Winbond Electronics had five patents in artificial intelligence during Q1 2024. The patent filed by Winbond Electronics Corp in Q1 2024 describes a dynamic sampling method and device for semiconductor manufacturing. This method involves generating a virtual image of a wafer, measuring critical patterns in the virtual image using virtual metrology, and determining whether to perform measurements on the wafer based on the virtual metrology result compared to a threshold. GlobalData’s report on Winbond Electronics gives a 360-degree view of the company including its patenting strategy. Buy the report here.
Winbond Electronics grant share with artificial intelligence as a theme is 60% in Q1 2024. Grant share is based on the ratio of number of grants to total number of patents.
Recent Patents
Application: Dynamic sampling method and device for semiconductor manufacture (Patent ID: US20240045408A1)
The patent filed by Winbond Electronics Corp. describes a dynamic sampling method and device for semiconductor manufacturing. The method involves generating an N-dimensional virtual image of a wafer based on design rules and quality control data, measuring a critical pattern in the virtual image using virtual metrology, determining if the measurement result exceeds a threshold, and deciding whether to perform a physical measurement on the wafer based on this determination. The device includes a processor and storage unit to implement this method, collecting necessary data from semiconductor devices, setting models for virtual image generation, and making decisions on sampling positions and measurements based on the virtual metrology results.
The dynamic sampling method and device proposed in the patent aim to optimize semiconductor manufacturing processes by utilizing virtual metrology to assess critical patterns on wafers before deciding on physical measurements. By generating N-dimensional virtual images and setting models based on design rules and quality control data, the method and device can efficiently determine when physical measurements are necessary, potentially saving time and resources in the manufacturing process. Additionally, the use of statistical thresholds and machine learning models enhances the accuracy and reliability of the decision-making process, ensuring that measurements are only performed when required based on the virtual metrology results.
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