  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\textsubscript{2}, CF\textsubscript{4}/O\textsubscript{2} and
SF\textsubscript{6}/O\textsubscript{2} 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.
