JP2024172792A - Substrate transport system and substrate transport method - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a substrate transfer system which enables reduction of worker's labor to exercise control over substrate transfer operation by a substrate transfer robot, and to provide a substrate transfer method.

SOLUTION: A substrate transfer system 100 includes an imaging part 43 which detects at least one of a substrate 210 transferred by a substrate transfer robot 20 and a substrate placement part 30a on which the substrate 210 is placed. The substrate transfer system 100 includes a control unit 60 which creates a learned model, which uses detection results of the imaging part 43 as an input and outputs transfer control information for controlling transfer operation of the substrate 210 by the substrate transfer robot 20, through machine learning.

SELECTED DRAWING: Figure 3

COPYRIGHT: (C)2024,JPO&INPIT

Description

This disclosure relates to a substrate transport system and a substrate transport method.

Conventionally, substrate transport methods for transporting substrates have been disclosed. Patent Document 1 discloses a substrate transport method in which a transport robot arranged in a substrate transport chamber transports a substrate to a mounting table in a substrate processing chamber where the substrate is processed. The transport robot has a fork on which the substrate is placed. Also, a position sensor that detects the substrate placed on the fork is arranged in the substrate transport chamber. In the substrate transport method described in Patent Document 1, the substrate placed on the fork is detected by the position sensor, and the transport path of the substrate by the transport robot is corrected to correct the positional deviation of the substrate relative to the fork.

Patent No. 6640321

However, when performing a substrate transport operation, it is necessary to teach the substrate transport robot, which is a transport robot that transports the substrate, the transport operation. In the above-mentioned Patent Document 1, while the positional deviation of the substrate relative to the fork that holds the substrate is detected by a position sensor, the transport operation of the substrate transport robot must be taught manually. Therefore, since the transport operation needs to be taught for each configuration of the substrate transport system, such as the position of the substrate placement part, the effort of teaching the substrate transport robot the transport operation is thought to be a burden for the worker. Therefore, it is desired to reduce the effort required by the worker to control the substrate transport operation by the substrate transport robot.

This disclosure has been made to solve the problems described above, and one objective of this disclosure is to provide a substrate transport system and a substrate transport method that can reduce the effort required by an operator to control the substrate transport operation by a substrate transport robot.

A substrate transport system according to a first aspect of this disclosure includes a substrate transport robot, a detection unit that detects at least one of a substrate transported by the substrate transport robot and a substrate placement part on which the substrate is placed, and a control unit that uses as input the detection result of at least one of the substrate and the substrate placement part by the detection unit and generates, through machine learning, a trained model that outputs transport control information for controlling the substrate transport operation by the substrate transport robot.

As described above, the substrate transport system according to the first aspect of this disclosure uses the detection results of at least one of the substrate and the substrate placement unit by the detection unit as input, and generates a trained model by machine learning that outputs transport control information for controlling the substrate transport operation by the substrate transport robot. This makes it possible to automatically control the substrate transport operation based on the output from the trained model generated by machine learning. As a result, the effort required by an operator to control the substrate transport operation by the substrate transport robot can be reduced compared to when the substrate transport operation is taught to the substrate placement unit manually.

A substrate transport method according to a second aspect of this disclosure detects at least one of a substrate transported by a substrate transport robot and a substrate placement part on which the substrate is placed, and uses the detection results of at least one of the substrate and the substrate placement part as input to generate a trained model through machine learning that outputs transport control information for controlling the substrate transport operation by the substrate transport robot.

As described above, the substrate transport method according to the second aspect of this disclosure uses the detection results of at least one of the substrate and the substrate placement unit as input, and generates a trained model by machine learning that outputs transport control information for controlling the substrate transport operation by the substrate transport robot. This makes it possible to automatically control the substrate transport operation based on the output from the trained model generated by machine learning. As a result, it is possible to provide a substrate transport method that can reduce the effort required by an operator to control the substrate transport operation by the substrate transport robot, compared to when the substrate transport operation is taught to the substrate placement unit manually.

The present disclosure reduces the effort required by workers to control the substrate transport operation of a substrate transport robot.

1 is a top view illustrating a substrate processing system in which a substrate transport system according to an embodiment of the present disclosure is disposed. 1 is a block diagram showing a configuration of a substrate transfer system according to an embodiment of the present disclosure. FIG. 1 is a perspective view showing a schematic configuration of a substrate transport system. 1 is a diagram showing an example of an image of a FOUP captured by an imaging unit disposed in a transport chamber. FIG. 11 is a diagram showing an example of an image of a load lock unit captured by an imaging unit disposed in a transfer chamber; FIG. 13 is a diagram showing an example of an image of an aligner captured by an imaging unit disposed in a transfer chamber. FIG. 1 is a diagram showing an example of an image captured by an imaging unit disposed in a substrate transport robot; FIG. 13 is a diagram for explaining the output of transport control information by a trained model. 11A and 11B are diagrams showing an example of positional deviation of a substrate with respect to a substrate placement part. 11A and 11B are diagrams showing an example of positional deviation of a substrate with respect to a substrate holding hand. FIG. 4 is a flow chart for explaining a substrate transport method according to the present embodiment.

Below, an embodiment of the present disclosure that embodies this disclosure will be described with reference to the drawings.

The substrate transfer system 100 according to this embodiment will be described with reference to Figures 1 to 10.

(Substrate Processing System)
As shown in FIG. 1, the substrate transfer system 100 is disposed in a substrate processing system 200. The substrate processing system 200 includes a processing device 201 that processes a substrate 210. The processing device 201 includes a substrate transfer robot 202, a load lock unit 203, a plurality of processing module units 204, and a transfer chamber 205. The substrate transfer robot 202 is a horizontal articulated robot that transfers the substrate 210. The load lock unit 203 is a room for taking in and taking out the substrate 210. That is, the substrate 210 is placed in the load lock unit 203 in order to transfer the substrate 210 to the processing device 201. The processing device 201 of the substrate processing system 200 includes two load lock units 203. The load lock unit 203 is connected to the substrate transfer system 100. The load lock unit 203 includes a substrate placement unit 203a on which the substrate 210 is placed. In the processing module section 204, processing such as resist coating and etching is performed on the substrate 210. A substrate transfer robot 202 is disposed in the transfer chamber 205. In the transfer chamber 205, the substrate transfer robot 202 transfers the substrate 210 between the load lock section 203 and the processing module section 204. The load lock section 203 is an example of a processing standby section.

The substrate transport system 100 transports the substrate 210 between a FOUP 101 (Front Opening Unify Pod) and a processing device 201 in a substrate processing system 200 in which processing is performed on the substrate 210. The substrate transport system 100 is an EFEM (Equipment Front End Module). The FOUP 101 is a container in which a plurality of substrates 210 are stored. The FOUP 101 has a substrate placement section 101a on which the substrate 210 is placed. In the FOUP 101, a plurality of substrates 210 are arranged in a vertical line. The FOUP 101 is an example of a substrate storage container. The substrate 210 is, for example, a silicon wafer having a disk shape.

(Substrate transport system)
2, the substrate transfer system 100 includes a transfer chamber 10, a substrate transfer robot 20, an aligner 30, an imaging unit 40, an illumination unit 50, and a control unit 60. The substrate transfer system 100 includes a target member 71, a target member 72, and a target member 73. The substrate transfer robot 20 includes a horizontal articulated robot arm 21 and a substrate holding hand 22. The imaging unit 40 includes an imaging unit 41, an imaging unit 42, an imaging unit 43, and an imaging unit 44. The imaging unit 40 is an example of a detection unit.

