Information display setting support program, information display setting support device, information display setting support system, and information display setting support method

Abstract

The information display setting support program causes a computer to execute a process of estimating parameter items necessary for setting a manufacturing device. The process of estimating the parameter items is performed by applying device information that identifies the manufacturing device and process information that identifies the manufacturing process to a first trained model for inferring parameter items that represent attribute information of the parameters to be set from the device information and the process information. Then, the computer executes a process of displaying the estimated parameter items on a display element.

Description

[Technical field]

[0001] The present disclosure relates to an information display setting support program, an information display setting support device, an information display setting support system, and an information display setting support method. [Background technology]

[0002] In order to operate a device appropriately, it is necessary to set various parameters that control the operation of the device to appropriate values. However, it is not easy to select appropriate parameters from a large number of parameters and set them to appropriate values. In view of this, a technology is known that makes it easy to set parameters by selecting and presenting parameters necessary for setting a device. For example, Patent Document 1 discloses a technology that supports setting work by selecting parameters that need to be set in order to make a device perform a desired operation. Specifically, a database (DB) of this support device stores a plurality of parameters and a plurality of keywords in advance in association with each other. When a user describes the contents to be set, this support device analyzes the description to extract keywords, searches the DB for parameters associated with the extracted keywords, selects them, and presents them as setting target candidates. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2007-26240 A Summary of the Invention [Problem to be solved by the invention]

[0004] In the technology of Patent Document 1, the contents of the description by the user affect the search results of the searched parameters. Therefore, depending on the contents of the description, the parameters that need to be set are not necessarily accurately searched and presented as setting candidates. As a result, there is a risk that irrelevant parameters are set. For this reason, support that enables appropriate parameters to be presented is required.

[0005] The present disclosure has been made in consideration of the above-mentioned problems, and aims to provide an information display setting support program, an information display setting support device, an information display setting support system, and an information display setting support method that provide support to enable the presentation of appropriate parameters. [Means for solving the problem]

[0006] In order to achieve the above object, an information display setting support program according to the present disclosure is a program for causing a computer to: a first trained model generated by any one of supervised learning, unsupervised learning, reinforcement learning, and semi-supervised learning; Apply it to the setting of manufacturing equipment Used A process is executed to estimate parameter items and display the estimated parameter items on a display device. Effect of the Invention

[0007] According to the present disclosure, the first trained model can assist in estimating and presenting parameter items required for setting manufacturing equipment identified from equipment information and process information. [Brief description of the drawings]

[0008] [Figure 1] FIG. 1 is a block diagram showing a functional configuration of an information display setting support device according to an embodiment; [Diagram 2] FIG. 2 is a diagram showing a functional configuration of a learning unit according to an embodiment; [Diagram 3] FIG. 2 is a diagram showing a functional configuration of an inference unit according to an embodiment; [Figure 4] FIG. 1 is a diagram showing an example of a hardware configuration of an information display setting support device according to an embodiment; [Diagram 5] FIG. 1 is a diagram showing an overview of a neural network according to an embodiment; [Figure 6] FIG. 13 is a diagram showing an example of a screen displaying information representing equipment, processes, data, and add-ons. [Figure 7] A diagram showing an example of parameter setting values. [Figure 8] Flowchart of learning process according to an embodiment [Figure 9] 1 is a flowchart showing a display setting support process according to an embodiment. [Figure 10] Flowchart of inference processing according to an embodiment [Figure 11] Flowchart of inference processing according to an embodiment DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0009] (Embodiment) Hereinafter, an information display setting support program, an information display setting support device, an information display setting support system, and an information display setting support method according to embodiments of the present disclosure will be described in detail with reference to the drawings. Note that the same or corresponding parts in the drawings are denoted by the same reference numerals.

[0010] An information display setting support system according to an embodiment of the present disclosure supports estimating and displaying parameter items necessary for setting a manufacturing device according to information input by a user. In this embodiment, a parameter item means attribute information of a parameter, such as a parameter's use, type, classification, and identification information. A plurality of parameter items are used to set a control device. A parameter value means a value set in a parameter item. Hereinafter, a parameter value is also referred to as a parameter setting value.

