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

Abstract

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

Description

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

[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.

[0002] In order to operate a device properly, various parameters that control the device's operation must be set 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 facilitates parameter setting by selecting and presenting parameters necessary for device configuration. For example, Patent Document 1 discloses a technology that supports configuration work by selecting parameters that need to be set to cause a device to perform a desired operation. Specifically, a database (DB) of this support device stores multiple parameters and multiple keywords in advance, linked to each other. When a user describes the settings they want to make, this support device analyzes the description to extract keywords, searches the DB for parameters linked to the extracted keywords, selects them, and presents them as setting candidates.

[0003] Japanese Patent Application Laid-Open No. 2007-26240

[0004] In the technology of Patent Document 1, the content of a user's description affects the search results for the searched parameters. Therefore, depending on the content of the description, parameters that need to be set may not be accurately searched and presented as setting candidates. As a result, there is a risk that irrelevant parameters will be set. Therefore, 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 appropriate parameters to be presented.

[0006] In order to achieve the above object, the information display setting support program of the present disclosure causes a computer to execute a process of applying device information that identifies a manufacturing device and process information that identifies a 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 process information, estimating the parameter items necessary for setting the manufacturing device, and displaying the estimated parameter items on a display element.

[0007] According to the present disclosure, the first trained model can provide assistance in estimating and presenting parameter items required for setting up manufacturing equipment identified from equipment information and process information.

[0008] FIG. 1 is a block diagram showing the functional configuration of an information display setting support device according to an embodiment; FIG. 2 is a diagram showing the functional configuration of a learning unit according to an embodiment; FIG. 3 is a diagram showing the functional configuration of an inference unit according to an embodiment; FIG. 4 is a diagram showing an example of the hardware configuration of an information display setting support device according to an embodiment; FIG. 5 is a diagram showing an overview of a neural network according to an embodiment; FIG. 6 is a diagram showing an example of a screen displaying information representing a device, a process, data, and add-ons; FIG. 7 is a diagram showing an example of parameter setting values;

[0009] 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 below with reference to the drawings. Note that identical or corresponding parts in the drawings are designated by the same reference numerals.

[0010] An information display setting support system according to an embodiment of the present disclosure supports the estimation and display of parameter items required for setting up a manufacturing device according to information input by a user. In this embodiment, a parameter item refers to attribute information of a parameter, such as the parameter's purpose, type, classification, and identification information. A plurality of parameter items are used to set up a control device. A parameter value refers to 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, the information display setting support system 1 according to the embodiment includes an information display setting support device 100 that estimates parameter items required for setting up 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 assistance 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 by a computer and includes a 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). By executing a program stored in the storage unit 120, the arithmetic processing unit 110 functions as an information receiving unit 111 that receives various types of 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 nonvolatile semiconductor memory, such as a flash memory, an EPROM (Erasable Programmable Read Only Memory), etc. The storage unit 120 may also include a nonvolatile memory, such as a magnetic disk, a flexible disk, an optical disk, a compact disk, a minidisk, or a DVD (Digital Versatile Disc).

[0016] The storage unit 120 includes an information storage unit 121 that stores various types of information including the information received by the information receiving unit 111, and a trained model storage unit 122 that stores trained models. The storage unit 120 also 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 received by the information receiving 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 trained 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" using a publicly known machine learning library, in which a set of data in which training data is labeled with a correct answer is trained. Note that the trained model may also 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 can 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. Note that the intermediate layer of the neural network may be composed of one or more.

[0020] The information receiving unit 111 of the arithmetic processing unit 110 receives information input in accordance with user operation. The user inputs device identification information and process identification information, for example, by operating an operation input unit (not shown) of the information display setting support device 100, such as a keyboard or 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 the information storage unit 121 in the storage unit 120. The control unit 112 serves as a control function that controls 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 learned models. Learned model 1 means a model obtained by learning based on learning information 1. Furthermore, learned model 2 means a model obtained by learning based on learning information 2. Details will be described later. Note that learned model 1 is an example of a first learned model according to the present disclosure. Furthermore, learned model 2 is an example of a second learned model according to the present disclosure.

