1
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Maher O, Jiménez M, Delacour C, Harnack N, Núñez J, Avedillo MJ, Linares-Barranco B, Todri-Sanial A, Indiveri G, Karg S. A CMOS-compatible oscillation-based VO 2 Ising machine solver. Nat Commun 2024; 15:3334. [PMID: 38637549 PMCID: PMC11026484 DOI: 10.1038/s41467-024-47642-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Accepted: 04/09/2024] [Indexed: 04/20/2024] Open
Abstract
Phase-encoded oscillating neural networks offer compelling advantages over metal-oxide-semiconductor-based technology for tackling complex optimization problems, with promising potential for ultralow power consumption and exceptionally rapid computational performance. In this work, we investigate the ability of these networks to solve optimization problems belonging to the nondeterministic polynomial time complexity class using nanoscale vanadium-dioxide-based oscillators integrated onto a Silicon platform. Specifically, we demonstrate how the dynamic behavior of coupled vanadium dioxide devices can effectively solve combinatorial optimization problems, including Graph Coloring, Max-cut, and Max-3SAT problems. The electrical mappings of these problems are derived from the equivalent Ising Hamiltonian formulation to design circuits with up to nine crossbar vanadium dioxide oscillators. Using sub-harmonic injection locking techniques, we binarize the solution space provided by the oscillators and demonstrate that graphs with high connection density (η > 0.4) converge more easily towards the optimal solution due to the small spectral radius of the problem's equivalent adjacency matrix. Our findings indicate that these systems achieve stability within 25 oscillation cycles and exhibit power efficiency and potential for scaling that surpasses available commercial options and other technologies under study. These results pave the way for accelerated parallel computing enabled by large-scale networks of interconnected oscillators.
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Affiliation(s)
- Olivier Maher
- IBM Research Europe - Zurich, Säumerstrasse 4, 8803 Rüschlikon, Zürich, Switzerland.
- Institute of Neuroinformatics, University of Zürich and ETH Zürich, Winterthurerstrasse 190, 8057 Zürich, Switzerland.
| | - Manuel Jiménez
- Instituto de Microelectrónica de Sevilla, IMSE-CNM (CSIC, Universidad de Sevilla), Av. Américo Vespucio 28, 41092, Sevilla, Spain
| | | | - Nele Harnack
- IBM Research Europe - Zurich, Säumerstrasse 4, 8803 Rüschlikon, Zürich, Switzerland
| | - Juan Núñez
- Instituto de Microelectrónica de Sevilla, IMSE-CNM (CSIC, Universidad de Sevilla), Av. Américo Vespucio 28, 41092, Sevilla, Spain
| | - María J Avedillo
- Instituto de Microelectrónica de Sevilla, IMSE-CNM (CSIC, Universidad de Sevilla), Av. Américo Vespucio 28, 41092, Sevilla, Spain
| | - Bernabé Linares-Barranco
- Instituto de Microelectrónica de Sevilla, IMSE-CNM (CSIC, Universidad de Sevilla), Av. Américo Vespucio 28, 41092, Sevilla, Spain
| | - Aida Todri-Sanial
- LIRMM, University of Montpellier, 56227, Montpellier, France
- Eindhoven University of Technology, Electrical Engineering Department, 5612AZ, Eindhoven, Netherlands
| | - Giacomo Indiveri
- Institute of Neuroinformatics, University of Zürich and ETH Zürich, Winterthurerstrasse 190, 8057 Zürich, Switzerland
| | - Siegfried Karg
- IBM Research Europe - Zurich, Säumerstrasse 4, 8803 Rüschlikon, Zürich, Switzerland.
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2
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Carter B, Hernandez UF, Miller DJ, Blaikie A, Horowitz VR, Alemán BJ. Coupled Nanomechanical Graphene Resonators: A Promising Platform for Scalable NEMS Networks. MICROMACHINES 2023; 14:2103. [PMID: 38004960 PMCID: PMC10672897 DOI: 10.3390/mi14112103] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Revised: 11/07/2023] [Accepted: 11/13/2023] [Indexed: 11/26/2023]
Abstract
Arrays of coupled nanoelectromechanical resonators are a promising foundation for implementing large-scale network applications, such as mechanical-based information processing and computing, but their practical realization remains an outstanding challenge. In this work, we demonstrate a scalable platform of suspended graphene resonators, such that neighboring resonators are persistently coupled mechanically. We provide evidence of strong coupling between neighboring resonators using two different tuning methods. Additionally, we provide evidence of inter-resonator coupling of higher-order modes, demonstrating the rich dynamics that can be accessed with this platform. Our results establish this platform as a viable option for realizing large-scale programmable networks, enabling applications such as phononic circuits, tunable waveguides, and reconfigurable metamaterials.
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Affiliation(s)
- Brittany Carter
- Department of Physics, University of Oregon, Eugene, OR 97403, USA (A.B.)
| | - Uriel F. Hernandez
- Department of Physics, University of Oregon, Eugene, OR 97403, USA (A.B.)
| | - David J. Miller
- Department of Physics, University of Oregon, Eugene, OR 97403, USA (A.B.)
| | - Andrew Blaikie
- Department of Physics, University of Oregon, Eugene, OR 97403, USA (A.B.)
| | | | - Benjamín J. Alemán
- Department of Physics, University of Oregon, Eugene, OR 97403, USA (A.B.)
