scholarly journals Intelligent systems based on photonics

2021 ◽  
Vol 11 (4) ◽  
pp. 422-436
Author(s):  
N.V. Golovastikov ◽  
◽  
S.P. Dorozhkin ◽  
V.A. Soife ◽  
◽  
...  
Keyword(s):  
Neural Networks ◽  
Intelligent Systems ◽  
Photonic Devices ◽  
Competitive Advantages ◽  
Planar Structures ◽  
Optical Neural Networks ◽  
Life Improvement ◽  
New Type ◽  
Photonic Sensors ◽  
Nanophotonic Structures

This paper discusses the prospects of photonics, shows the relevance and applicability of photonics research. The poten-tial of photonics technologies to answer the socio-economic challenges of the digital transformation age is revealed. Opportunities that emerge with the introduction of photonic devices to various technical systems designed for environ-mental protection and quality of life improvement are demonstrated. Concrete photonics structures and devices for such key applications as spectroscopy, analog optical calculations, and optical neural networks are closely examined. Possi-ble applications for photonic sensors and new type spectrometers are outlined, their competitive advantages explored. Various geometries of extra fine compact photonic spectrometers are presented: based on digital planar diagrams, inte-grated into the photonic waveguides, metasurfaces, diffraction gratings with varying parameters. The benefits of analog optical computations against conventional electronic devices are discussed. Various nanophotonic structures designed for differential and integral operators are studied, solutions for edge detection are proposed. The concept for artificial intelligence implementation on the photonics platform using optical neural networks is analyzed. Various solutions are examined: containing sequences of diffraction elements and based on Huygens–Fresnel principle, as well as planar structures comprised of waveguides that interact as Mach–Zehnder interferometer. SPIE estimation of the international photonics market proposes that the peak of interest for this field is yet to be achieved and photonics will claim its place in the future technological landscape.

scholarly journals Optical Axons for Electro-Optical Neural Networks

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6119
Author(s):  
Mircea Hulea ◽  
Zabih Ghassemlooy ◽  
Sujan Rajbhandari ◽  
Othman Isam Younus ◽  
Alexandru Barleanu
Keyword(s):  
Neural Networks ◽  
Optical Signal ◽  
Sensor Data ◽  
Line Of Sight ◽  
Optical Intensity ◽  
Digital Signals ◽  
Link Length ◽  
Optical Neural Networks ◽  
Analogue Signals ◽  
Neural Unit

Recently, neuromorphic sensors, which convert analogue signals to spiking frequencies, have been reported for neurorobotics. In bio-inspired systems these sensors are connected to the main neural unit to perform post-processing of the sensor data. The performance of spiking neural networks has been improved using optical synapses, which offer parallel communications between the distanced neural areas but are sensitive to the intensity variations of the optical signal. For systems with several neuromorphic sensors, which are connected optically to the main unit, the use of optical synapses is not an advantage. To address this, in this paper we propose and experimentally verify optical axons with synapses activated optically using digital signals. The synaptic weights are encoded by the energy of the stimuli, which are then optically transmitted independently. We show that the optical intensity fluctuations and link’s misalignment result in delay in activation of the synapses. For the proposed optical axon, we have demonstrated line of sight transmission over a maximum link length of 190 cm with a delay of 8 μs. Furthermore, we show the axon delay as a function of the illuminance using a fitted model for which the root mean square error (RMS) similarity is 0.95.

scholarly journals Generalization of Patterns by Identification with Polynomial Neural Network

2010 ◽  
Vol 61 (2) ◽  
pp. 120-124 ◽  
Author(s):  
Ladislav Zjavka
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Functional Dependence ◽  
Polynomial Neural Network ◽  
New Type ◽  
Input Variables ◽  
The Brain ◽  
Functional Output ◽  
Simplified Form

Generalization of Patterns by Identification with Polynomial Neural Network Artificial neural networks (ANN) in general classify patterns according to their relationship, they are responding to related patterns with a similar output. Polynomial neural networks (PNN) are capable of organizing themselves in response to some features (relations) of the data. Polynomial neural network for dependence of variables identification (D-PNN) describes a functional dependence of input variables (not entire patterns). It approximates a hyper-surface of this function with multi-parametric particular polynomials forming its functional output as a generalization of input patterns. This new type of neural network is based on GMDH polynomial neural network and was designed by author. D-PNN operates in a way closer to the brain learning as the ANN does. The ANN is in principle a simplified form of the PNN, where the combinations of input variables are missing.

Efficient On-Chip Training of Optical Neural Networks Using Genetic Algorithm

ACS Photonics ◽  
2021 ◽  
Author(s):  
Hui Zhang ◽  
Jayne Thompson ◽  
Mile Gu ◽  
Xu Dong Jiang ◽  
Hong Cai ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Neural Networks ◽  
Optical Neural Networks ◽  
On Chip

Planar structures of nano-voids in volume of a crystal

2021 ◽  
Author(s):  
E.A. Stepantsov
Keyword(s):  
Plastic Deformation ◽  
High Precision ◽  
Solid Phase ◽  
Crystal Surface ◽  
Ion Beam ◽  
Photonic Devices ◽  
Planar Structure ◽  
Compression Pressure ◽  
Planar Structures ◽  
Developed Surface

It was studied the possibility of solid phase intergrowth of optical Y-ZrO2 crystals with preliminarily developed one of their two contacting surfaces. The developing included creation of determined relief by argon ion beam milling through a mask with determined layout. The process of solid phase intergrowth of crystals with such developed surfaces was fulfilled in the same conditions, which were used at the similar procedure for crystals with undeveloped surfaces. During the process crystal samples were put together in contact in parallel crystallographic orientation along preliminary polished and etched surfaces. Then they were heated in vacuum up to temperature 1600°С. After that they were pressed to each other up to pressure 1.4 kN/mm2 for 4 hours with further cooling with rate 10°С/min down to room temperature. Decreasing of effective square of contacting surfaces on a value of total square of etched relief picture was taken into account at calculating of compression pressure. It was found out that solid phase intergrowth on undeveloped parts of the surfaces was realized with the same result, as it was in case of solid phase intergrowth of Y-ZrO2 crystals, the contacting surfaces of which had not been developed by Ar beam milling. It was shown that nano-voids is formed at the rest parts of the contacting surfaces of crystalline specimens during their solid phase intergrowth. As a result a planar structure of nano-voids is created in a volume of a crystal, fabricated by solid phase intergrowth of two crystalline samples with preliminarily developed surface of one of them by argon beam milling through special mask. It was demonstrated that a configuration of nano-voids planar structure corresponds to a picture of the relief of the developed crystal surface with precision not worse than +/- 1 µ. By chemical etching for dislocation holes of the crystal side surfaces, which are perpendicular to a plane of a planar structure of nano-voids, it was demonstrated that during of solid phase intergrowth process plastic deformation of the material did not have place even on micro-level, corresponding to thickness of etched relief. Full absence of even weak traces of plastic deformation in the zone of crystal specimen intergrowth is an explanation of so high precision correspondance of etched relief to configuration of planar structure of nono-voids. The shown results demonstrate the possibility of creation a planar structure of nano-voids inside of a crystal, corresponding to in advance determined picture with so high precision, that it gives new possibilities in designing of photonic devices.

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