Resistance switching memories are memristors
Source abstract
Abstract
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 “sine-wave-like” input voltage source, or current source. In particular, the pinched hysteresis loop shrinks and tends to a straight line as frequency increases. Though numerous examples of voltage vs. current pinched hysteresis loops have been published in many unrelated fields, such as biology, chemistry, physics, etc., and observed from many unrelated phenomena, such as gas discharge arcs, mercury lamps, power conversion devices, earthquake conductance variations, etc., we restrict our examples in this tutorial to solid-state and/or nano devices where copious examples of published pinched hysteresis loops abound. In particular, we sampled arbitrarily, one example from each year between the years 2000 and 2010, to demonstrate that the memristor is a device that does not depend on any particular material, or physical mechanism. For example, we have shown that spin-transfer magnetic tunnel junctions are examples of memristors. We have also demonstrated that both bipolar and unipolar resistance switching devices are memristors. The goal of this tutorial is to introduce some fundamental circuit-theoretic concepts and properties of the memristor that are relevant to the analysis and design of non-volatile nano memories where binary bits are stored as resistances manifested by the memristor’s continuum of equilibrium states. Simple pedagogical examples will be used to illustrate, clarify, and demystify various misconceptions among the uninitiated.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Advanced Memory and Neural Computing · Ferroelectric and Negative Capacitance Devices · Neural dynamics and brain function
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (14)
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