Patent Application
20250217670
The present invention relates to an information processing apparatus, an inference apparatus, a machine-learning apparatus, an information processing method, an inference method, and a machine-learning method.
A substrate processing apparatus that performs chemical mechanical polishing (CMP) is known as a type of substrate processing apparatuses that performs various processing on a substrate, such as a semiconductor wafer. In the substrate processing apparatus, for example, a substrate is chemically and mechanically polished by pressing the substrate against a polishing pad by a polishing head, which is referred to as a top ring, with a polishing liquid (e.g., slurry) being supplied onto the polishing pad from a liquid supply nozzle, while a polishing table having the polishing pad is being rotated. Then, in order to remove foreign matter, such as polishing debris adhering to the polished substrate, a cleaning tool is brought into contact with the polished substrate while supplying substrate cleaning fluid onto the polished substrate to scrub-clean the wafer W, and the substrate is then dried, so that a series of processes is completed. The next substrate is then processed.
When the above-described series of processes is repeated, wear or contamination of the cleaning tool gradually progresses, so that it is necessary to replace the cleaning tool. A replacement time of the cleaning tool has been managed based on, for example, an accumulated number of uses or an accumulated use time of the cleaning tool (for example, see Patent document 1).
Patent document 1: Japanese laid-open patent publication No. H10-242092
In Patent document 1, the accumulated number of uses or the accumulated use time of the cleaning tool is determined by accumulating the number of times or time at which the cleaning tool is in contact with the substrate using a counter or a timer. However, a state of the cleaning tool not only varies depending on operating conditions of the substrate processing apparatus when the cleaning tool is in contact with the substrate, but also varies depending on the operating conditions of the substrate processing apparatus when the cleaning tool is not in contact with the substrate, for example, when the cleaning tool is cleaned (i.e., self-cleaned) with cleaning-tool cleaning fluid. Therefore, the state of the cleaning tool cannot be determined in detail only by the management of the accumulated number of uses or the accumulated use time.
On the other hand, operating conditions of parts included in the substrate processing apparatus for cleaning the substrate (e.g., a substrate holder configured to hold the substrate, a cleaning-fluid supply structure configured to supply the cleaning fluid onto the substrate, a substrate cleaning structure configured to bring the cleaning tool in contact with the substrate to clean the substrate, and a cleaning-tool cleaning structure configured to clean the cleaning tool with the cleaning-tool cleaning fluid) are factors that affect the state of the cleaning tool. These operating conditions act complexly and mutually on the cleaning tool. Therefore, it is difficult to accurately analyze the effects of the respective operating conditions on the state of the cleaning tool.
In view of the above-mentioned drawbacks, it is an object of the present invention to provide an information processing apparatus, an inference apparatus, a machine-learning apparatus, an information processing method, an inference method, and a machine-learning method capable of appropriately predicting a state of a cleaning tool according to operating conditions of a substrate processing apparatus.
In order to achieve the above object, an information processing device according to an embodiment of the present invention, comprises:
According to the information processing apparatus of the embodiment of the present invention, the operating condition information including the substrate-holder state information, the cleaning-fluid-supply-structure state information, the substrate-cleaning-structure state information, and the cleaning-tool-cleaning-structure state information is input to the learning model, so that the cleaning-tool state information for the operating condition information can be predicted. Therefore, the state of the cleaning tool can be predicted appropriately according to the operating conditions of the substrate processing apparatus.
Objects, configurations, and effects other than those described above will be made clear in detailed descriptions of the invention described below.
Embodiments for practicing the present invention will be described below with reference to the drawings. In the following descriptions, scope necessary for the descriptions to achieve the object of the present invention will be schematically shown, scope necessary for the descriptions of relevant parts of the present invention will be mainly described, and parts omitted from the descriptions will be based on known technology.
The substrate processing system 1 includes, as its main components, substrate processing devices 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 with, for example, a general-purpose computer or dedicated computer (see
Each substrate processing device 2 is composed of a plurality of units and is configured to perform a series of substrate processes on one or more wafers W, such as loading, polishing, cleaning, drying, film-thickness measuring, and unloading. During the processes, the substrate processing device 2 controls operations of the units while referring to device setting information 265 including device parameters that have been set for the units and substrate recipe information 266 that determines operating conditions in the polishing process, the cleaning process, and the drying process.
The substrate processing device 2 is configured to transmit various reports R to the database device 3, the user terminal device 6, etc. according to the operation of each unit. The various reports R include, for example, process information that identifies a wafer W on which substrate processing is performed, device-state information that indicates a state of each unit when each process is performed, event information detected by the substrate processing device 2, and manipulation information of a user (an operator, a production manager, a maintenance manager, etc.) on the substrate processing device 2.
The database device 3 is an apparatus that manages production history information 30 on a history when substrate processing is performed using a cleaning tool for proper production, and cleaning-test information 31 on a history of a test of cleaning process (hereinafter referred to as “cleaning test”) that has been performed using a cleaning tool for test. In addition to the above information, the database device 3 may also store the device setting information 265 and the substrate recipe information 266. In that case, the substrate processing device 2 may refer to these information.
The database device 3 receives various reports R from the substrate processing device 2 when the substrate processing device 2 has performed substrate processing using the cleaning tool for proper production, and registers the various reports R in the production history information 30, so that the reports R on the substrate processing are accumulated in the production history information 30.
When the substrate processing device 2 performs the cleaning test using the cleaning tool for test, the database device 3 receives the various reports R (including at least the device-state information) from the substrate processing device 2, registers the reports R in the cleaning-test information 31, and registers test results of the cleaning test associated with the various reports R, so that the reports R and the test results on the cleaning test are accumulated in the cleaning-test information 31. The cleaning test may be performed in the substrate processing device 2 for proper production, or may be performed in a cleaning-test device (not shown) for test that can perform the same cleaning process as the substrate processing device 2. The cleaning tool for test and the cleaning-test device include various cleaning-tool measuring devices (not shown) for measuring conditions of the cleaning tool, such as a weight, a moisture content, a hardness, and a cleanliness of the cleaning tool. Measurement values of the cleaning-tool measuring devices are registered as test results in the cleaning-test information 31.
The machine-learning device 4 operates as a main configuration for a learning phase in machine learning. For example, the machine-learning device 4 acquires, as first learning data 11A, part of the cleaning-test information 31 from the database device 3, and performs the machine learning to create a first learning model 10A to be used in the information processing device 5. The first learning model 10A as a learned model is provided to the information processing device 5 via the network 7, a storage medium, or the like.
The information processing device 5 operates as a main configuration for an inference phase in the machine learning. When the cleaning process is performed by the substrate processing device 2 using the cleaning tool for proper production, the information processing device 5 predicts a state of the cleaning tool using the first learning model 10A created by the machine-learning device 4, and transmits cleaning-tool state information, which is a result of the prediction, to the database device 3, the user terminal device 6, etc. The timing at which the information processing device 5 predicts the cleaning-tool state information may be after the cleaning process (i.e., post-predicting process), during the cleaning process (i.e., real-time-predicting process), or before the cleaning process (i.e., pre-predicting process).
The user terminal device 6 is a terminal device used by a user. The user terminal device 6 may be a stationary device or a portable device. The user terminal device 6 receives various input manipulations via a display screen of an application program, a web browser, etc., and displays various information (e.g., a notification of event, the cleaning-tool state information, the production history information 30, the cleaning-test information 31, etc.) via the display screen.
The load-unload unit 21 includes first to fourth front load sections 210A to 210D on which wafer cassettes (FOUPs, etc.), each capable of storing a large number of wafers W along a vertical direction, are placed, a transfer robot 211 that is movable along the storage direction (vertical direction) of the wafers W in each wafer cassette, and a horizontally-moving mechanism 212 for moving the transfer robot 211 along an arrangement direction of the first to fourth front load sections 210A to 210D (i.e., along a direction of a shorter side of the housing 20).
