Rehovot, Israel

Miriam Brook


Average Co-Inventor Count = 2.0

ph-index = 1


Company Filing History:


Years Active: 2024

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1 patent (USPTO):Explore Patents

Title: Innovations by Miriam Brook in Semiconductor Defect Examination

Introduction

Miriam Brook is an accomplished inventor based in Rehovot, Israel. She has made significant contributions to the field of semiconductor technology, particularly in the area of defect examination. Her innovative approach combines machine learning with traditional examination tools to enhance the accuracy of semiconductor inspections.

Latest Patents

Miriam Brook holds a patent for a system and method of runtime defect examination on a semiconductor specimen. This patent outlines a process that begins with obtaining a first image of the semiconductor specimen using an examination tool configured with a specific focus plane. The system employs a machine learning model to determine if the first image is in focus. If the image is found to be out of focus, the examination tool undergoes focus calibration to select a new focus plane that optimizes the focus score. A second image is then captured and analyzed, ensuring that it is suitable for defect examination.

Career Highlights

Miriam Brook is currently employed at Applied Materials Israel Limited, where she continues to develop innovative solutions in semiconductor technology. Her work has been instrumental in advancing the methods used for defect detection, which is crucial for maintaining the quality and reliability of semiconductor devices.

Collaborations

Miriam collaborates with Dror Shemesh, contributing to the development of cutting-edge technologies in their field. Their partnership exemplifies the importance of teamwork in driving innovation and achieving significant advancements in semiconductor examination techniques.

Conclusion

Miriam Brook's contributions to semiconductor defect examination highlight her role as a leading inventor in the industry. Her innovative patent demonstrates the potential of integrating machine learning with traditional examination methods to improve accuracy and efficiency.

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