Plating apparatus and method for diagnosing the same

Abstract

Accurately understand the operating status of your plating equipment. A plating apparatus for forming a plating film on a substrate is provided, the plating apparatus comprising: a substrate holder configured to hold the substrate; a plating tank configured to accommodate a plating solution together with the substrate holder; an anode arranged in the plating tank so as to face the substrate held by the substrate holder; a film thickness measurement unit configured to measure the film thickness of the plating film formed on the substrate at each point on the substrate; and a control unit for controlling the plating apparatus, wherein the control unit comprises: an imaging subunit configured to generate an image map representing the film thickness distribution on the substrate based on the film thickness measurement values ​​at each point on the substrate; a machine learning model trained to output an estimated status related to the plating process of the plating apparatus when the image map is input; and a diagnostic subunit configured to diagnose the plating apparatus based on the estimated status output from the machine learning model.

Description

[Technical Field]

[0001] The present invention relates to a plating apparatus and a method for diagnosing the plating apparatus. [Background technology]

[0002] The plating apparatus includes a substrate holder for holding a substrate, a plating tank containing a plating solution, and an anode disposed in the plating tank so as to face the substrate held by the substrate holder.Technical developments have been made to improve the flatness of the plating film thickness formed on the substrate in the plating apparatus (see, for example, Patent Document 1). [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Patent No. 7373684 Summary of the Invention [Problem to be solved by the invention]

[0004] In order to improve the flatness of the plating film thickness formed on the substrate, it is necessary to accurately grasp the operating status of the plating equipment. [Means for solving the problem]

[0005] According to one embodiment, there is provided a plating apparatus for forming a plating film on a substrate, the plating apparatus comprising: a substrate holder configured to hold the substrate; a plating tank configured to accommodate a plating solution together with the substrate holder; an anode arranged in the plating tank so as to face the substrate held by the substrate holder; a film thickness measurement unit configured to measure the film thickness of the plating film formed on the substrate at each point on the substrate; and a control unit for controlling the plating apparatus, the control unit comprising: an imaging subunit configured to generate an image map representing the film thickness distribution on the substrate based on the film thickness measurement values ​​at each point on the substrate; a machine learning model trained to output an estimated status related to the plating process of the plating apparatus when the image map is input; and a diagnosis subunit configured to diagnose the plating apparatus based on the estimated status output from the machine learning model. [Brief explanation of the drawings]

[0006] [Figure 1] 1 is an overall configuration diagram of a plating apparatus according to an embodiment of the present invention; [Figure 2] FIG. 2 is a schematic cross-sectional side view of a plating module. [Figure 3] FIG. 2 is a partial schematic cross-sectional view of a substrate holder holding a substrate. [Figure 4] FIG. 2 is an overall plan view of a substrate held by a substrate holder. [Figure 5] 1 is a schematic diagram showing bumps formed on a substrate using a plating apparatus according to an embodiment of the present invention. [Figure 6] FIG. 2 is a schematic diagram showing an example of the arrangement of bumps formed on a substrate. [Figure 7] 1 is a block diagram of an exemplary system for implementing a method according to an embodiment of the present invention; [Figure 8A] FIG. 2 is an exemplary functional block diagram of a control unit according to an embodiment of the present invention. [Figure 8B]FIG. 2 is an exemplary functional block diagram of a system including a control unit according to an embodiment of the present invention. [Figure 8C] FIG. 2 is an exemplary functional block diagram of a system including a control unit according to an embodiment of the present invention. [Figure 8D] FIG. 2 is an exemplary functional block diagram of a system including a control unit according to an embodiment of the present invention. [Figure 9] FIG. 10 is a diagram illustrating an example of an image map. [Figure 10] FIG. 1 is a block diagram of an exemplary machine learning model according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0007] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Hereinafter, embodiments of the present invention will be described with reference to the accompanying drawings. In the drawings described below, identical or corresponding components are designated by the same reference numerals, and redundant description will be omitted.

[0008] FIG. 1 is a diagram illustrating the overall configuration of a plating apparatus 10 according to one embodiment of the present invention. As shown in FIG. 1, the plating apparatus 10 includes two cassette tables 102, an aligner 104 that aligns the positions of the substrate's orientation flat, notch, and other features to a predetermined direction, and a spin rinse dryer 106 that spins the plated substrate at high speed to dry it. The cassette table 102 carries a cassette 100 containing substrates such as semiconductor wafers. A load / unload station 120 is provided near the spin rinse dryer 106, where a substrate holder 30 is placed to load and unload the substrate. A transfer robot 122 is located at the center of these units 102, 104, 106, and 120, and transports substrates between these units.

