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: Methods and systems for temperature-based metrology calibration at a manufacturing system are provided. First metrology data corresponding to one or more first temperatures associated with a substrate following a completion of one or more portions of a substrate process at a manufacturing system is obtained. Second metrology data corresponding to a second temperature associated with the substrate following the completion of the substrate process is determined in view of calibration data associated with the substrate. The second temperature is different from each of the one or more first temperatures. In response to a determination, in view of the second metrology data, that a modification criterion associated with the substrate process is satisfied, the substrate process recipe is modified.

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: 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.

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.

Abstract: Methods and apparatus for processing a substrate are provided herein. For example, a gas supply configured for use with a processing chamber includes an ampoule that stores a precursor and comprises an input to receive a carrier gas and an output to provide a mixture of the carrier gas and the precursor to the processing chamber and a sensor assembly comprising a detector and an infrared source operably connected to an outside of an enclosure, through which the mixture flows, and a gas measurement volume disposed within the enclosure and along an inner wall thereof so that a concentration of the precursor in the mixture can be measured by the detector and transmitted to a controller.

Abstract: Disclosed herein are embodiments of sensor devices comprising a sensing component able to determine the presence of, detect, and/or quantify detectable species in a variety of environments and applications. The sensing components disclosed herein can comprise MOF materials, plasmonic nanomaterials, redox-active molecules, a metal, or any combinations thereof. In some exemplary embodiments, optical properties of the plasmonic nanomaterials and/or the redox-active molecules combined with MOF materials can be monitored directly to detect analyte species through their impact on external conditions surrounding the material or as a result of charge transfer to and from the plasmonic nanomaterial and/or the redox-active molecule as a result of interactions with the MOF material.

Abstract: Disclosed herein are embodiments of sensor devices comprising a sensing component able to determine the presence of, detect, and/or quantify detectable species in a variety of environments and applications. The sensing components disclosed herein can comprise MOF materials, plasmonic nanomaterials, or combinations thereof. In an exemplary embodiment, light guides can be coupled with the sensing components described herein to provide sensor devices capable of increased NIR detection sensitivity in determining the presence of detectable species, such as gases and volatile organic compounds. In another exemplary embodiment, optical properties of the plasmonic nanomaterials combined with MOF materials can be monitored directly to detect analyte species through their impact on external conditions surrounding the particle or as a result of charge transfer to and from the plasmonic material as a result of interactions with the plasmonic material and/or the MOF material.

Abstract: Disclosed herein are embodiments of sensor devices comprising a sensing component able to determine the presence of, detect, and/or quantify detectable species in a variety of environments and applications. The sensing components disclosed herein can comprise MOF materials, plasmonic nanomaterials, redox-active molecules, a metal, or any combinations thereof. In some exemplary embodiments, optical properties of the plasmonic nanomaterials and/or the redox-active molecules combined with MOF materials can be monitored directly to detect analyte species through their impact on external conditions surrounding the material or as a result of charge transfer to and from the plasmonic nanomaterial and/or the redox-active molecule as a result of interactions with the MOF material.

Abstract: Disclosed herein are embodiments of sensor devices comprising a sensing component able to determine the presence of, detect, and/or quantify detectable species in a variety of environments and applications. The sensing components disclosed herein can comprise MOF materials, plasmonic nanomaterials, or combinations thereof. In an exemplary embodiment, light guides can be coupled with the sensing components described herein to provide sensor devices capable of increased NIR detection sensitivity in determining the presence of detectable species, such as gases and volatile organic compounds. In another exemplary embodiment, optical properties of the plasmonic nanomaterials combined with MOF materials can be monitored directly to detect analyte species through their impact on external conditions surrounding the particle or as a result of charge transfer to and from the plasmonic material as a result of interactions with the plasmonic material and/or the MOF material.