Abstract: A method includes identifying a set of wafers, wherein each wafer is associated with a respective start time of a set of start times, determining whether the set of wafers includes an idle wafer, in response to determining that the set of wafers includes an idle wafer that is idle for a duration that exceeds a predefined threshold value, generating a modified set of start times by modifying at least the start time for the idle wafer, and initiating a computer simulation forecasting processing of the set of wafers using a wafer modification chamber and a wafer movement chamber based on the modified set of start times. The computer simulation uses a machine learning model trained based on a first duration to perform a first manufacturing task using the wafer modification chamber and a second duration to perform a second manufacturing task using the wafer movement chamber.

Abstract: Disclosed herein is technology for performing a simulation based optimization to identify a schedule for a manufacturing tool. An example method may include determining, by a processing device, resources of a manufacturing tool, wherein the resources comprise a first chamber and a second chamber; accessing task data indicating a first manufacturing task and a second manufacturing task; determining a duration to perform the first manufacturing task using the first chamber and a duration to perform the second manufacturing task using the second chamber; updating a machine learning model based on the duration to perform the first manufacturing task and the duration to perform the second manufacturing task; performing a set of computer simulations that uses the machine learning model and the task data to produce a set of simulation results; storing, by the processing device, a simulation result of the set of simulation results in a data store.

Abstract: Disclosed herein is technology for performing a simulation based optimization to identify a schedule for a manufacturing tool. An example method may include determining, by a processing device, resources of a manufacturing tool, wherein the resources comprise a first chamber and a second chamber; accessing task data indicating a first manufacturing task and a second manufacturing task; determining a duration to perform the first manufacturing task using the first chamber and a duration to perform the second manufacturing task using the second chamber; updating a machine learning model based on the duration to perform the first manufacturing task and the duration to perform the second manufacturing task; performing a set of computer simulations that uses the machine learning model and the task data to produce a set of simulation results; storing, by the processing device, a simulation result of the set of simulation results in a data store.

Abstract: A method and system for managing process jobs in a semiconductor fabrication facility is described. In one embodiment, the method includes receiving a plurality of process jobs associated with one or more priorities. The method further includes executing the plurality of process jobs in an order reflecting the priorities. The order is modifiable in real time upon receiving a new process job with a priority higher than the priorities of the plurality of process jobs.

Abstract: A method and system for managing process jobs in a semiconductor fabrication facility is described. In one embodiment, the method includes receiving a plurality of process jobs associated with one or more priorities. The method further includes executing the plurality of process jobs in an order reflecting the priorities. The order is modifiable in real time upon receiving a new process job with a priority higher than the priorities of the plurality of process jobs.