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

Abstract

To provide an information processing device capable of appropriately predicting reliability information of a polishing end point detection function showing the reliability of an end detection function for detecting that chemical mechanical polishing processing reaches an end point.SOLUTION: An information processing device 5 includes an information acquisition part 500 for acquiring reliability deterioration element state information including at least one of consumption state information showing a consumption state of a component of a substrate processing device 2 in chemical mechanical polishing processing of a substrate performed by the substrate processing device 2 and processing state information showing a processing state during polishing processing, and a state prediction part 501 for predicting the reliability information of the polishing end point detection function to the reliability deterioration element state information by inputting the reliability deterioration element state information acquired by the information acquisition part 500 in a learning model which learns a correlation between the reliability deterioration element state information and reliability information of a polishing end point detection function showing the reliability of an end point detection function for detecting that the chemical mechanical polishing processing reaches an end point.SELECTED DRAWING: Figure 13

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] As one of substrate processing apparatuses for performing various processes on substrates such as semiconductor wafers, substrate processing apparatuses for performing chemical mechanical polishing (CMP) processing are known. In the substrate processing apparatus, for example, a polishing table having a polishing pad is rotated while a polishing liquid (slurry) is supplied to the polishing pad from a liquid supply nozzle, and the substrate is pressed against the polishing pad by a polishing head called a top ring, thereby chemically and mechanically polishing the substrate. Then, in order to remove foreign matter such as polishing debris attached to the polished substrate, a cleaning tool is brought into contact with the polished substrate while a substrate cleaning fluid is supplied to the polished substrate, and the substrate is then dried, completing a series of processes and moving on to the processing of the next substrate.

[0003] In the above series of processes, if the polishing is insufficient, the insulation of the electronic circuit formed on the finished wafer may be insufficient, resulting in a short circuit. In addition, if the polishing is excessive, the cross-sectional area of ​​the wiring may be reduced, causing an increase in resistance, or the circuit itself may not be formed. Therefore, some recent polishing devices detect changes in frictional force, motor current, or physical quantities of the wafer to detect the end point of polishing. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Patent Publication No. 2021-028099 Summary of the Invention [Problem to be solved by the invention]

[0005] Patent Document 1 discloses a technology that uses a trained model that uses machine learning to learn waveforms of measurement data from the start of polishing to the end of polishing output from each of multiple types of endpoint detection sensors provided in a polishing unit during a past polishing operation, and inputs measurement data from the start of polishing to the present time output from each of multiple types of endpoint detection sensors during a new polishing operation, to estimate whether the present time is the endpoint timing indicating the end of polishing, thereby improving the accuracy of endpoint detection.

[0006] However, there is no conventional polishing apparatus having an end point detection technique that can determine whether the end point detection result is reliable, and the end point detection result can only be used as it is.

[0007] In view of the above 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 enable appropriate prediction of reliability information of a polishing endpoint detection function that indicates the reliability of the endpoint detection function that detects that a chemical mechanical polishing process has reached its endpoint. [Means for solving the problem]

[0008] In order to achieve the above object, an information processing device according to one aspect of the present invention comprises: Wear state information indicating a wear state of components of a substrate processing apparatus in a chemical mechanical polishing process of a substrate performed by the substrate processing apparatus, and processing state information indicating a processing state during polishing processing An information acquisition unit that acquires reliability degradation factor state information, the information including at least one of the following: a state prediction unit that predicts reliability information of the polishing end point detection function for the reliability degrading factor state information by inputting the reliability degrading factor state information acquired by the information acquisition unit into a learning model that has been trained by machine learning on a correlation between the reliability degrading factor state information and reliability information of a polishing end point detection function that indicates the reliability of an end point detection function that detects that the chemical mechanical polishing process has reached an end point. Effect of the Invention

[0009] According to an information processing device of one embodiment of the present invention, reliability reduction factor state information including at least one of wear state information indicating the wear state of components of the substrate processing device in a chemical mechanical polishing process and processing state information indicating the processing state during polishing processing is input into a learning model, and reliability information of the polishing end point detection function for the reliability reduction factor state information is predicted, so that reliability information of the polishing end point detection function indicating the reliability of the end point detection function that detects that the chemical mechanical polishing process has reached its end point can be appropriately predicted.

[0010] Other objects, configurations and effects will become apparent from the detailed description of the invention described below. [Brief description of the drawings]

[0011] [Figure 1] 1 is an overall configuration diagram showing an example of a substrate processing system 1. FIG. [Diagram 2] FIG. 2 is a plan view showing an example of a substrate processing apparatus 2. [Diagram 3] 2 is a perspective view showing an example of first to fourth polishing sections 22A to 22D. FIG. [Figure 4] 2 is a cross-sectional view showing a schematic example of a top ring 221. FIG. [Diagram 5] FIG. 2 is a block diagram showing an example of a substrate processing apparatus 2. [Figure 6] FIG. 9 is a hardware configuration diagram showing an example of a computer 900. [Figure 7] 2 is a data configuration diagram showing an example of production history information 30 managed by a database device 3. FIG. [Figure 8] 2 is a data configuration diagram showing an example of polishing test information 31 managed by the database device 3. FIG. [Figure 9] FIG. 2 is a block diagram showing an example of a machine learning device 4 according to the first embodiment. [Figure 10] 2 is a diagram showing an example of a first learning model 10A and first learning data 11A. FIG. [Figure 11]4 is a flowchart showing an example of a machine learning method performed by the machine learning device 4. [Figure 12] 1 is a block diagram showing an example of an information processing device 5 according to a first embodiment. [Figure 13] FIG. 2 is a functional explanatory diagram illustrating an example of an information processing device 5 according to the first embodiment. [Figure 14] 13 is a flowchart showing an example of an information processing method by the information processing device 5. [Figure 15] 13 is a schematic diagram for explaining an optical sensor 226 provided on a polishing table 220 according to a second embodiment. FIG. [Figure 16] FIG. 11 is a block diagram showing an example of a machine learning device 4a according to a second embodiment. [Figure 17] 1 is a diagram showing an example of a second learning model 10B and a first learning data set 11B. FIG. [Figure 18] FIG. 11 is a block diagram showing an example of an information processing device 5a according to a second embodiment. [Figure 19] FIG. 11 is a functional explanatory diagram illustrating an example of an information processing device 5a according to a second embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0012] Hereinafter, an embodiment for carrying out the present invention will be described with reference to the drawings. In the following, the scope necessary for the explanation to achieve the object of the present invention will be shown in a schematic manner, and the scope necessary for the explanation of the relevant part of the present invention will be mainly explained, and the parts that are omitted will be explained as being related to the publicly known technology.

[0013] (First embodiment) 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 series of substrate processing including a chemical mechanical polishing process (hereinafter referred to as a "polishing process") for polishing a surface of a substrate (hereinafter referred to as a "wafer") W such as a semiconductor wafer to a flat surface, a cleaning process for cleaning the wafer W after the polishing process, and the like.

[0014] 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, for example, configured as a general-purpose or dedicated computer (see FIG. 6 described later), and is connected to a wired or wireless network 7 so as to be capable of transmitting and receiving various data to and from each other (FIG. 1 shows transmission and reception of some data by dashed arrows). 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.

[0015] The substrate processing apparatus 2 is composed of a plurality of units, and performs each process, such as loading, polishing, cleaning, drying, film thickness measurement, unloading, etc., as a series of substrate processes on one or a plurality of wafers W. In this case, the substrate processing apparatus 2 controls the operation of each unit while referring to apparatus setting information 265 consisting of a plurality of apparatus parameters set for each unit, and substrate recipe information 266 that defines polishing process state information for the polishing process, cleaning process conditions for the cleaning process, etc.

[0016] 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 specifying the target wafer W when the substrate processing is performed, apparatus status information indicating the status of each unit when each process is performed, event information detected by the substrate processing apparatus 2, operation information of a user (operator, production manager, maintenance manager, etc.) with respect to the substrate processing apparatus 2, etc.

[0017] The database device 3 is a device that manages production history information 30 relating to the history of substrate processing performed on wafers W for actual production, and polishing test information 31 relating to the history of polishing test (hereinafter referred to as "polishing test") performed on dummy wafers for testing. Note that the database device 3 may store device setting information 265 and substrate recipe information 266 in addition to the above, and in that case, the substrate processing apparatus 2 may refer to these pieces of information.

[0018] When the substrate processing apparatus 2 performs substrate processing on the wafers W for actual production, the database device 3 receives various reports R from the substrate processing apparatus 2 at any time and registers them in the production history information 30, whereby the reports R regarding the substrate processing are accumulated in the production history information 30.

[0019] When the substrate processing apparatus 2 performs a polishing test on a test dummy wafer, the database device 3 receives various reports R (including at least apparatus status information) from the substrate processing apparatus 2 at any time and registers them in the polishing test information 31. The database device 3 also registers the test results of the polishing test in correspondence with the reports R, so that the polishing test information 31 accumulates the reports R and test results relating to the polishing test.

[0020] The dummy wafer is a jig simulating a wafer W. A dummy wafer sensor such as a pressure sensor or a temperature sensor is provided on the surface or inside of the dummy wafer to measure the state of the wafer W when the polishing process is performed, and the measurement value of the dummy wafer sensor is registered in the polishing test information 31 as a test result. The dummy wafer sensor may be provided at one or more locations on the substrate surface of the dummy wafer, or may be provided all over the surface. The polishing test may be performed in the substrate processing apparatus 2 for actual production, or in a polishing test apparatus (not shown) for testing that can reproduce the same polishing process as the substrate processing apparatus 2.

