CN111310911A - Apparatus, method, medium, photosensitive resin composition, and method for producing a laminate - Google Patents

Abstract

The invention provides a device, a method, a medium, a photosensitive resin composition and a method for manufacturing a laminated body, aiming at solving the problem of poor efficiency of composition obtained by repeated tests. The device provided by the invention comprises: a composition acquisition unit that acquires composition data indicating a composition of the photosensitive resin composition; a characteristic acquisition unit that acquires characteristic data indicating characteristics of the photosensitive resin composition; and a learning processing section that performs learning processing of a model for outputting recommended composition data representing a composition of the recommended photosensitive resin composition in response to input of target characteristic data representing a characteristic of the photosensitive resin composition as a target, using learning data including the acquired composition data and characteristic data.

Description

Apparatus, method, medium, photosensitive resin composition, and method for producing a laminate

technical field

The present invention relates to an apparatus, a method, a program, a method for producing a photosensitive resin composition, and a method for producing a photosensitive resin laminate.

Background technique

Heretofore, in the case of producing a photosensitive resin composition, in order to obtain the desired properties, a preferred composition has been found by trial and error by a skilled operator (for example, refer to Patent Document 1).

Patent Document 1: Japanese Patent Laid-Open No. 2017-114080

SUMMARY OF THE INVENTION

Invention to solve problem

However, finding the composition by trial and error is inefficient.

solution to the problem

In order to solve the above-mentioned problems, a first aspect of the present invention provides an apparatus. The apparatus may include a composition acquisition unit that acquires composition data representing the composition of the photosensitive resin composition. The apparatus may include a property acquisition unit that acquires property data representing properties of the photosensitive resin composition. The apparatus may be provided with a learning processing section that executes learning processing of a model for expressing a target photosensitive resin combination in response to an input using the learning data including the acquired composition data and characteristic data The target characteristic data of the characteristics of the material is output, and the recommended composition data indicating the composition of the recommended photosensitive resin composition is output.

In a second aspect of the present invention, an apparatus is provided. The apparatus may include a target characteristic acquisition unit that acquires target characteristic data indicating the characteristics of the target photosensitive resin composition. The device may include a target characteristic supply unit that supplies the target characteristic data acquired by the target characteristic acquisition unit to a model for outputting a photosensitive resin combination indicative of a recommended recommendation in response to the input of the target characteristic data Recommended composition data for the composition of the material. The apparatus may be provided with a recommended composition acquisition section that acquires recommended composition data output by the model in response to supplying the target characteristic data to the model.

In a third aspect of the present invention, a method is provided. The method may include a composition acquisition stage of acquiring composition data representing the composition of the photosensitive resin composition. The method may include a property acquisition stage of acquiring property data representing properties of the photosensitive resin composition. The method may include a learning process stage of performing a learning process of a model for responding to an input of a model representing the target photosensitive resin composition using the learning data including the acquired composition data and characteristic data. The target characteristic data of the characteristic is output, and the recommended composition data representing the composition of the recommended photosensitive resin composition is output.

In a fourth aspect of the present invention, a method is provided. The method may include a target characteristic acquisition stage of acquiring target characteristic data representing the characteristics of the target photosensitive resin composition. The method may include a target characteristic supply stage of supplying the target characteristic data acquired through the target characteristic acquisition stage to a model for outputting a composition representing the recommended photosensitive resin composition in response to the input of the target characteristic data recommended composition data. The method may include a recommendation composition acquisition stage of acquiring recommendation composition data output by the model in response to supplying the model with target characteristic data.

In the 5th aspect of this invention, the manufacturing method of the photosensitive resin composition is provided. The production method may include a step of determining the composition of the photosensitive resin composition based on the recommended composition data acquired by the method of the fourth aspect. The manufacturing method may include a stage of mixing raw materials for generating the photosensitive resin composition.

In a sixth aspect of the present invention, a method for producing a photosensitive resin laminate is provided. The production method may include a step of applying, on a base film, the fluid of the photosensitive resin composition obtained by mixing in the production method of the fifth aspect. The manufacturing method may include a step of providing a cover film on the applied photosensitive resin composition.

In a seventh aspect of the present invention, a program is provided. The program can cause the computer to function as a composition acquisition unit that acquires composition data representing the composition of the photosensitive resin composition. The program can cause the computer to function as a property acquisition unit that acquires property data representing the properties of the photosensitive resin composition. The program can cause the computer to function as a learning processing section that executes learning processing of a model for responding to an input of an object representing a target using the learning data including the acquired composition data and characteristic data. The target characteristic data of the characteristic of the photosensitive resin composition is output, and the recommended composition data which shows the composition of the recommended photosensitive resin composition are output.

An eighth aspect of the present invention provides a program. The program can cause the computer to function as a target characteristic acquisition unit that acquires target characteristic data representing the characteristics of the target photosensitive resin composition. The program may cause the computer to function as a target characteristic supply unit for supplying the target characteristic data acquired by the target characteristic acquisition unit to a model for outputting, in response to the input of the target characteristic data, the target characteristic data representing the recommendation. Recommended composition data for the composition of the photosensitive resin composition. The program can cause the computer to function as a recommended composition acquisition section that acquires recommended composition data output by the model in response to supplying the target characteristic data to the model.