[0009] The load / unload station 120 is equipped with a flat loading plate 152 that can slide laterally along rails 150. The two substrate holders 30 are placed horizontally in parallel on this loading plate 152, and after a substrate is transferred between one substrate holder 30 and the transport robot 122, the loading plate 152 is slid laterally, and a substrate is transferred between the other substrate holder 30 and the transport robot 122.

[0010] The plating apparatus 10 further includes a stocker 124, a pre-wet module 126, a pre-soak module 128, a first rinse module 130a, a blow module 132, a second rinse module 130b, and a plating module 110. The stocker 124 stores and temporarily places the substrate holder 30. The pre-wet module 126 immerses the substrate in pure water. The pre-soak module 128 etches away an oxide film on the surface of a conductive layer, such as a seed layer, formed on the surface of the substrate. The first rinse module 130a rinses the pre-soaked substrate together with the substrate holder 30 in a cleaning solution (such as pure water). The blow module 132 drains the substrate after cleaning. The second rinse module 130b rinses the plated substrate together with the substrate holder 30 in a cleaning solution. The load / unload station 120, the stocker 124, the pre-wet module 126, the pre-soak module 128, the first rinse module 130a, the blow module 132, the second rinse module 130b, and the plating module 110 are arranged in this order.

[0011] The plating module 110 is configured, for example, by housing multiple plating tanks 114 inside an overflow tank 136. In the example of Fig. 1, the plating module 110 has eight plating tanks 114. Each plating tank 114 is configured to house one substrate therein and to immerse the substrate in the plating solution held therein to perform plating such as copper plating on the surface of the substrate.

[0012] The plating apparatus 10 includes a transfer device 140, which is positioned beside each of these devices and uses, for example, a linear motor system, to transfer the substrate holder 30 together with the substrate between these devices. The transfer device 140 includes a first transfer device 142 and a second transfer device 144. The first transfer device 142 is configured to transfer substrates between the load / unload station 120, the stocker 124, the pre-wet module 126, the pre-soak module 128, the first rinse module 130a, and the blow module 132. The second transfer device 144 is configured to transfer substrates between the first rinse module 130a, the second rinse module 130b, the blow module 132, and the plating module 110. The plating apparatus 10 may include only the first transfer device 142 without the second transfer device 144.

[0013] On both sides of the overflow tank 136, a paddle driver 160 and a paddle follower 162 are arranged, which are located inside each plating tank 114 and drive paddles that act as stirring rods to stir the plating solution in the plating tank 114.

[0014] The plating apparatus 10 further includes a film thickness measurement unit 300 for measuring the film thickness of the plating film formed on the substrate, and a control unit 400 for performing various controls on each part of the plating apparatus 10 and analyzing various data related to the plating process in the plating apparatus 10. The film thickness measurement unit 300 may be provided as a part of the plating apparatus 10, either built-in or attached to the plating apparatus 10, as shown in FIG. 1, or may be located remotely from the plating apparatus 10 as an independent device separate from the plating apparatus 10. Similarly, the control unit 400 may be built-in or attached to the plating apparatus 10 as a part of the plating apparatus 10, as shown in FIG. 1, or may be located remotely from the plating apparatus 10 as an independent device separate from the plating apparatus 10, and may be communicatively connected to the plating apparatus 10 and the film thickness measurement unit 300 via a network such as a LAN (local area network) or the Internet.

[0015] An example of a series of plating processes using this plating apparatus 10 will be described below. First, a substrate is removed from the cassette 100 mounted on the cassette table 102 by the transfer robot 122 and transferred to the aligner 104. The aligner 104 aligns the positions of the orientation flat, notch, etc. to a predetermined direction. The substrate, whose direction has been aligned by the aligner 104, is then transferred to the load / unload station 120 by the transfer robot 122.

[0016] In the load / unload station 120, two substrate holders 30 housed in the stocker 124 are simultaneously grasped by the first transfer device 142 of the transfer device 140 and transferred to the load / unload station 120. The two substrate holders 30 are then simultaneously placed horizontally on the mounting plate 152 of the load / unload station 120. In this state, the transfer robot 122 transfers substrates to each substrate holder 30, and the transferred substrates are held by the substrate holders 30.

