Patent Application
20220152774
This application claims the priority benefits of Japanese application no. 2020-192585, filed on Nov. 19, 2020. The entirety of the above-mentioned patent application is hereby incorporated by reference herein and made a part of this specification.
The disclosure relates to a substrate processing apparatus and a substrate processing method.
A CMP (Chemical Mechanical Polishing) apparatus is known as an example of substrate processing apparatuses used in semiconductor processing. CMP apparatuses can be roughly divided into “face-up type (a system in which the surface to be polished of the substrate faces upward)” and “face-down type (a system in which the surface to be polished of the substrate faces downward)” depending on the direction in which the surface to be polished of the substrate is facing.
Patent Document 1 discloses that, in a face-up type CMP apparatus, a polishing pad having a smaller diameter than the substrate is brought into contact with the substrate while being rotated and swung to polish the substrate. It is disclosed that, in this CMP apparatus, a support member is provided around the substrate and the polishing pad swung to the outside of the substrate is supported by the support member, and the height and horizontal position of the support member can be adjusted.
Further, Patent Document 2 discloses that, in a transport system for transporting a substrate, a tilted part of a transport surface detection jig is detected by a transmission sensor from a side surface direction of the substrate to detect the tilt of the transport surface of the substrate. It is disclosed that, in the transport system described in Patent Document 2, an equation of the surface of the jig can be calculated by at least three orthogonal projection points.
The substrate to be polished by the CMP apparatus may have variations in thickness or surface profile due to manufacturing errors or the like. Therefore, in order to improve the uniformity of polishing of the surface to be polished, it is preferable to measure the thickness of the substrate to be processed in the substrate processing apparatus. However, if a sensor is provided on the side surface of the substrate as in the system described in Patent Document 2, the footprint of the substrate processing apparatus may become large.
In view of the above, the disclosure is to improve the uniformity of polishing of the surface to be polished.
An embodiment relates to a substrate processing apparatus, including: a table configured to support a substrate; a pad holder configured to hold a polishing pad that is configured to polish the substrate supported by the table; a drive module configured to swing the pad holder in a radial direction of the substrate; a support member having a support surface configured to support the polishing pad swung to outside of the table by the drive module; an imaging module configured to image a surface to be polished of the substrate supported by the table and the support surface; a storage part storing a learning model constructed by machine learning; a step estimation module learning the learning model by inputting imaging information obtained by the imaging module to the learning model, and estimating a step between the support surface and the surface to be polished by using the learning model; and an adjustment module configured to adjust a height of the support surface while polishing the substrate based on the step estimated.
Hereinafter, embodiments of a substrate processing apparatus and a substrate processing method according to the disclosure will be described with reference to the accompanying drawings. In the accompanying drawings, the same or similar elements are denoted by the same or similar reference numerals, and repeated descriptions of the same or similar elements may be omitted from the description of each embodiment. In addition, the features shown in an embodiment can be applied to other embodiments where no contradiction arises.
The table 100 is a member for supporting a substrate WF to be processed. In an embodiment, the table 100 has a support surface 100a for supporting the substrate WF and is configured to be rotatable by a drive mechanism such as a motor (not shown). A plurality of holes 102 are formed on the support surface 100a (see
The multi-axis arm 200 is a member that holds a plurality of processing tools for performing various processes on the substrate WF supported by the table 100, and is arranged adjacent to the table 100. The multi-axis arm 200 of the present embodiment is configured to hold a large-diameter polishing pad 222 for polishing the substrate WF, a cleaning tool 232 for cleaning the substrate WF, a small-diameter polishing pad 242 for finish polishing the substrate WF, and an atomizer 252 for discharging a liquid such as water to the substrate WF. In the present embodiment, the large-diameter polishing pad 222, the cleaning tool 232, the small-diameter polishing pad 242, and the atomizer 252 are respectively provided on a first arm 220, a second arm 230, a third arm 240, and a fourth arm 250 that extend radially. The multi-axis arm 200 further includes a drive module 280 for rotating, elevating, and swinging the polishing pads 222 and 242 with respect to the substrate WF supported by the table 100.
