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.

Abstract: A system and method for optimizing resource allocation in a real-time online auction system of a publication application is described. The method includes receiving campaign data including a total budget, target resource utilization curve, and maximum bid for each auction opportunity of the publication application, maintaining, in a memory, a dynamic adjustment factor for each campaign, applying a resource conservation algorithm by calculating an adjusted bid using the dynamic adjustment factor, tracking, in real-time, resource utilization for each campaign for the publication application, updating the dynamic adjustment factor based on a difference between target and actual resource utilization, to reduce computational load through adaptive bid adjustments, and outputting, to a network interface, the updated dynamic adjustment factor and the adaptive bid adjustments for use in subsequent auctions, to balance resource utilizations across multiple time periods.

Abstract: A multi-armed bandit (MAB) problem is obtained and a per-round regret lower bound is determined, wherein a corresponding regret is measured against a benchmark. The multi-armed bandit problem is provided to an algorithm that has a per-round regret that is close to the determined per-round regret lower bound, wherein the algorithm dynamically adapts to changes and discards irrelevant past information by alternating between recently pulled arms and unpulled arms having potential, wherein the alternating comprises updating an estimate of an expected reward of each arm within each epoch and an estimate for an error bound that captures an amount of error contained in the estimate of the expected reward for each arm within each epoch based on the auto-regressive temporal structure with trend components, and restarting the algorithm.