Method of constructing prediction model that predicts number of plateable substrates, method of constructing selection model for predicting component that causes failure, and method of predicting number of plateable substrates

A method of the present disclosure includes: plating a plurality of substrates using a substrate holder; determining a total number of substrates that have been plated using the substrate holder until a failure occurs in the substrate holder; determining a first processable number and a second processable number; generating a first data set constituted by a combination of first condition data and the first processable number, the first condition data representing a state of a component of the substrate holder; generating a second data set constituted by a combination of second condition data and the second processable number, the second condition data representing a state of a component of the substrate holder; and optimizing a parameter of a prediction model constituted by a neural network using training data including the first data set and the second data set.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is based on and claims priority from Japanese Patent Application No. 2018-233823, filed on Dec. 13, 2018, with the Japan Patent Office, the disclosure of which is incorporated herein in its entirety by reference.

TECHNICAL FIELD

The present disclosure relates to a method of constructing a prediction model that predicts the number of substrates that may be plated until a failure occurs in a substrate holder used in a plating apparatus, and in particular, a method of constructing a prediction model by machine learning such as deep learning. The present disclosure also relates to a method of predicting the number of substrates that may be plated using such a prediction model.

BACKGROUND

The plating apparatus immerses a substrate held by a substrate holder (e.g., a wafer) in a plating solution and applies a voltage between the substrate and an anode to deposit a conductive film on the surface of the substrate. The substrate holder includes a plurality of components such as a plurality of electrical contacts that establish an electrical connection between the substrate and a power source, a seal that isolates the electrical contacts from the plating solution, and a seal holder that holds the seal.

When a failure occurs in the substrate holder, it adversely affects the plating of the substrate. For example, when the seal is deformed, the plating solution enters the substrate holder, and the plating solution comes into contact with the electrical contacts. As a result, a conductive film having a target thickness is not formed on the substrate. Since the substrate holder has a component that contacts the substrate to be plated, the condition of the substrate holder greatly affects the plating result. Therefore, maintenance of the substrate holder is important to achieve favorable plating results.

DETAILED DESCRIPTION

It is desirable that the maintenance of the substrate holder is performed before the failure of the substrate holder occurs. However, it is difficult to accurately determine whether the maintenance of the substrate holder is necessary. For this reason, in the related art, there is a case where maintenance of the substrate holder is performed after a failure occurs in the substrate holder. However, in that case, since preparation for the maintenance may not be performed in advance, the maintenance takes time. Meanwhile, Japanese Patent Laid-Open Publication No. 2018-003102 describes a substrate holder inspection apparatus that includes an apparatus of inspecting an appearance of a substrate holder and cleans the substrate holder as necessary, but such a description only determines whether an abnormality occurs in the appearance of the substrate holder.

Therefore, the present disclosure provides a method of constructing a prediction model that may accurately predict when the maintenance of the substrate holder is required. Further, the present disclosure provides a method of constructing a selection model which may predict a component that causes a failure of the substrate holder from a plurality of components of the substrate holder that may cause the failure of the substrate holder. The present disclosure also provides a method of predicting the maintenance time of the substrate holder using such a prediction model.

According to an embodiment of the present disclosure, there is provided a method of constructing a prediction model which predicts the number of substrates that may be plated until a failure occurs in a substrate holder. The method includes: plating a plurality of substrates using the substrate holder; determining a total number of substrates that have been plated using the substrate holder until the failure occurs in the substrate holder; determining a first processable number and a second processable number which are numbers of substrates that may be plated until the failure occurs in the substrate holder; generating a first data set constituted by a combination of first condition data corresponding to the first processable number and the first processable number, the first condition data representing a state of a component of the substrate holder; generating a second data set constituted by a combination of second condition data corresponding to the second processable number and the second processable number, the second condition data representing the state of the component; and optimizing a parameter of the prediction model constituted by a neural network using training data including the first data set and the second data set.

According to an embodiment of the present disclosure, the first processable number is 0, the first condition data is defect condition data representing the state of the component of the substrate holder when the failure occurs, and the first data set is a defect data set including a combination of the defect condition data and 0.

According to an embodiment of the present disclosure, the second processable number is a processable number obtained by subtracting an intermediate number smaller than the total number from the total number, the second condition data is intermediate condition data representing a state of the component when plating the intermediate number of substrates, and the second data set is an intermediate data set including a combination of the intermediate condition data and the second processable number.

According to an embodiment of the present disclosure, the first processable number is a processable number obtained by subtracting a first intermediate number smaller than the total number from the total number, the first condition data is first intermediate condition data representing a state of the component when plating the first intermediate number of substrates, the first data set is a first intermediate data set constituted by a combination of the first intermediate condition data and the first processable number, the second processable number is a processable number obtained by subtracting a second intermediate number smaller than the first intermediate number from the total number, the second condition data is second intermediate condition data representing a state of the component when plating the second intermediate number of substrates, and the second data set is a second intermediate data set constituted by a combination of the second intermediate condition data and the second processable number.

According to an embodiment of the present disclosure, each of the first condition data and the second condition data includes any one of image data and surface shape data of the substrate holder.

According to an embodiment of the present disclosure, the prediction model includes a neural network having an input layer, at least two intermediate layers, and an output layer.

According to an embodiment of the present disclosure, the prediction model is updated by repeating steps from the plating the plurality of substrates, the determining the total number of substrates, the determining the first processable number and the second processable number, the generating the first data set, the generating the second data set, and the optimizing the parameter of the prediction model.

An embodiment of the present disclosure further includes: generating a selection data set including a numerical value set indicating that a cause of the failure of the substrate holder is in the component, reference condition data representing a state of other component of the substrate holder when the failure occurs, and defect condition data representing the state of the other component when the failure occurs; and optimizing a parameter of a selection model constituted by a neural network using the selection data set.

According to an embodiment of the present disclosure, there is provided a method of preparing a prediction model constructed using the method described above, inputting the latest condition data representing the state of the components of the currently used substrate holder to the prediction model, and outputting the number of processable substrates from the prediction model.

An embodiment of the present disclosure further includes writing the predictable number on an electronic tag attached to the currently used substrate holder.

According to an embodiment of the present disclosure, there is provided a method of constructing a selection model for predicting a component that causes a failure of a substrate holder from a plurality of components of the substrate holder that may cause the failure of the substrate holder. The method includes: plating a plurality of substrates until the failure occurs in a first substrate holder due to a first component of the first substrate holder; generating a first selection data set including a first numerical value set indicating that a cause of the failure of the first substrate holder is the first component, first reference condition data representing a state of a second component of the first substrate holder when the failure occurs, and first defect condition data representing a state of the first component when the failure occurs; optimizing a parameter of a selection model constituted by a neural network using the first selection data set; plating the plurality of substrates until a failure occurs in a second substrate holder due to the second component of the second substrate holder; generating a second selection data set including a second numerical value set indicating that a cause of the failure of the second substrate holder is the second component, second reference condition data representing a state of a first component of the second substrate holder when the failure occurs, and second defect condition data representing a state of the second component when the failure occurs; and further optimizing the parameter using the second selection data set.

According to an embodiment of the present disclosure, each of the first reference data and the first defect condition data is constituted by any one of image data and surface shape data of the first substrate holder, and each of the second reference condition data and the second defect condition data is constituted by any one of image data and surface shape data of the second substrate holder.

