Radical Sensor Substrate for In-Situ Plasma Concentration Sensing
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Solution Overview
Problem
Current semiconductor processing tools lack the ability to quantify radical species concentration in-situ, limiting the refinement of processing conditions and chamber matching.
Innovation Solution
The implementation of sensor substrates with catalytic and non-catalytic temperature sensors, coupled with AI/ML models, to measure radical species concentration and other process parameters in-situ, enabling precise control of semiconductor processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If test substrates are run to determine radical species concentration, then radical species concentration can be determined, but only the end effect of the radical species can be determined, not the radical species themselves
Solution Approach 1:
The patent introduces an intermediary substance (e.g., oxygen, nitrogen, or hydrogen gas) that reacts with radical species in a controlled manner to produce detectable signals. This intermediary acts as a mediator between the radical species and the detection system, allowing direct measurement of radical species concentration and composition without relying solely on end-effect measurements from test substrates.
2Manufacturing precision
If multiple sensors are integrated on a substrate to measure process parameters, then process uniformity and chamber matching are improved, but device complexity increases
Solution Approach 1:
The patent combines multiple different types of sensors (temperature sensors, pressure sensors, radical species sensors) onto a single substrate, merging their functions into one integrated device. This consolidation reduces the number of separate components needed, simplifies installation, and enables simultaneous measurement of multiple process parameters to improve process uniformity and chamber matching.
Solution Approach 2:
The sensor substrate is designed with multi-functionality, capable of measuring various process parameters (temperature, pressure, radical species concentration) simultaneously. This universal sensor platform can be used across different semiconductor processing tools and applications, reducing the need for tool-specific sensor configurations and simplifying overall system complexity.
3Manufacturing precision
If AI/ML models are used to control process parameters based on sensor readings, then processing precision is improved, but system complexity increases
Solution Approach 1:
The patent implements a feedback control system where sensors continuously monitor process parameters (temperature, pressure, radical species concentration) and provide real-time data to AI/ML models. These models analyze the feedback data and automatically adjust process parameters to maintain optimal processing conditions, improving precision while managing complexity through automated closed-loop control.
Solution Approach 2:
The AI/ML models utilize parameter changes in real-time, dynamically adjusting process parameters based on sensor readings and learned patterns. This approach enables precise control of semiconductor processing by continuously optimizing parameters such as gas flow rates, power levels, and temperature based on actual process conditions rather than fixed preset values.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables accurate measurement of radical species concentration and other process parameters, improving process uniformity and chamber matching by using sensor substrates with integrated temperature sensors and AI/ML models.
Implementation Method 1
a first sensor of a first type on the substrate, where a catalytic layer is provided as at least part of the first sensor
Implementation Method 2
obtaining temperature readings with the first sensor and the second sensor, and calculating a radical density of the plasma from a difference in the temperature readings
Data Source
AI summary
Embodiments disclosed herein include sensor devices. In an embodiment, a sensor device comprises a substrate, a first sensor of a first type on the substrate, where a catalytic layer is provided as at least part of the first sensor, a second sensor of the first type on the substrate and adjacent to the first sensor, and a lid over the substrate, where an opening through the lid is provided over the first sensor and the second sensor.