Semiconductor Tool Digital Twin for Predictive Part Ordering
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Solution Overview
Problem
Current semiconductor manufacturing processes lack effective methods for predicting part failures and maintenance needs, leading to inefficient maintenance schedules and increased operational costs.
Innovation Solution
An information processing system that utilizes a simulation model to analyze physical sensor data from semiconductor manufacturing apparatuses, comparing it with virtual sensor data to predict part failures and maintenance needs, and automatically orders replacement parts based on these predictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If maintenance is performed based on fixed schedules or reactive approaches, then operational simplicity is maintained, but part failures occur leading to increased downtime and costs
Solution Approach 1:
The system performs preliminary actions by predicting part failures before they occur using simulation models. The simulation execution unit runs models that forecast part degradation and failure risks, enabling maintenance to be scheduled proactively rather than reactively, thus improving reliability while maintaining manageable complexity
Solution Approach 2:
The system creates a virtual copy of the semiconductor manufacturing apparatus through simulation models. This digital twin (virtual sensor data) is compared with actual sensor data to predict part failures without requiring complex physical monitoring systems, resolving the contradiction between reliability improvement and system complexity
2Loss of time
If simulation models are used to predict part failures, then maintenance timing is optimized, but computational resources and processing time increase
Solution Approach 1:
The system applies partial action by focusing simulation computations only on critical parts identified through initial assessment. The simulation execution unit selectively models degradation processes for specific high-risk components rather than entire apparatuses, reducing computational energy consumption while still minimizing operational downtime through targeted predictions
Solution Approach 2:
The system performs preliminary computational analysis to identify which parts require detailed simulation modeling. By pre-screening components and only applying full simulation to those with highest failure risk, the system reduces overall computational energy consumption while maintaining effective downtime reduction through timely failure predictions
3Measurement precision
If physical sensor data is continuously monitored and compared with virtual sensor data, then part failure detection accuracy improves, but data processing complexity increases
Solution Approach 1:
The system extracts and compares only the critical parameters between physical sensor data and virtual sensor data. The simulation result determination unit identifies key degradation indicators from the simulation output and focuses comparison on these specific parameters rather than all available data, improving failure detection accuracy while keeping data processing complexity manageable
Solution Approach 2:
The system applies different analysis depths to different parts of the data. Critical parameters showing larger deviations between physical and virtual sensor data receive more detailed analysis, while parameters within acceptable ranges receive minimal processing. This localized quality approach improves detection accuracy for critical failures without uniformly increasing data processing complexity across all parameters
Data Source
AI summary
An information processing apparatus executes a simulation of a state of a process which is being performed in a semiconductor manufacturing apparatus, by using a simulation model of the semiconductor manufacturing apparatus. The information processing apparatus includes: a physical sensor data acquisition unit that acquires physical sensor data measured in the semiconductor manufacturing apparatus that is performing the process according to process parameters; a simulation execution unit that executes the simulation by the simulation model according to the process parameters, thereby outputting virtual sensor data; a simulation result determination unit that performs a pre-detection of a part of the semiconductor manufacturing apparatus that needs to be replaced, based on a difference between the physical sensor data and the virtual sensor data; and a part order unit that orders the part of the semiconductor manufacturing apparatus based on a result of the pre-detection.