A bifunctional superconducting cell as flux qubit and neuron

• Dmitrii S. Pashin,
• Pavel V. Pikunov,
• Marina V. Bastrakova,
• Andrey E. Schegolev,
• Nikolay V. Klenov and
• Igor I. Soloviev

Beilstein J. Nanotechnol. 2023, 14, 1116–1126, doi:10.3762/bjnano.14.92

• Proposed Bifunctional Cell In this and subsequent sections, we consider a parametric quantron (parametron) under the influence of unipolar pulses of external magnetic flux. It should be noted that this system has proven to be a basic element of neural networks such as the perceptron with a sigmoidal input

• from the ground to the excited state PLZ < 0.01. This estimate is important for evaluating the functioning of this circuit in adiabatic quantum neural networks, where there are strict requirements for the absence of excitation from the initial state for the implementation of sigmoidal activation

• feature allows for the use of the proposed scheme in superconducting neural networks, such as perceptrons, integrated into hybrid quantum-neuromorphic computers. Moreover, the temperature affects the steepness of the sigmoid function. Even the manifestation of hysteresis in flux-to-flux transformations

Prediction of cytotoxicity of heavy metals adsorbed on nano-TiO₂ with periodic table descriptors using machine learning approaches

• Joyita Roy,
• Souvik Pore and
• Kunal Roy

Beilstein J. Nanotechnol. 2023, 14, 939–950, doi:10.3762/bjnano.14.77

• nanomaterials based on structure similarities with known substances. Materials with similar structures are likely to produce similar toxicity through comparable mechanisms. The development of machine learning (ML) approaches, such as artificial neural networks (ANNs), decision trees, logistic regression (LR

Frontiers of nanoelectronics: intrinsic Josephson effect and prospects of superconducting spintronics

• Anatolie S. Sidorenko,
• Horst Hahn and
• Vladimir Krasnov

Beilstein J. Nanotechnol. 2023, 14, 79–82, doi:10.3762/bjnano.14.9

• : artificial neural networks; functional nanostructures; intrinsic Josephson effect; nanoelectronics; spintronics; The twenty-first century is marked by an explosive growth in the flow of information, which is necessary to process, archive, and transmit data through communication systems. For that purpose

• development in superconducting spintronics, based on functional nanostructures and Josephson junctions, has taken place [13][14]. The implementation of such devices in building blocks for quantum computers and for novel computers using non-von Neumann architecture with brain-like artificial neural networks

Design of surface nanostructures for chirality sensing based on quartz crystal microbalance

• Yinglin Ma,
• Xiangyun Xiao and
• Qingmin Ji

Beilstein J. Nanotechnol. 2022, 13, 1201–1219, doi:10.3762/bjnano.13.100

• higher interaction energy for the host–guest complexes to discriminate the enantiomers in the inclusion process [66]. Fietzek et al. measured the selective adsorption of chiral limonene in three different β-cyclodextrin (β-CD) derivatives by QCM and artificial neural networks (ANN) to evaluate the chiral

A superconducting adiabatic neuron in a quantum regime

• Marina V. Bastrakova,
• Dmitrii S. Pashin,
• Dmitriy A. Rybin,
• Andrey E. Schegolev,
• Nikolay V. Klenov,
• Igor I. Soloviev,
• Anastasiya A. Gorchavkina and
• Arkady M. Satanin

Beilstein J. Nanotechnol. 2022, 13, 653–665, doi:10.3762/bjnano.13.57

• obtained results indicate the conditions under which the neuron possesses the required sigmoid activation function. Keywords: Josephson junction; quantum neuron; quantum-classical neural networks; superconducting quantum interferometer; Introduction The implementation of machine learning algorithms is

• classical perceptron and a control quantum co-processor (designed for the rapid search of the perceptron synaptic weights) to work in a single chip in a millikelvin cryogenic stage of a cryocooler. For the practical implementation of such neural networks, we need synapses, which are also based on adiabatic

A new method for obtaining the magnetic shape anisotropy directly from electron tomography images

• Cristian Radu,
• Ioana D. Vlaicu and
• Andrei C. Kuncser

Beilstein J. Nanotechnol. 2022, 13, 590–598, doi:10.3762/bjnano.13.51

• system entities. Using the abovementioned information as data input in artificial intelligence systems, such as neural networks, in order to identify and/or predict materials with special properties, should be explored. Conclusion Magn3t software aims to provide a free, open source solution for the most