[0011] As shown in FIG. 1, an information display setting support system 1 according to an embodiment includes an information display setting support device 100 that estimates parameter items required for setting a manufacturing device, and a display device 300 that displays various information including the estimated parameter items.

[0012] The display device 300 displays the information output by the information display setting support device 100. The display device 300 includes, for example, a liquid crystal display. The display device 300 is an example of a display element according to the present disclosure.

[0013] The information display setting support device 100 is configured with a computer. The information display setting support device 100 includes a calculation processing unit 110 that executes data processing, and a storage unit 120 that stores data.

[0014] The arithmetic processing unit 110 includes, for example, a CPU (Central Processing Unit). The arithmetic processing unit 110 executes a program stored in the storage unit 120 to function as an information receiving unit 111 that receives various information, a control unit 112 that controls the processing of the information display setting support device 100, a learning unit 113 that generates a trained model, an inference unit 114 that uses the trained model to estimate parameter items and parameter values ​​required for setting the manufacturing device, and an output unit 115 that outputs the parameter items and parameter values.

[0015] The storage unit 120 includes a storage device, such as a non-volatile semiconductor memory, such as a flash memory, an EPROM (Erasable Programmable Read Only Memory), etc. The storage unit 120 may also include a non-volatile memory including a magnetic disk, a flexible disk, an optical disk, a compact disk, a mini disk, and a DVD (Digital Versatile Disc).

[0016] The storage unit 120 includes an information storage unit 121 that stores various information including the information received by the information receiving unit 111, and a trained model storage unit 122 that stores a trained model. The storage unit 120 further stores a program executed by the arithmetic processing unit 110.

[0017] The information storage unit 121 stores information including device identification information representing device identification information accepted by the information accepting unit 111 in accordance with a user's operation, process identification information representing process identification information, and information indicating parameter items and parameter values ​​estimated by the inference unit 114 using the learned model. Furthermore, learning information representing information for learning acquired by the learning unit 113 and inference information representing information for inference acquired by the inference unit 114 are stored in the information storage unit 121.

[0018] The trained model storage unit 122 stores the trained model generated by the learning unit 113. The trained model is generated by, for example, a "supervised learning method" that uses a known machine learning library and trains a set of data in which training data is labeled with a correct answer. Note that the trained model may be trained and generated by other learning methods such as an "unsupervised learning method," a "reinforcement learning method," or a "semi-supervised learning method."

[0019] The learning algorithm may be a known learning algorithm such as a neural network. The neural network has an input layer composed of a plurality of neurons to which different input parameters are input, an intermediate layer to which signals output from each neuron in the input layer are input, and an output layer to which signals output from the intermediate layer are input and which outputs output parameters. The intermediate layer of the neural network may be composed of one or more layers.

[0020] The information receiving unit 111 of the arithmetic processing unit 110 receives information input in accordance with a user's operation. The user inputs device identification information and process identification information, for example, by operating an operation input unit (not shown) in the information display setting support device 100, such as a keyboard or a mouse. The information receiving unit 111 receives the device identification information and process identification information input by the user, and stores the received information in an information storage unit 121 in the storage unit 120. The control unit 112 serves as a control function for controlling the operation of the information display setting support device 100.

[0021] As shown in FIG. 2, the learning unit 113 includes a data acquisition unit 1131 that acquires learning information 1 or learning information 2, and a model generation unit 1132 that generates a learned model. In this embodiment, learned model 1 and learned model 2 are generated as the learned models. Learned model 1 means a model obtained by learning based on learning information 1. Also, learned model 2 means a model obtained by learning based on learning information 2. Details will be described later. It should be noted that learned model 1 is an example of a first learned model according to the present disclosure. Also, learned model 2 is an example of a second learned model according to the present disclosure.

[0022] The data acquiring unit 1131 acquires learning information 1. In addition, the data acquiring unit 1131 acquires learning information 2. The learning information 1 and learning information 2 will be described in detail later.

[0023] The model generation unit 1132 generates a trained model 1 and a trained model 2 according to the input information of the learning information 1 and the learning information 2 output from the data acquisition unit 1131. The trained model 1 and the trained model 2 are stored in the trained model storage unit 122.

