Invention:
This invention is an interferometric system that captures two interferograms and utilizes a deep learning algorithm to process them. This invention enables the measurement of surface roughness and surface shape using the interferometric system and neural network to process the data. The deep learning portion of the system is in a compact form, allowing for on-machine measurements and a suitable attachment to the interferometric system.
Background:
The application of high precision optical elements is vast, from smart-phone camera lenses and telescopes to optical fibers and much more. With an increase in technological development, high precision optical elements with accurate and efficient fabrication processes are in high demand, placing ultrahigh requirements on the measurement tools to improve workpiece quality control and manage the machining process. As a recognized accurate testing method, interferometry has been a powerful method for non-contact surface metrology of optical elements. There are two unique interferometry methods to measure surface form and roughness. Currently, the commercial interferometric instruments have a separate procedure to acquire the surface form and roughness measurements. This adds additional costs, time and fabrication errors to the interferometric process of measurements.
Applications:
- Metrology
- Optical testing and measurement
- Interferometric systems
Advantages:
- On-machine deep learning
- All-in-one process for surface form and roughness measurements
- Cost efficient, due to reduced procedure
- Less fabrication errors potential due to reduced procedure
- Compact deep learning attachment
