Substrate Treatment Module Monitoring for Predictive Maintenance
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Solution Overview
Problem
In semiconductor manufacturing, modules such as mass flow controllers and RF generators deteriorate over time, leading to production stops and repetitive maintenance needs, as existing methods fail to predict and prevent module failures effectively.
Innovation Solution
A predictive maintenance method and device that determines whether analog data from substrate treatment exceeds predetermined thresholds, notifying users of module deterioration and allowing for proactive measures, using a system comprising a Unique Platform Controller, Process Module Controller, and storage medium to perform a learning phase and monitoring phase to establish allowable thresholds and associate data with related modules.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If modules are simply repaired or replaced when they deteriorate, then the module can be restored to working condition, but the same deterioration process repeats and production stops occur
Solution Approach 1:
The system performs preliminary actions by continuously monitoring analog data from modules and comparing it against thresholds determined during a learning phase. When deterioration trends are detected before complete failure, the system notifies operators to perform maintenance proactively, preventing production stops and breaking the cycle of repetitive deterioration and repair.
Solution Approach 2:
The system establishes a feedback loop by continuously acquiring analog data from modules, comparing it against predetermined thresholds, and providing notifications when deterioration is detected. This feedback mechanism enables operators to take corrective actions based on real-time module status, improving reliability and preventing production interruptions.
2Reliability
If maintenance is performed frequently to prevent module failure, then module reliability improves, but system downtime increases due to unnecessary maintenance
Solution Approach 1:
The system changes the parameter of maintenance timing from fixed-schedule to condition-based. By monitoring analog data parameters and comparing against dynamically determined thresholds, the system identifies when actual deterioration occurs, enabling maintenance to be performed only when necessary. This reduces unnecessary maintenance downtime while maintaining high reliability.
Solution Approach 2:
The learning phase performs preliminary action by establishing baseline thresholds for normal module operation. During the monitoring phase, the system compares actual analog data against these predetermined thresholds, enabling early detection of deterioration trends. This allows maintenance to be scheduled optimally—neither too early nor too late—minimizing downtime while preventing failures.
3Reliability
If analog data monitoring and threshold comparison systems are implemented, then early detection of module deterioration is enabled, but system complexity increases
Solution Approach 1:
The system enables self-service by automatically acquiring analog data from modules, comparing it against predetermined thresholds, and generating notifications without requiring complex external analysis systems. The controller itself performs the monitoring and comparison functions, simplifying the overall system architecture while maintaining high deterioration detection capability.
Solution Approach 2:
The learning phase performs preliminary action by automatically determining appropriate thresholds for each module and recipe combination. These predetermined thresholds are stored and reused during normal operation, eliminating the need for complex real-time threshold calculation algorithms and reducing system complexity during the monitoring phase.
4Reliability
If module deterioration is detected early, then proactive maintenance can be performed, but additional monitoring and analysis resources are required
Solution Approach 1:
The controller performs multiple functions using the same hardware resources: it controls substrate treatment processes, acquires analog data from modules, compares data against thresholds, and generates maintenance notifications. This multi-functionality eliminates the need for separate dedicated monitoring hardware, reducing resource requirements while enabling early deterioration detection.
Solution Approach 2:
The system uses existing analog data already being collected during normal substrate treatment operations for monitoring purposes. By repurposing data that would otherwise be discarded, the system enables deterioration detection without requiring additional sensors or data collection infrastructure, minimizing additional resource requirements.
Data Source
AI summary
Examples of a predictive maintenance method includes determining whether analog data measured in a substrate treatment that has used a recipe exceeds an allowable threshold which corresponds to the recipe and has been determined beforehand, and notifying, in a case where it is determined that the analog data exceeds the allowable threshold in the determination, a user that a relating module which has been associated with the analog data beforehand has deteriorated.