Data processing device, developing device, data processing method, and data processing program

Abstract

To temporarily recover waste liquid discharged from a development apparatus, regenerate the recovered waste liquid, return the regenerated liquid into a developer tank as new developer, and reuse the new developer.SOLUTION: A data processor is provided, comprising: a processing condition acquisition section which acquires a processing condition regarding developer; a developer data acquisition section which acquires developer data showing the composition of the developer; a recommended condition generation section which generates a recommended condition of additive to be added to the recovered liquid on the basis of the processing condition and the developer data in order to reuse recovered liquid recovered from waste liquid which has been discharged in a developing step; and an output section which outputs the recommended condition. The recommended condition generation section may have a prediction section which predicts recovered liquid data showing the composition of the recovered liquid on the basis of the processing condition and the developer data.SELECTED DRAWING: Figure 3

Description

[Technical Field]

[0001] The present invention relates to a data processing device, a developing device, a data processing method, and a data processing program. [Background technology]

[0002] Patent Document 1 states that "when making a printing plate made of a photosensitive resin, a development process is carried out in which the printing plate is exposed to light in a predetermined pattern and developed with a developer." [Prior art document] [Patent documents] [Patent Document 1] JP 2011-64796 A Summary of the Invention

[0003] A first aspect of the present invention provides a data processing device. The data processing device may include a processing condition acquisition unit that acquires processing conditions related to a developer. The data processing device may include a developer data acquisition unit that acquires developer data indicating the composition of the developer. The data processing device may include a recommended condition generation unit that generates recommended conditions for additives to be added to a recovered solution recovered from waste solution discharged in a development process, in order to reuse the recovered solution, based on the processing conditions and the developer data. The data processing device may include an output unit that outputs the recommended conditions.

[0004] The recommended condition generating section may include a predicting section that predicts recovered liquid data indicating a composition of the recovered liquid based on the processing conditions and the developing liquid data.

[0005] The processing conditions may include development processing conditions in the developing step. The prediction unit may include a waste liquid prediction unit that predicts waste liquid data indicating a composition of the waste liquid based on the development processing conditions and the developer data.

[0006] The waste liquid prediction unit may predict the waste liquid data using a waste liquid prediction model that has learned the relationship between the development processing conditions and the developer data, and the waste liquid data by machine learning.

[0007] The processing conditions may include waste liquid processing conditions in a waste liquid processing step. The prediction unit may include a recovered liquid prediction unit that predicts the recovered liquid data based on the waste liquid processing conditions and the waste liquid data.

[0008] The recovered liquid prediction unit may predict the recovered liquid data using a recovered liquid prediction model that learns the relationship between the waste liquid treatment conditions and the waste liquid data, and the recovered liquid data through machine learning.

[0009] The recommended condition generating unit may include a calculating unit that calculates the recommended conditions based on the recovered liquid data. The calculating unit may include a regenerated liquid calculating unit that calculates regenerated liquid data indicating a composition of a regenerated liquid obtained by adding the additive to the recovered liquid based on the recovered liquid data.

[0010] The calculation section may include a mixed solution calculation section that calculates mixed solution data indicating a composition of a mixed solution obtained by mixing the regenerated solution with the developer based on the developer data and the regenerated solution data.

[0011] The recommended condition generation unit may repeatedly execute the calculation process of the mixed liquid data for at least one candidate condition of the additive multiple times, using the calculated mixed liquid data as new developer data, and generate the recommended conditions based on a mixed liquid data group consisting of the mixed liquid data from multiple times.

[0012] The recommended condition generation unit may repeatedly perform the calculation process of the mixed liquid data group for each of a plurality of candidate conditions for the additive, and generate the recommended conditions based on the mixed liquid data group calculated for each of the plurality of candidate conditions.

[0013] The output section may output the mixed liquid data included in the mixed liquid data group calculated for at least one candidate condition out of the mixed liquid data group calculated for each of the plurality of candidate conditions.

[0014] The recommended condition generating section may further include a storage section that stores at least one predetermined candidate condition.

[0015] The recommended condition generation unit may obtain a list of a plurality of predetermined candidate conditions, and generate the recommended conditions by evaluating the mixed liquid data group calculated for each of the plurality of candidate conditions using a predetermined evaluation method.

[0016] The recommended condition generation unit may generate one or more candidate conditions using a predetermined search method, and may generate the recommended conditions by evaluating the mixed liquid data group calculated for each of a plurality of candidate conditions including the one or more generated candidate conditions using a predetermined evaluation method.

[0017] The one or more candidate conditions may be generated based on the type and amount of the additive.

[0018] The recommended condition generation unit may repeatedly perform the calculation process of the regenerated liquid data multiple times for at least one candidate condition of the additive, using the calculated regenerated liquid data as new developer data, and generate the recommended conditions based on a regenerated liquid data group consisting of the regenerated liquid data from multiple times.

[0019] The recommended condition generation unit may repeatedly perform the calculation process of the regenerated liquid data group for each of a plurality of candidate conditions for the additive, and generate the recommended conditions based on the regenerated liquid data group calculated for each of the plurality of candidate conditions.

[0020] The candidate conditions may include conditions regarding the type and amount of the additive.

[0021] The recommended conditions may include conditions regarding the types and amounts of the additives.

[0022] The developer may be a developer used for developing a resin original plate.

[0023] In a second aspect of the present invention, there is provided a development device, which may include the data processing device.

[0024] A third aspect of the present invention provides a data processing method. The data processing method may include acquiring processing conditions related to a developer. The data processing method may include acquiring developer data indicating the composition of the developer. The data processing method may include generating, based on the processing conditions and the developer data, recommended conditions for additives to be added to a recovered solution recovered from waste liquid discharged in a development process in order to reuse the recovered solution. The data processing method may include outputting the recommended conditions.

[0025] A third aspect of the present invention provides a data processing program. The data processing program may be executed by a computer. The data processing program may cause the computer to function as a processing condition acquisition unit that acquires processing conditions related to a developer. The data processing program may cause the computer to function as a developer data acquisition unit that acquires developer data indicating the composition of the developer. The data processing program may cause the computer to function as a recommended condition generation unit that generates recommended conditions for additives to be added to a recovered solution recovered from waste solution discharged in a development process, in order to reuse the recovered solution, based on the processing conditions and the developer data. The data processing program may cause the computer to function as an output unit that outputs the recommended conditions.

[0026] The above summary of the invention does not list all of the features of the present invention, and subcombinations of these features may also be inventions. [Brief explanation of the drawings]

[0027] [Figure 1] 1 shows an example of the configuration of a developing device 10 that develops an original. [Figure 2] 1 shows a schematic diagram of a developer recycling system 100. [Figure 3] 1 shows an example of a block diagram of a data processing device 300 according to the present embodiment. [Figure 4] An example of a flow in which the data processing device 300 according to this embodiment generates and outputs recommended conditions for additives will be shown. [Figure 5] FIG. 10 shows an example of a block diagram of a data processing device 300 according to a modified example of the present embodiment. [Figure 6] An example of a flow in which the data processing device 300 according to the modified example of this embodiment generates recommended conditions for additives will be shown. [Figure 7] 10 shows an example of output from a data processing device 300 according to a modified example of this embodiment. [Figure 8] An example of a flow in which the data processing device 300 according to the modified example of this embodiment generates recommended conditions for additives using a genetic algorithm will be described below. [Figure 9] An example of a flow in which the data processing device 300 according to the modified example of this embodiment generates recommended conditions for additives using the steepest descent method will be shown. [Figure 10] An example of a flow in which the data processing device 300 according to the modified example of this embodiment generates recommended conditions for additives using the simulated annealing method will be shown. [Figure 11] 99 illustrates an example computer 9900 in which aspects of the present invention may be embodied, in whole or in part. DETAILED DESCRIPTION OF THE INVENTION

[0028] The present invention will be described below through embodiments of the invention, but the following embodiments do not limit the scope of the invention according to the claims. Furthermore, not all of the combinations of features described in the embodiments are necessarily essential to the solution of the invention.

[0029] FIG. 1 shows an example of the configuration of a developing device 10 for developing a master plate. Currently, known printing methods include lithographic printing (offset printing), intaglio printing (gravure printing), and relief printing (flexographic printing). To perform these types of printing, a printing plate must be made according to the image to be printed. For example, when making a relief (flexographic) printing plate, a master plate including an infrared-sensitive layer and a photosensitive resin layer is subjected to processes such as back exposure, laser drawing, relief exposure, development, drying, and post-exposure to obtain the printing plate. The developing device 10 shown in this figure may be used, for example, in the development process of such relief printing platemaking. The developer used in the developing device 10 may be a developer used to develop a resin master plate. This developer may be an aqueous developer containing at least a surfactant, or a solvent developer containing a saturated or unsaturated hydrocarbon, an ester, an alcohol, or the like. Among these, aqueous developers are subject to significant changes in the concentrations of developer components during the development process and waste liquid treatment process due to the physical / chemical properties of the components contained therein, and therefore there are great benefits to predicting the components according to the present invention and optimizing the additives required for reusing the recovered solution. Specifically, when a general waste liquid treatment method such as distillation is used as the waste liquid treatment process for aqueous developers, the composition of the contained components is likely to change, making it difficult to reuse the recovered solution.