The transfer robot 211 is configured to be accessible to the wafer cassette placed on each of the first to fourth front load sections 210A to 210D, the substrate transport unit 23 (specifically, a lifter 232, which will be described later), the finishing unit 24 (specifically, first and second drying section 24E, 24F, which will be described later), and the film-thickness measuring unit 25. The transfer robot 211 includes upper and lower hands (not shown) for transporting the wafer W between the wafer cassette, the substrate transport unit 23, the finishing unit 24, and the film-thickness measuring unit 25. The lower hand is used when transporting the wafer W before processing of the wafer W, and the upper hand is used when transporting the wafer W after processing of the wafer W. When the wafer W is transported to and from the substrate transport unit 23 or the finishing unit 24, a shutter (not shown) provided on the first partition wall 200A is opened and closed.
The polishing unit 22 includes first to fourth polishing sections 22A to 22D each configured to perform the polishing process (planarization) on the wafer W. The first to fourth polishing sections 22A to 22D are arranged in parallel along the longitudinal direction of the housing 20.
Each of the first to fourth polishing sections 22A to 22D includes a polishing table 220 that rotatably supports a polishing pad 2200 having a polishing surface, a top ring (polishing head) 221 configured to hold the wafer W and to polish the wafer W while pressing the wafer W against the polishing pad 2200 on the polishing table 220, a polishing-fluid supply nozzle 222 configured to supply a polishing fluid onto the polishing pad 2200, a dresser 223 that rotatably supports a dresser disk 2230 and configured to dress the polishing pad 2200 by bringing the dresser disk 2230 into contact with the polishing surface of the polishing pad 2200, and an atomizer 224 configured to emit a cleaning fluid to the polishing pad 2200.
The polishing table 220 is supported by a polishing table shaft 220a. The polishing table 220 includes a rotating mechanism 220b configured to rotate the polishing table 220 about its own axis, and a temperature regulating mechanism 220c configured to regulate a surface temperature of the polishing pad 2200.
The top ring 221 is supported by a top-ring shaft 221a that is movable in the vertical direction. The top ring 221 includes a rotating mechanism 221c configured to rotate the top ring 221 about an axis of the top ring 221, a vertical movement mechanism 221d configured to move the top ring 221 in the vertical direction, and an oscillation mechanism 221e configured to rotate (or oscillate) the top ring 221 around a support shaft 221b as a pivot center.
The polishing-fluid supply nozzle 222 is supported by a support shaft 222a. The polishing-fluid supply nozzle 222 includes an oscillation mechanism 222b configured to rotate and move the polishing-fluid supply nozzle 222 around the support shaft 222a as a pivot center, a flow-rate regulator 222c configured to regulate a flow rate of the polishing fluid, and a temperature regulating mechanism 222d configured to regulate a temperature of the polishing fluid. The polishing fluid is a polishing liquid (e.g., slurry) or pure water, which may further include a chemical liquid, or may be a polishing liquid to which a dispersant is added.
The dresser 223 is supported by a dresser shaft 223a that is movable in the vertical direction. The dresser 223 includes a rotating mechanism 223c configured to rotate about its own axis, a vertical movement mechanism 223d configured to move the dresser 223 in the vertical direction, and an oscillation mechanism 223e configured to rotate and move the dresser 223 around a support shaft 223b as a pivot center.
The atomizer 224 is supported by a support shaft 224a. The atomizer 224 includes an oscillation mechanism 224b configured to rotate and move the atomizer 224 around the support shaft 224a as a pivot center, and a flow-rate regulator 224c configured to regulate a flow rate of the cleaning fluid. The cleaning fluid is a mixture of liquid (e.g., pure water) and gas (e.g., nitrogen gas), or liquid (e.g., pure water).
The wafer W is attracted and held on the lower surface of the top ring 221 and is moved to a predetermined polishing position above the polishing table 220. Thereafter, the wafer W is polished by being pressed by the top ring 221 against the polishing surface of the polishing pad 2200 on which the polishing fluid is supplied from the polishing-fluid supply nozzle 222.
As shown in
The first linear transporter 230A is arranged adjacent to the first and second polishing sections 22A and 22B and is configured to transport the wafer W to four transfer positions (which will be referred to as first to fourth transfer positions TP1 to TP4 in the order from the load-unload-unit-21-side). The second transfer position TP2 is a position where the wafer W is delivered to the first polishing section 22A, and the third transfer position TP3 is a position where the wafer W is delivered to the second polishing section 22B.
The second linear transporter 230B is arranged adjacent to the third and fourth polishing sections 22C and 22D and is configured to transport the wafer W to three transfer positions (which will be referred to as fifth to seventh transfer positions TP5 and TP7 in the order from the load-unload-unit-21-side). The sixth transfer position TP6 is a position where the wafer W is delivered to the third polishing section 22C, and the seventh transfer position TP7 is a position where the wafer W is delivered to the fourth polishing section 22D.
The swing transporter 231 is disposed adjacent to the fourth and fifth transfer positions TP4 and TP5. The swing transporter 231 has a hand that is movable between the fourth and fifth transfer positions TP4 and TP5. The swing transporter 231 is configured to transport the wafer W between the first and second linear transporters 230A and 230B and place the wafer W temporarily on the temporary station 233. The lifter 232 is disposed adjacent to the first transfer position TP1. The lifter 232 is configured to transport the wafer W between the first transfer position TP1 and the transfer robot 211 of the load-unload unit 21. When the wafer W is transported, the shutter (not shown) provided on the first partition wall 200A is opened and closed.
As shown in
The sections 24A to 24H of the finishing unit 24 are isolated from each other and are arranged along the first and second linear transporters 230A and 230B, for example, in an order of the first and second roll sponge cleaning sections 24A, 24B, the first transport section 24G, the first and second pen sponge cleaning sections 24C, 24D, the second transport section 24H, and the first and second drying sections 24E, 24F (in an order of distance from the load-unload unit 21). The finishing unit 24 sequentially performs a primary cleaning process on the wafer W after the polishing process using one or both of the first and second roll sponge cleaning sections 24A and 24B, a secondary cleaning process on the wafer W using one or both of the first and second pen sponge cleaning sections 24C and 24D, and a drying process on the wafer W using one or both of the first and second drying sections 24E and 24F. The order of the processes performed by the sections 24A to 24H of the finishing unit 24 may be changed as appropriate, or some of the processes may be omitted. For example, the cleaning process performed by the roll sponge cleaning sections 24A and 24B may be omitted, and the processes may be started from the cleaning process performed by the pen sponge cleaning sections 24C and 24D. The finishing unit 24 may include a buff cleaning section (not shown) instead of or in addition to any of the roll sponge cleaning sections 24A and 24B and the pen sponge cleaning sections 24C and 24D, and may perform a buff cleaning process. In this embodiment, each of the sections 24A to 24H of the finishing unit 24 are described as holding the wafer W in a horizontal posture (horizontally holding the wafer W), while each of the sections 24A to 24H may vertically hold or obliquely hold the wafer W.
The roll sponges 2400 and the pen sponges 2401 are made of synthetic resin, such as PVA or nylon, and have a porous structure. The roll sponges 2400 and the pen sponges 2401 function as cleaning tools for scrubbing the wafer W. The roll sponges 2400 and the pen sponges 2401 are removably attached to the first and second roll sponge cleaning sections 24A, 24B and the first and second pen sponge cleaning sections 24C, 24D, respectively.
The first transport section 24G includes therein a first transfer robot 246A that is movable in the vertical direction. The first transfer robot 246A is configured to be able to access the temporary station 233 of the substrate transport unit 23, the first and second roll sponge cleaning sections 24A, 24B, and the first and second pen sponge cleaning sections 24C, 24D, and has upper and lower hands (not shown) configured to transport the wafer W between them. For example, the lower hand is used to transport the wafer W before cleaning of the wafer W, and the upper hand is used to transport the wafer W after cleaning of the wafer W. When the wafer W is transported from the temporary station 233, a shutter (not shown) provided on the second partition wall 200B is opened and closed.
