Information processing device, inference device, machine learning device, information processing method, inference method, and machine learning method

Abstract

To provide an information processing device with which it is possible to analyze the occurrence cause of cracks in a board without relying on a user's experience and knowledge.SOLUTION: An information processing device 5 comprises: an information acquisition unit 500 which, in a substrate treatment process carried out by a substrate treatment device equipped with a polishing unit that performs a substrate polishing process and a substrate conveyance unit that performs substrate delivery to and from the polishing unit, acquires crack occurrence state information that includes crack state information that indicates a crack state when a crack has occurred to a substrate and device state information that indicates the state of the polishing unit when a substrate treatment process is carried out on the substrate; and a crack occurrence step identification unit 501 for inputting the crack occurrence state information having been acquired by the information acquisition unit 500 in accordance with the occurrence of a substrate crack to a learning model 11 having been trained by machine learning for a correlation between the crack occurrence state information and crack occurrence step information that indicates a step that is included in the substrate treatment steps and which is the occurrence cause of a substrate crack, thereby identifying a step that is the occurrence cause of the substrate crack.SELECTED DRAWING: Figure 17

Description

[Technical Field]

[0001] The present invention relates to an information processing device, an inference device, a machine learning device, an information processing method, an inference method, and a machine learning method. [Background technology]

[0002] One known type of substrate processing apparatus performs various processes on substrates such as semiconductor wafers, and is known to perform chemical mechanical polishing (CMP) processing. The substrates that undergo various processes in the substrate processing apparatus are formed in a thin plate shape, and therefore cracks may occur in the substrates not only during the process of actually polishing the substrates but also during the process of transferring the substrates between units (see, for example, Patent Documents 1 and 2). [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2020-188233 [Patent Document 2] Japanese Patent Application Laid-Open No. 2000-223380 Summary of the Invention [Problem to be solved by the invention]

[0004] If a crack occurs in a substrate for some reason during a substrate processing process performed by a substrate processing apparatus, it is necessary to inspect each unit of the substrate processing apparatus and check various apparatus parameters referenced by the substrate processing apparatus during operation. In particular, if it remains unclear which process, among the series of processes performed by the polishing unit that performs the polishing process and the substrate transport unit that transfers the substrate between the polishing unit, the process in which the crack occurred in the substrate cannot be performed effectively, and the crack may recur. Furthermore, analyzing the cause of a crack in a substrate largely depends on the experience and knowledge of the user of the substrate processing apparatus, and if the analysis is inappropriate, it may lead to more serious problems and reduced productivity.

[0005] In view of the above-mentioned problems, the present invention aims to provide an information processing device, an inference device, a machine learning device, an information processing method, an inference method, and a machine learning method that can analyze the cause of substrate cracks in a substrate processing device without relying on the experience or knowledge of the user. [Means for solving the problem]

[0006] In order to achieve the above object, an information processing device according to one aspect of the present invention comprises: an information acquisition unit that acquires crack occurrence state information including crack state information indicating the state of a crack when a crack occurs in the substrate and apparatus state information indicating the state of the polishing unit when the substrate processing process is performed on the substrate, in a substrate processing process performed by a substrate processing apparatus including a polishing unit that performs a polishing process on the substrate and a substrate transport unit that transfers the substrate between the polishing unit; The device is equipped with a crack occurrence process identification unit that identifies the process that causes cracks in the substrate by inputting the crack occurrence state information acquired by the information acquisition unit in response to the occurrence of a crack in the substrate into a learning model that has been trained through machine learning to determine the correlation between the crack occurrence state information and crack occurrence process information that indicates the process that causes cracks in the substrate among the processes included in the substrate processing process. [Effects of the Invention]

[0007] According to an information processing device of one aspect of the present invention, crack occurrence status information including crack status information and device status information obtained in response to the occurrence of a crack in a substrate is input into a learning model, thereby identifying the cause of the crack in the substrate, and therefore, it is possible to deal with the crack in the substrate quickly and appropriately without relying on the user's experience or knowledge.

[0008] Problems, configurations, and effects other than those described above will become apparent from the detailed description of the invention that follows. [Brief explanation of the drawings]

[0009] [Figure 1] 1 is an overall configuration diagram showing an example of a substrate processing system 1. FIG. [Figure 2] FIG. 2 is a plan view showing an example of a substrate processing apparatus 2. [Figure 3] FIG. 2 is a perspective view showing an example of first to fourth polishing sections 22A to 22D. [Figure 4] FIG. 2 is a cross-sectional view schematically illustrating an example of a top ring 221. [Figure 5] 10 is a plan view schematically illustrating an example of first and second linear transporters 230A and 230B. FIG. [Figure 6] 10 is a front view schematically showing an example of first and second linear transporters 230A and 230B. FIG. [Figure 7] FIG. 10 is a schematic diagram illustrating an example of a substrate receiving step. [Figure 8] FIG. 10 is a schematic diagram illustrating an example of a substrate delivery process. [Figure 9] FIG. 2 is a block diagram showing an example of a substrate processing apparatus 2. [Figure 10] 10 is a timing chart showing an example of a substrate processing process performed by the substrate processing apparatus 2. [Figure 11] FIG. 9 is a hardware configuration diagram showing an example of a computer 900. [Figure 12]3 is a data configuration diagram showing an example of history information 30 managed by the database device 3. FIG. [Figure 13] FIG. 2 is a block diagram showing an example of a machine learning device 4. [Figure 14] 1 is a diagram showing an example of a learning model 10 and learning data 11. FIG. [Figure 15] 10 is a flowchart showing an example of a machine learning method performed by the machine learning device 4. [Figure 16] FIG. 2 is a block diagram showing an example of an information processing device 5. [Figure 17] FIG. 2 is a functional explanatory diagram showing an example of an information processing device 5. [Figure 18] 10 is a flowchart showing an example of an information processing method by the information processing device 5. DETAILED DESCRIPTION OF THE INVENTION

[0010] Hereinafter, an embodiment for carrying out the present invention will be described with reference to the drawings. The scope necessary for the explanation to achieve the object of the present invention will be schematically shown, and the scope necessary for explaining the relevant parts of the present invention will be mainly explained, and the parts that are omitted from the explanation will be based on publicly known techniques.

[0011] 1 is an overall configuration diagram showing an example of a substrate processing system 1. The substrate processing system 1 according to this embodiment functions as a system for managing a substrate processing step in which a chemical mechanical polishing process (hereinafter referred to as a "polishing process") is performed on a substrate W such as a semiconductor wafer (hereinafter referred to as a "wafer").

[0012] The substrate processing system 1 mainly comprises a substrate processing apparatus 2, a database device 3, a machine learning device 4, an information processing device 5, and a user terminal device 6. Each of the devices 2 to 6 is configured, for example, by a general-purpose or dedicated computer (see FIG. 11 described later), and is connected to a wired or wireless network 7 so as to be able to mutually transmit and receive various data (in FIG. 1, transmission and reception of some data is indicated by dashed arrows). Note that the number of the devices 2 to 6 and the connection configuration of the network 7 are not limited to the example in FIG. 1 and may be changed as appropriate.

[0013] The substrate processing apparatus 2 is an apparatus that performs a polishing process to flatten the surface of a wafer W. The substrate processing apparatus 2 is composed of multiple units, and performs a series of operations on one or multiple wafers W, such as loading, polishing, cleaning, drying, film thickness measurement, and unloading. In doing so, the substrate processing apparatus 2 controls the operation of each unit by referring to apparatus setting information 265 consisting of multiple apparatus parameters set for each unit, and substrate recipe information 266 that defines the polishing conditions for the polishing process. The substrate processing apparatus 2 also includes a camera 201 attached at a position where it can photograph the wafer W.

[0014] The substrate processing apparatus 2 transmits various reports R to the database device 3, the user terminal device 6, etc. in accordance with the operation of each unit. The various reports R include, for example, process information that identifies the target wafer W when each process is performed, apparatus status information that indicates the status of each unit when each process is performed, image information captured by the camera 201, event information detected by the substrate processing apparatus 2, operation information of the user (operator, production manager, maintenance manager, etc.) on the substrate processing apparatus 2, etc.

[0015] The database device 3 is a device that manages history information 30 when the substrate processing apparatus 2 operates. The database device 3 receives various reports R from the substrate processing apparatus 2 as needed and registers them in the history information 30 for each substrate processing apparatus 2, whereby the contents of the reports R are accumulated in the history information 30 together with date and time information. In addition to the history information 30, the database device 3 may also store apparatus setting information 265 and substrate recipe information 266, and in this case, the substrate processing apparatus 2 may refer to these pieces of information.

[0016] The machine learning device 4 operates as a main player in the learning phase of machine learning, and, for example, acquires part of the history information 30 from the database device 3 as learning data 11, and generates a learning model 10 to be used in the information processing device 5 by machine learning. The trained learning model 10 is provided to the information processing device 5 via the network 7, a recording medium, or the like. In this embodiment, a case will be described in which supervised learning is adopted as the machine learning method.