3, the transfer chamber 10 is a housing having a rectangular parallelepiped shape. The transfer chamber 10 has a front surface 11 on the Y1 direction side and a back surface 12 on the Y2 direction side. The transfer chamber 10 also has a top surface 13 on the vertical upper side, which is the Z1 direction side. In the transfer chamber 10, the substrate 210 is transferred. The transfer chamber 10 houses a substrate transfer robot 20, an aligner 30, an imaging unit 40, an illumination unit 50, and a control unit 60. On the front surface 11 on the Y1 direction side of the transfer chamber 10, three load port units 11a to which the FOUP 101 is attached are arranged side by side in the horizontal direction. The load port unit 11a has an opening and closing mechanism. The load port unit 11a opens and closes the door unit of the attached FOUP 101. In addition, two load lock units 203 are connected to the back surface 12 on the Y2 direction side of the transfer chamber 10. An opening/closing mechanism is also arranged between the load lock section 203 and the rear surface 12. Note that FIG. 3 shows a state in which a FOUP 101 is attached to one of the three load port sections 11a on the X1 direction side.

The substrate transport robot 20 is disposed in the transport chamber 10. The substrate transport robot 20 transports the substrate 210 in the transport chamber 10. Specifically, the substrate transport robot 20 has a substrate holding hand 22, which is an end effector that holds the substrate 210, disposed at the tip of the robot arm 21. The substrate transport robot 20 transports the substrate 210 between the FOUP 101 attached to the load port section 11a and the load lock section 203. The substrate transport robot 20 transports the substrate 210 while holding the substrate 210 along a horizontal plane in the substrate holding hand 22.

The aligner 30 aligns the substrate 210 transported by the substrate transport robot 20. The aligner 30 is disposed in the transport chamber 10, close to the X2 direction, which is one side of the X direction, which is the left-right direction. The aligner 30 has a substrate placement part 30a on which the substrate 210 is placed. The aligner 30 aligns the rotational position along the circumferential direction of the main surface of the substrate 210 by rotating the substrate 210 placed on the substrate placement part 30a along a horizontal plane. The aligner 30 also detects the center position of the substrate 210.

In the substrate transport system 100, the unprocessed substrate 210 held by the substrate transport robot 20 is transported from the FOUP 101 to the aligner 30. The substrate 210 aligned by the aligner 30 is then transported again by the substrate transport robot 20 to the load lock unit 203. The substrate 210 after processing in the processing device 201 is transported from the load lock unit 203 to the FOUP 101 by the substrate transport robot 20. The substrate 210 transport operation by the substrate transport robot 20 includes both an operation of removing the substrate 210 by removing it from the substrate placement section 101a of the FOUP 101, the substrate placement section 203a of the load lock section 203, and the substrate placement section 30a of the aligner 30, and an operation of transporting the substrate 210 by placing it on the substrate placement section 101a of the FOUP 101, the substrate placement section 203a of the load lock section 203, and the substrate placement section 30a of the aligner 30.

The imaging unit 40 is, for example, a two-dimensional camera. Each of the imaging units 41, 42, 43, and 44 of the imaging unit 40 has an imaging element such as a CCD (Charge Coupled Device) and a CMOS (Complementary Metal Oxide Semiconductor). The imaging units 41, 42, and 43 are arranged above the transport chamber 10. The imaging unit 44 is arranged in the substrate holding hand 22 of the substrate transport robot 20. Specifically, three imaging units 41 are arranged on the upper side, which is the Z1 direction side of the rear surface 12 in the transport chamber 10, so as to correspond to the three load port units 11a to which the FOUP 101 is attached. The imaging unit 41 images the FOUP 101. Two imaging units 42 are arranged on the upper side, which is the Z1 direction side, of the front surface 11 in the transfer chamber 10 so as to correspond to the two load lock units 203. The imaging unit 41 images the load lock unit 203. The imaging unit 43 is arranged above the aligner 30 on the top surface 13 in the transfer chamber 10. The imaging unit 43 images the aligner 30. The imaging unit 44 is arranged on the Z1 direction side of the substrate holding hand 22. The imaging unit 44 is fixed to the substrate holding hand 22 so as to move integrally with the substrate holding hand 22. The imaging units 41, 42, and 43 are examples of transfer chamber imaging units. The imaging unit 44 is an example of a robot imaging unit.

As shown in FIG. 4, an image 80 is acquired by imaging unit 40. FIG. 4 shows an example of an image 80 of FOUP 101 captured by imaging unit 41 of imaging unit 40. The images 80 captured by each of imaging units 41, 42, 43, and 44 of imaging unit 40 are transmitted to control unit 60.

The image 80 captured by the imaging unit 41 includes the substrate 210 stored in the FOUP 101, the substrate placement portion 101a in the FOUP 101, and the target member 71. The target member 71 is a member that indicates the position of the substrate placement portion 101a in the FOUP 101. That is, the imaging unit 41 captures an image of the FOUP 101 to capture an image of the substrate 210 stored in the FOUP 101, the substrate placement portion 101a in the FOUP 101, and the target member 71. In the FOUP 101, the target members 71 are arranged, for example, on the front surface 11 in the transport chamber 10, one on each side of one load port portion 11a in the X direction, which is the left-right direction.

5, the captured image 80 of the load lock unit 203 captured by the imaging unit 42 includes the substrate 210 placed in the load lock unit 203, the substrate placement portion 203a in the load lock unit 203, and the target member 72. That is, the imaging unit 42 captures the substrate 210 placed in the load lock unit 203, the substrate placement portion 203a in the load lock unit 203, and the target member 72 by capturing an image of the load lock unit 203 from inside the transport chamber 10. In the load lock unit 203, the target members 72 are arranged, for example, on the rear surface 12 in the transport chamber 10, two on each side of the load lock unit 203 in the X direction, which is the left-right direction, so that they are located at the four corners of the load lock unit 203.

6, the captured image 80 of the aligner 30 captured by the imaging unit 43 includes the substrate 210 placed on the aligner 30, the substrate placement portion 30a of the aligner 30, and the target member 73. That is, the imaging unit 43 captures an image of the aligner 30 from the top surface 13 inside the transfer chamber 10 to capture an image of the substrate 210 placed on the substrate placement portion 30a, the substrate placement portion 30a, and the target member 73. In the aligner 30, the target members 72 are arranged, for example, at the four corners of the substrate placement portion 30a of the aligner 30 within the transfer chamber 10.

In addition, since the substrate transport robot 20 is disposed in the transport chamber 10, the substrate transport robot 20 may be included in the captured image 80 captured by each of the imaging units 41, 42, and 43. In the example of FIG. 6, the substrate transport robot 20 operating to move the substrate holding hand 22 holding the substrate 210 closer to the aligner 30 is included in the captured image 80 captured by the imaging unit 43. Note that the imaging units 41, 42, and 43 are disposed in a fixed state in the transport chamber 10, and the imaging target areas to be imaged by the imaging units 41, 42, and 43 are fixed.

