Abstract:
Technologies directed to an eco-efficiency monitoring and exploration platform for semiconductor manufacturing. One method includes receiving, by a processing device, first data indicating an update to a substrate fabrication system having a first configuration of manufacturing equipment and operating to one or more process procedures. The method further includes determining, by the processing device, using the first data with a digital replica, environmental resource data. The digital replica includes a digital reproduction of the substrate fabrication system. The environmental resource usage data indicates an environment resource consumption that corresponds to performing the one or more process procedures by the substrate fabrication system incorporating the update. The method further includes providing, by the processing device, the environmental resource usage data for display on a graphical user interface (GUI).
Abstract:
Technologies directed to an eco-efficiency monitoring and exploration platform for semiconductor manufacturing. One method includes receiving, by a processing device, first data indicating an update to a substrate fabrication system having a first configuration of manufacturing equipment and operating to one or more process procedures. The method further includes determining, by the processing device, using the first data with a digital replica, environmental resource data. The digital replica includes a digital reproduction of the substrate fabrication system. The environmental resource usage data indicates an environment resource consumption that corresponds to performing the one or more process procedures by the substrate fabrication system incorporating the update. The method further includes providing, by the processing device, the environmental resource usage data for display on a graphical user interface (GUI).
Abstract:
Methods and systems for reducing substrate particle scratching using machine learning are provided. A machine learning model is trained to predict process recipe settings for a substrate temperature control process to be performed for a current substrate at a manufacturing system. First training data and second training data are generated for the machine learning model. The first training data includes historical data associated with prior process recipe settings for a prior substrate temperature control process performed for a prior substrate at a prior process chamber. The second training data is associated with a historical scratch profile of one or more surfaces of the prior substrate after performance of the prior substrate temperature control process according to the prior process recipe settings. The first training data and the second training data are provided to train the machine learning model to predict which process recipe settings for the substrate temperature control process to be performed for the current substrate correspond to a target scratch profile for one or more surfaces of the current substrate.
Abstract:
Process recipe data associated a process to be performed for a substrate at a process chamber is provided as input to a trained machine learning model. A set of process recipe settings for the process that minimizes scratching on one or more surfaces of the substrate is determined based on one or more outputs of the machine learning model. The process is performed for the substrate at the process chamber in accordance with the determined set of process recipe settings.
Abstract:
Apparatus for the removal of exhaust gases are provided herein. In some embodiments, an apparatus may include a carrier for supporting one or more substrates in a substrate processing tool, the carrier having a first exhaust outlet, and an exhaust assembly including a first inlet disposed proximate the carrier to receive process exhaust from the first exhaust outlet of the carrier, a second inlet to receive a cleaning gas, and an outlet to remove the process exhaust and the cleaning gas.