A method that is used for self-learning manufacturing scheduling for a flexible manufacturing system that is used to produce at least a product is provided. The manufacturing system consists of processing entities that are interconnected through handling entities. The manufacturing scheduling will be learned by a reinforcement learning system on a model of the flexible manufacturing system. The model represents at least a behavior and a decision making of the flexible manufacturing system. The model is realized as a petri net.

An order of the processing entities and the handling entities is interchangeable, and therefore, the whole arrangement is very flexible.

An apparatus and method of diagnosing an unobserved operational parameter of a machine or apparatus. The method including obtaining a plurality of causal relationships between pairs of parameters of the machine or apparatus, wherein each pair includes a cause parameter and an effect parameter. For at least some of the parameters, a decomposition of the parameters into a plurality of information components is determined, based on the determined causal relationships between the parameters. The decomposition includes a synergistic information component including information obtained from a combination of at least two causal relationships having the parameter as effect parameter. A parameter is determined to include a negative synergistic information component. Based on the existence of the negative synergistic information component, it is diagnosed that an unobserved operational parameter provides a cause for the parameter including the negative synergistic information component.

A system and method of controlling a cluster tool apparatus, wherein the cluster tool includes one or more processing modules and a robot that is configured to move a semiconductor product to and from the one or more processing modules, the cluster tool configured for processing semiconductor products. The method of controlling the cluster tool apparatus to perform a processing cycle includes receiving a plurality of system parameters from a user interface, wherein the system parameters relate to one or more processing steps of the processing cycle, determining a schedule for performing the processing cycle utilizing the one or more processing modules, wherein the schedule being determined based on a semiconductor product residency parameter.

A system and method of controlling a cluster tool apparatus, wherein the cluster tool includes one or more processing modules and a robot that is configured to move a semiconductor product to and from the one or more processing modules, the cluster tool configured for processing semiconductor products. The method of controlling the cluster tool apparatus to perform a processing cycle includes receiving a plurality of system parameters from a user interface, wherein the system parameters relate to one or more processing steps of the processing cycle, determining a schedule for performing the processing cycle utilizing the one or more processing modules, wherein the schedule being determined based on a semiconductor product residency parameter.