WO2025240441A1

WO2025240441A1 - Computer vision for susceptor leveling and alignment

Info

Publication number: WO2025240441A1
Application number: PCT/US2025/029092
Authority: WO
Inventors: Aniketnitin Patil
Original assignee: Applied Materials Inc
Current assignee: Applied Materials Inc
Priority date: 2024-05-13
Filing date: 2025-05-13
Publication date: 2025-11-20
Anticipated expiration: 2026-11-13
Also published as: US20250349589A1; TW202605760A

Abstract

Methods and devices for aligning a susceptor are provided herein. Embodiments include creating a three-dimensional (3D) map of a susceptor and a ring within a substrate processing chamber based on camera data comprising image data associated with the susceptor and the ring. Embodiments further include adjusting a position of the susceptor based on the 3D map to create a gap having a target size between the susceptor and the ring.

Description

COMPUTER VISION FOR SUSCEPTOR LEVELING AND ALIGNMENT

BACKGROUND

Field

[0001] Embodiments of the present disclosure generally relate to semiconductor processing and, more specifically, to a machine learning-based process for susceptor alignment within a processing chamber.

Description of the Related Art

[0002] Semiconductor substrates are processed for a wide variety of applications, including the fabrication of integrated devices and microdevices. One method of processing substrates includes growing a film or layer on an upper surface of the substrate within a processing chamber. The layer may be deposited by exposing the substrate to processing gases while heating the substrate with a radiant heat source.

[0003] In substrate processing systems, a substrate may be transported from a substrate load lock chamber to a process chamber with a transport robot for processing. The transport robot may use an end effector for transferring a substrate onto a susceptor within a processing chamber. One of the challenges of substrate handling and positioning is aligning the susceptor and the substrate inside the processing chamber to ensure optimal processing. As an example, if the substrate is misaligned within the chamber (e.g., too close to a heating element within the chamber or tilted relative to a heating element), local temperature changes occur, resulting in temperature gradients across the substrate. This can cause non-uniformity in process results.

[0004] Current methods for aligning and/or positioning a susceptor within a processing chamber involve manual calibration. Such manual calibration is time intensive and prone to manual errors. Methods of automating the alignment and calibration process fail to provide a fully accurate representation of the position of the susceptor, which can result in the need for additional calibration or alignment on the backend which increases chamber downtime..

[0005] Therefore, there is a need for improved alignment techniques that provide for more efficient and accurate alignment. SUMMARY

[0006] Embodiments described herein generally relate to aligning a susceptor within a substrate processing chamber, and more particularly, to generating a three dimensional (3D) map of a susceptor and a ring within the processing chamber and then adjusting the position of the susceptor based on the 3D map.

[0007] In one embodiment, a method comprises creating a three- dimensional (3D) map of a susceptor and a ring within a substrate processing chamber based on camera data comprising image data associated with the susceptor and the ring; and adjusting a position of the susceptor based on the 3D map to create a gap having a target size between the susceptor and the ring.

[0008] In another embodiment, a method comprises creating a three- dimensional (3D) map of a susceptor and a ring within a substrate processing chamber by: receiving camera data comprising image data and depth data associated with the susceptor and the ring; creating the 3D map based on the camera data using a machine learning model trained, based on position data from a two-dimensional (2D) profilometer indicating a location of the susceptor relative to the ring, to create 3D maps; adjusting the position of the susceptor; receiving additional position data from the 2D profilometer; receiving additional camera data; and retraining the machine learning model using the additional position data, wherein the retrained machine learning model is used to update the 3D map; and adjusting the position of the susceptor based on providing the 3D map to an optimization algorithm to create a gap having a target size between the susceptor and the ring.

[0009] In another embodiment, processing chamber system configured for susceptor alignment comprises a processing chamber for processing substrates; a camera system configured to capture image data and depth data; a two-dimensional (2D) profilometer; a susceptor configured to hold a substrate; and a computing device capable of: creating a three-dimensional (3D) map of a susceptor and a ring within a substrate processing chamber based on camera data comprising image data associated with the susceptor and the ring and position data from a two-dimensional (2D) profilometer; and adjusting a position of the susceptor based on the 3D map to create a gap having a target size between the susceptor and the ring.