[0030] In this developing device 10, an initial developer is supplied as a new solution and stored in the developer tank. This initial developer may be, for example, a diluted stock solution prepared by diluting a stock developer solution containing 25% surfactant A, 25% surfactant B, and 25% pH adjuster C by 25 times. The developer stored in the developer tank is then supplied to the master while the master surface is rubbed with a developer brush to remove the uncured (unexposed) photosensitive resin composition, thereby performing the development process. During this process, the developer supplied to the master is collected in the developer tank and stored there again. Therefore, as the development process progresses, the developer stored in the developer tank gradually contains uncured photosensitive resin composition and the like as development residue. Furthermore, when the unexposed areas are removed from the master surface, the concentration of the developer components changes due to chemical reactions between the unexposed areas and some of the developer components, or the unexposed areas absorb or adsorb some of the developer components. For example, in the case of an aqueous developer that exhibits alkaline properties due to the presence of an alkaline surfactant, the surfactant concentration and pH decrease as the development process progresses. An increase in the concentration of development residues and a decrease in the concentration of developer components cause a decrease in development performance (e.g., development speed).

[0031] Therefore, in the developing device 10, it becomes necessary to perform maintenance on the developer in order to prevent an increase in the concentration of development residues and a decrease in the concentration of developer components. For example, in the developing device 10, each time one (or several) master plates are developed, a certain amount of developer in the developer tank may be discharged as waste, and the same amount of new developer may be added at a predetermined concentration (the order of discharge and addition may be reversed). Also, for example, in the developing device 10, each time a predetermined number of master plates are developed, all of the developer in the developer tank may be discharged as waste and replaced with new developer. There are no particular limitations on the maintenance method, as long as it can prevent an increase in the concentration of development residues and a decrease in the concentration of developer components.

[0032] For these reasons, the used developer used in the development process is discharged from the developer tank, resulting in a large amount of industrial waste fluid. In recent years, therefore, efforts have been considered to temporarily collect and regenerate the waste liquid discharged from the developing device 10, and then return the regenerated liquid to the developer tank as new developer for reuse.

[0033] 2 is a schematic diagram of a developer reuse system 100. The reuse system 100 includes a developing device 10, a waste liquid tank 20, a waste liquid treatment device 30, and a recovered liquid tank 40.

[0034] The developing device 10 performs the above-described development process. During this process, the developing device 10 performs developer maintenance. For example, in the developing device 10, every time one (or several) master plates are developed, a certain amount of the developer in the developer tank may be discharged as waste liquid, and the same amount of regenerated liquid may be added. That is, in the developing device 10, a mixed liquid obtained by mixing the developer that has not been discharged and the regenerated liquid remaining in the developer tank with the regenerated liquid may be used as new developer to perform the next development process. Also, for example, in the developing device 10, every time a predetermined number of master plates are developed, all of the developer in the developer tank may be discharged as waste liquid and replaced with regenerated liquid. That is, in the developing device 10, the replaced regenerated liquid may be used as new developer to perform the next development process.

[0035] The waste liquid tank 20 is a buffer tank that buffers the waste liquid discharged from the developer tank of the developing device 10. The waste liquid tank 20 buffers the waste liquid obtained from the developing device 10 and supplies it to the waste liquid treatment device 30.

[0036] The waste liquid treatment device 30 treats the waste liquid supplied from the waste liquid tank 20. For example, in the waste liquid treatment device 30, a flocculant is added to the waste liquid to flocculate the developer residue. Then, in the waste liquid treatment device 30, filtration is performed using a filter such as a nonwoven fabric to separate the developer residue from the developer components (which may also contain water). Then, in the waste liquid treatment device 30, the concentrated liquid or solid matter containing the developer residue is discarded. The waste liquid treated and recovered in this way will be called recovered liquid.

[0037] The waste liquid treatment device 30 can also separate the developer components (which may include water) in the waste liquid from the development residue by atomizing them with ultrasonic waves and recovering them. In such a case, the waste liquid treatment device 30 may have an atomization chamber equipped with an ultrasonic generator, and the waste liquid may be supplied into the atomization chamber and ultrasonically treated to recover the developer components in the form of a mist. The developer components recovered in this way are called recovered liquid.

[0038] In addition, the waste liquid treatment device 30 can employ waste liquid treatment methods such as, but not limited to, distillation, centrifugation, centrifugal filtration, membrane separation, and the like.

[0039] The recovered liquid tank 40 is a buffer tank that buffers the recovered liquid that has been treated in the waste liquid treatment device 30. However, such recovered liquid cannot be reused in the developing device 10 as is. As described above, this is partly because the concentration of developer components contained in the waste liquid is lower than that of the developer before use. In addition, regardless of the waste liquid treatment method adopted in the waste liquid treatment device 30, it is generally not possible to recover all of the developer components in the waste liquid, so the content of developer components in the recovered liquid is lower than that of the waste liquid before treatment. Therefore, when supplying such recovered liquid to the developing device 10 for reuse, a process of adjusting the concentration of developer components in the recovered liquid is required.

[0040] In the process of adjusting the concentration of the recovered liquid, the concentration of the developer components in the recovered liquid supplied from the recovered liquid tank 40 is adjusted. For example, the recovered liquid is regenerated into a regenerated liquid by adding an additive to the recovered liquid. The regenerated liquid is then supplied to the developing device 10. As a result, the regenerated liquid is returned to the developer tank and reused in the developing device 10.

[0041] Here, in the process of adjusting the concentration of the recovered liquid, a developer stock solution or a diluted stock solution may be used as an additive to adjust the concentration of the developer components in the recovered liquid. While such a concentration adjustment method has the advantages of easy concentration adjustment (low operational burden on the user) and high safety, it has the disadvantage of not being able to finely adjust the concentration of each component. Therefore, in the process of adjusting the concentration of the recovered liquid, each of the developer components may be used as an additive. That is, for example, surfactant A, surfactant B, and pH adjuster C may be used as additives.

[0042] In this case, when adjusting the concentration of the developer components in the recovered solution, it is not known under what conditions the additives should be added to the recovered solution, and it has been left to the user's intuition and experience. In such cases, the concentration of each developer component in the mixed solution or regenerated solution becomes too low or too high, resulting in problems such as not being able to achieve the desired development performance or providing excessive development performance.

[0043] 3 shows an example of a block diagram of a data processing device 300 according to this embodiment. In consideration of the above-mentioned problems, the data processing device 300 according to this embodiment acquires processing conditions for the developer and developer data indicating the composition of the developer, and generates and outputs recommended conditions for the additive to be added to the recovered solution based on these. As a result, the data processing device 300 according to this embodiment can inform the user of the conditions under which the additive should be added to the recovered solution.

[0044] The data processing device 300 according to this embodiment may be a computer such as a personal computer (PC), tablet computer, smartphone, workstation, server computer, or general-purpose computer, or may be a computer system in which multiple computers are connected. Such a computer system is also considered a computer in a broad sense. The data processing device 300 may also be implemented as one or more virtual computer environments executable within a computer. Alternatively, the data processing device 300 may be a dedicated computer designed for data processing, or may be dedicated hardware implemented using dedicated circuits. Furthermore, if the data processing device 300 is connectable to the Internet, the data processing device 300 may be implemented using cloud computing.

[0045] The data processing device 300 may also be configured as the same device as the developing device 10. That is, the developing device 10 that develops the master may include the data processing device 300 according to this embodiment. This allows the function of developing the master and the function of proposing recommended additive conditions to be provided by a single device. When the developing device 10 and the data processing device 300 are provided as a single device, the data processing device 300 can directly acquire the development processing conditions for the development step from the developing device 10. For example, the data processing device 300 can acquire actual measured values, such as the temperature of the developer during the development process, from the log data stored in the developing device 10, thereby enabling highly accurate prediction of waste solution data after the development process. Alternatively, the data processing device 300 may be configured as a separate device from the developing device 10. In this case, the data processing device 300 may be configured as a standalone device, or may be configured as the same device as the waste solution processing device 30, for example. This allows the function of developing the master and the function of proposing recommended additive conditions to be provided by separate devices.

[0046] The data processing device 300 includes a processing condition acquisition unit 310, a developer data acquisition unit 320, a recommended condition generation unit 330, and an output unit 340. Note that these blocks are functionally separated functional blocks and may not necessarily correspond to the actual device configuration. In other words, just because something is shown as a single block in this diagram does not necessarily mean that it is composed of a single device. Also, just because something is shown as separate blocks in this diagram does not necessarily mean that it is composed of separate devices.

[0047] The processing condition acquisition unit 310 acquires processing conditions related to the developer. Here, the processing conditions related to the developer may include, for example, development processing conditions for the development step. As an example, the processing condition acquisition unit 310 acquires development processing conditions for the development step from the developing device 10 via a network. Furthermore, the processing conditions related to the developer may include waste liquid processing conditions for the waste liquid processing step. As an example, the processing condition acquisition unit 310 acquires waste liquid processing conditions for the waste liquid processing step from the waste liquid processing device 30 via a network. However, this is not limiting. The processing condition acquisition unit 310 may acquire such development processing conditions and waste liquid processing conditions via user input or by reading them from various memory devices. The processing condition acquisition unit 310 supplies the acquired processing conditions to the recommended condition generation unit 330.