As shown in FIG. 7, the captured image 80 captured by the imaging unit 44 is an image captured with the imaging direction being the direction from the base end to the tip end of the substrate holding hand 22 of the substrate transport robot 20. For example, when the substrate transport robot 20 performs an operation to remove a substrate 210 from a FOUP 101, the captured image 80 captured by the imaging unit 44 includes the substrate placement portion 101a of the FOUP 101, the substrate 210 placed on the substrate placement portion 101a, and the target member 71. The captured image 80 captured by the imaging unit 44 also includes the tip portion of the substrate holding hand 22 of the substrate transport robot 20. The imaging direction of the imaging unit 44 is changed in accordance with the operation of the substrate holding hand 22.

As shown in FIG. 3, the illumination unit 50 irradiates illumination light in the transport chamber 10. The illumination unit 50 includes, for example, an LED (Light-Emitting Diode) that irradiates yellow light as illumination light. The illumination unit 50 irradiates illumination light at the timing when the imaging unit 40 performs imaging. The illumination unit 50 irradiates the illumination light to the area imaged by the imaging unit 40, for example, by irradiating the entire inside of the transport chamber 10 with illumination light diffused therethrough. The illumination unit 50 is disposed on each of the side portions on the X1 and X2 directions at the upper side, which is the Z1 direction side, in the transport chamber 10. The captured image 80 captured by the imaging unit 40 in a state where illumination light from the illumination unit 50 is irradiated is transmitted to the control unit 60.

As shown in FIG. 1, the control unit 60 includes a machine learning unit 61. The control unit 60 controls the operation of each unit of the substrate transport system 100. In this embodiment, the control unit 60 is a robot control unit that controls the operation of the substrate transport robot 20. That is, the control unit 60 is a robot controller. The control unit 60 includes, for example, a calculation device such as a CPU (Central Processing Unit). The control unit 60 also includes memories such as a RAM (Random Access Memory) and a ROM (Read Only Memory), and a storage device such as a hard disk. The control unit 60 executes control processing by the calculation device based on programs and parameters stored in the storage device. In this embodiment, the control unit 60 controls the capture of the captured image 80 by the imaging unit 40 and controls the transport operation of the substrate 210 by the substrate transport robot 20. For example, the control unit 60 includes a main CPU that controls the capture of the captured image 80, controls machine learning (described later), and controls the generation of command values for the transport operation of the substrate 210, and a servo CPU that controls the drive current output to a servo motor that serves as a drive source for operating the substrate transport robot 20 based on the command values for the transport operation from the main CPU. Note that the control unit 60 may be composed of a single CPU.

(Control of conveying operation)
As shown in FIG. 8, in this embodiment, the control unit 60 acquires transport control information 81 for controlling the transport operation of the substrate transport robot 20 based on a trained model 62 generated by machine learning using an image 80 captured by the imaging unit 40. The trained model 62 receives the captured images 80 captured by each of the imaging units 41, 42, 43, and 44 of the imaging unit 40 as input, and outputs transport control information 81 for controlling the transport operation of the substrate transport robot 20. The transport control information 81 includes instruction information for aligning the coordinate axes of the substrate transport robot 20 with the positions of the substrate placement unit 101a, the substrate placement unit 203a, and the substrate placement unit 30a. The transport control information 81 includes information indicating the position of the substrate 210 on the coordinate axes of the substrate transport robot 20, and information indicating the positions of the substrate placement unit 101a, the substrate placement unit 203a, and the substrate placement unit 30a. The transport control information 81 includes, for example, a command value as a control amount for operating the robot arm 21 of the substrate transport robot 20. Specifically, the transport control information 81 includes a command value for controlling the rotation angle of a motor that serves as a drive source for the robot arm 21. The trained model 62 is generated by the machine learning unit 61 of the control unit 60 and is stored in a storage device of the control unit 60.

<Generating trained models>
The control unit 60 generates a trained model 62 by machine learning in the machine learning unit 61. In the machine learning unit 61, the trained model 62 is generated by machine learning using deep reinforcement learning so as to input the captured image 80 and output the transport control information 81. The machine learning unit 61 performs machine learning using, for example, DQN (Deep Q-Network), which is reinforcement learning using deep learning. In the deep reinforcement learning, the machine learning unit 61 generates the trained model 62 by learning the transport control information 81 through repeated trials based on the captured image 80.

In deep reinforcement learning, the control unit 60 acquires in advance, as a target teaching image, for example, an image 80 in a state in which the center position of the substrate holding hand 22 is located at the center position of each of the substrate placement units 101a, 203a, and 30a, or an image 80 in a state in which the substrate holding hand 22 holds the center position of the placed substrate 210. Then, in deep reinforcement learning by the machine learning unit 61 of the control unit 60, the image 80 captured by each of the imaging units 41, 42, 43, and 44 is used as an input, and machine learning is performed by operating the substrate transport robot 20 through repeated trials by evaluating, for example, a value indicating the degree of agreement between the input image 80 and the teaching image acquired in advance. For example, the degree of agreement is evaluated based on the positional relationship between each of the target members 71, 72, and 73 and the substrate 210 or the substrate holding hand 22. In deep reinforcement learning, a trained model 62 is generated by performing repeated trials to obtain a high evaluation.

In this embodiment, each of target members 71, 72, and 73 is a plate-shaped member having a predetermined circular shape in the captured image 80, for example, as identified by the control unit 60 in machine learning that generates the trained model 62. Furthermore, each of target members 71, 72, and 73 has a color different from the surroundings, as identified in the captured image 80. For example, each of target members 71, 72, and 73 is black. Furthermore, the control unit 60 detects the board 210 included in the captured image 80.

The machine learning unit 61 of the control unit 60 performs machine learning using deep reinforcement learning to repeatedly try and learn the transport operation of the substrate transport robot 20, which includes both the operation of removing the substrate 210 placed on each of the substrate placement units 101a, 203a, and 30a, and the operation of loading the substrate 210 onto each of the substrate placement units 101a, 203a, and 30a. For example, in learning to acquire transport control information 81 for the transport operation for the FOUP 101, an image 80 captured by the imaging unit 41 capturing an image of the FOUP 101 and an image 80 captured by the imaging unit 44 arranged on the substrate transport robot 20 are acquired as detection results of the substrate 210 and the substrate placement unit 101a in the FOUP 101. The machine learning unit 61 then uses the captured image 80 as the detection result as input and performs machine learning using deep reinforcement learning with DQN to train the trained model 62 to output the transport control information 81. For example, the control unit 60 trains the trained model 62 by performing a preset number of trial operations.

When learning the operation of loading the substrate mounting part 101a, the operation of the substrate transport robot 20 is learned so that the position of the substrate mounting part 101a indicated by the target member 71 in the captured image 80 coincides with the position of the substrate holding hand 22 in the captured image 80, or the position of the substrate holding hand 22 detected based on the substrate 210 held by the substrate holding hand 22. When learning the operation of unloading the substrate 210 placed on the substrate mounting part 101a, the operation of the substrate transport robot 20 is learned so that the position of the substrate mounting part 101a detected based on the target member 71 in the captured image 80, or the substrate 210 placed on the substrate mounting part 101a coincides with the position of the substrate holding hand 22 detected based on the substrate holding hand 22 in the captured image 80. The machine learning unit 61 of the control unit 60 learns the transport control information 81 as a control amount in the transport operation for the load lock unit 203 and the transport control information 81 as a control amount in the transport operation for the aligner 30 by deep reinforcement learning, in the same way as the FOUP 101.

