A new memristor based on NiTi smart alloys

Kyriakides, Evripides; Hadjistassou, Constantinos; Georgiou, Julius

doi:10.1109/iscas.2012.6271506

Cited by 5 publications

(5 citation statements)

References 16 publications

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“…It has also received physical justification by a semi-empirical approach in [23]. Nevertheless, we deem that, for the purposes of a dynamic simulation model, the generalized sigmoidal proposed here is more convenient because (i) it emerges by simply inserting a multiplicative factor into equations ( 10) and (ii) the final form of the equations ( 4) and ( 5) comes out significantly simpler than if one used Richard's function in equations ( 8) through (10) instead. In fig.…”

Section: Model Descriptionmentioning

confidence: 96%

“…In the equations above, we arbitrarily introduced some extra constant multiplicative factors, a M A , a RA , a AR , a RM in general different for each transition. When equations (10) are inserted back into the respective differential equations in (4) and ( 5) and after integrating these again (considering ( 8) -( 10)), we derive a new, more general sigmoid function given by:…”

Section: Model Descriptionmentioning

confidence: 99%

“…Y (x; ν), which also depends on an extra steepness parameter ν, and has similar characteristics with the sigmoid used here. Richard's function has been used before in the description of the martensitic/austenitic transitions in SMAs (for instance see [10,23]). It has also received physical justification by a semi-empirical approach in [23].…”

Section: Model Descriptionmentioning

confidence: 99%

“…Finally, inserting equations (10) into equations ( 4) and ( 5), one gets, together with restriction (3), a system of differential equations whose solution gives the general dependence of each lattice fraction on temperature for any values of the four state variables ξ M , ξ A , ξ R , T .…”

Section: Model Descriptionmentioning

confidence: 99%

“…the difference of resistivity vs. temperature curves between the cooling and heating parts of the cycle) has been experimentally shown. Thus, NiTi behaves effectively as an extended memristor [8][9][10] with the temperature change playing the role of one internal state variable and the fraction of each lattice phase acting as additional state variables. Memristors are a very interesting and promising contribution to electronic technology of the 21st century coming from the 70s considered to implement the theoretically predicted fourth basic electrical element [11].…”

Section: Introductionmentioning

confidence: 99%

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Modeling the dynamical behavior of memristive {NiTi} alloy at constant stress for time-varying electric current input signals

Antoniades,

Stavrinides,

Picos

et al. 2021

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The dynamical electric behavior of a NiTi smart alloy thin filament when driven by time varying current pulses is studied by a structure-based phenomenological model that includes rate-based effects. The simulation model relates the alloy's electrical resistivity to the relative proportions of the three main structural phases namely Martensite, Austenite and R-phase, experimentally known to exist in NiTi alloy lattice structure. The relative proportions of the phases depend on temperature and applied stress. Temperature varies due to the self-heating of the filament by the Joule effect when a current pulse passes and also due to convective/radiative interchange with the ambient. The temperature variation with time causes structural phase transitions, which result in abrupt changes in the sample resistivity as the proportions of each lattice phase vary.The model is described by a system of four 1st-order nonlinear differential-algebraic equations yielding the temporal evolution of resistivity and output voltage across the filament for any given timevarying input current pulse. The model corresponds to a 4th-order extended memristor, described by four state variables, which are the proportions of each of the three NiTi lattice phases and temperature. Simulations are experimentally verified by comparing to measurements obtained for samples self-heated by triangular current input waveforms as well as for passively samples with no current input. Numerical results reproduce very well measurements of resistance vs. temperature at equilibrium as well as the full dynamics of experimentally observed I-V characteristic curves and resistance vs. driving current for time-varying current input waveforms of a wide range of frequencies (0.01-10 Hz).

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Section: Model Descriptionmentioning

confidence: 96%

Section: Model Descriptionmentioning

confidence: 99%