According to an embodiment of the present disclosure, a method includes: inputting latest condition data of a first component and a second component of a substrate holder representing a state of a component of a currently used substrate holder to the selection model constructed by the method described above; when a first certainty factor corresponding to the first component output from the selection model is higher than a second certainty factor corresponding to the second component, inputting the latest condition data of the first component to a prediction model corresponding to the first component; and outputting a number of predictable substrates of the substrate holder from the prediction model. The prediction model is a prediction model constructed by the method described above.

According to the present disclosure, it is possible to construct a prediction model that may accurately predict the number of substrates that may be plated until a failure occurs in the substrate holder. Further, according to the present disclosure, it is possible to construct a selection model that may predict a component that causes a failure of the substrate holder from a plurality of components of the substrate holder that may cause the failure of the substrate holder. Further, according to the present disclosure, it is possible to accurately predict the number of substrates that may be plated using the prediction model until a failure occurs in the substrate holder.

DESCRIPTION OF EMBODIMENT

Embodiments of the present disclosure will be described below with reference to the drawings.FIG. 1is an overall layout view of a plating apparatus. As illustrated inFIG. 1, the plating apparatus includes two cassette tables12on which a cassette10accommodating a substrate such as a wafer is mounted, an aligner14that aligns notches such as an orientation flat and a notch of the substrate in a predetermined direction, and a spin rinse dryer16that rotates the substrate after plating treatment at high speed to dry the substrate.

A fixing station20is provided in the vicinity of the spin rinse dryer16to dispose the substrate holder18and attach/detach the substrate to/from the substrate holder18. Further, a substrate transport device22including a transport robot that transports a substrate among the cassette10, the aligner14, the spin rinse dryer16, and the fixing station20is disposed. The fixing station20is provided with an imaging device101that photographs the substrate holder18and a three-dimensional measuring device102that measures the surface shape of the substrate holder18.

The imaging device101and the three-dimensional measuring device102are electrically connected to an arithmetic system110that executes a machine learning. The imaging device101and the three-dimensional measuring device102are configured to be capable of transmitting generated image data and surface shape data to the arithmetic system110. The arithmetic system110is constituted by at least one computer. The arithmetic system110includes a storage device110athat stores the image data and the surface shape data. The arithmetic system110further includes a processing device110bsuch as a central processing unit (CPU) or a graphic processing unit (GPU).

InFIG. 1, the arithmetic system110is schematically depicted. The arithmetic system110may be an edge server connected to the plating apparatus via a communication line, a cloud server connected to the plating apparatus via a network such as the Internet, or a fog computing device installed in the network (e.g., a gateway, a fog server, a router, etc.). The arithmetic system110may be a combination of a plurality of servers (computers). For example, the arithmetic system110may be a combination of an edge server which is disposed near the plating apparatus and a cloud server which is far from the plating apparatus. A plurality of servers (computers) constituting the arithmetic system110may or may not be connected to each other via a network such as the Internet.

Further, a stocker24that stores and temporarily holds the substrate holder18, a pre-wet tank26that hydrophilizes the surface of the substrate, and a pretreatment tank28that etches away an oxide film on the surface of a conductive film such as a seed layer formed on the surface of the substrate, a first water washing tank30athat washes the substrate after pretreatment, a blow tank32that drains the washed substrate, a second water washing tank30bthat washes the substrate after plating, and a plating tank34are arranged in this order. The plating tank34is configured by accommodating a plurality of plating cells38inside the overflow tank36, and each of the plating cells38accommodates a single substrate inside and is subjected to copper plating, metal plating (Sn, Au, Ag, Ni, Ru, or In plating), or alloy plating (Sn/Ag alloy, Sn/In alloy, etc.).

In addition, the plating apparatus is provided with a substrate holder transport device40that transports the substrate holder18together with the substrate, for example, employs a linear motor system. The substrate holder transport device40is provided with a first transporter42that transports a substrate among the fixing station20, the stocker24, and the pre-wet tank26, and a second transporter44that transports a substrate among the stocker24, the pre-wet tank26, the pretreatment tank28, the first water washing tank30a, the second water washing tank30b, the blow tank32, and the plating tank34. Only the first transporter42may be provided without the second transporter44. In this case, the first transporter42is configured to transport a substrate among the fixing station20, the stocker24, the pre-wet tank26, the pretreatment tank28, the first water washing tank30a, the second water washing tank30b, the blow tank32, and the plating tank34.

Further, the plating apparatus includes a controller115. The imaging device101, the three-dimensional measuring device102, the arithmetic system110, the fixing station20, the substrate transport device22, and the substrate holder transport device40are electrically connected to the controller115. Operations of the imaging device101, the three-dimensional measuring device102, the fixing station20, the substrate transport device22, and the substrate holder transport device40are controlled by the controller115. The arithmetic system110transmits the prediction result of the maintenance time to the controller115, and the controller115controls the imaging device101, the three-dimensional measuring device102, and the substrate holder transport device40based on the prediction result of the maintenance time.

A paddle driving device46is disposed inside each plating cell38adjacent to the overflow tank36of the plating tank34to drive a paddle as a stirring rod that stirs the plating solution (not illustrated).

The fixing station20includes a mounting plate52that may slide horizontally along a rail50. After displacing the two substrate holders18in parallel in a horizontal state on the mounting plate52and transferring the substrate between the one substrate holder18and the substrate transport device22, the mounting plate52is slid in the horizontal direction to transfer the substrate between the other substrate holder18and the substrate transport device22.

As illustrated inFIGS. 2 to 5, the substrate holder18includes a first holding member (base holding member)54which is made of, for example, vinyl chloride and has a rectangular flat plate shape, and a second holding member (movable holding member)58which is attached to the first holding member54via a hinge56so as to be opened and closed. Meanwhile, this example illustrates that the second holding member58is configured to be able to be opened and closed via the hinge56. However, for example, the second holding member58may be disposed at a position facing the first holding member54, and the second holding member58may be advanced toward the first holding member54so as to be opened and closed.

The second holding member58has a base60and a seal holder62. The seal holder62is made of, for example, vinyl chloride, and improves sliding with a slide plate64described below. When a substrate W is held by the substrate holder18, a seal (first seal protrusion)66is attached to the upper surface of the seal holder62so as to protrude inward in pressure contact with an outer peripheral portion of the surface of the substrate W and seal a gap between the substrate W and the second holding member58. Further, when the substrate W is held by the substrate holder18, a seal (second seal protrusion)68is attached to the surface of the seal holder62facing the first holding member54so as to protrude inward in pressure contact the first holding member54and seal a gap between the first holding member54and the second holding member58. The seal68is located outside the seal66.

The seal (first seal protrusion)66and the seal (second seal protrusion)68are endless seals. The seal66and the seal68may be a seal member such as an O-ring. In an embodiment, the second holding member58itself including the seal66and the seal68may be made of a material having a sealing function. In the present embodiment, the seal66and the seal68are annular and are arranged concentrically. When plating the substrate W, the substrate holder18holding the substrate W is disposed in the plating cell38in a vertical posture. When the substrate holder18is disposed in the plating cell in a horizontal posture, the seal68may be omitted.

