STMicroelectronics had 35 patents in artificial intelligence during Q2 2024. STMicroelectronics NV has filed patents for methods to generate classification predictions for wafer defect maps using machine learning models, a blood pressure monitoring device with inertial measurement units and machine learning processes, an electronic device with sensor processing unit for activity recognition, a sensor device with asymmetric lens for direction detection, and a sensor device with configurable digital analysis block for generating classification data based on sensor data. GlobalData’s report on STMicroelectronics gives a 360-degree view of the company including its patenting strategy. Buy the report here.
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STMicroelectronics had no grants in artificial intelligence as a theme in Q2 2024.
Recent Patents
Application: Machine learning techniques for wafer defect map classification (Patent ID: US20240202908A1)
The patent filed by STMicroelectronics NV describes methods and systems for generating classification predictions for wafer defect maps. The invention involves using machine learning models to generate reduced feature data and wafer defect pattern clusters based on vector representations of wafer defect map images. Each image is associated with a specific defect pattern cluster, and a classification prediction is generated based on this association. The system includes unsupervised classification circuitry that can preprocess images, generate vector representations, and utilize machine learning models such as Uniform Manifold Approximation and Projection and Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) for clustering.
Furthermore, the system can identify cluster representatives, calculate pattern similarity scores, and update a wafer defect pattern class corpus based on these scores. Additionally, there is a provision for a supervised classification circuitry to train a convolutional neural network machine learning model based on the updated wafer defect pattern class corpus for generating classification predictions. Overall, the patent focuses on utilizing machine learning techniques to efficiently classify and predict defects in wafer maps, enhancing the quality and accuracy of defect analysis in semiconductor manufacturing processes.
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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.