BRIEF DESCRIPTION OF THE DRAWINGS

[0010] So that the manner in which the above recited features of the present disclosure can be understood in detail, a more particular description of the disclosure, briefly summarized above, may be had by reference to embodiments, some of which are illustrated in the appended drawings. It is to be noted, however, that the appended drawings illustrate only exemplary embodiments and are therefore not to be considered limiting of scope, as the disclosure may admit to other equally effective embodiments.

[0011] Figure 1 depicts an example substrate processing system according to certain embodiments.

[0012] Figure 2 depicts an example of computing components for susceptor alignment.

[0013] Figure 3 depicts an example of views of a susceptor relative to a ring of a processing chamber according to certain embodiments.

[0014] Figure 4 is a flow diagram of example operations for aligning a susceptor within a substrate processing chamber, according to certain embodiments

[0015] To facilitate understanding, identical reference numerals have been used, where possible, to designate identical elements that are common to the figures. It is contemplated that elements and features of one embodiment may be beneficially incorporated in other embodiments without further recitation.

DETAILED DESCRIPTION

[0016] Embodiments described herein generally relate to aligning a susceptor within a substrate processing chamber. More particularly, embodiments described herein provide devices and methods for aligning a susceptor within a substrate processing chamber. [0017] Embodiments described herein provide for creating a three dimensional (3D) map of a susceptor and a ring within a substrate processing chamber based on data received from a camera system and a two-dimensional (2D) profilometer. The position of the susceptor may then be adjusted based on the 3D map, such as by using an optimization algorithm or machine learning techniques.

[0018] Embodiments described herein incorporate a camera system that is configured to capture both image data and depth data. The image and depth data are used along with the positional data received from the profilometer to generate the 3D map. For example, a neural network may be trained through a supervised learning process using the 2D profilometer data as ground truth to generate a 3D map based on the image and depth data. The susceptor may be moved (e.g., manually or automatically) and additional image, depth, and positional data may be obtained. The neural network may be retrained based on the additional data, and an updated 3D map may be created. Once a 3D map that provides an accurate representation of how susceptor positional adjustments impact the position of the susceptor relative to the ring is generated, an optimization algorithm or a machine learning process may be used to adjust the susceptor (manually, or by a robot or actuator) into a position specified by a user.

Example Processing Chamber Configured for Susceptor Alignment

[0019] Figure 1 is a schematic sectional view of a processing chamber 100 configured to perform susceptor alignments, according to one implementation. The process chamber 100 includes an upper body 156, a lower body 148 disposed below the upper body 156, and a flow module 112 disposed between the upper body 156 and the lower body 148. The processing chamber 100 also includes a processing volume 136 formed between an upper window 108 and a lower window 110. The processing volume 136 contains the susceptor 106 disposed therein and a pre-heat ring 133 is configured to be disposed around the periphery of the susceptor 106 while the susceptor 106 is in a processing position. [0020] A substrate support 101 configured to support substrates within the processing chamber 100 includes a susceptor 106 and a shaft 118. The susceptor 106 is supported on the shaft 118. The susceptor 106 is configured to hold a substrate 102. A transport robot, controlled by a computing system, such as a computing system that uses the computing components described in Figure 2, positions a substrate on the susceptor 106 for processing. The substrate 102 may be a silicon wafer that is processed inside the processing chamber 100. To facilitate positioning of the substrate 102 inside the processing chamber 100, the susceptor 106 may be raised, lowered, tilted, translated, and/or rotated (such as by rotating the substrate support 101 or raising/lowering the shaft). The susceptor 106 further includes lift pin holes 107 disposed therein. The lift pin holes 107 are sized to accommodate a respective lift pin

132 for placing and/or lifting of the substrate 102 to or from the susceptor 106. The lift pins 132 rest on lift pin stops 134 when the susceptor 106 is lowered from a processing position to a transfer position.