[0048] The developer data acquisition unit 320 acquires developer data indicating the composition of the developer. For example, the developer data acquisition unit 320 acquires developer data indicating the composition of the initial developer used in the development process from information such as a developer ingredient list. However, this is not limited to this. The developer data acquisition unit 320 may acquire developer data indicating the composition of the initial developer used in the development process via user input or by reading it from various memory devices. Such an initial developer may be an undiluted developer or a diluted undiluted developer obtained by diluting the undiluted developer. The developer data acquisition unit 320 supplies the acquired developer data to the recommended condition generation unit 330.

[0049] The recommended condition generating unit 330 generates recommended conditions for additives to be added to recovered liquid, in order to reuse the recovered liquid recovered from waste liquid discharged in the developing process, based on the processing conditions and the developer data. For example, the recommended condition generating unit 330 generates recommended conditions for additives based on the processing conditions acquired by the processing condition acquiring unit 310 and the developer data acquired by the developer data acquiring unit 320.

[0050] In this case, for example, the recommended condition generation unit 330 may generate the recommended conditions for additives using a learning model generated by machine learning the relationship between the processing conditions and developer data and the recommended conditions for additives. Note that any algorithm capable of generating a model capable of predicting the recommended conditions for additives may be used as the machine learning algorithm, such as a neural network, regression, support vector machine, random forest, decision tree, clustering, or principal component analysis.

[0051] Furthermore, such a learning model may be generated by a device other than the data processing device 300. This allows the data processing device 300 to externally execute the process of generating the learning model, thereby reducing the processing load. Alternatively, such a learning model may be generated internally in the data processing device 300. This allows the data processing device 300 to provide, as an integrated device, the function of proposing recommended conditions and the function of generating a learning model.

[0052] The recommended condition generation unit 330 inputs processing conditions and developer data into such a learning model and obtains the output of the learning model as the recommended conditions for the additive. In this way, the recommended condition generation unit 330 may generate recommended conditions for the additive using a learning model generated by machine learning. However, this is not limited to this. The recommended condition generation unit 330 may also generate recommended conditions for the additive by inputting processing conditions and developer data into a known function based on a theoretical formula or empirical rules. The recommended condition generation unit 330 supplies the generated recommended conditions for the additive to the output unit 340.

[0053] The output unit 340 outputs the recommended conditions. For example, the output unit 340 displays the recommended conditions for the additives generated by the recommended condition generation unit 330 on a monitor. However, this is not limited to this. The output unit 340 may output the recommended conditions for the additives as audio through a speaker, may print them out using a printer, or may send a signal to another functional unit or device. For example, if the process of adjusting the concentration of the recovered liquid is performed automatically using a device, a signal can be sent to the device.

[0054] FIG. 4 shows an example of a flow in which the data processing device 300 according to this embodiment generates and outputs recommended conditions for additives.

[0055] In step S410, the data processing device 300 acquires processing conditions. For example, the processing condition acquisition unit 310 acquires the development processing conditions for the development process from the development device 10 via a network. The processing condition acquisition unit 310 may acquire at least one of the following development processing conditions: the type of development device, the amount of developer, the temperature of the developer in the development process, the development time, the contact pressure of the development brush, the rotation speed of the development brush, the maintenance conditions for the developer, the size of the plate to be made, the image ratio, and the development depth. However, the invention is not limited to these. The processing condition acquisition unit 310 may acquire various conditions that may affect the recommended additive conditions as development processing conditions. While the temperature of the developer, the development time, and the like in the development process may be preset values, it is preferable to use actual measured values ​​acquired from log data, etc., because this improves the accuracy of generating the recommended conditions.

[0056] Furthermore, for example, the processing condition acquisition unit 310 acquires waste liquid processing conditions for the waste liquid processing step from the waste liquid processing device 30 via a network. In this case, for example, if the waste liquid processing device 30 employs a waste liquid processing method using a flocculant, the processing condition acquisition unit 310 may acquire at least one of the following as waste liquid processing conditions: flocculant addition conditions, filter characteristics, filtration type, and filtration rate. However, this is not limited to this. Furthermore, if ultrasonic atomization is employed as another waste liquid processing method, the processing condition acquisition unit 310 may acquire at least one of the following as waste liquid processing conditions: number of ultrasonic generators, input power, processing time, waste liquid temperature, and mist collection blower rotation speed. However, this is not limited to this. The processing condition acquisition unit 310 may acquire various conditions that may affect the recommended additive conditions as waste liquid processing conditions.

[0057] In this way, the processing conditions related to the developer acquired by the processing condition acquisition unit 310 may include, for example, development processing conditions in the development step and waste liquid treatment conditions in the waste liquid treatment step. The processing condition acquisition unit 310 acquires processing conditions related to the developer in this way, for example. The processing condition acquisition unit 310 supplies the acquired processing conditions to the recommended condition generation unit 330.

[0058] In step S420, the data processing device 300 acquires developer data. For example, the developer data acquisition unit 320 acquires developer data indicating the composition of the initial developer used in the development process from information such as a developer ingredient list. In the case of an alkaline aqueous developer containing a surfactant, the developer data acquisition unit 320 may acquire at least one of the concentration of development residue, the concentration of the surfactant, the concentration of a pH adjuster, and the pH value in the developer as developer data.

[0059] As an example, assume that the developing device 10 uses a diluted stock solution obtained by diluting a stock developer solution containing 25% surfactant A, 25% surfactant B, and 25% pH adjuster C by 25 times as the initial developer. In this case, the developer data acquisition unit 320 may acquire the following initial developer data: surfactant A concentration = 1%, surfactant B concentration = 1%, and pH adjuster C concentration = 1% in the diluted stock solution. However, this is not limited to this. The developer data acquisition unit 320 may acquire the concentrations and characteristics of various components that may affect development performance as developer data. The developer data acquisition unit 320 supplies the acquired developer data to the recommended condition generation unit 330.

[0060] In step S430, the data processing device 300 generates recommended conditions for additives. For example, the recommended condition generation unit 330 generates recommended conditions for additives to be added to a recovered solution to reuse the recovered solution recovered from waste solution discharged in the development process, based on the processing conditions acquired in step S410 and the developer data acquired in step S420. As an example, the recommended condition generation unit 330 inputs the processing conditions acquired in step S410 and the developer data acquired in step S420 into a pre-stored learning model, and acquires the output of the learning model as the recommended conditions for additives. In this way, the recommended condition generation unit 330 may generate recommended conditions for additives using a learning model generated by machine learning.

[0061] Here, when a stock developer solution is used as the additive, the recommended condition generating unit 330 may generate the amount of stock developer solution to be added to the recovered solution as the recommended condition for the additive. Furthermore, when a diluted stock solution is used as the additive, the recommended condition generating unit 330 may generate the dilution ratio and amount of the diluted stock solution to be added to the recovered solution, or the amount of stock developer solution and water for dilution as the recommended condition for the additive. Furthermore, when each component contained in the stock developer solution is used as an additive, the recommended condition generating unit 330 may generate each component and its amount to be added to the recovered solution as the recommended condition for the additive. Furthermore, at least two or more of the stock developer solution, diluted stock solution, and each component contained in the stock developer solution can be used in combination as the additive. For example, if a developer stock solution containing no pH adjuster is diluted and a pH adjuster is added to the diluted solution to create an initial developer, the additives may be the amounts of the developer stock solution and pH adjuster, the amounts of the diluted developer and pH adjuster, the amounts of each component contained in the developer stock solution and pH adjuster, or a combination of these. In this way, the recommended conditions generated by the recommended condition generation unit 330 may include conditions regarding the type and amount of additives. The recommended condition generation unit 330 supplies the generated recommended conditions for additives to the output unit 340.

[0062] In step S440, the data processing device 300 outputs the recommended conditions for the additives. For example, the output unit 340 outputs and displays the recommended conditions for the additives generated in step S430 on a monitor.

[0063] In this way, the data processing device 300 according to this embodiment acquires developer data indicating the processing conditions for the developer and the composition of the developer, and generates and outputs recommended conditions for the additives to be added to the recovered solution based on these. This allows the data processing device 300 according to this embodiment to inform the user of the conditions under which the additives should be added to the recovered solution. Therefore, when adjusting the concentrations of the developer components in the recovered solution in order to reuse the recovered solution, the user can adjust them to the desired concentrations without relying on intuition or experience.

[0064] In the example of the developer recycling system shown in FIG. 2, the developer can be recycled multiple times by repeatedly executing each process in the developing device 10 and the waste liquid treatment device 30. For example, if recycling is repeated 30 times, the recommended additive conditions can be optimized for each recycling. In such a case, the recommended additive conditions from the first to the 30th recycling may be different from each other. Alternatively, the recommended additive conditions can be proposed on the premise that the concentration adjustment is performed using the same additive conditions consistently over the 30 recycling cycles. The method for determining the recommended additive conditions (optimization method) will be described later.