[0017] The information processing device 5 operates as the main body of the inference phase of machine learning, and when a crack (including damage such as chips or cracks) occurs in a wafer W during a substrate processing process by the substrate processing device 2, it uses the learning model 10 generated by the machine learning device 4 to identify the process (crack occurrence process) that caused the crack in the wafer W, and transmits crack occurrence process information indicating that process to the database device 3, user terminal device 6, etc.

[0018] The user terminal device 6 is a terminal device used by a user, and may be a stationary device or a portable device. The user terminal device 6 accepts various input operations via a display screen such as an application program or a web browser, and displays various information (e.g., event notifications, crack occurrence process information, history information 30, etc.) via the display screen. The user terminal device 6 also includes a camera 60 that is built-in or can be externally connected.

[0019] The camera 201 of the substrate processing apparatus 2 and the camera 60 of the user terminal device 6 function as an imaging device that, for example, when a crack occurs in a wafer W, captures an image of the wafer W on which the crack has occurred (hereinafter referred to as a "cracked wafer") and generates image information. The imaging device may be, for example, like the camera 201 of the substrate processing apparatus 2, an imaging device that automatically captures an image of the cracked wafer W during a series of operations of the substrate processing apparatus 2, or like the camera 60 of the user terminal device 6, an imaging device that manually captures an image of the cracked wafer W based on a user's imaging operation. The imaging device may be either the camera 201 of the substrate processing apparatus 2 or the camera 60 of the user terminal device 6, or may be configured as an external device such as an appearance inspection device instead of or in addition to these. Furthermore, the camera 201 of the substrate processing apparatus 2 captures an image of all wafers W regardless of whether a crack has occurred in the wafer W. You can also take a W automatically.

[0020] (Substrate processing device 2) 2 is a plan view showing an example of a substrate processing apparatus 2. The substrate processing apparatus 2 is configured to include a load / unload unit 21, a polishing unit 22, a substrate transport unit 23, a cleaning unit 24, a film thickness measurement unit 25, and a control unit 26 inside a housing 20 that is substantially rectangular in plan view. The load / unload unit 21 is separated from the polishing unit 22, the substrate transport unit 23, and the cleaning unit 24 by a first partition wall 200A, and the substrate transport unit 23 and the cleaning unit 24 are separated from each other by a second partition wall 200B.

[0021] (load / unload unit) The load / unload unit 21 includes first to fourth front loading sections 210A to 210D on which wafer cassettes (FOUPs, etc.) capable of storing a large number of wafers W in the vertical direction are placed, a transport robot 211 that can move up and down along the storage direction (vertical direction) of the wafers W stored in the wafer cassettes, and a horizontal movement mechanism section 212 that moves the transport robot 211 along the arrangement direction of the first to fourth front loading sections 210A to 210D (the short side direction of the housing 20).

[0022] The transfer robot 211 is configured to be able to access the wafer cassettes placed on each of the first to fourth front load sections 210A to 210D, the substrate transfer unit 23 (specifically, the lifter 232 described below), the cleaning unit 24 (specifically, the drying chamber 241 described below), and the film thickness measurement unit 25, and is equipped with upper and lower two-stage hands (not shown) for transferring wafers W between them. The lower hand is used when transferring a wafer W before processing, and the upper hand is used when transferring a wafer W after processing. When transferring a wafer W to or from the substrate transfer unit 23 or the cleaning unit 24, a shutter (not shown) provided on the first partition wall 200A is opened and closed.

[0023] (Polishing unit) The polishing unit 22 includes first to fourth polishing sections 22A to 22D that respectively perform polishing (flattening) of the wafer W. The first to fourth polishing sections 22A to 22D are arranged side by side along the longitudinal direction of the housing 20.

[0024] 3 is a perspective view showing an example of the first to fourth polishing sections 22A to 22D. The first to fourth polishing sections 22A to 22D have the same basic configuration and functions.

[0025] Each of the first to fourth polishing units 22A to 22D includes a polishing table 220 on which a polishing pad 2200 having a polishing surface is attached, a top ring (polishing head) 221 for holding a wafer W and polishing the wafer W while pressing it against the polishing pad 2200 on the polishing table 220, a polishing liquid supply nozzle 222 for supplying a polishing liquid (slurry) or a dressing liquid (e.g., pure water) to the polishing pad 2200, a dresser 223 for dressing the polishing surface of the polishing pad 2200, and an atomizer 224 for spraying a mixed fluid of a liquid (e.g., pure water) and a gas (e.g., nitrogen gas) or a liquid (e.g., pure water) onto the polishing surface in the form of a mist.

[0026] The polishing table 220 is supported by a polishing table shaft 220a and includes a rotational movement mechanism 220b that rotates the polishing table 220 about its axis. The top ring 221 is supported by a top ring shaft 221a that is movable in the vertical direction and includes a rotational movement mechanism 221c that rotates the top ring 221 about its axis, a vertical movement mechanism 221d that moves the top ring 221 in the vertical direction, and a swivel movement mechanism 221e that swivels (oscillates) the top ring 221 around the support shaft 221b. The polishing liquid supply nozzle 222 is supported by a support shaft 222a and includes a swivel movement mechanism 222b that swivels the polishing liquid supply nozzle 222 around the support shaft 222a. The dresser 223 is supported by a dresser shaft 223a that can move in the vertical direction and includes a rotation movement mechanism 223c that rotates the dresser 223 around the axis of the dresser shaft 223a, a vertical movement mechanism 223d that moves the dresser 223 in the vertical direction, and a swivel movement mechanism 223e that swivels the dresser 223 around the support shaft 223b. The atomizer 224 is supported by a support shaft 224a and includes a swivel movement mechanism 224b that swivels the atomizer 224 around the support shaft 224a.

[0027] Note that, in FIG. 3, the specific configurations of the rotational movement mechanism units 220b, 221c, 223c, the up-down movement mechanism units 221d, 223d, and the swivel movement mechanism units 221e, 222b, 223e, 224b are omitted, but they are configured by appropriately combining, for example, actuators such as motors and air cylinders, driving force transmission mechanisms such as linear guides, ball screws, gears, belts, couplings, and bearings, and sensors such as linear sensors, encoder sensors, and limit sensors.

[0028] 4 is a cross-sectional view schematically illustrating an example of the top ring 221. The top ring 221 includes a top ring body 2210 attached to a top ring shaft 221a, a substantially disk-shaped carrier 2211 arranged inside the top ring body 2210, a membrane 2212 arranged below the carrier 2211 and pressing the wafer W against the polishing pad 2200, a substantially annular retaining ring 2213 arranged on the outer periphery of the carrier 2211 and directly pressing the polishing pad 2200, and a retaining ring air bag 2214 arranged between the top ring body 2210 and the retaining ring 2213 and pressing the retaining ring 2213 against the polishing pad 2200.

[0029] The membrane 2212 is made of an elastic film and has a plurality of concentric partition walls 2212e therein, thereby providing first to fourth membrane pressure chambers 2212a to 2212d concentrically arranged from the center of the top ring body 2210 toward the periphery. The membrane 2212 also has a plurality of holes 2212f on its underside for suction of the wafer W, functioning as a substrate holding surface for holding the wafer W. The retainer ring airbag 2214 is made of an elastic film and has a retainer ring pressure chamber 2214a therein. The configuration of the top ring 221 may be modified as needed. It may include a pressure chamber for pressing the entire carrier 2211. The number and shape of the membrane pressure chambers in the membrane 2212 may be modified as needed. The number and arrangement of the suction holes 2212f may also be modified as needed. The membrane 2212 may not necessarily have the suction holes 2212f.

[0030] First to fourth flow paths 2216A to 2216D are connected to the first to fourth membrane pressure chambers 2212a to 2212d, respectively, and a fifth flow path 2216E is connected to the retaining ring pressure chamber 2214a. The first to fifth flow paths 2216A to 2216E communicate with the outside via a rotary joint 2215 provided on the top ring shaft 221a and branch into first branch flow paths 2217A to 2217E and second branch flow paths 2218A to 2218E, respectively. Pressure sensors PA to PE are installed in the first to fifth flow paths 2216A to 2216E, respectively. The first branch flow paths 2217A to 2217E are connected to a gas supply source GS of pressurized fluid (air, nitrogen, etc.) via valves V1A to V1E, flow sensors FA to FE, and pressure regulators RA to RE. The second branch flow paths 2218A to 2218E are connected to a vacuum source VS via valves V2A to V2E, respectively, and are configured to be able to communicate with the atmosphere via valves V3A to V3E.

[0031] The wafer W is held by suction on the lower surface of the top ring 221 and moved to a predetermined polishing position on the polishing table 220. Then, a polishing liquid is supplied from a polishing liquid supply nozzle 222. The polishing surface of the polishing pad 2200 is polished by being pressed against it by the top ring 221. At this time, the top ring 221 independently controls pressure regulators RA to RE to adjust the pressure with which the wafer W is pressed against the polishing pad 2200 by the pressure fluid supplied to the first to fourth membrane pressure chambers 2212a to 2212d, for each region of the wafer W, and also adjust the pressure with which the retainer ring 2213 is pressed against the polishing pad 2200 by the pressure fluid supplied to the retaining ring pressure chamber 2214a. The pressures of the pressure fluids supplied to the first to fourth membrane pressure chambers 2212a to 2212d and the retaining ring pressure chamber 2214a are measured by pressure sensors PA to PE, respectively, and the flow rates of the pressurized gases are measured by flow sensors FA to FE, respectively.

