Abstract: Methods and systems for performing tasks associated with a manufacturing system using a multi-modal, multi-agentic framework. A user query to perform a task pertaining to a manufacturing system including a set of specialized subsystems that are each associated with an aspect of a manufacturing process of the manufacturing system is detected from a client device. A set of operations associated with the task is determined. Each operation is associated with a distinct artificial intelligence (AI) model. For each operation in the set, an instruction to perform the operation is transmitted to a computing device associated with the respective distinct AI model corresponding to the instructed operation. Data corresponding to an output of the respective distinct AI model is received from each computing device, which is used to generate a response to the user query. The generated response is provided to the client device.

Abstract: A method includes identifying an output image of a substrate defect based on a user sketch of the substrate defect and a text description associated with the substrate defect. The method further includes updating the output image based on user input to generate an updated output image. The method further includes causing, based on the updated output image, performance of a corrective action associated with substrate processing via a substrate processing system.

Abstract: A method includes obtaining, by a processing device, defect data for a substrate processed in a substrate processing system. The method further includes obtaining, by the processing device, context data associated with the substrate. The method further includes determining a troubleshooting guide associated with the defect data. The troubleshooting guide includes a sequence of troubleshooting operations, each associated with one or more probably root causes for the defect data. The method further includes determining a subset of context data based on the troubleshooting guide. The method further includes processing the defect data and the subset of context data using one or more trained machine learning models that output a predicted corrective action associated with a troubleshooting operation in the sequence of troubleshooting operations. The method further includes initiating the corrective action.

Abstract: A method includes obtaining defect data and context data in association with a substrate, and providing the defect data and the context data to a first trained machine learning model as input. The method further includes obtaining output from the first trained machine learning model based on the defect data and the context data. The output is indicative of a predicted root cause in association with the defect data. The method further includes performing a corrective action in view of the output.