[0065] FIG. 5 shows an example block diagram of a data processing device 300 according to a modified example of this embodiment. In FIG. 5, components having the same functions and configurations as those in FIG. 3 are denoted by the same reference numerals, and descriptions thereof will be omitted except for the following differences. In the data processing device 300 according to the above-described embodiment, the recommended condition generation unit 330 generates recommended conditions in one go from processing conditions and developer data. However, in this case, for example, the learning model that outputs the recommended conditions may become large, prolonging the time required for machine learning and potentially preventing accurate convergence of the machine learning. Therefore, in the data processing device 300 according to this modified example, the configuration of the recommended condition generation unit 330 is subdivided. In the data processing device 300 according to this modified example, the recommended condition generation unit 330 includes a prediction unit 510, a calculation unit 520, and a storage unit 530.

[0066] The prediction unit 510 predicts recovered liquid data indicating the composition of the recovered liquid based on the processing conditions acquired by the processing condition acquisition unit 310 and the developer data acquired by the developer data acquisition unit 320. More specifically, the prediction unit 510 may include a waste liquid prediction unit 512 and a recovered liquid prediction unit 514.

[0067] The waste liquid prediction unit 512 predicts waste liquid data indicating the composition of the waste liquid based on the development processing conditions and the developer data. For example, the waste liquid prediction unit 512 predicts the waste liquid data based on the development processing conditions acquired by the processing condition acquisition unit 310 and the developer data acquired by the developer data acquisition unit 320.

[0068] In this case, for example, the waste liquid prediction unit 512 may predict the waste liquid data using a waste liquid prediction model that has learned the relationship between the development processing conditions and developer data, and the waste liquid data through machine learning. Note that any algorithm that can generate a model that can predict the waste liquid data may be used as such a machine learning algorithm.

[0069] Furthermore, like the above-described learning model, such a waste liquid prediction model may be generated by a device other than the data processing device 300, or may be generated inside the data processing device 300. In generating such a waste liquid prediction model, machine learning may be performed using, as learning data, waste liquid data obtained by measuring the components of the waste liquid discharged from the developer tank, in addition to the development processing conditions and developer data, for example.

[0070] The waste liquid prediction unit 512 inputs the development processing conditions and developer data into such a waste liquid prediction model and obtains the output of the waste liquid prediction model as waste liquid data. In this way, the waste liquid prediction unit 512 may predict the waste liquid data using a waste liquid prediction model generated by machine learning. However, this is not limited to this. The waste liquid prediction unit 512 may also predict the waste liquid data by inputting the development processing conditions and developer data into a known function based on a theoretical formula or empirical rules. The waste liquid prediction unit 512 supplies the predicted waste liquid data to the recovered liquid prediction unit 514.

[0071] The recovered liquid prediction unit 514 predicts the recovered liquid data based on the waste liquid treatment conditions and the waste liquid data. For example, the recovered liquid prediction unit 514 predicts the recovered liquid data based on the waste liquid treatment conditions acquired by the treatment condition acquisition unit 310 and the waste liquid data predicted by the waste liquid prediction unit 512.

[0072] In this case, for example, the recovered liquid prediction unit 514 may predict the recovered liquid data using a recovered liquid prediction model that has learned the relationship between the waste liquid treatment conditions and waste liquid data, and the recovered liquid data through machine learning. Note that any algorithm that can generate a model that can predict the recovered liquid data may be used as such a machine learning algorithm.

[0073] Furthermore, like the learning model described above, such a recovered liquid prediction model may be generated by a device other than the data processing device 300, or may be generated inside the data processing device 300. In generating such a recovered liquid prediction model, machine learning may be performed using, for example, the waste liquid treatment conditions, the waste liquid data, and also the recovered liquid data obtained by measuring the components of the recovered liquid recovered in the waste liquid treatment device 30 as learning data.

[0074] The recovered liquid prediction unit 514 inputs the waste liquid treatment conditions and waste liquid data into the recovered liquid prediction model and obtains the output of the recovered liquid prediction model as recovered liquid data. In this way, the recovered liquid prediction unit 514 may predict the recovered liquid data using the recovered liquid prediction model generated by machine learning. However, this is not limited to this. The recovered liquid prediction unit 514 may also predict the recovered liquid data by inputting the waste liquid treatment conditions and waste liquid data into a known function based on a theoretical formula or empirical rules. The recovered liquid prediction unit 514 supplies the predicted recovered liquid data to the calculation unit 520.

[0075] The calculation unit 520 calculates recommended conditions for an additive to be added to the recovered liquid based on the recovered liquid data predicted by the prediction unit 510. More specifically, the calculation unit 520 may include a regenerated liquid calculation unit 522 and a mixed liquid calculation unit 524.

[0076] The regenerated liquid calculation unit 522 calculates regenerated liquid data indicating the composition of the regenerated liquid obtained by adding an additive to the recovered liquid based on the recovered liquid data. For example, the regenerated liquid calculation unit 522 calculates the regenerated liquid data based on the recovered liquid data predicted by the prediction unit 510 and candidate conditions for adding the additive. The regenerated liquid calculation unit 522 supplies the calculated regenerated liquid data to the mixed liquid calculation unit 524.

[0077] The mixed solution calculation unit 524 calculates mixed solution data indicating the composition of a mixed solution obtained by mixing a reclaimed solution with a developer in a developer tank of the developing device 10, based on the developer data and the reclaimed solution data. For example, the mixed solution calculation unit 524 calculates the mixed solution data based on the developer data acquired by the developer data acquisition unit 320 and the reclaimed solution data calculated by the reclaimed solution calculation unit 522. The mixed solution calculation unit 524 supplies the calculated mixed solution data to the developer data acquisition unit 320 for repeated calculations.

[0078] The storage unit 530 stores at least one predetermined candidate condition. The candidate conditions stored in the storage unit 530 may include conditions related to the type and amount of additives, similar to the recommended conditions generated by the recommended condition generation unit 330. For example, the storage unit 530 may store a list of multiple predetermined candidate conditions. That is, the storage unit 530 may store a list of multiple candidate conditions that differ in conditions related to at least one of the type and amount of additives. However, this is not limited to this. As described above, the storage unit 530 may store multiple candidate conditions as a list in advance, or, as will be described later, the recommended condition generation unit 330 may generate one or more candidate conditions using a predetermined search method. This will be described later.

[0079] In the data processing device 300 according to this modification, the recommended condition generating unit 330 generates recommended conditions for adding additives based on the mixed liquid data calculated by the mixed liquid calculating unit 524. This will be described in detail using a flow chart.

[0080] FIG. 6 shows an example of a flow in which the data processing device 300 according to the modified embodiment generates recommended conditions for additives.

[0081] In step S600, the data processing device 300 acquires processing conditions related to the developer. Step S600 may be the same as step S410 described above. That is, the processing condition acquisition unit 310 acquires development processing conditions in the development step and waste liquid treatment conditions in the waste liquid treatment step. In this way, the processing conditions related to the developer acquired by the processing condition acquisition unit 310 may include development processing conditions in the development step and waste liquid treatment conditions in the waste liquid treatment step. The processing condition acquisition unit 310 supplies the acquired processing conditions to the recommended condition generation unit 330.

[0082] In step S602, the data processing device 300 acquires the loop count J. Here, J indicates the number of loops for repeatedly calculating the mixed liquid data for each candidate condition. That is, for example, if J=100, the data processing device 300 repeatedly executes the mixed liquid data calculation process for each candidate condition 100 times. The data processing device 300 may acquire such loop count J, for example, via user input. For example, the loop count J can be determined based on the number of times the developer is recycled, a maintenance method for the developer after the development process in the developing device 10, etc.

[0083] In step S604, the data processing device 300 sets i=1 and m=1. Here, i indicates the number of originals to be developed (number of developed originals) in the case where, for example, in the developing device 10, as maintenance of the developer after the development process, a certain amount of the developer in the developer tank is discharged as waste liquid every time one original is developed, and the same amount of new developer is added at a predetermined concentration (the order of discharge and addition may be reversed). (Details will be explained below assuming this maintenance condition, but are not limited to this.) That is, at the time point i=1, the data processing device 300 calculates mixed liquid data indicating the composition of the mixed liquid obtained after one original is developed. Also, m indicates the index of the candidate conditions for adding additives. That is, at the time point m=1, the data processing device 300 calculates mixed liquid data for candidate condition 1, which is the first candidate condition. Therefore, at the time points i=1 and m=1, the data processing device 300 calculates the mixed liquid data after one reticle is developed for candidate condition 1.

[0084] In step S606, the data processing device 300 acquires a list of N candidate conditions. For example, the regenerated liquid calculation unit 522 may access the storage unit 530 and acquire a list of N candidate conditions stored in advance. Here, such candidate conditions may include conditions related to the type and amount of additives. That is, the regenerated liquid calculation unit 522 may acquire a list of N candidate conditions each having different conditions related to at least one of the type and amount of additives.

[0085] In step S610, the data processing device 300 acquires developer data. At the time point i=1, step S610 may be the same as step S420 described above. That is, the developer data acquisition unit 320 acquires developer data indicating the composition of the initial developer used in the development process from information such as a developer ingredient list. The developer data acquisition unit 320 supplies the acquired developer data to the recommended condition generation unit 330.