[0024] As shown in FIG. 3, the inference unit 114 includes a data acquisition unit 1141 that acquires inference information 1 or inference information 2, and an information inference unit 1142 that estimates parameter items and add-ons required for setting up the manufacturing equipment, or parameter values, by inputting the inference information 1 or inference information 2 into a learned model.

[0025] The data acquisition unit 1141 acquires information for inference 1. In addition, the data acquisition unit 1141 acquires information for inference 2. The details of the information for inference 1 and the information for inference 2 will be described later.

[0026] The information inference unit 1142 estimates parameter items and add-ons required for setting the manufacturing equipment by inputting the inference information 1 output from the data acquisition unit 1141 into the trained model 1. The information inference unit 1142 also estimates parameter values ​​required for setting the manufacturing equipment by inputting the inference information 2 output from the data acquisition unit 1141 into the trained model 2. The information inference unit 1142 is an example of an estimation means according to the present disclosure.

[0027] 1 outputs the parameter items, add-ons, and parameter values ​​estimated by the information inference unit 1142 to the display device 300. The output unit 115 is an example of an output means according to the present disclosure.

[0028] Next, an example of a hardware configuration of the information display setting support device 100 will be described with reference to Fig. 4. The information display setting support device 100 in Fig. 4 is realized by a computer such as a personal computer or a microcontroller.

[0029] The information display setting support device 100 comprises a processor 1001 that executes an operating program which is a program for the operation of the information display setting support device 100, memory 1002 that serves as the main storage area, an interface 1003 that realizes the communication function of the information display setting support device 100, and a secondary storage device 1004 that stores the operating program for executing processing, all of which are connected to each other via a bus 1000.

[0030] The processor 1001 is, for example, a CPU (Central Processing Unit). The processor 1001 loads an operation program stored in a secondary storage device 1004 into a memory 1002 and executes the program, thereby realizing each function of the information display setting support device 100.

[0031] The memory 1002 is a main storage device constituted by, for example, a RAM (Random Access Memory). The memory 1002 stores the operation program read by the processor 1001 from the secondary storage device 1004. The memory 1002 also functions as a work memory when the processor 1001 executes the operation program.

[0032] The interface 1003 is an I / O (Input / Output) interface such as a serial port, a USB (Universal Serial Bus) port, a network interface, etc. The interface 1003 realizes the communication function of the information display setting support device 100.

[0033] The secondary storage device 1004 is, for example, a flash memory, a hard disk drive (HDD), or a solid state drive (SSD). The secondary storage device 1004 stores the operation program executed by the processor 1001, the learning information 1 and 2, the inference information 1 and 2, the information accepted by the information accepting unit 111, and the trained models 1 and 2 generated by the model generating unit 1132 in the learning unit 113.

[0034] Next, an overview of the neural networks that constitute Trained Model 1 and Trained Model 2 will be described with reference to FIG. As shown in the figure, the neural network is composed of an input layer, a hidden layer, and an output layer, each of which includes a number of neurons. Here, there is one hidden layer, but the number of hidden layers is arbitrary.

[0035] For example, for trained model 1, each neuron X1 to X n is assigned a pair of a device and each process that the device executes, and each neuron Z1 to Z k Various parameter items and add-ons to be set in the device are assigned to each parameter item. Each parameter item has attribute information set therein, such as purpose, data type, data classification, and data name.

[0036] In this embodiment, as shown in FIG. 6, it is assumed that a "semiconductor manufacturing equipment" that manufactures semiconductors is specified as the equipment, and "development" is specified as the process. In this case, the input to the neuron in the input layer to which the pair of "semiconductor manufacturing equipment" and "development" is assigned is set to "1," and the input to other neurons is set to "0." As the processing progresses, the output of one or more neurons in the output layer to which the parameter items to be set for the combination of "semiconductor manufacturing equipment" and "development" are assigned will be "1", and the output of other neurons will be "0".

