Automated Endpoint Algorithm Engine for Plasma Etching
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Solution Overview
Problem
The manual process of identifying endpoint signatures in plasma processing is time-consuming and prone to errors due to the overwhelming volume of data from sensors, requiring expert knowledge and often resulting in suboptimal endpoint detection.
Innovation Solution
An automated algorithm engine analyzes sensor data to define an approximate endpoint period, identify potential endpoint signatures, and generate optimal endpoint algorithms, capable of handling multiple substrates and reducing noise, with minimal human intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual expert analysis is used to identify endpoint signatures, then detection accuracy may be improved, but time consumption increases significantly
Solution Approach 1:
The system performs self-service through automated algorithm construction. The endpoint detection algorithm automatically analyzes sensor data, identifies signatures, and optimizes parameters without requiring manual expert intervention, thereby reducing time consumption while maintaining detection accuracy
Solution Approach 2:
The patent replaces the manual mechanical analysis process with an automated computational system. The algorithm engine substitutes human experts by automatically processing sensor data, identifying endpoint signatures, and constructing detection algorithms, thus eliminating time-consuming manual operations
2Measurement precision
If comprehensive sensor data from all channels is analyzed, then detection accuracy improves, but data complexity and processing difficulty increase
Solution Approach 1:
The algorithm extracts only the relevant endpoint signature information from the comprehensive sensor data. By identifying and isolating the specific signals and patterns that indicate endpoint conditions, the system filters out unnecessary data complexity while maintaining detection accuracy
Solution Approach 2:
The patent segments the complex sensor data into manageable components for analysis. The algorithm divides the data processing into distinct stages: data acquisition, preprocessing, signature identification, and validation, making the complex analysis task tractable and systematic
3Reliability
If manual expert knowledge is required for algorithm construction, then detection reliability improves, but ease of operation deteriorates
Solution Approach 1:
The system automatically acquires and processes sensor data, identifies endpoint signatures, and constructs detection algorithms without requiring user expertise. This self-service capability maintains reliability through automated validation while significantly improving ease of operation for non-expert users
Solution Approach 2:
The patent introduces an intermediary algorithm engine that bridges the gap between raw sensor data and reliable endpoint detection. This intermediary automatically performs the complex analysis tasks that would otherwise require expert knowledge, making the system accessible to non-experts while maintaining detection reliability
Data Source
AI summary
A method for automatically identifying an optimal endpoint algorithm for qualifying a process endpoint during substrate processing within a plasma processing system is provided. The method includes receiving sensor data from a plurality of sensors during substrate processing of at least one substrate within the plasma processing system, wherein the sensor data includes a plurality of signal streams from a plurality of sensor channels. The method also includes identifying an endpoint domain, wherein the endpoint domain is an approximate period within which the process endpoint is expected to occur. The method further includes analyzing the sensor data to generate a set of potential endpoint signatures. The method yet also includes converting the set of potential endpoint signatures into a set of optimal endpoint algorithms. The method yet further includes importing one optimal endpoint algorithm of the set of optimal endpoint algorithms into production environment.