[0017] Next, two substrate holders 30 holding substrates are simultaneously grasped by the first transfer device 142 of the transfer device 140 and stored in the pre-wet module 126. Next, the substrate holders 30 holding the substrates processed in the pre-wet module 126 are transferred by the first transfer device 142 to the pre-soak module 128, where the oxide film on the substrates is etched. Subsequently, the substrate holders 30 holding the substrates are transferred to the first rinse module 130a, where the surfaces of the substrates are rinsed with pure water stored in the first rinse module 130a.

[0018] The substrate holders 30 holding the substrates after rinsing are transported by the second transport device 144 from the first rinse module 130a to the plating module 110 and stored in the plating tanks 114 filled with plating solution. The second transport device 144 sequentially repeats the above procedure to sequentially store the substrate holders 30 holding the substrates in the plating tanks 114 of the plating modules 110.

[0019] In each plating tank 114, a plating current is supplied between the anode (not shown) in the plating tank 114 and the substrate, and simultaneously the paddle driving unit 160 and the paddle following unit 162 move the paddle back and forth parallel to the surface of the substrate, thereby plating the surface of the substrate.

[0020] After plating is completed, the two substrate holders 30 holding the plated substrates are simultaneously gripped by the second transfer device 144 and transported to the second rinse module 130b, where the substrates are immersed in pure water contained in the second rinse module 130b to rinse the surfaces of the substrates with pure water. Next, the substrate holders 30 are transported by the second transfer device 144 to the blow module 132, where water droplets adhering to the substrate holders 30 are removed by blowing air or the like. Thereafter, the substrate holders 30 are transported by the first transfer device 142 to the load / unload station 120.

[0021] In the load / unload station 120, the processed substrate is removed from the substrate holder 30 by the transfer robot 122 and transferred to the spin rinse dryer 106. The spin rinse dryer 106 rotates the plated substrate at high speed to dry it. The dried substrate is returned to the cassette 100 by the transfer robot 122.

[0022] Thereafter, the thickness of the plating film formed on the substrate is measured in the film thickness measurement unit 300. The measurement data of the plating film thickness is provided to the control unit 400 for analysis.

[0023] 2 is a schematic cross-sectional side view of the above-mentioned plating module 110. As shown in the figure, the plating module 110 includes an anode holder 220 configured to hold an anode 221, a substrate holder 30 configured to hold a substrate W, a plating tank 114 containing a plating solution Q containing an additive, and an overflow tank 136 that receives and discharges the plating solution Q that overflows from the plating tank 114. The plating tank 114 and the overflow tank 136 are separated by a partition wall 255. The anode holder 220 and the substrate holder 30 are housed inside the plating tank 114. As described above, the substrate holder 30 holding the substrate W is transported by the second transport device 144 (see FIG. 1) and housed in the plating tank 114.

[0024] Although only one plating tank 114 is shown in FIG. 2, as described above, the plating module 110 may include multiple plating tanks 114 having the same configuration as that shown in FIG.

[0025] The anode 221 is electrically connected to a positive terminal 271 of a power supply 270 via an electrical terminal 223 provided on the anode holder 220. The substrate W is electrically connected to a negative terminal 272 of the power supply 270 via a power supply contact 242 and an electrical terminal 243 provided on the substrate holder 30. The power supply contact 242 of the substrate holder 30 is in contact with the periphery of the substrate W. The power supply 270 is configured to supply a plating current between the anode 221 connected to the positive terminal 271 and the substrate W connected to the negative terminal 272, and to measure the applied voltage between the positive terminal 271 and the negative terminal 272.

[0026] The anode holder 220 holding the anode 221 and the substrate holder 30 holding the substrate W are immersed in the plating solution Q in the plating tank 114, and are arranged facing each other so that the anode 221 and the surface W1 to be plated of the substrate W are approximately parallel. A plating current is supplied to the anode 221 and the substrate W from the power source 270 while they are immersed in the plating solution Q in the plating tank 114. As a result, metal ions in the plating solution Q are reduced on the surface W1 to be plated of the substrate W, and a film is formed on the surface W1 to be plated.

[0027] The anode holder 220 has an anode mask 225 for adjusting the electric field between the anode 221 and the substrate W. The anode mask 225 is a substantially plate-shaped member made of, for example, a dielectric material, and is provided on the front surface of the anode holder 220 (the surface facing the substrate holder 30). In other words, the anode mask 225 is disposed between the anode 221 and the substrate holder 30. The anode mask 225 has a first opening 225a in its substantially central portion, through which a current flows between the anode 221 and the substrate W. The diameter of the opening 225a is preferably smaller than the diameter of the anode 221. The anode mask 225 may be configured so that the diameter of the opening 225a is adjustable.

