CONFERENCE PROCEEDINGS
Advanced Search
Home > Proceedings > Volume PC12885 > Article
Presentation
12 March 2024 Deep neural networks for robust THz-TDS refractive index extraction
Nicholas T. Klokkou, Jon Gorecki, Ben Beddoes, Vasilis Apostolopoulos
Author Affiliations +
Proceedings Volume PC12885, Terahertz, RF, Millimeter, and Submillimeter-Wave Technology and Applications XVII; PC1288503 (2024) https://doi.org/10.1117/12.3005191
Event: SPIE OPTO, 2024, San Francisco, California, United States
Abstract
Terahertz Time-Domain Spectroscopy (THz-TDS) uses ultrafast lasers to emit and detect broadband, picosecond pulses with excellent signal-to-noise ratios and rapid data acquisition. Commercial spectrometers have become available and there is now great access to the technology. However, the data analysis remains complex and prone to errors due to multiple processing steps and variation in experimental setups, hindering its true breakout into industry. Machine learning, particularly the training of artificial neural networks with simulated data, has proven effective in various spectroscopic techniques, including refractive index extraction with THz-TDS. This approach allows controlled inclusion of analytical and experimental errors, enabling performant networks that are easier to characterize. We explore the use of deep neural networks for complex refractive index prediction that account for experimental and analytical errors, such as laser drift, compensating for imperfect experimental data and potentially superseding current extraction methods.
Conference Presentation
© (2024) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Citation Download Citation
Nicholas T. Klokkou, Jon Gorecki, Ben Beddoes, and Vasilis Apostolopoulos "Deep neural networks for robust THz-TDS refractive index extraction", Proc. SPIE PC12885, Terahertz, RF, Millimeter, and Submillimeter-Wave Technology and Applications XVII, PC1288503 (12 March 2024); https://doi.org/10.1117/12.3005191
ACCESS THE FULL ARTICLE
ORGANIZATIONAL
Sign in with credentials provided by your organization.
INSTITUTIONAL
Select your institution to access the SPIE Digital Library.
SELECT YOUR INSTITUTION
PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.
PERSONAL SIGN IN
No SPIE Account? Create one
PURCHASE THIS CONTENT
SUBSCRIBE TO DIGITAL LIBRARY
50 downloads per 1-year subscription
Members: $195
Non-members: $335 ADD TO CART
25 downloads per 1 - year subscription
Members: $145
Non-members: $250 ADD TO CART
PURCHASE SINGLE ARTICLE
Includes PDF, HTML & Video, when available
Members: $17.00
Non-members: $21.00 ADD TO CART
PROCEEDINGS
 PRESENTATION
WATCH
PRESENTATION SAVE TO MY LIBRARY
GET CITATION
Advertisement
Advertisement
KEYWORDS
Artificial neural networks
Spectroscopy
Refractive index
Terahertz spectroscopy
Education and training
Data modeling
Signal to noise ratio
RELATED CONTENT
Subscribe to Digital Library
Receive Erratum Email Alert
Back to Top