[0021] The machine learning device 4 operates as a subject of the learning phase of machine learning, and, for example, acquires a part of the polishing test information 31 from the database device 3 as first learning data 11A, and generates by machine learning a first learning model 10A used in the information processing device 5. The trained first learning model 10A is provided to the information processing device 5 via the network 7, a recording medium, or the like.

[0022] The information processing device 5 operates as a subject of the inference phase of machine learning, and uses the first learning model 10A generated by the machine learning device 4 to predict the state of the wafer W for production when the polishing process is performed by the substrate processing device 2 on the wafer W, and transmits the predicted result, that is, reliability information of the polishing endpoint detection function, to the database device 3, the user terminal device 6, etc. The timing at which the information processing device 5 predicts the reliability information of the polishing endpoint detection function may be after the polishing process is performed (post-prediction processing), may be while the polishing process is being performed (real-time prediction processing), or may be before the polishing process is performed (advance prediction processing).

[0023] 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 of, for example, an application program, a web browser, or the like, and displays various information (for example, event notifications, reliability information of the polishing endpoint detection function, production history information 30, polishing test information 31, etc.) via the display screen.

[0024] (Substrate processing device 2) 2 is a plan view showing an example of the 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 a 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 200A, and the substrate transport unit 23 and the cleaning unit 24 are separated from each other by a second partition 200B.

[0025] (Load / Unload Unit) The load / unload unit 21 includes first to fourth front load 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 load sections 210A to 210D (the short side direction of the housing 20).

[0026] The transfer robot 211 is configured to be accessible to the wafer cassettes placed on 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 provided with upper and lower two-stage hands (not shown) for transferring the wafer W between them. The lower hand is used when transferring the wafer W before processing, and the upper hand is used when transferring the wafer W after processing. When transferring the wafer W to the substrate transfer unit 23 or the cleaning unit 24, a shutter (not shown) provided on the first partition 200A is opened and closed.

[0027] (Polishing unit) The polishing unit 22 includes first to fourth polishing sections 22A to 22D that respectively perform a polishing process (flattening) of the wafer W. The first to fourth polishing sections 22A to 22D are mounted in a housing 20. are arranged side by side along the longitudinal direction.

[0028] 3 is a perspective view showing an example of the first to fourth polishing units 22A to 22D. The first to fourth polishing units 22A to 22D have the same basic configuration and functions.

[0029] 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 fluid supply nozzle 222 as a polishing fluid supply unit for supplying polishing fluid to the polishing pad 2200, a dresser 223 for dressing the polishing surface of the polishing pad 2200, an atomizer 224 for spraying cleaning fluid onto the polishing pad 2200, and an environmental sensor 225 for measuring the condition of the internal space of the housing 20 in which the polishing process is performed.

[0030] The polishing table 220 is supported by a polishing table shaft 220a and includes a rotation movement mechanism 220b that rotates the polishing table 220 about its axis via a polishing table rotary connector 2201, and a polishing pad surface temperature adjustment mechanism 220c that adjusts the surface temperature of the polishing pad 2200. The polishing pad surface temperature adjustment mechanism 220c has a radiation thermometer 220c1 above the polishing table 220 that measures the surface temperature of the polishing pad 2200 or the surface temperature of the grindstone.

[0031] The polishing table 220 of this embodiment also has a polishing table internal temperature adjustment mechanism 220d that adjusts the temperature of the polishing table 220 by supplying and discharging temperature control water to and from the inside of the polishing table 220. The polishing table internal temperature adjustment mechanism 220d has a temperature control water supply pipe 220d1 that supplies temperature control water to the inside of the polishing table 220 and a temperature control water discharge pipe 220d2 that discharges the temperature control water. The polishing table internal temperature adjustment mechanism 220d has a supply temperature control water thermometer 220d3 in the temperature control water supply pipe 220d1 that measures the temperature of the supplied temperature control water, and a discharge temperature control water thermometer 220d4 in the temperature control water discharge pipe 220d2 that measures the temperature of the discharged temperature control water.

[0032] The top ring 221 is supported by a top ring shaft 221a that can move in the vertical direction, and is equipped with a top ring rotational movement mechanism 221c that rotates the top ring 221 around its axis, a top ring up and down movement mechanism 221d that moves the top ring 221 in the vertical direction, and a top ring swing movement mechanism 221e that swings (swings) the top ring 221 around the top ring swing support shaft 221b as the center of rotation.

[0033] The top ring swing movement mechanism 221e includes a top ring swing support shaft 221b, a top ring swing arm 221f that swingably connects the top ring shaft 221a to the top ring swing support shaft 221b, and a top ring swing shaft motor 221g that rotates and drives the top ring swing support shaft 221b.

[0034] The top ring swing movement mechanism 221e has a top ring swing torque sensor 221h that detects the top ring swing torque applied to the top ring swing arm 221f at the connection between the top ring swing arm 221f and the top ring swing shaft motor 221g. Specifically, the top ring swing torque sensor 221h may detect the torque applied to the top ring swing arm 221f from the current value of the top ring swing shaft motor 221g. The current value of the top ring swing shaft motor 221g depends on the torque of the top ring swing arm 221f at the connection to the top ring swing shaft motor 221g. In this embodiment, the current value of the top ring swing shaft motor 221g may be the current value supplied to the top ring swing shaft motor 221g or a current command value generated in a driver (not shown). The top ring swing torque may be detected by other methods.

[0035] An acceleration sensor 221j and / or an amplitude sensor (not shown) for measuring vibration of the top ring 221 during polishing may be attached to the top ring 221. Furthermore, a sound level measuring device 221k for measuring noise during polishing may be provided near the top ring 221.

[0036] The polishing-fluid supply nozzle 222 is supported by a support shaft 222a and includes a swinging movement mechanism 222b for swinging the polishing-fluid supply nozzle 222 around the support shaft 222a, a polishing-fluid flow rate regulator 222c for adjusting the flow rate of the polishing fluid, and a polishing-fluid temperature control mechanism 222d for adjusting the temperature of the polishing fluid. The polishing fluid is a polishing liquid (slurry) or pure water, and may further include a chemical solution or may be a polishing liquid to which a dispersant has been added.

[0037] The dresser 223 is supported by a dresser shaft 223a that can move in the vertical direction, and is equipped with a dresser rotational movement mechanism 223c that rotates the dresser 223 around its axis, a dresser up-down movement mechanism 223d that moves the dresser 223 in the vertical direction, and a dresser swing movement mechanism 223e that swings the dresser 223 around the dresser swing support shaft 223b.

[0038] The dresser swing movement mechanism 223e includes a dresser swing support shaft 223b, a dresser swing arm 223f that swingably connects the dresser shaft 223a to the dresser swing support shaft 223b, and a dresser swing shaft motor 223g that rotates and drives the dresser swing support shaft 223b.

[0039] The dresser swing movement mechanism 223e has a dresser swing torque sensor 223h that detects the dresser swing torque applied to the dresser swing arm 223f at the connection between the dresser swing arm 223f and the dresser swing shaft motor 223g. Specifically, the dresser swing torque sensor 223h may detect the torque applied to the dresser swing arm 223f from the current value of the dresser swing shaft motor 223g. The current value of the dresser swing shaft motor 223g is an amount that depends on the torque of the dresser swing arm 223f at the connection to the dresser swing shaft motor 223g. In this embodiment, the current value of the dresser swing shaft motor 223g may be a current value supplied to the dresser swing shaft motor 223g or a current command value generated in a driver (not shown). Note that the dresser swing torque may be detected by other methods.

[0040] The atomizer 224 is supported by a support shaft 224a and includes an atomizer swing movement mechanism 224b that swings the atomizer 224 around the support shaft 224a, and a cleaning fluid flow rate regulator 224c that adjusts the flow rate of the cleaning fluid. The cleaning fluid is a mixture of a liquid (e.g., pure water) and a gas (e.g., nitrogen gas) or a liquid (e.g., pure water).

[0041] The environmental sensor 225 is composed of sensors arranged in the internal space of the housing 20, and includes, for example, a temperature sensor 225a that measures the temperature of the internal space, a humidity sensor 225b that measures the humidity of the internal space, and an air pressure sensor 225c that measures the air pressure of the internal space. Note that the environmental sensor 225 may include a camera (image sensor) that can capture images of the surface of the polishing pad 2200 during, before, and after the polishing process.

[0042] In addition, in FIG. 3, the specific configuration of each rotational movement mechanism 220b, 221c, 223c, each vertical movement mechanism 221d, 223d, and each oscillating movement mechanism 221e, 222b, 223e, 224b is omitted, but for example, they are configured by appropriately combining modules for generating driving force such as motors and actuators, driving force transmission mechanisms such as linear guides, ball screws, gears, belts, couplings, bearings, and sensors such as linear sensors, encoder sensors, limit sensors, torque sensors, etc.

