Nanophotonics: Application of Dressed Photons to Novel Photonic Devices and Systems

Ohtsu, Motoichi; Kawazoe, Tadashi; Yatsui, Takashi; Naruse, Makoto

doi:10.1109/jstqe.2008.918110

Cited by 76 publications

(47 citation statements)

References 51 publications

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“…In this section, we first briefly review the fundamental principles of optical excitation transfer involving optical nearfield interactions [2,3]. Conventionally, the interaction Hamiltonian between an electron and an electric field is given …”

Section: Optical Excitation Transfer Via Optical Near-field Intermentioning

confidence: 99%

“…As a consequence, in the case of cubic QDs, for instance, transitions to states described by quantum numbers containing an even number are prohibited [2]. In the case of optical near-field interactions, on the other hand, due to the steep electric field of optical near-fields in the vicinity of nanometer-scale matter, an optical transition that violates conventional optical selection rules is allowed [2,3]. Optical excitations in nanostructures, such as QDs, can be transferred to neighboring ones via optical near-field interactions [8][9][10].…”

Section: Rmentioning

confidence: 99%

“…However, there are some fundamental difficulties impeding the adoption of optical technologies in information processing and communication systems [2][3][4][5][6]. One problem is the poor integrability of optical devices in systems due to the diffraction limit of light.…”

Section: Introductionmentioning

confidence: 99%

“…Nanophotonics, on the other hand, which is based on local interactions between nanometer-scale materials via optical near-fields, is not restricted by conventional diffraction of light, allowing ultrahigh-density integration [2][3][4]. Optical excitation transfer between quantum dots (QDs) via optical near-field interactions will be one of the most important mechanisms in realizing novel devices and systems [2,3,7,8].…”

Section: Introductionmentioning

confidence: 99%

“…Optical excitation transfer between quantum dots (QDs) via optical near-field interactions will be one of the most important mechanisms in realizing novel devices and systems [2,3,7,8].…”

Section: Introductionmentioning

confidence: 99%

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Autonomous excitation transfer in quantum dot mixtures via network of optical near-field interactions at the nanoscale

Naruse

Peper

Leibnitz

et al. 2011

2011 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)

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Abstract-We examine autonomous optical excitation transfer in mixtures of different-sized quantum dots networked via optical near-fields at the nanometer scale. We theoretically and experimentally demonstrate optical excitation transfer via the network of optical near-field interactions among quantum dots. The topology-dependent efficiency of excitation transfer is also investigated. The results of our analysis of autonomous and energy-efficient light-matter interactions at the nanoscale, called nanophotonics, will provide useful insights into the design of robust and energy-efficient information and communications systems and networks.

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Section: Optical Excitation Transfer Via Optical Near-field Intermentioning

confidence: 99%

Section: Rmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Optical excitation transfer between quantum dots (QDs) via optical near-field interactions will be one of the most important mechanisms in realizing novel devices and systems [2,3,7,8].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Autonomous excitation transfer in quantum dot mixtures via network of optical near-field interactions at the nanoscale

Naruse

Peper

Leibnitz

et al. 2011

2011 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)

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Photonics for Neuromorphic Computing: Fundamentals, Devices, and Opportunities

Li,

Gong,

Huang

et al. 2024

Advanced Materials

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In the dynamic landscape of Artificial Intelligence (AI), two notable phenomena are becoming predominant: the exponential growth of large AI model sizes and the explosion of massive amount of data. Meanwhile, scientific research such as quantum computing and protein synthesis increasingly demand higher computing capacities. Neuromorphic computing, inspired by the mechanism and functionality of human brains, uses physical artificial neurons to do computations and is drawing widespread attention. Conventional electronic computing has experienced certain difficulties, particularly concerning the latency, crosstalk, and energy consumption of digital processors. As the Moore's law approaches its terminus, there is a urgent need for alternative computing architectures that can satisfy this growing computing demand and break through the von Neumann model. Recently, the expansion of optoelectronic devices on photonic integration platforms has led to significant growth in photonic computing, where photonic integrated circuits (PICs) have enabled ultrafast artificial neural networks (ANN) with sub‐nanosecond latencies, low heat dissipation, and high parallelism. Such non‐von Neumann photonic computing systems hold the promise to cater to the escalating requirements of AI and scientific computing. In this review, we study recent advancements in integrated photonic neuromorphic systems, and from the perspective of materials and device engineering, we lay out the scientific and technological breakthroughs necessary to advance the state‐of‐the‐art. In particular, we examine various technologies and devices employed in neuromorphic photonic AI accelerators, spanning from traditional optics to PICs. We evaluate the performances of different designs by energy efficiency in operations per joule (OP/J) and compute density in operations per squared millimeter per second (OP/mm2/s). Putting special emphasis on photonic components such as VCSEL lasers, optical interconnects, and frequency microcombs, we highlight the most recent breakthroughs in photonic engineering and materials science used to create advanced neuromorphic computing chips. Lastly, we recognize that existing technologies encounter obstacles in achieving photonic AI accelerators with peta‐level computing speed and energy efficiency, and we also explore potential approaches in new devices, fabrication, materials, and integration to drive innovation. As the current challenges and barriers in cost, scalability, footprint, and computing capacity are resolved one‐by‐one, photonic neuromorphic systems are bound to co‐exist with, if not replace, conventional electronic computers and transform the landscape of AI and scientific computing in the foreseeable future.This article is protected by copyright. All rights reserved

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Nonadiabatic Near-Field Optical Polishing and Energy Transfers in Spherical Quantum Dots

Nomura

Yatsui

Ohtsu

2009

Springer Series in Optical Sciences

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Nanophotonics: Application of Dressed Photons to Novel Photonic Devices and Systems

Cited by 76 publications

References 51 publications

Autonomous excitation transfer in quantum dot mixtures via network of optical near-field interactions at the nanoscale

Autonomous excitation transfer in quantum dot mixtures via network of optical near-field interactions at the nanoscale

Photonics for Neuromorphic Computing: Fundamentals, Devices, and Opportunities

Nonadiabatic Near-Field Optical Polishing and Energy Transfers in Spherical Quantum Dots

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