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Simulations of thin film sputter deposition require the separation of theplasma and material transport in the gas-phase from the growth/sputteringprocesses at the bounding surfaces. Interface models based on analyticexpressions or look-up tables inherently restrict this complex interaction to abare minimum. A machine learning model has recently been shown to overcome thisremedy for Ar ions bombarding a Ti-Al composite target. However, the chosennetwork structure (i.e., a multilayer perceptron) provides approximately 4million degrees of freedom, which bears the risk of overfitting the relevantdynamics and complicating the model to an unreliable extend. This work proposesa conceptually more sophisticated but parameterwise simplified regressionartificial neural network for an extended scenario, considering a variableinstead of a single fixed Ti-Al stoichiometry. A convolutional$\beta$-variational autoencoder is trained to reduce the high-dimensionalenergy-angular distribution of sputtered particles to a latent spacerepresentation of only two components. In addition to a primary decoder whichis trained to reconstruct the input energy-angular distribution, a secondarydecoder is employed to reconstruct the mean energy of incident Ar ions as wellas the present Ti-Al composition. The mutual latent space is hence conditionedon these quantities. The trained primary decoder of the variational autoencodernetwork is subsequently transferred to a regression network, for which only themapping to the particular latent space has to be learned. While obtaining acompetitive performance, the number of degrees of freedom is drasticallyreduced to 15,111 and 486 parameters for the primary decoder and the remainingregression network, respectively. The underlying methodology is general and caneasily be extended to more complex physical descriptions with a minimal amountof data required.

An efficient plasma-surface interaction surrogate model for sputtering processes based on autoencoder neural networks