[0032] (Substrate transport unit) 2, the substrate transfer unit 23 includes first and second linear transporters 230A and 230B that are horizontally movable along the arrangement direction of the first to fourth polishing sections 22A to 22D (the longitudinal direction of the housing 20), a swing transporter 231 disposed between the first and second linear transporters 230A and 230B, a lifter 232 disposed on the load / unload unit 21 side, and a temporary placement table 233 for wafers W disposed on the cleaning unit 24 side. Also, cameras 201 are installed at positions where they can photograph wafers W transferred by the first and second linear transporters 230A and 230B.

[0033] The first linear transporter 230A is disposed adjacent to the first and second polishing units 22A and 22B and is a mechanism for transporting wafers W between four transfer positions (first to fourth transfer positions TP1 to TP4, in order from the load / unload unit 21 side). The second transfer position TP2 is a position where a wafer W is transferred to or from the first polishing unit 22A, and the top ring 221 of the first polishing unit 22A is configured to be movable between the second transfer position TP2 and the polishing position by a swinging motion. The third transfer position TP3 is a position where a wafer W is transferred to or from the second polishing unit 22B, and the top ring 221 of the second polishing unit 22B is configured to be movable between the third transfer position TP3 and the polishing position by a swinging motion.

[0034] The second linear transporter 230B is disposed adjacent to the third and fourth polishing units 22C and 22D and is a mechanism for transporting wafers W between three transfer positions (fifth to seventh transfer positions TP5 to TP7, in order from the load / unload unit 21 side). The sixth transfer position TP6 is a position where a wafer W is transferred to or from the third polishing unit 22C, and the top ring 221 of the third polishing unit 22C is configured to be movable between the sixth transfer position TP6 and the polishing position by a swinging motion. The seventh transfer position TP7 is a position where a wafer W is transferred to or from the fourth polishing unit 22D, and the top ring 221 of the fourth polishing unit 22D is configured to be movable between the seventh transfer position TP7 and the polishing position by a swinging motion.

[0035] The swing transporter 231 is disposed adjacent to the fourth and fifth transfer positions TP4, TP5 and has a hand that is movable between the fourth and fifth transfer positions TP4, TP5. The swing transporter 231 is a mechanism that transfers the wafer W between the first and second linear transporters 230A, 230B and temporarily places the wafer W on the temporary placement table 233.

[0036] The lifter 232 is disposed adjacent to the first transfer position TP1 and is a mechanism for transferring the wafer W between the lifter 232 and the transfer robot 211 of the load / unload unit 21. When transferring the wafer W, a shutter (not shown) provided on the first partition wall 200A is opened and closed.

[0037] Fig. 5 is a plan view schematically showing an example of the first and second linear transporters 230A and 230B. Fig. 6 is a front view schematically showing an example of the first and second linear transporters 230A and 230B. Fig. 7 is a schematic view showing an example of a substrate receiving step. FIG. 8 is a schematic diagram showing an example of a substrate delivery step.

[0038] The first and second linear transporters 230A and 230B include a transfer hand 2300 for holding a wafer W, a vertical movement mechanism 2301 for moving the transfer hand 2300 in the vertical direction, a horizontal movement mechanism 2302 for moving the transfer hand 2300 and the vertical movement mechanism 2301 in the arrangement direction of the first to fourth polishing units 22A to 22D (the longitudinal direction of the housing 20), and retainer ring stations 2303 provided at respective positions (second transfer position TP2, third transfer position TP3, sixth transfer position TP6, and seventh transfer position TP7) for transferring the wafer W to and from the top ring 221. The first and second linear transporters 230A and 230B are configured with a plurality of sets of a transfer hand 2300, a vertical movement mechanism 2301, and a horizontal movement mechanism 2302.

[0039] The transfer hand 2300 has a shape that supports a portion of the outer periphery of the lower surface of the wafer W. The retainer ring station 2303 is disposed at a position facing the retainer ring 2213 of the top ring 221, and includes a plurality of push-up pins 2303a that push up the retainer ring 2213. The retainer ring station 2303 is installed at a position that does not interfere with the transfer hand 2300 when the transfer hand 2300 is raised by the vertical movement mechanism 2301 while being positioned below the retainer ring station 2303 by the horizontal movement mechanism 2302. The retainer ring station 2303 may include a release nozzle that supplies a fluid for releasing the wafer W.

[0040] 5 and 6 omit the specific configuration of the vertical movement mechanism 2301 and the horizontal movement mechanism 2302, but the vertical movement mechanism 2301 and the horizontal movement mechanism 2302 are configured by appropriately combining, for example, actuators such as motors and air cylinders, driving force transmission mechanisms such as linear guides, ball screws, gears, couplings, belts, and bearings, and sensors such as linear sensors, encoder sensors, and limit sensors.

[0041] 7, in a substrate receiving step in which the polishing unit 22 receives the unpolished wafer W from the substrate transfer unit 23, the top ring 221 not holding the wafer W descends, and the transfer hand 2300 holding the wafer W ascends. When the top ring 221 descends, the retainer ring 2213 is pushed up by the push-up pins 801, and the transfer hand 2300 further ascends, causing the upper surface of the wafer W to come into contact with the lower surface of the membrane 2212. At this time, for example, the second membrane pressure chamber 2212b corresponding to the position where the suction holes 2212f are formed is evacuated by the vacuum source VS, and the wafer W is suction-held by the membrane 2212. Then, the top ring 221 suction-holding the wafer W ascends, and the transfer hand 2300 that delivered the wafer W descends.

[0042] 8, in a substrate transfer process in which the polishing unit 22 transfers the polished wafer W to the substrate transfer unit 23, the top ring 221 suction-holding the wafer W descends, and the transfer hand 2300, which is not holding the wafer W, ascends. When the top ring 221 descends, the retainer ring 2213 is pushed up by the push-up pins 801, and the transfer hand 2300 further ascends, bringing the underside of the wafer W into close proximity with the transfer hand 2300. At this time, for example, the vacuum pumping for the second membrane pressure chamber 2212b corresponding to the position where the suction hole 2212f is formed is stopped, and pressure fluid is supplied to the third membrane pressure chamber 2212c located outside the second membrane pressure chamber 2212b, thereby releasing the wafer W from the membrane 2212. Then, the top ring 221, which has released the wafer W, ascends, and the transfer hand 2300, which has received the wafer W, descends.

[0043] (Cleaning unit) 2, the cleaning unit 24 includes first and second cleaning chambers 240A and 240B for cleaning the wafer W using a cleaning liquid, a drying chamber 241 for drying the wafer W, and first and second transfer chambers 242A and 242B for transferring the wafer W. The chambers of the cleaning unit 24 are partitioned and arranged along the first and second linear transporters 230A and 230B, for example, in the order of the first cleaning chamber 240A, the first transfer chamber 242A, the second cleaning chamber 240B, the first transfer chamber 242B, and the drying chamber 241 (in order from farthest from the load / unload unit 21).

[0044] (film thickness measurement unit) The film thickness measurement unit 25 is a measuring device that measures the film thickness of the wafer W before or after polishing processing, and is configured by, for example, an optical film thickness measuring device, an eddy current film thickness measuring device, etc. The transfer of the wafer W to each film thickness measurement module is performed by a transfer robot 211.

[0045] (control unit) 9 is a block diagram showing an example of the substrate processing apparatus 2. The control unit 26 is electrically connected to the units 21 to 25 and the camera 201, and functions as a control section that controls the units 21 to 25 and the camera 201 in an integrated manner.

[0046] The load / unload unit 21 includes a plurality of modules 2171 to 2177 each of which is composed of various actuators. p (e.g., a transport robot 211, etc.) and a plurality of modules 2171 to 217 p Each module is located in p A plurality of sensors 2181 to 218 detect data (detected values) necessary for control. q and each sensor 2181 to 218 q Based on the detected value, each module 2171 to 217 p and a sequencer 219 for controlling the operation of the

[0047] The polishing unit 22 includes a plurality of modules 2271 to 2277 each of which is composed of various actuators. r (For example, a polishing table 220, a top ring 221, a polishing liquid supply nozzle 222, a dresser 223, an atomizer 224, etc.) and a plurality of modules 2271 to 227 r Each module is located in 2271~227 r A plurality of sensors 2281 to 228 detect data (detected values) necessary for the control of s and each sensor 2281~228 s Based on the detected value of each module 2271 to 227 r and a sequencer 229 for controlling the operation of the

[0048] Sensors 2281 to 228 of polishing unit 22 sThese include, for example, a sensor that detects the rotation speed of the polishing table 220, a sensor that detects the rotation torque of the polishing table 220, a sensor that detects the rotation speed of the top ring 221, a sensor that detects the rotation torque of the top ring 221, a sensor that detects the oscillation position of the top ring 221, a sensor that detects the oscillation torque of the top ring 221, a sensor that detects the height of the top ring 221, a sensor that detects the lifting torque of the top ring 221, a sensor that detects the pressure (positive pressure and negative pressure) in the first to fourth membrane pressure chambers 2212a to 2212d and the retaining ring pressure chamber 2214a, a sensor that detects the flow rate of the pressurized fluid supplied to the first to fourth membrane pressure chambers 2212a to 2212d and the retaining ring pressure chamber 2214a, a sensor that detects the flow rate of the polishing liquid supplied from the polishing liquid supply nozzle 222, and a sensor that detects the dropping position of the polishing liquid supply nozzle 222.

