Applied Materials had 83 patents in artificial intelligence during Q2 2024. Applied Materials Inc has developed methods and systems utilizing machine learning models to optimize environmental resource usage during substrate processing, improve inspection accuracy by segmenting inspection images based on height profile patterns, center substrates in chambers using pyrometers and robot arms, detect anomalies in process runs by correlating sensor data, and estimate film thickness on semiconductor substrates using simulated training data for machine learning models. 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 artificial intelligence 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 a process chamber. The method involves inputting process recipes into machine learning models to predict environmental resource usage data and provide recommendations for modifying the recipes to reduce resource consumption. The system includes process chambers with sensors controlled by a system controller that utilizes trained machine learning models to output recommendations based on predicted environmental resource usage data.
The method and system outlined in the patent aim to improve the efficiency of substrate processing by analyzing environmental resource consumption associated with different process recipes. By utilizing machine learning models to predict resource usage data and provide recommendations for recipe modifications, the system can optimize processing conditions to reduce energy, gas, and water consumption. The system controller receives process recipes, compares predicted resource usage data, and outputs recommendations for modifying recipes to achieve reduced environmental impact. Additionally, the patent describes the training of machine learning models with historical data to further enhance the optimization process and improve overall resource efficiency in substrate processing.
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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.