In the present embodiment, the first arm 220, the second arm 230, the third arm 240, and the fourth arm 250 extend radially around a swing shaft 210 at intervals of 90 degrees counterclockwise in a plan view. The drive module 280 can rotationally drive the first to fourth arms 220 to 250 to move any of the large-diameter polishing pad 222, the cleaning tool 232, the small-diameter polishing pad 242, and the atomizer 252 onto the substrate WF. Further, the drive module 280 can move the polishing pads 222 and 242 onto the dresser 500. In the present embodiment, the drive module 280 can rotationally drive the first to fourth arms 220 to 250 to swing (repeatedly move) the polishing pads 222 and 242 in an arc pattern on the substrate WF. However, the drive module 280 may be configured so that the polishing pads 222 and 242 can be swung on the substrate WF separately from the rotational drive of the first to fourth arms 220 to 250. The drive module 280 may swing the polishing pads 222 and 242 in a straight line.
For example, when the polishing pad 222 is on the substrate WF, the substrate processing apparatus 1000 rotates the table 100 and rotates the polishing pad 222, and swings the polishing pad 222 with a rotation drive mechanism 212 while pressing the polishing pad 222 against the substrate WF to polish the substrate WF.
As shown in
Further, in the following description, the function of the support member 300 when the large-diameter polishing pad 222 is swung with respect to the substrate WF will be described as an example, but the same applies to the cleaning tool 232 or the small-diameter polishing pad 242.
The support member 300 is a member for supporting the polishing pad 222 that is swung to the outside of the table 100 by the rotation of the swing shaft 210. That is, the substrate processing apparatus 1000 is configured to uniformly polish the surface to be polished of the substrate WF by swinging (overhanging) the polishing pad 222 until it protrudes to the outside of the substrate WF when polishing the substrate WF. Here, when the polishing pad 222 is overhung, the pressure of the polishing pad 222 is concentrated on the peripheral edge of the substrate WF due to various factors such as the tilt of a pad holder 226, and the surface to be polished of the substrate WF may not be uniformly polished. Therefore, in the substrate processing apparatus 1000 of the present embodiment, the support members 300 for supporting the polishing pad 222 overhanging to the outside of the substrate WF are provided on both sides of the table 100.
Therefore, the polishing pad 222 does not protrude from the region of the surface to be polished of the substrate WF and the support surface 300a during swinging.
As shown in
The substrate processing apparatus 1000 includes the imaging module 600 for imaging the surface to be polished of the substrate WF supported by the table 100 and the support surface 300a of the support member 300. In the imaging module 600 of the present embodiment, as shown in
As shown in
The control module 800 may calculate the diameter of the substrate WF based on the alignment result of the substrate WF obtained by the centering mechanisms 400A, 400B, and 400C.
As shown in
As shown in
As shown in
The step estimation module 820 is configured to estimate a step (height difference, see
The state variable acquisition part 822 acquires the state variable SV every predetermined time (for example, several msec and several tens of msec). As an example, the predetermined time can be the same as or corresponding to a learning cycle of the learning model generation part 824. In the present embodiment, the input of information from various sensors to the control module 800 corresponds to the acquisition of the state variable SV by the state variable acquisition part 822. The state variable SV includes at least the imaging information 51 obtained by the imaging module 600. Here, the imaging information 51 includes the imaging information of the support surface 300a obtained by the first imaging device 602 and the imaging information of the surface to be polished of the substrate WF obtained by the second imaging device 604. The imaging information 51 may include a focus value and an F value of the imaging module 600 (imaging devices 602 and 604), and may include the position of each of a plurality of imaging elements in the imaging devices 602 and 604 and the focus value of the imaging element. Further, the state variable SV may include the height adjustment amount of the support member 300 made by the control module 800 or the output value (rotation torque command value or motor current) of the drive module 280 in the table 100 or the polishing pads 222 and 242 (multi-axis arm 200) in addition to the imaging information obtained by the imaging module 600. Besides, the state variable SV may include the thickness information or surface profile information of the substrate WF measured or estimated by another sensor or the like (not shown). Such a state variable SV may be acquired while the substrate WF is being polished, or may be acquired before or after the substrate WF is polished. Further, the state variable SV may include the information previously input to the substrate processing apparatus 1000 by a user. As an example, the state variable SV may include information on the material of the substrate WF.