[0049] The substrate transport unit 23 includes a plurality of modules 2371 to 2377 each of which is composed of various actuators. t (For example, first and second linear transporters 230A, 230B, swing transporter 231, lifter 232, etc.) are arranged in a plurality of modules 2371, and each module 2371 to 237 t Detects the data (detected values) required for control Multiple sensors 2381-238 u and each sensor 2381~238 u Based on the detected value of each module 2371 to 237 t and a sequencer 239 for controlling the operation of the

[0050] Sensors 2381 to 238 of the substrate transport unit 23 u These include, for example, a sensor for detecting the position of the transfer hand 2300, a sensor for detecting the height of the transfer hand 2300, a sensor for detecting the presence or absence of the wafer W relative to the transfer hand 2300, and the like.

[0051] The cleaning unit 24 includes a plurality of modules 2471 to 2477 each of which is composed of various actuators. v(for example, a first cleaning chamber 240A, a second cleaning chamber 240B, a drying chamber 241, etc.) and a plurality of modules 2471 to 247 v Each module is located in 2471~247 v A plurality of sensors 2481 to 248 detect data (detected values) necessary for the control of w and each sensor 2481 to 248 w Based on the detected value of each module 2471 to 247 v and a sequencer 249 for controlling the operation of the

[0052] The film thickness measurement unit 25 includes a plurality of modules 2571 to 2577 each of which is composed of various actuators. x (e.g., a film thickness measurement module, etc.) and a plurality of modules 2571 to 257 x Each module is located in 2571~257 x A plurality of sensors 2581 to 258 detect data (detected values) necessary for the control of y and each sensor 2581~258 y Based on the detected value of each module 2571~257 x and a sequencer 259 for controlling the operation of the

[0053] The control unit 26 includes a control unit 260, a communication unit 261, an input unit 262, an output unit 263, and a storage unit 264. The control unit 26 is configured, for example, by a general-purpose or dedicated computer (see FIG. 11 described later).

[0054] The communication unit 261 is connected to the network 7 and functions as a communication interface for transmitting and receiving various types of data. The input unit 262 accepts various input operations, and the output unit 263 functions as a user interface by outputting various types of information via a display screen, signal tower lighting, and buzzer sound.

[0055] The storage unit 264 stores various programs (such as an operating system (OS), application programs, and a web browser) and data (such as apparatus setting information 265 and substrate recipe information 266) used in the operation of the substrate processing apparatus 2. The apparatus setting information 265 and the substrate recipe information 266 are data that can be edited by the user via a display screen.

[0056] The control unit 260 controls the plurality of sensors 2181 to 2188 via a plurality of sequencers 219, 229, 239, 249, and 259 (hereinafter referred to as a "sequencer group"). q , 2281-228 s , 2381-238 u , 2481-248 w , 2581-258 y (hereinafter referred to as "sensor group"), and p , 2271-227 r , 2371-237 t , 2471-247 v , 2571-257 x (hereinafter referred to as "module group") operate in coordination to carry out a series of processes such as loading, polishing, cleaning, drying, film thickness measurement, and unloading.

[0057] Fig. 10 is a timing chart showing an example of the substrate processing process by the substrate processing apparatus 2. The substrate processing process shown in Fig. 10 represents, among the series of processes described above, a process in which the top ring 221 of the polishing unit 22 receives the unpolished wafer W from the first or second linear transporter 230A, 230B of the substrate transfer unit 23, polishes the wafer W, and transfers the polished wafer W to the first or second linear transporter 230A, 230B.

[0058] The substrate processing process includes a substrate receiving process S1 (FIG. 7) in which the top ring 221 receives the unpolished wafer W from the first or second linear transporter 230A, 230B; a pre-polishing swinging process S2 in which the top ring 221 moves the unpolished wafer W to a polishing position on the polishing table 220; a pre-polishing lowering process S3 in which the top ring 221 lowers the unpolished wafer W to a polishing height; a polishing process S4 in which the top ring 221 polishes the unpolished wafer W; a post-polishing lifting process S5 in which the top ring 221 lifts the polished wafer W to a moving height; a post-polishing swinging process S6 in which the top ring 221 moves the polished wafer W to a transfer position on the retainer ring station 2303; and a substrate transferring process S7 (FIG. 8) in which the top ring 221 transfers the polished wafer W to the first or second linear transporter 230A, 230B.

[0059] In this embodiment, the above substrate processing steps are performed between the first and second polishing sections 22A and 22B and the first linear transporter 230A, and between the third and fourth polishing sections 22C and 22D and the second linear transporter 230B.

[0060] (Hardware configuration of each device) 11 is a hardware configuration diagram showing an example of a computer 900. Each of the control unit 26 of the substrate processing apparatus 2, the database device 3, the machine learning device 4, the information processing device 5, and the user terminal device 6 is configured by a general-purpose or dedicated computer 900.

[0061] 11, the computer 900 includes, as its main components, a bus 910, a processor 912, a memory 914, an input device 916, an output device 917, a display device 918, a storage device 920, a communication I / F (interface) unit 922, an external device I / F unit 924, an I / O (input / output) device I / F unit 926, and a media input / output unit 928. Note that the above components may be omitted as appropriate depending on the application of the computer 900.

[0062] The processor 912 is composed of one or more arithmetic processing devices (such as a central processing unit (CPU), a micro-processing unit (MPU), a digital signal processor (DSP), or a graphics processing unit (GPU)), and operates as a control unit that controls the entire computer 900. The memory 914 stores various data and programs 930, and is composed of, for example, a volatile memory (such as a DRAM or SRAM) that functions as a main memory, a non-volatile memory (ROM), a flash memory, etc.

[0063] The input device 916 is composed of, for example, a keyboard, a mouse, a numeric keypad, an electronic pen, etc., and functions as an input unit. The output device 917 is composed of, for example, a sound (audio) output device, a vibration device, etc., and functions as an output unit. The display device 918 is composed of, for example, a liquid crystal display, an organic EL display, electronic paper, a projector, etc., and functions as an output unit. The input device 916 and the display device 918 may be integrated into one device, such as a touch panel display. The storage device 920 is composed of, for example, an HDD, an SSD (Solid State Drive), etc., and functions as a storage unit. The storage device 920 stores various data necessary for executing the operating system and the program 930.

[0064] The communication I / F unit 922 is connected by wire or wireless to a network 940 such as the Internet or an intranet (which may be the same as the network 7 in FIG. 1), and functions as a communication unit that transmits and receives data to and from other computers in accordance with a predetermined communication standard. The external device I / F unit 924 is connected by wire or wireless to an external device 950 such as a camera, printer, scanner, or reader / writer, and functions as a communication unit that transmits and receives data to and from the external device 950 in accordance with a predetermined communication standard. The I / O device I / F unit 926 is connected to I / O devices 960 such as various sensors and actuators, and functions as a communication unit that transmits and receives various signals and data, such as detection signals from sensors and control signals to actuators, between the I / O devices 960. The media input / output unit 928 is formed of a drive device such as a DVD drive or CD drive, and reads and writes data from and to media (non-transitory storage media) 970 such as DVDs and CDs.

[0065] In the computer 900 having the above configuration, the processor 912 loads a program 930 stored in the storage device 920 into the memory 914, executes the program, and controls each unit of the computer 900 via the bus 910. The program 930 may be stored in the memory 914 instead of the storage device 920. The program 930 may be recorded on the medium 970 in an installable file format or an executable file format and provided to the computer 900 via the media input / output unit 928. The program 930 may be provided to the computer 900 by being downloaded via the network 940 via the communication I / F unit 922. Furthermore, the computer 900 may implement various functions realized by the processor 912 executing the program 930 using hardware such as an FPGA or an ASIC.

[0066] The computer 900 is, for example, a desktop computer or a portable computer, and is an electronic device of any type. The computer 900 may be a client computer, a server computer, or a cloud computer. The computer 900 may also be applied to devices other than the devices 2 to 6.

[0067] (History information 30) 12 is a data configuration diagram showing an example of history information 30 managed by the database device 3. The history information 30 includes, as tables in which various reports R from the substrate processing apparatus 2 are classified and registered, for example, a process history table 300 relating to process information, an apparatus status history table 301 relating to apparatus status information (detected values ​​of sensors, command values ​​to actuators, etc.), and a crack occurrence history table 302 relating to image information and sketch information (described later). In addition to the above, the history information 30 also includes an event history table relating to event information, an operation history table relating to operation information, etc., but detailed description thereof will be omitted.

[0068] Each record in the process history table 300 may register, for example, a wafer ID, a cassette number, a slot number, the start time of each process S1 to S7, the end time of each process S1 to S7, the ID of the unit used in each process S1 to S7, etc. Note that the process history table 300 may also register information about processes other than the processes S1 to S7.