That is, in this embodiment, the imaging unit 40 captures images of the substrate 210, the substrate placement unit 101a and the target member 71, the substrate placement unit 203a and the target member 72, the substrate placement unit 30a and the target member 73, and the substrate transport robot 20 so as to detect the substrate 210, the substrate placement unit 101a, the substrate placement unit 203a, the substrate placement unit 30a, and the substrate transport robot 20. In other words, in this embodiment, the captured image 80 is a detection result in which the substrate 210, the substrate placement unit 101a, the substrate placement unit 203a, the substrate placement unit 30a, and the substrate transport robot 20 are detected. The control unit 60 then uses a trained model 62 generated by machine learning based on the images 80 captured by the imaging units 41, 42, 43, and 44 obtained as detection results to obtain transport control information 81 for controlling the transport operation of the substrate 210 to the FOUP 101, the load lock unit 203, and the aligner 30.

In this embodiment, the control unit 60 controls the operation of the substrate transport robot 20 based on the transport control information 81 acquired using the trained model 62 generated by machine learning based on the captured image 80. That is, in the substrate transport system 100 of this embodiment, the transport operation of the substrate transport robot 20 is controlled based on the acquired transport control information 81, and the transport operation of the substrate transport robot 20 is taught by automatic teaching using machine learning based on the captured image 80. Therefore, in this embodiment, the operator does not teach information indicating absolute position coordinates indicating the position of the substrate 210 and the positions of the substrate placement unit 101a, the substrate placement unit 203a, and the substrate placement unit 30a, and the transport control information 81, which is the output from the trained model 62 to which the captured image 80 is input, is acquired, and the relative positional relationship indicating how the substrate transport robot 20 should be operated from the state in which the current captured image 80 is acquired, is automatically taught.

Correction of deviation in conveying operation
In this embodiment, the control unit 60 acquires transport control information 81 by using a learned model 62 generated by machine learning, and controls the transport operation of the substrate transport robot 20 while correcting deviations in the transport operation by performing the transport operation of the substrate 210 based on the acquired transport control information 81.

For example, as shown in FIG. 9, the substrate 210 placed on the substrate placement unit 101a, the substrate placement unit 203a, and the substrate placement unit 30a may be misaligned with respect to the substrate placement unit 101a, the substrate placement unit 203a, and the substrate placement unit 30a. Note that FIG. 9 shows the misalignment of the substrate 210 placed on the substrate placement unit 203a, and the substrate 210 in the case where no misalignment occurs is shown by a dotted line. When a transport operation of the substrate 210 in which a positional deviation occurs is performed based on the set value, it is considered that a deviation occurs in the movement of the substrate 210 during the transport operation. Therefore, the machine learning unit 61 of the control unit 60 uses the learned model 62 while performing the transport operation to control the transport operation so as to correct the deviation of the transport operation of the substrate transport robot 20 based on the transport control information 81 acquired from the captured image 80. The control unit 60 controls the transport operation while correcting the misalignment, so that even if there is a misalignment in the substrate placement unit 101a, the substrate placement unit 203a, and the substrate 210 placed on the substrate placement unit 30a, the transport operation is performed to eliminate the misalignment.

As shown in FIG. 10, the substrate 210 held by the substrate holding hand 22 of the substrate transport robot 20 may be misaligned with respect to the substrate holding hand 22. In FIG. 10, similar to FIG. 9, the substrate 210 is shown by a dotted line when no misalignment occurs. Even in such a case, the control unit 60 uses the learned model 62 while performing the transport operation to control the transport operation so as to correct the misalignment of the transport operation of the substrate transport robot 20 based on the transport control information 81 acquired from the captured image 80, so that no misalignment occurs in the movement of the substrate 210 during the transport operation.

When the machine learning unit 61 generates the trained model 62 through machine learning using the captured image 80 as input, the control unit 60 controls the transport operation by the substrate transport robot 20 at a slower operating speed than the operating speed of a normal transport operation until learning of the trained model 62 is complete. That is, the control unit 60 acquires transport control information 81 for controlling the transport operation of the substrate transport robot 20 by repeatedly attempting the transport operation while performing machine learning using the captured image 80 as input at a relatively slow operating speed.

When learning of the trained model 62 by deep reinforcement learning is completed, the control unit 60 performs a transport operation by the substrate transport robot 20 at a relatively high operating speed based on the transport control information 81 output from the trained model 62 that has completed learning. For example, the control unit 60 determines that learning of the trained model 62 is completed when a preset number of trial operations are completed.

For example, when multiple substrates 210 stored in a FOUP 101 are transported sequentially, the control unit 60 performs machine learning to generate a trained model 62 by deep reinforcement learning, by performing a predetermined number of trials based on the captured image 80 with the operation speed of the transport operation relatively slow before performing the transport operation of the substrate 210. Then, based on the transport control information 81 based on the generated trained model 62, the control unit 60 performs another predetermined number of transport operations with the operation speed of the transport operation relatively slow, thereby performing learning to update the trained model 62 while performing machine learning again. Then, after the learning of the trained model 62 is completed, the control unit 60 controls the transport operation of the substrate transport robot 20 at a normal operation speed, which is a state in which the operation speed is relatively high. As an example, the trained model 62 is generated by performing machine learning with a predetermined number of trials before the substrate transport system 100 is shipped from the factory, and the trained model 62 is learned by performing machine learning with a further predetermined number of trials after the substrate transport system 100 is installed.

(Substrate transport method)
Next, a control process of the substrate transfer method by the substrate transfer system 100 of this embodiment will be described with reference to Fig. 11. The control process of the substrate transfer method of this embodiment is executed by the control unit 60 of the substrate transfer system 100.

First, in step S1, captured image 80 is acquired. Specifically, captured image 80 is acquired in which substrate 210, substrate placement unit 101a, substrate placement unit 203a, substrate placement unit 30a, target member 71, target member 72, target member 73, and substrate transport robot 20 are captured as detection results so that substrate 210, substrate placement unit 101a, substrate placement unit 203a, substrate placement unit 30a, and substrate transport robot 20 are detected. Note that in step S1, captured image 80 is acquired while substrate transport robot 20 is operated at a relatively low operating speed.

Next, in step S2, machine learning is performed to generate a trained model 62. Specifically, machine learning is performed using deep reinforcement learning so that the captured image 80 acquired in step S1 is input, and transport control information 81 for controlling the transport operation of the substrate transport robot 20 is output.

Next, in step S3, it is determined whether or not learning of the trained model 62 has been completed. If it is determined that learning of the trained model 62 has been completed, the process proceeds to step S4. If it is not determined that learning of the trained model 62 has been completed, the process returns to step S1. In step S3, it is determined whether or not learning of the trained model 62 has been completed, for example, based on whether or not machine learning has been performed a predetermined number of times.

In step S4, the operating speed of the substrate transport robot 20 is set to increase relatively significantly.

Next, in step S5, the transport operation of the substrate transport robot 20 is started based on the trained model 62 generated by the control processing from step S1 to step S3. That is, the transport operation is started based on the transport control information 81 according to the trained model 62 generated by machine learning using the captured image 80.

[Effects of this embodiment]
As described above, the substrate transport system 100 uses the captured image 80 as an input of the detection results of the substrate 210 and the substrate placement unit 101a, the substrate placement unit 203a, and the substrate placement unit 30a, and generates, by machine learning, the trained model 62 that outputs transport control information 81 for controlling the transport operation of the substrate 210 by the substrate transport robot 20. This makes it possible to automatically control the transport operation of the substrate 210 based on the output from the trained model 62 generated by machine learning. As a result, the effort required by an operator to control the transport operation of the substrate 210 by the substrate transport robot 20 can be reduced compared to the case where the transport operation of the substrate 210 is taught to the substrate placement unit 101a, the substrate placement unit 203a, and the substrate placement unit 30a by hand.

