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We have developed a deep generative model that can produce accurate opticalemission spectra and colour images of an ICP plasma using only the applied coilpower, electrode power, pressure and gas flows as inputs -- essentially anempirical surrogate collisional radiative model. An autoencoder was trained ona dataset of 812,500 image/spectra pairs in argon, oxygen,Ar/O\textsubscript{2}, CF\textsubscript{4}/O\textsubscript{2} andSF\textsubscript{6}/O\textsubscript{2} plasmas in an industrial plasma etchtool, taken across the entire operating space of the tool. The autoencoderlearns to encode the input data into a compressed latent representation andthen decode it back to a reconstruction of the data. We learn to map the plasmatool's inputs to the latent space and use the decoder to create a generativemodel. The model is very fast, taking just over 10 s to generate 10,000measurements on a single GPU. This type of model can become a building blockfor a wide range of experiments and simulations. To aid this, we have releasedthe 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 thedevelopment and use of this exciting area of deep learning. Anyone can try themodel, for free, on Google Colab.

Data-driven plasma modelling: Surrogate collisional radiative models of fluorocarbon plasmas from deep generative autoencoders