Kota Yanagihara
Papers
1
Total Citations
2
H-Index
1
About
Kota Yanagihara is a researcher specializing in computer vision and automated defect detection, with a particular focus on semiconductor manufacturing processes. His most cited work, "Automatic Chip Detection Using Differnet" (2022), introduces a novel deep learning approach for identifying chip defects on silicon wafers, addressing critical quality control challenges in the electronics industry. Although his publication record is still developing, with this paper garnering 2 citations to date, Yanagihara's contribution lies in applying differential neural networks to enhance the precision and efficiency of automated inspection systems. This work holds potential for reducing manufacturing errors and improving yield rates in chip production. As an emerging voice in applied machine learning for industrial automation, Yanagihara's research bridges the gap between theoretical computer vision models and practical, real-world manufacturing needs. His focus on defect detection aligns with broader industry trends toward smart manufacturing and Industry 4.0, positioning him as a promising contributor to the field.
Research Focus
Key Achievements
Top Papers
- 1Automatic Chip Detection Using Differnet2 citations · 2022