[0021] The shaft 118 is connected to a motion assembly 121. The motion assembly 121 includes one or more actuators and/or adjustment devices that provide movement and/or adjustment of the shaft 118 and/or the susceptor 106 within the processing volume 136. The motion assembly 121 includes a rotary actuator 122 that rotates the shaft 118 and/or the susceptor 106 about a longitudinal axis A of the process chamber 100. The motion assembly 121 further includes a vertical actuator 124 to lift and lower the susceptor 106 in the z-direction. The motion assembly includes a tilt adjustment device 126 that is used to adjust the planar orientation of the susceptor 106 and a lateral adjustment device 128 that is used to adjust the position of the shaft 118 and the susceptor 106 side to side within the processing volume 136. In other embodiments, the susceptor 106 may rotate about a longitudinal axis (e.g. the x-axis).

[0022] Processing chamber 100 includes a processing volume 136, a cavity in which the substrate 102 is processed on the susceptor 106. A preheat ring

133 is configured to surround the susceptor 106. When the susceptor 106 is aligned according to specifications provided by a user, the preheat ring 133 may form a narrow, substantially uniform gap with the edge 125 (e.g., outer diameter) of the susceptor 106. The susceptor 106 and the preheat ring 133 may each be substantially circular, and the preheat ring 133 may have a larger circumference than the susceptor 106. In such a manner, the susceptor 106 and the preheat ring 133 are aligned concentrically.

[0023] Processing chamber 100 further includes an upper lamp assembly 147 comprising upper lamps 141 as shown in Figure 1. The upper lamp assembly 147 (e.g., heating component) facilitates substrate processing by applying heat to substrate 102. Additionally, processing chamber 100 may comprise a camera system including cameras 153. Furthermore, processing chamber 100 may comprise one or more 2D profilometers 104, which may be configured to measure a profile between susceptor 106 and preheat ring 133 at various points.

[0024] The 2D profilometer 104 may be any type of 2D profilometer 104 as known in the art, such as a laser-based profilometer. 2D profilometer 104 may be configured to capture measurements of the exact position of the susceptor 106 relative to the preheat ring 133. The 2D profilometer data may offer measurements of a precision within, for example, 5 microns. Each measurement may measure one point along the circumference of the interface between the susceptor 106 and the preheat ring 133. To gather additional 2D profilometer data, the susceptor 106 may be rotated. After the rotation, the 2D profilometer 104 may measure the position of the susceptor 106 relative to the preheat ring 133 again. The rotation and measurement may be repeated to gather additional positional data as necessary for generating a 3D map, as discussed in further detail below with respect to Figure 2. Taking a higher number of 2D profilometer 104 measurements may result in a more accurate 3D map with a tradeoff of requiring a greater amount of time and resources.

[0025] The plurality of upper lamps 141 are disposed between an upper window 108 and a lid 154. The plurality of upper lamps 141 form an upper lamp assembly 147. The lid 154 includes the plurality of cameras 153. The lid 154 further includes the 2-D profilometer 104. A plurality of lower lamps 143 are disposed between the lower window 110 and a floor 152. The plurality of lower lamps 143 form a lower lamp assembly 145.

[0026] As shown in Figure 1 , a camera system may comprise three cameras 153 that are each configured to capture image data corresponding to susceptor 106 and preheat ring 133. In the example embodiment illustrated in Figure 1 , the cameras 153 also capture depth data because the cameras 153 are positioned at different locations along the circumference of the preheat ring 133. This stereo camera arrangement allows for the capturing of 3D image data (i.e., both image and depth data). Such 3D image data may be processed by a trained neural network in order to create a 3D map of the preheat ring 133 and the susceptor 106. Although not shown, in alternate embodiments the camera system may comprise a single camera that is configured to travel along the circumference of the preheat ring 133 and capture image data at various points along the circumference, thus enabling the camera to capture 3D image data in a similar manner as a stereo camera system. In other examples, the camera system includes a single camera and multiple light sources that are configured to add 3D depth perception to captured image data similar to images captured by stereo camera systems. The camera systems described above are included as example embodiments, and other camera configurations for capturing 3D image data may be used as well.