[0028] The plating module 110 further includes a regulation plate 230 for adjusting the electric field between the anode 221 and the substrate W. The regulation plate 230 is a substantially plate-shaped member made of, for example, a dielectric material, and is disposed between the anode mask 225 and the substrate holder 30 (substrate W). The regulation plate 230 has a second opening 230a through which a current flows between the anode 221 and the substrate W. The diameter of the opening 230a is preferably smaller than the diameter of the substrate W. The regulation plate 230 may be configured so that the diameter of the opening 230a is adjustable.

[0029] A paddle 235 is provided between the regulation plate 230 and the substrate holder 30 to agitate the plating solution Q near the plating surface W1 of the substrate W. The paddle 235 is a generally rod-shaped member and is provided in the plating tank 114 so as to face vertically. One end of the paddle 235 is fixed to a paddle driving device 236. The operation of the paddle driving device 236 is controlled by a controller (not shown), and the paddle 235 is moved horizontally along the plating surface W1 of the substrate W by the paddle driving device 236. This agitates the plating solution Q.

[0030] The plating tank 114 has a plating solution supply port 256 for supplying plating solution Q into the tank. The overflow tank 136 has a plating solution discharge port 257 for discharging plating solution Q that has overflowed from the plating tank 114. The plating solution supply port 256 is located at the bottom of the plating tank 114, and the plating solution discharge port 257 is located at the bottom of the overflow tank 136.

[0031] When plating solution Q is supplied to plating tank 114 from plating solution supply port 256, plating solution Q overflows from plating tank 114, flows over partition wall 255, and flows into overflow tank 136. Plating solution Q that has flowed into overflow tank 136 is discharged from plating solution discharge port 257, and impurities are removed by a filter or the like provided in plating solution circulation device 258. Plating solution Q from which impurities have been removed is supplied to plating tank 114 via plating solution supply port 256 by plating solution circulation device 258.

[0032] FIG. 3 is a partial schematic cross-sectional view of the substrate holder 30 holding the substrate W. FIG. 4 is an overall plan view of the substrate W held by the substrate holder 30. As shown in FIG. 3, a seed layer 301 is formed in advance on the surface of the substrate W, and a photoresist layer 302 is formed on the seed layer 301. The photoresist layer 302 has openings 302a where a plating film is to be formed. The substrate holder 30 includes a power supply contact 242 and a seal member 241. The power supply contact 242 is a member made of a conductive material and is in electrical contact with the outer edge portion 62 of the substrate W. The seal member 241 is made of a material such as resin and is in mechanical contact with the substrate W on the inner periphery side of the substrate W relative to the power supply contact 242. The seal member 241 mechanically holds the substrate W and prevents the plating solution Q from penetrating the outer edge portion 62 of the substrate W and exposing the power supply contact 242 to the plating solution Q. 4, the substrate holder 30 has a plurality of power supply contacts 242 along the outer periphery of the substrate W. In the example of Fig. 4, the substrate holder 30 has 12 power supply contacts 242 arranged at equal intervals so as to contact the outer edge 62 of the substrate W. Each power supply contact 242 is connected to the negative terminal 272 of the power source 270 (see Fig. 2) via electrical wiring (not shown) built into the substrate holder 30, and a plating current flows to the substrate W through these power supply contacts 242.

[0033] FIG. 5 is a schematic diagram showing bumps formed on a substrate W using a plating apparatus 10 according to one embodiment of the present invention. In the example of FIG. 5 and the example of FIG. 6 described below, a plated film is formed as a bump (or pad), which is a cylindrical metal structure. However, the present invention is not limited to forming bumps on a substrate; it also includes forming a uniform plated film over the entire surface of the substrate or over a wide area of ​​the substrate surface. Note that FIG. 5 shows only a portion of the substrate W. Referring to FIG. 5, a thin metal seed layer 501 is formed on the surface of the substrate W in advance. During plating, power is supplied to this seed layer 501 (i.e., the surface of the substrate W) via the power supply contact 242 of the substrate holder 30. A photoresist layer 502 is formed on the seed layer 501, and the photoresist layer 502 has a circular opening 502a where the bump will be formed. The substrate W on which the photoresist layer 502 has been formed is held by the substrate holder 30 and immersed in a plating solution Q in a plating tank 114 for plating. During plating, the surface of the substrate W other than the circular openings 502a in the photoresist layer 502 is shielded from the plating solution Q by the photoresist layer 502. As a result, a plating film grows only on the bottom surfaces of the openings 502a in the photoresist layer 502, forming bumps 503 on the substrate W. After the plating process, the photoresist layer 502 is removed (see the right side of FIG. 5).

