Optical computing is considered a promising solution for the growing demand for parallel computing in various cutting-edge fields that require high integration and high-speed computational capacity. We propose an optical computation architecture called diffraction casting (DC) for flexible and scalable parallel logic operations. In DC, a diffractive neural network is designed for single instruction, multiple data (SIMD) operations. This approach allows for the alteration of logic operations simply by changing the illumination patterns. Furthermore, it eliminates the need for encoding and decoding of the input and output, respectively, by introducing a buffer around the input area, facilitating end-to-end all-optical computing. We numerically demonstrate DC by performing all 16 logic operations on two arbitrary 256-bit parallel binary inputs. Additionally, we showcase several distinctive attributes inherent in DC, such as the benefit of cohesively designing the diffractive elements for SIMD logic operations that assure high scalability and high integration capability. Our study offers a design architecture for optical computers and paves the way for a next-generation optical computing paradigm.
Reservoir computing is a powerful tool for creating digital twins of a target systems. They can both predict future values of a chaotic timeseries to a high accuracy and also reconstruct the general properties of a chaotic attractor. In this. We show that their ability to learn the dynamics of a complex system can be extended to systems with multiple co-existing attractors, here a four-dimensional extension of the well-known Lorenz chaotic system.
Even parts of the phase space that were not in the training set can be explored with the help of a properly-trained reservoir computer. This includes entirely separate attractors, which we call "unseen". Training on a single noisy trajectory is sufficient. Because Reservoir Computers are substrate-agnostic, this allows the creation of conjugate autonomous reservoir computers for any target dynamical systems.
KEYWORDS: Nanophotonics, Holograms, Near field optics, Polarization, Nano opto mechanical systems, Near field, Nanolithography, Data processing, Holography, Prototyping
A nanophotonic hierarchical hologram works in both optical far-fields and near-fields, the former being associated
with conventional holographic images, and the latter being associated with the optical intensity distribution based on a
nanometric structure that is accessible only via optical near-fields. In principle, a structural change occurring at the
subwavelength scale does not affect the optical response functions, which are dominated by propagating light. Therefore,
the visual aspect of the hologram is not affected by such a small structural change on the surface, and retrieval in both
fields can be processed independently. We propose embedding a nanophotonic code, which is retrievable via optical
near-field interactions involving nanometric structures, within an embossed hologram. Due to the one-dimensional grid
structure of the hologram, evident polarization dependence appears in retrieving the code. Here we describe the basic
concepts, numerical simulations, and experimental demonstrations of a prototype nanophotonic hierarchical hologram
with a nanophotonic code and describe its optical characterization.
"Nanophotonics" uses the local interaction between nanometric particles via optical near-fields to bring "qualitative
innovation" to the field of optical technology. Optical near-field interactions respond hierarchically at the nanometer
scale, allowing unique nanophotonic functions. We defined two kinds of hierarchical optical near-field interactions:
those between optical far- and near-fields, and those in the optical near-field only. We demonstrated these hierarchical
effects numerically and experimentally using several prototype "nanophotonic architectures." The first, a "hierarchical
hologram," operated in both the far- and near-fields with few adverse effects. We also demonstrated hierarchical effects
in the optical near-field by core-shell metal nanostructures. Hierarchical nanoscale architectures could allow single
optical devices to perform multiple functions. The practical realization of such devices could have a major impact, for
example, in the field of optical security.
To decrease the sizes of photonic devices beyond the diffraction limit of light, we propose nanophotonic devices based
on optical near-field interactions between semiconductor quantum dots (QDs). To drive such devices, an optical signal
guide whose width is less than several tens of nanometers is required. Furthermore, unidirectional signal transfer is
essential to prevent nanophotonic devices operating incorrectly due to signals reflected from the destination. For
unidirectional signal transfer at the nanometer scale, we propose a nanophotonic signal transmitter based on optical nearfield
interactions between small QDs of the same size and energy dissipation in larger QDs that have a resonant exciton
energy level with the small QDs. To confirm such unidirectional energy transfer, we used time-resolved
photoluminescence spectroscopy to observe exciton energy transfer between the small QDs via the optical near-field, and
subsequent energy dissipation in the larger QDs. We estimated that the energy transfer time between resonant CdSe/ZnS
QDs was 135 ps, which is shorter than the exciton lifetime of 2.10 ns. Furthermore, we confirmed that exciton energy did
not transfer between nonresonant QD pairs. These results indicated that the proposed nanophotonic signal transmitters
based on optical near-field interactions and energy dissipation could be used to make multiple transmitters and selfdirectional
interconnections.
KEYWORDS: Near field optics, Particles, Resistance, Quantum dots, Metals, Information security, Signal processing, Nanoparticles, Excitons, Spatial frequencies
Recent advances in near-field optics and sub-wavelength-precision fabrication technology allow the design of optical
devices and systems at densities beyond those conventionally limited by the diffraction of light. Such higher integration
density, however, is only one of the benefits of optical near-fields over conventional optics and electronics. In this paper,
we exploit additional notable features in optical near-field interactions, which are physical hierarchy in optical nearfields
and the properties associated with energy dissipation processes. We present their theoretical backgrounds and their
applications. We deal with security aspects of optical near-field interactions by noticing environmental factors. We also
demonstrate hierarchical systems based on dipole-dipole interactions and angular spectrum representation of optical
near-fields as well as associating them with energy dissipation processes, which lead to another functionality such as
traceability of information.