[0027] The flow module 112 includes a plurality of process gas inlets 114, a plurality of flow module purge gas inlets 164, and one or more exhaust gas outlets 116. One or more cooling channels 146 are disposed below the plurality of process gas inlets 114. The cooling channels 146 may be annular. Further, holes or slits in the liner 163 may allow for gases to exhaust to gas outlets 116. It is contemplated that the positions of the slit inlet and the slit outlet may be switched depending on the chosen configuration. In some embodiments, a rotation purge gas may flow from a rotation inlet (not shown) at the bottom of the process chamber 100, around the shaft 118, and exit at the one or more exhaust gas outlets 116. In some embodiments, each of the different purge gases may be exhausted by exhaust vents, such as slits or holes in the liner 163, prior to reaching the one or more gas outlets 116. [0028] The liner 163 is disposed on the inner surface of the flow module 112 and protects the flow module 112 from reactive gases used during deposition processes. A liner purge gas may flow in between the liner 163 and the chamber body and exit at the one or more exhaust gas outlets 116. The flow module purge gas, the slit purge gas, the rotation purge gas, and/or the liner purge gas may be an inert gas, such as one or more of helium, hydrogen, nitrogen and argon. The flow module purge gas, the slit purge gas, the rotation purge gas, and/or the liner purge gas may further include an etchant for affecting selectivity of deposited film or cleaning areas that are being purged. In some embodiments, the liner 163 is split into an upper liner and a lower liner and the lower liner has holes formed along an inner surface thereof to form a vented lower liner whereby gas within the lower portion of the chamber may be vented directly to an exhaust.

[0029] The process gas inlet 114 is positioned to flow a gas parallel to the top surface 150 of a substrate 102, while the flow module purge gas inlets 164 are positioned to flow a purge gas beneath the susceptor 106. Precursor gases and/or process gases may include one or more of silane, dichlorosilane (DCS), trichlorosilane (TCS), or disilane (DS), tetraethylorthosilicate (TEOS), higher order silanes, such as trisilane and tetra silane. However, other gases are also contemplated. As shown in Figure 1 , a controller 120 is in communication with the process chamber 100 and is used to control processes, such as those described herein.

[0030] The various gas inlets, such as the plurality of process gas inlets 144 and the plurality of flow module purge gas inlets 164, may be configured to provide individual or multiple gas flows with varied gas parameters such as velocity, density, or composition. In some embodiments, as shown in Figure 1 , the one or more exhaust outlets 116 are located 180 degrees from the plurality of process gas inlets 114 and the plurality of flow module purge gas inlets 164 in a cross-flow configuration.

Example Computing Components for Susceptor Alignment [0031] Figure 2 illustrates an example of computing components for susceptor alignment. The computing components may be housed within the controller 120. Creating the 3D map 225 may comprise capturing one or more 2D profilometer measurements 205 of the interface between the susceptor 106 and the preheat ring 133 (e.g., by capturing a measurement, rotating the susceptor 106, capturing another measurement, and so on). Additionally, 3D image data 215 (e.g., image data and depth data) may be captured by the camera system. The 3D image data 215 may be provided to a machine learning model such as neural network 200. The 2D profilometer measurements 205 may be provided to the neural network 200 and used as ground truth labels to train the neural network 200 through a supervised learning process. For example, the neural network 200 may generate a mapping of a series of points along the circumference of the interface between the susceptor 106 and the preheat ring 133 based on the 2D profilometer measurements 205. The mapping may comprise detecting edges of the susceptor 106 and the preheat ring 133 based on the 2D profilometer measurements 205. When provided with the 3D image data 215, the neural network 200 may create a preliminary version of the 3D map 225. The creation of the preliminary map may comprise creating two circular profiles based on the detected edges, and then mapping the 3D image data 215 onto the circular profiles to create a preliminary version of the 3D map 225.