[0086] In step S620, the data processing device 300 predicts waste liquid data. For example, the waste liquid prediction unit 512 predicts the waste liquid data based on the development processing conditions acquired in step S600 and the developer data acquired in step S610. As an example, the waste liquid prediction unit 512 inputs the development processing conditions acquired in step S600 and the developer data acquired in step S610 into a pre-stored waste liquid prediction model and acquires the output of the waste liquid prediction model as waste liquid data. In this way, the waste liquid prediction unit 512 may predict the waste liquid data using a waste liquid prediction model that has learned the relationship between the development processing conditions and developer data and the waste liquid data through machine learning. The waste liquid prediction unit 512 supplies the predicted waste liquid data to the recovered liquid prediction unit 514.

[0087] In step S630, the data processing device 300 predicts the recovered liquid data. For example, the recovered liquid prediction unit 514 predicts the recovered liquid data based on the waste liquid treatment conditions acquired in step S600 and the waste liquid data predicted in step S620. As an example, the recovered liquid prediction unit 514 inputs the waste liquid treatment conditions acquired in step S600 and the waste liquid data predicted in step S620 into a pre-stored recovered liquid prediction model and acquires the output of the recovered liquid prediction model as recovered liquid data. In this manner, the recovered liquid prediction unit 514 may predict the recovered liquid data using a recovered liquid prediction model that has learned the relationship between the waste liquid treatment conditions, the waste liquid data, and the recovered liquid data through machine learning. The prediction unit 510 predicts the recovered liquid data indicating the composition of the recovered liquid based on, for example, the processing conditions acquired by the processing condition acquisition unit 310 and the developer data acquired by the developer data acquisition unit 320. The recovered liquid prediction unit 514 supplies the predicted recovered liquid data to the calculation unit 520.

[0088] In step S640, the data processing device 300 calculates regenerated liquid data. For example, the regenerated liquid calculation unit 522 calculates regenerated liquid data indicating the composition of the regenerated liquid when an additive is added to the recovered liquid according to candidate condition m, based on the recovered liquid data predicted in step S630. That is, at the time point m=1, the regenerated liquid calculation unit 522 calculates the composition of the regenerated liquid when an additive is added according to candidate condition 1 to the recovered liquid whose composition has been predicted in step S630. The regenerated liquid calculation unit 522 supplies the calculated regenerated liquid data to the mixed liquid calculation unit 524.

[0089] In step S650, the data processing device 300 calculates mixed solution data. For example, the mixed solution calculation unit 524 calculates mixed solution data indicating the composition of a mixed solution obtained by mixing the regenerated solution with the developer, based on the developer data acquired in step S610 and the regenerated solution data calculated in step S640. That is, the mixed solution calculation unit 524 calculates the composition of a mixed solution obtained by mixing the regenerated solution, the composition of which has been calculated in step S640, with the developer remaining in the developer tank. As an example, the mixed solution calculation unit 524 may calculate the concentrations of surfactant A, surfactant B, and pH adjuster C in the mixed solution as the mixed solution data.

[0090] In step S652, the data processing device 300 determines whether i=J. That is, the data processing device 300 determines whether the mixed liquid data has been repeatedly calculated for one candidate condition a predetermined number of times, J. If it is determined that i=J is not true (No), the data processing device 300 proceeds to step S654.

[0091] In step S654, the data processing device 300 increments i. The mixed liquid calculation unit 524 also supplies the mixed liquid data calculated in the immediately preceding step S650 to the developer data acquisition unit 320. The data processing device 300 then returns the process to step S610. In step S610 following step S654, the developer data acquisition unit 320 acquires the mixed liquid data calculated in the immediately preceding step S650 as new developer data. The recommended condition generation unit 330 then executes the mixed liquid data calculation process using this mixed liquid data as new developer data. That is, when i=2 and m=1, the recommended condition generation unit 330 calculates the mixed liquid data after developing two reticles under candidate condition 1 using the mixed liquid data after developing one reticle as new developer data. Similarly, at times i=3 and m=1, the recommended condition generating unit 330 calculates mixed liquid data after developing three reticles using the mixed liquid data after developing two reticles as new developer data for candidate condition 1. In this way, the recommended condition generating unit 330 repeatedly executes the mixed liquid data calculation process for one candidate condition using the mixed liquid data calculated immediately before as new developer data, and calculates a mixed liquid data group consisting of J sets of mixed liquid data.

[0092] On the other hand, if it is determined that i=J (Yes), the data processing device 300 proceeds to step S660. In step S660, the data processing device 300 determines whether m=N. That is, the data processing device 300 determines whether mixed liquid data groups have been calculated for all N candidate conditions. If it is determined that m=N is not true (No), the data processing device 300 proceeds to step S662.

[0093] In step S662, the data processing device 300 increments m and resets i to 1. Then, the data processing device 300 returns the process to step S610 and continues the flow. That is, when m=2, the data processing device 300 calculates a mixed liquid data group for candidate condition 2, which is the second candidate condition. At this time, in step S610, the developer data is initialized. That is, in step S610 when m=2 and i=1, the same developer data (developer solution data representing the initial developer solution composition) as that obtained when m=1 and i=1 is obtained. Similar processing is also performed when m is 3 or greater. In this way, the data processing device 300 repeatedly performs the mixed liquid data calculation processing for all N candidate conditions to calculate a mixed liquid data group.

[0094] On the other hand, if it is determined that m=N (Yes), the data processing device 300 proceeds to step S664. In step S664, the data processing device 300 records each candidate condition and the mixed liquid data group calculated for each candidate condition.

[0095] In step S670, the data processing device 300 generates recommended conditions. For example, the recommended condition generation unit 330 repeatedly executes a calculation process for mixed liquid data for at least one candidate condition for an additive, using the calculated mixed liquid data as new developer data, and generates the recommended conditions based on a mixed liquid data group consisting of the mixed liquid data from the multiple times. More specifically, the recommended condition generation unit 330 repeatedly executes a calculation process for a mixed liquid data group for each of a plurality of candidate conditions for the additive, and generates recommended conditions based on the mixed liquid data group calculated for each of the plurality of candidate conditions.

[0096] That is, by referring to each candidate condition recorded in step S664 and the mixed solution data group calculated for each candidate condition, the recommended condition generation unit 330 recognizes, for each of the N candidate conditions, fluctuation trends indicating how the concentrations of surfactant A, surfactant B, and pH adjuster C in the mixed solution change as J masters are developed. Then, the recommended condition generation unit 330 generates recommended conditions for additives to be added to the recovered solution based on the fluctuation trends in the mixed solution. For example, the recommended condition generation unit 330 calculates, for each component, an area S within which the fluctuation trends deviate from the recommended fluctuation characteristics. That is, the recommended condition generation unit 330 calculates an area SA within which the fluctuation trend of the concentration of surfactant A deviates from the recommended fluctuation characteristics, an area SB within which the fluctuation trend of the concentration of surfactant B deviates from the recommended fluctuation characteristics, and an area SC within which the fluctuation trend of the concentration of pH adjuster C deviates from the recommended fluctuation characteristics. The recommended condition generating unit 330 may then select, as the recommended condition, the candidate condition for which the sum of deviation areas SA+SB+SC is the smallest from among the N candidate conditions. At this time, if there is a more important component, the recommended condition generating unit 330 may select, as the recommended condition, the candidate condition for which the sum YASA+YBSB+YCSC (where YA to YC are weights) obtained by weighting and adding the deviation areas is the smallest.

[0097] Alternatively, the recommended condition generating unit 330 may determine the recommended conditions by searching for Pareto optimal solutions in the (SA, SB, SC) space or the (YASA, YBSB, YCSC) space. If multiple solutions are found, the recommended condition generating unit 330 may determine the recommended conditions by further filtering using cost minimization or the like, or may present multiple recommended conditions to the user and allow the user to determine the recommended conditions.

[0098] The recommended condition generating unit 330 can obtain a list of multiple predetermined candidate conditions in this manner, and generate recommended conditions by evaluating the mixed liquid data group calculated for each of the multiple candidate conditions using a predetermined evaluation method. The recommended condition generating unit 330 supplies the generated recommended conditions to the output unit 340.

[0099] In step S680, the data processing device 300 outputs the recommended additive conditions. For example, the output unit 340 displays and outputs the recommended additive conditions generated in step S670 on a monitor. At this time, the output unit 340 may output the mixed liquid data included in the mixed liquid data group calculated for at least one of the candidate conditions among the mixed liquid data groups calculated for each of the candidate conditions. For example, assume that one of the candidate conditions is selected as the recommended condition. In this case, the output unit 340 may output a part or all of the mixed liquid data group calculated for the one candidate condition. The output unit 340 may also output a part or all of the mixed liquid data group calculated for the other candidate conditions not selected as the recommended condition. In this way, the output unit 340 may output the trend of the mixed liquid data calculated multiple times. The output unit may also output the development performance corresponding to the trend of the mixed liquid data and the running cost corresponding to the recommended condition.

[0100] FIG. 7 shows an example of output from a data processing device 300 according to a modified example of this embodiment. In this figure, the horizontal axis represents the development plate number, i.e., i up to J=100. In this figure, the vertical axis represents the concentration of surfactant A in the mixed solution. This figure also shows the concentration of surfactant A in the mixed solution as an example of a mixed solution data group calculated for each of candidate conditions 1, 2, 3, and 4. Here, candidate condition 1 represents a condition in which a stock developer solution is added to the recovered solution at a ratio of 1:1. Candidate condition 2 represents a condition in which a stock developer solution is added to the recovered solution at a ratio of 0.8:1. Candidate condition 3 represents a condition in which a stock developer solution is added to the recovered solution at a ratio of 0.6:1. Candidate condition 4 represents a condition in which a stock developer solution is added to the recovered solution at a ratio of 0.4:1.