In a ninth aspect of the present invention, a storage medium is provided. The storage medium stores a program that, when executed by a computer, realizes the following stages: a composition acquisition stage, which acquires composition data representing the composition of the photosensitive resin composition; a property acquisition stage, which acquires property data representing the properties of the photosensitive resin composition; and The learning processing stage uses the learning data including the acquired composition data and the characteristic data to execute the learning processing of the model for responding to the input of the target characteristic data representing the characteristic of the target photosensitive resin composition , to output recommended composition data representing the composition of the recommended photosensitive resin composition.

In a tenth aspect of the present invention, a storage medium is provided. The storage medium stores a program that, when executed by a computer, realizes the following stages: a target property acquisition stage for acquiring target property data representing the properties of the target photosensitive resin composition; a target property supply stage for supplying to a model through the target property acquisition stage The acquired target characteristic data, the model for outputting recommended composition data representing the recommended composition of the photosensitive resin composition in response to the input of the target characteristic data; and the recommended composition acquisition stage in response to supplying the model to the The target feature data and the recommendations output by the model make up the data.

In addition, the above-mentioned summary of the invention does not enumerate all the essential characteristics of this invention. In addition, subcombinations of these feature groups can also constitute inventions otherwise.

Description of drawings

FIG. 1 shows a system 1 according to this embodiment.

FIG. 2 shows the learning method of the model 35 .

FIG. 3 shows the manufacturing method of the photosensitive resin composition using the mold 35 .

FIG. 4 shows a method of using the dry film 100 .

Figure 5 shows an example of a computer 2200 that may embody aspects of the invention in whole or in part.

Description of reference numerals

1: system; 2: manufacturing device; 3: learning processing device; 20: mixing unit; 21: generating unit; 32: composition acquiring unit; 33: characteristic acquiring unit; 34: learning processing unit; 35: model; 36: target Characteristic acquisition part; 37: Target characteristic supply part; 38: Recommended composition acquisition part; 39: Control part; 100: Dry film; 101: Cover film; 200: Substrate; 201: Insulating plate; line; 300: Mask; 2200: Computer; 2201: DVD-ROM; 2210: Main Controller; 2212: CPU; 2214: RAM; 2216: Graphics Controller; 2218: Display Device; 2220: Input/Output Controller; 2222: Communication interface; 2224: Hard disk drive; 2226: DVD-ROM drive; 2230: ROM; 2240: Input/output chip; 2242: Keyboard.

Detailed ways

Hereinafter, the present invention will be described based on the embodiments of the invention, but the following embodiments do not limit the invention according to the claims. In addition, not all combinations of the features described in the embodiments are necessarily required for the solution of the invention.

[1. System]

FIG. 1 shows a system 1 according to this embodiment. The system 1 includes a photosensitive resin composition manufacturing apparatus 2 and a learning processing apparatus 3 . The photosensitive resin composition may be photocurable or photosoluble (photodecomposable, photosoftening). The photosensitive resin composition may be in the form of a film, and in this case, the photosensitive resin composition is also referred to as a dry film. In the present embodiment, as an example, the photosensitive resin composition can be formed into a roll shape by laminating with a base film (carrier film, support film) and a cover film and then winding for easy storage and transportation, and in this case, The roll-shaped laminate is also referred to as a photosensitive resin laminate (dry film roll, dry film resist roll).

[1-1. Manufacturing equipment]

The manufacturing apparatus 2 is used for manufacturing the photosensitive resin composition. For example, the manufacturing apparatus 2 may include: a mixing unit 20 that mixes raw materials of the photosensitive resin composition to form a fluid of the photosensitive resin composition; and a generating unit 21 that generates a photosensitive resin composition from the fluid of the photosensitive resin composition resin laminate. The generation part 21 may have a filter part for filtering the raw material, a coating part for applying the fluid of the photosensitive resin composition on the base film to form the photosensitive resin composition into a film, and On the photosensitive resin composition of cloth, a cover film is provided (as an example, it is bonded), and the roll part etc. which wind up a laminated body are provided.

[1-2. Learning processing device]

The learning processing device 3 is an example of a device. The learning processing device 3 performs learning processing of the model 35 , and includes a composition acquisition unit 32 , a characteristic acquisition unit 33 , a learning processing unit 34 , and a model 35 . The learning processing device 3 also operates the model 35 , and includes a target characteristic acquisition unit 36 , a target characteristic supply unit 37 , a recommended composition acquisition unit 38 , and a control unit 39 .

[1-2-1. Composition acquisition section]

The composition acquisition unit 32 acquires composition data indicating the composition of the photosensitive resin composition. The composition acquisition unit 32 can acquire composition data of the photosensitive resin composition manufactured by the manufacturing apparatus 2 . The composition acquisition unit 32 may supply the acquired composition data to the learning processing unit 34 .

[1-2-2. Property Acquisition Department]

The characteristic acquisition unit 33 acquires characteristic data representing the characteristics of the photosensitive resin composition. The characteristic acquisition unit 33 can acquire characteristic data of the photosensitive resin composition manufactured by the manufacturing apparatus 2 . The characteristic acquisition unit 33 may supply the acquired characteristic data to the learning processing unit 34 . In the present embodiment, as an example, the characteristic acquisition unit 33 acquires characteristic data from the operator.