The second transport section 24H includes a second transfer robot 246B that is movable in the vertical direction. The second transfer robot 246B is configured to be accessible to the first and second pen sponge cleaning sections 24C, 24D and the first and second drying sections 24E, 24F, and has a hand (not shown) configured to transport the wafer W between them.
Each of the first and second roll sponge cleaning sections 24A and 24B includes a substrate holder 241 configured to hold the wafer W, a cleaning-fluid supply structure 242 configured to supply substrate cleaning fluid onto the wafer W, a substrate cleaning structure 240 configured to rotatably support the roll sponges 2400 and clean the wafer W by bringing the roll sponges 2400 in contact with the wafer W, a cleaning-tool cleaning structure 243 configured to clean (self-clean) the roll sponges 2400 with a cleaning-tool cleaning fluid, and an environmental sensor 244 configured to measure a condition of an internal space of the housing 20 where the cleaning process is performed.
The substrate holder 241 includes substrate holding mechanisms 241a configured to hold multiple portions of the side edge of the wafer W, and substrate rotating mechanisms 241b configured to rotate the wafer W about a third rotation axis perpendicular to the cleaning target surface of the wafer W. In the example of
The cleaning-fluid supply structure 242 includes cleaning-fluid supply nozzles 242a configured to supply substrate cleaning fluid to the cleaning target surface of the wafer W, swing movement mechanisms 242b configured to swing and move the cleaning-fluid supply nozzles 242a, flow-rate regulators 242c configured to regulate flow rate and pressure of the substrate cleaning fluid, and a temperature regulating mechanism 242d configured to regulate temperature of the substrate cleaning fluid. The substrate cleaning fluid may be either pure water (rinsing liquid), or a chemical liquid, or mixture of the pure water and the chemical liquid (for example, a concentration can be adjusted by adjusting flow rates of the pure water and the chemical liquid by the flow-rate regulators 242c). The cleaning-fluid supply nozzles 242a may be a nozzle for pure water and a nozzle for chemical liquid which are arranged separately, as shown in
The substrate cleaning structure 240 includes cleaning-tool rotating mechanisms 240a configured to rotate the roll sponges 2400 about first rotation axes parallel to the cleaning target surface of the wafer W, a vertical movement mechanism 240b configured to move at least one of the pair of roll sponges 2400 in the vertical direction so as to change heights of the roll sponges 2400 and a distance between the roll sponges 2400. The vertical movement mechanism 240b and the linear movement mechanism 240c function as a cleaning-tool movement mechanism configured to move the relative position of the roll sponges 2400 and the cleaning target surface of the wafer W.
The cleaning-tool cleaning structure 243 is arranged in a position that does not interfere with the wafer W. The cleaning-tool cleaning structure 243 includes a cleaning-tool cleaning tank 243a that can store and discharge cleaning-tool cleaning fluid, a cleaning-tool cleaning plate 243b disposed in the cleaning-tool cleaning tank 243a such that the roll sponge 2400 can be pressed against the cleaning-tool cleaning plate 243b, a flow-rate regulator 243c configured to regulate flow rate and pressure of the cleaning-tool cleaning fluid supplied to the cleaning-tool cleaning tank 243a, and a flow-rate regulator 243c configured to regulate flow rate and pressure of the cleaning-tool cleaning fluid flowing in the roll sponge 2400 and discharged through an outer circumferential surface of the roll sponge 2400 to the outside. The cleaning-tool cleaning fluid may be either pure water (rinsing liquid), or a chemical liquid, or mixture of the pure water and the chemical liquid (for example, a concentration can be adjusted by adjusting flow rates of the pure water and the chemical liquid by the flow-rate regulators 243c).
The environment sensors 244 include, for example, a temperature sensor 244a, a humidity sensor 244b, an atmospheric-pressure sensor 244c, an oxygen concentration sensor 244d, and a microphone (sound sensor) 244e. The environment sensors 244 may include a camera (image sensor) capable of generating an image of the surface of the wafer W or the roll sponge 2400, temperature distribution, air-flow distribution, or the like during or before or after the cleaning process. The object of the camera is not limited to visible light, and may be infrared light, ultraviolet light, or the like.
The first and second roll sponge cleaning sections 24A and 24B perform the primary cleaning process. Specifically, the wafer W is rotated by the substrate rotating mechanism 241b while the wafer W is held by the substrate holding mechanism 241a. Then, the substrate cleaning fluid is supplied from the cleaning-fluid supply nozzles 242a to the cleaning target surfaces of the wafer W. The roll sponges 2400 are rotated around their own axes by the cleaning-tool rotating mechanisms 240a and are placed in sliding contact with the cleaning target surfaces of the wafer W. The wafer W is cleaned by the sliding contact with the roll sponges 2400. Thereafter, the substrate cleaning structure 240 moves the roll sponge 2400 into the cleaning-tool cleaning tank 243a, where the roll sponge 2400 is rotated, or pressed against the cleaning-tool cleaning plate 243b, or supplied with the cleaning-tool cleaning fluid by the flow-rate regulator 243d, so that the roll sponge 2400 is cleaned.
Each of the first and second pen sponge cleaning sections 24C and 24D includes a substrate holder 241 configured to hold the wafer W, a cleaning-fluid supply structure 242 configured to supply substrate cleaning fluid onto the wafer W, a substrate cleaning structure 240 configured to rotatably support the pen sponge 2401 and clean the wafer W by bringing the pen sponge 2401 in contact with the wafer W, a cleaning-tool cleaning structure 243 configured to clean (self-clean) the pen sponge 2401 with a cleaning-tool cleaning fluid, and an environmental sensor 244 configured to measure a condition of an internal space of the housing 20 where the cleaning process is performed. The following describes are focused on configurations of the pen sponge cleaning sections 24C and 24D different from the roll sponge cleaning sections 24A and 24B.
The substrate holder 241 includes substrate holding mechanisms 241c configured to hold multiple portions of the side edge of the wafer W, and a substrate rotating mechanism 241d configured to rotate the wafer W about a third rotation axis perpendicular to the cleaning target surface of the wafer W. In the example of
The cleaning-fluid supply structure 242 has the same configurations as that shown in
The substrate cleaning structure 240 includes a cleaning-tool rotating mechanism 240d configured to rotate the pen sponge 2401 about a second rotation axis perpendicular to the cleaning target surface of the wafer W, a vertical movement mechanism 240e configured to move the pen sponge 2401 in the vertical direction, and a swing movement mechanism 240f configured to swing and move the pen sponge 2401 in the horizontal direction. The vertical movement mechanism 240e and the swing movement mechanism 240f function as a cleaning-tool movement mechanism configured to move the relative position of the pen sponge 2401 and the cleaning target surface of the wafer W.
The cleaning-tool cleaning structure 243 is arranged in a position that does not interfere with the wafer W. The cleaning-tool cleaning structure 243 includes a cleaning-tool cleaning tank 243e that can store and discharge cleaning-tool cleaning fluid, a cleaning-tool cleaning plate 243f disposed in the cleaning-tool cleaning tank 243e such that the pen sponge 2401 can be pressed against the cleaning-tool cleaning plate 243f, a flow-rate regulator 243g configured to regulate flow rate and pressure of the cleaning-tool cleaning fluid supplied to the cleaning-tool cleaning tank 243e, and a flow-rate regulator 243h configured to regulate flow rate and pressure of the cleaning-tool cleaning fluid flowing in the pen sponge 2401 and discharged through an outer circumferential surface of the pen sponge 2401 to the outside.