The learning model generation part 824 learns the learning model (estimated value of the step a with respect to the state variable SV) according to an arbitrary learning algorithm collectively called machine learning. The learning model generation part 824 repeatedly executes learning based on the state variable SV acquired by the state variable acquisition part 822. The learning model generation part 824 acquires a plurality of state variables SV, identifies the features of the state variables SV, and interprets the correlation. Further, the learning model generation part 824 interprets the correlation of the state variable SV to be acquired next time when the step a between the support member 300 and the substrate WF is estimated with respect to the current state variable SV. Then, the learning model generation part 824 optimizes the estimation of the step δh between the support member 300 and the substrate WF with respect to the acquired state variable SV by repeating the learning.
As an example, the learning model generation part 824 is constructed by supervised learning. Supervised learning may be performed at an installation site of the substrate processing apparatus 1000, at a manufacturing site, or at a dedicated learning site. As an example of supervised learning, the learning model generation part 824 may use the imaging information of the table 100 in a state where the substrate WF is not placed as teacher data.
Further, as an example of supervised learning, the learning model generation part 824 may use the imaging information of a reference substrate prepared in advance as teacher data. A substrate having a known thickness or plate surface profile can be used as the reference substrate. The reference substrate may have a uniform thickness, or may have a predetermined uneven pattern formed as the surface profile. Furthermore, as an example of supervised learning, the learning model generation part 824 may use the imaging information of the support surface 300a of the support member 300 as teacher data. In this case, a plurality of pieces of imaging information may be acquired for each height of the support surface 300a of the support member 300, and the height information of the support surface 300a for each piece of imaging information may be used as teacher data. As an example, in a state where the substrate WF is not placed or in a state where the reference substrate is placed, the support surface 300a and the table 100 (or the reference substrate) are imaged by the imaging module 600 while the height of the support surface 300a of the support member 300 is changed, and the imaging information can be used as teacher data.
Moreover, the learning model generation part 824 may execute reinforcement learning to learn the learning model. Reinforcement learning is a method of generating a learning model that rewards the action (output) executed for the current state (input) in a certain environment and obtains the maximum reward. As an example of performing reinforcement learning, the learning model generation part 824 has an evaluation value calculation part 825 that calculates an evaluation value based on the state variable SV, and a learning part 826 that learns the learning model based on the evaluation value. As an example, the evaluation value calculation part 825 may give a larger reward as the stability of the state variable SV becomes higher, that is, give a larger reward as the change between the state variable SV acquired last time and the state variable SV acquired this time becomes smaller. Further, as an example, the evaluation value calculation part 825 may give a larger reward as the step δh between the substrate WF being polished and the support member 300 becomes smaller and the estimated step δh approaches the value 0. Further, as an example, the evaluation value calculation part 825 may give a larger reward as the stability of the load in the drive module 280 becomes higher. In addition, as an example, the evaluation value calculation part 825 may give a larger reward as the energy consumption in the substrate processing apparatus 1000 becomes smaller. Further, as an example, the evaluation value calculation part 825 may give a larger reward as the time required for the polishing process in the substrate processing apparatus 1000 becomes shorter. Further, as an example, the evaluation value calculation part 825 may give a larger reward as the surface profile of the substrate WF becomes constant.
The adjustment module 830 is configured to adjust the height of the support surface 300a during polishing based on the step a between the substrate WF and the support surface 300a estimated by the step estimation module 820. In the present embodiment, the support member drive mechanism 380 for driving the support member 300 and the control module 800 for sending a command to the support member drive mechanism 380 function as the adjustment module. Based on the estimated value of the step δh between the substrate WF and the support surface 300a, the adjustment module 830 (control module 800) drives the support member drive mechanism 380 so that the step between the substrate WF and the support surface 300a becomes the value 0.
