (unit: sccm). The hydrogen ratio contained in the etching gas has four levels of values, namely (0, 0.25, 0.5, 0.75). The type of etching gas has four levels of values, namely (CF4, CF4+H2, CF4+H2, CF4+H2), corresponding to the respective values ​​of the hydrogen ratio contained in the etching gas.

[0083] The second parameter is the observed intensity of the H2F+ ion (mass m / z=21).

[0084] The third parameter is the etching rate.

[0085] The second recipe group is a set of all combinations of possible values ​​for each of the first parameters. Since the ICP power, bias power, etching gas pressure, etching gas flow rate, and hydrogen ratio in the etching gas have four, three, four, three, and four levels, respectively, the second recipe group is made up of 576 (=4 x 3 x 4 x 3 x 4) recipes. Note that the type of etching gas is omitted because there is a one-to-one correspondence between the value of the hydrogen ratio in the etching gas and the value of the type of etching gas.

[0086] The third recipe group is composed of all combinations of possible values ​​of each of the first parameters when the ICP power has seven levels of values ​​(400, 500, 600, 700, 800, 900, 1000) (unit: W), the bias power has five levels of values ​​(100, 150, 200, 250, 300) (unit: W), the etching gas pressure has seven levels of values ​​(10, 15, 20, 25, 30, 35, 40) (unit: mTorr), the etching gas flow rate has six levels of values ​​(50, 60, 70, 80, 90, 100) (unit: sccm), and the hydrogen ratio in the etching gas has four levels of values ​​(0, 0.25, 0.5, 0.75). The third recipe group is composed of 5880 (= 7 x 5 x 7 x 6 x 4) recipes.

[0087] The experimental design is a D-optimal design.

[0088] The training model 82 is Gaussian process regression.

[0089] Since the learning model 82 is Gaussian process regression, the prediction unit 394 extracts the second prediction recipe from the set remaining after excluding the recipes included in the current learning data 81 from the third recipe group, in order of the recipes whose surface or curve indicating the expected value is closest to the first predetermined value region.

[0090] (4-2) Results As a result, recipes for processing the processing object into the target object were selected simply by setting 45 recipes out of a total of 576 recipes in the plasma generating device 2. Of the 45 recipes, 2 recipes were selected for processing the processing object into the target object, and 43 recipes were used for the learning data 81.

[0091] In addition, the accuracy of the final learning model 82 (first learning model 821) was compared with that of the second learning model 822 generated using learning data obtained by removing the second parameter from the final learning data 81. FIG. 3 is a diagram showing a comparison of the accuracy of the learning models. The right diagram of FIG. 3 shows the accuracy of the first learning model 821, and the left diagram of FIG. 3 shows the accuracy of the second learning model 822. The plot points correspond to 43 recipes included in the final learning data 81. The horizontal axis is the etching rate obtained by setting the recipe in the plasma generating device 2. The vertical axis is the etching rate predicted by inputting the recipe into the learning model (in the first learning model 821 in the right diagram, in addition to the recipe, the value of the second parameter is also input into the learning model). Therefore, it is desirable that the plot points are arranged diagonally.

[0092] 3, it can be seen that the plot points based on the first learning model 821 are arranged more diagonally than the plot points based on the second learning model 822. The coefficient of determination for the first learning model 821 was 0.9347, and for the second learning model 822 was 0.8885.

[0093] (5) Features (5-1) Conventionally, there is a technology in which a learning model is generated in which parameters to be set in a plasma processing apparatus are used as explanatory variables and parameters indicating the evaluation of a processing object after processing are used as objective variables, and the learning model is used to search for values ​​of parameters to be set in the plasma processing apparatus in order to process the processing object into a target object.

[0094] In order to reduce the number of searches for parameter values ​​to be set in the plasma processing apparatus, it is desirable to further improve the prediction accuracy of the learning model.