[0043] 3, the specific configuration of each flow rate adjustment unit 222c, 224c is omitted, but for example, it is configured by appropriately combining modules for fluid adjustment such as pumps, valves, regulators, etc. and sensors such as flow rate sensors, pressure sensors, liquid level sensors, etc. Furthermore, while the specific configuration of the polishing table surface temperature adjustment mechanism unit 220c, the polishing table inside temperature adjustment mechanism unit 220d, and the polishing fluid temperature adjustment mechanism unit 222d is omitted, for example, it is configured by appropriately combining modules for temperature adjustment (conduction type, radiation (emission) type, convection type) such as heaters, heat exchangers, etc. and sensors such as temperature sensors, current sensors, etc.

[0044] 4 is a cross-sectional view showing a schematic 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 housed in the top ring body 2210, an elastic membrane 2212 disposed under the carrier 2211 and pressing the wafer W against the polishing pad 2200, a substantially annular retaining ring 2213 disposed on the outer periphery of the carrier 2211 and the elastic membrane 2212 and directly pressing the polishing pad 2200, and a retaining ring airbag 2214 disposed between the top ring body 2210 and the retaining ring 2213 and serving as a retaining ring pressing mechanism for pressing the retaining ring 2213 against the polishing pad 2200.

[0045] In this embodiment, a retaining ring airbag 2214 is used as the retaining ring pressing mechanism, but the retaining ring pressing mechanism may be a fluid actuator using air, water, oil, etc., an electric actuator using a ball screw, etc., or an elastic member including a spring or a bag-shaped bag.

[0046] The elastic membrane 2212 is made of an elastic membrane and has a plurality of concentric partition walls 2212e therein, thereby providing first to fourth elastic membrane pressure chambers 2212a to 2212d arranged concentrically from the center of the top ring body 2210 toward the outer periphery. The elastic membrane 2212 has a plurality of holes 2212f for suction of the wafer W on its lower surface, and functions as a substrate holding surface for holding the wafer W. The retainer ring airbag 2214 is made of an elastic membrane and has a retainer ring pressure chamber 2214a therein. The configuration of the top ring 221 may be changed as appropriate, and may include a pressure chamber for pressing the entire carrier 2211, the number and shape of the elastic membrane pressure chambers of the elastic membrane 2212 may be changed as appropriate, and the number and arrangement of the suction holes 2212f may be changed as appropriate. The elastic membrane 2212 may not have the suction holes 2212f.

[0047] The first to fourth flow paths 2216A to 2216D are connected to the first to fourth elastic membrane pressure chambers 2212a to 2212d, respectively, and the fifth flow path 2216E is connected to the retainer ring pressure chamber 2214a. The first to fifth flow paths 2216A to 2216E are connected to the outside via a top ring rotary connector 2215 provided on the top ring shaft 221a, and are branched into first branch flow paths 2217A to 2217E and second branch flow paths 2218A to 2218E, respectively. The first to fifth flow paths 2216A to 2216E are provided with pressure sensors PA to PE, respectively. The first branch flow paths 2217A to 2217E are connected to a gas supply source GS of pressure fluid (air, nitrogen, etc.) via valves V1A to V1E, flow rate 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.

[0048] 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. The wafer W is then polished by being pressed by the top ring 221 against the polishing surface of the polishing pad 2200 to which a polishing fluid is supplied from a polishing fluid supply nozzle 222. At this time, the top ring 221 independently controls the pressure regulators RA to RE. The pressure fluid supplied to the first through fourth elastic membrane pressure chambers 2212a through 2212d adjusts the pressure of the wafer W against the polishing pad 2200 for each region of the wafer W, and the pressure fluid supplied to the retaining ring pressure chamber 2214a adjusts the pressure of the retaining ring 2213 against the polishing pad 2200. The pressures of the pressure fluids supplied to the first through fourth elastic membrane pressure chambers 2212a through 2212d and the retaining ring pressure chamber 2214a are measured by pressure sensors PA through PE, respectively, and the flow rates of the pressurized gas are measured by flow sensors FA through FE, respectively.

[0049] (Substrate transport unit) As shown in FIG. 2, the substrate transport unit 23 includes first and second linear transporters 230A, 230B that can move horizontally along the arrangement direction of the first to fourth polishing sections 22A-22D (the longitudinal direction of the housing 20), a swing transporter 231 arranged between the first and second linear transporters 230A, 230B, a lifter 232 arranged on the load / unload unit 21 side, and a temporary placement table 233 for wafer W arranged on the cleaning unit 24 side.

[0050] The first linear transporter 230A is disposed adjacent to the first and second polishing units 22A and 22B, and is a mechanism for transporting the wafer 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 the wafer W is delivered to the first polishing unit 22A, and the third transfer position TP3 is a position where the wafer W is delivered to the second polishing unit 22B.

[0051] The second linear transporter 230B is disposed adjacent to the third and fourth polishing units 22C, 22D, and is a mechanism for transporting the wafer 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 the wafer W is delivered to the third polishing unit 22C, and the seventh transfer position TP7 is a position where the wafer W is delivered to the fourth polishing unit 22D.

[0052] The swing transporter 231 is disposed adjacent to the fourth and fifth transfer positions TP4, TP5, and has a hand movable between the fourth and fifth transfer positions TP4, TP5. The swing transporter 231 is a mechanism for transferring the wafer W between the first and second linear transporters 230A, 230B, and for temporarily placing the wafer W on the temporary placement table 233. 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 200A is opened and closed.

[0053] (Washing unit) 2, the cleaning unit 24 includes first and second cleaning chambers 240A and 240B for cleaning the wafer W using a cleaning tool, a drying chamber 241 for drying the wafer W, and first and second transfer chambers 242A and 242B for transporting the wafer W. The chambers of the cleaning unit 24 are arranged along the first and second linear transporters 230A and 230B in a partitioned state, for example, in the order of the first cleaning chamber 240A, the first transfer chamber 242A, the second cleaning chamber 240B, the second transfer chamber 242B, and the drying chamber 241 (in order of distance from the load / unload unit 21). The number and arrangement of the cleaning chambers 240A and 240B, the drying chamber 241, and the transfer chambers 242A and 242B are not limited to the example of FIG. 2, and may be changed as appropriate.

[0054] (Thickness measurement unit) 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, and is composed of, for example, an optical film thickness measuring device, an eddy current film thickness measuring device, etc. The wafer W is delivered to and from the module by a transfer robot 211.

[0055] (Control Unit) 5 is a block diagram showing an example of the substrate processing apparatus 2. A control unit 26 is electrically connected to each of the units 21 to 25 and comprehensively controls each of the units 21 to 25. In the following, the control system (modules, sensors, sequencer) of the polishing unit 22 will be described as an example, but the other units 21, 23 to 25 have the same basic configuration and functions, so description thereof will be omitted.

[0056] The polishing unit 22 includes a plurality of modules 2271 to 2277 that are arranged in each subunit (for example, a polishing table 220, a top ring 221, a polishing fluid supply nozzle 222, a dresser 223, an atomizer 224, etc.) of the polishing unit 22 and are to be controlled. r and multiple modules 2271-227 rEach module is located in r A plurality of sensors 2281 to 228 detect data (detection values) necessary for the control of s and each sensor 2281 to 228 s Based on the detection value of each module 2271 to 227 r and a sequencer 229 for controlling the operation of the

[0057] Sensors 2281 to 228 of the polishing unit 22 s These include, for example, a sensor for detecting the flow rate of the polishing fluid, a sensor for detecting the pressing force of the retaining ring pressing mechanism, a sensor for detecting the rotational torque of the top ring 221, a sensor for detecting the rotational torque of the polishing table 220, a timer for measuring the time until the end point is detected, an optical sensor or the like for detecting the end point, and an environmental sensor 225. In this embodiment, the rotational torque is measured by measuring the sliding resistance between the top ring 221 or the dresser 223 in contact with the polishing surface and the polishing table 220.

[0058] The control unit 26 includes a substrate processing 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 formed, for example, of a general-purpose or dedicated computer (see FIG. 6 described later).

[0059] The communication unit 261 is connected to the network 7 and functions as a communication interface for transmitting and receiving various data. The input unit 262 accepts various input operations, and the output unit 263 functions as a user interface by outputting various information via a display screen, signal tower lighting, and buzzer sound.

[0060] The storage unit 264 stores various programs (such as an operating system (OS), application programs, and a web browser) and data (such as equipment setting information 265 and substrate recipe information 266) used in the operation of the substrate processing apparatus 2. The equipment setting information 265 and the substrate recipe information 266 are data that can be edited by the user via a display screen.

[0061] The substrate processing control unit 260 controls a 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 the "sensor group") and a plurality of modules 2171 to 217 p , 2271-227 r , 2371-237 t , 2471-247 v , 2571-257 x (hereinafter referred to as "module group") operate in coordination to perform a series of substrate processing operations such as loading, polishing, cleaning, drying, film thickness measurement, and unloading.

[0062] (Hardware configuration of each device) 6 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.

[0063] 6, 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.

[0064] 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), a graphics processing unit (GPU), or a neural processing unit (NPU)) and controls the entire computer 900. The memory 914 stores various data and programs 930 and is composed of, for example, a volatile memory (DRAM, SRAM, etc.) that functions as a main memory, a non-volatile memory (ROM), a flash memory, etc.

[0065] 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 integrally configured, 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 the execution of the operating system and the program 930.

