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A digital twin is a numerical copy of an asset or a process, used to predict its physical behavior over time. Usually, a digital twin is based on physical models, constructed by simulating its different parts. It is then used to monitor and act on systems, based on digital state information, which is computed from real sensors data that feed the digital twin. Among the usages, we can cite predictive maintenance, planification, root cause analysis among others. We propose to adapt the technology to monitor and model complex processes by data driven, it can also be used in complement of physical simulation. Our proposal is a framework to create Artificial Intelligence (AI) models based on experimental data, then simulate new recipes and optimize the process, including constraints defined by the Process Engineer. AI models can be enriched with physical models; when available, they are used to create additional training data and to compare AI models with simulation. AI models require clean data, this procedure is tedious and time consuming. Depending on the process, it can be simplified by proposing automatic processes to clean and arrange data so that it can be used directly for training. The use of AI in comparison to classical physical models allows users to identify bias in their selection of parameters. It is used as a proxy for accurate optimization of the process under constraints. It can also serve to explore more efficiently the parameters space, by avoiding experiments that would lead to low performances. Finally, several tools are proposed to improve the understanding of the complete process and visualize the relationships between parameters and characteristics of the product. We propose an experimental setup using physical simulations of semiconductor materials to demonstrate the use of our digital twin pipeline.
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