Details
| Original language | English |
|---|---|
| Title of host publication | Optical System Alignment, Tolerancing, and Verification XIV |
| Editors | Jose Sasian, Richard N. Youngworth |
| Publisher | SPIE |
| Number of pages | 8 |
| ISBN (electronic) | 9781510654280 |
| Publication status | Published - 3 Oct 2022 |
| Event | SPIE Optical Engineering + Applications, 2022, San Diego, California, United States: Optics and Photonics for Information Processing XVI - San Diego, California, San Diego, United States Duration: 21 Aug 2022 → 25 Aug 2022 Conference number: 122250E |
Publication series
| Name | Proceedings of SPIE - The International Society for Optical Engineering |
|---|---|
| Volume | 12222 |
| ISSN (Print) | 0277-786X |
| ISSN (electronic) | 1996-756X |
Abstract
External Fabry-Perot resonators are widely used in the field of optics and are well established in areas such as frequency selection and spectroscopy. However, fine tuning and thus most efficient coupling of these resonators into the optical path is a time-consuming task, which is usually performed manually by trained personnel. The state of the art includes many different approaches for automatic alignment, which, however, are designed for special optical configurations and cannot be generalized. However, these approaches are only valid for individually designed optical systems and are not universally applicable. Moreover, none of these approaches address the identification of the spatial degrees of freedom of the resonator. Knowledge of this exact pose information can generally be integrated into the alignment process and has great potential for automation. In this work, convolutional neural networks (CNNs) are applied to identify the sensitive spatial degrees of freedom of a FabryPerot resonator in a simulation environment. For this purpose, well established CNN architectures, which are typically used for feature extraction, are adapted to this regression problem. The input of the CNNs was chosen to be the intensity profiles of the transversal modes, which can be obtained from the transmitted power behind the resonator. These modes are known to be highly correlated with the coupling quality and thus with the spatial location of resonators. To achieve an exact pose estimation, the CNN input consists of several images of mode profiles, which are propagated through an encoder structure followed by fully-connected layers providing the four spatial parameters as the network output. For training and evaluation, intensity images as well as resonator poses are obtained from a simulation of a free spectral range of a resonator. Finally, different encoder structures including a memory efficient, small self-developed network architecture are evaluated.
Keywords
- Fabry-Perot resonator, Neural networks, Optical assembly, Optical pose estimation
ASJC Scopus subject areas
- Materials Science(all)
- Electronic, Optical and Magnetic Materials
- Physics and Astronomy(all)
- Condensed Matter Physics
- Computer Science(all)
- Computer Science Applications
- Mathematics(all)
- Applied Mathematics
- Engineering(all)
- Electrical and Electronic Engineering
Cite this
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Optical System Alignment, Tolerancing, and Verification XIV. ed. / Jose Sasian; Richard N. Youngworth. SPIE, 2022. 122220D (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 12222).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Pose estimation of optical resonators using convolutional neural networks in a simulation environment
AU - Melchert, Nils Frederik
AU - Hedrich, Kolja
AU - Wiese, Leon Vincent
AU - Hinz, Lennart
AU - Reithmeier, Eduard
N1 - Conference code: 122250E
PY - 2022/10/3
Y1 - 2022/10/3
N2 - External Fabry-Perot resonators are widely used in the field of optics and are well established in areas such as frequency selection and spectroscopy. However, fine tuning and thus most efficient coupling of these resonators into the optical path is a time-consuming task, which is usually performed manually by trained personnel. The state of the art includes many different approaches for automatic alignment, which, however, are designed for special optical configurations and cannot be generalized. However, these approaches are only valid for individually designed optical systems and are not universally applicable. Moreover, none of these approaches address the identification of the spatial degrees of freedom of the resonator. Knowledge of this exact pose information can generally be integrated into the alignment process and has great potential for automation. In this work, convolutional neural networks (CNNs) are applied to identify the sensitive spatial degrees of freedom of a FabryPerot resonator in a simulation environment. For this purpose, well established CNN architectures, which are typically used for feature extraction, are adapted to this regression problem. The input of the CNNs was chosen to be the intensity profiles of the transversal modes, which can be obtained from the transmitted power behind the resonator. These modes are known to be highly correlated with the coupling quality and thus with the spatial location of resonators. To achieve an exact pose estimation, the CNN input consists of several images of mode profiles, which are propagated through an encoder structure followed by fully-connected layers providing the four spatial parameters as the network output. For training and evaluation, intensity images as well as resonator poses are obtained from a simulation of a free spectral range of a resonator. Finally, different encoder structures including a memory efficient, small self-developed network architecture are evaluated.
AB - External Fabry-Perot resonators are widely used in the field of optics and are well established in areas such as frequency selection and spectroscopy. However, fine tuning and thus most efficient coupling of these resonators into the optical path is a time-consuming task, which is usually performed manually by trained personnel. The state of the art includes many different approaches for automatic alignment, which, however, are designed for special optical configurations and cannot be generalized. However, these approaches are only valid for individually designed optical systems and are not universally applicable. Moreover, none of these approaches address the identification of the spatial degrees of freedom of the resonator. Knowledge of this exact pose information can generally be integrated into the alignment process and has great potential for automation. In this work, convolutional neural networks (CNNs) are applied to identify the sensitive spatial degrees of freedom of a FabryPerot resonator in a simulation environment. For this purpose, well established CNN architectures, which are typically used for feature extraction, are adapted to this regression problem. The input of the CNNs was chosen to be the intensity profiles of the transversal modes, which can be obtained from the transmitted power behind the resonator. These modes are known to be highly correlated with the coupling quality and thus with the spatial location of resonators. To achieve an exact pose estimation, the CNN input consists of several images of mode profiles, which are propagated through an encoder structure followed by fully-connected layers providing the four spatial parameters as the network output. For training and evaluation, intensity images as well as resonator poses are obtained from a simulation of a free spectral range of a resonator. Finally, different encoder structures including a memory efficient, small self-developed network architecture are evaluated.
KW - Fabry-Perot resonator
KW - Neural networks
KW - Optical assembly
KW - Optical pose estimation
UR - http://www.scopus.com/inward/record.url?scp=85140344866&partnerID=8YFLogxK
U2 - 10.1117/12.2633416
DO - 10.1117/12.2633416
M3 - Conference contribution
T3 - Proceedings of SPIE - The International Society for Optical Engineering
BT - Optical System Alignment, Tolerancing, and Verification XIV
A2 - Sasian, Jose
A2 - Youngworth, Richard N.
PB - SPIE
T2 - SPIE Optical Engineering + Applications, 2022, San Diego, California, United States
Y2 - 21 August 2022 through 25 August 2022
ER -