AMC Source Identification via CFD Inverse Modeling
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Solution Overview
Problem
The existing methods for identifying airborne molecular contamination (AMC) sources in semiconductor manufacturing are time-consuming and resource-intensive, leading to slow response times when AMC sensors alarm, potentially disrupting the manufacturing process.
Innovation Solution
A method involving distributed AMC sensors connected to a monitoring system, utilizing computational fluid dynamics (CFD) simulations to optimize sensor layout and perform inverse modeling, creating a spatial response probability distribution matrix to quickly identify and locate AMC sources, thereby reducing the time and resources required to address contamination issues.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional manual methods are used to identify AMC leakage sources, then measurement precision can be maintained, but loss of time and productivity deteriorate significantly
Solution Approach 1:
The patent replaces manual mechanical inspection methods with computational fluid dynamics (CFD) simulation and data processing systems. The system automatically processes sensor data, performs CFD simulations to model contamination flow patterns, and identifies leakage sources through algorithmic analysis, eliminating the need for manual time-consuming inspection while maintaining or improving identification accuracy.
Solution Approach 2:
The patent introduces CFD simulation as an intermediary between AMC sensor detections and source identification. The CFD model acts as a virtual mediator that traces contamination pathways from sensor locations back to potential leakage sources, enabling rapid identification without direct manual investigation of each potential source.
2Measurement precision
If more AMC sensors are distributed in the fab, then measurement precision and source location capability improve, but device complexity and cost increase
Solution Approach 1:
The patent performs preliminary CFD simulations to optimize sensor placement before actual deployment. By pre-calculating the most effective sensor locations based on simulated contamination flow patterns, the system achieves high source identification accuracy with a minimized sensor network, avoiding the need for dense sensor distribution and reducing overall system complexity.
Solution Approach 2:
The patent creates a multi-functional system where the same CFD simulation framework serves multiple purposes: optimizing sensor layout, identifying leakage sources, and analyzing contamination pathways. This universal approach allows the system to achieve high measurement precision without proportionally increasing complexity, as the same computational tools handle multiple tasks.
Data Source
AI summary
The present disclosure provides a method of identifying an airborne molecular contamination (AMC) leaking source in a fab. The method includes distributing a sensor in the fab, executing a forward computational fluid dynamics (CFD) simulation of an air flow in the fab, setting an inversed modeling of the forward CFD simulation of the air flow in the fab, building up a database of a spatial response probability distribution matrix of the sensor using an AMC measurement data in the fab, and identifying the AMC leaking source using the database of the spatial response probability distribution matrix of the sensor.