As illustrated inFIG. 5, the seal66is sandwiched between the seal holder62and a first fixing ring70aso as to be attached to the seal holder62. The first fixing ring70ais attached to the seal holder62via a fastener69asuch as a bolt. The seal68is sandwiched between the seal holder62and a second fixing ring70bso as to be attached to the seal holder62. The second fixing ring70bis attached to the seal holder62via a fastener69bsuch as a bolt.

A step portion is provided on the outer peripheral portion of the seal holder62of the second holding member58, and the slide plate64is rotatably attached to the step portion via a spacer65. The slide plate64is mounted so as not to escape by a presser plate72(see, e.g.,FIG. 3) which is attached to the side surface of the seal holder62so as to protrude outward. The slide plate64is made of, for example, titanium that has excellent corrosion resistance against acids and alkalis, and sufficient rigidity. The spacer65is made of a material having a low coefficient of friction, such as PTFE, so that the slide plate64may rotate smoothly.

Located on the outer side of the slide plate64, the first holding member54is provided with inverted L-shaped clampers74having protruding portions that protrude inward at an equal interval along the circumferential direction. Meanwhile, a protruding portion64bthat protrudes outward is provided at a position facing the clamper74along the circumferential direction of the slide plate64. Further, the lower surface of the inwardly protruding portion of the clamper74and the upper surface of the protruding portion64bof the slide plate64are tapered surfaces that are inclined in opposite directions along the rotation direction. Convex portions64aprotruding upward are provided at a plurality of locations (e.g., 3 locations) along the circumferential direction of the slide plate64. Thus, the slide plate64may be rotated by rotating the rotation pin of the fixing station20(not illustrated) and pushing the convex portion64afrom the side.

In a state where the second holding member58is opened, the substrate W is placed at the center of the first holding member54. Next, the second holding member58is closed via the hinge56, the slide plate64is rotated clockwise, and the protruding portion64bof the slide plate64is slid into the inner protruding portion of the clamper74, so that the first holding member54and the second holding member58are fastened and locked to each other via the tapered surfaces provided on the slide plate64and the clamper74, respectively, and the slide plate64is rotated counterclockwise to remove the protruding portion64bof the slide plate64from the inverted L-shaped clamper74and release the lock.

When the second holding member58is locked in this way (i.e., when the substrate holder18holds the substrate W), the lower end of the lower protruding portion on the inner peripheral surface of the seal66is uniformly pressed against the outer peripheral portion of the surface of the substrate W, and a gap between the second holding member58and the outer peripheral portion of the surface of the substrate W is sealed by the seal66. Similarly, the lower end of the lower protruding portion on the outer peripheral side of the seal68is uniformly pressed against the surface of the first holding member54, and a gap between the first holding member54and the second holding member58is sealed by the seal68.

The substrate holder18holds the substrate W by sandwiching the substrate W between the first holding member54and the second holding member58. The second holding member58has a circular opening58a. The opening58ais slightly smaller than the size of the substrate W. When the substrate W is sandwiched between the first holding member54and the second holding member58, the processed surface of the substrate W is exposed through the opening58a. Therefore, various processing liquids such as a prewetting liquid, a pretreatment liquid, and a plating liquid (to be described later) may come into contact with the exposed surface of the substrate W held by the substrate holder18. The exposed surface of the substrate W is surrounded by a seal (first seal protrusion)66.

When the substrate W is held by the substrate holder18, an internal space R1in which the inner peripheral side is sealed with the seal66and the outer peripheral side is sealed with the seal68is formed inside the substrate holder18, as illustrated inFIG. 5. A protruding portion82is provided at the center portion of the first holding member54to have a support surface80that protrudes in a ring shape in accordance with the size of the substrate W and contacts the outer peripheral portion of the substrate W to support the substrate W. A concave portion84is provided at a predetermined position along the circumferential direction of the protruding portion82.

In addition, as illustrated inFIG. 3, a plurality (12in the figure) of second electrical contacts86are arranged in each of the concave portions84, and the second electrical contacts86are respectively connected to a plurality of electric wires92extending from external electrical contacts91provided on a hand90. When the substrate W is disposed on the support surface80of the first holding member54, the end portion of the second electrical contact86is exposed on the surface of the first holding member54on the side of the substrate W in a springy state so as to contact the lower portion of the first electrical contact88illustrated inFIG. 5.

The first electrical contact88electrically connected to the second electrical contact86is fixed to the seal holder62of the second holding member58via a fastener89such as a bolt. The first electrical contact88has a leaf spring shape. The first electrical contact88is located on the outer side of the seal66, has a contact portion that protrudes inward in a leaf spring shape, and is easily bent at the contact portion with springiness due to its elastic force. When the substrate W is held by the first holding member54and the second holding member58, the contact portion of the first electrical contact88is configured to elastically contact the outer peripheral surface of the substrate W supported on the support surface80of the first holding member54.

The second holding member58is opened and closed by the weight of an air cylinder (not illustrated) and the second holding member58. That is, the first holding member54is provided with a through hole54a, and an air cylinder is provided at a position facing the through hole54awhen the substrate holder18is placed on the fixing station20. As a result, a piston rod is extended, the second holding member58is opened by pushing the seal holder62of the second holding member58upward with a pressing rod (not illustrated) via the through hole54a, and the second holding member58is closed by its own weight by contracting a piston rod.

A pair of substantially T-shaped hands90is provided at the end of the first holding member54of the substrate holder18to serve as support portions when the substrate holder18is transported or suspended. In the stocker24, the substrate holder18is suspended vertically by hooking the hand90on the upper surface of the peripheral wall of the stocker24. The suspended hand90of the substrate holder18is held by the transporter42or44of the substrate holder transport device40to transport the substrate holder18. Meanwhile, also in the pre-wet tank26, the pretreatment tank28, the first water washing tank30a, the second water washing tank30b, the blow tank32, and the plating tank34, the substrate holder18is suspended from the peripheral walls thereof via the hand90.

The substrate W used in the present embodiment is a circular substrate such as a wafer, but the present disclosure may also be applied to a rectangular substrate. Each component of the substrate holder that holds the rectangular substrate has a shape that matches the shape of the substrate. For example, the opening58adescribed above is a rectangular opening which is smaller than the size of the entire rectangular substrate. Other components such as the seals66and68are also shaped to match the shape of the rectangular substrate. The shape of each of other components is also changed as appropriate without departing from the technical idea described above.

As described above, the substrate holder18is a composite assembly constituted by a plurality of components such as the seals66and68, the first electrical contact88, the second electrical contact86, the external electrical contact91, the seal holder62, and the first holding member54. The components may be deformed or corrode as the substrate holder18is used to plate a plurality of substrates. For example, when the first electrical contact88is deformed, an appropriate current may not be caused to pass through the substrate. In another example, when the seals66and68are deformed, the plating solution enters the internal space R1of the substrate holder18and the plating solution comes into contact with the electrical contacts86and88. As a result, defective plating occurs. For this reason, it is important to perform maintenance of the substrate holder18before defective plating occurs.

In the present embodiment, the maintenance time of the substrate holder18is predicted using a prediction model constructed by machine learning. The prediction model is a model that predicts the number of substrates that may be plated using the substrate holder18until a failure occurs in the substrate holder18. In the present specification, machine learning refers to learning performed using a neural network, and includes deep learning. The machine learning is executed by the arithmetic system110configured by at least one computer. The arithmetic system110includes the storage device110athat stores a program which causes the arithmetic system110to execute machine learning, and a processing device110bthat performs computation according to the program.

