Abstract: Spectral data associated with one or more regions of a surface of a substrate is identified. The substrate has been processed according to one or more first operations of a process recipe that is unknown to a system controller for the manufacturing system. The spectral data is provided as input to a machine learning model that is trained to predict, based on given spectral data, a respective process recipe associated with the substrate and one or more operations of the respective process recipe that have already been performed. A determination is made, based on one or more outputs of the machine learning model, that the substrate is associated with the process recipe and that one or more second operations are yet to be performed. The substrate is caused to be processed according to the one or more second operations of the process recipe.

Abstract: A process recipe associated with a substrate at a manufacturing system is identified. A first set of measurements for the substrate is obtained from a substrate measurement subsystem. A second set of measurements for the substrate is obtained from one or more sensors of a chamber of the manufacturing system. A determination is made based on the obtained first set of measurements and the obtained second set of measurements of whether to modify the process recipe by at least one of modifying an operation of the process recipe or generating an instruction to prevent completion of execution of one or more operations of the process recipe.

Abstract: A non-transitory computer-readable storage medium stores instructions, which when executed by a processing device of a diagnostic server, cause the processing device to perform certain operations. The operations include receiving, from a processing chamber, (i) measurement values of a combined signal that is based on an injection of an alternating signal wave onto a first output signal of a controller of the processing chamber, and (ii) measurement values of a second output signal of the controller that incorporates feedback from the processing chamber. The operations further include generating, based on the measurement values of the combined signal and the measurement values of the second output signal of the controller, a baseline bode fingerprint pertaining to a state associated with the processing chamber. The operations further include storing, in computer storage, the baseline bode fingerprint to be used in performing diagnostics of the processing chamber.

Abstract: A method for determining whether to modify a manufacturing process recipe is provided. A substrate to be processed at a manufacturing system according to the first process recipe is identified. An instruction to transfer the substrate to a substrate measurement subsystem to obtain a first set of measurements for the substrate is generated. The first set of measurements for the substrate is received from the substrate measurement subsystem. An instruction to transfer the substrate from the substrate measurement subsystem to a processing chamber is generated. A second set of measurements for the substrate is received from one or more sensors of the processing chamber. A first mapping between the first set of measurements and the second set of measurements for the substrate is generated. The first set of measurements mapped to the second set of measurements for the substrate is stored.

Abstract: Spectral data associated with a first prior substrate and/or a second prior substrate is obtained. A metrology measurement value associated with the first portion of the first prior substrate is determined based on one or more metrology measurement values measured for at least one of a second portion of the first prior substrate or a third portion of a second prior substrate. Training data for training a machine learning model to predict metrology measurement values of a current substrate is generated. Generating the training data includes generating a first training input including the spectral data associated with the first prior substrate and generating a first target output for the first training input, the first target output including the determined metrology measurement value associated with the first portion of the first prior substrate. The training data is provided to train the machine learning model.

Abstract: A non-transitory computer-readable storage medium stores instructions, which when executed by a processing device of a diagnostic server, cause the processing device to perform certain operations. The operations include receiving, from a processing chamber, (i) measurement values of a combined signal that is based on an injection of an alternating signal wave onto a first output signal of a controller of the processing chamber, and (ii) measurement values of a second output signal of the controller that incorporates feedback from the processing chamber. The operations further include generating, based on the measurement values of the combined signal and the measurement values of the second output signal of the controller, a baseline bode fingerprint pertaining to a state associated with the processing chamber. The operations further include storing, in computer storage, the baseline bode fingerprint to be used in performing diagnostics of the processing chamber.

Abstract: A non-transitory computer-readable storage medium stores instructions, which when executed by a processing device of a diagnostic server, cause the processing device to perform certain operations. The operations include receiving, from a processing chamber, (i) measurement values of a combined signal that is based on an injection of an alternating signal wave onto a first output signal of a controller of the processing chamber, and (ii) measurement values of a second output signal of the controller that incorporates feedback from the processing chamber. The operations further include generating, based on the measurement values of the combined signal and the measurement values of the second output signal of the controller, a baseline bode fingerprint pertaining to a state associated with the processing chamber. The operations further include storing, in computer storage, the baseline bode fingerprint to be used in performing diagnostics of the processing chamber.

Abstract: Methods and apparatus perform backside via reveal processes using a centralized control framework for multiple process tools. In some embodiments, a method for performing a backside via reveal process may include receiving process tool operational parameters from process tools involved in the backside via reveal process by a central controller, receiving sensor metrology data from at least one or more of the process tools involved in the backside via reveal process, and altering the backside reveal process based, at least in part, on the process tool operational parameters and the sensor metrology data by adjusting two or more of the process tools involved in the backside via reveal process. The profile parameters are configured to prevent backside via breakage during a chemical mechanical polishing (CMP) process.