[0022] The data acquisition unit 1131 acquires learning information 1. The data acquisition unit 1131 also 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, respectively, in accordance with the input information of training information 1 and training 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 Figure 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 or parameter values ​​required for setting up the manufacturing equipment by inputting the inference information 1 or inference information 2 into a learned model.

[0025] The data acquisition unit 1141 acquires inference information 1. The data acquisition unit 1141 also acquires inference information 2. Details of the inference information 1 and the inference information 2 will be described later.

[0026] The information inference unit 1142 estimates parameter items and add-ons required for setting up 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 up 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 the 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 operating program stored in the secondary storage device 1004 into the memory 1002 and executes the program, thereby realizing each function of the information display setting assistance device 100.

[0031] The memory 1002 is a main storage device configured, for example, by a RAM (Random Access Memory). The memory 1002 stores the operating program that the processor 1001 reads from the secondary storage device 1004. The memory 1002 also functions as a work memory when the processor 1001 executes the operating 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 assistance 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 operating program executed by the processor 1001, the training information 1 and 2, the inference information 1 and 2, the information received by the information receiving unit 111, and the trained models 1 and 2 generated by the model generation unit 1132 in the training 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 Figure 5. As shown in the figure, the neural network is composed of an input layer, an intermediate layer, and an output layer, each of which includes a plurality of neurons. Here, there is one intermediate layer, but the number of intermediate layers is arbitrary.

[0035] Regarding the trained model 1, for example, each neuron X in the input layer １ ~X ｎ A pair of a device and each process executed by the device is assigned to each neuron Z １ ~Z ｋ Various parameter items and add-ons to be set in the device are assigned to each parameter item. Attribute information for each parameter item includes its purpose, data type, data classification, data name, etc.

[0036] In this embodiment, as shown in Figure 6, it is assumed that "semiconductor manufacturing equipment" that manufactures semiconductors is specified as the equipment and "development" as the process. In this case, the input to the neuron in the input layer to which the combination of "semiconductor manufacturing equipment" and "development" is assigned is set to "1," and the input to the 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 item to be set for the combination of "semiconductor manufacturing equipment" and "development" is assigned is set to "1," and the output of the other neurons is set to "0."

[0037] With improvements to the 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 then pulls down the process item to select "development" from the list. This information is reflected in the neurons in the input layer. Subsequently, when 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 parameter items. Also, add-ons refer to functions that are added to expand functionality. In the example of FIG. 6, recommended add-ons are displayed, but depending on the equipment, process, recommended data, etc., there may be cases where no additional add-ons are required to expand functionality, and therefore the recommended add-ons are not displayed. 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 "conveyance," a data type representing the data type of "axis," and a data type representing the data type of "actual drive axis." Note that the data type "axis" is an example of type information related to the present disclosure. Furthermore, 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 the trained model 2, all the inputs and outputs of the trained model 1 obtained in this way are used as inputs. In this case, each neuron X １ ~X ｎ On the other hand, each neuron Z in the output layer is assigned a set of "device" and "process", a parameter item, or an add-on. １ ~Z ｋ are assigned the parameter values ​​shown in Figure 7. 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 neuron to which the parameter value to be set is assigned will output a "1", and the outputs of the other neurons will be "0".

[0040] With the improved GUI, in computer processing, when a 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 the parameter setting values ​​shown in Figure 7. Note that Figure 7 shows an example of some of the many parameter setting values.

[0041] In the example of FIG. 6 , the above-mentioned learning information 1 corresponds to information representing a combination of an “equipment,” a “process,” a “recommended data,” and a “recommended add-on.” Furthermore, in the examples of FIGS. 6 and 7 , learning information 2 corresponds to information representing a combination of an “equipment,” a “process,” a “recommended data,” a “recommended add-on,” and a “parameter setting value” shown in FIG. 7 . Of the learning information 1, information representing a combination of an “equipment” and a “process” is referred to as “learning input information 1.” Furthermore, of the learning information 2, information representing a combination of an “equipment,” a “process,” a “recommended data,” and a “recommended add-on” is referred to as “learning input information 2.” The parameter items shown in FIG. 5 correspond to the “recommended data” shown in FIG. 6 . The add-ons shown in FIG. 5 correspond to the “recommended add-ons” shown in FIG. 6 . Furthermore, the parameter values ​​shown in FIG. 5 correspond to the “parameter setting value” shown in FIG. 7 .