[0095] The search device 3 of this embodiment searches for values ​​of one or more first parameters to be set in the plasma processing device 1 that processes a processing object into a target object using a process gas. The search device 3 includes a control unit 39 and a storage unit 31. The control unit 39 selects a plurality of first combinations consisting of possible values ​​of each of the first parameters. The control unit 39 acquires, for each of the first combinations, values ​​of one or more second parameters indicating the state of the process gas that has been converted into plasma, and values ​​of one or more third parameters indicating the evaluation of the processing object after processing, by setting each of the first combinations in the plasma processing device 1. The control unit 39 stores learning data 81 in which the combination of the first combination, the acquired values ​​of the second parameters, and the acquired values ​​of the third parameters are associated with each other in the first combination, in the storage unit 31. The control unit 39 generates a learning model 82 using the learning data 81. The learning model 82 uses the first parameter and the second parameter as explanatory variables and the third parameter as a target variable. The control unit 39 uses the learning model 82 to predict the values ​​of the first parameters such that the values ​​of the third parameters become predetermined values.

[0096] In the searching device 3 of this embodiment, the learning model 82 has, as explanatory variables, one or more second parameters indicating the state of the process gas turned into plasma, in addition to one or more first parameters set in the plasma processing device 1. As a result, the searching device 3 can further improve the prediction accuracy of the learning model 82.

[0097] (5-2) In the searching device 3 of this embodiment, the control unit 39 selects, by experimental design, from among a plurality of second combinations constituted by values ​​that each of the first parameters can take, a first combination having a smaller number of combinations than the second combination.

[0098] As a result, the searching device 3 can efficiently search for parameter values ​​to be set in the plasma processing device.

[0099] (5-3) In the searching device 3 of this embodiment, the first parameters include at least one of the plasma generation power, the bias power, the pressure of the process gas, the flow rate of the process gas, and the type of the process gas.

[0100] (5-4) In the searching device 3 of this embodiment, the second parameters include parameters that directly or indirectly represent at least one of the electron temperature, the electron density, the radical species, the radical density, the ion species, the ion density, and the ion energy.

[0101] (5-5) In the searching device 3 of this embodiment, the parameters indirectly representing the radical species and the parameters indirectly representing the radical density include at least one of the following: the emission wavelength of the plasma, the emission intensity of the emission wavelength of the plasma and the ratio of the emission intensities of each of the multiple emission wavelengths of the plasma, the mass of radicals in the plasma, the observed intensity of the mass of radicals in the plasma and the ratio of the observed intensities of each of the multiple masses of radicals in the plasma, or a value obtained by calculation using at least one of them.

[0102] (5-6) In the searching device 3 of this embodiment, the parameter indirectly representing the ion species and the parameter indirectly representing the ion density include at least one of the following: the emission wavelength of the plasma, the emission intensity of the emission wavelength of the plasma and the ratio of the emission intensities of each of the multiple emission wavelengths of the plasma, the mass of the ion, the observed intensity of the mass of the ion and the ratio of the observed intensities of each of the multiple masses of the ions, or a value obtained by calculation using at least one of them.

[0103] (5-7) In the searching device 3 of this embodiment, the third parameter includes at least one of the film thickness, the processed shape, the processing speed, the sputtering resistance, and the deposited film composition.

[0104] (5-8) In the searching device 3 of this embodiment, the control unit 39 acquires the values ​​of the second parameters and the values ​​of the third parameters for the predicted values ​​of the first parameters by setting the predicted values ​​of the first parameters in the plasma processing device 1. The control unit 39 updates the learning data 81 by adding data that associates the predicted values ​​of the first parameters with the acquired values ​​of the second parameters and the acquired values ​​of the third parameters to the learning data 81. The control unit 39 updates the learning model 82 by using the updated learning data 81.

[0105] As a result, the search device 3 can further improve the prediction accuracy of the learning model 82.

