Amorphous tantala ( ) thin films were deposited by reactive ion beam sputtering with simultaneous low energy assist or bombardment. Under the conditions of the experiment, the as-deposited thin films are amorphous and stoichiometric. The refractive index and optical band gap of thin films remain unchanged by ion bombardment. Around 20% improvement in room temperature mechanical loss and 60% decrease in absorption loss are found in samples bombarded with 100-eV . A detrimental influence from low energy bombardment on absorption loss and mechanical loss is observed. Low energy bombardment removes excess oxygen point defects, while bombardment introduces defects into the tantala films.
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Deep-learning-based image reconstruction for compressed ultrafast photography
Compressed ultrafast photography (CUP) is a computational optical imaging technique that can capture transient dynamics at an unprecedented speed. Currently, the image reconstruction of CUP relies on iterative algorithms, which are time-consuming and often yield nonoptimal image quality. To solve this problem, we develop a deep-learning-based method for CUP reconstruction that substantially improves the image quality and reconstruction speed. A key innovation toward efficient deep learning reconstruction of a large three-dimensional (3D) event datacube ( ) ( , spatial coordinate; , time) is that we decompose the original datacube into massively parallel two-dimensional (2D) imaging subproblems, which are much simpler to solve by a deep neural network. We validated our approach on simulated and experimental data.
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- Award ID(s):
- 2053080
- PAR ID:
- 10180513
- Publisher / Repository:
- Optical Society of America
- Date Published:
- Journal Name:
- Optics Letters
- Volume:
- 45
- Issue:
- 16
- ISSN:
- 0146-9592; OPLEDP
- Format(s):
- Medium: X Size: Article No. 4400
- Size(s):
- Article No. 4400
- Sponsoring Org:
- National Science Foundation
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