Tunable superconducting neurons for networks based on radial basis functions

• Andrey E. Schegolev,
• Nikolay V. Klenov,
• Sergey V. Bakurskiy,
• Igor I. Soloviev,
• Mikhail Yu. Kupriyanov,
• Maxim V. Tereshonok and
• Anatoli S. Sidorenko

Beilstein J. Nanotechnol. 2022, 13, 444–454, doi:10.3762/bjnano.13.37

• relevant for a number of niche tasks where performance and energy efficiency are critically important. In this paper, we consider the basic elements for superconducting neural networks on radial basis functions. We examine the static and dynamic activation functions of the proposed neuron. Special

• frequency range. When working with large data, it is necessary to create specialized neural networks at the hardware level to effectively solve such problems. Josephson digital circuits and analog receivers have been used for a long time to create software-defined radio-systems [18][19][20][21][22][23][24

• implementation of the key elements of the discussed neural networks is the focus of this work. Results and Discussion Model of tunable Gauss-neuron: numerical simulations A common architecture of the considered RBFNs [49] is presented in Figure 1a. These networks have only one hidden layer of neurons on which

The role of convolutional neural networks in scanning probe microscopy: a review

• Ido Azuri,
• Irit Rosenhek-Goldian,
• Neta Regev-Rudzki,
• Georg Fantner and
• Sidney R. Cohen

Beilstein J. Nanotechnol. 2021, 12, 878–901, doi:10.3762/bjnano.12.66

• , convolutional neural networks, and how it is transforming the acquisition and analysis of scanning probe data. Keywords: atomic force microscopy (AFM); deep learning; machine learning; neural networks; scanning probe microscopy (SPM); Review Introduction: traditional machine learning vs deep learning Machine

• . Neural networks were first proposed by Warren McCulloch and Walter Pitts in 1943 [25]. This provided the groundwork for the eventual use of artificial neural networks (ANNs) in machine learning. ANNs comprise an end-to-end process, where the neural network learns, extracts, and selects those features

• analogy of neural networks to the physiological ones was furthered in the 1962 work of Hubel and Wiesel, which showed that a set of neurons arranged in a column extending inwards from the brain surface all respond to stimuli of a specific orientation and location [27]. For instance, a particular column

Reducing molecular simulation time for AFM images based on super-resolution methods

• Zhipeng Dou,
• Jianqiang Qian,
• Yingzi Li,
• Rui Lin,
• Jianhai Wang,
• Peng Cheng and
• Zeyu Xu

Beilstein J. Nanotechnol. 2021, 12, 775–785, doi:10.3762/bjnano.12.61

• [36], which use the principle of signal sparsity and the learning ability of deep neural networks to achieve super-resolution tasks. The CS has the possibility of recovering the data almost perfectly from undersampled information [37], which is widely used in AFM imaging to reduce sampling time [38

Recent progress in actuation technologies of micro/nanorobots

• Ke Xu and
• Bing Liu

Beilstein J. Nanotechnol. 2021, 12, 756–765, doi:10.3762/bjnano.12.59

• cultured nerve cells to connect disconnected nerve clusters. It can guide the direction of axon growth and selectively reconstruct neural networks in vitro. The micro/nanorobot can be used as a basis for the targeted delivery of nerve cells and the formation of active neural networks in vitro, thereby

• promoting the research of neural networks and neural connectivity, offering reproducibility, selectivity, and precise connection. This provides a potential platform for advanced in vitro controllable models of artificial neural networks. With the research on single magnetic field-driven micro/nanorobots

Intracranial recording in patients with aphasia using nanomaterial-based flexible electronics: promises and challenges

• Qingchun Wang and
• Wai Ting Siok

Beilstein J. Nanotechnol. 2021, 12, 330–342, doi:10.3762/bjnano.12.27

• processing. This review presents findings on aphasia, an impairment in language and communication, and discusses how different brain imaging techniques, including positron emission tomography, magnetic resonance imaging, and iEEG, have advanced our understanding of the neural networks underlying language and

• electrodes must be implanted for clinical but not research purposes. These rules have restricted the location of electrodes that can be placed and have limited our understanding of more complex neural networks such as language processing that require accurate and precise recording of neuronal activity

