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Stan Williams of HP Labs also argued that ReRAM was a memristor. [21] However, others challenged this terminology and the applicability of memristor theory to any physically realizable device is open to question. [22] [23] [24] Whether redox-based resistively switching elements (ReRAM) are covered by the current memristor theory is disputed. [25]
A memristor (/ ˈ m ɛ m r ɪ s t ər /; a portmanteau of memory resistor) is a non-linear two-terminal electrical component relating electric charge and magnetic flux linkage.It was described and named in 1971 by Leon Chua, completing a theoretical quartet of fundamental electrical components which also comprises the resistor, capacitor and inductor.
Often the isolation capabilities are inferior to the use of transistors if the on/off ratio for the selector is not sufficient, limiting the ability to operate very large arrays in this architecture. Chalcogenide-based threshold switches have been demonstrated as a viable selector for high-density PCM arrays [22]
The main determinant of a memory system's cost is the density of the components used to make it up. Smaller components, and fewer of them, mean that more "cells" can be packed onto a single chip, which in turn means more can be produced at once from a single silicon wafer.
The current–voltage characteristics of four devices: a resistor with large resistance, a resistor with small resistance, a P–N junction diode, and a battery with nonzero internal resistance.
Thirty-seven years after he predicted its existence, a working solid-state memristor was created by a team led by R. Stanley Williams at Hewlett Packard. [ 5 ] [ 6 ] Alongside Tamas Roska , Chua also introduced the first algorithmically programmable analog cellular neural network (CNN) processor.
The reasons for this surge of interest are manifold. The performance of OFETs, which can compete with that of amorphous silicon (a-Si) TFTs with field-effect mobilities of 0.5–1 cm 2 V −1 s −1 and ON/OFF current ratios (which indicate the ability of the device to shut down) of 10 6 –10 8, has improved significantly.
Data compression ratio, also known as compression power, is a measurement of the relative reduction in size of data representation produced by a data compression algorithm. It is typically expressed as the division of uncompressed size by compressed size.