KR-20260066650-A - Heat transfer management in substrate support systems

KR20260066650AKR 20260066650 AKR20260066650 AKR 20260066650AKR-20260066650-A

Abstract

The method comprises: a step of identifying attribute data associated with a substrate support system; a step of identifying target performance data associated with a substrate support system; a step of determining zone configuration data associated with a substrate support system based on the attribute data and the target performance data; and a step of configuring the substrate support system based on the zone configuration data.

Inventors

차다, 알빈더 만모한 싱

Assignees

어플라이드 머티어리얼스, 인코포레이티드

Dates

Publication Date: 20260512
Application Date: 20240122
Priority Date: 20230908

Claims (20)

As a method, A step of identifying attribute data associated with a substrate support system; A step of identifying target performance data associated with the above-mentioned substrate support system; and A step of causing heat transfer management of the substrate support system based on the above attribute data and the above target performance data A method including
A method according to claim 1, wherein the step of causing the heat transfer management comprises the step of causing one or more material operations on the substrate support system.
In paragraph 1, the above attribute data is: Sensor data received from one or more sensors associated with the above-mentioned substrate support system; or Simulated data associated with the above substrate support system A method comprising one or more of the following.
A method according to claim 1, wherein the attribute data comprises one or more of measurement data of one or more components of the substrate support system; or one or more of processing chamber data of the processing chamber, wherein the substrate support system is disposed within the processing chamber, and the processing chamber data comprises one or more of flow rate data associated with a process gas flowing into the processing chamber, exhaust port location data associated with an exhaust port of the processing chamber, or pressure data associated with a pressure of the processing chamber.
A method according to claim 1, wherein the target performance data comprises one or more of a target heat map, a target etching map, or a target deposition map associated with the upper surface of the substrate support system.
In paragraph 2, the above one or more material operations are: Removal of material from the above substrate support system; Addition of material to the above substrate support system; or Surface treatment of the above substrate support system A method comprising one or more of the following.
In paragraph 2, A step of providing the above attribute data and the above target performance data as inputs to a trained machine learning model; A step of obtaining an output associated with prediction data from the above-mentioned trained machine learning model; and A step of determining one or more material operations to be performed on the substrate support system to satisfy the target performance data based on the above prediction data. A method that additionally includes
A method according to claim 7, wherein the trained machine learning model is trained based on data inputs including past attribute data and past target performance data of past substrate support systems, and target outputs including past material behaviors for said past substrate support systems.
A method according to paragraph 2, wherein one or more material actions form intentional asymmetric features in the substrate support system.
In paragraph 2, the substrate support system is: A ceramic puck housing a clamp electrode; housing heaters; or forming gas channels; A cooling plate forming cooling channels and gas channels; and Bonding material for bonding the ceramic puck to the cooling plate A method including
In claim 10, the method comprises one or more material operations forming protrusions on the cooling plate.
A method according to claim 10, wherein the one or more material actions include corrugating the cooling channels to cause perturbation of fluid flow to control heat transfer efficiency.
A method according to claim 10, wherein the one or more material operations comprises causing the gas channels to have one or more of the following: having a variable gas channel width, having a variable gas channel height, or being configured to flow different types of gas compositions.
A method according to claim 10, wherein the operation of one or more materials comprises making the gas channels multi-zone gas channels passing through the cooling plate, the bonding material, and the ceramic puck.
In paragraph 10, the above one or more material operations cause the cooling channels: pin; Stepwise approximation for representing a circular channel; Circular channel; Stacked; computer-generated regenerative channels A method to make one or more of them.
As a non-transient machine-readable storage medium for storing instructions, said instructions cause a processing device to: A step of identifying attribute data associated with a substrate support system; A step of identifying target performance data associated with the above-mentioned substrate support system; and A step of causing one or more material operations on the substrate support system based on the above attribute data and the above target performance data. A non-transient machine-readable storage medium that enables the performance of operations including
In paragraph 16, the above one or more material operations are: Removal of material from the above substrate support system; Addition of material to the above substrate support system; or Surface treatment of the above substrate support system A non-transient machine-readable storage medium comprising one or more of the following.
In Clause 16, the above operations are: A step of providing the above attribute data and the above target performance data as inputs to a trained machine learning model; A step of obtaining an output associated with prediction data from the above-mentioned trained machine learning model; and A step of determining one or more material operations to be performed on the substrate support system to satisfy the target performance data based on the above prediction data. A non-transient machine-readable storage medium additionally comprising
As a system, Memory; Processing device coupled to the above memory Includes, and the processing device is: Identify attribute data associated with the substrate support system; Identifying target performance data associated with the above-mentioned substrate support system; Based on the above attribute data and the above target performance data, causing one or more material operations to be performed on the substrate support system, System.
In paragraph 19, the above one or more material operations are: Removal of material from the above substrate support system; Addition of material to the above substrate support system; or Surface treatment of the above substrate support system A system including one or more of the following.

