88
2 Fundamental Properties of Mem-Elements
control parameters and of suitable window and memristance functions. The PSpice
architecture of the TEAM model is similar to the one originally presented in [62].
Another activation-type state model, where the state variable expresses the
memristance and the control signal is in voltage form, embedded in the PSpice
software program [75], enabled to capture the adaptive behavior of a unicellular
organism named amoeba through a simple memristor-based oscillator [76].
A further interesting model with threshold-activated state dynamics was proposed in [77] to explain Spike-Timing-Dependent-Plasticity (STDP) in neural
synapses.
An additional insightful discussion on the models available in the literature was
recently published in [78], where a novel model inspired from Simmons’ electron
tunneling theory [73], endowed with programming threshold capability and PSpice
circuit implementation, was also proposed.
The Boundary Condition Memristor (BCM) model is a simple yet accurate
boundary condition-based mathematical model for memristor nano-structures made
up of two layers with different conductivity levels, whose longitudinal extensions
depend on the time history of the input. In comparison with the classical BCM [31],
the generalized version [32] is augmented with programming threshold capability
[78], i.e., with tunable nonvolatile behavior.
Recently, in [79], assuming Pickett’s model [72] as reference for comparison,
various memristor models, including Biolek’s, the TEAM, and the BCM models,
were first compared on the basis of the ability to reproduce (after an optimization
process) the dynamics of the reference model in a particular simulation scenario, and
second employed in a couple of memristor-based circuits to investigate the variance
in the nonlinear dynamical behaviors they give rise to. The latter study revealed
the model-dependency of the dynamics of the memristor-based circuits, and thus
raised a warning against a blind faith in the memristor models and pointed out the
necessity to develop a universal mathematical model for exploring the full potential
of the memristor and unfolding its unique properties.
Appendix 3: Memristor Circuits and Systems
Due to its nature and properties (e.g., CMOS process compatibility, lower cost, zero
standby power, nanosecond switching speed, great scalability, and high density),
the memristor devices are enabling the exploration of alternative computing architectures and algorithms. Neuromorphic computing, computation-in-memory, and
accelerate architectures embedding memristors will be able to address emerging
applications, which are extremely demanding and/or have surpassed the capabilities
of today’s computation architectures and technologies. These new architectures will
have to realize at least partially the following: (a) eliminate the communication
and memory bottleneck, (b) support massive parallelism to increase the overall
performance, (c) drastically enhance energy efficiency (both static and dynamic) to
improve the computation efficiency, (d) be cheaper to manufacture, etc. Reliable,
2 Fundamental Properties of Mem-Elements
control parameters and of suitable window and memristance functions. The PSpice
architecture of the TEAM model is similar to the one originally presented in [62].
Another activation-type state model, where the state variable expresses the
memristance and the control signal is in voltage form, embedded in the PSpice
software program [75], enabled to capture the adaptive behavior of a unicellular
organism named amoeba through a simple memristor-based oscillator [76].
A further interesting model with threshold-activated state dynamics was proposed in [77] to explain Spike-Timing-Dependent-Plasticity (STDP) in neural
synapses.
An additional insightful discussion on the models available in the literature was
recently published in [78], where a novel model inspired from Simmons’ electron
tunneling theory [73], endowed with programming threshold capability and PSpice
circuit implementation, was also proposed.
The Boundary Condition Memristor (BCM) model is a simple yet accurate
boundary condition-based mathematical model for memristor nano-structures made
up of two layers with different conductivity levels, whose longitudinal extensions
depend on the time history of the input. In comparison with the classical BCM [31],
the generalized version [32] is augmented with programming threshold capability
[78], i.e., with tunable nonvolatile behavior.
Recently, in [79], assuming Pickett’s model [72] as reference for comparison,
various memristor models, including Biolek’s, the TEAM, and the BCM models,
were first compared on the basis of the ability to reproduce (after an optimization
process) the dynamics of the reference model in a particular simulation scenario, and
second employed in a couple of memristor-based circuits to investigate the variance
in the nonlinear dynamical behaviors they give rise to. The latter study revealed
the model-dependency of the dynamics of the memristor-based circuits, and thus
raised a warning against a blind faith in the memristor models and pointed out the
necessity to develop a universal mathematical model for exploring the full potential
of the memristor and unfolding its unique properties.
Appendix 3: Memristor Circuits and Systems
Due to its nature and properties (e.g., CMOS process compatibility, lower cost, zero
standby power, nanosecond switching speed, great scalability, and high density),
the memristor devices are enabling the exploration of alternative computing architectures and algorithms. Neuromorphic computing, computation-in-memory, and
accelerate architectures embedding memristors will be able to address emerging
applications, which are extremely demanding and/or have surpassed the capabilities
of today’s computation architectures and technologies. These new architectures will
have to realize at least partially the following: (a) eliminate the communication
and memory bottleneck, (b) support massive parallelism to increase the overall
performance, (c) drastically enhance energy efficiency (both static and dynamic) to
improve the computation efficiency, (d) be cheaper to manufacture, etc. Reliable,