[0037] With the improvement of GUI (Graphical User Interface), in computer processing, for example, a user pulls down the equipment item to select "semiconductor manufacturing equipment" from the list, and also pulls down the process item to select "development" from the list. This information is reflected in the neurons of the input layer. Next, when an output is obtained, recommended data representing recommended data suitable for the development process and recommended add-ons representing recommended add-ons are displayed. Note that the "recommended data" shown in FIG. 6 corresponds to a parameter item. Also, an add-on means a function that is added to expand the function. Also, in the example of FIG. 6, the recommended add-on is displayed, but depending on the equipment, process, recommended data, etc., there may be cases where the recommended add-on is not displayed because there is no need to add and expand the function. Note that "semiconductor manufacturing equipment" is an example of equipment information related to the present disclosure. Also, "development" is an example of process information related to the present disclosure.

[0038] In the example of FIG. 6, recommended data No. 1 to No. 6 are shown. For example, data No. 1 has a data name of "Axis0001", a use in the manufacturing process of "transport", a data type representing the data type of "axis", and a data type representing the data type of "actual drive axis". The data type "axis" is an example of type information according to the present disclosure. Also, the data type corresponds to the parameter item type, the data type corresponds to the parameter item type, and the data name corresponds to the parameter item name.

[0039] For Trained Model 2, all of the inputs and outputs of Trained Model 1 obtained in this manner are used as inputs. In this case, each neuron in the input layer, X1 to X n On the other hand, each neuron Z1 to Z k are assigned the parameter values ​​shown in FIG. Based on the input and output of trained model 1, a "1" is input to the corresponding neuron in the input layer, and a "0" is input to the other neurons. Then, among the neurons in the output layer, the output of the neuron to which the parameter value to be set is assigned will be "1", and the output of the other neurons will be "0".

[0040] With the improvement of the GUI, in terms of computer processing, when the user selects “OK” on the screen shown in Figure 6, the displayed information is supplied to the input layer of the neural network corresponding to trained model 2. When an output is obtained from the output layer, the GUI displays parameter setting values ​​exemplified in Fig. 7. Note that Fig. 7 shows an example of some of the many parameter setting values.

[0041] In the example of FIG. 6, the learning information 1 corresponds to information representing a combination of "equipment", "process", "recommended data", and "recommended add-on". In the examples of FIG. 6 and FIG. 7, the learning information 2 corresponds to information representing a combination of "equipment", "process", "recommended data", "recommended add-on", and "parameter setting value" shown in FIG. 7. Of the learning information 1, information representing a combination of "equipment" and "process" is called "learning input information 1". Of the learning information 2, information representing a combination of "equipment", "process", "recommended data", and "recommended add-on" is called "learning input information 2". The parameter item shown in FIG. 5 corresponds to "recommended data" shown in FIG. 6. The add-on shown in FIG. 5 corresponds to "recommended add-on" shown in FIG. 6. In addition, the parameter value shown in FIG. 5 corresponds to "parameter setting value" shown in FIG. 7.

[0042] In the three-layered neural network shown in Figure 5, multiple inputs are input layers X1 to X n When the value is entered into 11 ~W nm Multiplying by Y1 to Y m The hidden layers Y1 to Y m The result input to is further weighted by V 11 ~V mk and output layers Z1~Z k The output layers Z1 to Z k The output from is the weights W 11 ~W nm and the weight V 11 ~V mk It depends on the value of . In this case, n and k are integers of 4 or more, and m is an integer of 3 or more.

[0043] In this embodiment, the input layers X1 to X n When, for example, learning input information 1 is input to each neuron in the output layer Z1 to Z k The weight W is set so that the output from approaches the combination of "recommended data" and "recommended add-ons" in training information 1. 11 ~W nm and the weight V 11 ~V mk The weight W 11 ~W nm and the weight V 11 ~V mk Adjusting the weights W is the learning process in neural networks. 11 ~W nm and the weight V 11 ~V mk The weights W are updated, for example, by using the back propagation method. 11 ~W nm and the weight V 11 ~V mk The adjustment of the weights W is called learning in the model generation unit 1132. 11 ~W nm and the weight V 11 ~Vmk and are stored as trained model 1, for example, in the trained model storage unit 122 shown in FIG.

