Applied Materials had 106 patents in artificial intelligence during Q1 2024. Applied Materials Inc has developed systems and methods for examining semiconductor specimens using end-to-end learning models trained with training images and measurement data. They have also created diagnostic substrates with resonator arrays for extracting plasma parameters and a computer-based method for non-destructive depth-profiling of samples. Additionally, they have introduced a system and method for defect examination on semiconductor specimens and edge defect detection via image analytics for susceptor pockets in substrate processing systems. GlobalData’s report on Applied Materials gives a 360-degree view of the company including its patenting strategy. Buy the report here.

Applied Materials grant share with artificial intelligence as a theme is 22% in Q1 2024. Grant share is based on the ratio of number of grants to total number of patents.

Recent Patents

Application: End-to-end measurement for semiconductor specimens (Patent ID: US20240105522A1)

The patent filed by Applied Materials Inc. describes a computerized metrology system and method for examining semiconductor specimens using an end-to-end (E2E) learning model. The system includes a processing and memory circuitry (PMC) that obtains a runtime image of the specimen and processes it through the E2E learning model to generate specific runtime measurement data for metrology applications. The E2E learning model is trained using a training set of images and ground truth measurement data, along with cost functions to evaluate precision, correlation, and matching benchmarks. The system aims to replace multiple processing modules in traditional metrology systems and provides interpretability through visual representations like heatmaps.

Furthermore, the method outlined in the patent involves generating a training set of images and ground truth measurement data for a metrology application, training an E2E learning model using this data and cost functions to evaluate precision, correlation, and matching benchmarks. Once trained, the E2E learning model can process a runtime image of a semiconductor specimen to obtain specific runtime measurement data for the metrology application. The method also includes provisions for using a single machine learning model to replace multiple processing modules, reducing capture time for runtime images, and evaluating matching between different metrology tools. Overall, the system and method described in the patent aim to improve the efficiency and accuracy of semiconductor specimen examination through advanced machine learning techniques.

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