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Optics Letters

Nanometric summation architecture based on optical near-field interaction between quantum dots

Makoto Naruse, Tetsuya Miyazaki, Fumito Kubota, Tadashi Kawazoe, Kiyoshi Kobayashi, Suguru Sangu, Motoichi Ohtsu

Published Jan 15, 2005• 34 citations
Publisher

Abstract

A nanoscale data summation architecture is proposed and experimentally demonstrated based on the optical near-field interaction between quantum dots. Based on local electromagnetic interactions between a few nanometric elements via optical near fields, we can combine multiple excitations at a certain quantum dot, which allows construction of a summation architecture. Summation plays a key role for content-addressable memory, which is one of the most important functions in optical networks.

DOI
10.1364/ol.30.000201

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A nanoscale data summation architecture is proposed and experimentally demonstrated based on the optical near-field interaction between quantum dots. Based on local electromagnetic interactions between a few nanometric elements via optical near fields, we can combine multiple excitations at a certain quantum dot, which allows construction of a summation architecture. Summation plays a key role for content-addressable memory, which is one of the most important functions in optical networks.
Using density operator formalism, we discuss interdot excitation energy transfer dynamics driven by the optical near-field and phonon bath reservoir, as well as coherent excitation dynamics of a quantum dot system. As an effective interaction between quantum dots induced by the optical near-field, the projection operator method gives a renormalized dipole interaction, which is expressed as a sum of the Yukawa functions and is used as the optical near-field coupling of quantum dots. We examine one- and two-exciton dynamics of a three-quantum dot system suggesting a nanometric photonic switch, and numerically obtain a transfer time comparable with the recent experimental results for CuCl quantum dots.
In nanophotonic device operations, characteristic features on a nanometer scale, such as locally excited states, dependence on the excitation number, and spatial symmetry of a system, play an important role. Using these features, selective excitation energy transfer via an optical near field is shown for a quantum-dot system with discrete energy levels. This selectivity strongly depends on a dipole-inactive state of an exciton, which cannot be excited by the far-field light. Operation principles of logic gates, photon storage, and quantum information processing device, which are based on the selectivity, are proposed, and the temporal dynamics are investigated analytically and numerically by using quantum theory. Nanophotonic devices, which are constructed from quantum mechanical and classical dissipative systems, are expected to become one of a key technologies in future device architecture.
Optical computing accelerators, with high parallelism, large bandwidth, and low transmission loss, have the potential to enhance electronic computing in both computational power and energy efficiency. Photonic acceleration plays a crucial role in supporting computationally intensive operations, such as dynamic-static matrix multiplication, significantly improving overall efficiency. Existing photonic architectures for dynamic-static matrix multiplication depend on complex coherent optical systems or costly nano-optics fabrication, limiting scalability. This study introduces a novel quantum dot fluorescence-based dynamic-static matrix multiplication photonic acceleration architecture that eliminates the need for coherent light sources or intricate fabrication. By leveraging simple, cost-effective quantum dot preparation and printing techniques, this architecture has significant potential for large-scale, high-performance, low-cost photonic accelerators. We detail the mathematical and physical mechanisms of the proposed architecture, experimentally validate the key physical processes, and demonstrate its application in template matching for image recognition, achieving 95% accuracy.