Description

Heat transfer management in substrate support systems The present disclosure relates to management in manufacturing systems, such as substrate support systems, and specifically to heat transfer management in manufacturing systems. Products are produced by performing one or more manufacturing processes using manufacturing equipment. For example, substrate processing equipment is used to process substrates by performing processes on them in processing chambers. The following is a simplified summary of the present disclosure to provide a basic understanding of some aspects of the present disclosure. This summary is not an extensive overview of the present disclosure. It is not intended to identify the essential or important elements of the present disclosure, nor is it intended to describe any scope of specific embodiments of the present disclosure or any scope of the claims. Its sole purpose is to present some concepts of the present disclosure in a simplified form as an introduction to the more detailed description that follows. In an embodiment of the present disclosure, the method comprises: identifying attribute data associated with a substrate support system; identifying target performance data associated with a substrate support system; and, based on the attribute data and the target performance data, causing heat transfer management of the substrate support system. In another aspect of the present disclosure, a non-transient machine-readable storage medium for storing instructions, wherein the instructions, when executed, cause a processing device to perform operations including: identifying attribute data associated with a substrate support system; identifying target performance data associated with a substrate support system; and causing one or more material operations on a substrate support system based on the attribute data and the target performance data. In another aspect of the present disclosure, the system comprises a memory and a processing device coupled to the memory. The processing device identifies attribute data associated with a substrate support system; identifies target performance data associated with a substrate support system; and, based on the attribute data and the target performance data, causes one or more material operations to be performed on the substrate support system. In an embodiment of the present disclosure, the method comprises: identifying attribute data associated with a substrate support system; identifying target performance data associated with a substrate support system; determining zone configuration data associated with a substrate support system based on the attribute data and the target performance data; and configuring the substrate support system based on the zone configuration data. In another aspect of the present disclosure, a non-transient machine-readable storage medium for storing instructions, wherein the instructions, when executed, cause a processing device to perform operations including: identifying attribute data associated with a substrate support system; identifying target performance data associated with a substrate support system; determining zone configuration data associated with a substrate support system based on the attribute data and the target performance data; and causing the substrate support system to be configured based on the zone configuration data. In another aspect of the present disclosure, the system comprises a memory and a processing device coupled to the memory. The processing device identifies attribute data associated with a substrate support system; identifies target performance data associated with a substrate support system; determines zone configuration data associated with a substrate support system based on the attribute data and the target performance data; and causes the substrate support system to be configured based on the zone configuration data. The present disclosure is illustrated in the accompanying drawings as an example, not as a limitation. FIG. 1 is a block diagram illustrating an exemplary system architecture according to specific embodiments. FIG. 2 illustrates a data set generator for generating data sets for a machine learning model according to specific embodiments. FIG. 3 is a block diagram illustrating the determination of prediction data according to specific embodiments. FIGS. 4a–4f are flowcharts of methods associated with heat transfer management according to specific embodiments. FIGS. 5a–5u illustrate parts of substrate support systems according to specific embodiments. FIG. 6 is a block diagram illustrating a computer system according to specific embodiments. This specification describes techniques for heat transfer management in substrate support systems (e.g., wafer support systems). Products are produced by performing one or more manufacturing processes using manufacturing equipment. For example, substrate processing equipment is used to process substrates by performing processes on