[0034] 6 is a schematic diagram showing an example of the arrangement of bumps formed on a substrate W. In this example, multiple vertical lines 602 and multiple horizontal lines 604 are provided as bump non-formation regions on the substrate W. A large number of bumps 503 are formed at predetermined intervals on each of these lines 602, 604 in the areas other than the bump non-formation regions. The bumps 503 are present all over the surface of the substrate W. It is required in the plating process to eliminate or reduce the variation in the height BH (see FIG. 5) of each of these multiple bumps 503, i.e., to form a plating film with a uniform film thickness distribution on the substrate W.

[0035] FIG. 7 is a block diagram of an exemplary system 700 for implementing a method according to one embodiment of the present invention. The system 700 includes a plating apparatus 10, a film thickness measurement unit 300, and a control unit 400. The plating apparatus 10, the film thickness measurement unit 300, and the control unit 400 are communicatively connected to each other via a network 710, such as a local area network (LAN) or the Internet. Alternatively, the film thickness measurement unit 300 and the control unit 400 may be incorporated into the plating apparatus 10 as part of its configuration, as shown in FIG. 1 above. The control unit 400 includes a processor 402 and a memory 404. The memory 404 stores a program 406 for implementing a method according to one embodiment of the present invention. The processor 402 reads and executes the program 406 from the memory 404. This implements the method according to one embodiment of the present invention.

[0036] 8A is an exemplary functional block diagram of a control unit 400 according to an embodiment of the present invention. As shown in FIG. 8A, the control unit 400 includes an imaging subunit 410, a machine learning model 420, a diagnosis subunit 430, and a calculation subunit 440. The functions of the imaging subunit 410, the machine learning model 420, the diagnosis subunit 430, and the calculation subunit 440 are realized by the processor 402 reading and executing the corresponding programs 406 from the memory 404.

[0037] The imaging subunit 410 is configured to generate an image map representing the film thickness distribution on the substrate W based on the film thickness measurement values ​​at each point on the substrate W obtained from the film thickness measurement unit 300. For example, a plating film is formed on the substrate W in the plating apparatus 10, and after the plating process is completed, the substrate W is transported to the film thickness measurement unit 300. The film thickness measurement unit 300 measures the film thickness of the plating film at multiple measurement points on the substrate W and supplies a group of measurement data to the imaging subunit 410. The measurement points on the substrate W may be arranged at appropriate intervals; the greater the number of measurement points, the higher the resolution of the image map that can be generated. In an embodiment in which bumps (see FIGS. 5 and 6) are formed on the substrate W, for example, the measurement points may be set at the position of each bump (i.e., the film thickness measurement unit 300 measures the height BH of each bump). The imaging subunit 410 can generate an image map based on the film thickness measurement values ​​at multiple measurement points on the substrate W supplied from the film thickness measurement unit 300. The generation of the image map may include, for example, converting the film thickness values ​​into color values ​​(RGB values) or brightness values ​​according to a predetermined conversion formula or conversion rule. An example of the generated image map is shown in FIG. 9.

[0038] The machine learning model 420 is configured to receive as input the image map generated by the imaging subunit 410 and output an estimated status related to the plating process of the plating apparatus 10. The machine learning model 420 is pre-trained using a large number of sets of training data including various operational statuses of the plating apparatus 10 when the plating apparatus 10 plates a substrate and image maps corresponding to each of the operational statuses. For example, the machine learning model 420 estimates whether or not power is being supplied to the plurality of power supply contacts 242 of the substrate holder 30 based on the image map representing the film thickness distribution on the substrate W. In another example, the machine learning model 420 estimates the state of the opening 225a of the anode mask 225 based on the image map representing the film thickness distribution on the substrate W. The machine learning model 420 may be configured to estimate both the state of the opening 225a of the anode mask 225 and whether or not power is being supplied to the plurality of power supply contacts 242 of the substrate holder 30 based on the image map representing the film thickness distribution on the substrate W. In addition, the machine learning model 420 may be configured to estimate any status of the plating apparatus 10 related to the film thickness of the plating film on the substrate W, other than whether the power supply to the power supply contact 242 is good or bad and the state of the opening 225a of the anode mask 225, based on an image map representing the film thickness distribution on the substrate W.

