Mass Flow Controller Estimation Without a Separate Restrictor
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Solution Overview
Problem
Conventional mass flow controllers face challenges in accurately estimating fluid flow rates, especially in ultra-high-precision applications like semiconductor manufacturing, due to the complexity of fluid dynamics and the introduction of a restrictor which increases manufacturing complexity and reduces response time.
Innovation Solution
A mass flow controller design without a separate restrictor, utilizing a valve as the flow control mechanism, equipped with sensors for temperature and pressure measurements, and a processor applying a trained machine learning model to estimate flow rates based on valve position, temperature, and pressure data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a separate restrictor is introduced to control flow rate, then flow control precision is improved, but device complexity and manufacturing cost increase
Solution Approach 1:
The patent combines the restrictor function with the valve by providing a valve body with a controllable opening area, eliminating the need for a separate restrictor component. The valve body directly controls flow rate through its opening area, which is adjusted by the drive mechanism, thereby simplifying the device structure while maintaining flow control precision.
Solution Approach 2:
The valve body serves multiple functions: it acts as both the flow control element and the restrictor. By controlling the opening area of the valve body, the system achieves both flow regulation and flow restriction functions that were previously required separate components, reducing overall device complexity.
2Measurement precision
If a separate restrictor is introduced to control flow rate, then flow control precision is improved, but response time decreases
Solution Approach 1:
The patent merges the restrictor function into the valve body, eliminating the need for flow to pass through a separate restrictor component. This direct control approach reduces the number of flow path transitions and eliminates delays associated with separate restriction elements, thereby improving response time while maintaining precision through the controllable opening area.
Solution Approach 2:
The valve opening area is dynamically adjustable through the drive mechanism, allowing real-time control of flow rate without the fixed restriction characteristics of a separate restrictor. This dynamic control enables faster response to changing flow requirements while maintaining precise flow control through programmed opening area adjustments.
3Measurement precision
If machine learning model is applied to estimate flow rate, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent replaces traditional mechanical flow measurement methods with a machine learning-based estimation system. The processor applies a trained machine learning model to sensor data (pressure, temperature, valve opening area) to estimate flow rate, substituting complex physical measurement mechanisms with computational intelligence that achieves higher precision without additional mechanical complexity.
Solution Approach 2:
The machine learning model acts as an intermediary between the physical flow system and the measurement output. It processes sensor data through learned relationships to produce accurate flow rate estimates, bridging the gap between simple sensor measurements and precise flow determination without requiring complex direct measurement hardware.
Data Source
AI summary
A mass flow controller, including a block body having a flow path and a valve provided at least in part within the flow path. The mass flow controller may further include a valve position sensor, a first temperature sensor, a first pressure sensor located on an upstream side of the flow path from the valve, and a second pressure sensor located on a downstream side of the flow path from the valve. The mass flow controller may further include a processor configured to receive valve position data, first temperature data, first pressure data, and second pressure data. The processor may be further configured to estimate a flow rate of fluid in the flow path at least in part by applying a trained machine learning model to the valve position data, the first temperature data, the first pressure data, and the second pressure data.