[0044] 3 reads out the learned model 1 or the learned model 2 stored in the learned model storage unit 122. The information inference unit 1142 reads out the weight W 11 ~W nm and the weight V 11 ~V mk This is set in the neural network. n When, for example, learning input information 1, specifically, information representing a combination of “equipment” and “process”, is input to each neuron in the output layers Z1 to Z k From the above, recommended data and add-ons corresponding to the equipment and process, in other words, parameter items and add-ons, can be output.

[0045] The learning process by the learning unit 113 will be described below. The learning process shown in Fig. 8 starts when the information receiving unit 111 receives an operation by a user instructing execution of the learning process. Note that the learning process for generating and saving a trained model 1 will be described.

[0046] As described above, the data acquisition unit 1131 in the learning unit 113 acquires training data representing the learning information 1 (step S301). The training data is input to the learning unit 113 via a communication unit or the like (not shown). For the trained model 1, the training data is a pair of information on the input layer side that specifies a combination of "equipment" and "process", and a combination of "parameter item" and "recommended add-on" that should be set for that combination. For trained model 2, the training data is the combination of "equipment" and "process," "parameter items," and "recommended add-ons," which are the input information, and the output information is the parameter setting values.

[0047] The model generation unit 1132 repeatedly uses the teacher data output by the data acquisition unit 1131 to learn the relationship between the combination of the device and the process and the combination of the recommended data and the recommended add-on (step S302), and calculates the weight W 11 ~W nm , V 11 ~V mk The model generation unit 1132 stores the trained model 1 in the trained model storage unit 122 (step S303), and ends the learning process.

[0048] Next, a display setting support process for supporting parameter setting by displaying parameter items and parameter setting values ​​will be described with reference to Fig. 9. When a user starts the information display setting support device 100, the display setting support process shown in Fig. 9 is started.

[0049] The control unit 112 determines whether the information receiving unit 111 has acquired information for inference 1 in accordance with a user's operation (step S101). Information representing the equipment and process in this embodiment corresponds to information for inference 1. In the example of FIG. 6, information representing the equipment "semiconductor manufacturing equipment" and the process "development" corresponds to information for inference 1. The acquired information for inference 1 is stored in the information storage unit 121.

[0050] When it is determined that the information for inference 1 has been acquired (step S101: Yes), the control unit 112 inputs the information for inference 1, specifically, information representing the equipment "semiconductor manufacturing equipment" and the process "development", to the inference unit 114 (step S102). Then, the inference process 1 is executed (step S103). Note that when it is determined that the information for inference 1 has not been acquired (step S101: No), the process of step S101 is repeated.

[0051] In the inference process 1 in step S103, the process shown in FIG. 10 is performed. Specifically, the data acquisition unit 1141 acquires information for inference 1 (step S103a). The information inference unit 1142 performs inference based on the information for inference 1 and the trained model 1 output by the data acquisition unit 1141 (step S103b). In the example of FIG. 6, the information inference unit 1142 estimates recommended data, in other words, parameter items and recommended add-ons corresponding to the equipment and the process, based on information representing the equipment "semiconductor manufacturing equipment" and the process "development" and the trained model 1. Note that there may be no recommended add-ons depending on the equipment and the process.

[0052] The information inference unit 1142 outputs the estimated parameter items and add-ons (step S103c). The process proceeds to step S104 shown in FIG.

[0053] The output unit 115 outputs the parameter items and the add-ons output by the information inference unit 1142 to the display device 300. The display device 300 displays the parameter items and the add-ons output from the output unit 115 (step S104).

[0054] The control unit 112 determines whether the information receiving unit 111 has received an instruction to display the parameter setting values ​​according to the user's operation (step S105). As an actual screen display, the screen shown in FIG. 6 is an example of the information receiving unit 111. The user selects an icon indicating "OK" by clicking to instruct the display of the parameter setting values. As a result, the parameter setting values ​​exemplified in FIG. 7 are displayed.

[0055] When it is determined that an instruction to display parameter settings has been received (step S105: Yes), the control unit 112 inputs inference information 2 to the inference unit 114 (step S106). In this embodiment, information representing the device, the process, the recommended data, and the recommended add-ons exemplified in Fig. 6 corresponds to the inference information 2. Inference process 2 is executed (step S107).

