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Ren, Zhihao; Dong, Bowei; Qiao, Qifeng; Liu, Xinmiao; Liu, Jinyuan; Zhou, Guangya; Lee, Chengkuo

doi:10.1002/inf2.12297

Cited by 5 publications

(6 citation statements)

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“…69–72 The SEIRA substrates are sub-wavelength nanoantennas or metamaterials which are artificial sub-wavelength structures with extraordinary physical properties distinct from the intrinsic properties of naturally available materials. 73–85 The nanofabrication technologies for preparing SEIRA/SERS substrates include chemical preparation methods, photolithography, electron beam lithography, magnetron sputtering, electron beam evaporation, and more. 86–107 The widely used theory for modeling SERS/SEIRA includes perturbation theory, 108 temporal coupled-mode theory, 109,110 coupled harmonic oscillator theory, 111 and so on.…”

Section: Concept Of Machine Learning Enhancing Sers and Seiramentioning

confidence: 99%

Machine learning-augmented surface-enhanced spectroscopy toward next-generation molecular diagnostics

Zhou

Ren

et al. 2023

Nanoscale Adv.

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Section: Concept Of Machine Learning Enhancing Sers and Seiramentioning

confidence: 99%

Machine learning-augmented surface-enhanced spectroscopy toward next-generation molecular diagnostics

Zhou

Ren

et al. 2023

Nanoscale Adv.

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Section: Cloud Computing Applicationsmentioning

confidence: 99%

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confidence: 99%

Advances in Machine‐Learning Enhanced Nanosensors: From Cloud Artificial Intelligence Toward Future Edge Computing at Chip Level

Zhang,

Liu,

Zhou

et al. 2023

Small Structures

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Machine‐learning‐enhanced nanosensors are rapidly emerging as a promising solution in the field of sensor technology, as traditional sensors encounter limitations of data analysis in their development. Since the inception of machine‐learning algorithms being applied to enhance nanosensors, they have gained significant attention due to their adaptive and predictive capabilities, which promise to dramatically improve efficiency in data collection and processing applications. Herein, a comprehensive overview of technological innovation is provided by reviewing the latest developments in cloud computing, edge computing, and the burgeoning realm of neuromorphic computing. Cloud computing has emerged as a powerhouse, harnessing formidable computational capabilities to process vast volumes of high‐dimensional data. Then, the research directions for various applications of these cloud artificial intelligence (AI)‐enhanced nanosensors are outlined. Moreover, the integration of AI and nanosensor technology into chip‐level edge computing, although promising, still faces challenges such as energy‐efficient hardware development, algorithm optimization, and scalability for mass production. Finally, a forward‐looking perspective on the future of machine‐learning‐enhanced nanosensors is provided, delineating the challenges and opportunities for further research and innovation in this exciting field.

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confidence: 99%

Recent Progress in Silicon‐Based Photonic Integrated Circuits and Emerging Applications

Xiao,

Liu,

et al. 2023

Advanced Optical Materials

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In recent years, with the further ministration of the semiconductor device in integrated circuits, power consumption and data transmission bandwidth have become insurmountable obstacles. As an integrated technology, photonic integrated circuits (PICs) have a promising potential in the post‐Moore era with more advantages in data processing, communication, and diversified sensing applications for their ultra‐high process speed and low power consumption. Silicon photonics is believed to be an encouraging solution to realize PICs because of the mature CMOS process. The past decades have witnessed a huge growth in silicon PICs. However, there is still a demand for the development of silicon PICs to enable powerful chip‐scale systems and new functionalities. In this paper, a review of the photonic components, functional blocks, and emerging applications for PICs is offered. The common photonic components are classified into several sections, including on‐chip light sources, fiber‐to‐chip couplers, photonic resonators, waveguide‐based sensors, on‐chip photodetectors, and modulators. The functional blocks of the PICs mentioned in this review are photonic memories and photonic neural networks. Finally, the paper concludes with emerging applications for further study.

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Abstract: Bigradient all‐dielectric metamaterials provide subwavelength‐scale on‐chip metalenses solution for dense photonic integrated circuits by structural engineering. (DOI: https://doi.org/10.1002/inf2.12264)

Cited by 5 publications

References 0 publications

Machine learning-augmented surface-enhanced spectroscopy toward next-generation molecular diagnostics

Machine learning-augmented surface-enhanced spectroscopy toward next-generation molecular diagnostics

Advances in Machine‐Learning Enhanced Nanosensors: From Cloud Artificial Intelligence Toward Future Edge Computing at Chip Level

Recent Progress in Silicon‐Based Photonic Integrated Circuits and Emerging Applications

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