The state of each component of the substrate holder18gradually changes as the substrate is repeatedly plated. A change in the state of each component may cause a failure of the substrate holder18. Examples of failure of the substrate holder18include liquid leakage and poor energization. The liquid leakage refers to a state where the plating solution enters the internal space R1of the substrate holder18due to an insufficient sealing function. The poor energization refers to a state where a desired current does not flow through the substrate held by the substrate holder18. When liquid leakage or poor conduction occurs, the plating apparatus may not form a film having the intended thickness on the surface of the substrate.

There are various causes of liquid leakage and poor conduction.FIG. 6is a table listing the types of state change of each component of the substrate holder18that may cause liquid leakage and poor energization. InFIG. 6, a circle (O) indicates that a change in the state of the component is highly likely to cause liquid leakage or poor energization, and a triangle (Δ) indicates that it is unclear whether the change in the state of the component may cause liquid leakage or poor energization. Further, the table illustrated inFIG. 6lists the types of data used to detect each state change. For example, the discoloration of the seal holder62refers to a state change that may cause liquid leakage, and the discoloration of the seal holder62is detected based on image data. The deformation of the electrical contacts86and88refers to a state change that may cause poor energization, and the deformation of the electrical contacts86and88is detected based on surface shape data.

A specific example of the state change of each component is as follows. However, the state change of each component is not limited to the following specific examples.

First electrical contact88: deformation, peeling of Au surface film, metal deposition, crystallization of copper sulfate contained in the plating solution

Second electrical contact86: deformation, peeling of Au surface film, metal deposition, crystallization of copper sulfate contained in the plating solution

External electrical contact91: deformation, peeling of Au surface film, metal deposition, crystallization of copper sulfate contained in the plating solution

Specific examples of deformation of each of the components include deformation caused by application of an external force, distortion caused by internal stress, and corrosion of the components.

In order to construct a prediction model that predicts a failure of the substrate holder18, at least one of the image data and the surface shape data of each component is used. For example, the surface shape data is used to construct a prediction model that predicts a failure of the substrate holder18based on deformation of the seals66and68, and the image data is used to construct a prediction model that predicts a failure of the substrate holder18based on discoloration of the seals66and68.

Before holding a substrate to be plated, the image data and the surface shape data of each component of the substrate holder18are generated by the imaging device101and the three-dimensional measuring device102, respectively. The arithmetic system110acquires the image data and the surface shape data from the imaging device101and the three-dimensional measuring device102, and stores the image data and surface shape data in the storage device110a. In an embodiment, the image data and the surface shape data of each component of the substrate holder18may be generated after the plated substrate is taken out from the substrate holder18.

The imaging device101is a camera provided with an image sensor such as a CCD or a CMOS. The three-dimensional measuring device102is a device that may measure the surface shape of a target object, and, for example, a laser displacement meter is used. More specifically, the three-dimensional measuring device102measures the positions of a plurality of measurement points on the surface of the component, and outputs the X coordinate, the Y coordinate, and the Z coordinate of each measurement point as a position measurement value.

The arithmetic system110counts the cumulative number of substrates plated using a particular substrate holder18after the use of the substrate holder18is started. Specifically, each time a substrate is plated using the substrate holder18, the arithmetic system110counts the cumulative number of substrates plated using the substrate holder18. Further, each time the image data and the surface shape data are acquired from the imaging device101and the three-dimensional measuring device102, the arithmetic system110associates the image data and the surface shape data with the current cumulative number of plated substrates. Then, the arithmetic system110stores the image data and the surface shape data in the storage device110ain association with the current cumulative number of plated substrates.

After holding the substrate to be plated, the substrate holder18is connected to a leakage inspection device117. The leakage inspection device117inspects whether the seals66and68of the substrate holder18are functioning normally. The leakage inspection device117forms a positive pressure or a negative pressure in the internal space R1provided in the substrate holder18by the seals66and68, and issues an alarm signal indicating that a failure has occurred in the substrate holder18when the pressure in the internal space R1(a positive pressure or a negative pressure) exceeds allowable values within a predetermined time. When the pressure in the internal space R1changes greatly, the plating solution may enter the internal space R1during plating of the substrate. This means that a failure has occurred in the substrate holder18(i.e., liquid leakage). The leakage inspection device117is electrically connected to the controller115.

Further, the substrate holder18is connected to an energization inspection device118while holding the substrate to be plated. The energization inspection device118sends a predetermined current to the substrate through the external electrical contact91of the substrate holder18and measures the internal resistance of the substrate holder18. The internal resistance of the substrate holder18is a combined resistance of the substrate held by the external electrical contact91, an electric wire92, the first electrical contact88, the second electrical contact86, and the substrate holder18. The internal resistance of the substrate holder18may vary according to a contact state between the first electrical contact88and the substrate, and a contact state between the first electrical contact88and the second electrical contact86. For example, when the first electrical contact88corrodes, a contact resistance between the first electrical contact88and the substrate changes. As a result, the internal resistance of the substrate holder18changes.

When the measured value of the internal resistance is out of a predetermined setting range, the energization inspection device118issues an alarm signal indicating that a failure has occurred in the substrate holder18. When the measured value of the internal resistance is out of the setting range, it is estimated that a failure occurs in at least one of the external electrical contact91, the first electrical contact88, and the second electrical contact86. As a result, a film having a desired thickness may not be deposited on the substrate. This means that a failure (i.e., a poor energization) has occurred in the substrate holder18. The energization inspection device118is electrically connected to the controller115.

In an embodiment, the internal resistance of the substrate holder18may be measured not on the substrate to be plated but on the substrate holder18that holds a dummy substrate. Examples of the dummy substrate are a blanket substrate that has no pattern formed on the surface thereof, and a substrate that has a conductive film such as copper coated on the surface.

The image data and the surface shape data are sent to the arithmetic system110and stored in the storage device110a. The storage device110astores a plurality of prediction models which predict the number of substrates that may be plated until a failure occurs in the substrate holder18. When any one of the image data and the surface shape data is input to each prediction model, the prediction model outputs the predicted number of substrates that may be plated using the substrate holder18.

The plurality of prediction models are provided corresponding to at least a plurality of components of the substrate holder18(the seal holder62, the electrical contacts86and88, the seals66and68, etc.). Further, a plurality of prediction models are provided for each type of failure of the substrate holder18(liquid leakage, poor energization). This is because the predicted number of substrates that may be plated using the substrate holder18may vary for each component of the substrate holder18and may vary for each type of failure of the substrate holder18. In an embodiment, a plurality of prediction models may be provided for each type of state change of the component of the substrate holder18. That is, as many prediction models as the number of circles (O) and triangles (Δ) illustrated in the table ofFIG. 6may be provided. Further, in an embodiment, only one prediction model may be provided for the substrate holder18.

Each of the plurality of prediction models is a model constituted by a neural network. The arithmetic system110constructs a prediction model by learning parameters of each prediction model (weights, etc.) using training data that includes at least one selected in advance from image data and surface shape data. The parameters of the prediction model may include a bias in addition to the weight.