- Materials Science Institute, University of Oregon, Eugene, OR 97403, USA
- Center for Optical, Molecular, and Quantum Science, University of Oregon, Eugene, OR 97403, USA
- Phil and Penny Knight Campus for Accelerating Scientific Impact, University of Oregon, Eugene, OR 97403, USA
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3
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Krasecki V, Sharma A, Cavell AC, Forman C, Guo SY, Jensen ET, Smith MA, Czerwinski R, Friederich P, Hickman RJ, Gianneschi N, Aspuru-Guzik A, Cronin L, Goldsmith RH. The Role of Experimental Noise in a Hybrid Classical-Molecular Computer to Solve Combinatorial Optimization Problems. ACS CENTRAL SCIENCE 2023; 9:1453-1465. [PMID: 37521801 PMCID: PMC10375572 DOI: 10.1021/acscentsci.3c00515] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Indexed: 08/01/2023]
Abstract
Chemical and molecular-based computers may be promising alternatives to modern silicon-based computers. In particular, hybrid systems, where tasks are split between a chemical medium and traditional silicon components, may provide access and demonstration of chemical advantages such as scalability, low power dissipation, and genuine randomness. This work describes the development of a hybrid classical-molecular computer (HCMC) featuring an electrochemical reaction on top of an array of discrete electrodes with a fluorescent readout. The chemical medium, optical readout, and electrode interface combined with a classical computer generate a feedback loop to solve several canonical optimization problems in computer science such as number partitioning and prime factorization. Importantly, the HCMC makes constructive use of experimental noise in the optical readout, a milestone for molecular systems, to solve these optimization problems, as opposed to in silico random number generation. Specifically, we show calculations stranded in local minima can consistently converge on a global minimum in the presence of experimental noise. Scalability of the hybrid computer is demonstrated by expanding the number of variables from 4 to 7, increasing the number of possible solutions by 1 order of magnitude. This work provides a stepping stone to fully molecular approaches to solving complex computational problems using chemistry.
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Affiliation(s)
- Veronica
K. Krasecki
- Department
of Chemistry, University of Wisconsin-Madison, Madison, Wisconsin 53706, United States
| | - Abhishek Sharma
- Department
of Chemistry, University of Glasgow, Glasgow, G12 8QQ, United Kingdom
| | - Andrew C. Cavell
- Department
of Chemistry, University of Wisconsin-Madison, Madison, Wisconsin 53706, United States
| | - Christopher Forman
- Department
of Chemistry, Northwestern University, Evanston, Illinois 60208, United States
| | - Si Yue Guo
- Department
of Chemistry, University of Toronto, Toronto, Ontario MS5 3H6, Canada
| | - Evan Thomas Jensen
- Department
of Chemistry, University of Wisconsin-Madison, Madison, Wisconsin 53706, United States
| | - Mackinsey A. Smith
- Department
of Chemistry, University of Wisconsin-Madison, Madison, Wisconsin 53706, United States
| | - Rachel Czerwinski
- Department
of Chemistry, University of Wisconsin-Madison, Madison, Wisconsin 53706, United States
| | - Pascal Friederich
- Department
of Chemistry, University of Toronto, Toronto, Ontario MS5 3H6, Canada
| | - Riley J. Hickman
- Department
of Chemistry, University of Toronto, Toronto, Ontario MS5 3H6, Canada
| | - Nathan Gianneschi
- Department
of Chemistry, Northwestern University, Evanston, Illinois 60208, United States
| | - Alán Aspuru-Guzik
- Department
of Chemistry, University of Toronto, Toronto, Ontario MS5 3H6, Canada
| | - Leroy Cronin
- Department
of Chemistry, University of Glasgow, Glasgow, G12 8QQ, United Kingdom
| | - Randall H. Goldsmith
- Department
of Chemistry, University of Wisconsin-Madison, Madison, Wisconsin 53706, United States
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4
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Choi JG, Park J, Kang MG, Kim D, Rieh JS, Lee KJ, Kim KJ, Park BG. Voltage-driven gigahertz frequency tuning of spin Hall nano-oscillators. Nat Commun 2022; 13:3783. [PMID: 35773256 PMCID: PMC9246901 DOI: 10.1038/s41467-022-31493-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2021] [Accepted: 06/20/2022] [Indexed: 11/09/2022] Open
Abstract
Spin Hall nano-oscillators (SHNOs) exploiting current-driven magnetization auto-oscillation have recently received much attention because of their potential for neuromorphic computing. Widespread applications of neuromorphic devices with SHNOs require an energy-efficient method of tuning oscillation frequency over broad ranges and storing trained frequencies in SHNOs without the need for additional memory circuitry. While the voltage-driven frequency tuning of SHNOs has been demonstrated, it was volatile and limited to megahertz ranges. Here, we show that the frequency of SHNOs is controlled up to 2.1 GHz by an electric field of 1.25 MV/cm. The large frequency tuning is attributed to the voltage-controlled magnetic anisotropy (VCMA) in a perpendicularly magnetized Ta/Pt/[Co/Ni]n/Co/AlOx structure. Moreover, the non-volatile VCMA effect enables cumulative control of the frequency using repetitive voltage pulses which mimic the potentiation and depression functions of biological synapses. Our results suggest that the voltage-driven frequency tuning of SHNOs facilitates the development of energy-efficient neuromorphic devices.
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Affiliation(s)
- Jong-Guk Choi
- Department of Materials Science and Engineering, KAIST, Daejeon, 34141, Korea
| | | | - Min-Gu Kang
- Department of Materials Science and Engineering, KAIST, Daejeon, 34141, Korea
| | - Doyoon Kim
- School of Electrical Engineering, Korea University, Seoul, 02841, Korea
| | - Jae-Sung Rieh
- School of Electrical Engineering, Korea University, Seoul, 02841, Korea
| | | | - Kab-Jin Kim
- Department of Physics, KAIST, Daejeon, 34141, Korea.
| | - Byong-Guk Park
- Department of Materials Science and Engineering, KAIST, Daejeon, 34141, Korea.