[0039] The diagnostic subunit 430 is configured to diagnose the plating apparatus 10 based on the estimated status output from the machine learning model 420 (e.g., an estimate of whether power is being supplied to the plurality of power supply contacts 242 of the substrate holder 30). For example, the diagnostic subunit 430 determines, based on the estimated status, that a problem such as a power supply failure has occurred in a specific power supply contact among the plurality of power supply contacts 242. The diagnostic subunit 430 may be configured to monitor the estimated status output from the machine learning model 420 over time and predict the occurrence of a failure or abnormality in the plating apparatus 10 based on the change in the estimated status over time.

[0040] The calculation subunit 440 is configured to simulate film thickness values ​​at each point on the substrate W according to predetermined simulation conditions. The calculation subunit 440 is used for the purpose of creating, by simulation, training data for the machine learning model 420. By using the calculation subunit 440, even if the amount of training data obtained by actually performing a plating process on the substrate W using the plating apparatus 10 is small, a variety of training data can be generated by simulation, thereby reducing the effort required to collect a sufficient amount of training data required to train the machine learning model 420.

[0041] FIG. 10 is a diagram illustrating an exemplary machine learning model 420 according to an embodiment of the present invention. The machine learning model 420 is configured by a neural network including an input layer 422 having multiple input nodes 421, one or more hidden layers (intermediate layers) 424, each having multiple nodes 423, and an output layer 426 having multiple output nodes 425. Each node is connected to multiple nodes in adjacent layers with a strength characterized by a weighting parameter. An image map representing the film thickness distribution on the substrate W, generated by the imaging subunit 410, is input to the input layer 422. The output layer 426 outputs an estimated status of the plating apparatus 10. The weighting parameters between each node of the neural network are adjusted so that this estimated status matches the status of the plating apparatus 10 in the training data. The parameter adjustment of the neural network is performed repeatedly using multiple sets of training data. This allows the machine learning model 420 to be trained.

[0042] The training data for training the machine learning model 420 includes first training data based on actual plating processing of substrates W. That is, the first training data is collected by actually plating multiple substrates in the plating apparatus 10. More specifically, plating processing is performed on one substrate by the plating apparatus 10. An operational status of the plating apparatus 10 is acquired during the plating processing. The operational status of the plating apparatus 10 may be, for example, a status regarding the quality of power supply to the multiple power supply contacts 242 of the substrate holder 30. The operational status of the plating apparatus 10 may also include a status regarding the state of the opening 225a of the anode mask 225 in the anode holder 220. Furthermore, the operational status of the plating apparatus 10 may include any other status of the plating apparatus 10 related to the thickness of the plating film on the substrate W. After plating processing on one substrate is completed, the film thickness measurement unit 300 measures the film thickness of the substrate, and the imaging subunit 410 generates an image map from the film thickness measurement results. The operational status of the plating apparatus 10 acquired during the plating process and the image map obtained from the imaging subunit 410 for this single substrate constitute one set of first training data. By repeating the same procedure for multiple substrates, multiple sets of first training data are collected. Note that the process conditions for the plating processes performed on the multiple substrates may vary. This allows for the creation of a wide variety of sets of first training data.

[0043] The training data for training the machine learning model 420 may include second training data based on a simulation in addition to the first training data described above. Specifically, the second training data is generated based on the results of a simulation performed by the calculation subunit 440. The calculation subunit 440 calculates the thickness of the plating film at each point on the substrate according to predetermined simulation conditions that specify the operating status of the plating apparatus 10 (e.g., the amount of power supplied from each power supply contact 242 of the substrate holder 30, the size of the opening 225a in the anode mask 225, etc.). The imaging subunit 410 generates an image map from the film thickness values ​​calculated by the calculation subunit 440. The operating status of the plating apparatus 10 used in the simulation and the image map generated by the imaging subunit 410 from the simulation results constitute one set of second training data. Multiple sets of second training data are collected by repeating the simulation while changing various simulation conditions.

[0044] In this manner, the machine learning model 420 is trained using training data including the first training data and the second training data. When an image map generated from film thickness measurements of a plated substrate is input to the trained machine learning model 420, the trained machine learning model 420 can estimate the status of the plating apparatus 10 when plating the substrate. For example, the trained machine learning model 420 can estimate whether the power supply to the multiple power supply contacts 242 of the substrate holder 30 is good or bad when the plating apparatus 10 plated the substrate. Based on this estimation result, the diagnostic subunit 430 can determine, for example, that a specific power supply contact among the multiple power supply contacts 242 is experiencing a problem, such as a power supply failure. The estimation of the status of the plating apparatus 10 (i.e., the estimated status output from the machine learning model 420) may be based on a single image map obtained from a single plated substrate, or may be based on multiple image maps obtained from multiple substrates plated in the same plating tank among the multiple plating tanks 114.