[0069] For example, a unit ID, a sensor ID (or an actuator ID), time-series data, etc. are registered in each record of the device state history table 301. The time-series data is the detection value of the sensor (or the command value to the actuator) sampled at a predetermined time interval.

[0070] Each record in the crack occurrence history table 302 is registered with, for example, a wafer ID, acquisition time, crack occurrence state information, crack occurrence process information, etc. As the crack occurrence state information, image information of the cracked wafer W photographed by either the camera 201 of the substrate processing apparatus 2 or the camera 60 of the user terminal device 6, or sketch information of the cracked wafer W sketched by the user, is registered. As the crack occurrence process information, a crack occurrence process specified by the user or the information processing device 5 is registered.

[0071] By referring to the process history table 300 and the equipment status history table 301, it is possible to determine whether each of the processes S1 to S7 included in the substrate processing process has been performed on the wafer W identified by the wafer ID. As the apparatus status information, time series data of each sensor (or time series data of each actuator) can be extracted. In addition, by further referring to the crack occurrence history table 302, crack occurrence status information and crack occurrence process information for the wafer W identified by the wafer ID can be extracted.

[0072] (Machine Learning Device 4) 13 is a block diagram showing an example of the machine learning device 4. The machine learning device 4 includes a control unit 40, a communication unit 41, a learning data storage unit 42, and a trained model storage unit 43.

[0073] The control unit 40 functions as a learning data acquisition unit 400 and a machine learning unit 401. The communication unit 41 is connected to external devices (e.g., the substrate processing apparatus 2, the database device 3, the information processing apparatus 5, and the user terminal device 6) via the network 7, and functions as a communication interface for transmitting and receiving various types of data.

[0074] The learning data acquisition unit 400 is connected to an external device via the communication unit 41 and the network 7, and acquires learning data 11 consisting of crack occurrence state information as input data and crack occurrence process information as output data. The learning data 11 is data used as teacher data (training data), verification data, and test data in supervised learning. The crack occurrence process information is data used as a correct answer label in supervised learning.

[0075] The learning data storage unit 42 is a database that stores a plurality of sets of learning data 11 acquired by the learning data acquisition unit 400. The specific configuration of the database that constitutes the learning data storage unit 42 may be designed as appropriate.

[0076] The machine learning unit 401 performs machine learning using multiple sets of learning data 11 stored in the learning data storage unit 42. That is, the machine learning unit 401 inputs multiple sets of learning data 11 to the learning model 10 and causes the learning model 10 to learn the correlation between the crack occurrence state information and the crack occurrence process information contained in the learning data 11, thereby generating a trained learning model 10.

[0077] The trained model storage unit 43 is a database that stores the trained learning model 10 (specifically, a group of adjusted weight parameters) generated by the machine learning unit 401. The trained learning model 10 stored in the trained model storage unit 43 is provided to an actual system (e.g., an information processing device 5) via the network 7, a recording medium, or the like. Note that although the training data storage unit 42 and the trained model storage unit 43 are shown as separate storage units in FIG. 13, they may also be configured as a single storage unit.

[0078] 14 is a diagram showing an example of the learning model 10 and the learning data 11. The learning data 11 used for machine learning of the learning model 10 is composed of crack occurrence state information including crack state information and equipment state information, and crack occurrence process information.

[0079] The crack state information included in the crack occurrence state information is information indicating the crack state when a crack occurs in the wafer W during the substrate processing process performed by the substrate processing apparatus 2. The crack state information is image information of a photograph of the cracked wafer W or sketch information of a sketch of the cracked wafer W.

[0080] The image information is obtained by photographing the broken wafer W in a plan view by the substrate processing apparatus 2 (camera 201) or the user terminal device 6 (camera 60). The broken wafer W is, for example, a wafer If the W is broken into multiple pieces, it is preferable to photograph the pieces in their original state so that the cracks are visible. The image information may be either a monochrome image or a color image, and may be either a two-dimensional image or a three-dimensional image.

[0081] The sketch information is, for example, a drawing of a crack that occurs when the wafer W is cracked, along the outline of the notched wafer W. The sketch information may be generated, for example, by a user drawing lines representing the crack state of the wafer W using a drawing application program on the user terminal device 6, or by a user writing the crack state of the wafer W by hand on paper and reading the handwritten drawing using an image reading device such as a scanner.

[0082] The apparatus state information included in the crack occurrence state information is information indicating the state of the polishing unit 22 when the substrate processing step is performed on the cracked wafer W.

[0083] The apparatus status information includes at least one of the following statuses of the polishing unit 22: the oscillation position of the top ring 221, the height of the top ring 221, the pressures in the first to fourth membrane pressure chambers 2212a to 2212d (membrane pressures), the flow rates of pressurized fluid supplied to the first to fourth membrane pressure chambers 2212a to 2212d (membrane flow rates), the pressure in the retaining ring pressure chamber 2214a (retaining ring airbag pressure), and the flow rate of pressurized fluid supplied to the retaining ring pressure chamber 2214a (retaining ring airbag flow rate).The pressures in the first to fourth membrane pressure chambers 2212a to 2212d and the retaining ring pressure chamber 2214a include the pressures when the pressurized fluids are supplied, the pressures when the chambers are opened to the atmosphere, and the pressures when a vacuum is drawn.

[0084] The apparatus status information may include information indicating the status of the substrate transfer unit 23, in addition to the status of the polishing unit 22. In this case, the apparatus status information includes, as the status of the substrate transfer unit 23, at least one of the position of the transfer hand 2300 (LTP horizontal position), the height of the transfer hand 2300 (LTP height), and the presence or absence of the wafer W relative to the transfer hand 2300 (presence or absence of the LTP substrate).

[0085] The crack occurrence process information is information indicating a process (crack occurrence process) among the processes S1 to S7 included in the substrate processing process that may cause cracks in the wafer W. The processes S1 to S7 included in the substrate processing process include a substrate receiving process S1, a pre-polishing oscillation process S2, a pre-polishing lowering process S3, a polishing process S4, a post-polishing raising process S5, a post-polishing oscillation process S6, and a substrate delivering process S7 as shown in FIG.

[0086] The learning data acquisition unit 400 acquires learning data 11 by referring to the history information 30 and accepting an input operation by the user via the user terminal device 6 as necessary.

[0087] For example, when a crack occurs in a wafer W, the learning data acquisition unit 400 uses a wafer ID that identifies the cracked wafer W to refer to the process history table 300 and the equipment status history table 301 in the history information 30, thereby acquiring, as equipment status information, time-series data of the sensor group when a substrate processing process was performed on the cracked wafer W. The learning data acquisition unit 400 also uses the wafer ID to refer to the crack occurrence history table 302 in the history information 30, thereby acquiring crack status information for the cracked wafer W. Note that, instead of acquiring crack status information from the history information 30, the learning data acquisition unit 400 may acquire image information captured by a user's shooting operation or sketch information generated by a user's drawing operation or reading operation.

[0088] Furthermore, the learning data acquisition unit 400 can acquire the data of the cracked wafer W by the user. As a result of analyzing the states of the polishing unit 22 and the substrate transport unit 23 based on the characteristics of the cracks (such as the direction and number of cracks) and the apparatus status information, the crack occurrence process information is acquired by accepting a user's designation operation that specifies a crack occurrence process for the cracked wafer W. Note that, instead of accepting a user's designation operation, if the crack occurrence process information as a result of prior analysis by the user has been registered in the crack occurrence history table 302 of the history information 30, the learning data acquisition unit 400 may acquire the crack occurrence process information by referring to the crack occurrence history table 302 of the history information 30 using a wafer ID that identifies the cracked wafer W.

[0089] The learning model 10 employs, for example, a convolutional neural network (CNN) structure and includes an input layer 100, an intermediate layer 101, and an output layer 102. Synapses (not shown) that connect each neuron are laid between each layer, and each synapse is associated with a weight. A group of weight parameters consisting of the weights of each synapse is adjusted by machine learning.

[0090] The input layer 100 has neurons whose number corresponds to the number of pixels in the crack occurrence state information (image information or sketch information) as input data, and the pixel value of each pixel is input to each neuron. The intermediate layer 101 is composed of, for example, a convolutional layer 101a, a pooling layer 101b, and a fully connected layer 101c. The fully connected layer 101c has inputs whose number corresponds to the device state information as input data, as well as the pooling layer 101b, and each neuron receives feature values ​​of the image information from the pooling layer 101b and variable values ​​indicating the device state information (e.g., time-series data of a sensor group). The output layer 102 has neurons whose number corresponds to the crack occurrence steps (each of steps S1 to S7) in the crack occurrence step information as output data, and the judgment results (inference results) for each of steps S1 to S7 are output as output data. That is, the learning model 10 is a multi-class classification model, and outputs the scores (reliabilities) when classified into each of the steps S1 to S7 as numerical values ​​within a predetermined range (for example, 0 to 1).

[0091] In this embodiment, the apparatus status information is acquired as time-series data of a group of sensors as shown in FIG. 14 , but this may be changed as appropriate depending on the configuration of the polishing unit 22 (particularly the top ring 221) or the substrate transport unit 23. Furthermore, the apparatus status information may be a command value to an actuator, a parameter converted from a detected value of a sensor or a command value to an actuator, or a parameter calculated based on detected values ​​of multiple sensors. As described above, when changing the definition of the apparatus status information, the data configuration of the input data in the learning model 10 and the learning data 11 may be changed as appropriate.

