Company Filing History:
Years Active: 2023-2024
Title: The Innovations of Soo-Yeon Mo
Introduction
Soo-Yeon Mo is a prominent inventor based in Hwaseong-si, South Korea. He has made significant contributions to the field of semiconductor technology, particularly in the area of overlay key centering systems. With a total of two patents to his name, his work showcases the intersection of deep learning and precision engineering.
Latest Patents
Soo-Yeon Mo's latest patents include a "Deep learning-based overlay key centering system and method thereof." This invention provides a method to precisely measure and examine the alignment state of fine patterns on a semiconductor substrate. The process involves collecting an input data set from at least one device, which includes measurement image data of an overlay key and label data that contains information on the position and bounding box size of the overlay. The model is trained using this data, optimizing its algorithm to minimize loss values.
Another notable patent is the "Correlation-based overlay key centering system and method thereof." This method focuses on centering a correlation-based overlay by resizing an overlay target image and defining symmetrical templates based on a diagonal. It calculates rough and fine center coordinates to accurately position the overlay key, enhancing the precision of semiconductor manufacturing processes.
Career Highlights
Soo-Yeon Mo is currently employed at Auros Technology, Inc., where he continues to innovate in the semiconductor field. His expertise in deep learning applications has positioned him as a valuable asset to his company and the industry at large.
Collaborations
He collaborates with talented coworkers, including Ga-Min Kim and Hyo-Sik Ham, who contribute to the innovative environment at Auros Technology, Inc. Their teamwork fosters a culture of creativity and technical excellence.
Conclusion
Soo-Yeon Mo's contributions to semiconductor technology through his innovative patents demonstrate his commitment to advancing the field. His work not only enhances manufacturing precision but also showcases the potential of integrating deep learning into engineering solutions.