An information processing device of the present invention includes a first acquisition unit, a second acquisition unit, and a machine learning processing unit. The first acquisition unit acquires total event status information. The second acquisition unit acquires time-series detection result information. The machine learning processing unit performs one or both of learning processing and determination processing. In the learning processing, a learning model is generated by performing machine learning with the time-series detection result information acquired by the second acquisition unit as an input for each piece of the total event status information acquired by the first acquisition unit. In the determination processing, a determination is performed on the generated learning model by inputting the time-series detection result information acquired by the second acquisition unit for each piece of the total event status information acquired by the first acquisition unit.

It is very difficult to schedule a single-arm cluster tool with wafer revisiting such that wafer residency time constraints are satisfied. The present invention conducts a study on this challenging problem for a single-arm cluster tool with atomic layer deposition (ALD) process. With a so called p-backward strategy being applied, a Petri net model is developed to describe the dynamic behavior of the system. Based on the model, existence of a feasible schedule is analyzed, schedulability conditions are derived, and scheduling algorithms are presented if there is a schedule. A schedule is obtained by simply setting the robot waiting time if schedulable and it is very computationally efficient. The obtained schedule is shown to be optimal. Illustrative examples are given to demonstrate the proposed approach.