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5
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Momeni A, Fleury R. Electromagnetic wave-based extreme deep learning with nonlinear time-Floquet entanglement. Nat Commun 2022; 13:2651. [PMID: 35552403 PMCID: PMC9098897 DOI: 10.1038/s41467-022-30297-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Accepted: 04/22/2022] [Indexed: 11/29/2022] Open
Abstract
Wave-based analog signal processing holds the promise of extremely fast, on-the-fly, power-efficient data processing, occurring as a wave propagates through an artificially engineered medium. Yet, due to the fundamentally weak non-linearities of traditional electromagnetic materials, such analog processors have been so far largely confined to simple linear projections such as image edge detection or matrix multiplications. Complex neuromorphic computing tasks, which inherently require strong non-linearities, have so far remained out-of-reach of wave-based solutions, with a few attempts that implemented non-linearities on the digital front, or used weak and inflexible non-linear sensors, restraining the learning performance. Here, we tackle this issue by demonstrating the relevance of time-Floquet physics to induce a strong non-linear entanglement between signal inputs at different frequencies, enabling a power-efficient and versatile wave platform for analog extreme deep learning involving a single, uniformly modulated dielectric layer and a scattering medium. We prove the efficiency of the method for extreme learning machines and reservoir computing to solve a range of challenging learning tasks, from forecasting chaotic time series to the simultaneous classification of distinct datasets. Our results open the way for optical wave-based machine learning with high energy efficiency, speed and scalability. Wave-based analog signal processing has been challenging for complex nonlinear operations such as data forecasting or classification. The authors propose here an analog neuromorphic platform for optical wave-based machine learning characterized by energy efficiency, speed and scalability.
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Affiliation(s)
- Ali Momeni
- Laboratory of Wave Engineering, School of Electrical Engineering, Swiss Federal Institute of Technology in Lausanne (EPFL), Lausanne, Switzerland
| | - Romain Fleury
- Laboratory of Wave Engineering, School of Electrical Engineering, Swiss Federal Institute of Technology in Lausanne (EPFL), Lausanne, Switzerland.
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6
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Akther A, Ushakov Y, Balanov AG, Savel'ev SE. Deterministic modeling of the diffusive memristor. CHAOS (WOODBURY, N.Y.) 2021; 31:073111. [PMID: 34340321 DOI: 10.1063/5.0056239] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/07/2021] [Accepted: 06/21/2021] [Indexed: 06/13/2023]
Abstract
Recently developed diffusive memristors have gathered a large amount of research attention due to their unique property to exhibit a variety of spiking regimes reminiscent to that found in biological cells, which creates a great potential for their application in neuromorphic systems of artificial intelligence and unconventional computing. These devices are known to produce a huge range of interesting phenomena through the interplay of regular, chaotic, and stochastic behavior. However, the character of these interplays as well as the instabilities responsible for different dynamical regimes are still poorly studied because of the difficulties in analyzing the complex stochastic dynamics of the memristive devices. In this paper, we introduce a new deterministic model justified from the Fokker-Planck description to capture the noise-driven dynamics that noise has been known to produce in the diffusive memristor. This allows us to apply bifurcation theory to reveal the instabilities and the description of the transition between the dynamical regimes.
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Affiliation(s)
- A Akther
- Department of Physics, Loughborough University, Loughborough LE11 3TU, United Kingdom
| | - Y Ushakov
- Department of Physics, Loughborough University, Loughborough LE11 3TU, United Kingdom
| | - A G Balanov
- Department of Physics, Loughborough University, Loughborough LE11 3TU, United Kingdom
| | - S E Savel'ev
- Department of Physics, Loughborough University, Loughborough LE11 3TU, United Kingdom
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7
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Higher-order and long-range synchronization effects for classification and computing in oscillator-based spiking neural networks. Neural Comput Appl 2021. [DOI: 10.1007/s00521-020-05177-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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8
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Dombroski A, Oakley K, Arcadia C, Nouraei F, Chen SL, Rose C, Rubenstein B, Rosenstein J, Reda S, Kim E. Implementing parallel arithmetic via acetylation and its application to chemical image processing. Proc Math Phys Eng Sci 2021; 477:rspa.2020.0899. [DOI: 10.1098/rspa.2020.0899] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2020] [Accepted: 03/30/2021] [Indexed: 09/01/2023] Open
Abstract
Chemical mixtures can be leveraged to store large amounts of data in a highly compact form and have the potential for massive scalability owing to the use of large-scale molecular libraries. With the parallelism that comes from having many species available, chemical-based memory can also provide the physical substrate for computation with increased throughput. Here, we represent non-binary matrices in chemical solutions and perform multiple matrix multiplications and additions, in parallel, using chemical reactions. As a case study, we demonstrate image processing, in which small greyscale images are encoded in chemical mixtures and kernel-based convolutions are performed using phenol acetylation reactions. In these experiments, we use the measured concentrations of reaction products (phenyl acetates) to reconstruct the output image. In addition, we establish the chemical criteria required to realize chemical image processing and validate reaction-based multiplication. Most importantly, this work shows that fundamental arithmetic operations can be reliably carried out with chemical reactions. Our approach could serve as a basis for developing more advanced chemical computing architectures.