[0066] The communication I / F unit 922 is connected to a network 940 (which may be the same as the network 7 in FIG. 1) such as the Internet or an intranet by wire or wirelessly, and functions as a communication unit that transmits and receives data to and from other computers according to a predetermined communication standard. The external device I / F unit 924 is connected to an external device 950 such as a camera, a printer, a scanner, a reader / writer by wire or wirelessly, and functions as a communication unit that transmits and receives data to and from the external device 950 according to a predetermined communication standard. The I / O device I / F unit 926 is connected to an I / O device 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 device 960. The media input / output unit 928 is composed of a drive device such as a DVD drive, a CD drive, and reads and writes data from and to a medium (non-temporary storage medium) 970 such as a DVD or a CD.

[0067] In the computer 900 having the above configuration, the processor 912 calls up the 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 in 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 downloading the program 930 via the communication I / F unit 922 over the network 940. In addition, the computer 900 may realize various functions realized by the processor 912 executing the program 930, for example, with hardware such as an FPGA or an ASIC.

[0068] 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.

[0069] (Production history information 30) 7 is a data configuration diagram showing an example of production history information 30 managed by database device 3. The production history information 30 includes, for example, a wafer history table 300 for each wafer W and a polishing history table 301 for apparatus status information in a polishing process, as tables in which reports R obtained when substrate processing is performed on wafers W for this production are classified and registered. In addition to the above, the production history information 30 also includes a cleaning history table for apparatus status information in a cleaning process, an event history table for event information, and an operation history table for operation information, but detailed description thereof will be omitted.

[0070] For example, a wafer ID, a cassette number, a slot number, a start time and an end time of each process, a unit ID used, etc. are registered in each record of the wafer history table 300. Note that although a polishing process and a cleaning process are exemplified in Fig. 7, other processes are also registered in the same manner.

[0071] In each record of the polishing history table 301, for example, a wafer ID, wear state information, processing state information, and the like are registered.

[0072] The wear state information is information that can be obtained before polishing and indicates the wear state of each component of the substrate processing apparatus 2. The wear state information is, for example, information indicating the condition of the polishing pad 2200, information indicating the condition of the top ring rotary connector 2215, information indicating the condition of the polishing table rotary connector 2201, information indicating the condition of the dresser 223, and information indicating the condition of the substrate processing control unit 260. The information indicating each condition includes at least the usage time and the number of processed wafers of each component.

[0073] The processing state information is information that can be acquired during polishing and indicates the processing state of the substrate processing apparatus 2. The processing state information is, for example, detection values ​​of each sensor sampled at a predetermined time interval by a group of sensors such as a flow rate sensor of the polishing fluid, a pressing force sensor of the retaining ring pressing mechanism, a rotational torque sensor of the top ring 221, or a rotational torque sensor of the polishing table 220, which are included in the substrate processing apparatus 2. Furthermore, the processing state information is, for example, a statistical value of the time until the end point is detected for each wafer W, and a statistical value of the sensor time series data for each wafer W.

[0074] By referring to the polishing history table 301, it is possible to extract time-series data of each sensor as the apparatus state of the substrate processing apparatus 2 when the polishing process was performed on the wafer W identified by the wafer ID.

[0075] (Polishing Test Information 31) 8 is a data configuration diagram showing an example of the polishing test information 31 managed by the database device 3. The polishing test information 31 includes a polishing test table 310 in which the report R and the test results obtained when the polishing test was performed are classified and registered.

[0076] For example, a test ID, wear state information, processing state information, test result information, etc. are registered in each record of the polishing test table 310. The wear state information and processing state information of the polishing test table 310 are information indicating the state of each part in the polishing test, and the data structure is similar to that of the polishing history table 301, so detailed description will be omitted.

[0077] The test result information is information indicating the state of the test polishing apparatus when the polishing process is performed in the polishing test. The test result information may be a measured value measured by a polishing apparatus measuring device provided in the test polishing apparatus. The test result information shown in FIG. 8 includes the number of processed wafers 1, 2, ..., ...m, ..., n and the usage time t1, t2, ..., ...tm, ..., tn after the start of the polishing process. It also includes the reliability of the end point detection at each time tm1, tm2, ..., ...tmm, ..., tmn included in the polishing process period of the mth wafer, the sign of reliability degradation, the type of component that causes reliability degradation, and the type of process TR1 to TR4 that causes reliability degradation.

[0078] By referring to the polishing test table 310, it is possible to extract time-series data of each sensor indicating the state of the polishing unit 22 when the polishing process was performed in the polishing test identified by the test ID, and the reliability of the end-point detection function at that time. For example, a sign of a decrease in the reliability of the end-point detection function can be detected using data from a first target period of the polishing process prior to the time tmm when the reliability of the end-point detection function decreased, and the presence or absence of a decrease in the reliability of the end-point detection function can be detected using data from a second target period including the time tmm when the reliability of the end-point detection function decreased.

[0079] (Machine Learning Device 4) 9 is a block diagram showing an example of the machine learning device 4 according to the first embodiment. The machine learning device 4 includes a machine learning control unit 40, a communication unit 41, a learning data storage unit 42, and a trained model storage unit 43.

[0080] The machine learning 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 device 5, the user terminal device 6, a polishing test device (not shown), etc.) via the network 7, and functions as a communication interface for transmitting and receiving various data.

[0081] The learning data acquisition unit 400 is connected to an external device via the communication unit 41 and the network 7, and acquires first learning data 11A configured as a set of reliability reduction factor state information as input data and reliability information of the polishing endpoint detection function as output data. The first learning data 11A is data used as teacher data (training data), verification data, and test data in supervised learning. Moreover, the reliability information of the polishing endpoint detection function is data used as a correct answer label in supervised learning.

[0082] The learning data storage unit 42 is a database that stores a plurality of sets of the first learning data 11A 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.

[0083] The machine learning unit 401 performs machine learning using multiple sets of first learning data 11A stored in the learning data storage unit 42. That is, the machine learning unit 401 inputs multiple sets of first learning data 11A to the first learning model 10A, and generates a trained first learning model 10A by having the first learning model 10A learn the correlation between reliability reduction factor state information included in the first learning data 11A and reliability information of the polishing endpoint detection function.

[0084] The trained model storage unit 43 is a database that stores the trained first learning model 10A (specifically, a group of adjusted weight parameters) generated by the machine learning unit 401. The trained first learning model 10A stored in the trained model storage unit 43 is provided to an actual system (e.g., an information processing device 5) via a 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. 9, they may be configured as a single storage unit.

[0085] The number of first learning models 10A stored in the trained model storage unit 43 is not limited to one, and multiple learning models with different conditions may be stored, such as machine learning methods, differences in the mechanisms and materials of the top ring 221, types of elastic membranes 2212, types of retaining rings 2213, types of polishing pads 2200, types of polishing fluids, types of data included in reliability degradation factor state information, types of data included in reliability information of the polishing end point detection function, etc. In this case, the training data storage unit 42 may store multiple types of training data having data configurations corresponding to multiple training models with different conditions, respectively.

[0086] 10 is a diagram showing an example of a first learning model 10A and first learning data 11A. The first learning data 11A used for machine learning of the first learning model 10A is composed of reliability reduction factor state information and reliability information of a polishing endpoint detection function.

[0087] The reliability degradation factor status information constituting the first learning data 11A includes wear status information indicating the wear status of the components of the substrate processing apparatus 2, and polishing processing status information indicating the processing status of the substrate processing apparatus 2 that can be obtained during polishing processing.

[0088] The wear state information included in the reliability degradation factor state information is information indicating wear states of the components of the substrate processing apparatus 2. The wear state information includes, for example, at least one of the condition of the polishing pad 2200, the condition of the top ring rotary connector 2215, the condition of the polishing table rotary connector 2201, the condition of the dresser 223, and the condition of the substrate processing controller 260.

[0089] The condition of the polishing pad 2200 includes at least the usage time of the polishing pad 2200 and the number of wafers processed by the polishing pad 2200. The condition of the polishing pad 2200 may be set based on, for example, the cumulative number of rotations of the polishing table 220, the rotation speed of the polishing table 220, the rotation torque of the polishing table 220, whether or not the polishing pad 220 has been dressed, whether or not the polishing pad 220 has been replaced, an image of the surface, the surface shape, flatness, cleanliness, wetness, etc. The condition of the polishing pad 2200 may change over time during the polishing process, for example.

[0090] The condition of the top ring rotary connector 2215 includes at least the usage time of the top ring 221 and the number of wafers processed by the top ring 221. The condition of the top ring rotary connector 2215 may be set based on, for example, the accumulated number of rotations of the top ring 221, the rotation speed of the top ring 221, the rotation torque of the top ring 221, etc. The condition of the top ring 221 may change over time during the polishing process, for example.

[0091] The condition of the polishing table rotary connector 2201 includes at least the usage time of the polishing table 220 and the number of wafers processed by the polishing table 220. The condition of the polishing table rotary connector 2201 may be set based on, for example, the cumulative number of rotations of the polishing table 220, the rotation speed of the polishing table 220, the rotation torque of the polishing table 220, etc. The condition of the polishing table 220 may change over time during the polishing process, for example.

[0092] The condition of the dresser 223 includes at least the usage time of the dresser 223 and the number of wafers processed by the polishing table 220. The condition of the dresser 223 may be set based on, for example, the cumulative number of rotations of the dresser 223, the rotation speed of the dresser 223, the rotation torque of the dresser 223, whether or not the dresser has been dressed, whether or not the dresser has been replaced, an image of the surface, the surface shape, flatness, cleanliness, wetness, etc. The condition of the dresser 223 may change over time during the polishing process, for example.

