Modeling and Simulation of Vanadium Dioxide Relaxation Oscillators

Maffezzoni, P.; Daniel, Luca; Shukla, Nikhil; Datta, Suman; Raychowdhury, Arijit

doi:10.1109/tcsi.2015.2452332

Cited by 74 publications

(74 citation statements)

References 31 publications

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“…At RCOUP ≤ 2 kΩ, as a result of the phase failure, the oscillation stops and the switches jump into a stable state. This phase failure seems to be associated with the synchronization instability of the coupled oscillator system itself, 25 and its aperiodicity is due to the presence of internal noise in the system.…”

Section: Discussionmentioning

confidence: 99%

“…8d) more accurately as compared to the work. 25 The main parameters of the tested switches differ by no more than 10%. To simulate the oscillations, LTspice software for electrical circuits has been applied.…”

Section: Single-oscillator Circuit Based On Vo2 Switchmentioning

confidence: 95%

“…The numerical model fairly accurately describes the dynamic currentvoltage characteristic that sets it apart from the simplified models. 25 On the other hand, physical mechanisms affecting the forward and backward switching time are identified, and this, in turn, determines the applicability limits of the proposed model. The operation of coupled VO2 oscillators with strong and weak coupling, as well as an example of the circuit for simulating the generation of bursting neural activity is demonstrated.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Switching dynamics of single and coupled VO₂-based oscillators as elements of neural networks

Velichko

Belyaev

Putrolaynen

et al. 2017

Int. J. Mod. Phys. B

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In the present paper, we report on the switching dynamics of both single and coupled VO2-based oscillators, with resistive and capacitive coupling, and explore the capability of their application in oscillatory neural networks. Based on these results, we further select an adequate SPICE model to describe the modes of operation of coupled oscillator circuits. Physical mechanisms influencing the time of forward and reverse electrical switching, that determine the applicability limits of the proposed model, are identified. For the resistive coupling, it is shown that synchronization takes place at a certain value of the coupling resistance, though it is unstable and a synchronization failure occurs periodically. For the capacitive coupling, two synchronization modes, with weak and strong coupling, are found. The transition between these modes is accompanied by chaotic oscillations. A decrease in the width of the spectrum harmonics in the weak-coupling mode, and its increase in the strongcoupling one, is detected. The dependences of frequencies and phase differences of the coupled oscillatory circuits on the coupling capacitance are found. Examples of operation of coupled VO2 oscillators as a central pattern generator are demonstrated.

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Section: Discussionmentioning

confidence: 99%

Section: Single-oscillator Circuit Based On Vo2 Switchmentioning

confidence: 95%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Switching dynamics of single and coupled VO₂-based oscillators as elements of neural networks

Velichko

Belyaev

Putrolaynen

et al. 2017

Int. J. Mod. Phys. B

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“…Thermal coupling mechanism is based on mutual thermal effect of switches due to their Joule heating and dependence of gate threshold voltage Uth on temperature. The choice of this coupling type is determined by the simplicity of a computing model realization when oscillators are electrically separated from each other unlike in capacitive or resistive couplings [14], [15], [18], [21]. An example of oscillators' interaction organization via thermal coupling of VO2 switches is shown in Fig.…”

Section: Oscillator Circuit and Methods Of Onn Organizationmentioning

confidence: 99%

A Model of an Oscillatory Neural Network with Multilevel Neurons for Pattern Recognition and Computing

2019

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The article presents a study on a new type of an oscillatory neural network (ONN) that uses multilevel neurons for pattern recognition on the basis of high order synchronization effect. The feature of this network architecture is a single oscillator (neuron) at the output with multilevel variation of its synchronization value with the main oscillator thus allowing classifications of an input pattern into a set of classes. ONN model is realized on thermally-coupled VO2-oscillators. ONN training was performed by using the trial-and-error method for the network parameters selection. It is shown that ONN is capable to classify 512 visual patterns (as a cell array 3x3, distributed by symmetry into 102 classes) into a set of classes with the maximum number of elements up to P=11. Classification capability of a network is studied depending on the interior noise level and synchronization effectiveness parameter. The obtained results allow designing multilevel output cascades of neural networks with high net data throughput and it may be applied in ONNs with various coupling mechanisms and oscillators topology.

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“…As one can see from formula (3), the oscillation frequency F 1 depends on many parameters determined by the external circuit ( C , R s , V DD ) and VO 2 ‐switch parameters ( V th , V h , R on , R off ). This accounts for the large spread of frequencies (ranging from kilohertz to megahertz) observed in different studies . Obviously, EBM affects only the switch parameters.…”

Section: Resultsmentioning

confidence: 94%

Electron beam modification of vanadium dioxide oscillators

Belyaev

Velichko

Putrolaynen

et al. 2016

Phys. Status Solidi C

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The paper presents the results of a study of electron‐beam modification (EBM) of VO2‐switch I–V curve threshold parameters and the self‐oscillation frequency of a circuit containing such a switching device. EBM in vacuum is reversible and the parameters are restored when exposed to air at pressure of 150 Pa. At EBM with a dose of 3 C cm−2, the voltages of switching‐on (Vth) and off (Vh), as well as the OFF‐state resistance Roff, decrease down to 50% of the initial values, and the oscillation frequency increases by 30% at a dose of 0.7 C cm−2. Features of physics of EBM of an oscillator are outlined considering the contribution of the metal and semiconductor phases of the switching channel. Controlled modification allows EBM forming of switches with preset parameters. Also, it might be used in artificial oscillatory neural networks for pattern recognition based on frequency shift keying.

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Modeling and Simulation of Vanadium Dioxide Relaxation Oscillators

Cited by 74 publications

References 31 publications

Switching dynamics of single and coupled VO₂-based oscillators as elements of neural networks

Switching dynamics of single and coupled VO₂-based oscillators as elements of neural networks

A Model of an Oscillatory Neural Network with Multilevel Neurons for Pattern Recognition and Computing

Electron beam modification of vanadium dioxide oscillators

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Modeling and Simulation of Vanadium Dioxide Relaxation Oscillators

Cited by 74 publications

References 31 publications

Switching dynamics of single and coupled VO2-based oscillators as elements of neural networks

Switching dynamics of single and coupled VO2-based oscillators as elements of neural networks

A Model of an Oscillatory Neural Network with Multilevel Neurons for Pattern Recognition and Computing

Electron beam modification of vanadium dioxide oscillators

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Switching dynamics of single and coupled VO₂-based oscillators as elements of neural networks

Switching dynamics of single and coupled VO₂-based oscillators as elements of neural networks