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Software-defined meta-optics
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AI
DOI：10.1063/5.0164387.png)
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摘要
En 中文
Rapid advancements in autonomous systems and the Internet of Things have necessitated the development of compact and low-power image sensors to bridge the gap between the digital and physical world. To that end, sub-wavelength diffractive optics, commonly known as meta-optics, have garnered significant interest from the optics and photonics community due to their ability to achieve multiple functionalities within a small form factor. Despite years of research, however, the performance of meta-optics has often remained inferior compared to that of traditional refractive optics. In parallel, computational imaging techniques have emerged as a promising path to miniaturize optical systems, albeit often at the expense of higher power and latency. The lack of desired performance from either meta-optical or computational solutions has motivated researchers to look into a jointly optimized meta-optical-digital solution. While the meta-optical front end can preprocess the scene to reduce the computational load on the digital back end, the computational back end can in turn relax requirements on the meta-optics. In this Perspective, we provide an overview of this up-and-coming field, termed here as software-defined meta-optics. We highlight recent contributions that have advanced the current state of the art and point out directions toward which future research efforts should be directed to leverage the full potential of subwavelength photonic platforms in imaging and sensing applications. Synergistic technology transfer and commercialization of meta-optic technologies will pave the way for highly efficient, compact, and low-power imaging systems of the future.
Keyword:
MACHINE VISION
期刊
IF:
3.6
论文数:
10.4W
被引数:
17.8W
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