[0106] (5-9) The plasma processing apparatus 1 of this embodiment processes a processing object into a target object using a process gas. The plasma processing apparatus 1 includes a processing chamber 70, a plasma generating apparatus 2, and a search device 3. The processing object is placed in the processing chamber 70. The plasma generating apparatus 2 generates a plasma-converted process gas in the processing chamber 70 according to the setting of each value of one or more first parameters, and performs plasma processing on the processing object. The search device 3 searches for each value of the first parameters to be set in the plasma generating apparatus 2. The search device 3 has a control unit 39 and a storage unit 31. The control unit 39 selects a plurality of first combinations consisting of values ​​that each of the first parameters can take. The control unit 39 acquires, for each of the first combinations, the values ​​of one or more second parameters indicating the state of the plasma-converted process gas and the values ​​of one or more third parameters indicating the evaluation of the processing object after processing, by setting each of the first combinations in the plasma generating apparatus 2. The control unit 39 stores learning data 81 in the storage unit 31, which associates the first combination with each of the acquired values ​​of the second parameter and each of the acquired values ​​of the third parameter. The control unit 39 uses the learning data 81 to generate a learning model 82. The learning model 82 has the first parameter and the second parameter as explanatory variables and the third parameter as a response variable. The control unit 39 uses the learning model 82 to predict each value of the first parameter such that each value of the third parameter becomes a predetermined value.

[0107] In the plasma processing apparatus 1 of this embodiment, the learning model 82 has, as explanatory variables, one or more second parameters indicating the state of the plasmatized process gas, in addition to one or more first parameters set in the plasma generating apparatus 2. As a result, the plasma processing apparatus 1 can further improve the prediction accuracy of the learning model 82.

[0108] (5-10) The search method of the present embodiment searches for values ​​of one or more first parameters to be set in a plasma processing apparatus 1 that processes a processing object into a target object using a process gas. The search method includes a selection step S2, an acquisition step S4, a generation step S6, and prediction steps S7 and S8. The selection step S2 selects a plurality of first combinations consisting of possible values ​​of each of the first parameters. The acquisition step S4 acquires values ​​of one or more second parameters indicating the state of the plasma-converted process gas and values ​​of one or more third parameters indicating the evaluation of the processing object after processing for each of the first combinations by setting each of the first combinations in the plasma processing apparatus 1. The generation step S6 stores learning data 81 in the storage unit 31, in which the combinations of the first combinations are associated with the acquired values ​​of the second parameters and the acquired values ​​of the third parameters. The generation step S6 generates a learning model 82 using the learning data 81. The learning model 82 uses the first and second parameters as explanatory variables and the third parameter as a response variable. Prediction steps S7 and S8 use the learning model 82 to predict the values ​​of the first parameters such that the values ​​of the third parameters are predetermined values.

[0109] In the search method of the present embodiment, the learning model 82 has, as explanatory variables, one or more second parameters indicating the state of the process gas turned into plasma, in addition to one or more first parameters set in the plasma processing apparatus 1. As a result, the search method can further improve the prediction accuracy of the learning model 82.

[0110] (6) Variations (6-1) Variation 1A In this embodiment, the plasma processing apparatus 1 includes the searching apparatus 3. However, the searching apparatus 3 may be separated from the plasma processing apparatus 1. In this case, the communication line 90 is, for example, the Internet.

[0111] (6-2) Although the embodiments of the present disclosure have been described above, it will be understood that various changes in form and details can be made without departing from the spirit and scope of the present disclosure described in the claims. [Explanation of symbols]

[0112] 1. Plasma processing equipment 2. Plasma generating device 3 Search device 31 Storage section 39 Control Unit 70 Processing Room 81 Training Data 82 Learning Model S2 Selection Steps S4 Acquisition Step S6 Generation Steps S7, S8 prediction steps [Prior art documents] [Patent documents]

[0113] [Patent Document 1] JP 2019-165123 A

Claims

1. A searching device (3) for searching values ​​of one or more first parameters to be set in a plasma processing device (1) that processes a processing object into a target object using a process gas, the searching device comprising: A control unit (39), a storage unit (31), Equipped with The control unit is selecting a plurality of first combinations consisting of possible values ​​of each of the first parameters; By setting each of the first combinations in the plasma processing apparatus, a value of one or more second parameters indicating a state of the process gas that has been converted into plasma and a value of one or more third parameters indicating an evaluation of the processing target after processing are obtained for each of the first combinations; storing learning data (81) in the storage unit, the learning data associating the first combination with each of the acquired values ​​of the second parameters and each of the acquired values ​​of the third parameters; and using the learning data, generating a learning model (82) in which the first parameters and the second parameters are explanatory variables and the third parameter is a target variable; predicting, using the learning model, values ​​of each of the first parameters such that each of the third parameters has a predetermined value; Search device (3).