[0056] If it is determined that the instruction to display the parameter setting values ​​has not been received (step S105: No), the process of step S105 is repeated.

[0057] In the inference process 2 in step S107, the process shown in Fig. 11 is performed. Specifically, the data acquisition unit 1141 acquires information for inference 2 (step S107a). The information inference unit 1142 performs inference based on the information for inference 2 and the trained model 2 output by the data acquisition unit 1141 (step S107b). In the example of Fig. 6, the information inference unit 1142 estimates parameter setting values ​​corresponding to the device, process, recommended data, and recommended add-on based on information representing the device, process, recommended data, and recommended add-on, and the trained model 2.

[0058] The information inference unit 1142 outputs the inferred parameter setting value (step S107c). The process proceeds to step S108 shown in FIG.

[0059] The output unit 115 outputs the parameter setting values ​​output by the information inference unit 1142 to the display device 300. The display device 300 displays the parameter setting values ​​output from the output unit 115 as shown in Fig. 7 (step S108). The display setting support process then ends.

[0060] As described above, the information display setting support system 1 according to the embodiment can support the estimation and presentation of parameter items and add-ons required for setting a manufacturing device, which are identified from a combination of device information and process information, using the trained model 1. In addition, the combination of device information and process information, the estimated parameter items and add-ons, and the trained model 2 can support the estimation and presentation of parameter setting values.

[0061] (Modification) In the embodiment, it has been described that the parameter items or parameter setting values ​​are output using the learned models 1 and 2 learned by the model generation unit 1132 of the information display setting support device 100. However, it is also possible to obtain a learned model from outside the information display setting support device 100 and output the parameter items or parameter setting values ​​to the display device 300 based on this learned model.

[0062] In addition, the various data in the storage unit 120 of the information display setting support device 100 may be stored in a cloud-type server external to the information display setting support device 100.

[0063] In the embodiment, the information display setting support device 100 generates a trained model 1 that infers data items and add-ons, but the present disclosure is not limited to this. The information display setting support device 100 may generate a trained model 1 that infers only parameter items, and apply device information that identifies a manufacturing device and process information that identifies a manufacturing process to the generated trained model 1 to estimate only the parameter items. In this case, the add-on is not output from the information inference unit 1142, and therefore the add-on is not input to the trained model 2.

[0064] In the embodiment, the display device 300 displayed multiple pieces of data representing the data names "Axis0001", "Axis0002", ... as exemplified in Figure 7, but it is also possible to provide a setting screen for a single piece of data and display only the parameter values ​​that require setting. Furthermore, in a parameter setting screen as exemplified in Fig. 7, the display device 300 may highlight some of the parameter setting values. For example, in the case of data with the data name "Axis0002" and the use "position control" as shown in Fig. 6, parameter setting values ​​related to position control such as "absolute position management setting" and "absolute position reference setting" as shown in Fig. 7 may be highlighted. By highlighting the parameter setting values ​​by making the background color red or displaying the characters in a larger size, the user can easily notice them. Note that the use "position control" as exemplified in Fig. 6 is an example of use information according to the present disclosure.

[0065] The information display setting support device 100 may further include a folder generating unit that organizes data. In the case of Figs. 6 and 7, the folder generating unit may generate a folder for "axis", and further generate a folder for "development" in the folder for "axis", and may generate folders for data names "Axis0001", "Axis0002", "Axis0003", and "Axis0004" in the folder, respectively, and may store parameter items and parameter setting values ​​corresponding to each data name in each generated folder. This allows the parameter items and parameter setting values ​​to be organized. The folder for "development" is an example of a process information folder according to the present disclosure. The data names "Axis0001", "Axis0002", "Axis0003", and "Axis0004" are examples of identification information according to the present disclosure. The folders with the data names "Axis0001", "Axis0002", "Axis0003", and "Axis0004" are examples of parameter item name folders according to the present disclosure. The "Axis" folder is an example of a type folder according to the present disclosure.

