Plasma Chamber Temperature Estimation Using Time-Series Process Data
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Solution Overview
Problem
In semiconductor fabrication processes, accurately estimating temperature inside a plasma processing space is challenging due to changes in conditions, leading to reduced estimation accuracy when direct temperature measurement is not feasible.
Innovation Solution
A temperature estimation apparatus that uses a time series model generated from historical process and temperature data to estimate temperature changes within the plasma processing space, allowing for real-time monitoring and adjustment of plasma processing conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If indirect temperature estimation is used in plasma processing, then temperature measurement becomes feasible without installing sensors inside the processing space, but estimation accuracy deteriorates when conditions inside the processing space change
Solution Approach 1:
The system performs preliminary actions by acquiring process conditions before plasma processing starts and using this data to generate or update a time series model in advance. This preliminary modeling enables accurate temperature estimation during processing without requiring real-time sensor installation, thus resolving the contradiction between measurement feasibility and accuracy.
Solution Approach 2:
The system implements feedback by successively inputting time series process data during plasma processing and continuously updating temperature estimates. The model learns from actual process data and adjusts predictions in real-time, maintaining high accuracy even when conditions change, while still avoiding the need for physical temperature sensors inside the processing space.
2Device complexity
If process conditions are kept constant for temperature estimation, then estimation simplicity is maintained, but adaptability to changing conditions inside the processing space is reduced
Solution Approach 1:
The system applies dynamics by using a time series model that can adapt to changing conditions. Instead of relying on static process conditions, the model processes time series data that captures dynamic changes in the plasma processing environment. This allows the estimation system to remain relatively simple in structure while being highly adaptive to condition changes through its ability to process sequential data.
Solution Approach 2:
The system implements parameter changes by utilizing time series process data that captures variations in processing conditions over time. The model learns relationships between changing parameters and temperature, enabling it to adapt to different processing scenarios without requiring a completely different estimation approach for each condition change.
Data Source
AI summary
A temperature estimation apparatus includes an estimation unit configured to successively estimate temperature data by successively inputting given time series process data relating to conditions inside a processing space in which plasma processing is performed, into a time series model generated in advance that correlates data values, in each time period, of time series process data relating to conditions inside the processing space, with a data value, at a respective time point, of time series temperature data measured inside the processing space.