2. the control unit selects, by an experimental design method, the first combination having a smaller number of combinations than the second combinations from among a plurality of second combinations constituted by values ​​that each of the first parameters can take. A search device (3) according to claim 1.

3. The first parameter includes at least one of a plasma generating power, a bias power, a pressure of the process gas, a flow rate of the process gas, and a type of the process gas. A searching device (3) according to claim 1 or 2.

4. The second parameter includes a parameter directly or indirectly representing at least one of an electron temperature, an electron density, a radical species, a radical density, an ion species, an ion density, and an ion energy. A searching device (3) according to claim 1 or 2.

5. The parameter indirectly representing the radical species and the parameter indirectly representing the radical density are an emission wavelength of the plasma and an emission intensity of the emission wavelength of the plasma; A ratio of emission intensities of a plurality of emission wavelengths of the plasma; and the mass of radicals in the plasma and the observed intensity of the mass of radicals in the plasma; the ratio of observed intensities of each of the radical masses in the plasma; and or a value obtained by performing a calculation using at least one of The searching device according to claim 4.

6. The parameter indirectly representing the ion species and the parameter indirectly representing the ion density are an emission wavelength of the plasma and an emission intensity of the emission wavelength of the plasma; A ratio of emission intensities of a plurality of emission wavelengths of the plasma; and the mass of the ion and the observed intensity of the mass of the ion; the ratio of observed intensities of the masses of the ions; or a value obtained by performing a calculation using at least one of The searching device according to claim 4.

7. The third parameter includes at least one of a film thickness, a processing shape, a processing speed, a sputtering resistance, and a deposition film composition. A searching device (3) according to claim 1 or 2.

8. The control unit is The predicted values ​​of the first parameters are set in the plasma processing apparatus, and the values ​​of the second parameters and the third parameters are obtained for the predicted values ​​of the first parameters; updating the learning data by adding data associating the predicted values ​​of each of the first parameters, the acquired values ​​of each of the second parameters, and the acquired values ​​of each of the third parameters to the learning data, and updating the learning model using the updated learning data; A searching device (3) according to claim 1 or 2.

9. A plasma processing apparatus (1) for processing an object to be processed into a target object using a process gas, comprising: A processing chamber (70) in which the processing object is placed; a plasma generating device (2) that generates the process gas in a plasma state in the processing chamber in accordance with the setting of each value of one or a plurality of first parameters, and performs plasma processing on the object to be processed; A search device (3) that searches for each value of the first parameter to be set in the plasma generation device; Equipped with The search device has a control unit (39) and a storage unit (31), The control unit is selecting a plurality of first combinations consisting of possible values ​​of each of the first parameters; By setting each of the first combinations in the plasma generating device, a value of one or more second parameters indicating a state of the process gas that has been converted into plasma and a value of one or more third parameters indicating an evaluation of the processing target after processing are obtained for each of the first combinations; storing learning data (81) in the storage unit, the learning data associating the first combination with each of the acquired values ​​of the second parameters and each of the acquired values ​​of the third parameters; and using the learning data, generating a learning model (82) in which the first parameters and the second parameters are explanatory variables and the third parameter is a target variable; predicting, using the learning model, values ​​of each of the first parameters such that each of the third parameters has a predetermined value; A plasma processing apparatus (1).

10. A method for searching for values ​​of one or more first parameters to be set in a plasma processing apparatus (1) that processes a processing object into a target object using a process gas, the method comprising: A selection step (S2) of selecting a plurality of first combinations constituted by values ​​that each of the first parameters can take; an acquisition step (S4) of acquiring, for each of the first combinations set in the plasma processing apparatus, values ​​of one or more second parameters indicating a state of the process gas plasmatized and values ​​of one or more third parameters indicating an evaluation of the processing target after processing; a generation step (S6) of storing learning data in a storage unit, in which the first combination is associated with each of the acquired values ​​of the second parameters and each of the acquired values ​​of the third parameters, and generating a learning model using the learning data, in which the first parameters and the second parameters are explanatory variables and the third parameter is a target variable; A prediction step (S7, S8) of predicting values ​​of the first parameters using the learning model such that the values ​​of the third parameters are predetermined values; Equipped with Search method.

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