The control unit 60 generates a trained model 62 by machine learning using deep reinforcement learning. This allows the transport operation of the substrate transport robot 20 to be controlled using the trained model 62 trained by machine learning using deep reinforcement learning, which is reinforcement learning using deep learning. Therefore, in the machine learning for generating the trained model 62, by using deep learning to extract features when performing reinforcement learning, the effort required by the operator to control the transport operation of the substrate 210 by the substrate transport robot 20 can be further reduced.

The control unit 60 inputs the captured image 80 as the detection result, and generates a trained model 62 by machine learning that outputs transport control information 81 including instruction information for aligning the coordinate axes of the substrate transport robot 20 with the positions of the substrate placement unit 101a, the substrate placement unit 203a, and the substrate placement unit 30a. This allows the substrate transport robot 20 to automatically be instructed on its transport operation based on the transport control information 81 including the instruction information output from the trained model 62, thereby reducing the effort required by the operator to control the substrate transport robot 20's transport operation of the substrate 210.

The imaging unit 40 as a detection unit detects the substrate 210, the substrate placement unit 101a, the substrate placement unit 203a, and the substrate placement unit 30a, as well as the substrate transport robot 20 by capturing an image of the substrate. The control unit 60 receives the captured image 80 as the detection result of the substrate 210, the substrate placement unit 101a, the substrate placement unit 203a, and the substrate placement unit 30a, and the captured image 80 as the detection result of the substrate transport robot 20, and generates a trained model 62 that outputs transport control information 81. This allows the trained model 62 to learn the substrate 210, the substrate placement unit 101a, the substrate placement unit 203a, and the substrate placement unit 30a, as well as the captured image 80 captured by the substrate transport robot 20, and outputs the transport control information 81, thereby improving the accuracy of the transport control information 81 output by the trained model 62. Therefore, the transport operation of the substrate transport robot 20 can be controlled with greater accuracy.

The control unit 60 also serves as a robot control unit that controls the transport operation of the substrate transport robot 20. The control unit 60 also acquires transport control information 81 by using the learned model 62, and controls the transport operation of the substrate transport robot 20 while correcting deviations in the transport operation by performing the transport operation of the substrate 210 based on the acquired transport control information 81. As a result, even if a deviation in the operation of the substrate transport robot 20 or a deviation in the transport operation such as a deviation in the placement position of the substrate 210 occurs, the transport operation of the substrate transport robot 20 can be controlled to correct the deviation by using the learned model 62. Therefore, by automatically correcting the deviation in the transport operation, the transport operation by the substrate transport robot 20 can be performed with high accuracy.

The substrate transport system 100 includes an imaging unit 40 that captures at least one of the substrate 210, the substrate placement unit 101a, the substrate placement unit 203a, and the substrate placement unit 30a, and the target member 71, the target member 72, and the target member 73 indicating the positions of the substrate placement unit 101a, the substrate placement unit 203a, and the substrate placement unit 30a. The control unit 60 receives an image 80 captured by the imaging unit 40 acquired as a detection result, and generates a trained model 62 by machine learning that outputs transport control information 81. As a result, the substrate 210, the substrate placement unit 101a, the substrate placement unit 203a, and the substrate placement unit 30a, and the target member 71, the target member 72, and the target member 73 can be easily detected by imaging by the imaging unit 40, compared to detection using a position sensor such as a photoelectric sensor. This improves the accuracy of identification using the trained model 62 generated by machine learning using the captured image 80 as input, thereby improving the accuracy of the substrate 210 transport operation by the substrate transport robot 20.

The substrate transport system 100 includes a substrate placement unit 101a, a substrate placement unit 203a, and a target member 71, a target member 72, and a target member 73 indicating the positions of the substrate placement unit 30a. The imaging unit 40 captures the target members 71, 72, and 73. The control unit 60 receives an image 80 including the target members 71, 72, and 73 acquired as a detection result, and generates a trained model 62 by machine learning that outputs transport control information 81. As a result, even if the shapes of the substrate placement unit 101a, the substrate placement unit 203a, and the substrate placement unit 30a are unclear in the image 80, the trained model 62 can acquire the transport control information 81 based on the image 80 including the target members 71, 72, and 73. Therefore, when the shapes of the substrate placement portion 101a, the substrate placement portion 203a, and the substrate placement portion 30a are unclear in the captured image 80, it is possible to prevent a decrease in the accuracy of control of the transport operation using the trained model 62 generated by machine learning based on the captured image 80.

The target members 71, 72, and 73 have a predetermined shape in the captured image 80 so that they can be identified in the machine learning that generates the trained model 62. As a result, since the target members 71, 72, and 73 have a predetermined shape, the target members 71, 72, and 73 can be easily detected from the captured image 80. Therefore, the positions of the substrate placement unit 101a, the substrate placement unit 203a, and the substrate placement unit 30a in the captured image 80 can be easily detected, so that the accuracy of the trained model 62 generated in the machine learning can be improved. Therefore, the transport operation of the substrate transport robot 20 using the trained model 62 can be controlled with even greater accuracy.

The substrate transport system 100 includes a transport chamber 10 in which a substrate transport robot 20 is disposed and in which a substrate 210 is transported. The imaging unit 40 is disposed in the transport chamber 10. This allows the transport operation of the substrate transport robot 20 to be controlled using a trained model 62 based on an image 80 captured in the transport chamber 10. As a result, the background in the image 80 can be kept constant, so that the transport operation of the substrate transport robot 20 can be controlled more accurately using the trained model 62.

The imaging unit 40 includes imaging units 41, 42, and 43 as a transport chamber imaging unit arranged in the transport chamber 10, and imaging unit 44 as a robot imaging unit arranged in the substrate transport robot 20. The control unit 60 receives the captured images 80 by the imaging units 41, 42, and 43, and the captured images 80 by the imaging unit 44, and generates a trained model 62 by machine learning to output transport control information 81. As a result, since the imaging units 40 are arranged in each of the transport chamber 10 and the substrate transport robot 20, machine learning can be performed to generate the trained model 62 using the captured images 80 captured from multiple angles as input. Therefore, the accuracy of the trained model 62 can be improved, and the transport operation of the substrate transport robot 20 can be controlled more accurately using the trained model 62.

Imaging units 41, 42, and 43 serving as the transfer chamber imaging units are disposed above transfer chamber 10. This prevents imaging units 41, 42, and 43 from interfering with the operation of substrate transfer robot 20, and allows substrate placement units 101a, 203a, and 30a to be imaged from above. This improves the visibility of substrate placement units 101a, 203a, and 30a in captured image 80, and allows more accurate control of the transfer operation of substrate transfer robot 20 using trained model 62 based on captured image 80.

The substrate transport robot 20 transports the substrate 210 to the FOUP 101, which is a substrate storage container in which the substrate 210 is stored, in the transport chamber 10. The imaging unit 41 captures at least one of the substrate 210 stored in the FOUP 101, the substrate placement section 101a on which the substrate 210 is placed in the FOUP 101, and the target member 71 indicating the position of the substrate placement section 101a in the FOUP 101. The control unit 60 receives an input of an image 80 including at least one of the substrate 210 stored in the FOUP 101, the substrate placement section 101a on which the substrate 210 is placed in the FOUP 101, and the target member 71 indicating the position of the substrate placement section 101a in the FOUP 101, and generates a trained model 62 by machine learning that outputs transport control information 81. This allows the learned model 62 to be used to obtain transport control information 81 for controlling the transport operation of the substrate transport robot 20 for the FOUP 101, thereby reducing the effort required by the operator to control the transport operation for the FOUP 101.

