Applied Materials had 86 patents in future of work during Q2 2024. The patents filed by Applied Materials Inc in Q2 2024 focus on methods utilizing machine learning models to optimize environmental resource usage during substrate processing, centering substrates in chambers using pyrometers and robot arms, updating equipment constants based on trained machine learning models, generating corrective actions for processing chambers based on performance metrics and trace data, and implementing an electronic device manufacturing system with a tool server for data collection and corrective actions. GlobalData’s report on Applied Materials gives a 360-degree view of the company including its patenting strategy. Buy the report here.
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Applied Materials had no grants in future of work as a theme in Q2 2024.
Recent Patents
Application: Machine and deep learning techniques for predicting ecological efficiency in substrate processing (Patent ID: US20240210916A1)
The patent filed by Applied Materials Inc. describes a method and system for optimizing environmental resource consumption during substrate processing in process chambers. The method involves inputting process recipes into machine learning models to predict environmental resource usage data and provide recommendations for modifying the process recipes to reduce resource consumption. The system includes process chambers with sensors controlled by a system controller that utilizes machine learning models to optimize resource consumption based on predicted data.
The method and system outlined in the patent aim to improve environmental sustainability by reducing energy, gas, and water consumption during substrate processing. By utilizing machine learning models to predict resource usage data and provide recommendations for process recipe modifications, the system can optimize resource consumption in process chambers. Additionally, the system controller can compare predicted resource usage data for different process recipes to determine the most efficient option. The patent also describes training multiple machine learning models to output predicted measurements and environmental resource usage data, further enhancing the system's capabilities for optimizing resource consumption.
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GlobalData Patent Analytics tracks bibliographic data, legal events data, point in time patent ownerships, and backward and forward citations from global patenting offices. Textual analysis and official patent classifications are used to group patents into key thematic areas and link them to specific companies across the world’s largest industries.
