Processing Chamber Recovery Prediction After Preventive Maintenance
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Solution Overview
Problem
Conventional methods for chamber seasoning in semiconductor fabrication facilities require unnecessary quantities of wafers and cause delays due to reliance on test samples for determining chamber recovery, leading to reduced productivity and inefficient maintenance.
Innovation Solution
A method and system utilizing a machine learning model to monitor and predict chamber conditions post-preventative maintenance, enabling in-situ monitoring and simulation of chamber conditions without relying on test samples, thereby optimizing the seasoning process and reducing downtime.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If chamber seasoning is performed using conventional test sample methods, then chamber recovery can be determined, but unnecessary quantities of wafers are consumed and delays occur
Solution Approach 1:
The patent replaces the mechanical/physical test sample processing method with an optical monitoring system using sensors and image processing. The system captures images of the chamber interior during seasoning and uses computer vision algorithms to detect when the chamber wall coating reaches the desired state, eliminating the need for separate test wafer processing to determine chamber readiness.
Solution Approach 2:
The patent creates a visual copy or representation of the chamber interior state through imaging sensors and image processing. Instead of physically testing with wafers, the system creates a digital model of the chamber wall coating progression and uses this copy to determine when seasoning is complete, reducing material consumption and time.
2Reliability
If chamber seasoning is performed using conventional test sample methods, then chamber recovery can be determined, but downtime increases due to delays
Solution Approach 1:
The patent enables continuous monitoring of the chamber seasoning process through imaging sensors that capture the chamber interior at multiple time points. This continuous visual feedback allows the system to determine chamber readiness without interrupting the seasoning process for test wafer processing, maintaining continuous useful action and reducing downtime.
Solution Approach 2:
The patent implements a feedback system where images of the chamber interior are continuously captured and analyzed to monitor the progression of chamber wall coating during seasoning. This real-time feedback allows for dynamic adjustment and early termination of seasoning when the desired state is reached, eliminating the fixed-time or test-sample-based approach that causes delays.
3Loss of information
If conventional chamber monitoring methods are used, then chamber conditions can be observed, but unnecessary seasoning cycles occur reducing efficiency
Solution Approach 1:
The patent replaces conventional indirect monitoring methods with direct optical imaging and computer vision analysis. The system captures images of the chamber interior and uses image processing algorithms to directly measure and quantify the chamber wall coating thickness and uniformity, providing precise information that prevents unnecessary seasoning cycles.
Data Source
AI summary
The subject matter of this specification can be implemented in, among other things, methods, systems, computer-readable storage medium. A method can include a processing device receiving sensor data indicating a state of an environment of a processing chamber processing a series of substrates according to a substrate processing procedure. The series of substrates may be processed subsequent to a preventive maintenance procedure. The processing device may determine a first set of values based on the sensor data. The set of values may indicate a likelihood an associated substrate comprises a first process result that meets a threshold condition of the substrate processing procedure. The processing device may further predict a first test result based on the first set of values. The first test result may indicate a likelihood a first substrate processed subsequent to processing the series of substrates comprises a second process result that meets the threshold condition.