[0101] The dotted line in this figure indicates the recommended fluctuation characteristics in the concentration of surfactant A. Here, the fluctuation characteristics of the concentration of surfactant A are shown when waste liquid is not reused, that is, when a certain amount of developer in the developer tank is discarded as waste liquid and the same amount of new liquid is added each time one master is developed. Note that, for convenience of explanation, only the concentration of surfactant A is shown as an output example here, but the output unit 340 may also output mixed liquid data sets calculated for each candidate condition for surfactant B and pH adjuster C.

[0102] Then, assume that candidate condition 3 has the smallest sum of deviation areas SA+SB+SC among candidate conditions 1 to 4. In this case, the recommended condition generating unit 330 may generate 0.6 (candidate condition 3) as the recommended condition, and the output unit 340 may output this.

[0103] In this way, when generating recommended conditions, the data processing device 300 according to this modification predicts waste liquid data and predicts recovered liquid data based on the predicted waste liquid data. Then, regenerated liquid data is calculated based on the predicted recovered liquid data, and mixed liquid data is calculated based on the calculated regenerated liquid data. In this way, according to the data processing device 300 according to this modification, recommended conditions are not generated all at once, but data for generating recommended conditions is generated sequentially in accordance with the flow of the reuse system 100. As a result, according to the data processing device 300 according to this modification, the function for generating recommended conditions can be subdivided.

[0104] Furthermore, the data processing device 300 according to this modification repeatedly executes the calculation process of the mixed solution data for at least one candidate condition of the additive multiple times, using the calculated mixed solution data as new developer data, and generates recommended conditions based on a mixed solution data group consisting of the mixed solution data from multiple times. As a result, the data processing device 300 according to this modification can generate recommended conditions by taking into consideration how the composition of the mixed solution changes when the regenerated solution is returned to the developer tank and mixed with the developer each time an original is developed.

[0105] Furthermore, the data processing device 300 according to this modification repeatedly executes the calculation process of the mixed liquid data group for each of a plurality of candidate conditions for the additive, and generates recommended conditions based on the mixed liquid data group calculated for each of the plurality of candidate conditions. This makes it possible to select the candidate condition having the most favorable fluctuation characteristics of the mixed liquid composition from the plurality of candidate conditions.

[0106] Furthermore, the data processing device 300 according to this modification outputs the mixed liquid data included in the mixed liquid data group calculated for at least one of the candidate conditions out of the mixed liquid data groups calculated for each of the plurality of candidate conditions. This makes it possible for the data processing device 300 according to this modification to inform the user how the composition of the mixed liquid will change when at least one candidate condition is adopted, thereby indicating to the user the appropriateness of the recommended conditions.

[0107] In the above description, the storage unit 530 stores a list of a plurality of predetermined candidate conditions, and the recommended condition generating unit 330 performs a full search of the plurality of predetermined candidate conditions. However, this is not limiting. The recommended condition generating unit 330 may generate one or more candidate conditions using a predetermined search method. Some explanations of this will be given below.

[0108] 8 shows an example of a flow in which a data processing device 300 according to a modified example of this embodiment generates recommended conditions for additives using a genetic algorithm. In FIG. 8, the same processes as those in FIG. 6 are denoted by the same reference numerals, and explanations thereof will be omitted hereinafter except for differences.

[0109] In step S800, the data processing device 300 randomly generates N candidate conditions. Accordingly, in the subsequent steps S610 to S664, the data processing device 300 calculates and records a mixed liquid data group for each of the N randomly generated candidate conditions.

[0110] In step S802, the data processing device 300 sets i=1 and k=1, where k indicates the index of the candidate condition to be added.

[0111] In step S804, the data processing device 300 uses the N candidate conditions at that time point to add a candidate conditions by crossover and mutation.

[0112] Steps S810 to S854 are the same as steps S610 to S654, and therefore description thereof will be omitted here. That is, in steps S810 to S854, the data processing device 300 repeatedly executes the mixed liquid data calculation process for one candidate condition out of the added a candidate conditions, using the mixed liquid data calculated immediately before as new developer data, to calculate a mixed liquid data group.

[0113] In step S860, the data processing device 300 determines whether k=a. That is, the data processing device 300 determines whether mixed liquid data groups have been calculated for all of the added a candidate conditions. If it is determined that k=a is not true (No), the data processing device 300 proceeds to step S862.

[0114] In step S862, the data processing device 300 increments k and resets i to 1. Then, the data processing device 300 returns the process to step S810 and continues the flow. In this way, the data processing device 300 calculates a mixed liquid data group by repeatedly executing the mixed liquid data calculation process for all of the added a candidate conditions.

[0115] On the other hand, if it is determined that k=a (Yes), the data processing device 300 proceeds to step S864. In step S864, the data processing device 300 records each of the added a candidate conditions and the mixed liquid data group calculated for each candidate condition.

[0116] In step S870, the data processing device 300 narrows down the N+a candidate conditions to N. At this time, the data processing device 300 may narrow down the candidate conditions based on, for example, a mixed liquid data group calculated for each candidate condition (for example, based on a recommended range in the mixed liquid data).

[0117] In step S880, the data processing device 300 determines whether the termination condition is met. At this time, the data processing device 300 may determine that the termination condition is met, for example, when a relatively good candidate condition is found, or when the addition of a candidate condition is repeated a predetermined number of times. If it is determined that the termination condition is not met (No), the data processing device 300 returns the process to step S802 and continues the flow.

[0118] On the other hand, if it is determined that the termination condition is met (Yes), the data processing device 300 proceeds to step S670. Therefore, the data processing device 300 generates recommended conditions based on the mixed liquid data group calculated for the narrowed-down N candidate conditions.

[0119] In this way, the data processing device 300 according to this modification may generate recommended conditions based on the mixed liquid data group calculated for the N candidate conditions finally determined using the genetic algorithm.

[0120] 9 shows an example of a flow in which a data processing device 300 according to a modified example of this embodiment generates recommended conditions for additives using the steepest descent method. In FIG. 9, the same processes as those in FIG. 6 are denoted by the same reference numerals, and explanations thereof will be omitted hereinafter except for differences.

[0121] In step S900, the data processing device 300 acquires an initial value of the candidate condition. For example, the data processing device 300 acquires a randomly generated candidate condition as the initial value of the candidate condition.

[0122] In step S902, the data processing device 300 generates multiple candidate conditions by slightly shifting each variable from its initial value by Δ. Here, the data processing device 300 shifts each variable individually by ±Δ, so that it generates twice the number of candidate conditions as the number of variables.

[0123] Steps S910 to S954 are similar to steps S610 to S654, and therefore description thereof will be omitted here. That is, in steps S910 to S954, the data processing device 300 repeatedly executes the mixed liquid data calculation process for one candidate condition out of the plurality of candidate conditions generated, using the mixed liquid data calculated immediately before as new developing solution data, to calculate a mixed liquid data group.

[0124] In step S960, the data processing device 300 determines whether any candidate conditions for which a mixed liquid data group has not been calculated remain among the generated candidate conditions. If it is determined that any candidate conditions remain (Yes), the data processing device 300 proceeds to step S962.

[0125] In step S962, the data processing device 300 increments m and resets i to 1. Then, the data processing device 300 returns the process to step S910 and continues the flow. In this way, the data processing device 300 calculates a mixed liquid data group by repeatedly executing the mixed liquid data calculation process for all of the generated plurality of candidate conditions.

[0126] On the other hand, if it is determined that no candidate conditions remain (No), the data processing device 300 proceeds to step S970. In step S970, the data processing device 300 determines whether or not the termination condition is satisfied. In this case, the data processing device 300 may determine that the termination condition is satisfied when, for example, the slope of the evaluation function for the candidate conditions is equal to or less than a predetermined threshold, or when the generation of candidate conditions has been repeated a predetermined number of times. If it is determined that the termination condition is not satisfied (No), the data processing device 300 proceeds to step S980.

[0127] In step S980, the data processing device 300 acquires the next candidate condition. At this time, the data processing device 300 may calculate the next candidate condition based on the mixed liquid data group calculated for each candidate condition (for example, based on the recommended range in the mixed liquid data).

[0128] In step S982, the data processing device 300 generates multiple candidate conditions by slightly shifting each variable from the next candidate condition by Δ. Here, the data processing device 300 shifts each variable individually by ±Δ, so that it generates twice the number of candidate conditions as the number of variables.

[0129] In step S990, the data processing device 300 sets i = 1 and m = 1. Then, the data processing device 300 returns the process to step S910 and continues the flow. In this way, the data processing device 300 calculates a mixed liquid data group by repeatedly executing the mixed liquid data calculation process for all of the multiple candidate conditions generated from the next candidate condition.

[0130] On the other hand, if it is determined that the termination condition is met (Yes), the data processing device 300 proceeds to step S670. Therefore, the data processing device 300 generates recommended conditions based on the mixed liquid data group calculated for the multiple candidate conditions generated so far.