The substrate transport robot 20 transports the substrate 210 between a FOUP 101 serving as a substrate storage container and a load lock unit 203 serving as a processing waiting unit on which the substrate 210 is placed in order to deliver the substrate 210 to a processing device 201 that processes the substrate 210. The imaging unit 42 images at least one of the substrate 210 placed on the substrate placement unit 203a in the load lock unit 203, the substrate placement unit 203a in the load lock unit 203, and a target member 72 indicating the position of the substrate placement unit 203a in the load lock unit 203. The control unit 60 receives as input a captured image 80 including at least one of the substrate 210 placed on the substrate placement portion 203a in the load lock unit 203, the substrate placement portion 203a in the load lock unit 203, and a target member 72 indicating the position of the substrate placement portion 203a in the load lock unit 203, and generates a trained model 62 by machine learning that outputs transport control information 81. As a result, the trained model 62 generated by machine learning based on the captured image 80 can be used to control the transport operation of the substrate transport robot 20 for the load lock unit 203, thereby reducing the effort required by the operator to control the transport operation for the load lock unit 203.

The control unit 60 also serves as a robot control unit that controls the transport operation of the substrate transport robot 20. When the control unit 60 generates a trained model 62 by machine learning using the captured image 80 as an input as a detection result, the control unit 60 controls the transport operation with the operating speed of the substrate transport robot 20 kept relatively slow until learning of the trained model 62 is complete. As a result, by keeping the operating speed relatively slow while the trained model 62 is being learned, blurring of the captured image 80 acquired for machine learning can be suppressed, thereby improving the accuracy of the trained model 62 generated by machine learning. As a result, the accuracy of control of the transport operation based on the trained model 62 can be improved.

[Modification]
It should be noted that the embodiments disclosed herein are illustrative and not restrictive in all respects. The scope of the present disclosure is indicated by the claims, not by the description of the embodiments described above, and further includes all modifications (variations) within the meaning and scope of the claims.

For example, in the above embodiment, the control unit 60 that controls the transport operation of the substrate transport robot 20 generates the trained model 62 by performing machine learning using DQN, which is reinforcement learning using deep learning, but the present disclosure is not limited to this. In the present disclosure, the trained model may be learned by a control device that is a learning device separate from the control unit that controls the operation of the substrate transport robot. In that case, the transport control information acquired in the learning device based on the trained model is output to the control unit that controls the transport operation, and the transport operation of the substrate transport robot is controlled based on the transport control information. In addition, the machine learning algorithm for generating the trained model is not limited to DQN. For example, reinforcement learning using a multilayer fully connected neural network, which is deep reinforcement learning other than DQN, may be performed. In addition, machine learning of the trained model may be performed by reinforcement learning such as Q-learning that does not use deep learning. In addition, in machine learning, the completion of learning and updating of the trained model may be determined when a predetermined operation time has elapsed, rather than a predetermined number of trials.

In the above embodiment, an example was shown in which the imaging unit 40 as a detection unit captures the substrate 210, the substrate placement unit 101a of the FOUP 101, the substrate placement unit 203a of the load lock unit 203, the substrate placement unit 30a of the aligner 30, the target member 71, the target member 72, and the target member 73, and the substrate transport robot 20, and machine learning of the trained model 62 that outputs transport control information 81 is performed using the captured image 80 as an input as a detection result, but the present disclosure is not limited to this. In the present disclosure, the captured image used for learning does not need to include the substrate transport robot. Also, the captured image may include at least one of the substrate, the substrate placement unit, and the target member. For example, when learning the substrate removal operation, machine learning may be performed based on the captured image of the substrate so as to detect only the substrate that is placed. Furthermore, when learning the loading operation for placing a substrate, machine learning may be performed based on an image captured of either the substrate placement portion or the target member to detect the substrate placement portion. That is, the substrate transport system according to the present disclosure does not need to include a target member. The substrate placement portion itself may be detected in the captured image without arranging a target member. Furthermore, instead of imaging by the imaging portion, a position sensor such as a photoelectric sensor that emits detection light may be provided as the detection portion. In that case, a trained model that uses the detection result by the position sensor as input and outputs transport control information is generated by machine learning.

In addition, in the above embodiment, an example was shown in which the transport operation by the substrate transport robot 20 is controlled to include both the operation of transporting the substrate 210 and the operation of transporting the substrate 210 by learning both the operation of transporting the substrate 210 and the operation of transporting the substrate 210, but the present disclosure is not limited to this. In the present disclosure, transport control information for controlling either one of the transport operations of transporting the substrate or the operation of transporting the substrate may be learned.

In the above embodiment, an example was shown in which the transport operation of the substrate transport robot 20 was controlled while correcting the deviation in the transport operation by performing the transport operation of the substrate 210 based on the transport control information 81, but the present disclosure is not limited to this. In the present disclosure, the deviation in the transport operation may be corrected by arithmetic processing such as a rule-based algorithm based on the detection result of the substrate, separately from learning by machine learning. In addition, an example was shown in which the transport operation is controlled so as to correct the deviation in the transport operation of the substrate transport robot based on the transport control information acquired from the captured image as the detection result using the trained model while performing the transport operation, but the trained model may not be used while performing the transport operation. That is, the control amount for performing the transport operation may be obtained by performing machine learning in advance, and the control amount based on the transport control information acquired by machine learning may be set as a set value to control the transport operation. That is, the transport operation may be automatically taught by machine learning, and the transport operation itself may be performed based on a set value as a fixed value set by automatic teaching.

In the above embodiment, the target members 71, 72, and 73 are circular plate-shaped members, but the present disclosure is not limited to this. In the present disclosure, the target members may be spherical, cylindrical, or prismatic with a polygonal cross section, or may be a solid having a predetermined shape such as a cross shape. The target members may be marker members such as AR markers. The target members may be removed after learning is completed. In addition to the substrate placement unit, a target member indicating the position of the substrate transport robot or the position of the substrate holding hand may be placed. In that case, the substrate transport robot may be detected based on the detection result or captured image of the target member, and machine learning may be performed. In addition, a target member transported by the substrate transport robot may be placed instead of the substrate, and machine learning may be performed based on the detection result or captured image of the target member placed instead of the substrate. In addition, the target member may be placed at a position where the substrate is placed on the substrate placement unit, such as inside the substrate storage container. Additionally, the number of target members per substrate placement section may be one or multiple.

In the above embodiment, an example is shown in which the transfer chamber 10 in which the substrate transfer robot 20, the imaging unit 40, and the illumination unit 50 are arranged is provided, but the present disclosure is not limited to this. In the present disclosure, the transfer chamber does not have to be provided. Also, either the imaging unit or the illumination unit may be arranged outside the transfer chamber in which the substrate is transferred. For example, when the substrate transfer method according to the present disclosure is performed in a transfer chamber of a processing device that processes a substrate, the ceiling member of the transfer chamber of the processing device may be made of a transparent material such as glass, and the imaging unit and the illumination unit may be arranged above the outside of the transfer chamber. Also, when the ceiling member of the transfer chamber is made of a transparent material, an external light source may be used to avoid disposing the illumination unit.