The environment sensors 244 include, for example, a temperature sensor 244a, a humidity sensor 244b, an atmospheric-pressure sensor 244c, an oxygen concentration sensor 244d, and a microphone (sound sensor) 244e. The environment sensors 244 may include a camera (image sensor) capable of generating an image of the surface of the wafer W or the pen sponge 2401, temperature distribution, air-flow distribution, or the like during or before or after the cleaning process. The object of the camera is not limited to visible light, and may be infrared light, ultraviolet light, or the like.
The first and second pen sponge cleaning sections 24C and 24D perform the secondary cleaning process. Specifically, the wafer W is rotated by the substrate rotating mechanism 241d while the wafer W is held by the substrate holding mechanism 241c. Then, the substrate cleaning fluid is supplied from the cleaning-fluid supply nozzles 242a to the cleaning target surface of the wafer W. The pen sponge 2401 is rotated around its own axis by the cleaning-tool rotating mechanisms 240d and is placed in sliding contact with the cleaning target surface of the wafer W. The wafer W is cleaned by the sliding contact with the pen sponge 2401. Thereafter, the substrate cleaning structure 240 moves the pen sponge 2401 into the cleaning-tool cleaning tank 243e, where the pen sponge 2401 is rotated, or pressed against the cleaning-tool cleaning plate 243f, or supplied with the cleaning-tool cleaning fluid by the flow-rate regulator 243h, so that the pen sponge 2401 is cleaned.
Each of the first and second drying sections 24E and 24F includes a substrate holder 241 configured to hold the wafer W, a drying-fluid supply structure 245 configured to supply substrate drying fluid to the wafer W, a housing 20 where the drying process is performed, and an environment sensor 244 configured to measure a condition of an internal space of the housing 20 where the drying process is performed.
The substrate holder 241 includes substrate holding mechanisms 241e configured to hold multiple portions of the side edge of the wafer W, and a substrate rotating mechanism 241f configured to rotate the wafer W about a third rotation axis perpendicular to the cleaning target surface of the wafer W.
The drying-fluid supply structure 245 includes drying-fluid supply nozzles 245a configured to supply substrate drying fluid onto the cleaning target surface of the wafer W, a vertical movement mechanism 245b configured to move the drying-fluid supply nozzles 245a in the vertical direction, a swing movement mechanism 245c configured to swing and move the drying-fluid supply nozzles 245a in the horizontal direction, a flow-rate regulator 245d configured to regulate flow rate and pressure of the substrate drying fluid, and a temperature regulating mechanism 245e configured to regulate a temperature of the substrate drying fluid. The vertical movement mechanism 245b and the swing movement mechanism 245c function as a drying-fluid supply nozzle movement mechanism configured to move a relative position of the drying-fluid supply nozzles 245a and the cleaning target surface of the wafer W. The substrate drying fluid is, for example, IPA vapor and pure water (rinsing liquid). The drying-fluid supply nozzles 245a have a nozzle for IPA vapor and a nozzle for pure water provided separately, as shown in
The environment sensors 244 include, for example, a temperature sensor 244a, a humidity sensor 244b, an atmospheric-pressure sensor 244c, an oxygen concentration sensor 244d, and a microphone (sound sensor) 244e. The environment sensors 244 may include a camera (image sensor) capable of generating an image of the surface of the wafer W, temperature distribution, air-flow distribution, or the like during or before or after the drying process.
The first and second drying sections 24E and 24F are configured to perform the drying process. Specifically, the wafer W is rotated by the substrate rotating mechanism 241f while the wafer W is held by the substrate holding mechanisms 241e. Then, the substrate drying fluid is supplied from the drying-fluid supply nozzles 245a onto the cleaning target surface of the wafer W, while the drying-fluid supply nozzles 245a are moved toward the side edge of the wafer W (in the radially outward direction). Thereafter, the wafer W is dried by being rotated at high speed by the substrate rotating mechanism 241f.
In
The film-thickness measuring unit 25 is a measuring device that measures the film thickness of the wafer W before or after the polishing process. The film-thickness measuring unit 25 is, for example, an optical film-thickness measuring device, an eddy current type film-thickness measuring device, or the like. The transfer robot 211 transports the wafer W to and from each film-thickness measuring module.
The finishing unit 24 includes modules 2471 to 247r to be controlled, which are disposed in sub-units (e.g., the first and second roll sponge cleaning sections 24A, 24B, the first and second pen sponge cleaning sections 24C, 24D, the first and second drying sections 24E, 24F, the first and second transport sections 24G, 24H, etc.), respectively, sensors 2481 to 248s arranged in the modules 2471 to 247r, respectively, for detecting data (i.e., detection values) necessary for controlling the modules 2471 to 247r, and a sequencer 249 for controlling the operations of the modules 2471 to 247r based on the detection values obtained by the sensors 2481 to 248s.
Examples of the sensors 2481 to 248s of the finishing unit 24 include a sensor configured to detect a holding pressure when the substrate holding mechanism 241a, 241c holds the substrate, a sensor configured to detect a rotation speed of the substrate holding mechanism 241a, 241c, a sensor configured to detect a rotation torque of the substrate rotation mechanism 241b, 241d, a sensor configured to detect a flow rate of the substrate cleaning fluid, a sensor configured to detect a pressure of the substrate cleaning fluid, a sensor configured to detect position coordinates of the cleaning-fluid supply structure 242 that can be converted into a dropping position of the substrate cleaning fluid, a sensor configured to detect a temperature of the substrate cleaning fluid, a sensor configured to detect a concentration of the substrate cleaning fluid, a sensor configured to detect a rotation speed of the cleaning-tool rotating mechanisms 240a, a sensor configured to detect a rotation torque of the cleaning-tool rotating mechanisms 240a, a sensor configured to detect position coordinates of the cleaning-tool moving mechanism (the vertical movement mechanism 240b, 240e, the linear movement mechanism 240c, the swing movement mechanism 240f), a sensor configured to detect a movement speed of the cleaning-tool moving mechanism, a sensor configured to detect a movement torque of the cleaning-tool moving mechanism, a sensor configured to detect a pressing load when the cleaning tool (the roll sponge 2400, the pen sponge 2401) is brought into contact with the wafer W or the cleaning-tool cleaning plate 243b, 243f, a sensor configured to detect a flow rate of the cleaning-tool cleaning fluid, a sensor configured to detect a pressure of the cleaning-tool cleaning fluid, a sensor configured to detect cleanliness of the cleaning-tool cleaning fluid (for example, a concentration, a particle diameter, or the number of particles for each particle diameter of particles contained in a waste liquid of the cleaning-tool cleaning tank 243a, 243e), and the environmental sensor 244.
The control unit 26 includes a control section 260, a communication section 261, an input section 262, an output section 263, and a storage section 264. The control unit 26 is comprised of, for example, a general-purpose or dedicated computer (see
The communication section 261 is coupled to the network 7 and functions as a communication interface for transmitting and receiving various data. The input section 262 receives various input operations. The output section 263 functions as a user interface by outputting various information via a display screen, lighting of signal tower, or buzzer sound.
The memory section 264 stores therein various programs (operating system (OS), application programs, web browser, etc.) and data (the device setting information 265, the substrate recipe information 266, etc.) used in the operations of the substrate processing device 2. The device setting information 265 and the substrate recipe information 266 are data that can be edited by the user via the display screen.
The control section 260 obtains detection values of the multiple sensors 2181 to 218q, 2281 to 228s, 2381 to 238u, 2481 to 248w, 2581 to 258y (hereinafter referred to as “sensor group”) via multiple sequencers 219, 229, 239, 249, 259 (hereinafter referred to as “sequencer group”). The control section 260 operates the multiple modules 2171 to 217p, 2271 to 227r, 2371 to 237t, 2471 to 247v, and 2571 to 257x (hereinafter referred to as “module group”) in cooperation to perform a series of substrate processes including loading, polishing, cleaning, drying, film-thickness measuring, and unloading.
