Tokyo Electron had two patents in edge computing during Q2 2024. The patents filed by Tokyo Electron Ltd in Q2 2024 focus on information processing methods, apparatus, and systems for substrate processing. The first patent involves acquiring time series data from sensors with different sampling periods, learning first models based on this data, and using the models to output estimation results related to the substrate processing apparatus. The second patent involves acquiring feature values from data processed by first learning models, learning a second model based on these feature values, and using the second model to output estimation results. GlobalData’s report on Tokyo Electron gives a 360-degree view of the company including its patenting strategy. Buy the report here.
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Tokyo Electron had no grants in edge computing as a theme in Q2 2024.
Recent Patents
Application: Information processing method, information processing apparatus, and substrate processing system (Patent ID: US20240202606A1)
The patent filed by Tokyo Electron Ltd. describes an information processing method, apparatus, and system for acquiring time series data from sensors with different sampling periods in a substrate processing apparatus. The method involves learning first learning models based on the data from each sensor individually and using these models to estimate information related to the substrate processing apparatus. The system includes edge devices connected to sensors, a host device, and an apparatus group server, with each edge device acquiring data, learning models, and outputting feature values to the host device, which in turn communicates with the server to store and process the data for further learning and estimation.
The information processing method and system outlined in the patent aim to efficiently process data from sensors in a substrate processing apparatus by utilizing learning models tailored to each sensor's data and integrating them into a comprehensive system involving edge devices, a host device, and an apparatus group server. The method involves extracting feature values, performing learning and estimation processes, and updating models based on estimation results to enhance the accuracy and effectiveness of the information processing system. The system's architecture allows for seamless communication and coordination between different components to optimize the processing of time series data from sensors in a substrate processing environment.
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