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Affiliation(s)
- Amanda Dombroski
- Department of Chemistry, Brown University, Providence, RI 02912, USA
| | - Kady Oakley
- Department of Chemistry, Brown University, Providence, RI 02912, USA
| | | | - Farnaz Nouraei
- School of Engineering, Brown University, Providence, RI 02912, USA
| | - Shui Ling Chen
- Department of Chemistry, Brown University, Providence, RI 02912, USA
| | - Christopher Rose
- School of Engineering, Brown University, Providence, RI 02912, USA
| | - Brenda Rubenstein
- Department of Chemistry, Brown University, Providence, RI 02912, USA
| | - Jacob Rosenstein
- School of Engineering, Brown University, Providence, RI 02912, USA
| | - Sherief Reda
- School of Engineering, Brown University, Providence, RI 02912, USA
| | - Eunsuk Kim
- Department of Chemistry, Brown University, Providence, RI 02912, USA
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9
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Mallick A, Bashar MK, Truesdell DS, Calhoun BH, Joshi S, Shukla N. Using synchronized oscillators to compute the maximum independent set. Nat Commun 2020; 11:4689. [PMID: 32943644 PMCID: PMC7499257 DOI: 10.1038/s41467-020-18445-1] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2020] [Accepted: 08/20/2020] [Indexed: 12/03/2022] Open
Abstract
Not all computing problems are created equal. The inherent complexity of processing certain classes of problems using digital computers has inspired the exploration of alternate computing paradigms. Coupled oscillators exhibiting rich spatio-temporal dynamics have been proposed for solving hard optimization problems. However, the physical implementation of such systems has been constrained to small prototypes. Consequently, the computational properties of this paradigm remain inadequately explored. Here, we demonstrate an integrated circuit of thirty oscillators with highly reconfigurable coupling to compute optimal/near-optimal solutions to the archetypally hard Maximum Independent Set problem with over 90% accuracy. This platform uniquely enables us to characterize the dynamical and computational properties of this hardware approach. We show that the Maximum Independent Set is more challenging to compute in sparser graphs than in denser ones. Finally, using simulations we evaluate the scalability of the proposed approach. Our work marks an important step towards enabling application-specific analog computing platforms to solve computationally hard problems. Designing efficient analog dynamical systems for solving hard optimization problems remains a challenge. Here, the authors demonstrate a dynamical system of thirty oscillators with reconfigurable coupling to compute optimal/near-optimal solutions to the hard Maximum Independent Set problem with over 90% accuracy.
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Affiliation(s)
- Antik Mallick
- Department of Electrical and Computer Engineering, University of Virginia, Charlottesville, VA, 22904, USA
| | - Mohammad Khairul Bashar
- Department of Electrical and Computer Engineering, University of Virginia, Charlottesville, VA, 22904, USA
| | - Daniel S Truesdell
- Department of Electrical and Computer Engineering, University of Virginia, Charlottesville, VA, 22904, USA
| | - Benton H Calhoun
- Department of Electrical and Computer Engineering, University of Virginia, Charlottesville, VA, 22904, USA
| | - Siddharth Joshi
- Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN, 46556, USA
| | - Nikhil Shukla
- Department of Electrical and Computer Engineering, University of Virginia, Charlottesville, VA, 22904, USA.
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10
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Jenkins AS, Alvarez LSE, Freitas PP, Ferreira R. Digital and analogue modulation and demodulation scheme using vortex-based spin torque nano-oscillators. Sci Rep 2020; 10:11181. [PMID: 32636523 PMCID: PMC7341870 DOI: 10.1038/s41598-020-68001-6] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2020] [Accepted: 05/18/2020] [Indexed: 11/09/2022] Open
Abstract
In conventional communications systems, information is transmitted by modulating the frequency, amplitude or phase of the carrier signal, which often occurs in a binary fashion over a very narrow bandwidth. Recently, ultra-wideband signal transmission has gained interest for local communications in technologies such as autonomous local sensor networks and on-chip communications, which presents a challenge for conventional electronics. Spin-torque nano-oscillators (STNOs) have been studied as a potentially low power highly tunable frequency source, and in this report we expand on this to show how a specific dynamic phase present in vortex-based STNOs makes them also well suited as Wideband Analogue Dynamic Sensors (WADS). This multi-functionality of the STNOs is the basis of a new modulation and demodulation scheme, where nominally identical devices can be used to transmit information in both a digital or analogue manner, with the potential to allow the highly efficient transmittance of data.
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Affiliation(s)
- Alex S Jenkins
- International Iberian Nanotechnology Laboratory, INL, Av. Mestre José Veiga s/n, 4715-330, Braga, Portugal.
| | - Lara San Emeterio Alvarez
- International Iberian Nanotechnology Laboratory, INL, Av. Mestre José Veiga s/n, 4715-330, Braga, Portugal
| | - Paulo P Freitas
- International Iberian Nanotechnology Laboratory, INL, Av. Mestre José Veiga s/n, 4715-330, Braga, Portugal
| | - Ricardo Ferreira
- International Iberian Nanotechnology Laboratory, INL, Av. Mestre José Veiga s/n, 4715-330, Braga, Portugal
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11
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Li Y, Cao W, Amin VP, Zhang Z, Gibbons J, Sklenar J, Pearson J, Haney PM, Stiles MD, Bailey WE, Novosad V, Hoffmann A, Zhang W. Coherent Spin Pumping in a Strongly Coupled Magnon-Magnon Hybrid System. PHYSICAL REVIEW LETTERS 2020; 124:117202. [PMID: 32242705 PMCID: PMC7489308 DOI: 10.1103/physrevlett.124.117202] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/01/2019] [Accepted: 01/23/2020] [Indexed: 05/25/2023]
Abstract
We experimentally identify coherent spin pumping in the magnon-magnon hybrid modes of yttrium iron garnet/permalloy (YIG/Py) bilayers. By reducing the YIG and Py thicknesses, the strong interfacial exchange coupling leads to large avoided crossings between the uniform mode of Py and the spin wave modes of YIG enabling accurate determination of modification of the linewidths due to the dampinglike torque. We identify additional linewidth suppression and enhancement for the in-phase and out-of-phase hybrid modes, respectively, which can be interpreted as concerted dampinglike torque from spin pumping. Furthermore, varying the Py thickness shows that both the fieldlike and dampinglike couplings vary like 1/sqrt[t_{Py}], verifying the prediction by the coupled Landau-Lifshitz equations.