[0093] The condition of the substrate process controller 260 includes at least the usage time of the substrate process controller 260 and the number of wafers processed by the substrate process controller 260. The condition of the substrate process controller 260 may change over time during the polishing process, for example.

[0094] The processing state information included in the reliability degradation factor state information is information that can be acquired during polishing, and indicates the processing state of the substrate processing apparatus 2. The processing state information includes, for example, at least one of the flow rate of the polishing fluid, the pressing force of the retaining ring pressing mechanism, the rotation torque of the top ring 221, the rotation torque of the polishing table 220, the oscillation torque of the top ring 221, the vibration of the top ring 221, the oscillation torque of the dresser 223, the noise during polishing, the temperature of the polishing surface, the temperature of the temperature control water for the polishing table 220, the statistical value of the time until the end point detection for each wafer, and the statistical value of the sensor time series data for each wafer.

[0095] The flow rate of the polishing fluid may be the flow rate of the polishing fluid supplied from the polishing fluid supply nozzle 222. In this embodiment, the pressing force of the retaining ring pressing mechanism may be the pressure in the retaining ring pressure chamber 2214a of the retaining ring airbag 2214, the flow rate of the pressurized fluid supplied to the retaining ring pressure chamber 2214a, or the like. The rotational torque of the top ring 221 may be determined from the motor current or the like that drives the top ring 221. The rotational torque of the polishing table 220 may be determined from the motor current or the like that drives the polishing table 220.

[0096] The swing torque of the top ring 221 may be a top ring swing torque applied to a top ring swing arm 221f detected by a top ring swing torque sensor 221h, etc. The vibration of the top ring 221 may be a vibration of the top ring 221 during polishing measured by an acceleration sensor 221j attached to the top ring 221, etc. The swing torque of the dresser 223 may be a dresser swing torque applied to a dresser swing arm 223f detected by a dresser swing torque sensor 223h, etc. The noise during polishing may be a noise during polishing measured by a sound level measuring device 221k provided near the top ring 221, etc. The temperature of the polishing surface may be a surface temperature of the polishing pad 2200 or a surface temperature of the grindstone measured by a radiation thermometer 220c1 installed above the polishing table 220. The temperature of the temperature-control water on the polishing table 220 may be the temperature of the temperature-control water supplied, measured by the supply temperature-control water thermometer 220d3.

[0097] The statistical value of the time until the end point is detected for each wafer may be obtained from the time until the end point is detected for each measured wafer. Also, the measured time until the end point is detected for each wafer may be divided into predetermined ranges, and the statistical value may be obtained from the number of data for each range. The statistical value of the sensor time series data for each wafer may be obtained from the time series data measured by sensors of the components such as the flow rate, pressing force, and rotational torque. Also, the measured time series data until the end point is detected may be divided into predetermined ranges, and the number of data for each range may be obtained.

[0098] The statistical values ​​obtained in this way may be used as they are, may be processed to remove noise that is easily picked up by measuring instruments, or may be used as the results of statistical processing, etc. In any case, it is sufficient to be able to obtain the variation in the results.

[0099] In this embodiment, since a retaining ring airbag 2214 is used as the retaining ring pressing mechanism, the top ring status information included in the polishing process status information includes the pressure within the retaining ring pressure chamber 2214a (retaining ring airbag pressure) and the flow rate of the pressurized fluid supplied to the retaining ring pressure chamber 2214a (retaining ring airbag flow rate).

[0100] When using another pressing mechanism as the retaining ring pressing mechanism, the pressing force of the retaining ring pressing mechanism in the processing state information included in the reliability reduction factor state information may be the amount of an element that adjusts the pressing force of the retaining ring pressing mechanism. For example, when an electric actuator is used as the retaining ring pressing mechanism, When a motor is used, the pressing force of the electric actuator may be the amount of current that adjusts the pressing force of the electric actuator, etc. When an elastic member such as a spring or a bag is used as the retaining ring pressing mechanism, the pressing force of the retaining ring pressing mechanism may be the pressing force of the elastic member and the vertical position that adjusts the pressing force of the elastic member, etc.

[0101] The reliability information of the polishing end point detection function constituting the first learning data 11A is information indicating the reliability of the polishing end point detection function of the wafer W subjected to the polishing process in a state as indicated by the reliability degradation cause state information. In this embodiment, the reliability information of the polishing end point detection function is information on the current reliability of the end point detection, information on the signs of reliability degradation, information on the types of components that cause reliability degradation, and information on the types of processes that cause reliability degradation.

[0102] The learning data acquiring unit 400 acquires the first learning data 11A by referring to the polishing test information 31 and, if necessary, by accepting an input operation by a user via the user terminal device 6. For example, the learning data acquiring unit 400 acquires, as reliability degradation factor status information, wear state information indicating the wear state of the components of the substrate processing apparatus 2 when the polishing test identified by the test ID was performed and processing state information indicating the processing state of the substrate processing apparatus 2 that can be acquired during polishing, by referring to the polishing test table 310 of the polishing test information 31.

[0103] In the present embodiment, the reliability reduction factor state information is acquired as time-series data of the sensor group. However, the reliability reduction factor state information may be appropriately changed according to the configuration of the polishing unit 22 (particularly, the top ring 221 and the polishing table 220). As the reliability reduction factor state information, a command value to the module may be used, a parameter converted from the detection value of the sensor or the command value to the module may be used, or a parameter calculated based on the detection values ​​of a plurality of sensors may be used. Furthermore, the reliability reduction factor state information may be acquired as time-series data of the entire polishing processing period, or as time-series data of a target period that is a part of the polishing processing period, or as time-point data at a specific target time, such as the time when the reliability of the polishing end point detection function is reduced. As described above, when changing the definition of the reliability reduction factor state information, the data configuration of the input data in the first learning model 10A and the first learning data 11A may be appropriately changed.

[0104] In addition, the learning data acquisition unit 400 refers to the polishing test table 310 of the polishing test information 31 to acquire test result information when a polishing test identified by the same test ID was conducted as reliability information of the polishing end point detection function corresponding to the above-mentioned reliability reduction factor status information.

[0105] In this embodiment, the reliability information of the polishing end point detection function is described as being information on the current reliability of end point detection, information on signs of reliability degradation, information on the type of components that are the cause of reliability degradation, and information on the type of process that is the cause of reliability degradation, as shown in FIG. 10 .

[0106] The reliability information of the current end point detection is information on the percentage of reliability of the current end point detection. Reliability may be classified from 0 to 100%. For example, if the reliability of the current end point detection is 100%, it can be determined that the current end point detection is reliable, and if the reliability of the current end point detection is 0%, it can be determined that the current end point detection is unreliable.

[0107] The reliability degradation predictor information is information on the time or number of processed wafers until the reliability of the end point detection is degraded or the end point is not detected. For example, if there is a high possibility that the reliability of the end point detection will be degraded in the polishing process several hours from now or several wafers from now, some kind of action can be taken on the substrate processing apparatus 2 before that happens.

[0108] The type information of components that cause reliability degradation is stored in advance as final inspection information. For example, if the top ring is the cause of the decrease in the reliability of the end point detection, some kind of action can be taken on the top ring.

[0109] The information on the type of process that causes a decrease in reliability is information on the type of process that causes a decrease in the reliability of the end point detection among the processes prepared in advance. For example, if the cause of the decrease in the reliability of the end point detection is a film formation process, some kind of measures can be taken for the strength, film thickness, variation, etc. of the film in the film formation process.

[0110] 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) that connect each neuron are laid between each layer, and each synapse is associated with a weight. A weight parameter group consisting of the weights of each synapse is adjusted by machine learning.

[0111] The input layer 100 has neurons in a number corresponding to the reliability degradation factor state information as input data, and each value of the reliability degradation factor state information is input to each neuron. The output layer 102 has neurons in a number corresponding to the reliability information of the polishing endpoint detection function as output data, and a prediction result (inference result) of the reliability information of the polishing endpoint detection function with respect to the reliability degradation factor state information is output as output data.

[0112] When the first learning model 10A is configured as a regression model, the reliability information of the polishing endpoint detection function is output as a numerical value normalized to a predetermined range (e.g., 0 to 1). When the first learning model 10A is configured as a classification model, the reliability information of the polishing endpoint detection function is output as a score (accuracy) for each class as a numerical value normalized to a predetermined range (e.g., 0 to 1).

[0113] An inference result corresponding to a numerical value is set in advance in the "predetermined range (0 to 1)." For example, in the case of reliability information of the current end point detection, the inference result "predetermined range (0 to 1)" may be divided into a plurality of ranges, and the current reliability of the end point detection (0 to 100%) may be set for each divided range. In addition, in the case of predictive information of reliability decrease, the inference result "predetermined range (0 to 1)" may be divided into a plurality of ranges, and the predictive time until reliability decrease may be set for each divided range.

[0114] In the case of type information of a component that is a factor of reliability degradation, a predetermined threshold is set within the "predetermined range (0 to 1)" that is the inference result of each component, and if the output value is below the threshold, the component is set as "not a factor of reliability degradation," and if it exceeds the threshold, it is set as "a factor of reliability degradation." Note that each component may be prepared in advance, and a respective threshold may be set for each.