[0092] In this embodiment, the substrate processing process performed by the substrate processing apparatus 2 is described as being divided into seven steps S1 to S7 as shown in FIGS. 10 and 14. However, the divisions of the substrate processing process may be changed as appropriate, and the divisions may be made more detailed or more general. The substrate processing process may also include a step prior to the substrate receiving step S1 or a step subsequent to the substrate transferring step S7. Furthermore, if the polishing process is performed in two stages, such as the first polishing unit 22A followed by the second polishing unit 22B, or the third polishing unit 22C followed by the fourth polishing unit 22D, or in three or more stages, the substrate processing process may include a series of these steps, i.e., multiple stages of the substrate processing process. Changing the definition of the substrate processing process as described above can be achieved by appropriately changing the data configuration of the learning model 10 and the output data in the training data 11.

[0093] (machine learning methods) FIG. 15 is a flowchart showing an example of a machine learning method performed by the machine learning device 4.

[0094] First, in step S100, the learning data acquisition unit 400 acquires a desired number of pieces of learning data 11 from the history information 30 or the like as a preliminary preparation for starting machine learning, and stores the acquired learning data 11 in the learning data storage unit 42. The number of pieces of learning data 11 to be prepared here may be set in consideration of the inference accuracy required for the learning model 10 to be finally obtained.

[0095] Next, in step S110, in order to start machine learning, the machine learning unit 401 prepares a pre-learning learning model 10. The pre-learning learning model 10 prepared here is configured with the neural network model exemplified in Fig. 12, and the weights of each synapse are set to initial values.

[0096] Next, in step S120, the machine learning unit 401 acquires, for example, one set of training data 11 at random from the multiple sets of training data 11 stored in the training data storage unit .

[0097] Next, in step S130, the machine learning unit 401 inputs crack occurrence state information (input data) included in one set of learning data 11 to the input layer of the prepared learning model 10 before learning (or during learning). As a result, crack occurrence process information (output data) is output as an inference result from the output layer of the learning model 10, and this output data was generated by the learning model 10 before learning (or during learning). Therefore, in the state before learning (or during learning), the output data output as an inference result indicates information different from the crack occurrence process information (correct label) included in the learning data 11.

[0098] Next, in step S140, the machine learning unit 401 performs machine learning by comparing the crack occurrence process information (correct label) included in the set of learning data 11 acquired in step S120 with the crack occurrence process information (output data) output from the output layer as an inference result in step S130, and performing a process of adjusting the weight of each synapse (back propagation).In this way, the machine learning unit 401 causes the learning model 10 to learn the correlation between the crack occurrence state information and the crack occurrence process information.

[0099] Next, in step S150, the machine learning unit 401 determines whether a predetermined learning termination condition has been met, for example, based on the evaluation value of an error function based on the crack occurrence process information (correct label) contained in the learning data 11 and the crack occurrence process information (output data) output as an inference result, or the remaining number of unlearned learning data 11 stored in the learning data storage unit 42.

[0100] In step S150, if the machine learning unit 401 determines that the learning termination condition is not satisfied and that machine learning should be continued (No in step S150), the process returns to step S120, and the processes of steps S120 to S140 are performed multiple times on the learning model 10 under training using unlearned training data 11. On the other hand, in step S150, if the machine learning unit 401 determines that the learning termination condition is satisfied and that machine learning should be terminated (Yes in step S150), the process proceeds to step S160.

[0101] Then, in step S160, the machine learning unit 401 stores the trained learning model 10 (adjusted weight parameter group) generated by adjusting the weights associated with each synapse in the trained model storage unit 43, and ends the series of machine learning methods shown in Fig. 15. In the machine learning method, step S100 corresponds to a learning data storage step, steps S110 to S150 correspond to a machine learning step, and step S160 corresponds to a trained model storage step.

[0102] As described above, the information processing device 5 and information processing method according to this embodiment can provide a learning model 10 that can identify (infer) the cause of a crack in a wafer W from crack occurrence status information including crack status information and device status information acquired in response to the occurrence of a crack in the wafer W.

[0103] (Information processing device 5) Fig. 16 is a block diagram showing an example of the information processing device 5. Fig. 17 is a functional explanatory diagram showing an example of the information processing device 5. The information processing device 5 includes a control unit 50, a communication unit 51, and a trained model storage unit 52.

[0104] The control unit 50 functions as an information acquisition unit 500, a crack occurrence process identification unit 501, and an output processing unit 502. The communication unit 51 is connected to external devices (e.g., the substrate processing apparatus 2, the database device 3, the machine learning device 4, and the user terminal device 6) via the network 7, and functions as a communication interface for transmitting and receiving various types of data.

[0105] The information acquisition unit 500 is connected to external devices via the communication unit 51 and the network 7, and acquires crack occurrence state information including crack state information and apparatus state information. For example, when the information acquisition unit 500 receives a wafer ID identifying a cracked wafer W from the user terminal device 6, the information acquisition unit 500 uses the wafer ID to refer to the process history table 300 and the apparatus state history table 301 in the history information 30, thereby acquiring, as apparatus state information, time-series data of the sensor group when the substrate processing process was performed on the cracked wafer W. The information acquisition unit 500 also uses the wafer ID to refer to the crack occurrence history table 302 in the history information 30, thereby acquiring crack state information for the cracked wafer W. Note that, instead of acquiring crack state information from the history information 30, the information acquisition unit 500 may acquire image information captured by a user's shooting operation or sketch information generated by a user's drawing operation or reading operation.

[0106] As described above, the crack occurrence process identifying unit 501 inputs the crack occurrence state information acquired by the information acquiring unit 500 in response to the occurrence of a crack in the wafer W as input data to the learning model 10, thereby identifying the process (crack occurrence process) that is the cause of the crack in the wafer W. In this case, the crack occurrence process identifying unit 501 acquires, for example, scores for each process S1 to S7 as output data from the learning model 10, and generates crack occurrence process information by identifying the process corresponding to the maximum value of the scores (substrate receiving process S1 in the example of FIG. 17) as the crack occurrence process.

[0107] The trained model storage unit 52 is a database that stores trained learning models 10 used by the crack occurrence process identification unit 501. The number of learning models 10 stored in the trained model storage unit 52 is not limited to one, and multiple trained models with different conditions, such as machine learning techniques, types of data included in crack occurrence state information, and types of data included in crack occurrence process information, may be stored and selectively used. The trained model storage unit 52 may also be substituted by a storage unit of an external computer (e.g., a server-type computer or a cloud-type computer), in which case the crack occurrence process identification unit 501 simply needs to access the external computer.

[0108] The output processing unit 502 performs output processing for outputting the crack occurrence process information generated by the crack occurrence process identification unit 501. For example, the output processing unit 502 may transmit the crack occurrence process information to the user terminal device 6, thereby causing a display screen based on the crack occurrence process information to be displayed on the user terminal device 6, or may transmit the crack occurrence process information to the database device 3, thereby registering the crack occurrence process information in the history information 30.

[0109] (Information processing method) 18 is a flowchart showing an example of an information processing method by the information processing device 5. The following describes an operation when, when a crack occurs in a wafer W, a user operates the user terminal device 6 to analyze the cause of the crack in the wafer W.

[0110] First, in step S200, when the user performs an input operation on the user terminal device 6 to input a wafer ID that identifies the cracked wafer W and a photographing operation to photograph the cracked wafer W with the camera 60, the user terminal device 6 transmits the wafer ID and the image information photographed by the camera 60 to the information processing device 5 as cracked state information.

[0111] Next, in step S210, the information acquisition unit 500 of the information processing device 5 receives the wafer ID and crack state information (image information) transmitted in step S200. In step S211, the information acquisition unit 500 uses the wafer ID received in step S210 to refer to the process history table 300 and the equipment status history table 301 in the history information 30 to acquire equipment status information when the substrate processing process was performed on the cracked wafer W. As a result, in step S212, the information acquisition unit 500 acquires image information as crack state information indicating the crack state of the wafer W identified by the wafer ID, and crack occurrence state information including equipment status information when the substrate processing process was performed on the wafer W.

[0112] Next, in step S220, the crack occurrence process identification unit 501 inputs the crack occurrence state information acquired in step S210 as input data into the learning model 10, thereby generating crack occurrence process information for the crack occurrence state information as output data, and identifying the crack occurrence process of the wafer W.

[0113] Next, in step S230, the output processing unit 502 performs output processing for outputting the crack occurrence process information generated in step S220, and transmits the crack occurrence process information to the user terminal device 6. Note that the destination of the crack occurrence process information may be the database device 3 in addition to or instead of the user terminal device 6.

[0114] Next, in step S240, when the user terminal device 6 receives the crack occurrence process information transmitted in step S230 as a response to the transmission process of step S200, it displays a display screen based on the crack occurrence process information, thereby allowing the user to visually recognize the crack occurrence process of the cracked wafer W. In the above information processing method, steps S210 to S212 correspond to an information acquisition process, step S220 corresponds to a crack occurrence process identification process, and step S230 corresponds to an output processing process.