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Affiliation(s)
- Yi Li
- Department of Physics, Oakland University, Rochester, MI 48309, USA
- Materials Science Division, Argonne National Laboratory, Argonne, IL 60439, USA
| | - Wei Cao
- Materials Science and Engineering, Department of Applied Physics and Applied Mathematics, Columbia University, New York, New York 10027, USA
| | - Vivek P. Amin
- Maryland Nanocenter, University of Maryland, College Park, MD 20742, USA
- Physical Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, Maryland 20899, USA
| | - Zhizhi Zhang
- Materials Science Division, Argonne National Laboratory, Argonne, IL 60439, USA
- School of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Jonathan Gibbons
- Materials Science Division, Argonne National Laboratory, Argonne, IL 60439, USA
| | - Joseph Sklenar
- Department of Physics and Astronomy, Wayne State University, Detroit, MI 48202, USA
| | - John Pearson
- Materials Science Division, Argonne National Laboratory, Argonne, IL 60439, USA
| | - Paul M. Haney
- Physical Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, Maryland 20899, USA
| | - Mark D. Stiles
- Physical Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, Maryland 20899, USA
| | - William E. Bailey
- Materials Science and Engineering, Department of Applied Physics and Applied Mathematics, Columbia University, New York, New York 10027, USA
| | - Valentine Novosad
- Materials Science Division, Argonne National Laboratory, Argonne, IL 60439, USA
| | - Axel Hoffmann
- Materials Science Division, Argonne National Laboratory, Argonne, IL 60439, USA
| | - Wei Zhang
- Department of Physics, Oakland University, Rochester, MI 48309, USA
- Materials Science Division, Argonne National Laboratory, Argonne, IL 60439, USA
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12
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A programmable chemical computer with memory and pattern recognition. Nat Commun 2020; 11:1442. [PMID: 32188858 PMCID: PMC7080730 DOI: 10.1038/s41467-020-15190-3] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2019] [Accepted: 02/20/2020] [Indexed: 11/08/2022] Open
Abstract
Current computers are limited by the von Neumann bottleneck, which constrains the throughput between the processing unit and the memory. Chemical processes have the potential to scale beyond current computing architectures as the processing unit and memory reside in the same space, performing computations through chemical reactions, yet their lack of programmability limits them. Herein, we present a programmable chemical processor comprising of a 5 by 5 array of cells filled with a switchable oscillating chemical (Belousov-Zhabotinsky) reaction. Each cell can be individually addressed in the 'on' or 'off' state, yielding more than 2.9 × 1017 chemical states which arise from the ability to detect distinct amplitudes of oscillations via image processing. By programming the array of interconnected BZ reactions we demonstrate chemically encoded and addressable memory, and we create a chemical Autoencoder for pattern recognition able to perform the equivalent of one million operations per second.
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13
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Grollier J, Querlioz D, Camsari KY, Everschor-Sitte K, Fukami S, Stiles MD. Neuromorphic Spintronics. NATURE ELECTRONICS 2020; 3:10.1038/s41928-019-0360-9. [PMID: 33367204 PMCID: PMC7754689 DOI: 10.1038/s41928-019-0360-9] [Citation(s) in RCA: 172] [Impact Index Per Article: 43.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/26/2019] [Accepted: 12/18/2019] [Indexed: 05/06/2023]
Abstract
Neuromorphic computing uses basic principles inspired by the brain to design circuits that perform artificial intelligence tasks with superior energy efficiency. Traditional approaches have been limited by the energy area of artificial neurons and synapses realized with conventional electronic devices. In recent years, multiple groups have demonstrated that spintronic nanodevices, which exploit the magnetic as well as electrical properties of electrons, can increase the energy efficiency and decrease the area of these circuits. Among the variety of spintronic devices that have been used, magnetic tunnel junctions play a prominent role because of their established compatibility with standard integrated circuits and their multifunctionality. Magnetic tunnel junctions can serve as synapses, storing connection weights, functioning as local, nonvolatile digital memory or as continuously varying resistances. As nano-oscillators, they can serve as neurons, emulating the oscillatory behavior of sets of biological neurons. As superparamagnets, they can do so by emulating the random spiking of biological neurons. Magnetic textures like domain walls or skyrmions can be configured to function as neurons through their non-linear dynamics. Several implementations of neuromorphic computing with spintronic devices demonstrate their promise in this context. Used as variable resistance synapses, magnetic tunnel junctions perform pattern recognition in an associative memory. As oscillators, they perform spoken digit recognition in reservoir computing and when coupled together, classification of signals. As superparamagnets, they perform population coding and probabilistic computing. Simulations demonstrate that arrays of nanomagnets and films of skyrmions can operate as components of neuromorphic computers. While these examples show the unique promise of spintronics in this field, there are several challenges to scaling up, including the efficiency of coupling between devices and the relatively low ratio of maximum to minimum resistances in the individual devices.
