Seoul, South Korea

Yoonsung Bae


Average Co-Inventor Count = 9.0

ph-index = 1


Company Filing History:


Years Active: 2025

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1 patent (USPTO):Explore Patents

Title: Yoonsung BAE - Innovating Semiconductor Manufacturing through Deep Learning

Introduction

Yoonsung BAE is a prominent inventor based in Seoul, South Korea. With a focus on advancing semiconductor manufacturing processes, he has developed innovative methods that combine deep learning and spectral analysis to enhance production efficiency and accuracy.

Latest Patents

Yoonsung BAE holds a patent titled "Method of training deep learning model for predicting pattern characteristics and method of manufacturing semiconductor device." This invention outlines a sophisticated approach to semiconductor device manufacturing, which involves forming patterns on wafers and employing a spectral optical system to measure the spectrum of these patterns. The use of a trained deep learning model enables the analysis of the spectrum data to predict pattern characteristics effectively. The innovation emphasizes the importance of domain knowledge that includes noise-inducing factors to optimize the manufacturing process.

Career Highlights

Currently, Yoonsung BAE is affiliated with Samsung Electronics, a leading global technology company recognized for its innovations in electronics and semiconductor solutions. His work at Samsung highlights his commitment to pushing the boundaries of semiconductor technology, particularly through the integration of advanced analytical techniques.

Collaborations

Yoonsung has collaborated with talented peers, including Seungho GWAK and Kwangrak Kim, contributing to a dynamic team environment that fosters creativity and innovation. Together, they work on projects that aim to enhance semiconductor processes and improve production methodologies.

Conclusion

Yoonsung BAE's contributions to the field of semiconductor manufacturing through his patented methods showcase the intersection of technology and innovation. As the industry evolves, his work demonstrates the potential of deep learning in improving manufacturing processes, setting new standards for efficiency and accuracy in semiconductor technology.

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