To realize the optical devices required by future systems, we have proposed nanometer-scale photonic integrated circuits (i.e., nanophotonic ICs). These devices consist of nanometer-scale dots, and an optical near field is used as the signal carrier. Since an optical near field is free from the diffraction of light due to its size-dependent localization and resonance features, nanophotonics enables the fabrication, operation, and integration of nanometric devices. To drive a nanophotonic device with an external conventional diffraction-limited photonic device, a far/near-field conversion device is required. Here, we review the use of a nanometer-scale waveguide as such a conversion device for nanophotonic ICs. Furthermore, the fabrication of a nanophotonic device using an optical near-field is introduced.
Here we show our architectural approaches to nanophotonics to benefit from unique physical properties obtained by local interactions between nanometric elements, such as quantum dots, via optical near fields, that provide ultra high-density integration capability beyond the diffraction limit of light. We discuss a memory-based architecture and a simple hierarchical architecture. By using resonant energy levels between quantum dots and inter-dot interactions, nanometric data summation and broadcast architectures are demonstrated including their proof-of-principle experimental verifications using CuCl quantum dots. Through such architectural and physical insights, we are seeking nanophotonic information systems for solving the integration density limited by diffraction limit of light and providing ultra low-power operations as well as unique functionalities which are only achievable using optical near-field interactions.
Optical interconnections and integrated optoelectronic devices are expected to be promising candidates that expand interconnection bandwidth between large-scale integrated circuits (LSIs). We have constructed an optoelectronic parallel computing system that has a reconfigurable free- space parallel optical interconnection module called OCULAR- II. It has a multi-layer architecture that eliminates the data transfer bottleneck between optoelectronic processing modules by reconfigurable free-space optical interconnections. An optoelectronic processing module is composed of a two-dimensional processing element array where each pixel has its own optical output channel by a VCSEL and optical input channel. The optical interconnection is integrated into a compact module where an optically addressable phase only spatial light modulator and an imaging optical system are compactly fabricated. Each component of the OCULAR-II system has been designed to be modular and compact. Therefore, just cascading optoelectronic processing modules and optical interconnection modules makes a pipelined parallel processing system. In the optical interconnection module, a custom designed Fourier Transform lens has been used to reduce the working distance of the lens system. A computer generated hologram (CGH) is written on a liquid crystal display (LCD) that is coupled by a fiber optic plate (FOP) to the optically addressable SLM. The interconnection topology between optoelectronic chips is controlled by changing the CGH patterns, which is calculated in advance. A global interconnectivity among processor arrays is also achievable since the communication channels are constructed via optical path in free space. The data broadcasting between processors that are located spatially far away can be efficiently implemented by free-space optical links in OCULAR-II's optical interconnection module.
The temporal resolution of a confocal laser microscope, by which three- dimensional data of specimens are obtained, usually suffers the slow speed of image acquisition devices, such as CCDs, or scanning mirrors. Here we propose a confocal microscope system where parallel optoelectronic devices are employed aiming at obtaining high frame rate of three-dimensional data that provides real time analysis of dynamical properties and adaptive feedback control of the specimen. A smart pixel yields pixel-parallel data processing capability owing to its integrated optical devices and electronic processing circuits fabricated on the same chip. A vertical-cavity surface-emitting laser (VCSEL) array is introduced for the parallel probing beams by which the optical system is simplified. In addition, pixel parallel illumination control is achievable. We have performed basic experiments on a syst em composed on an 8x8x VCSEL array, a silicon photodetector array, and processing element (PE) array. Each PE contains an arithmetic logic unit (ALU), 24- bit local memory, and electrical connections to neighboring pixels, that provides fast processing versatility such as the moving object recognition. Vibrating a sample by a piezo micro stage, we have successfully obtained a dynamical property, which is a one-dimensional moment of the moving sample, on the basis of the data obtained by the experimental system over the range of conventional video frame rate.
The extraordinary increase of digital image contents requires the development of methods to classify or quickly search video sequences in movie databases. In this paper, we suggest a novel algorithm based on Eigen value decomposition to construct and search video databases, and that can be implemented using smart pixel optoelectronic systems. By successive iterations the images are progressively classified in sets and subsets in a tree-type configuration. To evaluate this method, a movie containing 2,262 frames has been analyzed, and a successful classification of these images in function of their contents was obtained. The realization of this algorithm on a smart pixel system called OCULAR-II is also discussed, and a demonstration of the database search algorithm on the OCULAR-II system is described.
A multi-layered optoelectronic parallel processing system, which is called Optoelectronic Computer Using Laser Arrays with Reconfiguration is shown. This system consists of layers of processing modules, which are composed of electronic programmable processing element array each having parallel optical input/output connected by optical interconnection modules. Every module is designed to be modular and cascadable. The algorithms for this system are also shown which exploit the aggregate bandwidth supplied by optics and the computation versatility given by electronic processors.
Recent rapid advances in technologies such as modulators, vertical cavity surface-emitting laser (VCSEL) arrays, smart-pixel processing elements (PEs) integrating electronic circuitry with optical inputloutput channels, and micro-optic components has allowed the construction of parallel optoelectronic computing systems. These systems utilise the high bandwidth, high density and global nature of optical communication paths to overcome some of the limitations of conventional interconnections. Globally interconnected parallel optical processors are suitable for applications such as sorting, FFTs, and signal processing, and high level image processing. The latter is of interest in our laboratory, specifically with its application to machine vision. However, our system, to be described below, is a general purpose machine due to the programmability of the processing elements (PEs) and by reconfiguring optical interconnections. We can therefore implement a variety of operations on our system: image processing, arithmetic, matrix operations, sorting, and signal processing.
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