[0066] Furthermore, when the information representing the parameter items and the add-ons is displayed, the control unit 112 may install an add-on function representing the function of the displayed add-on in the information display setting support device 100. This improves the efficiency of development for users. Note that, in the embodiment, the reason why the recommended add-ons are input to the trained model 2 is that some add-ons can generate parameter items after installation, and the parameter setting values ​​of the parameter items generated by installing the add-ons are also output from the trained model 2. In other words, the information display setting support device 100 sets not only the parameter setting values ​​of the parameter items, but also the parameter setting values ​​of the parameter items generated by installing the add-ons.

[0067] In the embodiment, the user selects information that identifies the manufacturing equipment, for example, "semiconductor manufacturing equipment," as shown in Fig. 6, but the present disclosure is not limited to this. For example, information that represents a manufacturing line may be displayed, and the user may select information that identifies the manufacturing line.

[0068] Various embodiments and modifications are possible without departing from the broad spirit and scope of the present disclosure. The above-described embodiments are for explaining the present disclosure and do not limit the scope of the present disclosure. That is, the scope of the present disclosure is indicated by the claims, not the embodiments. Various modifications made within the scope of the claims and the scope of the disclosure equivalent thereto are considered to be within the scope of the present disclosure. [Explanation of symbols]

[0069] 1 Information display setting support system, 100 Information display setting support device, 110 Calculation processing unit, 111 Information receiving unit, 112 Control unit, 113 Learning unit, 114 Inference unit, 115 Output unit, 120 Memory unit, 121 Information storage unit, 122 Learned model storage unit, 300 Display device, 1000 Bus, 1001 Processor, 1002 Memory, 1003 Interface, 1004 Secondary storage device, 1131, 1141 Data acquisition unit, 1132 Model generation unit, 1142 Information inference unit.

Claims

1. On the computer, applying equipment information that identifies a manufacturing equipment and process information that identifies a manufacturing process to a first trained model for inferring parameter items that represent attribute information of a parameter to be set from the equipment information and the process information, the first trained model being generated by any one of supervised learning, unsupervised learning, reinforcement learning, and semi-supervised learning, to estimate parameter items used in setting the manufacturing equipment; displaying the estimated parameter items on a display device; An information display setting support program that executes processing.

2. The parameter items include use information representing information on a use in the manufacturing process.

2. The information display setting support program according to claim 1.

3. 3. The information display setting support program according to claim 1, applying the device information, the process information, and the estimated parameter items to a second trained model for inferring parameter setting values ​​representing setting values ​​of the parameters from the device information, the process information, and the estimated parameter items, the second trained model being generated by any one of supervised learning, unsupervised learning, reinforcement learning, and semi-supervised learning, to estimate the parameter setting values; displaying the estimated parameter setting values ​​on a display element; An information display setting support program that causes the computer to further execute a process.

4. 4. The information display setting support program according to claim 3, highlighting a portion of the parameter settings; An information display setting support program that causes the computer to further execute a process.

5. 3. The information display setting support program according to claim 2, The parameter item further includes type information indicating a type of the parameter and identification information of the parameter item, generating a type folder representing a folder according to the type information, generating a process information folder representing a folder of the process information within the type folder, generating parameter item name folders representing folders of the identification information of the parameter items within the process information folder, and storing parameter setting values ​​representing setting values ​​of the parameters corresponding to the identification information in each of the generated parameter item name folders; An information display setting support program that causes the computer to further execute a process.

6. 6. The information display setting support program according to claim 1, further comprising: The first trained model infers add-ons used in the identified manufacturing process in addition to the parameter items, further displaying the inferred add-on on a display device; An information display setting support program that causes the computer to further execute a process.

7. an estimation means for applying equipment information identifying a manufacturing equipment and process information identifying a manufacturing process to a first trained model for inferring parameter items representing attribute information of a parameter to be set from the equipment information and the process information, the first trained model being generated by any one of supervised learning, unsupervised learning, reinforcement learning, and semi-supervised learning, to estimate parameter items used in setting the manufacturing equipment; an output means for displaying the estimated parameter items on a display device; Equipped with Information display setting support device.

8. An information display setting support device according to claim 7; A display device having the display element. Information display setting support system.

9. An information display setting support method executed by the information display setting support system according to claim 8, comprising: Applying the device information and the process information to the first trained model to estimate the parameter items; displaying the estimated parameter items on the display element; Information display setting support method.