Generating TCN Models From Parallel Devs Models: Semiconductor Manufacturing Systems

Machine learning models have the potential to augment the simulation of discrete-event dynamical systems, which is of considerable interest. Such models should serve the central purpose of capturing the temporal dynamics of event-based systems. In this paper, we use simulations of Parallel DEVS (PDEVS) models of a benchmark semiconductor fabrication manufacturing system to generate ARIMA, RNN, LSTM, and TCN models of the same. Single/multi-stage manufacturing statistical and deep learning models are developed and evaluated for different experimental scenarios. We generate Temporal Convolutional Neural (TCN) network models and evaluate their uni/multivariate throughput and turnaround time series by varying wafer lot configurations and sizes. The results show the predicted time series generated by TCN models can approximate the accuracies of simulated PDEVS models while achieving many-fold execution speedup.