Location History:
- Rehovot, IL (2022)
- Givatayim, IL (2022 - 2023)
Company Filing History:
Years Active: 2022-2025
Title: Tal Ben-Shlomo: Innovator in Semiconductor Technology
Introduction
Tal Ben-Shlomo is a prominent inventor based in Givatayim, Israel. He has made significant contributions to the field of semiconductor technology, holding a total of 4 patents. His work focuses on enhancing the examination and segmentation processes of semiconductor specimens through innovative machine learning techniques.
Latest Patents
One of Tal's latest patents is titled "Machine learning based examination of a semiconductor specimen and training thereof." This patent describes a system and method for runtime examination of a semiconductor specimen. The method involves obtaining a runtime image with a low signal-to-noise ratio and processing it using a machine learning model to derive specific examination data. The model is trained using various samples to ensure accuracy in the examination application.
Another notable patent is "Segmentation of an image of a semiconductor specimen." This invention provides a method for segmenting images of fabricated semiconductor specimens. It includes obtaining a probability map that predicts pixel probabilities corresponding to structural elements in the image. The method enhances the repeatability of segmentation through simulation and label mapping.
Career Highlights
Tal Ben-Shlomo is currently employed at Applied Materials Israel Limited, where he continues to develop cutting-edge technologies in semiconductor examination and processing. His expertise in machine learning applications has positioned him as a key player in the industry.
Collaborations
Tal has collaborated with notable colleagues, including Shalom Elkayam and Shaul Cohen. Their combined efforts contribute to the advancement of semiconductor technologies and innovations.
Conclusion
Tal Ben-Shlomo's work in semiconductor technology exemplifies the impact of innovative thinking in the field. His patents reflect a commitment to improving examination and segmentation processes, showcasing the potential of machine learning in this critical area.