2023/06/26 by Gregory A. Daly, Daly, Gregory A., Jonathan E. Fieldsend +5 · 1 citation
Computer Science · Engineering · #Building Energy and Comfort Optimization #FOS: Physical sciences #Generative Adversarial Networks and Image Synthesis #Plasma Physics (physics.plasm-ph)
paper · pdf · doi:10.48550/arxiv.2306.14785
openalex publication_date 2023/06/26 · openalex created_date 2023/06/28 · openalex updated_date 2026/07/28
We have developed a deep generative model that can produce accurate optical emission spectra and colour images of an ICP plasma using only the applied coil power, electrode power, pressure and gas flows as inputs -- essentially an empirical surrogate collisional radiative model. An autoencoder was trained on a dataset of 812,500 image/spectra pairs in argon, oxygen, Ar/O\textsubscript2, CF\textsubscript4/O\textsubscript2 and SF\textsubscript6/O\textsubscript2 plasmas in an industrial plasma etch tool, taken across the entire operating space of the tool. The autoencoder learns to encode the input data into a compressed latent representation and then decode it back to a reconstruction of the data. We learn to map the plasma tool's inputs to the latent space and use the decoder to create a generative model. The model is very fast, taking just over 10 s to generate 10,000 measurements on a single GPU. This type of model can become a building block for a wide range of experiments and simulations. To aid this, we have released the underlying dataset of 812,500 image/spectra pairs used to train the model, the trained models and the model code for the community to accelerate the development and use of this exciting area of deep learning. Anyone can try the model, for free, on Google Colab.