Gas Delivery Network Feedback Control for Precise Precursor Flow
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Solution Overview
Problem
Existing substrate processing systems face challenges in accurately delivering gases and precursors to processing chambers, leading to inefficiencies and potential hardware issues due to unoptimized delivery settings.
Innovation Solution
Implementing a method that measures gas and precursor parameters using sensors within the delivery systems, and adjusts delivery settings based on model parameter values to achieve optimal gas and precursor delivery settings (GDSS and PDSS) to ensure precise control and maintenance of model parameter values (gas MPV and precursor MPV).
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional gas delivery systems operate without real-time monitoring and adjustment, then the system complexity is reduced, but the manufacturing precision and reliability of substrate processing deteriorate due to unoptimized delivery settings
Solution Approach 1:
The patent implements feedback control by continuously measuring gas flow rates and precursor delivery parameters using sensors, comparing these measurements against model parameter values (MPV), and automatically adjusting delivery settings to achieve optimal values. This closed-loop feedback mechanism ensures precise gas delivery while maintaining consistent substrate processing quality.
Solution Approach 2:
The patent replaces manual or open-loop mechanical control of gas delivery with an automated control system that uses sensors, processors, and actuators. The system substitutes mechanical adjustment procedures with electronic control based on real-time measurements and pre-determined model parameters, improving precision without proportionally increasing mechanical complexity.
2Reliability
If gas delivery settings are optimized using real-time measurement and adjustment, then the reliability of substrate processing is improved, but the device complexity increases due to additional sensors and control mechanisms
Solution Approach 1:
The feedback control system continuously monitors gas flow rates and precursor delivery parameters, compares them against model parameter values, and automatically adjusts delivery settings to maintain optimal conditions. This ensures reliable substrate processing by detecting and correcting deviations from desired parameters in real-time, improving consistency and reducing defects.
Solution Approach 2:
The system uses pre-determined model parameter values (MPV) that represent optimal delivery conditions for different substrate processing scenarios. By having these optimal parameters established beforehand, the system can quickly adjust to maintain reliability without requiring complex real-time optimization algorithms, thereby reducing control system complexity.
3Productivity
If model parameter values are used to guide delivery settings, then the productivity of substrate processing is improved through optimized delivery, but the measurement precision requirements increase to accurately detect parameter deviations
Solution Approach 1:
The system monitors and adjusts critical parameters such as gas flow rate, temperature, and precursor delivery rate to maintain them at optimal model parameter values. By focusing measurement and control efforts on these key parameters rather than all possible variables, the system achieves high productivity with manageable measurement precision requirements.
Solution Approach 2:
The feedback mechanism compares measured parameters against model parameter values and triggers adjustments only when deviations exceed acceptable thresholds. This selective feedback approach allows the system to maintain high productivity while tolerating moderate measurement noise, as long as the measurement precision is sufficient to detect meaningful deviations from optimal parameters.
Data Source
AI summary
A method of substrate processing includes delivering one or more precursors from a precursor delivery system to the processing chamber according to a first precursor delivery system setting (PDSS). A precursor parameter is measured using a sensor disposed within the precursor delivery system. The method also includes determining a second PDSS based upon a comparison between a precursor model parameter value (precursor MPV) and the measured precursor parameter, wherein the second PDSS is selected to achieve the precursor MPV. Thereafter, the one or more precursors are delivered according to the second PDSS.