Deep learning in optical metrology: a review

🤖 Plain-English Summary

With the advances in scientific foundations and technological implementations, optical metrology has become versatile problem-solving backbones in manufacturing, fundamental research, and engineering applications, such as quality control, nondestructive testing, experimental mechanics, and biomedicine. The open challenges faced by the current deep-learning approach in optical metrology are then discussed.

🔑 Key Findings

• In recent years, deep learning, a subfield of machine learning, is emerging as a powerful tool to address problems by learning from data, largely driven by the availability of massive datasets, enhanced computational power, fast data storage, and novel training algorithms for the deep neural network.
• It is currently promoting increased interests and gaining extensive attention for its utilization in the field of optical metrology.
• Unlike the traditional "physics-based" approach, deep-learning-enabled optical metrology is a kind of "data-driven" approach, which has already provided numerous alternative solutions to many challenging problems in this field with better performances.

🔗 Related Resources