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Poster Session I

4:30 pm – 6:30 pm, Tuesday October 14 Session DT4 COEX, Lobby E
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Data-Driven Approach to Plasma Electron Temperature Estimation Using VI Probe Harmonics

Poster 43
Presenter: Yonghyun Kim (Korea Institute of Fusion Energy)
Author: Jong-Sik Kim (Korea Institute of fusion energy)

This study presents a machine learning approach to predict plasma electron temperature (𝑇ₑ) using harmonic data from a VI-probe, typically used for RF power measurements. Experiments were conducted in an Inductively Coupled Plasma (ICP) reactor with argon gas, under RF power from 50 to 1000 W and pressure between 5 and 100 mTorr. Voltage, current, and phase of the 13.56 MHz RF signal were measured up to the 15th harmonic using a VI-probe, while 𝑇ₑ was measured simultaneously with a Langmuir probe. The VI-probe data served as input features, and 𝑇ₑ as the target variable, for training regression models including linear regression and random forest. Performance was evaluated using RMSE, MSE, and 𝑅ÂČ. Feature importance analysis identified key contributors to prediction. The models achieved high accuracy with 𝑅ÂČ exceeding 0.9, though overfitting was observed due to limited training data. These findings demonstrate the potential of using VI-probe data for reliable, real-time, and automated plasma diagnostics, without the need for complex sensor systems

Funding acknowledgement

This work was supported by the National Research Council of Science & Technology(NST) grant by the Korea government(MSIT)(No.CRC-20-01-NFRI).

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