[1-2-3. Learning Processing Department]

The learning processing unit 34 executes the learning processing of the model 35 using the input learning data. The learning data may include composition data from the composition acquisition section 32 and characteristic data from the characteristic acquisition section 33 .

[1-2-4. Model]

The model 35 is for outputting recommended composition data representing the recommended composition of the photosensitive resin composition in response to input of target characteristic data representing the characteristics of the target photosensitive resin composition. Furthermore, the model 35 may be stored on a server external to the learning processing device 3 .

[1-2-5. Target Characteristic Acquisition Department]

The target characteristic acquisition unit 36 acquires target characteristic data indicating the characteristics of the target photosensitive resin composition. In the present embodiment, as an example, the target characteristic acquisition unit 36 acquires target characteristic data from the operator. The target characteristic acquisition unit 36 may supply the acquired target characteristic data to the target characteristic supply unit 37 .

[1-2-6. Target Characteristic Supply Department]

The target characteristic supply unit 37 supplies the target characteristic data from the target characteristic acquisition unit 36 to the model 35 .

[1-2-7. Recommended composition acquisition department]

The recommended composition acquisition section 38 acquires recommended composition data output by the model 35 in response to the supply of the target characteristic data to the model 35 . The recommended composition acquisition unit 38 may supply the acquired recommended composition data to the control unit 39 . The recommended composition acquisition unit 38 may output the recommended composition data to the outside of the learning processing device 3 .

[1-2-8. Control Department]

The control unit 39 supplies the control condition data to the manufacturing apparatus 2, and causes the manufacturing apparatus 2 to operate under the control conditions indicated by the control condition data. For example, by supplying the recommended composition data to the manufacturing apparatus 2, the control unit 39 may cause the mixing unit 20 of the manufacturing apparatus 2 to mix the raw materials in the composition indicated by the recommended composition data to manufacture the photosensitive resin composition.

According to the above system 1, the learning process of the model 35 for outputting the recommended composition data in response to the input of the target characteristic data is performed using the learning data including the composition data and the characteristic data. Then, by inputting the target properties to the model 35, the composition of the photosensitive resin composition for generating the target properties is output. Therefore, the composition of the photosensitive resin composition for preparing the target characteristic can be obtained without repeated tests by a skilled worker.

[2. Composition data]

The composition represented by the composition data may be the presence or absence of a raw material that can generate the photosensitive resin composition, or the compound contained in the raw material (for example, the name or structural formula of a specific compound contained in the raw material represented by a generic name). The composition represented by the composition data may be the content ratio of the compound contained in the raw material, and the content ratio may be 0. The raw materials that can generate the photosensitive resin composition may include alkali-soluble polymers, ethylenically unsaturated bond-containing compounds, photopolymerization initiators, groups having acid-decomposable groups (as an example, groups that are deprotected by acid) ) and at least one of a phenolic resin, a photoacid generator, a dissolution inhibitor, a sensitizer, a polymerization inhibitor, an adhesive and a plasticizer.

Among them, the alkali-soluble polymer may be a binder polymer in a photocurable resin composition, and may be, for example, a carboxyl group-containing polymer (as an example, a polymer represented by the following chemical formula (1)). The ethylenically unsaturated bond-containing compound may be a monomer in the photocurable resin composition, and may be a monomer represented by the following chemical formula (2) as an example. A photopolymerization initiator can bond between monomers by exposure in a photocurable resin composition. The resin and phenolic resin having a repeating unit containing an acid-decomposable group may be a polymer in a photo-soluble resin composition, and may be dissolved and decomposed by an acid. The photoacid generator generates an acid by exposure to light in a photosoluble resin composition. Dissolution inhibitors are also called anti-solvents, and inhibit the dissolution of components in an alkaline aqueous solution. As an example, a photosensitizer may be sufficient as a sensitizer, but other kinds of sensitizers, such as N-phenylglycine, may be sufficient. The polymerization inhibitor may be a substance that inhibits the polymerization reaction under the influence of light and heat. The adhesive is a substance that improves the adhesiveness of the photosensitive resin composition on the surface of the substrate. A plasticizer is added for the purpose of imparting flexibility to the photosensitive resin composition or facilitating processing.

[Chemical formula 1]

[Chemical formula 2]

[3. Characteristic data]

The properties represented by the property data may be, for example, the film thickness of the photosensitive resin composition, the shortest development time, the sensitivity to light, the transmittance, the resolution, the minimum resist line width, the adhesion to the substrate, and the developer. At least one of foamability, developer cohesiveness, edge-melting properties, flexibility of cured film, adhesion to base film or cover film, hue stability, peeling time, peeling sheet size, and hiding properties.

Here, the development of the photosensitive resin composition may refer to exposing the dry film of the photosensitive resin composition to harden or solubilize the photosensitive resin composition in the exposed area, and then exposing the photosensitive resin composition in the exposed area or the non-exposed area to light. The resin composition is removed, and a negative or positive image corresponding to the exposed area is revealed. When the photosensitive resin composition is photocurable (also referred to as a negative type), the shortest development time is also referred to as a break point, and represents the shortest time in which the photosensitive resin composition is developed. As an example, the shortest developing time may be the time until all the photosensitive resin compositions are removed without exposing the photosensitive resin composition laminated on the substrate and developing by spraying of an alkaline solution. The shortest development time may be a time when parameters that may affect the development time, such as the number of spray ports and spray pressure, are set to fixed values.