Abstract: Methods and systems for detecting and correcting substrate process drift using machine learning are provided. Data associated with processing each of a first set of substrates at a manufacturing system according to a process recipe is provided as input to a trained machine learning model. One or more outputs are obtained from the trained machine learning model. An amount of drift of a first set of metrology measurement values for the first set of substrates from a target metrology measurement value is determined from the one or more outputs. Process recipe modification identifying one or more modifications to the process recipe is also determined. For each modification, an indication of a level of confidence that a respective modification to the process recipe satisfies a drift criterion for a second set of substrates is determined.

Abstract: Methods and apparatus perform backside via reveal processes using a centralized control framework for multiple process tools. In some embodiments, a method for performing a backside via reveal process may include receiving process tool operational parameters from process tools involved in the backside via reveal process by a central controller, receiving sensor metrology data from at least one or more of the process tools involved in the backside via reveal process, and altering the backside reveal process based, at least in part, on the process tool operational parameters and the sensor metrology data by adjusting two or more of the process tools involved in the backside via reveal process. The profile parameters are configured to prevent backside via breakage during a chemical mechanical polishing (CMP) process.

Abstract: A method for training a machine learning model to predict metrology measurements of a current substrate being processed at a manufacturing system is provided. Training data for the machine learning model is generated. A first training input including historical spectral data and/or historical non-spectral data associated with a surface of a prior substrate previously processed at the manufacturing system is generated. A first target output for the first training input is generated. The first target output includes historical metrology measurements associated with the prior substrate previously processed at the manufacturing system. Data is provided to train the machine learning model on (i) a set of training inputs including the first training input, and (ii) a set of target outputs including a first target output.

Abstract: A method for determining whether to modify a manufacturing process recipe is provided. A substrate to be processed at a manufacturing system according to the first process recipe is identified. An instruction to transfer the substrate to a substrate measurement subsystem to obtain a first set of measurements for the substrate is generated. The first set of measurements for the substrate is received from the substrate measurement subsystem. An instruction to transfer the substrate from the substrate measurement subsystem to a processing chamber is generated. A second set of measurements for the substrate is received from one or more sensors of the processing chamber. A first mapping between the first set of measurements and the second set of measurements for the substrate is generated. The first set of measurements mapped to the second set of measurements for the substrate is stored.

Abstract: A method for a substrate measurement subsystem is provided. An indication is received that a substrate being processed at a manufacturing system has been loaded into a substrate measurement subsystem. First positional data of the substrate within the substrate measurement subsystem is determined. One or more portions of the substrate to be measured by one or more sensing components of the substrate measurement subsystem are determined based on the first positional data of the substrate and a process recipe for the substrate. Measurements of each of the determined portions of the substrate are obtained by one or more sensing components of the substrate measurement subsystem. The obtained measurements of each of the determined portions of the substrate are transmitted to a system controller.

Abstract: A non-transitory computer-readable storage medium stores instructions, which when executed by a processing device of a diagnostic server, cause the processing device to perform certain operations. The operations include receiving, from a processing chamber, (i) measurement values of a combined signal that is based on an injection of an alternating signal wave onto a first output signal of a controller of the processing chamber, and (ii) measurement values of a second output signal of the controller that incorporates feedback from the processing chamber. The operations further include generating, based on the measurement values of the combined signal and the measurement values of the second output signal of the controller, a baseline bode fingerprint pertaining to a state associated with the processing chamber. The operations further include storing, in computer storage, the baseline bode fingerprint to be used in performing diagnostics of the processing chamber.

Abstract: In an aspect, in general, a system and method compensate for a misalignment characteristic of one or more acceleration sensors fixed to a sprung mass of a vehicle, each acceleration sensor having a location on the vehicle and a desired orientation relative to the vehicle.

Abstract: In an aspect, in general, a system and method compensate for a misalignment characteristic of one or more acceleration sensors fixed to a sprung mass of a vehicle, each acceleration sensor having a location on the vehicle and a desired orientation relative to the vehicle.

Abstract: An active wheel damper. An active suspension damps vertical displacement of an unsprung mass in a frequency range and reduces vertical displacement of a sprung mass in another frequency range.

Abstract: In an active suspension system for actively suspending a plant, a fail-safe system controlled by a failure-detector has a selectively-activated damper coupled to the plant. The damper may be a separate element from the actuator. Under normal circumstances, the damper is deactivated and therefore generates no damping force. However, if the failure detector detects an abnormal state in the system, it activates the damper, thereby causing a damping force that resists motion of the plant.

Abstract: An active wheel damper. An active suspension damps vertical displacement of an unsprung mass in a frequency range and reduces vertical displacement of a sprung mass in another frequency range.