Deep learning via CNN for identification of blue core phenomenon in helicon plasma discharge

Abstract

Based on deep learning image recognition techniques, a convolutional neural network model for discharge mode recognition of helicon plasma was trained. The accuracy of the model was evaluated using functions such as F1-scores and the confusion matrix. The final recognition accuracy was more than 98.18% after 30 iterations. Interpretable analysis was done using methods such as gradient-weighted class activation mapping to verify the model's robustness as well as repeatability. The model identification results were compared with Langmuir probe diagnostic results. It was found a good fit between the model and the probe results, corroborating the correctness of the model. The present model can well identify the critical power of entering W mode in the discharge process of helicon plasma. As the discharge database expands, it has great potential for recognizing the higher-order discharge modes based on deep learning.