[0032] The supervised learning process may comprise taking the output of the neural network 200 (e.g., the preliminary version of the 3D map 225), comparing it to the 2D profilometer measurements 205, and then adjusting parameters of the neural network 200 based on differences between the 3D map 225 and the 2D profilometer measurements 205. The susceptor 106 may be rotated, additional 2D profilometer measurements 205 and 3D image data 215 may be taken, updates may be made to the 3D map 225, and the neural network 200 may be retrained for a threshold number of iterations or until the output 3D map 225 matches a threshold number of 2D profilometer measurements 205. Additionally, the supervised learning process may further comprise raising, lowering, tilting, and/or making translational adjustments to the position of the susceptor 106. Making such positional adjustments may allow the 3D map 225 to capture how performing such movements affects the actual position of the susceptor 106. For example, if performing a particular movement with the transport robot causes the susceptor 106 to move to a particular position, the 3D map 225 may store this information. Thus, by using the 3D map 225, a computing system may be able to determine how to move the transport robot in order to cause the susceptor 106 to reach a given positon.

[0033] The 3D map 225 may be provided to an optimization engine 210. Optimization engine 210 may comprise one or more processors configured to optimize the position of the susceptor 106 according to specifications provided by a user. For example, the user may specify that the susceptor 106 should be level with the preheat ring 133, and the gap between the susceptor 106 and the preheat ring 133 should be equidistant along the circumference of the interface between the susceptor 106 and the preheat ring 133 (for example, within a predefined tolerance or threshold).

[0034] Optimization engine 210 may use an optimization algorithm to adjust the position of the susceptor 106. For example, if the 3D map 225 indicates that a side of the substrate support 101 is tilted upward relative to a specified tilt, the susceptor 106 may be iteratively tilted downward by an increment (e.g., the side of the substrate support 101 may be tilted downward by a predetermined increment, such as an increment of 0.01 degrees, or another increment) until the 3D map 225 indicates that the specified tilt is reached; if the 3D map 225 then indicates that the substrate support 101 is tilted downward relative to the specified tilt after an incremental adjustment, the susceptor 106 may be tilted upward by a smaller increment, and so on. As another example, if the 3D map 225 indicates that the gap between the susceptor 106 and the preheat ring 133 is smaller on the left side of the susceptor 106 than on the right side of the susceptor 106, the susceptor 106 may be iteratively moved to the right by an increment (e.g., ten microns, or another increment) until the 3D map 225 indicates that the gap is an equal length on both sides; if the 3D map 225 indicates that the gap is smaller on the right side than on the left after an incremental adjustment, the susceptor 106 may then be moved to the left by a smaller increment, and so on. In alternate embodiments, the optimization engine 210 comprises a machine learning model that is trained to optimize the position of the susceptor 106 based on the 3D map and the position specified by the user.

Example Views of a Susceptor Relative to a Ring of a Processing Chamber

[0035] Figure 3 illustrates example views 300a-d of a susceptor 106 (specifically, the susceptor 106) relative to a preheat ring 133 according to certain embodiments. View 300a illustrates a plan view of a susceptor 106 and a preheat ring 133. In the example shown in view 300a, the susceptor 106 is off-center (e.g., non-concentric) relative to the preheat ring 133. Because the susceptor 106 is off-center, a substrate that is supported by the susceptor 106 may also be off-center relative to the preheat ring 133, resulting in an improperly aligned gap 315. As discussed above, when substrates are improperly positioned within a processing chamber (e.g., off-center relative to the chamber and/or chamber components such as the preheat ring 133), non-uniform ity may occur in the processing results due to non-uniform heating. For example, a temperature gradient may result on the surface of the substrate due to one part of the substrate being too close to a heat source and/or another part of the substrate being too far away from the heat source.