• decoding for a long time [84]. The development allows for the observation of brain activity in freely moving animals. Researchers are able to investigate the correlation between behaviour and neural networks. The adaption of nanomaterial-based flexible electronics in iEEG recordings offers a great

Functional nanostructures for electronics, spintronics and sensors

• Anatolie S. Sidorenko

Beilstein J. Nanotechnol. 2020, 11, 1704–1706, doi:10.3762/bjnano.11.152

• theoretical and experimental investigations of S/F superlattices. Such superlattices can be used as tunable kinetic inductivity synapses in artificial neural networks of a superconducting computer with non-von Neumann architecture. A further example by Novikov et al. [14] demonstrated the concept of “read-out

Atomic defect classification of the H–Si(100) surface through multi-mode scanning probe microscopy

• Jeremiah Croshaw,
• Thomas Dienel,
• Taleana Huff and
• Robert Wolkow

Beilstein J. Nanotechnol. 2020, 11, 1346–1360, doi:10.3762/bjnano.11.119

• optimizing sample preparation, quantifying how defects affect device operation [9], or by using convolutional neural networks to autonomously identify defects [10][11][12], a comprehensive understanding of the many varieties of defects is needed. Native silicon atoms at the unreconstructed (unterminated

Controlling the proximity effect in a Co/Nb multilayer: the properties of electronic transport

• Sergey Bakurskiy,
• Mikhail Kupriyanov,
• Nikolay V. Klenov,
• Igor Soloviev,
• Andrey Schegolev,
• Roman Morari,
• Yury Khaydukov and
• Anatoli S. Sidorenko

Beilstein J. Nanotechnol. 2020, 11, 1336–1345, doi:10.3762/bjnano.11.118

• superlattices can be used as tunable kinetic inductors designed for artificial neural networks representing the information in a “current domain”. Keywords: cryogenic computing; spin-valve; superconducting neural network; superconducting spintronics; Introduction Multilayer superconductor/ferromagnetic (S/F

• elements, including synapses. This new type of application will be discussed in more detail. The creation of artificial neural networks is one of the current trends in the development of superconductor electronics [10][11][12][13][14][15]. Such an artificial neural network contains layers of elements that

• nonlinearly transform the incoming signal (neurons), which is connected by linear tunable connections (synapses). There are more than 106 synapses in the neural networks that are used in these applications. The energy dissipation at these interconnects is a serious problem, which motivates the search for

Anomalous current–voltage characteristics of SFIFS Josephson junctions with weak ferromagnetic interlayers

• Tairzhan Karabassov,
• Anastasia V. Guravova,
• Aleksei Yu. Kuzin,
• Elena A. Kazakova,
• Shiro Kawabata,
• Boris G. Lvov and
• Andrey S. Vasenko

Beilstein J. Nanotechnol. 2020, 11, 252–262, doi:10.3762/bjnano.11.19

• , e.g., single-flux quantum circuits [46][47], spintronic devices [48], memory elements [49][50][51][52][53][54][55][56][57][58] and spin-valves [59][60][61][62][63][64][65], magnetoelectronics [66][67][68], qubits [69], artificial neural networks [70], microrefrigerators [71][72], and low-temperature

A novel method to remove impulse noise from atomic force microscopy images based on Bayesian compressed sensing

• Yingxu Zhang,
• Yingzi Li,
• Zihang Song,
• Zhenyu Wang,
• Jianqiang Qian and
• Junen Yao

Beilstein J. Nanotechnol. 2019, 10, 2346–2356, doi:10.3762/bjnano.10.225

• further improve the denoising performance, machine learning [10] and neural networks [11][12] are introduced to help remove the impulse noise. First, machine learning or neural networks are used to improve the accuracy of the recognition of noisy pixels. Then, the noise pixels are replaced by the median

• density. In addition, impulse noise filtering methods using machine learning [10], support vector machines [38], or neural networks [12] encounter the same problem as the adaptive median filter. When the noise density is lower than 0.5, the values of PSNR and SSIM acquired by the proposed method remain

Nitrogen-vacancy centers in diamond for nanoscale magnetic resonance imaging applications

• Alberto Boretti,
• Lorenzo Rosa,
• Jonathan Blackledge and
• Stefania Castelletto