As shown in
The processor 912 includes one or more arithmetic processing unit(s) (CPU (Central Processing Unit), MPU (Micro-processing unit), DSP (digital signal processor), GPU (Graphics Processing Unit), etc.), and operates as a controller configured to control the entire computer 900. The memory 914 stores various data and programs 930, and includes, for example, a volatile memory (DRAM, SRAM, etc.) that functions as a main memory, a non-volatile memory (ROM), a flash memory, etc.
The input device 916 includes, for example, a keyboard, a mouse, a numeric keypad, an electronic pen, etc., and functions as an input section. The output device 917 includes, for example, a sound (voice) output device, a vibration device, etc., and functions as an output section. The display device 918 includes, for example, a liquid crystal display, an organic EL display, electronic paper, a projector, etc., and functions as an output section. The input device 916 and the display device 918 may be configured integrally, such as a touch panel display. The storage device 920 includes, for example, HDD (Hard Disk Drive), SSD (Solid State Drive), etc., and functions as a storage section. The storage device 920 stores various data necessary for executing the operating system and the programs 930.
The communication I/F section 922 is coupled to a network 940, such as the Internet or an intranet (which may be the same as the network 7 in
In the computer 900 having the above configurations, the processor 912 calls the program 930 stored in the storage device 920 into the memory 914 and executes the program 930, and controls each part of the computer 900 via the buses 910. The program 930 may be stored in the memory 914 instead of the storage device 920. The program 930 may be stored in the medium 970 in an installable file format or an executable file format, and may be 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 and the communication I/F unit 922. The computer 900 performs various functions realized by the processor 912 executing the programs 930. The computer 900 may include hardware, such an FPGA, an ASIC, etc. for executing the above-described various functions.
The computer 900 is, for example, a stationary computer or a portable computer, and is an electronic device in arbitrary form. The computer 900 may be a client computer, a server computer, or a cloud computer. The computer 900 may be applied to devices other than the devices 2 to 6.
For example, a wafer ID, a cassette number, a slot number, start time and end time of each process, a used unit ID, etc. are registered in each record of the wafer history table 300. The polishing process, the cleaning process, and the drying process are illustrated as examples in
For example, a wafer ID, substrate-holder state information, cleaning-fluid-supply-structure state information, substrate-cleaning-structure state information, cleaning-tool-cleaning-structure state information, device internal-environment information, and processing result information are registered in each record of the cleaning history table 301.
The substrate-holder state information is information indicating a state of the substrate holder 241 in the cleaning process. The substrate-holder state information is, for example, detection values of each sensor (or command values to each module) sampled at predetermined time intervals by the sensor group (or the module group) of the substrate holder 241.
The cleaning-fluid-supply-structure state information is information indicating a state of the cleaning-fluid supply structure 242 in the cleaning process. The cleaning-fluid-supply-structure state information is, for example, detection values of each sensor (or command values to each module) sampled at predetermined time intervals by the sensor group (or the module group) of the cleaning-fluid supply structure 242.
The substrate-cleaning-structure state information is information indicating a state of the substrate cleaning structure 240 in the cleaning process. The substrate-cleaning-structure state information is, for example, detection values of each sensor (or command values to each module) sampled at predetermined time intervals by the sensor group (or the module group) of the substrate cleaning structure 240.
The cleaning-tool-cleaning-structure state information is information indicating a state of the cleaning-tool cleaning structure 243 in the cleaning process. The cleaning-tool-cleaning-structure state information is, for example, detection values of each sensor (or command values to each module) sampled at predetermined time intervals by the sensor group (or the module group) of the cleaning-tool cleaning structure 243.
The device internal-environment information is information indicating a state of the internal space of the substrate processing device 2 formed by the housing 20. The internal space of the substrate processing device 2 is a space in which the finishing unit 24 is arranged. The device internal-environment information is, for example, detection values of each sensor sampled by the environmental sensor 244 at predetermined time intervals. When the internal space of the substrate processing device 2 is separated for each of the sub-units of the finishing unit 24 (e.g., the first and second roll sponge cleaning sections 24A, 24B, the first and second pen sponge cleaning sections 24C, 24D, the first and second drying sections 24E, 24F), the environment sensor 224 is installed for each of the sub-units of the finishing unit 24, and the device internal-environment information is obtained for each of the sub-units of the finishing unit 24.
The processing result information is information indicating a result of the cleaning process. The processing result information includes the cumulative number of wafers W or the cumulative use time that has been used or spent in the cleaning process using the cleaning tool (the roll sponge 2400, the pen sponge 2401) since the cleaning tool has been replaced.
By referring to the cleaning history table 301, time-series data of each sensor (or time-series data of each module) can be extracted as a state of the substrate processing device 2 when the cleaning process is performed on the wafer W identified by the wafer ID.
For example, a test ID, the substrate-holder state information, the cleaning-fluid-supply-structure state information, the substrate-cleaning-structure state information, the cleaning-tool-cleaning-structure state information, the device internal-environment information, the processing result information, the test result information, etc. are registered in each record of the cleaning-test table 310. The substrate-holder state information, the cleaning-fluid-supply-structure state information, the substrate-cleaning-structure state information, the cleaning-tool-cleaning-structure state information, the device internal-environment information, and the processing result information of the cleaning-test table 310 are information indicating the state of each unit in the cleaning test. Their data configurations are the same as those of the cleaning history table 301, and therefore detailed explanations will be omitted.
The test result information is information indicating the state of the cleaning tool for test when the cleaning process is performed in the cleaning test. The test result information is measurement values sampled at predetermined time intervals by the cleaning-tool measuring device provided to the cleaning tool for test or the cleaning-test device. The test result information shown in
By referring to the cleaning-test table 310, time-series data (or time-series data of each module) indicating a state of the substrate cleaning device (the substrate holder 241, the cleaning-fluid supply structure 242, the substrate cleaning structure 240, and the cleaning-tool cleaning structure 243) when the cleaning process is performed using the cleaning tool for test in the cleaning test identified by the test ID, and time-series data indicating the state of the cleaning tool for test at that time can be extracted.
The control section 40 functions as a learning-data acquisition section 400 and a machine-learning section 401. The communication section 41 is coupled to external devices (e.g., the substrate processing device 2, the database device 3, the information processing device 5, the user terminal device 6, the cleaning-test device (not shown), etc.) via the network 7. The communication section 41 serves as a communication interface configured to transmit and receive various data.
The learning-data acquisition section 400 is coupled to an external device via the communication section 41 and the network 7. The learning-data acquisition section 400 acquires the first learning data 11A including operating condition information as input data and the cleaning-tool state information as output data. The first learning data 11A is data used as teacher data (or training data), verification data, and test data in supervised learning. The cleaning-tool state information is used as ground-truth label or correct label in the supervised learning.
The learning-data storage section 42 is a database that stores multiple sets of first learning data 11A acquired by the learning-data acquisition section 400. The specific configuration of the database that constitutes the learning-data storage section 42 may be designed as appropriate.
The machine-learning section 401 performs the machine learning using the multiple sets of first learning data 11A stored in the learning-data storage section 42. Specifically, the machine-learning section 401 inputs the multiple sets of first learning data 11A to the first learning model 10A and causes the first learning model 10A to learn a correlation between the operating condition information and the cleaning-tool state information included in the first learning data 11A to thereby create the first learning model 10A as a learned model.
