Abstract: Systems, apparatus, and methods are disclosed for foreline diagnostics and control. A foreline coupled to a chamber exhaust is instrumented with one or more sensors, in some embodiments placed between the chamber exhaust and an abatement system. The one or more sensors are positioned to measure pressure in the foreline as an indicator of conductance. The sensors are coupled to a trained machine learning model configured to provide a signal when the foreline needs a cleaning cycle or when preventive maintenance should be performed. In some embodiments, the trained machine learning predicts when cleaning or preventive maintenance will be needed.

Abstract: Aspects generally relate to methods, systems, and apparatus for conducting a calibration operation for a plurality of mass flow controllers (MFCs) of a substrate processing system. In one aspect, a corrected flow curve is created for a range of target flow rates across a plurality of setpoints. In one implementation, a method of conducting a calibration operation for a plurality of mass flow controllers (MFCs) of a substrate processing system includes prioritizing the plurality of MFCs for the calibration operation. The prioritizing includes determining an operation time for each MFC of the plurality of MFCs, and ranking the plurality of MFCs in a rank list according to the operation time for each MFC. The method includes conducting the calibration operation for the plurality of MFCs according to the rank list and during an idle time for the substrate processing system.

Abstract: Aspects generally relate to methods, systems, and apparatus for conducting a calibration operation for a plurality of mass flow controllers (MFCs) of a substrate processing system. In one aspect, a corrected flow curve is created for a range of target flow rates across a plurality of setpoints. In one implementation, a method of conducting a calibration operation for a plurality of mass flow controllers (MFCs) of a substrate processing system includes prioritizing the plurality of MFCs for the calibration operation. The prioritizing includes determining an operation time for each MFC of the plurality of MFCs, and ranking the plurality of MFCs in a rank list according to the operation time for each MFC. The method includes conducting the calibration operation for the plurality of MFCs according to the rank list and during an idle time for the substrate processing system.

Abstract: Embodiments disclosed herein include methods of processing substrates in a tool. In an embodiment, the method of processing the substrate in the tool, comprises receiving an augmented recipe with a machine learning (ML) and/or an artificial intelligence (AI) module. In an embodiment, the augmented recipe comprises, a recipe for processing the substrate in the tool, and a matrix identifier that corresponds to one or more substrate properties. In an embodiment the method further comprises using the ML and/or AI module to retrieve a data set from a database, where the data set is associated with the matrix identifier, and using the ML and/or AI module to modify the augmented recipe to form a modified recipe, where the modification is dependent on the data set.

Abstract: Aspects generally relate to methods, systems, and apparatus for conducting a calibration operation for a plurality of mass flow controllers (MFCs) of a substrate processing system. In one aspect, a corrected flow curve is created for a range of target flow rates across a plurality of setpoints. In one implementation, a method of conducting a calibration operation for a plurality of mass flow controllers (MFCs) of a substrate processing system includes prioritizing the plurality of MFCs for the calibration operation. The prioritizing includes determining an operation time for each MFC of the plurality of MFCs, and ranking the plurality of MFCs in a rank list according to the operation time for each MFC. The method includes conducting the calibration operation for the plurality of MFCs according to the rank list and during an idle time for the substrate processing system.

Abstract: An apparatus for controlling temperature profile of a substrate within an epitaxial chamber includes a bottom center pyrometer and a bottom outer pyrometer to respectively measure temperatures at a center location and an outer location of a first surface of a susceptor of an epitaxy chamber, a top center pyrometer and a top outer pyrometer to respectively measure temperatures at a center location and an outer location of a substrate disposed on a second surface of the susceptor opposite the first surface, a first controller to receive signals, from the bottom center pyrometer and the bottom outer pyrometer, and output a feedback signal to a first heating lamp module that heats the first surface based on the measured temperatures of the first surface, and a second controller to receive signals, from the top center pyrometer, the top outer pyrometer, the bottom center pyrometer, and the bottom outer pyrometer, and output a feedback signal to a second heating lamp module that heats the substrate based on the mea

Abstract: Systems, apparatus, and methods are disclosed for foreline diagnostics and control. A foreline coupled to a chamber exhaust is instrumented with one or more sensors, in some embodiments placed between the chamber exhaust and an abatement system. The one or more sensors are positioned to measure pressure in the foreline as an indicator of conductance. The sensors are coupled to a trained machine learning model configured to provide a signal when the foreline needs a cleaning cycle or when preventive maintenance should be performed. In some embodiments, the trained machine learning predicts when cleaning or preventive maintenance will be needed.

Abstract: A method for processing a substrate within a processing chamber comprises receiving a first radiation signal corresponding to a film on a target element disposed within the processing chamber, analyzing the first radiation signal, and controlling the processing of the substrate based on the analyzed first radiation signal. The processing chamber includes a substrate support configured to support the substrate within a processing volume and a controller coupled to a first sensing device configured to receive the first radiation signal.