[0131] In this way, the data processing device 300 according to this modification may generate recommended conditions based on a group of mixed liquid data calculated for a plurality of candidate conditions generated using the steepest descent method. Note that, although the above description has shown an example in which the data processing device 300 uses the steepest descent method to generate a plurality of candidate conditions, other gradient methods such as the conjugate gradient method may also be used.

[0132] 10 shows an example of a flow in which a data processing device 300 according to a modified example of this embodiment generates recommended conditions for additives using the simulated annealing method. In FIG. 10, the same processes as those in FIG. 6 are denoted by the same reference numerals, and explanations thereof will be omitted hereinafter except for differences.

[0133] In step S1000, the data processing device 300 acquires an initial value of the candidate condition. For example, the data processing device 300 acquires a randomly generated candidate condition as the initial value of the candidate condition.

[0134] In steps S610 to S652, the data processing device 300 repeatedly executes the mixed liquid data calculation process for the initial values ​​of the candidate conditions, using the mixed liquid data calculated immediately before as new developer data, to calculate a mixed liquid data group.

[0135] In step S1002, the data processing device 300 records the initial values ​​of the candidate conditions and the mixed liquid data group calculated for the initial values ​​of the candidate conditions.

[0136] In step S1004, the data processing device 300 resets i to 1.

[0137] In step S1006, the data processing device 300 randomly generates the next candidate condition.

[0138] The processing of steps S1010 to S1054 is the same as the processing of steps S610 to S654, and therefore description thereof will be omitted here. That is, for the next candidate condition, the data processing device 300 repeatedly executes the calculation processing of mixed liquid data using the mixed liquid data calculated immediately before as new developing solution data, and calculates a mixed liquid data group.

[0139] In step S1060, data processing device 300 determines whether to accept the next candidate condition. At this time, data processing device 300 may determine to accept the next candidate condition if the next candidate condition is superior to the recorded candidate condition, for example. If it is determined not to accept the next candidate condition (No), data processing device 300 skips the next step S1070.

[0140] On the other hand, if it is determined that the request is accepted (Yes), the data processing device 300 proceeds to step S1070. In step S1070, the data processing device 300 updates the recorded candidate condition and mixed liquid data group to the next candidate condition and the mixed liquid data group calculated for the next candidate condition.

[0141] In step S1080, the data processing device 300 determines whether the termination condition is satisfied. At this time, the data processing device 300 may determine that the termination condition is satisfied when, for example, the probability that the next candidate condition will be accepted is below a predetermined threshold, or may determine that the termination condition is satisfied when the generation of the next candidate condition has been repeated a predetermined number of times. If it is determined that the termination condition is not satisfied (No), the data processing device 300 returns the process to step S1004.

[0142] On the other hand, if it is determined that the termination condition is met (Yes), the data processing device 300 proceeds to step S670. Then, the data processing device 300 determines the finally recorded candidate condition as the recommended condition.

[0143] In this way, the data processing device 300 according to this modification may determine the candidate conditions that are finally recorded as the recommended conditions using the simulated annealing method.

[0144] For example, in this manner, the recommended condition generation unit 330 can generate one or more candidate conditions using a predetermined search method, and then evaluate the mixed solution data calculated for each of the multiple candidate conditions, including the generated one or more candidate conditions, using a predetermined evaluation method to generate recommended conditions. Furthermore, such one or more candidate conditions may be generated based on the type and amount of additive. Thus, with the data processing device 300 according to this modification, it is not necessary to prepare all of the multiple candidate conditions in advance.

[0145] The above description also illustrates an example in which the data processing device 300 generates recommended conditions based on a set of mixed liquid data. This is effective when the next development process is performed in the developing device 10 using a mixed liquid obtained by mixing the developer remaining in the developer tank without being discharged with the reclaimed liquid as a new developer. However, in the developing device 10, for example, every time a predetermined number of masters are developed, all of the developer in the developer tank may be discarded as waste liquid and replaced with the reclaimed liquid. In such a case, the recommended condition generating unit 330 does not need to calculate the mixed liquid data.

[0146] That is, the calculation unit 520 does not need to include the mixed solution calculation unit 524. In this case, the regenerated solution calculation unit 522 simply supplies the calculated regenerated solution data to the developer data acquisition unit 320. The developer data acquisition unit 320 then acquires the regenerated solution data calculated by the regenerated solution calculation unit 522 as new developer data. In this case, the recommended condition generation unit 330 repeatedly executes the calculation process for regenerated solution data multiple times for at least one candidate condition for the additive, using the calculated regenerated solution data as new developer data, and generates recommended conditions based on a regenerated solution data group consisting of the multiple regenerated solution data. Furthermore, the recommended condition generation unit 330 repeatedly executes the calculation process for the regenerated solution data group for each of multiple candidate conditions for the additive, and generates recommended conditions based on the regenerated solution data group calculated for each of the multiple candidate conditions. Since this flow simply omits the process of calculating the mixed solution data, a detailed description thereof will be omitted here.

[0147] Various embodiments of the present invention may be described with reference to flowcharts and block diagrams, where the blocks may represent (1) stages of a process in which operations are performed or (2) sections of an apparatus responsible for performing the operations. Particular stages and sections may be implemented by dedicated circuitry, programmable circuitry provided with computer-readable instructions stored on a computer-readable medium, and / or a processor provided with computer-readable instructions stored on a computer-readable medium. Dedicated circuitry may include digital and / or analog hardware circuitry, and may include integrated circuits (ICs) and / or discrete circuits. Programmable circuitry may include reconfigurable hardware circuitry, including logical AND, OR, XOR, NAND, NOR, and other logical operations, flip-flops, registers, memory elements such as field programmable gate arrays (FPGAs), programmable logic arrays (PLAs), and the like.

[0148] A computer-readable medium may include any tangible device capable of storing instructions that are executed by an appropriate device, such that the computer-readable medium having instructions stored thereon comprises an article of manufacture containing instructions that can be executed to create means for performing the operations specified in the flowcharts or block diagrams. Examples of computer-readable media may include electronic, magnetic, optical, electromagnetic, and semiconductor storage media. More specific examples of computer-readable media may include floppy disks, diskettes, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), electrically erasable programmable read-only memory (EEPROM), static random access memory (SRAM), compact disc read-only memory (CD-ROM), digital versatile disc (DVD), Blu-ray (RTM) disc, memory stick, integrated circuit card, and the like.

[0149] The computer readable instructions may include either assembler instructions, Instruction Set Architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state-setting data, or source or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk®, JAVA®, C++, etc., and conventional procedural programming languages ​​such as the “C” programming language or similar programming languages.

[0150] The computer-readable instructions may be provided to a processor or programmable circuitry of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus, either locally or over a wide-area network (WAN) such as a local area network (LAN), the Internet, etc., which executes the computer-readable instructions to create means for performing the operations specified in the flowcharts or block diagrams. Examples of processors include computer processors, processing units, microprocessors, digital signal processors, controllers, microcontrollers, etc.

[0151] 11 illustrates an example of a computer 9900 in which aspects of the present invention may be embodied, in whole or in part. Programs installed on the computer 9900 may cause the computer 9900 to function as or perform operations associated with an apparatus or one or more sections of the apparatus according to embodiments of the present invention, and / or to perform a process or steps of a process according to embodiments of the present invention. Such programs may be executed by the CPU 9912 to cause the computer 9900 to perform specific operations associated with some or all of the blocks in the flowcharts and block diagrams described herein.

[0152] The computer 9900 according to this embodiment includes a CPU 9912, a RAM 9914, a graphics controller 9916, and a display device 9918, which are interconnected by a host controller 9910. The computer 9900 also includes input / output units such as a communication interface 9922, a hard disk drive 9924, a DVD drive 9926, and an IC card drive, which are connected to the host controller 9910 via an input / output controller 9920. The computer also includes legacy input / output units such as a ROM 9930 and a keyboard 9942, which are connected to the input / output controller 9920 via an input / output chip 9940.

[0153] The CPU 9912 operates according to programs stored in the ROM 9930 and RAM 9914, thereby controlling each unit. The graphics controller 9916 retrieves image data generated by the CPU 9912 into a frame buffer or the like provided in the RAM 9914 or into the graphics controller itself, and causes the image data to be displayed on the display device 9918.

[0154] The communication interface 9922 communicates with other electronic devices via a network. The hard disk drive 9924 stores programs and data used by the CPU 9912 in the computer 9900. The DVD drive 9926 reads programs or data from the DVD-ROM 9901 and provides the programs or data to the hard disk drive 9924 via the RAM 9914. The IC card drive reads programs and data from an IC card and / or writes programs and data to an IC card.

[0155] The ROM 9930 stores therein a boot program or the like that is executed by the computer 9900 upon activation, and / or programs that depend on the hardware of the computer 9900. The input / output chip 9940 may also connect various input / output units to the input / output controller 9920 via a parallel port, a serial port, a keyboard port, a mouse port, etc.

[0156] The programs are provided by a computer-readable medium such as a DVD-ROM 9901 or an IC card. The programs are read from the computer-readable medium, installed in the hard disk drive 9924, RAM 9914, or ROM 9930, which are also examples of computer-readable media, and executed by the CPU 9912. The information processing described in these programs is read by the computer 9900, and causes cooperation between the programs and the various types of hardware resources described above. An apparatus or method may be configured by implementing information manipulation or processing in accordance with the use of the computer 9900.