[0115] In addition, in the case of type information of a process that is a factor of reliability degradation, a predetermined threshold value is set within the "predetermined range (0 to 1)" that is the inference result of each process, and if the output value is equal to or less than the threshold value, the process is set as "not a factor of reliability degradation," and if it exceeds the threshold value, it is set as "a factor of reliability degradation." Note that each process may be prepared in advance, and a respective threshold value may be set for each process.

[0116] (Machine learning methods) FIG. 11 is a flowchart showing an example of a machine learning method performed by the machine learning device 4.

[0117] First, in step S100, the learning data acquisition unit 400 starts machine learning. As a preliminary preparation for this, a desired number of first learning data 11A are acquired from the polishing test information 31 or the like, and the acquired first learning data 11A are stored in the learning data storage unit 42. The number of first learning data 11A to be prepared here may be set in consideration of the inference accuracy required for the first learning model 10A to be finally obtained.

[0118] Next, in step S110, the machine learning unit 401 prepares a first learning model 10A before learning in order to start machine learning. The first learning model 10A before learning prepared here is configured with the neural network model exemplified in Fig. 10, and the weight of each synapse is set to an initial value.

[0119] Next, in step S120, the machine learning unit 401 acquires, for example, one set of first training data 11A randomly from the multiple sets of first training data 11A stored in the training data storage unit .

[0120] Next, in step S130, the machine learning unit 401 inputs the polishing process state information (input data) included in the 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, reliability information (output data) of the polishing endpoint detection function is output as an inference result from the output layer 102 of the first learning model 10A, and 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 indicates information different from the reliability information (correct label) of the polishing endpoint detection function included in the first learning data 11A.

[0121] Next, in step S140, the machine learning unit 401 performs machine learning by comparing the reliability information (correct label) of the polishing end point detection function included in the set of first learning data 11A acquired in step S120 with the reliability information (output data) of the polishing end point detection function output from the output layer as an inference result in step S130, and performing a process (backpropagation) of adjusting the weight of each synapse. In this way, the machine learning unit 401 causes the first learning model 10A to learn the correlation between the reliability reduction factor state information and the reliability information of the polishing end point detection function.

[0122] Next, in step S150, the machine learning unit 401 determines whether a predetermined learning termination condition has been satisfied, for example, based on an evaluation value of an error function based on the reliability information (correct label) of the polishing end point detection function included in the first learning data 11A and the reliability information (output data) of the polishing end point detection function output as an inference result, or based on the remaining number of unlearned first learning data 11A stored in the learning data memory unit 42.

[0123] In step S150, if the machine learning unit 401 determines that the learning end condition is not satisfied and machine learning is to be continued (No in step S150), the process returns to step S120 and performs steps S120 to S140 on the first learning model 10A being trained multiple times using untrained first training data 11A. On the other hand, in step S150, if the machine learning unit 401 determines that the learning end condition is satisfied and machine learning is to be terminated (Yes in step S150), the process proceeds to step S160.

[0124] Then, in step S160, the machine learning unit 401 stores the trained first learning model 10A (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. 11. 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.

[0125] As described above, according to the machine learning device 4 and machine learning method of this embodiment, a first learning model 10A can be provided that is capable of predicting (inferring) reliability information of the polishing end point detection function, which indicates the state of the wafer W, from reliability degradation factor state information including wear state information and processing state information.

[0126] (Information processing device 5) Fig. 12 is a block diagram showing an example of the information processing device 5 according to the first embodiment. Fig. 13 is a functional explanatory diagram showing an example of the information processing device 5 according to the first embodiment. The information processing device 5 includes an information processing control unit 50, a communication unit 51, and a trained model storage unit 52.

[0127] The information processing control unit 50 functions as an information acquiring unit 500, a state predicting 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 data.

[0128] The information acquisition unit 500 is connected to an external device via the communication unit 51 and the network 7, and acquires reliability degradation factor status information including wear status information and machining status information.

[0129] For example, when performing "real-time prediction processing" of reliability information of the polishing end point detection function for a wafer W during polishing processing, the information acquisition unit 500 receives report R regarding reliability degradation factor status information from the substrate processing apparatus 2 performing the polishing processing at any time, and thereby acquires wear status information and processing status information as reliability degradation factor status information at any time while the polishing processing is being performed on the wafer W.

[0130] As described above, the state prediction unit 501 inputs the reliability degradation factor state information acquired by the information acquisition unit 500 as input data into the first learning model 10A, and predicts reliability information of the polishing end point detection function for the wafer W undergoing polishing processing indicated in the reliability degradation factor state information.

[0131] The trained model storage unit 52 is a database that stores the trained first trained model 10A used in the state prediction unit 501. The number of the first trained model 10A stored in the trained model storage unit 52 is not limited to one, and a plurality of trained models having different states, such as machine learning methods, differences in the mechanisms and materials of the top ring 221, types of the elastic membrane 2212, types of the retaining ring 2213, types of the polishing pad 2200, types of polishing fluids, types of data included in reliability reduction factor state information, types of data included in reliability information of the polishing end point detection function, etc., may be stored and selectively usable. The trained model storage unit 52 may be substituted by a storage unit of an external computer (for example, a server-type computer or a cloud-type computer), and in that case, the state prediction unit 501 may access the external computer.

[0132] The output processing unit 502 performs output processing for outputting the reliability information of the polishing endpoint detection function generated by the state prediction unit 501. For example, the output processing unit 502 may transmit the reliability information of the polishing endpoint detection function to the user terminal device 6, so that a display screen based on the reliability information of the polishing endpoint detection function may be displayed on the user terminal device 6, or the output processing unit 502 may transmit the reliability information of the polishing endpoint detection function to the database device 3, so that the reliability information of the polishing endpoint detection function may be registered in the production history information 30.

[0133] (Information processing method) FIG. 14 is a flowchart showing an example of an information processing method by the information processing device 5. An example of an operation in which a user operates the user terminal device 6 to perform a "prediction process" of reliability information of the polishing end point detection function for a specific wafer W will be described below.

[0134] First, in step S200, when the user performs an input operation to input, into the user terminal device 6, a wafer ID that identifies a wafer W that is the target of prediction, the user terminal device 6 transmits the wafer ID to the information processing device 5.

[0135] Next, in step S210, the information acquiring unit 500 of the information processing device 5 receives the wafer ID transmitted in step S200. In step S211, the information acquiring unit 500 refers to the polishing history table 301 of the production history information 30 using the wafer ID received in step S210 to acquire reliability degradation factor state information when the polishing process was performed on the wafer W identified by the wafer ID.

[0136] Next, in step S220, the state prediction unit 501 inputs the reliability degradation factor state information acquired in step S211 as input data into the first learning model 10A, thereby generating reliability information of the polishing end point detection function for the reliability degradation factor state information as output data, and predicts the state of the wafer W.

[0137] Next, in step S230, as an output process for outputting the reliability information of the polishing endpoint detection function generated in step S220, the output processing unit 502 transmits the reliability information of the polishing endpoint detection function to the user terminal device 6. Note that the destination of the reliability information of the polishing endpoint detection function may be the database device 3 in addition to or instead of the user terminal device 6.

[0138] Next, in step S240, when the user terminal device 6 receives the reliability information of the polishing end point detection function transmitted in step S230 as a response to the transmission process of step S200, a display screen is displayed based on the reliability information of the polishing end point detection function, allowing the user to visually confirm the state of the wafer W. In the above information processing method, steps S210 and S211 correspond to an information acquisition step, step S220 corresponds to a state prediction step, and step S230 corresponds to an output processing step.

[0139] As described above, according to the information processing device 5 and information processing method of this embodiment, reliability reduction factor information in the polishing process is input into the first learning model 10A, and reliability information of the polishing end point detection function for the reliability reduction factor status information is predicted, so that reliability information of the polishing end point detection function indicating the reliability of the end point detection function that detects that the chemical mechanical polishing process has reached its end point can be appropriately predicted.

[0140] Second Embodiment The second embodiment differs from the first embodiment in that an optical sensor is used as a polishing end point detection function. In the following, a machine learning device 4a and an information processing device 5a according to the second embodiment will be described, focusing on the differences from the first embodiment.

[0141] FIG. 15 is a schematic diagram for explaining an optical sensor 226 provided on a polishing table 220 according to the second embodiment.

[0142] 15, an optical sensor 226 for detecting the state of the film on the wafer W is embedded inside the polishing table 220. This optical sensor 226 irradiates light onto the wafer W and detects the state of the film on the wafer W (such as film thickness) from the intensity of the light reflected from the wafer W (reflection intensity or reflectance).

[0143] The polishing pad 2200 is provided with a light-transmitting layer for transmitting light from the optical sensor 226. The light-transmitting portion 2200a is attached to the polishing pad 2200. The light-transmitting portion 2200a is made of a material with high transmittance, such as quartz glass, a glass material, or pure water (with a transparent fluid supply unit and flow path not shown). Alternatively, the light-transmitting portion 2200a may be configured by providing a through-hole in the polishing pad 2200 and flowing a transparent fluid such as pure water from a transparent fluid supply unit from below while the through-hole is blocked by the wafer W. The light-transmitting portion 2200a is disposed at a position passing through the center of the wafer W held by the top ring 221.

