A Versatile Memristor Model With Nonlinear Dopant Kinetics

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Memristor-based memory: The sneak paths problem and solutions (2013, background): “In addition, several models for memristors has been introduced [10–16].”
TEAM: ThrEshold Adaptive Memristor Model (2013, methodology): “Window function described by (8) according to [14].”
STDP and STDP variations with memristors for spiking neuromorphic learning systems (2013, background): “…has recently been demonstrated (with extraordinary impact among the research community) in nanoscale two-terminal devices, such as certain titanium-dioxide (Strukov et al., 2008; Borghetti et al., 2009; Prodromakis et al., 2011, 2012a) and amorphous Silicon (Jo et al., 2009) cross-point switches.”
Multi-terminal memtransistors from polycrystalline monolayer molybdenum disulfide (2018, result): “Equation (3) captures the non-linear kinetics of the dopants near the contacts using a window function where the degree of non-linearity is defined by a positive integer p (see Methods).(28) This model agrees well with the experimental data (Fig.”
Integration of nanoscale memristor synapses in neuromorphic computing architectures (2013, background): “TiO2-based memristor models [33, 34] hypothesized that solidstate devices comprise a mixture of TiO2 phases, a stoichiometric and a reduced one (TiO2 − x), that can facilitate distinct resistive states via controlling the displacement of oxygen vacancies and thus the extent of the two phases.”