In the present embodiment, the data representing the state of each component of the substrate holder18is constituted by image data and surface shape data. In an embodiment, only image data or surface shape data may be used as data representing the state of each component of the substrate holder18.

FIG. 7is a schematic view illustrating an example of a prediction model. As illustrated inFIG. 7, the prediction model is a neural network having an input layer201, a plurality of intermediate layers (also referred to as hidden layers)202, and an output layer203. The prediction model illustrated inFIG. 7includes four intermediate layers202, but the configuration of the prediction model is not limited to the example illustrated inFIG. 7. Machine learning which is performed using a neural network having many intermediate layers202is called deep learning.

In the prediction model using image data, numerical values representing red, green, and blue of each pixel constituting the image data are input to the input layer201. In the prediction model using the surface shape data, the X coordinate value, the Y coordinate value, and the Z coordinate value that represent the position of the measurement point on the surface of the component of the substrate holder18are input to the input layer201. In any case, the output layer203outputs the number of substrates that may be plated until a failure occurs in the substrate holder18. In the following description, the number of substrates output from the prediction model is referred to as a predictable number.

The arithmetic system110optimizes the parameters of the prediction model (weights, etc.) by machine learning using training data, and improves the accuracy of the prediction model.FIG. 8is a flowchart illustrating an embodiment of a method for optimizing the parameters of a prediction model. In step1, a plurality of substrates are plated using one new substrate holder18. The plating of the substrates is performed by the plating apparatus illustrated inFIG. 1. Examples of the “new substrate holder18” include not only unused substrate holders but also maintained substrate holders. Image data and surface shape data are generated by the imaging device101and the three-dimensional measuring instrument102before each substrate is held by the substrate holder18. The arithmetic system110acquires image data and surface shape data from the imaging device101and the three-dimensional measuring device102, and stores such data in the storage device110aof the arithmetic system110together with the cumulative number of plated substrates.

Plating of a plurality of substrates using the new substrate holder18is performed until a failure occurs in the substrate holder18. The user may know that a failure has occurred in the substrate holder18from an alarm signal issued from the leakage inspection device117or the energization inspection device118. When a failure occurs in the substrate holder18, the user takes out the substrate holder18from the plating apparatus, disassembles the substrate holder18, and specifies the cause of the failure of the substrate holder18. Further, the user gives to the arithmetic system110information that a failure has occurred in the substrate holder18due to a change in the state of the specified component using an input device, a communication device, or the like (not illustrated). Specifically, the user teaches the arithmetic system110the components to be used for the construction (learning) of the prediction model. For example, when the failure of the substrate holder18occurs due to deformation of the first electrical contact88, the user gives to the arithmetic system110information that the component to be used for construction (learning) of the prediction model is the first electrical contact88.

In step2, the arithmetic system110determines the total number of substrates plated using the substrate holder18until the above-described failure occurs, and stores the determined total number of substrates in the storage device110a. The total number of substrates is the cumulative number of substrates plated using the substrate holder18from the time when the use of the substrate holder18is started to the time when a failure occurs in the substrate holder18. The total number of substrates corresponds to the latest cumulative number stored in the storage device110aof the arithmetic system110. Further, the number of substrates that may be plated using the substrate holder18when a failure occurs is 0.

In step3, the arithmetic system110determines defect condition data representing the state of the component of the substrate holder18that is the cause of the failure of the substrate holder18. The defect condition data is condition data representing the state of the component of the substrate holder18that has caused the failure of the substrate holder18, and more specifically, is condition data representing the state of the component when the failure occurs in the substrate holder18. The defect condition data constitutes parts of the image data and the surface shape data stored in the storage device110a. The defect condition data includes any one of the image data and the surface shape data of the components. For example, when the failure of the substrate holder18occurs due to deformation of the first electrical contact88, the defect condition data is the image data of the first electrical contact88, that is, the latest image data of the first electrical contact88when the failure of the substrate holder18occurs.

In step4, the arithmetic system110generates a defect data set constituted by a combination of defect condition data and 0. The numerical value 0 is the number of substrates that may be plated using the substrate holder18, that is, the number of substrates that may be processed. The defect condition data is condition data corresponding to the numerical value 0.

In step5, the arithmetic system110determines the number of substrates that may be processed by subtracting, from the total number, an intermediate number that is smaller than the total number obtained in step2. For example, in a case where the total number of substrates when a failure occurs in the substrate holder18is 500 and the intermediate number is 200, the processable number is 300 (500−200).

In step6, the arithmetic system110determines intermediate condition data corresponding to the processable number determined in step5. The intermediate condition data is condition data representing the state of the component of the substrate holder18that is a cause of the failure of the substrate holder18, and more specifically, is condition data representing the state of the component when the intermediate number of substrates is plated. Similarly to the defect condition data, the intermediate condition data constitutes parts of the image data and the surface shape data stored in the storage device110a. The intermediate condition data is constituted by any one of the image data and the surface shape data of the component, and is the same type of data as the defect condition data. For example, when the defect condition data is surface shape data, the intermediate condition data is also surface shape data.

In step7, the arithmetic system110generates an intermediate data set constituted by a combination of the intermediate condition data and the processable number of substrates determined in step5.

Steps5,6, and7are repeated a predetermined number of times. More specifically, the arithmetic system110generates a plurality of intermediate data sets by repeatedly determining the processable number and generating the intermediate data set while changing the intermediate number. For example, in a case where the total number of plated substrates is 500 when a failure occurs in the substrate holder18, the intermediate number is set to 100, 200, 300, and 400, respectively. The plurality of intermediate numbers are preferably evenly distributed between 0 and the total number. In the present embodiment, the number of substrates that may be processed is determined to be 400 (500−100), 300 (500−200), 200 (500−300), and 100 (500−400), respectively.

The arithmetic system110determines intermediate condition data corresponding to the processable number 400 (i.e., intermediate condition data of the component when 100 substrates are plated), intermediate condition data corresponding to the processable number 300 (i.e., intermediate condition data of the component when 200 substrates are plated), intermediate condition data corresponding to the processable number 200 (i.e., intermediate condition data of the component when 300 substrates are plated), and intermediate condition data corresponding to the processable number 100 (i.e., intermediate condition data of the component when 400 substrates are plated).

Further, the arithmetic system110generates an intermediate data set constituted by a combination of intermediate condition data of components when 100 substrates are plated and the processable number 400, an intermediate data set constituted by a combination of intermediate condition data of components when 200 substrates are plated and the processable number 300, an intermediate data set constituted by a combination of intermediate condition data of components when 300 substrates are plated and the processable number 200, and an intermediate data set constituted by a combination of intermediate condition data of components when 400 substrates are plated and the processable number 100.

In step8, the arithmetic system110generates training data including the defect data set described above and the plurality of intermediate data sets described above. In an embodiment, the training data may not include a defect data set. In this case, the training data includes only a plurality of intermediate data sets.

In step9, the arithmetic system110constructs a prediction model constituted by a neural network using the training data. More specifically, the arithmetic system110optimizes parameters of the prediction model (weights, etc.) by deep learning using the training data. The defect condition data (e.g., image data of a discolored seal holder62) is input to the input layer201of the prediction model. The arithmetic system110determines the optimal parameter of the prediction model that may minimize a difference between the output value from the output layer203and 0. Similarly, intermediate condition data is input to the input layer201of the prediction model. The arithmetic system110determines the optimal parameter of the prediction model that may minimize a difference between the output value from the output layer203and the processable number (e.g., 300). In this way, the arithmetic system110performs deep learning using the training data and optimizes the parameters of the prediction model. The prediction model constructed by deep learning is stored in the storage device110aof the arithmetic system110.

