Topic
Memistor
About: Memistor is a research topic. Over the lifetime, 608 publications have been published within this topic receiving 34905 citations.
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TL;DR: A Random Weight Change algorithm is adopted and implemented in circuits for its learning of memristor-based multilayer neural networks and its hardware-based learning architecture is investigated.
Abstract: Implementation of memristor-based multilayer neural networks and their hardware-based learning architecture is investigated in this paper. Two major functions of neural networks which should be embedded in synapses are programmable memory and analog multiplication. "Memristor", which is a newly developed device, has two such major functions in it. In this paper, multilayer neural networks are implemented with memristors. A Random Weight Change algorithm is adopted and implemented in circuits for its learning. Its hardware-based learning on neural networks is two orders faster than its software counterpart.
2 citations
Posted Content•
TL;DR: In this article, a spin-torque memristor was used to displace the magnetic domain wall at low current densities and thus to minimize the energy cost of the memory.
Abstract: Memristors are non-volatile nano-resistors. Their resistance can be tuned by applied currents or voltages and set to a large number of levels between two limit values. Thanks to these properties, memristors are ideal building blocks for a number of applications such as multilevel non-volatile memories and artificial nano-synapses, which are the focus of this work. A key point towards the development of large scale memristive neuromorphic hardware is to build these neural networks with a memristor technology compatible with the best candidates for the future mainstream non-volatile memories. Here we show the first experimental achievement of a memristor compatible with Spin-Torque Magnetic Random Access Memory. The resistive switching in our spin-torque memristor is linked to the displacement of a magnetic domain wall by spin-torques in a perpendicularly magnetized magnetic tunnel junction. We demonstrate that our magnetic synapse has a large number of intermediate resistance states, sufficient for neural computation. Moreover, we show that engineering the device geometry allows leveraging the most efficient spin torque to displace the magnetic domain wall at low current densities and thus to minimize the energy cost of our memristor. Our results pave the way for spin-torque based analog magnetic neural computation.
2 citations
Patent•
23 Jun 2015TL;DR: The continuous-level memristor emulator as discussed by the authors uses two currentfeedback operational-amplifiers (CFOAs) and uses an OTA-based circuit in place of a diode resistive network to provide a continuous level of memristance instead of two binary states.
Abstract: The continuous-level memristor emulator is a circuit that uses off-the-shelf components to emulate a memristor. The circuit uses two current-feedback operational-amplifiers (CFOAs) and uses an operational transconductance amplifier (OTA)-based circuit in place of a diode resistive network to provide a continuous level of memristance instead of two binary states. The OTA is forced to work in its nonlinear region by the voltage VDC applied to its positive input terminal. Thus, the transfer function of the OTA-based circuit will be a nonlinear function. Experimental testing shows that the continuous-level memristor emulator is operational as a memristor, and the emulator may be used, e.g., in place of a memristor in a multivibrator circuit.
2 citations
01 Aug 2016
TL;DR: It was concluded that as the resistor values increases, frequency of the signal decreases and Vout will also increased and the implementations of this device in security application.
Abstract: Maintaining the security of communication is very crucial nowadays. It is important in cryptographic security to have strong keys and is secretive. Random number generators are used to combat this problem by producing different and unique identification for each user in a network. Memristors has been studied as a potential tool in hardware security because of its energy efficiency and the nanotechnology fabrication process variations is more unique and random than the traditional complementary metal-oxide-semiconductor (CMOS) processes. This paper analyzes a memristor based ring oscillator random number generator design and how the relationship between the frequency and resistor or memristor affects the randomness of the generator and the implementations of this device in security application. It was concluded that as the resistor values increases, frequency of the signal decreases and Vout will also increased.
2 citations
TL;DR: In this paper , it was shown that a resistor with memory is not a memristor and is simply an inductor with memory, which casts further doubts that ideal memristors do actually exist in nature or may be easily created in the lab.
Abstract: A simple and unambiguous test has been recently suggested [J. Phys. D: Applied Physics, 52, 01LT01 (2018)] to check experimentally if a resistor with memory is indeed a memristor, namely a resistor whose resistance depends only on the charge that flows through it, or on the history of the voltage across it. However, although such a test would represent the litmus test for claims about memristors (in the ideal sense), it has yet to be applied widely to actual physical devices. In this paper, we experimentally apply it to a current-carrying wire interacting with a magnetic core, which was recently claimed to be a memristor (so-called `$\Phi$ memristor') [J. Appl. Phys. 125, 054504 (2019)]. The results of our experiment demonstrate unambiguously that this `$\Phi$ memristor' is not a memristor: it is simply an inductor with memory. This demonstration casts further doubts that ideal memristors do actually exist in nature or may be easily created in the lab.
2 citations