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Affiliation(s)
- J. Grollier
- Unité Mixte de Physique CNRS, Thales, Univ. Paris-Sud, Université Paris-Saclay, 91767 Palaiseau, France
| | - D. Querlioz
- Centre de Nanosciences et de Nanotechnologies, Univ. Paris-Sud, CNRS, Université Paris-Saclay, 91405 Orsay, France
| | - K. Y. Camsari
- School of Electrical & Computer Engineering, Purdue University, West Lafayette, Indiana 47907 USA
| | - K. Everschor-Sitte
- Institute of Physics, Johannes Gutenberg University Mainz, D-55099 Mainz, Germany
| | - S. Fukami
- Research Institute of Electrical Communication, Tohoku University, Sendai, Miyagi 9808577, Japan
| | - M. D. Stiles
- National Institute of Standards and Technology, Gaithersburg, Maryland 20899, USA
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14
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Doster J, Hoenl S, Lorenz H, Paulitschke P, Weig EM. Collective dynamics of strain-coupled nanomechanical pillar resonators. Nat Commun 2019; 10:5246. [PMID: 31748570 PMCID: PMC6868224 DOI: 10.1038/s41467-019-13309-9] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2019] [Accepted: 10/30/2019] [Indexed: 11/09/2022] Open
Abstract
Semiconductur nano- and micropillars represent a promising platform for hybrid nanodevices. Their ability to couple to a broad variety of nanomechanical, acoustic, charge, spin, excitonic, polaritonic, or electromagnetic excitations is utilized in fields as diverse as force sensing or optoelectronics. In order to fully exploit the potential of these versatile systems e.g. for metamaterials, synchronization or topologically protected devices an intrinsic coupling mechanism between individual pillars needs to be established. This can be accomplished by taking advantage of the strain field induced by the flexural modes of the pillars. Here, we demonstrate strain-induced, strong coupling between two adjacent nanomechanical pillar resonators. Both mode hybridization and the formation of an avoided level crossing in the response of the nanopillar pair are experimentally observed. The described coupling mechanism is readily scalable, enabling hybrid nanomechanical resonator networks for the investigation of a broad range of collective dynamical phenomena.
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Affiliation(s)
- J Doster
- Department of Physics, University of Konstanz, Universitätsstrasse 10, 78457, Konstanz, Germany
| | - S Hoenl
- Department of Physics, University of Konstanz, Universitätsstrasse 10, 78457, Konstanz, Germany.,IBM Research - Zurich, Säumerstrasse 4, CH-8803, Rüschlikon, Switzerland
| | - H Lorenz
- Fakultät für Physik and Center for NanoScience (CeNS), Ludwig-Maximilians-Universität, Geschwister-Scholl-Platz 1, 80539, München, Germany
| | - P Paulitschke
- Fakultät für Physik and Center for NanoScience (CeNS), Ludwig-Maximilians-Universität, Geschwister-Scholl-Platz 1, 80539, München, Germany
| | - E M Weig
- Department of Physics, University of Konstanz, Universitätsstrasse 10, 78457, Konstanz, Germany.
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15
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Nanoscale true random bit generator based on magnetic state transitions in magnetic tunnel junctions. Sci Rep 2019; 9:15661. [PMID: 31666671 PMCID: PMC6821798 DOI: 10.1038/s41598-019-52236-z] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2019] [Accepted: 09/16/2019] [Indexed: 11/08/2022] Open
Abstract
We present an investigation into the in-plane field induced free layer state transitions found in magnetic tunnel junctions. By applying an ac current into an integrated field antenna, the magnetisation of the free layer can be switched between the magnetic vortex state and the quasi-uniform anti-parallel state. When in the magnetic vortex state, the vortex core gyrates a discrete number of times, and at certain frequencies there is a 50% chance of the core gyrating n or n - 1 times, leading to the proposal of a novel nanoscale continuous digital true random bit generator.
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16
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Vanag VK. Hierarchical network of pulse coupled chemical oscillators with adaptive behavior: Chemical neurocomputer. CHAOS (WOODBURY, N.Y.) 2019; 29:083104. [PMID: 31472522 DOI: 10.1063/1.5099979] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/13/2019] [Accepted: 06/26/2019] [Indexed: 06/10/2023]
Abstract
We consider theoretically a network of pulse coupled oscillators with time delays. Each oscillator is described by the Oregonator-like model for the Belousov-Zhabotinsky (BZ) reaction. Different groups of oscillators constitute five functional units: (1) a central pattern generator (CPG), (2) a "reader" unit that can identify dynamical modes of the CPG, (3) an antenna (A) unit that receives external signals and responds on them by generating different dynamical modes, (4) another reader unit for identification of the dynamical modes in the A unit, and (5) a decision making unit that switches the current dynamical mode of the CPG to the mode that is similar to the current mode in the A unit. We call this network a chemical neurocomputer, since chemical BZ reaction occurs in each micro-oscillator, while pulse connectivity of these cells is inspired by the brain.
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Affiliation(s)
- Vladimir K Vanag
- Center for Nonlinear Chemistry, Immanuel Kant Baltic Federal University, 14 A. Nevskogo Str., Kaliningrad 236041, Russia
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17
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A Method for Evaluating Chimeric Synchronization of Coupled Oscillators and Its Application for Creating a Neural Network Information Converter. ELECTRONICS 2019. [DOI: 10.3390/electronics8070756] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
This paper presents a new method for evaluating the synchronization of quasi-periodic oscillations of two oscillators, termed “chimeric synchronization”. The family of metrics is proposed to create a neural network information converter based on a network of pulsed oscillators. In addition to transforming input information from digital to analogue, the converter can perform information processing after training the network by selecting control parameters. In the proposed neural network scheme, the data arrives at the input layer in the form of current levels of the oscillators and is converted into a set of non-repeating states of the chimeric synchronization of the output oscillator. By modelling a thermally coupled VO2-oscillator circuit, the network setup is demonstrated through the selection of coupling strength, power supply levels, and the synchronization efficiency parameter. The distribution of solutions depending on the operating mode of the oscillators, sub-threshold mode, or generation mode are revealed. Technological approaches for the implementation of a neural network information converter are proposed, and examples of its application for image filtering are demonstrated. The proposed method helps to significantly expand the capabilities of neuromorphic and logical devices based on synchronization effects.
