Deep neural network and meta-learning-based reactive sputtering with small data sample counts

Although several studies have focused on the application of deep-learning techniques in manufacturing processes, the lack of relevant datasets remains a major challenge. Hence, this paper presents a meta-learning approach to resolve the few-shot regression problem encountered in manufacturing applications. The proposed approach is based on data augmentation using conventional regression models and optimization-based meta-learning. The resulting deep neural network can be employed to optimize the reactive-sputtering process used in the fabrication of thin, compounded films of titanium and nitride. The performance of the proposed meta learning approach is compared to the conventional regression models, including support vector regression, Bayesian ridge regression, and Gaussian process regression, which exhibit state-of-the-art performance for regression over small data sample counts. The proposed meta-learning approach outperformed the baseline regression models when tested by varying the training sample counts from 5 to 40, resulting in a decrease in the root mean square error to 74.6% of that observed in the conventional models to predict the stoichiometric ratio of the film produced during the reactive sputtering process. This is remarkable because regression performed over a small number of data is usually considered unsuitable for deep-learning approaches. Therefore, this approach exhibits considerable potential for usage in different manufacturing applications because of its capability to handle a range of dataset sizes.