When the photosensitive resin composition is photocurable, the sensitivity to light is also referred to as the minimum curing exposure amount, and represents the minimum exposure amount that can form an image corresponding to the exposure area. For example, regarding the sensitivity to light, when the photosensitive resin composition is photocurable, it may be generated on the substrate when the dry film of the photosensitive resin composition laminated on the substrate is exposed and developed. The minimum exposure amount (mJ/cm ² ) of the photosensitive resin composition that is cured and remains. As an example, the sensitivity to light can be calculated from the lowest transmittance after curing of the photosensitive resin composition by exposing the photosensitive resin composition using a mask whose transmittance is gradually different.

Transmittance, also known as transmittance, refers to the transmittance of light. The wavelength of the transmitted light may be a wavelength for curing or solubilizing the photosensitive resin composition.

Resolution represents the density of a developable image. As an example, the resolution may not occur when a plurality of thin resist lines are formed while changing the exposure width by exposing and developing the dry film of the photosensitive resin composition laminated on the copper substrate. The minimum resist width for collapse of the resist or deformation due to exposure to exposure. The film thickness of the resist may be arbitrary.

As an example, the adhesiveness with the substrate may be formed by exposing and developing the dry film of the photosensitive resin composition laminated on the copper substrate, and it may not be possible when a plurality of resists having different thicknesses are formed. Minimum resist width at which resist collapse, stripping occurs.

The developing solution foaming property shows the foaming property when developing the photosensitive resin composition using a developing solution. As a developer solution foamability, the value measured by various well-known methods can be used.

The developer cohesiveness shows cohesion when the photosensitive resin composition is developed using a developer. As the developer cohesiveness, values measured by various known methods can be used.

The edge-melting property represents the amount of the photosensitive resin composition extruded from the end face of the dry film roll to the outside due to the winding pressure during storage of the dry film roll. The smaller the edge-melting property, the longer the shelf life of the dry film, the more preferred. As the edge melting properties, values measured by various known methods can be used.

Cured film flexibility means the flexibility of the photocurable photosensitive resin composition after development. For example, the flexibility of the cured film may be such that no resist is generated when a dry film of the photosensitive resin composition laminated on a flexible substrate is exposed and developed and then wound around a plurality of cylinders with different diameters The smallest diameter of the rupture. As an example, the flexibility of the cured film can be measured using a mandrel bending test apparatus.

Adhesion to the base film or cover film means the adhesion of the dry film to the base film or cover film. For example, the tackiness may be the force required to peel off the dry film from the base film or cover film using a universal tensile machine (Japanese: Tensilon apparatus). When the viscosity is too large or too small, the usability of the dry film roll will be deteriorated, so it is preferably within an appropriate range.

The peeling time shows the peelability when peeling the photosensitive resin composition from the board|substrate. For example, the peeling time may be the time until the photosensitive resin composition is peeled off from the substrate when immersed in an alkaline peeling solution in order to develop the dry film after being laminated on the substrate and exposed to light. The peeling time is preferably short.

Hue stability means the stability of the color of the photosensitive resin composition. As hue stability, the value measured by various well-known methods can be used.

The peeling sheet size represents the size of the photosensitive resin composition (also referred to as a peeling sheet) peeled from the substrate. The size of the peeling sheet may be the size of the peeling sheet that has been peeled off from the substrate and finely divided by a water-washing spray. The release sheet is preferably small.

The hiding property represents the cracking rate in the case where the cover hole of the substrate is covered with a dry film. For example, the hiding property may be the number of cracked cover holes when a dry film is laminated on a substrate having cover holes having a diameter of 1 mm to 10 mm and exposed to development. The smaller the hiding property, the more preferable.

[3. Work]

[3-1. Learning processing of the model]

FIG. 2 shows the learning method of the model 35 . The system 1 performs the learning of the model 35 through the processing of steps S11 to S15.

In step S11, the control part 39 makes the manufacturing apparatus 2 manufacture the photosensitive resin composition. When the process of steps S11 to S15 is performed a plurality of times, the control unit 39 may change the composition of the photosensitive resin composition each time to manufacture.

In step S13, the composition acquisition part 32 and the characteristic acquisition part 33 acquire the composition data and characteristic data of the photosensitive resin composition manufactured by the process of step S11, respectively. The composition acquisition unit 32 may acquire composition data from at least one of the operator and the manufacturing apparatus 2, or may acquire composition data from another external apparatus (not shown). The characteristic acquisition unit 33 may acquire characteristic data from at least one of an operator and a measurement device (not shown) for measuring characteristics, or may acquire characteristic data from another external device (not shown). The measurement apparatus may be arranged outside the manufacturing apparatus 2 or inside. Characteristics can be acquired at a plurality of positions of the photosensitive resin composition. In addition, the acquisition process of the composition data in the process of step S13 may be performed before the process of step S11.