In the above embodiment, an example is shown in which the imaging unit 41, the imaging unit 42, and the imaging unit 43 are arranged above the transport chamber 10, and the imaging unit 44 is arranged in the substrate holding hand 22 of the substrate transport robot 20, but the present disclosure is not limited to this. In the present disclosure, the imaging unit does not need to be arranged in the substrate transport robot. The imaging unit does not need to be arranged in the transport chamber. When the imaging unit is arranged in the transport chamber, the imaging unit may be arranged below the transport chamber. The imaging unit may include a stereo camera or a 3D camera such as a TOF (Time Of Flight) system. Multiple imaging units may be arranged for one imaging target. For example, multiple imaging units may be arranged to image one substrate placement unit. The imaging unit may be configured to detect infrared light. In that case, an illumination unit that irradiates infrared light and a target member that reflects infrared light may be arranged. The imaging unit may be arranged inside the substrate storage container. The imaging unit may also be placed inside the processing standby unit.

In addition, in the above embodiment, an example was shown in which the operation speed was relatively slow until learning of the trained model 62 was completed, but the present disclosure is not limited to this. In the present disclosure, the operation speed of the transport operation may not be slowed down even when training of the trained model is performed.

In addition, in the above embodiment, an example is shown in which the substrate transport robot 20 has a horizontally articulated robot arm 21, but the present disclosure is not limited to this. In the present disclosure, the substrate transport robot may have a vertically articulated robot arm instead of a horizontally articulated robot arm. In addition, the substrate transport robot may have a linear movement mechanism such as a slide mechanism.

In the above embodiment, the substrate transport system 100 of the present disclosure is an EFEM that transports the substrate 210 to the processing device 201, but the present disclosure is not limited to this. In the present disclosure, the substrate transport system may be something other than an EFEM. For example, the substrate transport system may be a stocker or sorter that transports substrates from a substrate storage container to a substrate storage container. The substrate transport system may also transport substrates that have been transported by a belt conveyor or the like, rather than a substrate storage container, to a substrate storage container or a substrate placement section. The substrate transport method of the present disclosure may also be applied to a system that transports substrates on the processing device side. That is, when transporting substrates between a load lock section and a processing module section, the substrates may be transported by the substrate transport method of the present disclosure.

In the above embodiment, an example has been shown in which the illumination unit 50 irradiates illumination light at the timing when the image capture unit 40 captures an image, but the present disclosure is not limited to this. In the present disclosure, the illumination unit may be constantly irradiated with illumination light. Furthermore, the control unit may control the irradiation of illumination light based on the captured image through control processing. For example, the amount of light, wavelength, etc. of the illumination light may be controlled based on the luminance value of the captured image.

In the above embodiment, an example is shown in which two illumination units 50 including LEDs that irradiate yellow light as illumination light are arranged, but the present disclosure is not limited to this. In the present disclosure, the illumination unit may include a light source device other than an LED. Furthermore, the illumination light from the illumination unit may have a wavelength of a specified color, such as orange, other than yellow. The illumination unit may irradiate light including multiple wavelengths, such as white light. Furthermore, the illumination light may be light with a wavelength other than visible light, such as infrared light. Furthermore, the number of illumination units may be one, or three or more.

In addition, in the above embodiment, an example is shown in which three FOUPs 101 are attached as substrate storage containers to the transport chamber 10 of the substrate transport system 100, and three imaging units 40 are arranged to correspond to the FOUPs 101, but the present disclosure is not limited to this. In the present disclosure, the number of substrate storage containers attached to the transport chamber may be two or less, or four or more. Also, one imaging unit may be arranged for multiple substrate storage containers. In that case, images of substrates transported to the multiple substrate storage containers are captured by a common imaging unit.

In the above embodiment, an example has been shown in which the trained model 62, which uses the captured image 80 captured by the imaging unit 40 as input and outputs the transport control information 81, is trained by machine learning, but the present disclosure is not limited to this. In the present disclosure, image processing such as noise removal, edge enhancement, and contrast change may be performed on the captured image to be input to the trained model before inputting it to the trained model. In addition, the output from the trained model may not be transport control information including command values for operating the robot arm, but may be transport control information including coordinates indicating the positions of the detected substrate, substrate placement unit, and substrate transport robot.

The functions of the elements disclosed herein can be performed using circuits or processing circuits, including general purpose processors, special purpose processors, integrated circuits, ASICs (Application Specific Integrated Circuits), conventional circuits, and/or combinations thereof, configured or programmed to perform the disclosed functions. Processors are considered processing circuits or circuits because they include transistors and other circuits. In this disclosure, a circuit, unit, or means is hardware that performs the recited functions or hardware that is programmed to perform the recited functions. The hardware may be hardware disclosed herein or other known hardware that is programmed or configured to perform the recited functions. If the hardware is a processor, which is considered a type of circuit, the circuit, means, or unit is a combination of hardware and software, and the software is used to configure the hardware and/or the processor.

[Aspects]
It will be appreciated by those skilled in the art that the exemplary embodiments described above are examples of the following aspects.

(Item 1)
A substrate transport robot;
a detection unit that detects at least one of a substrate transported by the substrate transport robot and a substrate placement unit on which the substrate is placed;
a control unit that uses a detection result of at least one of the substrate and the substrate placement unit by the detection unit as input and generates a trained model through machine learning, the trained model outputting transport control information for controlling the transport operation of the substrate by the substrate transport robot.

(Item 2)
The substrate transport system of item 1, wherein the control unit generates the trained model through machine learning using deep reinforcement learning.

(Item 3)
3. The substrate transport system according to claim 1, wherein the control unit uses the detection result as an input and generates the trained model through machine learning to output the transport control information including teaching information for aligning coordinate axes between the substrate transport robot and the position of the substrate mounting unit.

(Item 4)
the detection unit detects the substrate transport robot in addition to at least one of the substrate and the substrate placement unit;
The control unit receives the detection results of at least one of the substrate and the substrate mounting unit and the detection results of the substrate transport robot as input, and generates the trained model that outputs the transport control information.

(Item 5)
The control unit is
A robot control unit that controls the transport operation of the substrate transport robot;
5. The substrate transport system according to any one of claims 1 to 4, wherein the transport control information is acquired by using the trained model, and the transport operation of the substrate is performed based on the acquired transport control information, thereby controlling the transport operation of the substrate transport robot while correcting deviations in the transport operation.

(Item 6)
the detection unit includes an imaging unit that images at least one of the substrate, the substrate placement unit, and a target member that indicates a position of the substrate placement unit;
The control unit receives an image captured by the imaging unit obtained as the detection result by the detection unit as an input, and generates the trained model that outputs the transport control information through machine learning.

(Item 7)
The target member indicates a position of the substrate placement part,
The imaging unit captures an image of the target member,
7. The substrate transport system according to claim 6, wherein the control unit receives the captured image including the target member obtained as the detection result as an input, and generates the trained model by machine learning to output the transport control information.

(Item 8)
8. The substrate transportation system of claim 7, wherein the target member has a predetermined shape in the captured image so as to be identified in machine learning that generates the trained model.

(Item 9)
a transfer chamber in which the substrate transfer robot is disposed and the substrate is transferred;
9. The substrate transfer system according to any one of items 6 to 8, wherein the imaging unit is disposed in the transfer chamber.