[0115] As described above, according to the information processing device 5 and information processing method of this embodiment, crack occurrence state information including crack state information and apparatus state information obtained in response to the occurrence of a crack in the wafer W is input into the learning model 10, thereby identifying the cause of the crack in the wafer W, and therefore, the crack in the wafer W can be dealt with quickly and appropriately without relying on the user's experience or knowledge.

[0116] (Other embodiments) The present invention is not limited to the above-described embodiment, and various modifications can be made without departing from the spirit and scope of the present invention, all of which are included in the technical concept of the present invention.

[0117] In the above embodiment, the database device 3, the machine learning device 4, and the information processing device 5 have been described as being configured as separate devices. However, these three devices may be configured as a single device, or any two of these three devices may be configured as a single device. At least one of the machine learning device 4 and the information processing device 5 may be incorporated into the control unit 26 of the substrate processing apparatus 2 or the user terminal device 6.

[0118] In the above embodiment, a case has been described in which a neural network is used as the learning model 10 for realizing machine learning by the machine learning unit 401, but other machine learning models may also be used. Examples of other machine learning models include tree types such as decision trees and regression trees, ensemble learning such as bagging and boosting, and neural network types (deep learning) such as recurrent neural networks, convolutional neural networks, and LSTM. hierarchical clustering, non-hierarchical clustering, k-nearest neighbors, k-means, etc. Examples include rastering type, principal component analysis, factor analysis, multivariate analysis such as logistic regression, and support vector machines.

[0119] (Machine learning programs and information processing programs) The present invention can also be provided in the form of a program (machine learning program) that causes the computer 900 to function as each unit included in the machine learning device 4, or a program (machine learning program) that causes the computer 900 to execute each step included in the machine learning method. The present invention can also be provided in the form of a program (information processing program) that causes the computer 900 to function as each unit included in the information processing device 5, or a program (information processing program) that causes the computer 900 to execute each step included in the information processing method according to the above embodiment.

[0120] (Inference device, inference method and inference program) The present invention can be provided not only in the form of the information processing device 5 (information processing method or information processing program) according to the above embodiment, but also in the form of an inference device (inference method or inference program) used to infer crack occurrence process information. In this case, the inference device (inference method or inference program) can include a memory and a processor, and the processor executes a series of processes. The series of processes includes an information acquisition process (information acquisition process) for acquiring crack occurrence state information including crack state information indicating the state of a crack when a crack occurs in a wafer W during a substrate processing process performed by the substrate processing apparatus 2 and apparatus state information indicating the state of the polishing unit 22 when the substrate processing process was performed on the wafer W, and an inference process (inference process) for inferring, when the crack occurrence state information is acquired in the information acquisition process in response to the occurrence of a crack in the wafer W, a process (crack occurrence process) that caused the crack to occur in the wafer W among the processes S1 to S7 included in the substrate processing process.

[0121] By providing it in the form of an inference device (inference method or inference program), it can be more easily applied to various devices than when it is implemented in an information processing device. It will be naturally understood by those skilled in the art that when the inference device (inference method or inference program) infers the crack occurrence process, it may apply the inference method implemented by the crack occurrence process identification unit using the machine learning device 4 and the trained learning model 10 generated by the machine learning method according to the above embodiment. [Explanation of symbols]

[0122] 1... substrate processing system, 2... substrate processing apparatus, 3... database device, 4...machine learning device, 5...information processing device, 6...user terminal device, 7...network, 10...Learning model, 11...Learning data, 20...housing, 21...load / unload unit, 22... Polishing unit, 22A to 22D... Polishing section, 23... Substrate transport unit, 24... cleaning unit, 25... film thickness measurement unit, 26... control unit, 30... history information, 40... control unit, 41... communication unit, 42... learning data storage unit, 43...Trained model memory unit, 50...control unit, 51...communication unit, 52...trained model storage unit, 60...camera, 201...camera, 220... polishing table, 221... top ring, 222... polishing liquid supply nozzle, 223...Dresser, 224...Atomizer, 230A...first linear transporter, 230B...second linear transporter, 231...swing transporter, 232...lifter, 233...temporary stand, 260...control unit, 261...communication unit, 262...input unit, 263...output unit, 264...storage unit, 300...process history table, 301...equipment status history table, 302...Crack occurrence history table, 400...learning data acquisition unit, 401...machine learning unit, 500: information acquisition unit, 501: crack occurrence process identification unit, 502: output processing unit, 900...Computer 2200... Polishing pad, 2210... Top ring body, 2211... Carrier, 2212...membrane, 2212a to 2212d...membrane pressure chamber, 2213...Retainer ring, 2214...Retainer ring airbag, 2214a...retainer ring pressure chamber, 2300...Transport hand, 2301...Up-down movement mechanism, 2302...Horizontal movement mechanism, 2303…Retainer ring station

Claims

1. an information acquisition unit that acquires crack occurrence state information including crack state information indicating the state of a crack when a crack occurs in the substrate and apparatus state information indicating the state of the polishing unit when the substrate processing process is performed on the substrate, in a substrate processing process performed by a substrate processing apparatus including a polishing unit that performs a polishing process on the substrate and a substrate transport unit that transfers the substrate between the polishing unit; a crack occurrence process identifying unit that identifies the process that causes cracks in the substrate by inputting the crack occurrence state information acquired by the information acquiring unit in response to the occurrence of a crack in the substrate into a learning model that has been trained by machine learning to determine a correlation between the crack occurrence state information and crack occurrence process information that indicates the process that causes cracks in the substrate among the processes included in the substrate processing process, The device status information included in the crack occurrence status information is The state of the polishing unit is: the position of a top ring of the polishing unit; the height of the top ring; the pressure in a pressure chamber provided in the top ring; and at least one of a flow rate of pressure fluid supplied to the pressure chamber; Information processing device.

2. The device status information included in the crack occurrence status information is In addition to the state of the polishing unit, the state of the substrate transport unit is also indicated; The state of the substrate transport unit is: the position of the substrate transport unit; The height of the substrate transport unit, and at least one of a presence / absence state of the substrate in the substrate transport unit, The information processing device according to claim 1 .

3. In a substrate processing process performed by a substrate processing apparatus including a polishing unit for polishing a substrate and a substrate transport unit for transferring the substrate between the polishing unit and the substrate, an information acquiring unit that acquires crack occurrence state information including crack state information indicating the state of a crack when a crack occurs in the substrate and apparatus state information indicating the state of the polishing unit when the substrate processing step is performed on the substrate; a crack occurrence process identifying unit that identifies the process that causes cracks in the substrate by inputting the crack occurrence state information acquired by the information acquiring unit in response to the occurrence of a crack in the substrate into a learning model that has been trained by machine learning to determine a correlation between the crack occurrence state information and crack occurrence process information that indicates the process that causes cracks in the substrate among the processes included in the substrate processing process, The steps included in the substrate processing step are: a substrate receiving step in which the polishing unit receives the substrate before the polishing process from the substrate transport unit; a pre-polishing oscillation step in which the polishing unit moves the substrate before the polishing process to a polishing position; a pre-polishing lowering step in which the polishing unit lowers the substrate before the polishing process to a polishing height; a polishing step in which the polishing unit performs a polishing process on the substrate before the polishing process; a post-polishing raising step in which the polishing unit raises the substrate after the polishing process to a moving height; a post-polishing swinging step in which the polishing unit moves the polished substrate to a transfer position; and a substrate transfer step in which the polishing unit transfers the substrate after the polishing process to the substrate transport unit; Information processing device.

4. An inference device comprising a memory and a processor, The processor: In a substrate processing process performed by a substrate processing apparatus including a polishing unit that performs a polishing process on a substrate and a substrate transport unit that transfers the substrate between the polishing unit and the substrate processing apparatus, an information acquisition process is performed to acquire crack occurrence state information including crack state information that indicates the state of a crack when a crack occurs in the substrate and apparatus state information that indicates the state of the polishing unit when the substrate processing process is performed on the substrate; When the crack occurrence state information is acquired in the information acquisition process in response to the occurrence of a crack in the substrate, an inference process is executed to infer which process among the processes included in the substrate processing process is the cause of the occurrence of a crack in the substrate; The device status information included in the crack occurrence status information is The state of the polishing unit is: the position of a top ring of the polishing unit; the height of the top ring; the pressure in a pressure chamber provided in the top ring; and at least one of a flow rate of pressure fluid supplied to the pressure chamber; Reasoning device.

5. The device status information included in the crack occurrence status information is In addition to the state of the polishing unit, the state of the substrate transport unit is also indicated; The state of the substrate transport unit is: the position of the substrate transport unit; The height of the substrate transport unit, and at least one of a presence / absence state of the substrate in the substrate transport unit, The inference device according to claim 4.