In step S15, the learning processing unit 34 executes the learning processing of the model 35 using the learning data including the acquired composition data and characteristic data. In addition, in the present embodiment, the model 35 is, as an example, a neural network of a recurrent (recurrent) type or a time-delay type. other machine learning algorithms. For example, the model 35 may include nodes corresponding to each element of the learning data in the input layer, and nodes corresponding to each raw material of the recommended composition in the output layer. The number of nodes corresponding to one element of the learning data in the input layer may be one or multiple. There can be an intermediate layer (hidden layer) containing one or more nodes between the input layer and the output layer. The learning processing unit 34 can perform the learning processing by adjusting the weights of the edges connecting the nodes and the offset values of the output nodes.

[3-2. Model application processing]

FIG. 3 shows the manufacturing method of the photosensitive resin composition using the mold 35 . The system 1 manufactures the photosensitive resin composition by the process of steps S21-S27.

In step S21 , the target characteristic acquisition unit 36 acquires target characteristic data of the target photosensitive resin composition. The target characteristic data may include, for at least one characteristic, a target range for that characteristic. The target range can be defined by at least one of an upper limit value and a lower limit value.

In step S23 , the target characteristic supply unit 37 supplies the acquired target characteristic data to the model 35 .

In step S25 , the recommended composition acquisition section 38 acquires recommended composition data output by the model 35 in response to the supply of the target characteristic data to the model 35 . The recommended composition acquisition unit 38 may perform bagging (also referred to as guided aggregation) of the target characteristic data to generate a plurality of local target characteristic data obtained by sampling only a part of the target characteristic. The recommendation composition acquisition unit 38 may respectively supply a plurality of local target characteristic data to the model 35 to acquire a plurality of recommended composition data. The recommended composition acquisition unit 38 can acquire a set of recommended composition data representing a composition that may achieve all the characteristics in the target characteristic data and the achievement probability of some characteristics of the target by aggregating the acquired pieces of recommended composition data. The achievement probability of the goal may be a value obtained by multiplying the achievement probabilities of all the characteristics. As an example, the achievement probability of all the characteristics may be weighted and multiplied by the weighting coefficient of each characteristic. The recommended composition acquisition unit 38 can acquire a set of the recommended composition data and the probability distribution of at least one characteristic value by using the target characteristic data including the target range of the characteristic.

In addition, the recommended composition acquisition unit 38 may use a genetic algorithm and repeatedly use the model 35 to acquire recommended composition data. For example, after supplying the target characteristic data (or local target characteristic data) to the model 35 to obtain a plurality of recommended composition data, the recommended composition acquisition unit 38 may perform any one of selection, crossover, and mutation on the recommended composition data to obtain a plurality of recommended composition data. Generate multiple recommendation composition data for the next generation. As an example, the crossover may mean that the content ratio of at least a part of the raw material is exchanged between two pieces of recommended composition data. The mutation may mean changing the content ratio of at least a part of the raw materials in the recommended composition data. The recommended composition acquisition unit 38 may supply the generated next-generation recommended composition data to the model 35 to acquire a plurality of characteristic data, and extract recommended composition data of which all characteristics satisfy the target among the plurality of recommended composition data supplied to the model 35 . Afterwards, the recommended composition acquisition unit 38 may perform any one of selection, crossover and mutation on the extracted recommended composition data to generate a plurality of recommended composition data of the next generation, and extract the characteristic data in the recommended composition data that meets the target. The composition data is recommended, and the above-mentioned processing is repeated, thereby obtaining the recommended composition data in which the achievement probability of the target exceeds the standard probability.

In step S27, the control part 39 supplies the recommended composition data to the manufacturing apparatus 2, and makes the manufacturing apparatus 2 manufacture the photosensitive resin composition with the composition shown by the recommended composition data. When the control unit 39 acquires a plurality of recommended composition data from the recommended composition acquisition unit 38 , the control unit 39 may display the recommended composition data and supply any one of the recommended composition data selected by the operator to the manufacturing apparatus 2 . In this case, the control unit 39 displays the acquired pieces of recommendation composition data in descending order of the achievement probability of the goal. In addition, when the operator selects any one characteristic, the control unit 39 may display a plurality of pieces of recommended composition data in descending order of the achievement probability of the target of the characteristic.

In the manufacturing apparatus 2, the composition of the photosensitive resin composition to be manufactured is determined based on the recommended composition data acquired from the learning processing apparatus 1, and the raw materials (components) corresponding to the determined composition can be mixed to manufacture the photosensitive resin. combination. As an example, a solvent for dissolving other components or a dispersion medium for dispersing other components may be contained in the components of the photosensitive resin composition, and the mixing unit 20 of the manufacturing apparatus 2 may mix the components to adjust the fluid of the photosensitive resin composition. (coating liquid). In addition, the manufacturing apparatus 2 can apply the fluid of the mixed photosensitive resin composition on the base film. The manufacturing apparatus 2 can remove the solvent and the dispersion medium by drying after coating the photosensitive resin composition. Thereby, the laminated body of a base film and a film-like photosensitive resin composition is manufactured. The manufacturing apparatus 2 can manufacture the photosensitive resin laminated body by providing the cover film for protecting the photosensitive resin composition on the photosensitive resin composition. In addition, at least one of the determination of the composition based on the recommended composition data, the mixing of the components, and the manufacture of the laminated body may be performed by an operator without using the manufacturing apparatus 2 . In addition, after manufacturing the photosensitive resin composition, the relearning of the model 35 can be performed by the process of the above-mentioned steps S11-S15.

