Abstract: Multi-pixel sensors such as camera sensors may be configured to capture two-dimensional and/or three-dimensional images of the interior of a process chamber or other fabrication tool. The sensors may be configured to capture pixelated electromagnetic radiation intensity information from within the interior of such process chamber before, during, and/or after processing of a substrate in the chamber. Such sensors may also be utilized for control, predictive, and/or diagnostic applications.

Abstract: Various embodiments herein relate to systems, methods, and media for generating digital twins of semiconductor manufacturing equipment. In some embodiments, a digital twin of a process chamber of semiconductor manufacturing equipment is provided. comprising one or more non-transitory machine readable media comprising logic configured to implement; a first model of a first location of the process chamber; and a second model of a second location of the process chamber, wherein the first model is coupled to the second model, and wherein the first model and the second model are each of a model type that is one of: 1) an AI/ML model; 2) an HFS model; and 3) a closed-form solution, and wherein the first model and the second model each represent a class of physical phenomena that is one of: 1) thermal characteristics; 2) plasma characteristics; 3) fluid dynamics; 4) structural characteristics; and 5) chemical reactions.

Abstract: Multi-pixel sensors such as camera sensors may be configured to capture two-dimensional and/or three-dimensional images of the interior of a process chamber or other fabrication tool. The sensors may be configured to capture pixelated electromagnetic radiation intensity information from within the interior of such process chamber before, during, and/or after processing of a substrate in the chamber. Such sensors may also be utilized for control, predictive, and/or diagnostic applications.

Abstract: Various embodiments herein relate to systems and methods for predictive maintenance for semiconductor manufacturing equipment.

Abstract: Defects on a substrate comprising electronic components can be classified with a computational defect analysis system that may be implemented in multiple stages. For example, a first stage classification engine may process metrology data to produce an initial classification of defects. A second stage classification engine may use the initial classification, along with manufacturing information and/or prior defect knowledge to output probabilities that the defects are caused by one or more potential sources.

Abstract: Defects on a substrate comprising electronic components can be classified with a computational defect analysis system that may be implemented in multiple stages. For example, a first stage classification engine may process metrology data to produce an initial classification of defects. A second stage classification engine may use the initial classification, along with manufacturing information and/or prior defect knowledge to output probabilities that the defects are caused by one or more potential sources.

Abstract: Provided herein are systems and methods for optimizing feature fill processes. The feature fill optimization systems and methods may be used to optimize feature fill from a small number of patterned wafer tests. The systems and methods may be used for optimizing enhanced feature fill processes including those that include inhibition and/or etch operations along with deposition operations. Results from experiments may be used to calibrate a feature scale behavioral model. Once calibrated, parameter space may be iteratively explored to optimize the process.

Abstract: A system includes a camera mounted external to and adjacent to a window of a processing chamber configured to process semiconductor substrates. The window allows the camera to view a component in the processing chamber. The camera is configured to generate a video signal indicative of a status of the component during a process being performed in the processing chamber. The system further includes a controller coupled to the processing chamber. The controller is configured to control the camera, process the video signal from the camera, determine the status of the component based on the processing of the video signal, and determine whether to terminate the process based on the status of the component.

Abstract: Provided herein are systems and methods for optimizing feature fill processes. The feature fill optimization systems and methods may be used to optimize feature fill from a small number of patterned wafer tests. The systems and methods may be used for optimizing enhanced feature fill processes including those that include inhibition and/or etch operations along with deposition operations. Results from experiments may be used to calibrate a feature scale behavioral model. Once calibrated, parameter space may be iteratively explored to optimize the process.

Abstract: Defects on a substrate comprising electronic components can be classified with a computational defect analysis system that may be implemented in multiple stages. For example, a first stage classification engine may process metrology data to produce an initial classification of defects. A second stage classification engine may use the initial classification, along with manufacturing information and/or prior defect knowledge to output probabilities that the defects are caused by one or more potential sources.

Abstract: A system includes a camera mounted external to and adjacent to a window of a processing chamber configured to process semiconductor substrates. The window allows the camera to view a component in the processing chamber. The camera is configured to generate a video signal indicative of a status of the component during a process being performed in the processing chamber. The system further includes a controller coupled to the processing chamber. The controller is configured to control the camera, process the video signal from the camera, determine the status of the component based on the processing of the video signal, and determine whether to terminate the process based on the status of the component.