[0144] 15, the optical sensor 226B includes a light source 226a, a light emitting optical fiber 226b as a light emitting unit that irradiates the light from the light source 226a onto the polished surface of the wafer W, a light receiving optical fiber 226c as a light receiving unit that receives reflected light from the polished surface, a spectrometer unit 226d having therein a spectrometer that disperses the light received by the light receiving optical fiber 226c and a plurality of light receiving elements that accumulate the light dispersed by the spectrometer as electrical information, an operation control unit 226e that controls the turning on and off of the light source 226a and the timing of starting reading of the light receiving elements in the spectrometer unit 226d, and a power source 226f that supplies power to the operation control unit 226e. Note that power is supplied to the light source 226a and the spectrometer unit 226d via the operation control unit 226e.

[0145] The light emitting end of the light emitting optical fiber 226b and the light receiving end of the light receiving optical fiber 226c are configured to be approximately perpendicular to the polished surface of the wafer W. For example, a photodiode array with 128 elements can be used as the light receiving element in the spectrometer unit 226d. The spectrometer unit 226d is connected to the operation control unit 226e. Information from the light receiving element in the spectrometer unit 226d is sent to the operation control unit 226e, and spectral data of the reflected light is generated based on this information. That is, the operation control unit 226e reads the electrical information stored in the light receiving element to generate the spectral data of the reflected light. This spectral data indicates the intensity of the reflected light resolved according to wavelength, and changes depending on the film thickness.

[0146] The operation controller 226e is connected to the optical sensor controller 226g. In this manner, the spectrum data generated by the operation controller 226e is transmitted to the optical sensor controller 226g. The optical sensor controller 226g calculates a characteristic value associated with the film thickness of the wafer W based on the spectrum data received from the operation controller 226e, and uses the calculated characteristic value as a monitoring signal to perform end point detection. The optical sensor controller 226g may be included in the substrate processing controller 260.

[0147] Fig. 16 is a block diagram showing an example of a machine learning device 4a according to the second embodiment. Fig. 17 is a diagram showing an example of a second learning model 10B and second learning data 11B. The second learning data 11B is used for machine learning of the second learning model 10B.

[0148] The reliability degradation factor state information constituting the second learning data 11B has the condition of the optical sensor 226 and the condition of the transparent liquid supply unit added as consumption state information, and the light reflection intensity information of the optical sensor 226 and the transparent liquid flow rate information added as processing state information. Note that other reliability degradation factor state information constituting the second learning data 11B is similar to that of the first embodiment, and therefore description thereof will be omitted.

[0149] The condition of the optical sensor 226 includes at least the usage time of the optical sensor 226 and the number of wafers processed by the optical sensor 226. The condition of the optical sensor 226 may be set based on, for example, the usage time of the light source 226a such as a lamp of the optical sensor 226, the temperature of the optical sensor 226, etc. The condition of the optical sensor 226 may change over time during the polishing process, for example.

[0150] The condition of the pure water transparent liquid supply unit should be at least the same as the usage time of the pure water supply unit and the and the number of wafers processed by the polishing unit 22. The condition of the transparent liquid supply unit may be set based on, for example, an accumulated supply flow rate of the transparent liquid supply unit. The condition of the transparent liquid supply unit may change over time during the polishing process.

[0151] The light reflection intensity information of the optical sensor 226 may be the intensity of light emitted by the optical sensor 226 and reflected by the wafer W. The transparent liquid information may be the flow rate of transparent liquid such as pure water supplied from a transparent liquid supply unit.

[0152] The learning data acquiring unit 400 acquires the second learning data 11B by referring to the polishing test information 31 and, if necessary, by accepting an input operation by a user via the user terminal device 6. For example, the learning data acquiring unit 400 acquires, as reliability degradation factor status information, wear state information and processing state information (time-series data of each sensor that each component has) at the time when a polishing test identified by a test ID was performed, by referring to the polishing test table 310 of the polishing test information 31.

[0153] Fig. 18 is a block diagram showing an example of the information processing device 5a functioning as the information processing device 5a according to the second embodiment. Fig. 19 is a functional explanatory diagram showing an example of the information processing device 5a according to the second embodiment.

[0154] The information acquiring unit 500 acquires reliability degradation factor state information including wear state information and processing state information, as in the first embodiment.

[0155] When performing “post-prediction processing” of reliability information of the polishing end point detection function for a wafer W after polishing processing has already been performed, the information acquisition unit 500 may refer to the polishing history table 301 of the production history information 30 to acquire wear state information and processing state information at the time when polishing processing was performed on the wafer W as reliability degradation factor state information.

[0156] As described above, the state prediction unit 501 inputs the reliability degradation factor state information acquired by the information acquisition unit 500 as input data into the second learning model 10B, and predicts reliability information of the polishing end point detection function for the wafer W undergoing polishing processing indicated in the reliability degradation factor state information.

[0157] As described above, according to the information processing device 5a and information processing method of this embodiment, reliability reduction factor state information including wear state information and processing state information in the polishing process is input into the second learning model 10B, and the reliability of the polishing end point detection function for the reliability reduction factor state information is predicted, so that the reliability of the polishing end point detection function for the wafer W due to the polishing process can be appropriately predicted.

[0158] In the second embodiment, the optical sensor 226 is used to detect the polishing end point of the wafer W, but other polishing end point detection functions may be used. For example, an eddy current sensor may be used as another example of the polishing end point detection function.

[0159] The eddy current sensor has an excitation coil, and when the magnetic field lines generated by the excitation coil connected to a high-frequency AC power source pass through a conductive film, they generate eddy currents on the surface of the wafer W. The magnitude of these eddy currents changes depending on the resistance of the metal film, i.e., the thickness of the metal film. Meanwhile, when an eddy current flows, magnetic field lines are generated from the eddy current in the opposite direction to the magnetic field lines generated by the excitation coil. The detection coil measures the strength of these magnetic field lines generated in the opposite direction, allowing the change in the thickness of the metal film to be measured.

[0160] The eddy current sensor is installed under the polishing table 220 shown in FIG. The eddy current sensor generates magnetic lines of force in a direction penetrating the polishing table 220. When the polishing table 220 rotates, the eddy current sensor rotates together with the polishing table 220 and passes below the wafer W held by the top ring 221. At this time, if a conductive film exists on the surface of the wafer W, the magnetic lines of force will pass through the conductive film, making it possible to measure the thickness of the conductive film.

[0161] As polishing of the wafer W progresses, the metal film on the surface of the wafer W decreases, and the resistance of the metal film increases accordingly. This reduces the eddy currents generated by the magnetic lines of force generated from the coil of the eddy current sensor, and the strength of the magnetic lines of force generated by the eddy currents also decreases. The eddy current sensor converts the changes in the magnetic lines of force generated by the eddy currents into a voltage corresponding to the amount of change in film thickness using the circuitry and software within the sensor.

[0162] In this way, the eddy current sensor can detect the polishing end point by measuring and storing the detected voltage for the wafer W after polishing in advance and comparing the stored voltage value with the voltage value during polishing of the wafer W. Note that the analog processing portion of the eddy current sensor may be replaced with digital processing. By using digital processing, the performance and stability of the eddy current sensor are improved.

[0163] When an eddy current sensor is used, the condition of the eddy current sensor may be used as wear state information, and magnetic field line strength information of the eddy current sensor may be used as machining state information.

[0164] The condition of the eddy current sensor includes at least the usage time of the eddy current sensor and the number of wafers processed by the eddy current sensor. The condition of the eddy current sensor may be set based on, for example, the usage time of the excitation coil. The condition of the eddy current sensor may change over time during the polishing process.

[0165] The magnetic field line strength information of the eddy current sensor may be the magnetic field line strength generated in the reverse direction and detected by the eddy current sensor.

[0166] As another example of the polishing end point detection function, a rotation torque method may be used, which may detect at least one of the rotation torque of a polishing table rotation motor of the rotation movement mechanism 220b that rotates the polishing table 220 and the rotation torque of a top ring rotation motor of the top ring rotation movement mechanism 221c that rotates the top ring 221.

[0167] When the wafer W is planarized, the polishing resistance decreases rapidly, making it possible to detect when polishing of the wafer W is completed.

[0168] The polishing end point of the wafer W may be detected by any one of an optical sensor, an eddy current sensor, and a torque sensor, or by a combination of all of them.

[0169] (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.

[0170] In the above embodiment, the database device 3, the machine learning device 4, and the information processing device 5 are described as being configured as separate devices, but the three devices may be configured as a single device, or any two of the three devices may be configured as a single device. In addition, at least one of the machine learning device 4 and the information processing device 5 may be incorporated in the control unit 26 or the user terminal device 6 of the substrate processing device 2.

[0171] In the above embodiment, the substrate processing apparatus 2 has been described as including each of the units 21 to 25. However, it is sufficient for the substrate processing apparatus 2 to include at least the polishing unit 22, and the other units may be omitted.

[0172] In the above embodiment, a case has been described in which a neural network is used as a learning model for implementing machine learning by the machine learning unit 401, but other machine learning models may 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, recurrent neural networks, convolutional neural networks, and neural network types such as LSTM (including deep learning). (including hierarchical clustering, non-hierarchical clustering, k-nearest neighbors, k-means, etc.) Examples of such methods include multivariate analyses such as filtering, principal component analysis, factor analysis, and logistic regression, as well as support vector machines.