[4. How to use the photosensitive resin composition]

FIG. 4 shows the usage method of the dry film 100 of the photosensitive resin composition. In this embodiment, as an example, the photosensitive resin composition is used to form metal wiring on a printed wiring board (for example, a mother board).

First, as shown in the parts (1) to (2), the photocurable dry film 100 is pasted on the substrate 200 . In the present embodiment, as an example, the dry film 100 and the cover film 101 are attached to the substrate 200 while peeling off the base film on the dry film roll. In addition, the substrate 200 may be a substrate in which the surface of the insulating plate 201 is covered with the copper foil 202 . The cover film 101 may be made of polyethylene terephthalate (PET).

Next, as shown in part (3), the dry film 100 is exposed to light through the mask 300 . The light to be irradiated may be visible light or light having a wavelength of 450 nm or less (an example, ultraviolet light). Thereby, the dry film 100 in the exposed area is cured.

Next, as shown in section (4), the uncured dry film 100 is removed and developed. For example, the uncured dry film 100 is dissolved in a weakly alkaline aqueous solution (as an example, a Na ₂ CO ₃ aqueous solution).

Next, as shown in part (5), etching is performed using the remaining dry film 100 as a resist. Thereby, the copper foil 202 of the part not covered with the resist is removed, and the copper wire 203 is formed from the remaining copper foil 202 . For etching, an acidic aqueous solution (as an example, a CuCl ₂ aqueous solution) can be used.

Then, as shown in part (6), the remaining dry film 100, that is, the photosensitive resin composition after curing is removed. To remove the remaining dry film 100, for example, at least the contact portion of the dry film 100 and the copper wire 203 may be immersed in a strong alkaline aqueous solution (for example, a NaOH aqueous solution), and then the insulating plate 201 and the copper wire 203 may be removed from the insulating plate 201 and the copper wire 203. The dry film 100 is peeled off.

In addition, in the above-described embodiment, the learning processing device 3 has been described as including the composition acquisition unit 32 , the characteristic acquisition unit 33 , and the learning processing unit 34 , but may not include at least one of them. In this case, the learning processing device 3 may not perform the learning processing of the model 35, but may use the model 35 that has completed the learning to perform the acquisition of the recommendation composition data. In addition, the learning processing device 3 has been described as having the target characteristic acquiring unit 36 , the target characteristic supplying unit 37 , and the recommended composition acquiring unit 38 , but may not include at least one of them. In this case, the learning processing device 3 may perform the learning processing of the model 35 without performing the processing of acquiring the recommended composition data using the model 35 . In addition, although the learning processing apparatus 3 was demonstrated to have the control part 39 and the model 35, it is not necessary to have at least one of them. For example, the control unit 39 and the model 35 may be provided in an external device of the learning processing device 3 (for example, a control device of the manufacturing device 2).

Moreover, although the photosensitive resin composition was demonstrated as being used as a resist in the formation process of the metal wiring on a printed wiring board, it can also be used as a resist in the manufacturing process of a semiconductor.

Additionally, various embodiments of the present invention may be described with reference to flowcharts and block diagrams, where blocks may represent stages of a process for performing operations (1) or portions of apparatuses having the functionality to perform operations (2). Particular stages and portions may be implemented by special purpose circuits, programmable circuits supplied with computer readable instructions stored on a computer readable medium, and/or processors supplied with computer readable instructions stored on a computer readable medium. accomplish. Application-specific circuits may include digital and/or analog hardware circuits, and may include integrated circuits (ICs) and/or discrete circuits. Programmable circuits may include reconfigurable hardware circuits including, for example, logical AND (AND), logical OR (OR), logical exclusive OR (XOR), logical AND NOT (NAND), logical OR NOT (NOR), and Other logic operations, flip-flops, registers, field programmable gate arrays (FPGAs), programmable logic arrays (PLAs), etc. such storage elements, and the like.

The computer-readable medium may include any tangible device capable of storing instructions for execution by a suitable device, with the result that the computer-readable medium having the instructions stored thereon is provided with the product of Instructions that can be executed by the unit of the operation specified in the block diagram. As examples of the computer-readable medium, electronic storage media, magnetic storage media, optical storage media, electromagnetic storage media, semiconductor storage media, and the like may be included. As more specific examples of the computer-readable medium, Floppy (registered trademark) floppy disk, magnetic disk, hard disk, 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 (Memory stick), integrated circuit card, etc.

Computer readable instructions may include any of source code and object code described in any combination of one or more programming languages, including assembly instructions, Instruction Set Architecture (ISA) Instructions, machine instructions, machine-related instructions, microcode, firmware instructions, state setting data, or object-oriented programming languages such as Smalltalk, JAVA (registered trademark), C++, and the "C" programming language or equivalent This existing procedural programming language.

Computer readable instructions may be provided to the processor or programmable circuitry of a general purpose computer, special purpose computer, or other programmable data processing device via a local or local area network (LAN), the Internet, etc. such as a wide area network (WAN), in order to create a Computer readable instructions are executed by means of performing the operations specified in the flowchart or block diagram. Examples of processors include computer processors, processing units, microprocessors, digital signal processors, controllers, microcontrollers, and the like.