[0036] As discussed above, a 3D map of the susceptor 106 and the preheat ring 133 may be generated and used to adjust the positon of the susceptor 106 to a specified position. For example, the specified position may be a position where the gap between the susceptor 106 and the preheat ring 133 is uniform within a specified tolerance, as shown in view 300b. In view 300b, a target gap 325 between the susceptor 106 and the preheat ring 133 is a uniform gap. For example, the target gap 325 of example 300b may be achieved by moving the susceptor 106 of example 300a downward by an increment until the 3D map indicates that the susceptor 106 is located in the center of the preheat ring 133. If an overcorrection occurs (i.e. , the susceptor 106 is moved such that the gap is smaller at the bottom than at the top, as indicated by the 3D map), the susceptor 106 may be adjusted upward by a smaller increment until the gap is uniform (or uniform within a specified threshold). [0037] View 300c illustrates an example where the susceptor 106 is not level (e.g., co-planar)_ with the preheat ring 133. Thus, a substrate that is carried by the susceptor 106 may also not be level with the preheat ring 133. As discussed above, when substrates are improperly positioned within a processing chamber, non-uniform ity may occur in the processing results. For example, a temperature gradient may result on the surface of the substrate due to one part of the substrate being too close to a heat source and/or another part of the substrate being too far away from the heat source.

[0038] View 300d illustrates an example where the susceptor 106 is level with the preheat ring 133. When the susceptor 106 is level with the preheat ring 133, the target gap 325 may be achieved. The susceptor 106 may be leveled by tilting the side of the susceptor 106 shown in 300c upward by an increment until the 3D map indicates that the susceptor 106 becomes level with the preheat ring 133. If an overcorrection occurs (i.e., the susceptor 106 is tilted such that the susceptor 106 is tilted upward relative to the preheat ring 133, as indicated by the 3D map), the susceptor 106 may be adjusted upward by a smaller increment until the susceptor 106 is level with the preheat ring 133 (or level within a specified threshold).

Example Operations for Susceptor Alignment

[0039] Figure 4 is a flow diagram of example operations 400 for susceptor alignment. Operations 400 may be performed by a computing device comprising one or more processors, such as the computing device as discussed with respect to Figure 2.

[0040] Operations 400 begin at block 410, with creating a three-dimensional (3D) map of a susceptor and a preheat ring within a substrate processing chamber based on camera data comprising image data associated with the susceptor and the ring. In certain embodiments, creating the 3D map is further based on position data received from a two-dimensional (2D) profilometer. Certain embodiments provide that the 3D map is created by a machine learning model that is trained through a supervised learning process involving the position data to create 3D maps. In some embodiments, creating the 3D map is further based on: receiving additional position data from the 2D profilometer after the adjusting of the position of the susceptor; and retraining the machine learning model using the additional position data, wherein the retrained machine learning model is used to update the 3D map. Certain embodiments provide that the supervised learning process comprises iteratively adjusting parameters of the machine learning model until a characteristic of the 3D map matches a characteristic indicated by the position data. In certain embodiments, the supervised learning process comprises iteratively adjusting parameters of the machine learning model based on comparing a characteristic of the 3D map output by the machine learning model in response to the camera data to a characteristic indicated by the position data (e.g., to optimize one or more variables, such as model accuracy, such as via an objective function). In some embodiments, the camera data is provided by a camera system comprising three cameras positioned along a circumference of the ring. According to certain embodiments, the camera data is provided by a camera system comprising a camera configured to move along a circumference of the ring. Some embodiments provide that the camera data is provided by a camera system comprising a camera and multiple light sources, wherein the multiple light sources are configured to allow the camera to capture the depth data.

[0041] Operations 400 continue at block 420, with adjusting a position of the susceptor based on the 3D map to create a gap having a target size between the susceptor and the ring. According to some embodiments, the gap is adjusted based on using an optimization algorithm to adjust the position of the susceptor based on the 3D map. Some embodiments provide that the gap is adjusted based on using a second machine learning model that is trained to adjust the position of the susceptor based on the 3D map. In certain embodiments, the gap between the susceptor and the ring is substantially equidistant. According to certain embodiments, adjusting the position of the susceptor further comprises adjusting the susceptor so that the susceptor is substantially level with the preheat ring.

[0042] While the foregoing is directed to implementations of the present disclosure, other and further implementations of the disclosure may be devised without departing from the basic scope thereof, and the scope thereof is determined by the claims that follow.

Claims

What is claimed is:

1 . A method of positioning a substrate susceptor, comprising: creating a three-dimensional (3D) map of a susceptor and a ring within a substrate processing chamber based on camera data comprising image data associated with the susceptor and the ring; and adjusting a position of the susceptor based on the 3D map to create a gap having a target size between the susceptor and the ring.