The learned-model storage section 43 is a database configured to store the first learning model 10A as the learned model (i.e., adjusted weight parameter group) created by the machine-learning section 401. The first learning model 10A as the learned model stored in the learned-model storage section 43 is provided to a real system (e.g., the information processing device 5) via the network 7, a storage medium, or the like. Although the learning-data storage section 42 and the learned-model storage section 43 are shown as separate storage sections in
The number of first learning model 10A stored in the learned-model storage section 43 is not limited to one. For example, a plurality of learning models may be stored in the learned-model storage section 43 for different conditions, such as for machine-learning methods, types of wafer W (size, thickness, film type, etc.), types of cleaning tool, a difference in mechanism of the substrate cleaning device (the substrate holder 241, the cleaning-fluid supply structure 242, the substrate cleaning structure 240, and the cleaning-tool cleaning structure 243), types of substrate cleaning fluid and types of cleaning-tool cleaning fluid, types of data including in the operating condition information, types of data including in the cleaning-tool state information, etc. In that case, a plurality of types of learning data having data configurations corresponding respectively to the plurality of learning models for the different conditions may be stored in the learning-data storage section 42.
The operating condition information constituting the first learning data 11A include the substrate-holder state information indicating the state of the substrate holder 241 in the cleaning process of the wafer W performed by the substrate processing device 2, the cleaning-fluid-supply-structure state information indicating the state of the cleaning-fluid supply structure 242, the substrate-cleaning-structure state information indicating the state of the substrate cleaning structure 240, and the cleaning-tool-cleaning-structure state information indicating the state of the cleaning-tool cleaning structure 243.
The substrate-holder state information included in the operating condition information includes at least one of the number of holding points at which the substrate holding mechanism 241a, 241c holds the substrate, the holding pressure at which the substrate holding mechanism 241a, 241c holds the substrate, the rotation speed of the substrate holding mechanism 241a, 241c, the rotation torque of the substrate rotating mechanism 241b, 241d, and the condition of the substrate holding mechanism 241a, 241c. The condition of the substrate holding mechanism 241a, 241c represents a degree of wear and a degree of dirt of the substrate holding mechanism 241a, 241c, which is set based on a use condition of the substrate holding mechanism 241a, 241c (use time, pressure during use, and replaced or not replaced). The condition of the substrate holding mechanism 241a, 241c may change over time during the cleaning process, for example.
The cleaning-fluid-supply-structure state information included in the operating condition information includes at least one of flow rate of the substrate cleaning fluid, pressure of the substrate cleaning fluid, dropping position of the substrate cleaning fluid, temperature of the substrate cleaning fluid, and concentration of the substrate cleaning fluid. In the case where the substrate cleaning fluid is a plurality of types of fluid, the cleaning-fluid-supply-structure state information may include flow rate, pressure, dropping position, temperature, and concentration of each fluid.
The substrate-cleaning-structure state information included in the operating condition information includes at least one of the rotation speed of the cleaning-tool rotating mechanism 240a, the rotation torque of the cleaning-tool rotating mechanism 240a, the position coordinates of the cleaning-tool moving mechanism (the vertical movement mechanism 240b, 240e, the linear movement mechanism 240c, the swing movement mechanism 240f), the movement speed of the cleaning-tool moving mechanism, the movement torque of the cleaning-tool moving mechanism, the pressing load at which the cleaning tool is brought into contact with the wafer W, and the condition of the cleaning tool. The condition of the cleaning tool indicates a condition of the cleaning tool at a point in time before a target point-in-time in the cleaning-tool state information. The condition of the cleaning tool is represented by a degree of wear or a degree of dirt of the cleaning tool, which is set based on, for example, a use condition of the cleaning tool (use time, pressing load during use, replaced or not replaced, the rotation speed of the wafer W, the number of wafers processed, and an image of the surface of the cleaning tool). The condition of the cleaning tool may change over time during the cleaning process, for example.
The cleaning-tool-cleaning-structure state information included in the operating condition information includes at least one of the rotation speed of the cleaning-tool rotating mechanism 240a, the rotation torque of the cleaning-tool rotating mechanism 240a, the position coordinates of the cleaning-tool moving mechanism (the vertical movement mechanism 240b, 240e, the linear movement mechanism 240c, the swing movement mechanism 240f), the movement speed of the cleaning-tool moving mechanism, the movement torque of the cleaning-tool moving mechanism, the pressing load at which the cleaning tool is brought into contact with the cleaning-tool cleaning plate 243b, 243f, the flow rate of the cleaning-tool cleaning fluid, the pressure of the cleaning-tool cleaning fluid, and the cleanliness of the cleaning-tool cleaning fluid (discharge sides of the cleaning-tool cleaning tank 243a, 243e).
The operating condition information may further include device internal-environment information indicating an environment of a space in which the cleaning process is performed. The device internal-environment information included in the operating condition information includes at least one of the temperature, the humidity, the atmospheric pressure, the air flow, the oxygen concentration, and the sound of the internal space (for each of the sub-units of the finishing unit 24) formed by the housing 20. Further, the operating condition information may further include processing result information indicating a result of the cleaning process. The processing result information included in the operating condition information may include at least one of the cumulative number of wafers W and the cumulative use time that has been used or spent in the cleaning process using the cleaning tool since the cleaning tool has been replaced.
The cleaning-tool state information constituting the first learning data 11A is information indicating the state of the cleaning tool when the substrate processing device 2 operates under the operating conditions indicated by the operating condition information. In this embodiment, the cleaning-tool state information is condition information indicating a condition of the cleaning tool. The condition information includes, for example, at least one of the weight, the moisture content, the hardness, and the cleanliness of the cleaning tool at a target point-in-time included in the cleaning-process period from start to end of the cleaning process (i.e., time required for cleaning per wafer). The cleaning-process period includes for example, an operation in which the substrate cleaning structure 240 brings the cleaning tool into contact with the wafer W to clean the wafer W, an operation in which the cleaning-fluid supply structure 242 supplies the substrate cleaning fluid onto the wafer W, and an operation in which the cleaning-tool cleaning structure 243 cleans the cleaning tool with the cleaning-tool cleaning fluid.
The learning-data acquisition section 400 acquires the first learning data 11A by referring to the cleaning-test information 31 and receiving, as necessary, the input manipulations of the user through the user terminal device 6. For example, the learning-data acquisition section 400 refers to the cleaning-test table 310 of the cleaning-test information 31, thereby acquiring, as the operating condition information, the substrate-holder state information, the cleaning-fluid-supply-structure state information, the substrate-cleaning-structure state information, and the cleaning-tool-cleaning-structure state information (time-series data of sensors of the substrate holder 241, the cleaning-fluid supply structure 242, the substrate cleaning structure 240, and the cleaning-tool cleaning structure 243) when the cleaning test identified by the test ID is performed.
In this embodiment, a case will be described where the operating condition information is acquired as the time-series data of the sensor group as shown in
The learning-data acquisition section 400 refers to the cleaning-test table 310 of the cleaning-test information 31, thereby acquiring, as the cleaning-tool state information for the operating condition information, the test result information (e.g., the time-series data (see
In this embodiment, a case will be described where the cleaning-tool state information is the condition information as shown in
The first learning model 10A employs, for example, a neural network structure, and includes an input layer 100, an intermediate layer 101, and an output layer 102. Synapses (not shown) connecting neurons are placed between the layers, and each synapse is associated with a weight. A weight parameter group including weights of the synapses is adjusted by the machine learning.
The input layer 100 has neurons corresponding to the operating condition information as the input data, and each value of the operating condition information is input to each neuron. The output layer 102 has neuron(s) corresponding to the cleaning-tool state information as the output data, and a prediction result (inference result) of the cleaning-tool state information for the operating condition information is output as the output data. When the first learning model 10A is constituted of a regression model, the cleaning-tool state information is output as a numerical value normalized to a predetermined range (e.g., 0 to 1). When the first learning model 10A is constituted of a classification model, the cleaning-tool state information is output as a numerical value normalized to a predetermined range (e.g., 0 to 1) as a score (confidence) for each class.
First, in step S100, the learning-data acquisition section 400 obtains, from the cleaning-test information 31 or the like, a desired number of first learning data 11A as advance preparation for starting the machine learning, and stores the obtained first learning data 11A in the learning-data storage section 42. The number of first learning data 11A to be prepared may be set in consideration of the inference accuracy required for the first learning model 10A finally obtained.