[0157] For example, when communication is performed between the computer 9900 and an external device, the CPU 9912 may execute a communication program loaded into the RAM 9914 and instruct the communication interface 9922 to perform communication processing based on the processing described in the communication program. Under the control of the CPU 9912, the communication interface 9922 reads transmission data stored in a transmission buffer processing area provided in the RAM 9914, the hard disk drive 9924, the DVD-ROM 9901, or a recording medium such as an IC card, and transmits the read transmission data to the network, or writes received data received from the network to a reception buffer processing area or the like provided on the recording medium.

[0158] The CPU 9912 may also cause all or a necessary portion of a file or database stored on an external recording medium such as a hard disk drive 9924, a DVD drive 9926 (DVD-ROM 9901), an IC card, etc. to be read into the RAM 9914, and perform various types of processing on the data on the RAM 9914. The CPU 9912 then writes back the processed data to the external recording medium.

[0159] Various types of information, such as various types of programs, data, tables, and databases, may be stored on the recording medium and subjected to information processing. The CPU 9912 may perform various types of processing on data read from the RAM 9914, including various types of operations, information processing, conditional judgment, conditional branching, unconditional branching, information search / replacement, etc., as described throughout this disclosure and specified by the instruction sequences of the programs, and write the results back to the RAM 9914. The CPU 9912 may also search for information in a file, database, etc. on the recording medium. For example, if multiple entries each having an attribute value of a first attribute associated with an attribute value of a second attribute are stored on the recording medium, the CPU 9912 may search for an entry that matches a condition specified by the attribute value of the first attribute from among the multiple entries, read the attribute value of the second attribute stored in the entry, and thereby obtain the attribute value of the second attribute associated with the first attribute that satisfies a predetermined condition.

[0160] The programs or software modules described above may be stored in a computer-readable medium on or near the computer 9900. A recording medium such as a hard disk or RAM provided in a server system connected to a dedicated communication network or the Internet can also be used as a computer-readable medium, thereby providing the programs to the computer 9900 via the network.

[0161] Although the present invention has been described above using embodiments, the technical scope of the present invention is not limited to the scope described in the above embodiments. It will be apparent to those skilled in the art that various modifications and improvements can be made to the above embodiments. It is clear from the claims that such modifications and improvements can also be included within the technical scope of the present invention.

[0162] It should be noted that the execution order of each process, such as operations, procedures, steps, and stages, in the devices, systems, programs, and methods shown in the claims, specifications, and drawings is not specifically stated as "before," "prior to," etc., and that the processes can be performed in any order unless the output of a previous process is used in a subsequent process. Even if the operational flow in the claims, specifications, and drawings is described using "first," "next," etc. for convenience, this does not mean that the processes must be performed in this order. [Explanation of symbols]

[0163] 10 Developing device 20 Waste liquid tank 30 Waste liquid treatment equipment 40 Recovery liquid tank 100 Reuse System 300 Data processing device 310 Processing condition acquisition unit 320 Developer data acquisition unit 330 Recommended Condition Generation Unit 340 Output Section 510 Prediction Department 512 Wastewater Prediction Department 514 Recovery liquid prediction unit 520 Calculation Unit 522 Regeneration liquid calculation section 524 Mixed liquid calculation section 530 Storage section 9900 Computer 9901 DVD-ROM 9910 Host Controller 9912 CPU 9914 RAM 9916 Graphics Controller 9918 Display Device 9920 Input / Output Controller 9922 Communication Interface 9924 Hard Disk Drive 9926 DVD drive 9930 ROM 9940 I / O chip 9942 keyboard

Claims

1. a processing condition acquisition unit that acquires processing conditions related to a developer; a developer data acquisition unit that acquires developer data indicating a composition of the developer; a recommended condition generating unit that generates recommended conditions for additives to be added to a recovered solution recovered from a waste solution discharged in a developing step, in order to reuse the recovered solution, based on the processing conditions and the developer data; an output unit that outputs the recommended conditions; Equipped with the recommended condition generating unit includes a predicting unit that predicts recovered liquid data indicating a composition of the recovered liquid based on the processing conditions and the developing liquid data. Data processing device.

2. the processing conditions include development processing conditions in the development step, 2. The data processing apparatus according to claim 1, wherein said prediction section includes a waste liquid prediction section that predicts waste liquid data indicating a composition of said waste liquid based on said development processing conditions and said developer data.

3. The data processing device according to claim 2 , wherein the waste liquid prediction unit predicts the waste liquid data using a waste liquid prediction model that learns a relationship between the development processing conditions and the developer data and the waste liquid data by machine learning.

4. The treatment conditions include waste liquid treatment conditions in a waste liquid treatment step, 4. The data processing apparatus according to claim 2, wherein the prediction unit includes a recovered liquid prediction unit that predicts the recovered liquid data based on the waste liquid treatment conditions and the waste liquid data.

5. The data processing device according to claim 4 , wherein the recovered liquid prediction unit predicts the recovered liquid data using a recovered liquid prediction model that learns a relationship between the waste liquid treatment conditions and the waste liquid data and the recovered liquid data through machine learning.

6. the recommended condition generating unit further includes a calculating unit that calculates the recommended conditions based on the collected liquid data, The data processing device according to claim 1 , wherein the calculation unit includes a regenerated liquid calculation unit that calculates regenerated liquid data indicating a composition of a regenerated liquid obtained by adding the additive to the recovered liquid based on the recovered liquid data.

7. 7. The data processing device according to claim 6, wherein the calculation unit includes a mixed solution calculation unit that calculates mixed solution data indicating a composition of a mixed solution obtained by mixing the regenerated solution with the developer based on the developer data and the regenerated solution data.

8. 8. The data processing device according to claim 7, wherein the recommended condition generating unit repeatedly executes the calculation process of the mixed solution data a plurality of times for at least one candidate condition of the additive, using the calculated mixed solution data as new developer data, and generates the recommended conditions based on a mixed solution data group consisting of the mixed solution data from a plurality of times.

9. 9. The data processing device according to claim 8, wherein the recommended condition generation unit repeatedly executes the calculation process of the mixed liquid data group for each of a plurality of candidate conditions for the additive, and generates the recommended conditions based on the mixed liquid data group calculated for each of the plurality of candidate conditions.

10. 10. The data processing device according to claim 9, wherein the output unit outputs the mixed liquid data included in the mixed liquid data group calculated for at least one candidate condition out of the mixed liquid data group calculated for each of the plurality of candidate conditions.

11. The data processing device according to claim 8 , wherein the recommended condition generating unit further includes a storage unit that stores at least one predetermined candidate condition.

12. 12. The data processing device according to claim 9, wherein the recommended condition generation unit acquires a list of a plurality of predetermined candidate conditions, and generates the recommended conditions by evaluating the mixed liquid data group calculated for each of the plurality of candidate conditions using a predetermined evaluation method.

13. 12. The data processing device according to claim 9, wherein the recommended condition generation unit generates one or more candidate conditions using a predetermined search method, and evaluates the mixed liquid data group calculated for each of a plurality of candidate conditions including the generated one or more candidate conditions using a predetermined evaluation method to generate the recommended conditions.

14. The data processing device according to claim 13 , wherein the one or more candidate conditions are generated based on the type and amount of the additive.

15. 7. The data processing device according to claim 6, wherein the recommended condition generation unit repeatedly performs the calculation process of the regenerated liquid data for at least one candidate condition of the additive, using the calculated regenerated liquid data as new developer data, and generates the recommended conditions based on a regenerated liquid data group consisting of the regenerated liquid data from multiple times.

16. 16. The data processing device according to claim 15, wherein the recommended condition generation unit repeatedly executes the calculation process of the regenerated liquid data group for each of a plurality of candidate conditions for the additive, and generates the recommended conditions based on the regenerated liquid data group calculated for each of the plurality of candidate conditions.

17. The data processing device according to claim 8 , wherein the candidate conditions include conditions related to the type and amount of the additive.

18. The data processing device according to claim 1 , wherein the recommended conditions include conditions regarding the type and amount of the additive.

19. The data processing device according to claim 1 , wherein the developer is a developer used for developing a resin original.

20. A master developing device comprising a data processing device according to any one of claims 1 to 19.

21. Obtaining processing conditions for a developer; acquiring developer data indicative of a composition of the developer; generating recommended conditions for additives to be added to a recovered solution recovered from a waste solution discharged in a development step, in order to reuse the recovered solution, based on the processing conditions and the developer data; outputting the recommended conditions; Equipped with generating the recommended conditions includes predicting recovered liquid data indicating a composition of the recovered liquid based on the processing conditions and the developer data; Data processing methods.

22. When executed by a computer, the computer is a processing condition acquisition unit that acquires processing conditions related to a developer; a developer data acquisition unit that acquires developer data indicating a composition of the developer; a recommended condition generating unit that generates recommended conditions for additives to be added to a recovered solution recovered from a waste solution discharged in a developing step, in order to reuse the recovered solution, based on the processing conditions and the developer data; an output unit that outputs the recommended conditions; and make it work, the recommended condition generating unit includes a predicting unit that predicts recovered liquid data indicating a composition of the recovered liquid based on the processing conditions and the developing liquid data. Data processing program.

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