Before inputting the defect condition data and the intermediate condition data to the prediction model, the defect condition data and the intermediate condition data may be pre-processed. Specifically, the arithmetic system110reduces the capacity of the defect condition data and the intermediate condition data by deleting a part of the defect condition data and a part of the intermediate condition data that do not contribute to a prediction of the number of substrates that may be processed. For example, since the seals66and68are annular, the arithmetic system110deletes the image data of the area inside the seals66and68from the image data sent from the imaging device101. By such pre-processing, the capacity of the defect condition data and the intermediate condition data may be reduced, and the load on the arithmetic system110and the learning speed may be improved.

All processes from step1to step9are repeated a plurality of times, and the prediction model is updated using new training data. That is, a new substrate holder (e.g., another substrate holder having the same structure as the substrate holder18or a maintained substrate holder18) is prepared, and a plurality of substrates are plated using the new substrate holder until a failure occurs in the new substrate holder. The arithmetic system110generates new training data and updates the prediction model by further optimizing the parameters of the prediction model. The component that has caused the failure of the new substrate holder is the same as the component that has caused the failure of the substrate holder18previously used for substrate plating. The types of defect condition data and intermediate condition data included in the new training data are also the same as the types of defect condition data and intermediate condition data included in the previously generated training data.

Similarly, a plurality of prediction models corresponding to a plurality of components of the substrate holder that may cause a failure are constructed. Specifically, a plurality of pieces of training data are generated using a plurality of substrate holders having the same structure, and parameters of a plurality of prediction models are optimized using the plurality of pieces of training data. The arithmetic system110stores the prediction models in the storage device110a. A plurality of prediction models may be constructed by the number of circles (O) and triangles (Δ) in the table illustrated inFIG. 6. In an embodiment, only one prediction model may be constructed.

The plating apparatus illustrated inFIG. 1plates a substrate using another substrate holder having the same structure as the substrate holder18used to construct the prediction model. Before plating the substrate, the imaging device101and the three-dimensional measuring device102generate image data and surface shape data of each component of the substrate holder, and the energization inspection device118measures the internal resistance of the substrate holder. The image data and the surface shape data are sent to the arithmetic system110and stored in the storage device110a.

The arithmetic system110predicts the number of substrates that may be plated by using all the generated prediction models (learned models) until a failure occurs in the substrate holder currently used by the plating apparatus. That is, the arithmetic system110inputs the latest condition data representing the state of each component of the substrate holder that is currently used to all prediction models. The latest condition data is constituted by image data or surface shape data of each component of the substrate holder currently used. Each prediction model outputs the number of substrates that may be plated until a failure occurs in the substrate holder currently used by the plating apparatus, that is, the number of predictable substrates.

In the present embodiment, since the arithmetic system110determines the number of predictable substrates using all prediction models, the obtained plurality of predictable substrates may vary. Therefore, the arithmetic system110selects the smallest predictable number from a plurality of number of predictable substrates.

In the plating apparatus, a plurality of substrate holders having the same structure are used. An electronic tag to which a radio frequency identification (RFID) technique (see reference numeral “95” inFIG. 3) is applied is attached to each of the substrate holders. The electronic tag is called an RFID tag or an RF tag. The electronic tag includes an electric circuit, and information may be transmitted and received in a contactless manner with a reader and a writer (not illustrated). The identification number of the substrate holder to which the electronic tag is attached is written in advance in the electronic tag. The arithmetic system110transmits the predictable number (i.e., the smallest predictable number) to the writer, and the writer writes the predictable number in the electronic tag of the substrate holder.

As the substrate holder is used for substrate plating, the number of predictable substrates output from the prediction model decreases. Every time all prediction models output a plurality of predictable substrates, the arithmetic system110determines the smallest predictable number and transmits the smallest predictable number to the writer. The writer rewrites the predictable number already written in the electronic tag of the substrate holder.

The reader reads the predictable number from the electronic tag of the substrate holder, and displays the predictable number on a display (not illustrated). In this way, the user may know the number of substrates that may be predicted for each substrate holder. As a result, maintenance of each substrate holder may be performed at an appropriate timing.

Since the identification number of the substrate holder and the number of predictable substrates are written on the electronic tag, even after the substrate holder is taken out from the plating apparatus, the number of predictable substrates, which is stored in the arithmetic system110or the controller115, is not referred to, and it is possible to know the number of predictable substrates unique to the substrate holder and the operation management of the substrate holder is facilitated.

The arithmetic system110includes, in addition to the above-described plurality of prediction models, a selection model which predicts a component that causes a failure of the substrate holder18from a plurality of components of the substrate holder18that may cause the failure of the substrate holder18. The selection model is configured to calculate a certainty factor for each component. The certainty factor is an index value indicating the probability that each component causes a failure of the substrate holder18, and is represented by a numerical value from 0 to 100. The certainty factor may be represented by a numerical value from 0 to 1. The certainty factor is sometimes called a score. The predictable number output from the prediction model constructed for the component with high certainty is a predictable number with high reliability.

In the present embodiment, two selection models are provided corresponding to two types of failure of the substrate holder18(liquid leakage and poor energization).FIG. 9is a schematic view illustrating an example of a selection model. As illustrated inFIG. 9, the selection model is a neural network having an input layer301, a plurality of intermediate layers (also referred to as hidden layers)302, and an output layer303. The selection model illustrated inFIG. 9includes four intermediate layers302, but the configuration of the selection model is not limited to the example illustrated inFIG. 9.

Data representing the states of a plurality of components of the substrate holder18when a failure occurs in the substrate holder18is input to the input layer301of the selection model. In the present embodiment, the image data and the surface shape data of the seal holder62, the first holding member54, the first electrical contact88, the second electrical contact86, the external electrical contact91, the seals66and68, and the slide plate64illustrated in the table ofFIG. 6, respectively, are input to the input layer301of the selection model. The output layer303of the selection model outputs a plurality of certainty factors corresponding to a plurality of components. The output layer303outputs the number of certainty factors which is equal to or greater than the number of components of the substrate holder18that may cause a failure of the substrate holder18.

The arithmetic system110optimizes the parameters (weights, etc.) of the selection model by deep learning using training data, and constructs the selection model.FIG. 10is a flowchart illustrating an embodiment of a method for optimizing parameters of a selection model.

In step1, a plurality of substrates are plated using one new substrate holder18. The plating of a plurality of substrates using the new substrate holder18is performed until a failure occurs in the substrate holder18. Examples of the “new substrate holder18” include not only unused substrate holders but also maintained substrate holders.

When a failure occurs in the substrate holder18, the user takes out the substrate holder18from the plating apparatus, disassembles the substrate holder18, and specifies the cause of the failure of the substrate holder18. Further, the user gives to the arithmetic system110information that a failure has occurred in the substrate holder18due to a change in the state of the specified Nth component using an input device, a communication device, or the like (not illustrated). Specifically, the user teaches the arithmetic system110the Nth component to be used for the construction (learning) of the selection model. The Nth component is any one of a plurality of components that may cause a failure of the substrate holder18. In the example illustrated inFIG. 6, the Nth component is any one of the seal holder62, the first holding member54, the first electrical contact88, the second electrical contact86, the external electrical contact91, the seals66and68, and the slide plate64. For example, when the failure of the substrate holder18occurs due to deformation of the first electrical contact88, the user gives to the arithmetic system110information that the component to be used for the construction (learning) of the selection model is the first electrical contact88.

