Appendix 2: Memristor Modeling and Simulation
85
However, the slowdown of Moore’s Law [54] has shifted interest back to unconventional devices that can potentially outperform CMOS established technologies.
Indeed, the situation has drastically changed after the announcement of HP in
2008 that has made clear how the memristor approach permits to formulate an
accurate circuit model of resistive switching devices. The integration of the two
fields, i.e., memristor theory and resistive switching devices, is also fundamental due
to the emerging multidisciplinary research in bio-inspired computing architectures,
which fosters a broader understanding of complex physical phenomena in material science and biological systems. Furthermore, advancements in measurement
instrumentation and analytical tools allowed for observing and quantifying the
resistive switching mechanism at fine volume resolution, also promoting the idea
that memristive effects are more prominent at the nanoscale. In this context, the
memristor theory developed by L. O. Chua offers a full framework which HP
has adapted to include the complex physical processes responsible for resistive
switching in the form of a wrap-around macromodel [55]. As a result, the coalition
of the two fields has sparked an explosion of interest in the research community
and the paradigm in the resistive switching community has somewhat changed.
The majority of measured resistive switching effects are now described from a
macroscopic point of view as memristive effects, by extending memristor theory to
memristive device and systems (see Sect. 2.3). Following this line of reasoning, L.
O. Chua has promoted the idea that all resistive switching memories are memristors
[5]: “All 2-terminal non-volatile memory devices based on resistance switching are
memristors, regardless of the device material and physical operating mechanisms.
They all exhibit a distinctive fingerprint characterized by a pinched hysteresis
loop confined to the first and the third quadrants of the v − i plane whose
contour shape in general changes with both the amplitude and frequency of any
periodic ssinewave-like input voltage source, or current source.” Based on this
fundamental observation, many solid-state and/or nano-resistive switching devices
can now be regarded as memristors. These include several types of Random Access
Memories (RAMs) as ferroelectric random access memory (FeRAM), magnetic
RAM (MRAM), Phase-Change RAM (PCRAM) and Resistive RAM (ReRAM),
and Atomic Switch [12, 55–58].
Appendix 2: Memristor Modeling and Simulation
Much progress has been achieved over the past few years to develop accurate models
for the dynamics of resistance switching memories, to establish solid foundations on
memristor theory and to embed the mathematical descriptions of these devices into
commercially available software packages, but a comprehensive body of knowledge
allowing a thorough exploration of the full potential of memristors in future
electronics has not been developed yet. The fact that there exist numerous physical
memristors, composed of both organic and inorganic materials, and that researchers
continue to investigate new materials or fabrication process methodologies to
85
However, the slowdown of Moore’s Law [54] has shifted interest back to unconventional devices that can potentially outperform CMOS established technologies.
Indeed, the situation has drastically changed after the announcement of HP in
2008 that has made clear how the memristor approach permits to formulate an
accurate circuit model of resistive switching devices. The integration of the two
fields, i.e., memristor theory and resistive switching devices, is also fundamental due
to the emerging multidisciplinary research in bio-inspired computing architectures,
which fosters a broader understanding of complex physical phenomena in material science and biological systems. Furthermore, advancements in measurement
instrumentation and analytical tools allowed for observing and quantifying the
resistive switching mechanism at fine volume resolution, also promoting the idea
that memristive effects are more prominent at the nanoscale. In this context, the
memristor theory developed by L. O. Chua offers a full framework which HP
has adapted to include the complex physical processes responsible for resistive
switching in the form of a wrap-around macromodel [55]. As a result, the coalition
of the two fields has sparked an explosion of interest in the research community
and the paradigm in the resistive switching community has somewhat changed.
The majority of measured resistive switching effects are now described from a
macroscopic point of view as memristive effects, by extending memristor theory to
memristive device and systems (see Sect. 2.3). Following this line of reasoning, L.
O. Chua has promoted the idea that all resistive switching memories are memristors
[5]: “All 2-terminal non-volatile memory devices based on resistance switching are
memristors, regardless of the device material and physical operating mechanisms.
They all exhibit a distinctive fingerprint characterized by a pinched hysteresis
loop confined to the first and the third quadrants of the v − i plane whose
contour shape in general changes with both the amplitude and frequency of any
periodic ssinewave-like input voltage source, or current source.” Based on this
fundamental observation, many solid-state and/or nano-resistive switching devices
can now be regarded as memristors. These include several types of Random Access
Memories (RAMs) as ferroelectric random access memory (FeRAM), magnetic
RAM (MRAM), Phase-Change RAM (PCRAM) and Resistive RAM (ReRAM),
and Atomic Switch [12, 55–58].
Appendix 2: Memristor Modeling and Simulation
Much progress has been achieved over the past few years to develop accurate models
for the dynamics of resistance switching memories, to establish solid foundations on
memristor theory and to embed the mathematical descriptions of these devices into
commercially available software packages, but a comprehensive body of knowledge
allowing a thorough exploration of the full potential of memristors in future
electronics has not been developed yet. The fact that there exist numerous physical
memristors, composed of both organic and inorganic materials, and that researchers
continue to investigate new materials or fabrication process methodologies to