2. The method of Claim 1 , wherein creating the 3D map is further based on position data received from a two-dimensional (2D) profilometer.

3. The method of Claim 2, wherein the 3D map is created by a machine learning model that is trained through a supervised learning process involving the position data to create 3D maps.

4. The method of Claim 3, wherein creating the 3D map is further based on: receiving additional position data from the 2D profilometer after the adjusting of the position of the susceptor; and retraining the machine learning model using the additional position data, wherein the retrained machine learning model is used to update the 3D map.

5. The method of Claim 3, wherein the supervised learning process comprises iteratively adjusting parameters of the machine learning model until a characteristic of the 3D map matches a characteristic indicated by the position data.

6. The method of Claim 1 , wherein the gap is adjusted based on using an optimization algorithm to adjust the position of the susceptor based on the 3D map.

7. The method of Claim 1 , wherein the gap is adjusted based on using a machine learning model that is trained to adjust the position of the susceptor based on the 3D map.

8. The method of Claim 1 , wherein the gap between the susceptor and the ring is substantially equidistant.

9. The method of Claim 1 , wherein adjusting the position of the susceptor further comprises adjusting the susceptor so that the susceptor is substantially level with the ring.

10. The method of Claim 1 , wherein the susceptor is positioned on a shaft, and the shaft is configured to translate the susceptor within a substrate processing chamber and tilt the susceptor in order to adjust the gap.

11 . The method of Claim 1 , wherein the camera data is provided by a camera system comprising three cameras positioned along a circumference of the ring.

12. A method of positioning a substrate susceptor, comprising: creating a three-dimensional (3D) map of a susceptor and a ring within a substrate processing chamber by: receiving camera data comprising image data and depth data associated with the susceptor and the ring; creating the 3D map based on the camera data using a machine learning model trained, based on position data from a two-dimensional (2D) profilometer indicating a location of the susceptor relative to the ring, to create 3D maps; adjusting the position of the susceptor; receiving additional position data from the 2D profilometer; receiving additional camera data; and retraining the machine learning model using the additional position data, wherein the retrained machine learning model is used to update the 3D map; and adjusting the position of the susceptor based on providing the 3D map to an optimization algorithm to create a gap having a target size between the susceptor and the ring.

13. A processing chamber system configured for susceptor alignment, comprising: a processing chamber for processing substrates; a susceptor configured to hold a substrate; a ring; a camera system configured to capture camera data comprising image data associated with the susceptor and the ring; and a computing device capable of: creating a three-dimensional (3D) map of the susceptor and the ring within the processing chamber based on camera data comprising image data associated with the susceptor and the ring; and adjusting a position of the susceptor based on the 3D map to create a gap having a target size between the susceptor and the ring.

14. The processing chamber system of Claim 13, wherein creating the 3D map is further based on position data received from a two-dimensional (2D) profilometer.

15. The processing chamber system of Claim 14, wherein the 3D map is created by a machine learning model that is trained through a supervised learning process involving the position data to create 3D maps.

16. The processing chamber system of Claim 15, wherein creating the 3D map is further based on: receiving additional position data from the 2D profilometer after the adjusting of the position of the susceptor; and retraining the machine learning model using the additional position data, wherein the retrained machine learning model is used to update the 3D map.

17. The processing chamber system of Claim 15, wherein supervised learning process comprises iteratively adjusting parameters of the machine learning model until a characteristic of the 3D map matches a characteristic indicated by the position data.

18. The processing chamber system of Claim 13, wherein the gap is adjusted based on using an optimization algorithm to adjust the position of the susceptor based on the 3D map.

19. The processing chamber system of Claim 13, wherein the gap is adjusted based on using a machine learning model that is trained to adjust the position of the susceptor based on the 3D map.

20. The processing chamber system of Claim 13, wherein the susceptor is positioned on a shaft, and the shaft is configured to translate the susceptor within a substrate processing chamber and tilt the susceptor in order to adjust the gap.