PDF Solutions. has been granted a patent for a computer-implemented method that classifies wafers in semiconductor fabrication. The method utilizes a rule-based model and a machine learning model, allowing user interaction to confirm or modify classifications, thereby enhancing the accuracy of wafer classification over time. GlobalData’s report on PDF Solutions gives a 360-degree view of the company including its patenting strategy. Buy the report here.
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According to GlobalData’s company profile on PDF Solutions, CMOS gate array designing was a key innovation area identified from patents. PDF Solutions's grant share as of July 2024 was 63%. Grant share is based on the ratio of number of grants to total number of patents.
Wafer classification using collaborative machine learning and user input
The patent US12038802B2 outlines a computer-implemented method for classifying wafers during semiconductor fabrication. The process begins with displaying wafer identification information on a graphical user interface (GUI) for multiple wafers at a specific fabrication step. An initial classification for a selected wafer is determined using a rule-based model, followed by a predicted classification generated by a machine learning model that incorporates both the initial classification and user input. The GUI allows users to interactively update classifications and enter their own classifications, ultimately leading to a final classification that is saved for future reference. The method also includes features for retrieving past classifications and training the machine learning model based on user feedback and classification data.
Additionally, the method provides mechanisms for user interaction, such as confirming or modifying classifications and drilling down into detailed wafer information. It supports feedback from multiple users, which can help resolve classification conflicts by incorporating additional information, including the timing of user feedback. The system is designed to enhance the accuracy of wafer classification through continuous learning and adaptation, as the machine learning models are periodically retrained based on the accumulated data. Overall, this patent presents a comprehensive approach to improving wafer classification in semiconductor manufacturing, leveraging both rule-based and machine learning techniques within an interactive user interface.
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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.