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18
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Mancilla-Almonacid D, Leon AO, Arias RE, Allende S, Altbir D. Synchronization of two spin-transfer-driven nano-oscillators coupled via magnetostatic fields. Phys Rev E 2019; 99:032210. [PMID: 30999469 DOI: 10.1103/physreve.99.032210] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2018] [Indexed: 06/09/2023]
Abstract
The magnetization dynamics of nano-oscillators may be excited by both magnetic fields and spin-polarized currents. While the dynamics of single oscillators has been well characterized, the synchronization of several ones is not fully understood yet. An analytical and numerical study of the nonlinear dynamics of two magnetostatically coupled spin valves driven by spin-transfer torques is presented under the macrospin approximation. The oscillators interact via magnetostatic fields and exhibit a robust synchronized magnetization motion. We describe the magnetization dynamics of the system using the Landau-Lifshitz-Gilbert-Slonczewski equation. Using a modal decomposition technique, we describe the dynamics, synchronization, and competition of oscillatory modes as a function of the current density, and the geometrical parameters of the setup. Simulations of the Landau-Lifshitz-Gilbert-Slonczewski equation show good agreement with an approximate analytic solution.
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Affiliation(s)
- D Mancilla-Almonacid
- Departamento de Física, CEDENNA, Universidad de Santiago de Chile, USACH, Av. Ecuador 3493, Santiago, Chile
| | - Alejandro O Leon
- Instituto de Física, Pontificia Universidad Católica de Valparaíso, Casilla 4059, Chile
| | - R E Arias
- Departamento de Física, CEDENNA, Facultad de Ciencias Físicas y Matemáticas, Universidad de Chile, Av. Blanco Encalada 2008, Santiago, Chile
| | - S Allende
- Departamento de Física, CEDENNA, Universidad de Santiago de Chile, USACH, Av. Ecuador 3493, Santiago, Chile
| | - D Altbir
- Departamento de Física, CEDENNA, Universidad de Santiago de Chile, USACH, Av. Ecuador 3493, Santiago, Chile
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19
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Romera M, Talatchian P, Tsunegi S, Abreu Araujo F, Cros V, Bortolotti P, Trastoy J, Yakushiji K, Fukushima A, Kubota H, Yuasa S, Ernoult M, Vodenicarevic D, Hirtzlin T, Locatelli N, Querlioz D, Grollier J. Vowel recognition with four coupled spin-torque nano-oscillators. Nature 2018; 563:230-234. [PMID: 30374193 DOI: 10.1038/s41586-018-0632-y] [Citation(s) in RCA: 98] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2017] [Accepted: 07/31/2018] [Indexed: 11/10/2022]
Abstract
In recent years, artificial neural networks have become the flagship algorithm of artificial intelligence1. In these systems, neuron activation functions are static, and computing is achieved through standard arithmetic operations. By contrast, a prominent branch of neuroinspired computing embraces the dynamical nature of the brain and proposes to endow each component of a neural network with dynamical functionality, such as oscillations, and to rely on emergent physical phenomena, such as synchronization2-6, for solving complex problems with small networks7-11. This approach is especially interesting for hardware implementations, because emerging nanoelectronic devices can provide compact and energy-efficient nonlinear auto-oscillators that mimic the periodic spiking activity of biological neurons12-16. The dynamical couplings between oscillators can then be used to mediate the synaptic communication between the artificial neurons. One challenge for using nanodevices in this way is to achieve learning, which requires fine control and tuning of their coupled oscillations17; the dynamical features of nanodevices can be difficult to control and prone to noise and variability18. Here we show that the outstanding tunability of spintronic nano-oscillators-that is, the possibility of accurately controlling their frequency across a wide range, through electrical current and magnetic field-can be used to address this challenge. We successfully train a hardware network of four spin-torque nano-oscillators to recognize spoken vowels by tuning their frequencies according to an automatic real-time learning rule. We show that the high experimental recognition rates stem from the ability of these oscillators to synchronize. Our results demonstrate that non-trivial pattern classification tasks can be achieved with small hardware neural networks by endowing them with nonlinear dynamical features such as oscillations and synchronization.