[0173] In the above embodiment, the test result information is information indicating the state when a polishing process is performed in a polishing test using a dummy wafer in a test device, but it may be continuously acquired as information indicating the state when an actual wafer is polished using an actual polishing unit 22 equipped with a sensor that detects the state of each component. The continuously acquired test result information is continuously learned by the machine learning device 4.

[0174] Furthermore, in a polishing unit 22 in which no sensor is installed, the test result information may be continuously acquired by a person determining a decrease in the reliability of the polishing end point detection function and labeling the data.

[0175] Furthermore, information continuously acquired using the actual polishing unit 22 may be uploaded to the cloud, and after machine learning is performed in the cloud, the learned model may be deployed to the substrate processing apparatus 2. Also, a processing method may be learned within the substrate processing apparatus 2 without uploading to the cloud.

[0176] (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. Moreover, 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.

[0177] (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 reliability information of a polishing endpoint detection function. 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 step) for acquiring reliability reduction factor state information, and an inference process (inference step) for inferring reliability information of the polishing endpoint detection function indicating the reliability of the polishing endpoint detection function of the substrate subjected to the polishing process based on the reliability reduction factor state information when the reliability reduction factor state information is acquired in the information acquisition process.

[0178] By providing the inference device (inference method or inference program) in the form of an inference device, It is obvious to those skilled in the art that when the inference device (the inference method or the inference program) infers reliability information of the polishing endpoint detection function, the inference method implemented by the state prediction unit may be applied using the trained learning model generated by the machine learning device and the machine learning method according to the above-described embodiments. [Explanation of symbols]

[0179] 1... substrate processing system, 2... substrate processing apparatus, 3... database device, 4, 4a...machine learning device, 5, 5a...information processing device, 6...user terminal device, 7...network, 10...Learning model, 10A...First learning model, 10B...Second learning model, 11A: first learning data, 11B: second 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 measuring unit, 26: control unit, 30...Production history information, 31...Polishing test information, 40...machine learning control unit, 41...communication unit, 42...learning data storage unit, 43...Trained model memory unit, 50: information processing control unit, 51: communication unit, 52: trained model storage unit, 220: polishing table; 221: top ring; 222: polishing fluid supply nozzle; 223...dresser, 224...atomizer, 225...environment sensor 260: substrate processing control unit, 21: communication unit, 262: input unit, 263: output unit, 264: storage unit, 300...wafer history table, 301...polishing history table, 310...polishing test table, 400...learning data acquisition unit, 401...machine learning unit, 500: information acquisition unit; 501: state prediction unit; 502: output processing unit; 900…Computer 2200: polishing pad, 2210: top ring body, 2211: carrier, 2212...elastic membrane, 2212a to 2212d...elastic membrane pressure chamber, 2213...Retainer ring, 2214...Retainer ring airbag (retainer ring pressing mechanism), 2214a...retainer ring pressure chamber, 226...Optical sensor

Claims

1. an information acquiring unit that acquires reliability degradation factor status information, the reliability degradation factor status information including at least one of wear status information indicating a wear status of components of a substrate processing apparatus in a chemical mechanical polishing process of a substrate performed by the substrate processing apparatus and processing status information indicating a processing status during polishing; a state prediction unit that predicts reliability information of the polishing end point detection function for the reliability degrading factor state information by inputting the reliability degrading factor state information acquired by the information acquisition unit into a learning model that has been trained by machine learning on a correlation between the reliability degrading factor state information and reliability information of a polishing end point detection function that indicates the reliability of an end point detection function that detects that the chemical mechanical polishing process has reached an end point. Information processing device.

2. the substrate processing apparatus further includes a polishing table that rotatably supports a polishing pad, a top ring that presses a substrate against the polishing pad, a dresser that rotatably supports a dresser disk and brings the dresser disk into contact with the polishing pad to dress the polishing pad, and a substrate processing control unit that comprehensively controls the entire substrate processing apparatus, The consumption state information is The condition of the polishing pad; the condition of a rotary connector that rotatably mounts the top ring and the polishing table; the condition of the dresser; and a condition of the substrate processing controller; The information processing device according to claim 1 .

3. The substrate processing apparatus further includes a transparent liquid supply unit that supplies a transparent liquid to the polishing pad, and an optical sensor that measures a reflection intensity of light emitted by a lamp to detect that the chemical mechanical polishing process has reached an end point, The consumption state information is the condition of the optical sensor; a condition of the transparent liquid supply unit; The information processing device according to claim 2 .

4. the substrate processing apparatus includes a polishing table that rotatably supports a polishing pad, a top ring that presses a substrate against the polishing pad, and a polishing fluid supply unit that supplies a polishing fluid to the polishing pad; The top ring is a top ring body that is moved by a rotational movement mechanism, a vertical movement mechanism, and a swing movement mechanism; an elastic membrane that is accommodated in the top ring body and presses the substrate against the polishing pad in response to a pressure fluid supplied to an elastic membrane pressure chamber; a retainer ring disposed on an outer periphery of the elastic membrane and pressing against the polishing pad; a retainer ring pressing mechanism that adjusts the pressing force of the retainer ring; Equipped with The processing state information is Flow rate of the polishing fluid The pressing force of the retaining ring pressing mechanism, a rotation torque of the top ring and the polishing table; A swing torque of the top ring, Vibration of the top ring; A swing torque of the dresser, Noise generated during polishing of the top ring; the temperature of the polishing surface of the top ring during polishing; the temperature of the water for adjusting the temperature of the polishing table; A statistical value of the time to end point detection for each substrate; and a statistical value of the sensor time series data for each substrate; 4. The information processing device according to claim 2 or 3.

5. The substrate processing apparatus further includes a transparent liquid supply unit that supplies a transparent liquid to the polishing pad, and an optical sensor that detects that the chemical mechanical polishing process has reached an end point. The processing state information is the optical reflection intensity of the optical sensor, and a flow rate of the transparent liquid. The information processing device according to claim 4.

6. The reliability information of the polishing end point detection function is Current endpoint detection reliability information, Information on signs of declining reliability, Information on the type of components of the substrate processing apparatus that cause a decrease in reliability; and information on the type of the substrate processing process that causes a decrease in reliability; The information processing device according to claim 1 .

7. An inference device comprising a memory and a processor, The processor, an information acquisition process for acquiring reliability degradation factor status information, the information including at least one of wear status information indicating a wear status of components of a substrate processing apparatus in a chemical mechanical polishing process of a substrate performed by the substrate processing apparatus, and processing status information indicating a processing status during polishing; When the reliability degradation factor status information is acquired in the information acquisition process, an inference process is executed to infer reliability information of a polishing end point detection function indicating reliability of an end point detection function that detects that the chemical mechanical polishing process has reached an end point with respect to the polishing process status information. Reasoning device.

8. a learning data storage unit for storing a plurality of sets of learning data consisting of reliability degradation factor status information, including at least one of wear status information indicating wear status of components of a substrate processing apparatus and processing status information indicating a processing status during polishing, in a chemical mechanical polishing process of a substrate performed by the substrate processing apparatus, and reliability information of a polishing end point detection function indicating the reliability of an end point detection function that detects that the chemical mechanical polishing process has reached an end point; a machine learning unit that inputs a plurality of sets of the learning data into a learning model to cause the learning model to learn a correlation between the reliability degradation factor state information and reliability information of the polishing endpoint detection function; A trained model storage unit that stores the trained model in which the correlation is trained by the machine learning unit. Machine learning device.

9. an information acquiring step of acquiring reliability degradation factor status information, the information including at least one of wear status information indicating a wear status of components of a substrate processing apparatus in a chemical mechanical polishing process of a substrate performed by the substrate processing apparatus, and processing status information indicating a processing status during polishing; a state prediction process for predicting reliability information of the polishing end point detection function for the reliability degrading factor state information by inputting the reliability degrading factor state information acquired by the information acquisition process into a learning model that has been trained by machine learning on a correlation between the reliability degrading factor state information and reliability information of a polishing end point detection function that indicates the reliability of an end point detection function that detects that the chemical mechanical polishing process has reached an end point. Information processing methods.

10. An inference method executed by an inference device having a memory and a processor, comprising: The processor, an information acquiring step of acquiring reliability degradation factor status information, the information including at least one of wear status information indicating a wear status of components of a substrate processing apparatus in a chemical mechanical polishing process of a substrate performed by the substrate processing apparatus, and processing status information indicating a processing status during polishing; When the reliability degradation factor status information is acquired in the information acquisition step, an inference step is performed of inferring reliability information of a polishing end point detection function indicating reliability of an end point detection function that detects that the chemical mechanical polishing process has reached an end point with respect to the polishing process status information. Reasoning method.

11. a learning data storage process for storing a plurality of sets of learning data consisting of reliability degradation factor status information, including at least one of wear status information indicating the wear status of components of a substrate processing apparatus and processing status information indicating the processing status during polishing, in a chemical mechanical polishing process of a substrate performed by the substrate processing apparatus, and reliability information of a polishing end point detection function indicating the reliability of an end point detection function that detects that the chemical mechanical polishing process has reached an end point; 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 reliability degradation factor state information and the reliability information of the polishing endpoint detection function; and a learning data storage step of storing a plurality of sets of the learning models in which the correlation has been learned by the machine learning step in a learning data storage unit. Machine learning methods.

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