Next, the procedure of the substrate processing method including the adjustment of the height position of the support member 300 according to the present embodiment will be described.
Subsequently, the table 100 is rotated and the polishing pad 222 is pressed against the substrate WF while being rotated (S130: pressing step). Subsequently, the polishing pad 222 is swung (S140: swinging step). Subsequently, the support surface 300a of the support member 300 and the surface to be polished of the substrate WF are imaged (imaging step), and the state variable including the imaging information is acquired (S150). Subsequently, the learning model is learned and generated based on the acquired state variable (S160). Subsequently, the state variable is input to the learning model to estimate the step δh between the support surface 300a and the surface to be polished of the substrate WF (S170: step estimation step). Subsequently, the height of the support surface 300a is adjusted while the substrate WF is being polished based on the estimated step a (S180: adjustment step).
Then, the processes of S150 to S180 are repeatedly executed until the polishing is completed (S190, No), and when the polishing is completed (S190, Yes), the substrate processing method is completed.
According to the substrate processing apparatus 1000 of the present embodiment described above, the surface to be polished of the substrate WF supported by the table 100 and the support surface 300a of the support member 300 are imaged by the imaging module 600, and the step a between the support surface 300a and the surface to be polished of the substrate WF is estimated based on the imaging information. Then, the substrate processing apparatus 1000 adjusts the height of the support surface 300a during polishing based on the estimated step a. According to such a substrate processing apparatus 1000, the height of the support surface 300a can be suitably adjusted during polishing, and the uniformity of polishing of the surface to be polished can be improved. Moreover, since the imaging module 600 is provided to face the support member 300 and the table 100, the substrate processing apparatus 1000 of the present embodiment can realize the above functions and effects without increasing the footprint.
In the above-described embodiment, the control module 800 (step estimation module 820) estimates the step δh between the support surface 300a of the support member 300 and the surface to be polished of the substrate WF. In addition to this, the control module 800 may be capable of measuring the thickness of the substrate WF based on the imaging information obtained by the imaging module 600 and the learning model. As an example, the control module 800 may measure the thickness of the substrate WF based on the step δh between the support surface 300a and the substrate WF and the height position of the support surface 300a.
Further, the control module 800 may be capable of measuring the surface profile of the substrate WF based on the imaging information obtained by the imaging module 600 and the learning model. As an example, the control module 800 may image the surface to be polished of the substrate WF with the imaging module 600 while rotating the substrate WF, and estimate the thickness of the substrate WF for each circumferential position (or the step δh between the support surface 300a and the surface to be polished) based on the imaging information and the learning model, thereby measuring the surface profile of the substrate WF.
Further, in the above-described embodiment, the imaging module 600 is used to estimate the step δh between the support surface 300a of the support member 300 and the surface to be polished of the substrate WF. In addition to this, the imaging module 600 may be used to detect a notch (not shown) formed in advance on the substrate WF. In this way, the imaging module 600 serves as both a mechanism for detecting the notch and a mechanism for estimating the step δh between the support surface 300a and the surface to be polished of the substrate WF, and therefore the number of parts in the substrate processing apparatus 1000 can be reduced.
Although the embodiments of the disclosure have been described above based on some examples, the above-described embodiments of the disclosure are for facilitating the understanding of the disclosure and do not limit the disclosure. The disclosure can be modified and improved without departing from the spirit thereof, and it goes without saying that the disclosure includes an equivalent thereof. In addition, within the range where at least a part of the above-mentioned problem can be solved or at least a part of the effect can be achieved, any combination or omission of each component described in the claims and specification is possible.
At least the following technical ideas are grasped from the above-described embodiment.
| Number | Date | Country | Kind |
|---|---|---|---|
| 2020-192585 | Nov 2020 | JP | national |