[0042] In the three-layer neural network shown in FIG. 5, multiple inputs are input to the input layer X １ ~Xｎ When input to the １１ ~W ｎｍ Multiplied by Y １ ~Y ｍ The hidden layer Y １ ~Y ｍ The result input to is further weighted by V １１ ~V ｍk and multiply it by Z １ ~Z ｋ The output layer Z １ ~Z ｋ The output result from １１ ~W ｎｍ and weight V １１ ~V ｍk Here, n and k are integers of 4 or more, and m is an integer of 3 or more.

[0043] In this embodiment, the input layer X １ ~X ｎ When, for example, learning input information 1 is input to each neuron in the output layer Z １ ~Z ｋ The weight W is set so that the result output from １１ ~W ｎｍ and weight V １１ ~V ｍk The weight W １１ ~W ｎｍ and weight V １１ ~V ｍk Adjusting the weights W is the learning process in neural networks. １１ ~W ｎｍ and weight V １１ ~V ｍk The weight W is updated using, for example, the back propagation method. １１ ~W ｎｍ and weight V １１ ~V ｍk The adjustment of the weights W is called learning in the model generation unit 1132. １１ ~W ｎｍ and weight V １１ ~Vｍk and is stored as, for example, trained model 1 in the trained model storage unit 122 shown in FIG. 2 .

[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 weight V １１ ~V ｍk and are set in the neural network. This results in the input layer X １ ~X ｎ When, for example, learning input information 1, specifically, information representing a combination of "device" and "process", is input to each neuron in the output layer Z １ ~Z ｋ From this, 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 performed 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 the user instructing execution. The learning process will be described below, which generates and saves a trained model 1.

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

[0047] The model generation unit 1132 repeatedly uses the training 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 training 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 inference information 1 in accordance with the user's operation (step S101). Information representing the equipment and process in this embodiment corresponds to inference information 1. In the example of Figure 6, information representing the equipment "semiconductor manufacturing equipment" and the process "development" corresponds to inference information 1. The acquired inference information 1 is stored in the information storage unit 121.

[0050] If it is determined that the inference information 1 has been acquired (step S101: Yes), the control unit 112 inputs the inference information 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 if it is determined that the inference information 1 has not been acquired (step S101: No), the process of step S101 is repeated.

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

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

[0053] The output unit 115 outputs the parameter items and add-ons output by the information inference unit 1142 to the display device 300. The display device 300 displays the parameter items and 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 parameter setting values ​​in accordance with a user operation (step S105). The screen shown in FIG. 6 is an example of an actual screen display of the information receiving unit 111. The user selects an icon indicating "OK" by clicking to instruct the display of parameter setting values. As a result, the parameter setting values ​​illustrated in FIG. 7 are displayed.

[0055] If 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, the information representing the device, process, recommended data, and recommended add-ons, as exemplified in Figure 6, corresponds to 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 inference process 2 in step S107, the process shown in Fig. 11 is performed. Specifically, the data acquisition unit 1141 acquires inference information 2 (step S107a). The information inference unit 1142 performs inference based on the inference information 2 output by the data acquisition unit 1141 and trained model 2 (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 trained model 2.

[0058] The information inference unit 1142 outputs the inferred parameter setting values ​​(step S107c), and 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 provide support for estimating and presenting 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. Furthermore, the combination of device information and process information, the estimated parameter items and add-ons, and the trained model 2 can provide support for estimating and presenting parameter setting values.

[0061] (Variant Example) In the embodiment, it has been described that the parameter items or parameter setting values ​​are output using the trained models 1 and 2 trained by the model generation unit 1132 of the information display setting support device 100, but it is also possible to obtain a trained 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 trained model.

