Susceptor Edge Defect Detection via Image Analytics
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Solution Overview
Problem
Conventional manual inspection of substrate processing equipment parts is time-consuming, inaccurate, and subjective, leading to potential production of defective substrates and unnecessary maintenance.
Innovation Solution
A method and system that utilize image analytics to identify and predict defects in the edges of susceptor pockets by analyzing images captured from substrate processing equipment, and subsequently trigger corrective actions such as cleaning, repair, or replacement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual inspection is used to inspect susceptor edges, then inspection can be performed, but it is time-consuming and inaccurate
Solution Approach 1:
The patent replaces manual mechanical inspection with an automated image analytics system that uses cameras to capture images of susceptor edges and processing devices to analyze them. This substitution eliminates human labor while improving both speed and consistency of inspection, directly resolving the contradiction between inspection accuracy and time consumption.
Solution Approach 2:
The system creates digital copies (images) of the susceptor edges and analyzes these copies rather than physically inspecting the actual edges. This copying approach allows for rapid, repeated inspections without contacting the physical object, thereby reducing inspection time while maintaining measurement precision through digital image analysis.
2Reliability
If manual inspection is used, then inspection can be performed, but it is subjective and leads to unnecessary maintenance
Solution Approach 1:
The image analytics system provides objective, data-driven feedback about susceptor edge conditions through quantitative analysis of images. This feedback mechanism eliminates subjectivity by using measurable criteria (pixel analysis, edge detection algorithms) to determine condition thresholds, thereby improving inspection consistency and enabling maintenance decisions based on actual measured conditions rather than operator judgment.
Solution Approach 2:
By replacing subjective human judgment with automated computational analysis, the system eliminates variability in inspection consistency. The processing devices use standardized algorithms to analyze images, ensuring that inspection results are reproducible and objective, thus improving reliability while optimizing maintenance efficiency through data-driven decision-making.
3Manufacturing precision
If conventional inspection methods are used, then equipment can be monitored, but manufacturing precision is compromised
Solution Approach 1:
The system creates digital images as copies of the susceptor edges and performs analysis on these images rather than direct physical measurement. This approach simplifies the inspection process while maintaining manufacturing precision, as digital image analysis can detect subtle edge variations and defects that directly impact substrate quality without requiring complex physical measurement equipment.
Solution Approach 2:
The patent introduces digital images as an intermediary between the physical susceptor edges and the analysis process. Instead of directly measuring the edges with complex equipment, the system captures optical images and uses processing algorithms to extract relevant information, thereby reducing device complexity while maintaining or improving manufacturing precision through non-contact analysis.
Data Source
AI summary
A method includes identifying one or more images of an edge of a susceptor pocket formed in an upper surface of a susceptor of a substrate processing system. An angle identification component is disposed in the susceptor pocket. The method further includes causing, based on the one or more images, performance of an action associated with the susceptor.