Semiconductor Tool Diagnostics Using Soundness Indicators
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Solution Overview
Problem
Existing apparatus diagnostic systems for plasma etching apparatuses are inflexible and require modifications when new components or sensors are added, and lack the ability to analyze conditions using arbitrary triggers, leading to inefficient maintenance and prolonged downtime.
Innovation Solution
A system that collects sensor data from plasma etching apparatuses via a LAN and executes registered analysis algorithms without modifying the system program, using arbitrary triggers to calculate soundness indicators and perform predictive maintenance with AI-based learning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the system is modified one by one for each new component or sensor added to the apparatus, then the system can accommodate new components, but the rate of operation decreases due to frequent modifications and maintenance interruptions
Solution Approach 1:
The patent implements a universal data collection framework that can accommodate any sensor or component through standardized interfaces. The system uses a common data structure and analysis algorithm that works with diverse sensor types without requiring system modifications, enabling the system to handle new components while maintaining continuous operation and high productivity.
2Reliability
If non-regular maintenance work is performed due to unexpected malfunctions, then apparatus reliability is maintained, but downtime increases significantly due to resource adjustment and part procurement
Solution Approach 1:
The patent implements continuous monitoring and predictive analysis that detects component degradation trends before failure occurs. By analyzing sensor data patterns and predicting potential malfunctions in advance, the system enables scheduled maintenance during planned downtime, preventing unexpected breakdowns and eliminating the need for urgent resource adjustment and part procurement.
Solution Approach 2:
The system continuously collects sensor data, analyzes it using predefined algorithms, and provides feedback on component health status. This feedback mechanism enables early detection of degradation patterns, allowing maintenance to be scheduled at optimal times rather than responding to failures, thereby reducing unplanned downtime while maintaining reliability.
3Ease of operation
If the analysis trigger is limited to recipe information only, then the analysis system is simple to operate, but the measurement precision of component state evaluation is insufficient
Solution Approach 1:
The patent implements a dynamic trigger system that automatically selects analysis triggers based on the specific component being evaluated and its operational characteristics. The system can switch between recipe-based triggers, time-based triggers, and event-based triggers depending on the component type, providing both ease of operation through automation and high measurement precision through appropriate trigger selection.
Data Source
AI summary
An apparatus diagnostic system for diagnosing conditions of a semiconductor manufacturing apparatus includes an apparatus diagnostic apparatus that outputs soundness indicators by a first algorithm with sensor data collected from the semiconductor manufacturing apparatus as an input to the first algorithm, outputs threshold spatial data under normal conditions of the semiconductor manufacturing apparatus by a second algorithm with the soundness indicators as an input to the second algorithm, and diagnoses conditions of the semiconductor manufacturing apparatus by a third algorithm with the soundness indicators and the threshold spatial data as an input to the third algorithm. The soundness indicators are indicators concerning a degree of soundness of conditions of the semiconductor manufacturing apparatus.