Next, in step S110, the machine-learning section 401 prepares the first learning model 10A before learning for starting the machine learning. The first learning model 10A prepared before learning in this embodiment is composed of the neural network model as shown in
Next, in step S120, the machine-learning section 401 randomly obtains, for example, one set of first learning data 11A from the multiple sets of first learning data 11A stored in the learning-data storage section 42.
Next, in step S130, the machine-learning section 401 inputs the operating condition information (input data) included in the one set of first learning data 11A to the input layer 100 of the prepared first learning model 10A before learning (or during learning). As a result, the cleaning-tool state information (the output data) is output as the inference result from the output layer 102 of the first learning model 10A. However, the output data is generated by the first learning model 10A before learning (or during learning). Therefore, in the state before learning (or during learning), the output data output as the inference result may indicate different information from the cleaning-tool state information (ground-truth label or correct label) included in the first learning data 11A.
Next, in step S140, the machine-learning section 401 performs the machine learning by comparing the cleaning-tool state information (ground-truth label or correct label) included in the one set of first learning data 11A acquired in the step S120 with the cleaning-tool state information (output data) output as the inference result from the output layer in the step S130, and adjusting the weight of each synapse (backpropagation). In this way, the machine-learning section 401 causes the first learning model 10A to learn a correlation between the operating condition information and the cleaning-tool state information.
Next, in step S150, the machine-learning section 401 determines whether a predetermined learning end condition is satisfied. For example, this determination is made based on an evaluation value of an error function based on the cleaning-tool state information (ground-truth label or correct label) included in the first learning data 11A and the cleaning-tool state information (output data) output as the inference result, or the remaining number of unlearned first learning data 11A stored in the learning-data storage section 42.
In the step S150, if the machine-learning section 401 has determined that the learning end condition is not satisfied and the machine learning is to be continued (“No” in the step S150), the process returns to the step S120, and the steps S120 to S140 are performed on the first learning model 10A multiple times using the unlearned first learning data 11A. On the other hand, if the machine-learning section 401 has determined that the learning end condition is satisfied and the machine learning is to be terminated (“Yes” in the step S150) in the step S150, the process proceeds to step S160.
Then, in the step S160, the machine-learning section 401 stores, in the learned-model storage section 43, the first learning model 10A as the learned model (adjusted weight parameter group) created by adjusting the weight associated with each synapse. The sequence of machine-learning processes shown in
As described above, the machine-learning device 4 and the machine-learning method according to the present embodiment can provide the first learning model 10A capable of predicting (inferring) the cleaning-tool state information indicating the state of the cleaning tool (the roll sponge 2400, the pen sponge 2401) from the operating condition information including the substrate-holder state information, the cleaning-fluid-supply-structure state information, the substrate-cleaning-structure state information, and the cleaning-tool-cleaning-structure state information.
The control section 50 functions as an information acquisition section 500, a state prediction section 501, and an output processing section 502. The communication section 51 is coupled to the external devices (e.g., the substrate processing device 2, the database device 3, the machine-learning device 4, the user terminal device 6, etc.) via the network 7, and serves as a communication interface configured to transmit and receive various data.
The information acquisition section 500 is coupled to an external device via the communication section 51 and the network 7 and acquires the operating condition information including the substrate-holder state information, the cleaning-fluid-supply-structure state information, the substrate-cleaning-structure state information, and the cleaning-tool-cleaning-structure state information.
For example, when the “post-predicting process” of the cleaning-tool state information is performed for the wafer W on which the cleaning process has already been performed, the information acquisition section 500 refers to the cleaning history table 301 of the production history information 30, thereby acquiring the operating condition information including the substrate-holder state information, the cleaning-fluid-supply-structure state information, the substrate-cleaning-structure state information, and the cleaning-tool-cleaning-structure state information when the cleaning process is performed on the wafer W. When the “real-time-predicting process” of the cleaning-tool state information is performed for the wafer W during the cleaning process, the information acquisition section 500 receives the report R on the device-state information from the substrate processing device 2 performing the cleaning process, thereby acquiring the operating condition information including the substrate-holder state information, the cleaning-fluid-supply-structure state information, the substrate-cleaning-structure state information, and the cleaning-tool-cleaning-structure state information during the cleaning process of the wafer W. When the “pre-predicting process” of the cleaning-tool state information is performed for the wafer W before the cleaning process, the information acquisition section 500 receives the substrate recipe information 266 from the substrate processing device 2 that is to perform the cleaning process of the wafer W and simulates the device-state information when the substrate processing device 2 operates according to the substrate recipe conditions 266, thereby acquiring the operating condition information including the substrate-holder state information, the cleaning-fluid-supply-structure state information, the substrate-cleaning-structure state information, and the cleaning-tool-cleaning-structure state information when the cleaning process is to be performed on the wafer W.
As described above, the state prediction section 501 inputs the operating condition information acquired by the information acquisition section 500 as the input data to the first learning model 10A, thereby predicting the cleaning-tool state information (in this embodiment, the condition information) indicating the state of the cleaning tool when the substrate processing device 2 operates under the operating conditions indicated by the operating condition information
The learned-model storage section 52 is a database configured to store the first learning model 10A as the learned models used in the state prediction section 501. The number of first learning model 10A stored in the learned-model storage section 52 is not limited to one. For example, a plurality of learned models may be stored in the learned-model storage section 52 for different conditions, such as such as for machine-learning methods, types of wafer W (size, thickness, film type, etc.), types of cleaning tool, a difference in mechanism of the substrate cleaning device (the substrate holder 241, the cleaning-fluid supply structure 242, the substrate cleaning structure 240, and the cleaning-tool cleaning structure 243), types of substrate cleaning fluid and types of cleaning-tool cleaning fluid, types of data including in the operating condition information, types of data including in the cleaning-tool state information, etc. The plurality of learned models may be selectively used. In this embodiment, the learned-model storage section 52 stores at least two types of first learning models 10A, one corresponding to the roll-sponge cleaning section 24A, 24B using the roll sponges 2400 and the other corresponding to the pen-sponge cleaning section 24C, 24D using the pen sponge 2401. The learned-model storage section 52 may be a storage section of an external computer (e.g., a server type computer or a cloud type computer). In that case, the state prediction section 501 accesses the external computer.
The output processing section 502 performs output processing to output the cleaning-tool state information generated by the state prediction section 501. For example, the output processing section 502 may transmit the cleaning-tool state information to the substrate processing device 2 or the user terminal device 6, so that a display screen based on the cleaning-tool state information may be displayed on the substrate processing device 2 or the user terminal device 6. The output processing section 502 may transmit the cleaning-tool state information to the database device 3, so that the cleaning-tool state information may be registered in the production history information 30.
First, in step S200, when the user performs an input operation of inputting a wafer ID for identifying a wafer W to be predicted on the user terminal device 6, the user terminal device 6 transmits the wafer ID to the information processing device 5.
Next, in step S210, the information acquisition section 500 of the information processing device 5 receives the wafer ID transmitted in the step S200. In step S211, the information acquisition section 500 uses the wafer ID received in the step S210 to refer to the cleaning history table 301 of the production history information 30, thereby acquiring the operating condition information when the cleaning process is performed on the wafer W identified by the wafer ID.
Next, in step S220, the state prediction section 501 inputs the operating condition information acquired in the step S211 as input data to the first learning model 10A, thereby generating, as output data, the cleaning-tool state information corresponding to the operating condition information and predicting the state of the cleaning tool.
Next, in step S230, the output processing section 502 transmits the cleaning-tool state information to the user terminal device 6 as an output process for outputting the cleaning-tool state information generated in the step S220. The cleaning-tool state information may be transmitted to the database device 3 in addition to or instead of the user terminal device 6.
