Abstract: Technologies directed to cooling flow according to predicted cooling parameters for substrate processing are described. In some embodiments, a method includes receiving first data indicative of a process recipe for processing a substrate in a processing chamber of a substrate processing system. The method further includes inputting the first data into a model. The model includes a digital twin configured to represent thermal characteristics of the processing chamber. The method further includes receiving, via the model, a predicted value of a parameter associated with a flow of coolant through a cooling loop of the processing chamber. The method further includes causing coolant to flow through the cooling loop based on the predicted value of the parameter during execution of the process recipe in the processing chamber.

Abstract: In one aspect of the present disclosure, a method includes obtaining, by a processing device, input data indicative of a first set of process parameters. The method further includes providing the input data to a first process model. The method further includes obtaining, from the first process model, first predictive output indicative of performance of a first process operation in accordance with the first set of process parameters. The method further includes providing the first predictive output to a second process model. The method further includes obtaining, from the second process model, second predictive output indicative of performance of a second process operation, different than the first process operation or a repetition of the first process operation, in accordance with the first set of process parameters. The method further includes performing a corrective action in view of the second predictive output.

Abstract: A method includes receiving, via a graphical user interface (GUI), by a processing device, a first user input to view data associated with a first process chamber in a first chamber data mode. Data of the first chamber data mode includes data of a process operation performed in the first process chamber. The method further includes providing, for display on the GUI, first display data of the first data chamber mode responsive to receiving the first user input. The method further includes receiving a second user input to view data associated with the first process chamber in a second chamber data mode. Data of the second chamber data mode includes data of a virtual process operation performed by a virtual representation of the first process chamber. The method further includes providing, for display on the GUI, second display data of the second data chamber mode.

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: A method and apparatus for virtually sensing a temperature of a hardware component of semiconductor processing chamber are disclosed. In one or more embodiments, a method of operation for a processing chamber suitable for use in semiconductor manufacturing includes receiving a process recipe for a manufacturing process and monitoring a first temperature of a first hardware component of the processing chamber using a sensor. The method further includes synthesizing, using a model of the processing chamber, a first virtual temperature of a second hardware component of the processing chamber based on the received process recipe and the first temperature of the first hardware component.

Abstract: A first selection of a first fabrication process and/or first manufacturing equipment to perform manufacturing operations of the first fabrication process is received. The first selection is input into a digital replica of the first manufacturing equipment, where the digital replica outputs physical conditions of the first fabrication process. Environmental resource usage data indicative of a first environmental resource consumption of the first fabrication process run on the first manufacturing equipment based on the physical conditions of the first fabrication process is determined. A modification to the first fabrication process that reduces the environmental resource consumption of the first fabrication process run on the first manufacturing equipment is determined. Applying the modification to the first fabrication and/or providing the modification for display by a graphical user interface (GUI) is performed.

Abstract: Systems and methods are provided for use in appending log entries to a data structure. One exemplary method includes receiving, at a communication device, a log entry from a terminal and signing the log entry with a private key of a key pair specific to the communication device. The method also includes transmitting the signed log entry to an identity provider (IDP) and receiving, by the communication device, from the IDP, a signed, encrypted log entry. The method further includes verifying, by the communication device, a signature of the signed, encrypted log entry based on a public key associated with a key pair specific to the IDP and then appending the encrypted log entry to a digital identity included in the communication device.

Abstract: Technologies directed to cooling flow according to predicted cooling parameters for substrate processing are described. In some embodiments, a method includes receiving first data indicative of a process recipe for processing a substrate in a processing chamber of a substrate processing system. The method further includes inputting the first data into a model. The model includes a digital twin configured to represent thermal characteristics of the processing chamber. The method further includes receiving, via the model, a predicted value of a parameter associated with a flow of coolant through a cooling loop of the processing chamber. The method further includes causing coolant to flow through the cooling loop based on the predicted value of the parameter during execution of the process recipe in the processing chamber.

Abstract: A method including receiving, by a processing device, a first selection of at least one of a first fabrication process or first manufacturing equipment to perform manufacturing operations of the first fabrication process. The method can further include inputting the first selection into a digital replica of the first manufacturing equipment wherein the digital replica outputs physical conditions of the first fabrication process. The method may further include determining environmental resource usage data indicative of a first environmental resource consumption of the first fabrication process run on the first manufacturing equipment based on the physical conditions of the first fabrication process. The processing device may further determine a modification to the first fabrication process that reduces the environmental resource consumption of the first fabrication process run on the first manufacturing equipment.

Abstract: Systems and methods are provided for use in appending log entries to a data structure. One exemplary method includes receiving, at a communication device, a log entry from a terminal and signing the log entry with a private key of a key pair specific to the communication device. The method also includes transmitting the signed log entry to an identity provider (IDP) and receiving, by the communication device, from the IDP, a signed, encrypted log entry. The method further includes verifying, by the communication device, a signature of the signed, encrypted log entry based on a public key associated with a key pair specific to the IDP and then appending the encrypted log entry to a digital identity included in the communication device.

Abstract: Systems and methods are provided for leveraging different data repositories. One example computer-implemented method includes receiving a transfer request for a transfer from a sender in a first region to a recipient in a second region and determining whether the transfer request satisfies a regulation associated with the first and/or second region for information related to the recipient and/or sender. The method also includes submitting an identity request for the recipient and/or sender to an identity network, in response to the transfer request failing to satisfy the regulation, and receiving information regarding the recipient and/or sender, in response to the identity request, from the identity network, where the information includes identifying information for the recipient and/or sender. The method then includes compiling and submitting a transfer request for the transfer to an associated network, whereby the transfer request is submitted to be completed.

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: A method includes receiving measurement data from multiple sensors positioned along a delivery line that delivers a liquid as a gas to one of a gas panel or a processing chamber; simulating, using a computer-generated model, one or more process parameters associated with the delivery line and a plurality of heater jackets positioned around the delivery line; comparing the measurement data with values of the one or more process parameters; and determining, based on at least a threshold deviation between the measurement data and the values of the one or more process parameters, that a fault exists that is associated with maintaining temperature within the delivery line consistent with a gaseous state of the liquid.

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: 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, software systems and processes to develop surrogate model-based optimizers for controlling and optimizing flow and pressure of purges between a showerhead and a heater having a substrate support to control non-uniformity inherent in a processing chamber due to geometric configuration and process regimes. The flow optimizer process utilizes experimental data from optimal process space coverage models, generated simulation data and statistical machine learning tools (i.e. regression models and global optimizers) to predict optimal flow rates for any user-specified process regime.

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.