Figure 5 shows an example of a computer 2200 that may embody aspects of the invention in whole or in part. A program installed in the computer 2200 can cause the computer 2200 to function as an operation associated with the apparatus according to the embodiment of the present invention or one or more parts of the apparatus, or can cause the computer 2200 to perform the operation or the 1 or more parts, and/or enable computer 2200 to perform a process or stage of a process to which an embodiment of the invention relates. Such programs are executed by the CPU 2212 in order to cause the computer 2200 to perform specific operations associated with some or all of the blocks of the flowcharts and block diagrams described in this specification.

The computer 2200 of the present embodiment includes a CPU 2212 , a RAM 2214 , a graphics controller 2216 , and a display device 2218 , which are connected to each other through a main controller 2210 . The computer 2200 also includes an input/output unit such as a communication interface 2222 , a hard disk drive 2224 , a DVD-ROM drive 2226 , and an IC card drive, which are connected to the main controller 2210 via the input/output controller 2220 . The computer also includes a conventional input/output unit such as a ROM 2230 and a keyboard 2242, which are connected to the input/output controller 2220 via the input/output chip 2240.

The CPU 2212 operates according to the programs stored in the ROM 2230 and the RAM 2214, thereby controlling each unit. Graphics controller 2216 acquires image data generated by CPU 2212 in a frame buffer or the like provided to RAM 2214 or itself, and the image data is displayed on display device 2218.

The communication interface 2222 communicates with other electronic devices via a network. The hard drive 2224 holds programs and data used by the CPU 2212 within the computer 2200. The DVD-ROM drive 2226 reads programs or data from the DVD-ROM 2201 and supplies the programs or data to the hard disk drive 2224 via the RAM 2214. The IC card driver reads programs and data from and/or writes programs and data into the IC card.

The ROM 2230 stores therein a boot program and the like executed by the computer 2200 upon activation, and/or a program dependent on the hardware of the computer 2200 . The input/output chip 2240 may also be used to connect various input/output units to the input/output controller 2220 via parallel ports, serial ports, keyboard ports, mouse ports, and the like.

The program is provided by a computer-readable medium such as a DVD-ROM 2201 or an IC card. The program is read from a computer-readable medium, installed on the hard disk drive 2224, RAM 2214, or ROM 2230, which are also examples of the computer-readable medium, and executed by the CPU 2212. The information processing described in these programs is read into the computer 2200, and cooperation between the programs and the various types of hardware resources described above is realized. The apparatus or method may be constituted by implementing operations or processing of information with the use of the computer 2200 .

For example, in the case of performing communication between the computer 2200 and an external device, the CPU 2212 may execute a communication program loaded into the RAM 2214, and instruct the communication interface 2222 to perform communication processing based on the processing described in the communication program. The communication interface 2222 reads the transmission data stored in the transmission buffer processing area provided in the recording medium such as the RAM 2214, the hard disk drive 2224, the DVD-ROM 2201 or the IC card under the control of the CPU 2212, and converts the read data to the transmission buffer processing area. Send data to the network, or write received data received from the network into a receive buffer processing area provided on the recording medium, or the like.

In addition, the CPU 2212 can read into the RAM 2214 all or a desired part of a file or database stored in an external recording medium such as a hard disk drive 2224, a DVD-ROM drive 2226 (DVD-ROM 2201), an IC card, or the like, and Various types of processing are performed on the data on RAM 2214 . The CPU 2212 next writes the processed data back to the external recording medium.

Various types of information such as various types of programs, data, tables, and databases can be stored in a recording medium and subjected to information processing. The CPU 2212 can execute various types of operations, information processing, conditional judgment, conditional branching, unconditional branching, and information retrieval/information specified by the instruction sequence of the program described anywhere in the present invention on the data read out from the RAM 2214. Various types of processing, including substitution, etc., write the results back to RAM 2214. In addition, the CPU 2212 can retrieve information in files, databases, and the like within the recording medium. For example, when a plurality of entries each having an attribute value of the first attribute associated with the attribute value of the second attribute are stored in the recording medium, the CPU 2212 may search and designate the first attribute from the plurality of entries. The attribute value of the second attribute associated with the first attribute that satisfies the predetermined condition is acquired by reading the attribute value of the second attribute stored in the entry in which the condition of the attribute value of the attribute matches.

The programs or software modules described above may be stored in a computer-readable medium on or near the computer 2200 . In addition, 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 be used as a computer-readable medium, thereby supplying the program to the computer 2200 via the network.

As mentioned above, although this invention was demonstrated using embodiment, the technical scope of this invention is not limited to the range described in the said embodiment. It should be apparent to those skilled in the art that various changes or improvements can be added to the above-described embodiments. It is clear from the description of the claims that such modifications or improvements are also included in the protection scope of the present invention.

It should be noted that, with regard to the execution order of the actions, processes, steps and stages in the apparatus, system, program and method shown in the claims, description and drawings, as long as there is no special indication of "before ... ", "before", etc., and it is not the case where the output of the preceding processing is used in the following processing, the above-mentioned execution order can be implemented in an arbitrary order. The operation flow in the claims, the description, and the drawings is described using "first," "next," etc. for convenience, but it does not mean that it must be implemented in this order.