6. An inference device comprising a memory and a processor, The processor: A polishing unit for polishing a substrate and a transfer unit for transferring the substrate between the polishing unit. and an information acquisition process for acquiring crack occurrence state information including crack state information indicating a crack state when a crack occurs in the substrate, and apparatus state information indicating a state of the polishing unit when the substrate processing process is performed on the substrate, in a substrate processing process performed by a substrate processing apparatus having a substrate transport unit that performs the polishing process. When the crack occurrence state information is acquired in the information acquisition process in response to the occurrence of a crack in the substrate, an inference process is executed to infer which process among the processes included in the substrate processing process is the cause of the occurrence of a crack in the substrate; The steps included in the substrate processing step are: a substrate receiving step in which the polishing unit receives the substrate before the polishing process from the substrate transport unit; a pre-polishing oscillation step in which the polishing unit moves the substrate before the polishing process to a polishing position; a pre-polishing lowering step in which the polishing unit lowers the substrate before the polishing process to a polishing height; a polishing step in which the polishing unit performs a polishing process on the substrate before the polishing process; a post-polishing raising step in which the polishing unit raises the substrate after the polishing process to a moving height; a post-polishing swinging step in which the polishing unit moves the polished substrate to a transfer position; and a substrate transfer step in which the polishing unit transfers the substrate after the polishing process to the substrate transport unit; Reasoning device.

7. In a substrate processing process performed by a substrate processing apparatus including a polishing unit that performs a polishing process on a substrate and a substrate transport unit that transfers the substrate between the polishing unit and the substrate processing apparatus, an information acquisition process is performed to acquire crack occurrence state information including crack state information that indicates the state of a crack when a crack occurs in the substrate and apparatus state information that indicates the state of the polishing unit when the substrate processing process is performed on the substrate; a crack occurrence process identification process for identifying the process that causes cracks in the substrate by inputting the crack occurrence state information acquired by the information acquisition process in response to the occurrence of a crack in the substrate into a learning model that has been trained by machine learning to determine the correlation between the crack occurrence state information and crack occurrence process information that indicates the process that causes cracks in the substrate among the processes included in the substrate processing process, The device status information included in the crack occurrence status information is The state of the polishing unit is: the position of a top ring of the polishing unit; the height of the top ring; the pressure in a pressure chamber provided in the top ring; and at least one of a flow rate of pressure fluid supplied to the pressure chamber; Information processing methods.

8. The device status information included in the crack occurrence status information is In addition to the state of the polishing unit, the state of the substrate transport unit is also indicated; The state of the substrate transport unit is: the position of the substrate transport unit; The height of the substrate transport unit, and at least one of a presence / absence state of the substrate in the substrate transport unit, The information processing method according to claim 7.

9. In a substrate processing process performed by a substrate processing apparatus including a polishing unit that performs a polishing process on a substrate and a substrate transport unit that transfers the substrate between the polishing unit and the substrate, crack state information indicating the crack state when a crack occurs in the substrate and crack information including apparatus state information indicating the state of the polishing unit when the substrate processing process is performed on the substrate an information acquisition step of acquiring occurrence state information; a crack occurrence process identification process for identifying the process that causes cracks in the substrate by inputting the crack occurrence state information acquired by the information acquisition process in response to the occurrence of a crack in the substrate into a learning model that has been trained by machine learning to determine the correlation between the crack occurrence state information and crack occurrence process information that indicates the process that causes cracks in the substrate among the processes included in the substrate processing process, The steps included in the substrate processing step are: a substrate receiving step in which the polishing unit receives the substrate before the polishing process from the substrate transport unit; a pre-polishing oscillation step in which the polishing unit moves the substrate before the polishing process to a polishing position; a pre-polishing lowering step in which the polishing unit lowers the substrate before the polishing process to a polishing height; a polishing step in which the polishing unit performs a polishing process on the substrate before the polishing process; a post-polishing raising step in which the polishing unit raises the substrate after the polishing process to a moving height; a post-polishing swinging step in which the polishing unit moves the polished substrate to a transfer position; and a substrate transfer step in which the polishing unit transfers the substrate after the polishing process to the substrate transport unit; Information processing methods.

10. An inference method executed by an inference device having a memory and a processor, The processor: In a substrate processing process performed by a substrate processing apparatus including a polishing unit that performs a polishing process on a substrate and a substrate transport unit that transfers the substrate between the polishing unit and the substrate processing apparatus, an information acquisition process is performed to acquire crack occurrence state information including crack state information that indicates the state of a crack when a crack occurs in the substrate and apparatus state information that indicates the state of the polishing unit when the substrate processing process is performed on the substrate; When the crack occurrence state information is acquired in the information acquisition step in response to the occurrence of a crack in the substrate, an inference step is executed to infer which of the steps included in the substrate processing step is the cause of the occurrence of a crack in the substrate; The device status information included in the crack occurrence status information is The state of the polishing unit is: the position of a top ring of the polishing unit; the height of the top ring; the pressure in a pressure chamber provided in the top ring; and at least one of a flow rate of pressure fluid supplied to the pressure chamber; Reasoning method.

11. The device status information included in the crack occurrence status information is In addition to the state of the polishing unit, the state of the substrate transport unit is also indicated; The state of the substrate transport unit is: the position of the substrate transport unit; The height of the substrate transport unit, and at least one of a presence / absence state of the substrate in the substrate transport unit, The inference method of claim 10.

12. An inference method executed by an inference device having a memory and a processor, The processor: In a substrate processing process performed by a substrate processing apparatus including a polishing unit that performs a polishing process on a substrate and a substrate transport unit that transfers the substrate between the polishing unit and the substrate, crack state information indicating a crack state when a crack occurs in the substrate, and an information acquiring step of acquiring crack occurrence state information including apparatus state information indicating the state of the polishing unit when the substrate processing step is performed; When the crack occurrence state information is acquired in the information acquisition step in response to the occurrence of a crack in the substrate, an inference step is executed to infer which of the steps included in the substrate processing step is the cause of the occurrence of a crack in the substrate; The steps included in the substrate processing step are: a substrate receiving step in which the polishing unit receives the substrate before the polishing process from the substrate transport unit; a pre-polishing oscillation step in which the polishing unit moves the substrate before the polishing process to a polishing position; a pre-polishing lowering step in which the polishing unit lowers the substrate before the polishing process to a polishing height; a polishing step in which the polishing unit performs a polishing process on the substrate before the polishing process; a post-polishing raising step in which the polishing unit raises the substrate after the polishing process to a moving height; a post-polishing swinging step in which the polishing unit moves the polished substrate to a transfer position; and a substrate transfer step in which the polishing unit transfers the substrate after the polishing process to the substrate transport unit; Reasoning method.

13. a learning data storage step of storing in a learning data storage unit a plurality of sets of learning data, each set consisting of crack occurrence state information including crack state information indicating the state of a crack when a crack occurs in the substrate, and crack occurrence process information including apparatus state information indicating the state of the polishing unit when the substrate processing step is performed on the substrate, and crack occurrence process information indicating the process among the processes included in the substrate processing step that causes a crack to occur in the substrate, in a substrate processing step performed by a substrate processing apparatus including a polishing unit that performs a polishing process on the substrate and a substrate transport unit that transfers the substrate between the polishing unit and the substrate; a machine learning process of inputting a plurality of sets of the learning data into a learning model to allow the learning model to learn a correlation between the crack occurrence state information and the crack occurrence process information; a learned model storage step of storing the learned model, which has learned the correlation through the machine learning step, in a learned model storage unit; The device status information included in the crack occurrence status information is The state of the polishing unit is: the position of a top ring of the polishing unit; the height of the top ring; the pressure in a pressure chamber provided in the top ring; and at least one of a flow rate of pressure fluid supplied to the pressure chamber; Machine learning methods.

14. The device status information included in the crack occurrence status information is In addition to the state of the polishing unit, the state of the substrate transport unit is also indicated; The state of the substrate transport unit is: the position of the substrate transport unit; The height of the substrate transport unit, and at least one of a presence / absence state of the substrate in the substrate transport unit, The machine learning method of claim 13.

15. In a substrate processing process performed by a substrate processing apparatus including a polishing unit that performs a polishing process on a substrate and a substrate transport unit that transfers the substrate between the polishing unit and the substrate processing apparatus, crack occurrence state information including crack state information that indicates the state of a crack when a crack occurs in the substrate and apparatus state information that indicates the state of the polishing unit when the substrate processing process is performed on the substrate, and a cause of the crack occurrence in the substrate in each process included in the substrate processing process a learning data storage step of storing a plurality of sets of learning data in a learning data storage unit, the learning data being composed of the crack occurrence process information indicating the process; a machine learning process of inputting a plurality of sets of the learning data into a learning model to allow the learning model to learn a correlation between the crack occurrence state information and the crack occurrence process information; a learned model storage step of storing the learned model, which has learned the correlation through the machine learning step, in a learned model storage unit; The steps included in the substrate processing step are: a substrate receiving step in which the polishing unit receives the substrate before the polishing process from the substrate transport unit; a pre-polishing oscillation step in which the polishing unit moves the substrate before the polishing process to a polishing position; a pre-polishing lowering step in which the polishing unit lowers the substrate before the polishing process to a polishing height; a polishing step in which the polishing unit performs a polishing process on the substrate before the polishing process; a post-polishing raising step in which the polishing unit raises the substrate after the polishing process to a moving height; a post-polishing swinging step in which the polishing unit moves the polished substrate to a transfer position; and a substrate transfer step in which the polishing unit transfers the substrate after the polishing process to the substrate transport unit; Machine learning methods.

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