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Affiliation(s)
- Miguel Romera
- Unité Mixte de Physique, CNRS, Thales, Université Paris-Sud, Université Paris-Saclay, Palaiseau, France
| | - Philippe Talatchian
- Unité Mixte de Physique, CNRS, Thales, Université Paris-Sud, Université Paris-Saclay, Palaiseau, France
| | - Sumito Tsunegi
- National Institute of Advanced Industrial Science and Technology (AIST), Spintronics Research Center, Tsukuba, Ibaraki, Japan
| | - Flavio Abreu Araujo
- Unité Mixte de Physique, CNRS, Thales, Université Paris-Sud, Université Paris-Saclay, Palaiseau, France.,Institute of Condensed Matter and Nanosciences, UC Louvain, Louvain-la-Neuve, Belgium
| | - Vincent Cros
- Unité Mixte de Physique, CNRS, Thales, Université Paris-Sud, Université Paris-Saclay, Palaiseau, France
| | - Paolo Bortolotti
- Unité Mixte de Physique, CNRS, Thales, Université Paris-Sud, Université Paris-Saclay, Palaiseau, France
| | - Juan Trastoy
- Unité Mixte de Physique, CNRS, Thales, Université Paris-Sud, Université Paris-Saclay, Palaiseau, France
| | - Kay Yakushiji
- National Institute of Advanced Industrial Science and Technology (AIST), Spintronics Research Center, Tsukuba, Ibaraki, Japan
| | - Akio Fukushima
- National Institute of Advanced Industrial Science and Technology (AIST), Spintronics Research Center, Tsukuba, Ibaraki, Japan
| | - Hitoshi Kubota
- National Institute of Advanced Industrial Science and Technology (AIST), Spintronics Research Center, Tsukuba, Ibaraki, Japan
| | - Shinji Yuasa
- National Institute of Advanced Industrial Science and Technology (AIST), Spintronics Research Center, Tsukuba, Ibaraki, Japan
| | - Maxence Ernoult
- Unité Mixte de Physique, CNRS, Thales, Université Paris-Sud, Université Paris-Saclay, Palaiseau, France.,Centre de Nanosciences et de Nanotechnologies, CNRS, Université Paris-Sud, Université Paris-Saclay, Orsay, France
| | - Damir Vodenicarevic
- Centre de Nanosciences et de Nanotechnologies, CNRS, Université Paris-Sud, Université Paris-Saclay, Orsay, France
| | - Tifenn Hirtzlin
- Centre de Nanosciences et de Nanotechnologies, CNRS, Université Paris-Sud, Université Paris-Saclay, Orsay, France
| | - Nicolas Locatelli
- Centre de Nanosciences et de Nanotechnologies, CNRS, Université Paris-Sud, Université Paris-Saclay, Orsay, France
| | - Damien Querlioz
- Centre de Nanosciences et de Nanotechnologies, CNRS, Université Paris-Sud, Université Paris-Saclay, Orsay, France.
| | - Julie Grollier
- Unité Mixte de Physique, CNRS, Thales, Université Paris-Sud, Université Paris-Saclay, Palaiseau, France.
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20
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A New Method of the Pattern Storage and Recognition in Oscillatory Neural Networks Based on Resistive Switches. ELECTRONICS 2018. [DOI: 10.3390/electronics7100266] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Development of neuromorphic systems based on new nanoelectronics materials and devices is of immediate interest for solving the problems of cognitive technology and cybernetics. Computational modeling of two- and three-oscillator schemes with thermally coupled VO2-switches is used to demonstrate a novel method of pattern storage and recognition in an impulse oscillator neural network (ONN), based on the high-order synchronization effect. The method allows storage of many patterns, and their number depends on the number of synchronous states Ns. The modeling demonstrates attainment of Ns of several orders both for a three-oscillator scheme Ns ~ 650 and for a two-oscillator scheme Ns ~ 260. A number of regularities are obtained, in particular, an optimal strength of oscillator coupling is revealed when Ns has a maximum. Algorithms of vector storage, network training, and test vector recognition are suggested, where the parameter of synchronization effectiveness is used as a degree of match. It is shown that, to reduce the ambiguity of recognition, the number coordinated in each vector should be at least one unit less than the number of oscillators. The demonstrated results are of a general character, and they may be applied in ONNs with various mechanisms and oscillator coupling topology.
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21
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Fang Y, Yashin VV, Dickerson SJ, Balazs AC. Tuning the synchronization of a network of weakly coupled self-oscillating gels via capacitors. CHAOS (WOODBURY, N.Y.) 2018; 28:053106. [PMID: 29857671 DOI: 10.1063/1.5026589] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
We consider a network of coupled oscillating units, where each unit comprises a self-oscillating polymer gel undergoing the Belousov-Zhabotinsky (BZ) reaction and an overlaying piezoelectric (PZ) cantilever. Through chemo-mechano-electrical coupling, the oscillations of the networked BZ-PZ units achieve in-phase or anti-phase synchronization, enabling, for example, the storage of information within the system. Herein, we develop numerical and computational models to show that the introduction of capacitors into the BZ-PZ system enhances the dynamical behavior of the oscillating network by yielding additional stable synchronization modes. We specifically show that the capacitors lead to a redistribution of charge in the system and alteration of the force that the PZ cantilevers apply to the underlying gel. Hence, the capacitors modify the strength of the coupling between the oscillators in the network. We utilize a linear stability analysis to determine the phase behavior of BZ-PZ networks encompassing different capacitances, force polarities, and number of units and then verify our findings with numerical simulations. Thus, through analytical calculations and numerical simulations, we determine the impact of the capacitors on the existence of the synchronization modes, their stability, and the rate of synchronization within these complex dynamical systems. The findings from our study can be used to design robotic materials that harness the materials' intrinsic, responsive properties to perform such functions as sensing, actuation, and information storage.
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Affiliation(s)
- Yan Fang
- Department of Electrical and Computer Engineering, University of Pittsburgh, 1238/940(1/2) Benedum Hall, Pittsburgh, Pennsylvania 15261, USA
| | - Victor V Yashin
- Department of Chemical Engineering, University of Pittsburgh, 1238/940(1/2) Benedum Hall, Pittsburgh, Pennsylvania 15261, USA
| | - Samuel J Dickerson
- Department of Electrical and Computer Engineering, University of Pittsburgh, 1238/940(1/2) Benedum Hall, Pittsburgh, Pennsylvania 15261, USA
| | - Anna C Balazs
- Department of Chemical Engineering, University of Pittsburgh, 1238/940(1/2) Benedum Hall, Pittsburgh, Pennsylvania 15261, USA
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