Semiconductor Manufacturing Device, Push-up Unit, and Method of Manufacturing Semiconductor Device
US-2024312825-A1 · Sep 19, 2024 · US
US2025349589A1 · US · A1
| Field | Value |
|---|---|
| Publication number | US-2025349589-A1 |
| Application number | US-202418662802-A |
| Country | US |
| Kind code | A1 |
| Filing date | May 13, 2024 |
| Priority date | May 13, 2024 |
| Publication date | Nov 13, 2025 |
| Grant date | — |
A practical reading order for non-experts. Skip the full description unless you need deep technical detail.
What the patent document calls the invention.
A short plain-language summary of the technical disclosure.
Who owns or filed the patent and who is credited as inventor.
Filing, priority, publication, and grant dates set the timeline.
The legal scope of protection — read this for what is actually claimed.
Technology tags used to group this patent with similar filings.
Prior art links and similar publications in this corpus.
Official abstract text for this publication.
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.
Opening claim text (preview).
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 created 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 created 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 comprises an arm and a substrate holder, wherein the arm of the susceptor is configured to translate the substrate holder within a substrate processing chamber and tilt the substrate holder in order to create 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 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. 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 created 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 created 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 comprises an arm and a substrate holder, wherein the arm of the susceptor is configured to translate the substrate holder within a substrate processing chamber and tilt the substrate holder in order to create the gap.
using optical controlling means · CPC title
characterised by control arrangements for positioning, e.g. centring a tool relative to a hole in the workpiece, additional detection means to correct position (G05B19/19 takes precedence) · CPC title
the criterion being a learning criterion · CPC title
Electricity · mapped topic
Related publications grouped by family.
Answers are generated from the same data shown on this page.