Yield Development Engineer
Intel
Seattle, WA, United States
Yessie Kim, Master of Applied Data Science, is a data scientist at Intel with expertise in statistical analysis, machine learning, and computer vision. In her current role, she focuses on building and deploying AI solutions that enhance defect detection accuracy and process efficiency in semiconductor manufacturing. Yessie earned her Master’s degree in Applied Data Science from the University of Michigan, where she built a strong foundation in data analytics, modeling, and data-driven decision-making.
In her professional work, Yessie has developed machine learning models for inline defect classification from SEM images of wafers, applying advanced techniques in feature engineering, random forests, convolutional neural networks (CNN), and YOLO-based architectures. She contributed to building a scalable AI framework for high-throughput inspection systems, improving classification accuracy and efficiency while reducing manual review time. Beyond modeling, she designed and delivered end-to-end dashboard solutions that turn raw data into actionable insights, enhancing collaboration across engineering teams.
Recognized for her composure and precision under pressure, Yessie combines logical, structured problem-solving with strong interpersonal skills, making her an effective collaborator in both technical and business environments. She views coding as a creative process for solving complex challenges and takes pride in delivering reliable, data-driven solutions.
Generic SEM Defect Classification AI Framework for High-Throughput Inline Optical Inspection Systems
Thursday, October 9, 2025
2:25pm - 2:45pm MT