[0045] The machine learning model 420 may be further trained using additional training data. The additional training data may be, for example, feature quantities extracted from the image map. For example, the feature quantity may be used to identify whether the film thickness distribution is generally concave or convex from the image map. Furthermore, the feature quantity may be used to extract the center position of a concave or convex surface from the image map when the film thickness distribution is generally concave or convex. Furthermore, the feature quantity may be used to extract the position of a locally low or high area in the film thickness distribution from the image map. The additional training data may also include statistical data (e.g., average, maximum, minimum, etc.) calculated from the film thickness measurement values ​​obtained from the film thickness measurement unit 300 or the film thickness values ​​calculated by the calculation subunit 440. The machine learning model 420 is configured so that these feature quantities and / or statistical data are input to the input layer 422. Configuring the machine learning model 420 in this manner can further improve the accuracy of the estimated status of the plating apparatus 10 output from the machine learning model 420.

[0046] The machine learning model 420 may be further configured to output setting parameters suggesting optimal process conditions for the plating apparatus 10. For example, when the amount of power supply to a specific one of the plurality of power supply contacts 242 of the substrate holder 30 is reduced, the machine learning model 420 may output setting parameters for the plating apparatus 10 that can achieve a better plating process, in addition to outputting an estimated status regarding the quality of the power supply to the plurality of power supply contacts 242. Such setting parameters may include, for example, an instruction to increase the amount of power supplied from the power supply contact 242 with the reduced amount of power. Alternatively, the setting parameters may include an instruction to adjust the opening 225a of the anode mask 225 so as to increase the electric field in the plating solution Q around the power supply contact 242 with the reduced amount of power.

[0047] Note that the functional blocks of the control unit 400 shown in FIG. 8A are merely an example and do not limit the present invention. For example, as shown in FIG. 8B, the control unit 400 may include only the machine learning model 420 and the diagnostic subunit 430, and the machine learning model 420 may receive image maps as input from two imaging subunits 410 (the imaging subunit 410 provided corresponding to the film thickness measurement unit 300 and the imaging subunit 410 provided corresponding to the calculation subunit 440) located outside the plating apparatus 10. Alternatively, as shown in FIG. 8C, only the calculation subunit 440 and the imaging subunit 410 corresponding to the calculation subunit 440 may be located outside the plating apparatus 10. Furthermore, as shown in FIG. 8D, the control unit 400 may include the imaging subunit 410, the machine learning model 420, and the diagnostic subunit 430, and the film thickness measurement unit 300 and the calculation subunit 440 may be located outside the plating apparatus 10. In addition, in FIGS. 8B to 8D, the functions of each unit / subunit / model are the same as those in FIG. 8A.

[0048] Although the embodiments of the present invention have been described above based on several examples, the above-described embodiments of the invention are intended to facilitate understanding of the present invention and are not intended to limit the present invention. The present invention may be modified or improved without departing from the spirit thereof, and the present invention naturally includes equivalents thereof. Furthermore, any combination or omission of the components described in the claims and specification is possible within the scope of solving at least part of the above-described problems or achieving at least part of the effects. [Explanation of symbols]

[0049] 10 Plating equipment 300 Film Thickness Measurement Unit 400 Control Unit 402 processor 404 Memory 406 Program 410 Imaging Subunit 420 Machine Learning Models 430 Diagnostic Subunit 440 Computational Subunit 700 System 710 Network

Claims

1. A plating apparatus for forming a plating film on a substrate, comprising: a substrate holder configured to hold the substrate; a plating tank configured to contain a plating solution together with the substrate holder; an anode disposed in the plating tank so as to face the substrate held by the substrate holder; a control unit for controlling the plating apparatus; Equipped with The control unit a machine learning model that is trained to output an estimated status related to the plating process of the plating apparatus when an image map representing the thickness distribution of the plating film formed on the substrate is input; a diagnostic subunit configured to perform a diagnosis on the plating apparatus based on the estimated status output from the machine learning model; A plating apparatus comprising:

2. A plating apparatus for forming a plating film on a substrate, comprising: a substrate holder configured to hold the substrate; a plating tank configured to contain a plating solution together with the substrate holder; an anode disposed in the plating tank so as to face the substrate held by the substrate holder; a control unit for controlling the plating apparatus; Equipped with The control unit an imaging subunit configured to generate an image map representing a film thickness distribution on the substrate based on film thickness measurements at each point on the substrate of a plating film formed on the substrate; a machine learning model trained to receive the image map as an input and output an estimated status associated with the plating process of the plating device; a diagnostic subunit configured to perform a diagnosis on the plating apparatus based on the estimated status output from the machine learning model; A plating apparatus comprising:

3. A plating apparatus for forming a plating film on a substrate, comprising: a substrate holder configured to hold the substrate; a plating tank configured to contain a plating solution together with the substrate holder; an anode disposed in the plating tank so as to face the substrate held by the substrate holder; a film thickness measurement unit configured to measure the film thickness of the plating film formed on the substrate at each point on the substrate; a control unit for controlling the plating apparatus; Equipped with The control unit an imaging subunit configured to generate an image map representing a film thickness distribution on the substrate based on the film thickness measurements at each point on the substrate; a machine learning model trained to receive the image map as an input and output an estimated status associated with the plating process of the plating device; a diagnostic subunit configured to perform a diagnosis on the plating apparatus based on the estimated status output from the machine learning model; A plating apparatus comprising:

4. the substrate holder includes a plurality of power supply contacts for supplying power to the substrate; the estimated status of the plating apparatus relates to whether power is being supplied to the plurality of power supply contacts of the substrate holder; The plating apparatus according to any one of claims 1 to 3.

5. an anode mask disposed near the anode and having an opening for adjusting an electric field in the plating solution in the plating tank; the estimated status of the plating apparatus is related to the state of the openings in the anode mask; The plating apparatus according to claim 4.

6. 2. The plating apparatus of claim 1, wherein the machine learning model is trained using first training data based on actual plating processing of the substrate, and the first training data includes an actual operating status of the plating apparatus during the plating processing and the image map generated based on film thickness measurements of the plated substrate.

7. The machine learning model is trained using first training data based on an actual plating process on the substrate, the first training data including an actual operating status of the plating device during the plating process and the image map generated based on film thickness measurements of the plated substrate; the control unit further comprises a calculation subunit configured to simulate a film thickness value at each point on the substrate according to a predetermined simulation condition; the imaging subunit is further configured to generate an image map based on the film thickness values ​​from the calculation subunit; The machine learning model is further trained using second training data based on a simulation, and the second training data is the plating equipment assumed in the simulation.

4. The plating apparatus according to claim 2, further comprising: an operational status of the plating apparatus; and the image map generated based on the simulated film thickness values.

8. 8. The plating apparatus according to claim 7, wherein the first and second teacher data further include feature amounts extracted from the image map.

9. 9. The plating apparatus according to claim 8, wherein the feature value includes at least one of: (i) whether the film thickness distribution is concave or convex as a whole; (ii) if the film thickness distribution is concave or convex as a whole, the center position of the concave or convex; and (iii) the position of a locally low or high area in the film thickness distribution.

10. 8. The plating apparatus according to claim 7, wherein the first and second teacher data further include statistical data calculated from the measured film thickness values ​​or the simulated film thickness values.

11. 4. The plating apparatus according to claim 1, wherein the diagnostic subunit is further configured to predict the occurrence of a failure or abnormality in the plating apparatus based on a time change in the estimated status output from the machine learning model.

12. The plating apparatus includes a plurality of plating tanks, The plating apparatus according to claim 11 , wherein the estimated status is an output from the machine learning model for the image maps obtained from a plurality of substrates respectively processed in the same plating tank among the plurality of plating tanks.

13. The plating apparatus according to claim 1 , wherein the machine learning model is further trained to output setting parameters of the plating apparatus that suggest optimal process conditions for the plating apparatus.

14. A diagnostic method for a plating apparatus that forms a plating film on a substrate, comprising: acquiring a film thickness measurement value of a plating film formed on the substrate at each point on the substrate from a film thickness measurement unit; generating an image map representing a film thickness distribution on the substrate based on the film thickness measurements at each point on the substrate; deriving an estimated status of the plating equipment from the image map using a machine learning model, the machine learning model being trained to receive the image map as an input and output the estimated status associated with a plating process of the plating equipment; performing a diagnosis on the plating equipment based on the estimated status from the machine learning model; A diagnostic method for a plating apparatus, comprising:

15. The plating apparatus includes a plurality of plating tanks, 15. The plating apparatus diagnostic method of claim 14, wherein the estimated status is an output from the machine learning model for the image maps obtained from a plurality of substrates respectively processed in the same plating tank among the plurality of plating tanks.

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