(Item 10)
the imaging unit includes a transfer chamber imaging unit disposed in the transfer chamber and a robot imaging unit disposed in the substrate transfer robot,
10. The substrate transport system according to item 9, wherein the control unit receives the captured image by the transport chamber imaging unit and the captured image by the robot imaging unit as input, and generates the trained model by machine learning to output the transport control information.

(Item 11)
Item 11. The substrate transfer system according to item 10, wherein the transfer chamber imaging unit is disposed above the transfer chamber.

(Item 12)
the substrate transport robot performs an operation of transporting the substrate to a substrate storage container in the transport chamber;
the imaging unit images at least one of the substrate stored in the substrate storage container, the substrate placement portion on which the substrate is placed in the substrate storage container, and the target member indicating a position of the substrate placement portion in the substrate storage container;
The control unit receives as input an image including at least one of the substrate stored in the substrate storage container, the substrate placement portion on which the substrate is placed in the substrate storage container, and the target member indicating the position of the substrate placement portion in the substrate storage container, and generates the trained model by machine learning to output the transport control information.

(Item 13)
the substrate transport robot performs a transport operation of the substrate between the substrate storage container and a processing standby unit on which the substrate is placed to be delivered to a processing device that performs processing on the substrate;
the imaging unit images at least one of the substrate placed on the substrate placement part in the processing standby unit, the substrate placement part in the processing standby unit, and the target member indicating a position of the substrate placement part in the processing standby unit;
Item 13. The substrate transport system of item 12, wherein the control unit receives as input the captured image including at least one of the substrate placed on the substrate mounting part in the processing waiting unit, the substrate mounting part in the processing waiting unit, and the target member indicating the position of the substrate mounting part in the processing waiting unit, and generates the trained model by machine learning to output the transport control information.

(Item 14)
The control unit is
A robot control unit that controls the transport operation of the substrate transport robot;
A substrate transport system according to any one of claims 1 to 13, wherein when the trained model is generated by machine learning using the detection results as input, the transport operation is controlled with the operating speed of the substrate transport robot kept relatively slow until learning of the trained model is completed.

(Item 15)
Detecting at least one of a substrate transported by a substrate transport robot and a substrate placement portion on which the substrate is placed;
A substrate transport method, which uses detection results of at least one of the substrate and the substrate mounting part as input, and generates a trained model through machine learning that outputs transport control information for controlling the transport operation of the substrate by the substrate transport robot.

10: Transport chamber 20: Substrate transport robot 30a, 101a, 203a: Substrate placement unit 40: Imaging unit 41, 42, 43: Imaging unit (transport chamber imaging unit)
44 Imaging unit (robot imaging unit)
50 Illumination unit 60 Control unit 62 Trained model 71, 72, 73 Target member 80 Captured image 81 Transport control information 100 Substrate transport system 101 FOUP (substrate storage container)
201 Processing device 203 Load lock unit (processing standby unit)
210 Substrate

Claims (15)

A substrate transport robot;
a detection unit that detects at least one of a substrate transported by the substrate transport robot and a substrate placement unit on which the substrate is placed;
a control unit that uses the detection result of at least one of the substrate and the substrate placement unit by the detection unit as input and generates a trained model through machine learning, the trained model outputting transport control information for controlling the substrate transport operation by the substrate transport robot. The substrate transport system of claim 1, wherein the control unit generates the trained model through machine learning using deep reinforcement learning. The substrate transport system according to claim 1 or 2, wherein the control unit uses machine learning to generate the trained model, which receives the detection results as input and outputs the transport control information including instruction information for aligning coordinate axes between the substrate transport robot and the position of the substrate placement unit. the detection unit detects the substrate transport robot in addition to at least one of the substrate and the substrate placement unit;
3. The substrate transport system according to claim 1, wherein the control unit receives as input the detection results of at least one of the substrate and the substrate mounting unit and the detection results of the substrate transport robot, and generates the trained model that outputs the transport control information. The control unit is
A robot control unit that controls the transport operation of the substrate transport robot;
The substrate transport system of claim 1 or 2, further comprising: a first transport control information acquisition unit that acquires a transport control information for a substrate transport robot based on the acquired transport control information; and a second transport operation of the substrate transport robot that acquires a transport control information for a substrate transport robot based on the acquired transport control information. the detection unit includes an imaging unit that images at least one of the substrate, the substrate placement unit, and a target member that indicates a position of the substrate placement unit;
3. The substrate transport system according to claim 1, wherein the control unit receives an image captured by the imaging unit obtained as the detection result by the detection unit as an input, and generates the trained model that outputs the transport control information through machine learning. The target member indicates a position of the substrate placement part,
The imaging unit captures an image of the target member,
The substrate transport system according to claim 6 , wherein the control unit receives the captured image including the target member obtained as the detection result as an input, and generates the trained model that outputs the transport control information by machine learning. The substrate transport system of claim 7, wherein the target member has a predetermined shape in the captured image so as to be identified in machine learning that generates the trained model. a transfer chamber in which the substrate transfer robot is disposed and the substrate is transferred;
The substrate transfer system according to claim 6 , wherein the imaging unit is disposed in the transfer chamber. the imaging unit includes a transfer chamber imaging unit disposed in the transfer chamber and a robot imaging unit disposed in the substrate transfer robot,
The substrate transport system according to claim 9 , wherein the control unit receives the captured image by the transport chamber imaging unit and the captured image by the robot imaging unit as input, and generates the trained model that outputs the transport control information by machine learning. The substrate transfer system according to claim 10, wherein the transfer chamber imaging unit is disposed above the transfer chamber. the substrate transport robot performs an operation of transporting the substrate to a substrate storage container in the transport chamber;
the imaging unit images at least one of the substrate stored in the substrate storage container, the substrate placement portion on which the substrate is placed in the substrate storage container, and the target member indicating a position of the substrate placement portion in the substrate storage container;
The substrate transport system of claim 9, wherein the control unit receives as input the captured image including at least one of the substrate stored in the substrate storage container, the substrate mounting portion on which the substrate is placed in the substrate storage container, and the target member indicating the position of the substrate mounting portion in the substrate storage container, and generates the trained model that outputs the transport control information through machine learning. the substrate transport robot performs a transport operation of the substrate between the substrate storage container and a processing standby unit on which the substrate is placed to be delivered to a processing device that performs processing on the substrate;
the imaging unit images at least one of the substrate placed on the substrate placement part in the processing standby unit, the substrate placement part in the processing standby unit, and the target member indicating a position of the substrate placement part in the processing standby unit;
13. The substrate transport system of claim 12, wherein the control unit receives as input the captured image including at least one of the substrate placed on the substrate mounting part in the processing waiting unit, the substrate mounting part in the processing waiting unit, and the target member indicating the position of the substrate mounting part in the processing waiting unit, and generates the trained model that outputs the transport control information through machine learning. The control unit is
A robot control unit that controls the transport operation of the substrate transport robot;
The substrate transport system of claim 1 or 2, wherein when the trained model is generated by machine learning using the detection results as input, the transport operation is controlled with the operating speed of the substrate transport robot kept relatively slow until learning of the trained model is completed. Detecting at least one of a substrate transported by a substrate transport robot and a substrate placement portion on which the substrate is placed;
A substrate transport method, which uses detection results of at least one of the substrate and the substrate mounting part as input, and generates a trained model through machine learning that outputs transport control information for controlling the transport operation of the substrate by the substrate transport robot.

Images