Next, in step S240, upon receiving the cleaning-tool state information transmitted in the step S230, the user terminal device 6 displays a display screen based on the cleaning-tool state information as a response to the transmission process in the step S200. As a result, the state of the cleaning tool can be visually recognized by the user. In the above information processing method, the steps S210 and S211 correspond to an information acquisition process, the step S220 corresponds to a state prediction process, and the step S230 corresponds to an output processing process.
As described above, the information processing device 5 and the information processing method according to the present embodiment inputs the operating condition information including the substrate-holder state information, the cleaning-fluid-supply-structure state information, the substrate-cleaning-structure state information, and the cleaning-tool-cleaning-structure state information in the cleaning process to the first learning model 10A, so that the cleaning-tool state information (condition information) corresponding to the operating condition information can be predicted. Therefore, the state of the cleaning tool can be predicted appropriately according to the operating conditions of the substrate processing apparatus 2.
A second embodiment differs from the first embodiment in that the cleaning-tool state information represents at least one of remaining service life information indicating a remaining service life of the cleaning tool (the roll sponge 2400, the pen sponge 2401), and cleaning quality information indicating a cleaning quality of the cleaning tool. In this embodiment, a machine-learning device 4a and an information processing device 5a according to the second embodiment will be described, focusing on differences from the first embodiment.
The learning-data acquisition section 400 acquires the second learning data 11B by referring to the cleaning-test information 31 and receiving, as necessary, input manipulations of the user through the user terminal device 6. Example of the cleaning-test information 31 is as follows. When the cleaning process is repeatedly performed using the cleaning tool for test or the cleaning-test device, the remaining service life information at a point when the service life of the cleaning tool has been reached is set to “0” as the test-result information. A value representing the registered remaining service life information increases as the time goes back to the past. The cleaning quality information measured by a measuring device, such as an optical microscope or a scanning electron microscope (SEM), is also registered as the test result information in the cleaning-test information 31. The learning-data acquisition section 400 acquires the remaining service life information and the cleaning quality information by acquiring the test result information of the cleaning test specified by the test ID from the cleaning test table 310 of the cleaning-test information 31.
The machine-learning section 401 inputs multiple sets of second learning data 11B to the second learning model 10B, and causes the second learning model 10B to learn a correlation between the operating condition information and the cleaning-tool state information (at least one of the remaining service life information and the cleaning quality information) included in the second learning data 11B, thereby creating the second learning model 10B as a learned model.
The information acquisition section 500 acquires the operating condition information including the substrate-holder state information, the cleaning-fluid-supply-structure state information, the substrate-cleaning-structure state information, and the cleaning-tool-cleaning-structure state information, as well as the first embodiment.
As described above, the state prediction section 501 inputs the operating condition information acquired by the information acquiring section 500 as input data to the second learning model 10B, thereby predicting the cleaning-tool state information (at least one of the remaining service life information and the cleaning quality information) indicating the state of the cleaning tool when the substrate processing device 2 operates under the operating conditions indicated by the operating condition information.
The output processing section 502 performs output processing to output the cleaning-tool state information (at least one of the remaining service life information and the cleaning quality information) generated by the state prediction section 501, as well as the first embodiment. For example, the output processing section 502 may transmit the cleaning-tool state information to the substrate processing device 2 or the user terminal device 6, so that a display screen based on the cleaning-tool state information may be displayed on the substrate processing device 2 or the user terminal device 6. The output processing section 502 may transmit the cleaning-tool state information to the database device 3, so that the cleaning-tool information may be registered in the production history information 30. At this time, the output processing section 502 may transmit information for displaying a replacement previous-notice of the cleaning tool, a procedure manual for a replacing operation, a time required for the replacing operation, a price of a replacement part, etc. to the substrate processing device 2 or the user terminal device 6, for example, when the remaining service life of the cleaning tool indicated by the remaining service life information is below a predetermined reference number for the previous notice or a predetermined reference time for the previous notice, or when the cleaning quality indicated by the cleaning quality information is below a predetermined reference quality. When the substrate processing device 2 has a function of automatically replacing the cleaning tool, the output processing section 502 may transmit an instruction to automatically replace the cleaning tool to the substrate processing device 2, or may transmit an instruction to order the replacement part for the cleaning tool to an inventory management device (not shown) configured to manage an inventory of the cleaning tool.
As described above, the information processing device 5a and the information processing method according to the present embodiment input the operating condition information including the substrate-holder state information, the cleaning-fluid-supply-structure state information, the substrate-cleaning-structure state information, and the cleaning-tool-cleaning-structure state information in the cleaning process to the second learning model 10B, so that the cleaning-tool state information (at least one of the remaining service life information and the cleaning quality information) for the operating condition information can be predicted. Therefore, the state of the cleaning tool can be predicted appropriately according to the operating conditions of the substrate processing apparatus 2.
The present invention is not limited to the above-described embodiments, and various modifications can be made and used without deviating from the scope of the present invention. All of them are included in the technical concept of the present invention.
In the above-described embodiments, the database device 3, the machine-learning device 4, and the information processing device 5 are described as being configured as separate devices, but these three devices may be configured as a single device. In one embodiment, any two of these three devices may be configured as a single device. Further, 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 device 2 or the user terminal device 6.
In the above embodiment, the substrate processing device 2 has been described as including the units 21 to 25, but the substrate processing device 2 may have at least the function of performing the cleaning process (the roll-sponge cleaning sections 24A, 24B or pen-sponge cleaning sections 24C, 24D) among the finishing unit 24, and the other units may be omitted.
In the embodiments described above, the neural network is employed as the learning model that implements the machine learning performed by the machine-learning section 401, while any other machine-learning model may be employed. Examples of the other machine-learning model include tree type (e.g., decision tree, regression tree), ensemble learning (e.g., bagging, boosting), neural network type including deep learning (e.g., recurrent neural network, convolutional neural network, LSTM), clustering type (e.g., hierarchical clustering, non-hierarchical clustering, k-nearest neighbor algorithm, k-means clustering), multivariate analysis (e.g., principal component analysis, factor analysis, logistic regression), and support vector machine.
The present invention can be provided in a form of a program (machine learning program) that causes the computer 900 to function as each section of the machine-learning devices 4, and in a form of a program (machine learning program) that causes the computer 900 to execute each process of the machine-learning method. Further, the present invention can be provided in a form of a program (information processing program) that causes the computer 900 to function as each section included in the information processing devices 5, and in a form of a program (information processing program) that causes the computer 900 to execute each process of the information processing method according to the above-described embodiments.
The present invention can be provided not only in a form of the information processing device 5 (information processing method or information processing program) according to the above-described embodiments, but also in a form of an inference apparatus (inference method or information processing program) used for inferring the cleaning-tool state information. In that case, the inference apparatus (inference method or inference program) may include a memory and a processor. The processor may execute a series of processes. The series of processes includes an information acquisition processing (information acquisition process) of acquiring the operating condition information, and an inference processing (inference process) of inferring the cleaning-tool state information (the condition information, the remaining service life information, or the cleaning quality information) indicating the state of the cleaning tool when the substrate processing device 2 operates under the operating conditions indicated by the operating condition information when the operating condition information is acquired in the information acquisition processing.
The form of the inference apparatus (inference method or inference program) can be applied to various devices more easily than when the information processing device is implemented. It is readily understood by a person skilled in the art that the state prediction section may be applied with use of the learning model as the learned model created by the machine-learning device and the machine-learning method according to the above-described embodiments when the inference apparatus (inference method or inference program) infers the cleaning-tool state information.
The present invention is applicable to an information processing apparatus, an inference apparatus, a machine-learning device, an information processing method, an inference method, and a machine-learning method.
| Number | Date | Country | Kind |
|---|---|---|---|
| 2022-056744 | Mar 2022 | JP | national |
| Filing Document | Filing Date | Country | Kind |
|---|---|---|---|
| PCT/JP2023/007808 | 3/2/2023 | WO |
