Abstract: A method, apparatus, and system for controlling a multi-chamber system configured to process substrates are described herein. In some embodiments, a method comprises automatically determining, by each digital twin device a first data set associated with a corresponding process chamber of process chambers. The plurality of digital twin devices captures and models characteristics and processes of process chambers and generates control inputs for controlling the process chambers or processes executed by the chambers during the manufacturing of substrates. The method further comprises automatically generating, by each digital twin device, a second data set that comprises control inputs, and automatically transmitting the second data set to the process chamber.

Abstract: A method, apparatus, and system for controlling a multi-chamber process system for substrate processing are described herein. In some embodiments, a method comprises determining, by each digital twin device, of a plurality of digital twin devices, a first data set associated with at least one process chamber of a plurality of chamber processes, and the corresponding processes for processing a plurality of substrates. Each digital twin device comprises one or more computational models. The first data set comprises measurements reported by probes or sensors within the at least one chamber process, or data collected and reported by internal sensors of the digital twin device. The method further automatically generating, by each digital twin device a second data set based on, at least in part, the first data set, and by executing one or more computational models of the digital twin device.

Abstract: A method, apparatus, and system for controlling a physical twin chamber configured to process substrates are described herein. In some embodiments, a method comprises determining, by a digital twin device, characteristics of a physical twin chamber and generating control inputs for controlling the physical twin chamber. The digital twin device comprises one or more computational models for determining the characteristics of the physical twin and for generating the control inputs. The digital twin device determines a first data set associated with the physical twin chamber. The first data set comprises process data collected by sensors configured to measure attributes of the physical twin chamber. Based on the first data, the digital twin device automatically generates a second data set based on the generated control inputs and transmits the second data set to the physical twin chamber for controlling the process performed on the substrates by the physical twin chamber.

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 apparatus for controlling precursor flow are provided. In embodiments, the methods and apparatus apparatus for controlling precursor flow to a deposition chamber, includes: an ampoule to output a precursor; a sensor assembly communicatively coupled to the ampoule; and a control system, wherein the control system is configured to calibrate the sensor assembly during flow of a precursor or a chemical standard through the sensor assembly.

Abstract: A method includes receiving, from sensors, sensor data associated with processing a substrate via a processing chamber of substrate processing equipment. The sensor data includes a first subset received from one or more first sensors and a second subset received from one or more second sensors, the first subset being mapped to the second subset. The method further includes identifying model input data and model output data. The model output data is output from a physics-based model based on model input data. The method further includes training a machine learning model with data input including the first subset and the model input data, and target output data including the second subset and the model output data to tune calibration parameters of the machine learning model. The calibration parameters are to be used by the physics-based model to perform corrective actions associated with the processing chamber.

Abstract: Methods and apparatus for controlling precursor flow are provided. In embodiments, the methods and apparatus apparatus for controlling precursor flow to a deposition chamber, includes: an ampoule to output a precursor; a sensor assembly communicatively coupled to the ampoule; and a control system, wherein the control system is configured to calibrate the sensor assembly during flow of a precursor or a chemical standard through the sensor assembly.

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: Methods and apparatus for controlling precursor flow are provided. In embodiments, the methods and apparatus apparatus for controlling precursor flow to a deposition chamber, includes: an ampoule to output a precursor a sensor assembly communicatively coupled to the ampoule; and a control system, wherein the control system is configured to calibrate the sensor assembly during flow of a precursor or a chemical standard through the sensor assembly.