In step2, the arithmetic system110determines defect condition data representing the state of the Nth component that causes the failure of the substrate holder18. More specifically, the defect condition data is condition data representing the state of the Nth component when the failure occurs in the substrate holder18. The defect condition data includes any one of the image data and the surface shape data of the Nth component.

In step3, the arithmetic system110generates a selection data set including a numerical value set indicating that the cause of the failure of the substrate holder18is the Nth component, reference condition data representing the state of other components of the substrate holder18when the failure of the substrate holder18occurs, and the defect condition data. The other components of the substrate holder18may cause a failure of the substrate holder18and are components other than the Nth component.

The numerical value set indicating that the cause of the failure of the substrate holder18is the Nth component is a combination of 100 and 0 indicating the certainty factor. The numerical value set is a numerical value set representing a certainty factor that the user investigates the substrate holder in which the failure occurs in step1and determines that the change has been caused by the state change of the Nth component. Specifically, the numerical value corresponding to the Nth component specified as the cause of the failure is 100, and the numerical values corresponding to the other components are all 0o.

A plurality of components may cause a failure. Therefore, in an embodiment, the numerical value corresponding to each of the two or more components may be 100. When it is not possible to determine that the cause of the failure is in the Nth component, the numerical value corresponding to the Nth component may be a number smaller than 100. For example, when there is a high possibility that the cause of the failure of the substrate holder18is in the Nth component, but it cannot be determined, the numerical value corresponding to the Nth component may be 80.

In the example illustrated inFIG. 6, examples of the components that may cause the failure of the substrate holder18include the seal holder62, the first holding member54, the first electrical contact88, the second electrical contact86, the external electrical contact91, the seal66and68, and the slide plate64. For example, when the Nth component is the seal holder62, the other components include the first holding member54, the first electrical contact88, the second electrical contact86, the external electrical contact91, the seals66and68, and the slide plate64. Therefore, the reference condition data of the other components is reference condition data representing the states of the first holding member54, the first electrical contact88, the second electrical contact86, the external electrical contact91, the seals66and68, and the slide plate64. The reference condition data includes at least one of the image data and the surface shape data of each of the other components.

In step4, the arithmetic system110optimizes the parameters (weights, etc.) of the selection model illustrated inFIG. 9by deep learning using the selection data set generated in step3above. Specifically, the defect condition data and the reference condition data are input to the input layer301of the selection model. For example, in a case where the seal holder62is discolored when a failure occurs in the substrate holder18, image data of the seal holder62is input to the input layer301as defect condition data, and the image data and the surface shape data of the components other than the seal holder62are input to the input layer301as reference condition data. The arithmetic system110determines the optimal parameter of the selection model that may minimize a difference between the output value set from the output layer303and the numerical value set generated in step3above.

The processes from step1to step4inFIG. 10are repeated until a selection data set is generated for all of the plurality of components of the substrate holder18that may cause a failure of the substrate holder18, and the parameters of the selection model are optimized using all the generated selection data sets. That is, a new substrate holder (e.g., another substrate holder having the same structure as that of the substrate holder18or a maintained substrate holder18) is prepared, and a plurality of substrates are plated using the new substrate holder until a failure occurs in the new substrate holder due to a component different from the Nth component. The arithmetic system110generates a new selection data set and further optimizes the parameters of the selection model. The initial value of N illustrated inFIG. 10is 1, and N=N+1 indicates that a component having the substrate holder is changed to another component. In the embodiment illustrated inFIG. 6, a selection data set is generated for all of the seal holder62, the first holding member54, the first electrical contact88, the second electrical contact86, the external electrical contact91, the seals66and68, and the slide plate64, and the processes from step1to step4are repeated until the parameters of the selection model are optimized using all of the generated selection data sets. In this way, a selection model which may predict a component that causes a failure of the substrate holder18is constructed. The constructed selection model is stored in the storage device110aof the arithmetic system110. Meanwhile,FIG. 10conceptually illustrates that a selection data set corresponding to a component of the substrate holder18that may cause a failure and learning using the selection data set (optimization of the model) are necessary. Actually, in order to perform a prediction with high accuracy, it is necessary to repeatedly perform a learning using a sufficient amount of selection data sets for one component.

The plating apparatus illustrated inFIG. 1plates a substrate using a substrate holder having the same structure as the substrate holder18used for the construction of the prediction model and the selection model. Before plating the substrate, the imaging device101and the three-dimensional measuring device102generate image data and surface shape data of each component of the substrate holder, and the energization inspection device118measures the internal resistance of the substrate holder. The image data and the surface shape data are sent to the arithmetic system110and stored in the storage device110a.

The arithmetic system110inputs all the latest condition data representing the respective states of the plurality of components of the substrate holder currently used by the plating apparatus to the input layer301of the selection model. Each of the latest condition data is constituted by the image data or the surface shape data of each component of the substrate holder currently used. In the example illustrated inFIG. 6, the image data and the surface shape data of all the components are input to the input layer301of the selection model. The selection model outputs a plurality of certainty factors corresponding to a plurality of components.

The arithmetic system110selects at least one component based on the plurality of certainty factors output from the selection model. Basically, the component with the highest certainty is selected.

The arithmetic system110stores a reference value in the storage device110a, and selects all the components having a certainty factor greater than the reference value. In the following example, component A and component B are selected.

Component A: certainty factor of 80%

Component B: certainty factor of 70%

Component C: certainty factor of 10%

Component D: certainty factor of 5%

Component E: certainty factor of 1%

In the above example, when the reference value is 85%, all the components are not selected. In this case, the upper M components are selected (M is a natural number smaller than the total number of all components). Alternatively, when the difference between the certainty factor of the upper K-th component and the certainty factor of the upper K+1-th component is larger than a preset value, the upper K components are selected (K is a natural number smaller than M). For example, in the above example, when the preset value is 40%, since the difference between the certainty factor of component B and the certainty factor of component C is 60%, component A and component B are selected.

The arithmetic system110uses the prediction model constructed for at least one selected component to calculate the predicted number of substrates that may be plated until a failure occurs in the substrate holder currently used by the plating apparatus. That is, the arithmetic system110inputs the image data and the surface shape data to a prediction model constructed for at least one selected component. The prediction model outputs the predicted number of substrates that may be plated until a failure occurs in the substrate holder, that is, the number of predictable substrates. When a plurality of components are selected, the plurality of prediction models corresponding to the plurality of components respectively output the number of predictable substrates. The arithmetic system110selects the smallest predictable number among the plurality of number of predictable substrates.