[0062] Furthermore, 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, add-ons are not output from the information inference unit 1142, and therefore add-ons are not input to the trained model 2.

[0064] In the embodiment, the display device 300 displays multiple data items representing data names "Axis0001," "Axis0002," etc., as illustrated in FIG. 7 . However, a setting screen for a single data item may be provided, displaying only the parameter values ​​that require setting. Furthermore, on the parameter setting screen illustrated in FIG. 7 , the display device 300 may highlight some of the parameter settings. For example, in the case of data item "Axis0002" with the purpose "position control" shown in FIG. 6 , parameter settings related to position control, such as "absolute position management setting" and "absolute position reference setting" shown in FIG. 7 , may be highlighted. Highlighting the parameter settings, such as by using a red background color or large font, can make them more easily noticeable to the user. The purpose "position control" illustrated in FIG. 6 is an example of purpose information according to the present disclosure.

[0065] The information display setting support device 100 may further include a folder generation unit that organizes data. In the case of FIGS. 6 and 7 , the folder generation unit may generate an “Axis” folder, and then generate a “Development” folder within the “Axis” folder. Within the “Development” folder, folders with data names “Axis0001,” “Axis0002,” “Axis0003,” and “Axis0004” may be generated, respectively, and parameter items and parameter setting values ​​corresponding to each data name may be stored within the generated folders. This organizes the parameter items and parameter setting values. The “Development” folder 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 information representing parameter items and add-ons is displayed, the control unit 112 may install add-on functions representing the functions of the displayed add-ons into the information display setting assistance device 100. This improves the efficiency of user development. Note that, in the embodiment, the reason why recommended add-ons are input into 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 assistance 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] 6, the user selects information that identifies the manufacturing equipment, for example, "semiconductor manufacturing equipment." However, the present disclosure is not limited to this. For example, information representing 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. Furthermore, the above-described embodiments are intended to illustrate the present disclosure and are not intended to limit the scope of the present disclosure. That is, the scope of the present disclosure is defined by the claims, not the embodiments. Various modifications made within the scope of the claims and within the scope of the meaning of the disclosure equivalent thereto are considered to be within the scope of the present disclosure.

[0069] 1 Information display setting support system, 100 Information display setting support device, 110 Arithmetic processing unit, 111 Information reception 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. An information display setting support program that causes a computer to execute the following process: apply 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 parameters to be set from the equipment information and the process information, thereby estimating parameter items necessary for setting the manufacturing equipment; and display the estimated parameter items on a display element.

2. The information display setting support program according to claim 1, wherein the parameter items include use information representing information on use in the manufacturing process.

3. An information display setting assistance program according to claim 1 or 2, further causing the computer to execute the following processes: 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, thereby estimating the parameter setting values; and displaying the estimated parameter setting values ​​on a display element.

4. An information display setting support program according to claim 3, further causing the computer to execute a process of highlighting a part of the parameter setting values.

5. An information display setting support program according to claim 2, wherein the parameter items further include type information indicating the type of the parameters and identification information of the parameter items, and the program further causes the computer to execute the following processes: generate a type folder indicating a folder according to the type information, generate a process information folder within the type folder indicating a folder of the process information, generate parameter item name folders within the process information folders indicating folders of the identification information of the parameter items, and store parameter setting values ​​indicating setting values ​​of the parameters corresponding to the identification information in the parameter item name folders thus generated.

6. An information display setting assistance program according to any one of claims 1 to 5, wherein the first trained model infers add-ons to be used in the identified manufacturing process in addition to the parameter items, and further displays the inferred add-ons on a display element.

7. An information display setting assistance device comprising: an estimation means for 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 parameters to be set from the equipment information and the process information, thereby estimating parameter items necessary for setting the manufacturing equipment; and an output means for displaying the estimated parameter items on a display element.

8. An information display setting support system comprising: the information display setting support device according to claim 7; and a display device having the display element.

9. An information display setting support method, comprising: 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 parameters to be set from the equipment information and the process information, thereby estimating parameter items necessary for setting the manufacturing equipment; and displaying the estimated parameter items on a display element.

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