In this paper we present a sub-bandgap photodetector consisting of a metal grating on a thin metal patch on silicon, which makes use of the enhancement produced by the excitation of surface plasmon polaritons at the metal-silicon interface. The grating is defined via e-beam lithography and Au lift-off on a Au patch defined beforehand by optical lithography on doped p-type silicon. The surface plasmon polaritons are absorbed by the metal, leading to the creation of hot holes that can cross into the silicon where they are collected as the photocurrent. Physical characterization of intermediate structure is provided along with responsivity measurements at telecom wavelengths. Results are promising in terms of responsivity, with a value of 13 mA/W measured at 1550 nm - this is among the highest values reported to date for sub-bandgap detectors based on internal photoemission. The Schottky photodetector can be used in, e.g., non-contact wafer probing or in short-reach optical communications applications.

Nanoantennas are key optical components for several applications including photodetection and biosensing. Here we present an array of metal nano-dipoles supporting surface plasmon polaritons (SPPs) integrated into a silicon-based Schottky-contact photodetector. Incident photons coupled to the array excite SPPs on the Au nanowires of the antennas which decay by creating "hot" carriers in the metal. The hot carriers may then be injected over the potential barrier at the Au-Si interface resulting in a photocurrent. High responsivities of 100 mA/W and practical minimum detectable powers of -12 dBm should be achievable in the infra-red (1310 nm). The device was then investigated for use as a biosensor by computing its bulk and surface sensitivities. Sensitivities of ∼ 250 nm/RIU (bulk) and ∼ 8 nm/nm (surface) in water are predicted. We identify the mode propagating and resonating along the nanowires of the antennas, we apply a transmission line model to describe the performance of the antennas, and we extract two useful formulas to predict their bulk and surface sensitivities. We prove that the sensitivities of dipoles are much greater than those of similar monopoles and we show that this difference comes from the gap in dipole antennas where electric fields are strongly enhanced.

Surface plasmon photodetectors are of broad interest. They are promising for several applications including telecommunications, photovoltaic solar cells, photocatalysis, color-sensitive detection, and sensing, as they can provide highly enhanced fields and strong confinement (to subwavelength scales). Such photodetectors typically combine a nanometallic structure that supports surface plasmons with a photodetection structure based on internal photoemission or electron-hole pair creation. Photodetector architectures are highly varied, including waveguides, gratings, nanoparticles, nanoislands, or nanoantennas. We review the operating principles behind surface plasmon photodetectors based on the internal photoelectric effect, and we survey and compare the most recent and leading edge concepts reported in the literature. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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We propose a plasmonic gold nanodipole array on silicon, forming a Schottky contact thereon, and covered by water. The behavior of this array under normal excitation has been extensively investigated. Trends have been found and confirmed by identification of the mode propagating in nanodipoles and its properties. This device can be used to detect infrared radiation below the bandgap energy of the substrate via internal photoelectric effect (IPE). Also we estimate its responsivity and detection limit. Finally, we assess the potential of the structure for bulk and surface (bio) chemical sensing. Based on modal results an analytical model has been proposed to estimate the sensitivity of the device. Results show a good agreement between numerical and analytical interpretations.

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Open radio access network (O-RAN), driven by O-RAN Alliance is based on the disaggregation of the traditional RAN systems into radio unit (RU), distributed unit (DU) and central unit (CU) components. It provides a unique opportunity to reduce the cost of wireless network deployment by using open-source software, serving as a foundation for O-RAN compliant functions, and by utilizing low-cost, generic white-box hardware for radio components. Relying on the two core pillars of openness and intelligence, there has been a coordinated global effort from operators and equipment providers to enhance the RAN architecture and improve its performance through virtualized network elements and open interfaces that incorporate intelligence in RAN. With the increased complexity of 5G networks and the demand to fulfill requirements, intelligence is becoming a key factor for automated deployment, operation, and optimization of open wireless networks. The first thrust of this paper surveys the AI/ML architecture in O-RAN specifications, key discussion points and future standardization directions, respectively. In the second part, we introduce a proof-of-concept use case on AI-driven network optimization within the near real-time RAN intelligent controller (near-RT RIC) and non-real time RIC (non-RT RIC). In particular, we investigate the user admission control problem, led by a deep learning-based algorithm, implemented as an xApp for network performance enhancement. Extensive system-level simulations are performed with NS-3 LTE to assess the proposed admission control algorithm. Accordingly, the proposed dynamic algorithm shows a significant admission control performance improvement and flexibility, compared to existing admission control static techniques, while satisfying the stringent quality of service targets of admitted devices. Finally, the paper offers insightful conclusions and findings on the AI-based modeling, model inference performance, key performance challenges and future research directions, respectively.

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