Claims (12)

1. An apparatus, comprising:

a composition acquisition unit that acquires composition data indicating a composition of the photosensitive resin composition;

a characteristic acquisition unit that acquires characteristic data indicating characteristics of the photosensitive resin composition; and

a learning processing section that performs learning processing of a model for outputting recommended composition data representing a composition of the recommended photosensitive resin composition in response to input of target characteristic data representing a characteristic of the photosensitive resin composition as a target, using learning data including the acquired composition data and the characteristic data.

2. An apparatus, comprising:

a target property acquisition unit that acquires target property data indicating a property of a target photosensitive resin composition;

a target characteristic supply unit that supplies the target characteristic data acquired by the target characteristic acquisition unit to a model for outputting recommended composition data representing a recommended composition of the photosensitive resin composition in response to input of the target characteristic data; and

a recommended composition acquisition section that acquires the recommended composition data that is output by the model in response to the target characteristic data being supplied to the model.

3. The device according to claim 1 or 2,

the photosensitive resin composition is in the form of a film.

4. The device according to any one of claims 1 to 3,

the photosensitive resin composition is used for forming metal wiring.

5. The device according to any one of claims 1 to 4,

the composition of the photosensitive resin composition is:

the presence or absence of at least 1 of an alkali-soluble polymer, an ethylenically unsaturated bond-containing compound, a photopolymerization initiator, a resin having a repeating unit containing an acid-decomposable group, a phenol resin, a photoacid generator, a dissolution inhibitor, a sensitizer, a polymerization inhibitor, an adhesion agent, and a plasticizer,

a compound contained in at least 1 of an alkali-soluble polymer, an ethylenically unsaturated bond-containing compound, a photopolymerization initiator, a resin having a repeating unit containing an acid-decomposable group, a phenol resin, a photoacid generator, a dissolution inhibitor, a sensitizer, a polymerization inhibitor, an adhesion agent, and a plasticizer, or,

a content ratio of a compound contained in at least 1 of an alkali-soluble polymer, an ethylenically unsaturated bond-containing compound, a photopolymerization initiator, a resin having a repeating unit containing an acid-decomposable group, a phenol resin, a photoacid generator, a dissolution inhibitor, a sensitizer, a polymerization inhibitor, an adhesion agent, and a plasticizer.

6. The device according to any one of claims 1 to 5,

the characteristics of the photosensitive resin composition are:

the photosensitive resin composition has at least 1 of film thickness, shortest development time, sensitivity to light, transmittance, resolution, minimum resist line width, adhesion to a substrate, developer foamability, developer cohesiveness, edge melting property, cured film flexibility, adhesiveness to a base film or a cover film, hue stability, peeling time, peeling sheet size, and covering property.

7. A method, characterized in that it comprises the following phases:

a composition acquisition step of acquiring composition data indicating the composition of the photosensitive resin composition;

a characteristic acquisition step of acquiring characteristic data representing characteristics of the photosensitive resin composition; and

a learning processing stage of performing learning processing of a model for outputting recommended composition data representing a composition of the recommended photosensitive resin composition in response to input of target characteristic data representing a characteristic of the photosensitive resin composition as a target, using learning data including the acquired composition data and the characteristic data.

8. A method, characterized in that it comprises the following phases:

a target characteristic acquisition step of acquiring target characteristic data indicating characteristics of a target photosensitive resin composition;

a target characteristic supply step of supplying the target characteristic data acquired by the target characteristic acquisition step to a model for outputting recommended composition data representing a recommended composition of the photosensitive resin composition in response to input of the target characteristic data; and

a recommended composition acquisition phase of acquiring the recommended composition data output by the model in response to the supply of the target characteristic data to the model.

9. A method for producing a photosensitive resin composition, comprising the steps of:

determining a composition of a photosensitive resin composition based on the recommended composition data obtained by the method according to claim 8; and

mixing raw materials for producing the photosensitive resin composition.

10. A method for manufacturing a photosensitive resin laminate, comprising the steps of:

coating a fluid of the photosensitive resin composition obtained by mixing by the production method according to claim 9 on a base film; and

and providing a cover film on the coated photosensitive resin composition.

11. A storage medium storing a program which, when executed by a computer, implements the stages of:

a composition acquisition step of acquiring composition data indicating the composition of the photosensitive resin composition;

a characteristic acquisition step of acquiring characteristic data representing characteristics of the photosensitive resin composition; and

a learning processing stage of performing learning processing of a model for outputting recommended composition data representing a composition of the recommended photosensitive resin composition in response to input of target characteristic data representing a characteristic of the photosensitive resin composition as a target, using learning data including the acquired composition data and the characteristic data.

12. A storage medium storing a program which, when executed by a computer, implements the stages of:

a target characteristic acquisition step of acquiring target characteristic data indicating characteristics of a target photosensitive resin composition;

a target characteristic supply step of supplying the target characteristic data acquired by the target characteristic acquisition step to a model for outputting recommended composition data representing a recommended composition of the photosensitive resin composition in response to input of the target characteristic data; and

a recommended composition acquisition phase of acquiring the recommended composition data output by the model in response to the supply of the target characteristic data to the model.

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