Beilstein J. Nanotechnol. 2019, 10, 2128–2151, doi:10.3762/bjnano.10.207

• was dispersed in cell media and then applied at a concentration of 6 μg/mL to the primary cultures while performing a routine change of cell media. ODMR from NVs within the NDs in the neural networks was used for sensing the temperature from thousands of NDs, which were probed simultaneously using a

Unipolar magnetic field pulses as an advantageous tool for ultrafast operations in superconducting Josephson “atoms”

• Daria V. Popolitova,
• Nikolay V. Klenov,
• Igor I. Soloviev,
• Sergey V. Bakurskiy and
• Olga V. Tikhonova

Beilstein J. Nanotechnol. 2019, 10, 1548–1558, doi:10.3762/bjnano.10.152

• of developments in algorithmic and adiabatic quantum computers, artificial metamaterials, and quantum neural networks. Hence, they seem to be very promising for studies of novel types of fast quantum-state control or initialization [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15]. In this work, we

• ultrafast state initialization for algorithmic quantum computers and quantum neural networks as well as in the fast control of the magnetic properties of media from Josephson meta-atoms. (a) The potential energy and the eigenfunctions (with energies E1 ,E2, E3, E4) of the three-junction qubit (described in

Beyond Moore’s technologies: operation principles of a superconductor alternative

• Igor I. Soloviev,
• Nikolay V. Klenov,
• Sergey V. Bakurskiy,
• Mikhail Yu. Kupriyanov,
• Alexander L. Gudkov and
• Anatoli S. Sidorenko

Beilstein J. Nanotechnol. 2017, 8, 2689–2710, doi:10.3762/bjnano.8.269

• mentioned localization of information and high non-linearity of Josephson junctions make superconductor circuits to be ideally suited for the implementation of unconventional computational paradigms like cellular automata [94][95], artificial neural networks [96][97][98] or quantum computing [99][100][101

Optical techniques for cervical neoplasia detection

• Tatiana Novikova

Beilstein J. Nanotechnol. 2017, 8, 1844–1862, doi:10.3762/bjnano.8.186

• watchful waiting accompanied by HPV/Pap tests or active treatment is further needed [34][35][36][37][38]. The use of spectra classification algorithms (e.g., Bayesian variable selection, neural networks, library approach, multivariate statistical analysis) may bring its own set of the problems: high

Adiabatic superconducting cells for ultra-low-power artificial neural networks

• Andrey E. Schegolev,
• Nikolay V. Klenov,
• Igor I. Soloviev and
• Maxim V. Tereshonok

Beilstein J. Nanotechnol. 2016, 7, 1397–1403, doi:10.3762/bjnano.7.130

• . We optimize their parameters for application in three-layer perceptron and radial basis function networks. Keywords: adiabatic superconductor cells; artificial neural networks; energy efficiency; Josephson effect; superconductivity; Findings Artificial neural networks (ANNs) are famous for their

The eNanoMapper database for nanomaterial safety information

• Nina Jeliazkova,
• Charalampos Chomenidis,
• Philip Doganis,
• Bengt Fadeel,
• Roland Grafström,
• Barry Hardy,
• Janna Hastings,
• Markus Hegi,
• Vedrin Jeliazkov,
• Nikolay Kochev,
• Pekka Kohonen,
• Cristian R. Munteanu,
• Haralambos Sarimveis,
• Bart Smeets,
• Pantelis Sopasakis,
• Georgia Tsiliki,
• David Vorgrimmler and
• Egon Willighagen

Beilstein J. Nanotechnol. 2015, 6, 1609–1634, doi:10.3762/bjnano.6.165

Growth and structural discrimination of cortical neurons on randomly oriented and vertically aligned dense carbon nanotube networks

• Christoph Nick,
• Sandeep Yadav,
• Ravi Joshi,
• Christiane Thielemann and
• Jörg J. Schneider

Beilstein J. Nanotechnol. 2014, 5, 1575–1579, doi:10.3762/bjnano.5.169

• between the aggregated networks. These results are in line with the results after using micro-stamped structures of carbon nanotubes, which led to the self-assembly of neural networks [28] and to the formation of neural networks on islands of unordered CNTs [12]. After 21 days in vitro the neural network