This inventor holds 1 USPTO granted patent. Top assignee: Kla Corporation. Active years: 2026.
Company Filing History:
Years Active: 2026
Title: Innovations by Samad Jafarzanjani
Introduction
Samad Jafarzanjani is an accomplished inventor based in Ann Arbor, MI (US). He has made significant contributions to the field of x-ray scatterometry through his innovative patent. His work focuses on employing machine learning techniques to enhance the efficiency of electromagnetic response modeling.
Latest Patents
Samad Jafarzanjani holds a patent titled "Methods and systems for x-ray scatterometry measurements employing a machine learning based electromagnetic response model." This patent describes methods and systems for estimating values of parameters of interest from x-ray scatterometry measurements with reduced computational effort. The approach utilizes a trained machine learning (ML) based electromagnetic (EM) response model to estimate these values through regression. The training data set comprises sets of Design Of Experiments (DOE) values of parameters of interest and corresponding DOE values of various electromagnetic response metrics. In certain instances, values of parameters of interest are derived from measured images based on regression using a sequence of trained ML based electromagnetic response models. Additionally, input values used to train the ML based EM response model are scaled based on model output variation.
Career Highlights
Samad Jafarzanjani is currently associated with Kla Corporation, where he applies his expertise in machine learning and electromagnetic modeling. His innovative approach has positioned him as a key contributor to advancements in x-ray scatterometry.
Collaborations
Samad collaborates with Mohsen Mahvash, working together to push the boundaries of their research and development efforts in the field.
Conclusion
Samad Jafarzanjani's contributions to the field of x-ray scatterometry through his innovative patent demonstrate his commitment to advancing technology. His work not only enhances measurement techniques but also showcases the potential of machine learning in scientific applications.