Hessam S. Sarjoughian

Vamsi Krishna Pendyala, Hessam S. Sarjoughian, Bala Potineni et al.

Advancements in high-computing devices increase the necessity for improved and new understanding and development of smart manufacturing factories. Discrete-event models with simulators have been shown to be critical to architect, designing, building, and operating the manufacturing of semiconductor chips. The diffusion, implantation, and lithography machines have intricate processes due to their feedforward and feedback connectivity. The dataset collected from simulations of the factory models holds the promise of generating valuable machine-learning models. As surrogate data-based models, their executions are highly efficient compared to the physics-based counterpart models. For the development of surrogate models, it is beneficial to have publicly available benchmark simulation models that are grounded in factory models that have concise structures and accurate behaviors. Hence, in this research, a dataset is devised and constructed based on a benchmark model of an Intel semiconductor fabrication factory. The model is formalized using the Parallel Discrete-Event System Specification and executed using the DEVS-Suite simulator. The time series dataset is constructed using discrete-event time trajectories. This dataset is further analyzed and used to develop baseline univariate and multivariate machine learning models. The dataset can also be utilized in the machine learning community for behavioral analysis based on formalized and scalable component-based discrete-event models and simulations.

Abdurrahman Alshareef, Hessam S. Sarjoughian

This paper presents our findings for using activity modeling for simulation (validation), model checking (verification), and execution purposes. Each is needed to tackle system complexity and further research into behavioral modeling. We argue different models implicate different understandings and expectations. We emphasize some distinctions with demonstrations using the Discrete Event System Specification with an exemplary model. In particular, the continuous-time base in models helps observe some inherent limitations and strengths in acquiring each capability. The temporal characterization of input, output, and state, or the lack thereof, is at the core of developing behavioral specifications. We use DEVS to arrive at the capability of validating simulations for activity models. We use Constrained-DEVS for the verification of activity models. We show how some executions can be derived, whether from the specification itself or with considerations for simulation and model checking.