In calculating the number of predictable substrates using only the prediction model described above, it is not possible to know which component ultimately causes a failure in the substrate holder being used, so that it is necessary to input the latest condition corresponding to each component to the prediction model corresponding to each component. Meanwhile, in calculating the number of predictable substrates using the selection model and the prediction model described above, all of the latest condition data representing the respective states of the plurality of components of the substrate holder currently used by the plating apparatus is first input to the input layer301of the selection model. The certainty factor output from the output layer of the selection model may vary for each component. That is, the certainty factor corresponding to the component that has a sign causing the failure is high, and the certainty factor corresponding to the component member that has no sign causing the failure is low. In this way, since it is possible to predict the component that may cause the failure of the substrate holder by using the selection model, the number of predictable substrates may be calculated using only the latest condition data and the prediction model corresponding to the component. In an embodiment, the reference value described above may be set to be low at the initial stage of use of the substrate holder, and may be set to increase as the number of processed substrate holders increases.

In an embodiment, the arithmetic system110may include a first server (first computer) and a second server (second computer). The storage device110aand the processing device110bof the arithmetic system110are constituted by a first storage device and a first processing device in the first server, and a second storage device and a second processing device in the second server. For example, the construction and update of the prediction model and the selection model described above may be executed by the first server, and the calculation of the number of predictable substrates and the calculation of the certainty factor using the prediction model and the selection model may be executed by the second server.

In an embodiment, the prediction model and the selection model may be constructed in advance in a system different from the arithmetic system110(a server or a computer), the constructed prediction model and selection model may be installed in the arithmetic system110, and only the calculation of the number of predictable substrates and the calculation of the certainty factor using the prediction model and the selection model may be executed by the arithmetic system110.

Next, with reference to the flowcharts illustrated inFIGS. 11 and 12, descriptions will be made on an embodiment of a process of determining whether the substrate holder18mounted on the plating apparatus may be used. In step1, the substrate holder18accommodated in the stocker24is gripped by the first transporter42of the substrate holder transport device40and transported to the fixing station20. Then, the substrate holder18is lowered to a horizontal state, whereby the substrate holder18is placed on the mounting plate52of the fixing station20.

In step2, the air cylinder of the fixing station20is operated to open the second holding member58of the substrate holder18, and the image data and the surface shape data of each component of the substrate holder18are photographed by the imaging device101and the three-dimensional measuring device102, respectively.

FIG. 13AandFIG. 13Bare schematic views illustrating an example of an imaging method of a component of the substrate holder18and a surface shape measurement method.FIG. 13Ais a schematic view illustrating an example of an imaging method and a surface shape measurement method of the seal holder62, the first electrical contact88, the seal66, and the seal68.FIG. 13Bis a schematic view illustrating an example of an imaging method and a surface shape measurement method of the first holding member54and the second electrical contact86. The imaging device101photographs a plurality of components of the substrate holder18and generates image data of each component. The three-dimensional measuring device102measures the surface shape of a plurality of components of the substrate holder18and generates the surface shape data of each component.

Referring back toFIG. 11, in step3, the imaging device101and the three-dimensional measuring device102transmit the image data and the surface shape data generated in step2to the arithmetic system110. In step4, the controller115issues a command to the fixing station20to hold the substrate to be plated on the substrate holder18. Thereafter, the substrate holder18is connected to the leakage inspection device117(step5).

In step6, the leakage inspection device117inspects whether the seals66and68of the substrate holder18are functioning normally. The leakage inspection device117forms a positive pressure or a negative pressure in the internal space R1provided in the substrate holder18by the seals66and68, and issues an alarm signal indicating that a failure has occurred in the substrate holder18when the pressure in the internal space R1(a positive pressure or a negative pressure) exceeds allowable values within a predetermined time. The alarm signal is transmitted to the controller115. Upon receiving the alarm signal, the controller115issues a command to the substrate holder transport device40, grips the substrate holder18with the first transporter42of the substrate holder transport device40, and returns the substrate holder18to a predetermined location of the stocker24(step7).

When the leakage inspection device117generates an alarm signal, the user may execute step8. In step8, the user may take out the substrate holder18from the plating apparatus, disassemble the substrate holder18, and specify the cause of the failure of the substrate holder18. Further, the user may give to the arithmetic system110information indicating that a failure has occurred in the substrate holder18due to a change in the state of the specified component using an input device, a communication device, or the like (not illustrated). That is, the user may teach the arithmetic system110the components to be used for the construction (learning) of the prediction model.

When the seals66and68of the substrate holder18are functioning normally, the substrate holder18is connected to the energization inspection device118while holding the substrate to be plated (step9). In step10, the energization inspection device118measures the internal resistance of the substrate holder18and compares the measured resistance value with a predetermined setting range. When the measured resistance value is out of the setting range, the energization inspection device118issues an alarm signal indicating that a failure has occurred in the substrate holder18. The alarm signal is transmitted to the controller115. Upon receiving the alarm signal, the controller115issues a command to the substrate holder transport device40, grips the substrate holder18with the first transporter42of the substrate holder transport device40, and returns the substrate holder18to a predetermined location of the stocker24(step11).

When the energization inspection device118generates an alarm signal, the user may execute step12. In step12, the user may take out the substrate holder18from the plating apparatus, disassemble the substrate holder18, and specify the cause of the failure of the substrate holder18. Further, the user may give to the arithmetic system110information indicating that a failure has occurred in the substrate holder18due to a change in the state of the specified component using an input device, a communication device, or the like (not illustrated). That is, the user may teach the arithmetic system110the components to be used for the construction (learning) of the prediction model.

When the resistance value measured in step10is within the setting range, the arithmetic system110inputs each data transmitted in step3to the prediction model, and outputs the number of predictable substrates (step13).

In step14, the arithmetic system110compares the predictable number output in step12with a predetermined threshold value. When the predictable number is larger than the threshold value, the arithmetic system110terminates a process of determining whether the substrate holder18may be used, and transmits a use permission signal to the controller115. The controller115causes the plating apparatus to execute a plating process (to be described later).

When the number of predictable substrates is smaller than the threshold value, the arithmetic system110issues an alarm signal indicating that maintenance of the substrate holder18is necessary. The alarm signal is transmitted to at least the controller115. Upon receiving the alarm signal, the controller115issues a command to the substrate holder transport device40, grips the substrate holder18with the first transporter42of the substrate holder transport device40, and returns the substrate holder18to a predetermined location of the stocker24(step15).

Hereinafter, an embodiment of the plating process will be described. The substrate is held by the substrate holder18in a state where the surface to be plated is exposed from an opening58aof the substrate holder18. The substrate is electrically connected to the plurality of first electrical contacts88at a portion not touching the plating solution. The external electrical contact91of each substrate holder18contacts a power supply electrode (not illustrated) disposed on the edge of each plating cell38. The power supply electrode is electrically connected to a conductive film such as a seed layer of the substrate through the external electrical contact91, the electric wire92, the second electrical contact86, and the first electrical contact88. The power supply electrode is electrically connected to a power source (not illustrated).

After suspending the substrate holder18in the plating cell38filled with the plating solution, a plating voltage is applied between the anode in the plating cell38(not illustrated) and the substrate, so that the surface of the substrate is plated. After the plating is finished, the substrate holder18is transported to the second water washing tank30b, and the surface of the substrate is washed. After the substrate washing, the substrate and substrate holder18are dried in the blow tank32. The first transporter42of the substrate holder transport device40grips the dried substrate holder18and places the substrate holder18on the mounting plate52of the fixing station20. The dried substrate is taken out from the substrate holder18and returned to a cassette10.