Using memristive properties common for titanium dioxide thin film devices, we designed a simple write algorithm to tune device conductance at a specific bias point to 1% relative accuracy (which is roughly equivalent to seven-bit precision) within its dynamic range even in the presence of large variations in switching behavior. The high precision state is nonvolatile and the results are likely to be sustained for nanoscale memristive devices because of the inherent filamentary nature of the resistive switching. The proposed functionality of memristive devices is especially attractive for analog computing with low precision data. As one representative example we demonstrate hybrid circuitry consisting of an integrated circuit summing amplifier and two memristive devices to perform the analog multiply-and-add (dot-product) computation, which is a typical bottleneck operation in information processing.

https://iopscience.iop.org/article/10.1088/0957-4484/23/7/075201/pdf

High precision tuning of state for memristive devices by adaptable variation-tolerant algorithm

Using memristive properties common for the titanium dioxide thin film devices, we designed a simple write algorithm to tune device conductance at a specific bias point to 1% relative accuracy (which is roughly equivalent to 7-bit precision) within its dynamic range even in the presence of large variations in switching behavior. The high precision state is nonvolatile and the results are likely to be sustained for nanoscale memristive devices because of the inherent filamentary nature of the resistive switching. The proposed functionality of memristive devices is especially attractive for analog computing with low precision data. As one representative example we demonstrate hybrid circuitry consisting of CMOS summing amplifier and two memristive devices to perform analog multiply and accumulate computation, which is a typical bottleneck operation in information processing.

High-Precision Tuning of State for Memristive Devices by Adaptable Variation-Tolerant Algorithm

Memristors have been proposed for a number of applications from nonvolatile memory to neuromorphic systems. Unlike conventional devices based solely on electron transport, memristors operate on the principle of resistive switching (RS) based on redistribution of ions. To date, a number of experimental and modeling studies have been reported to probe the RS mechanism; however, a complete physical picture that can quantitatively describe the dynamic RS behavior is still missing. Here, we present a quantitative and accurate dynamic switching model that not only fully accounts for the rich RS behaviors in memristors in a unified framework but also provides critical insight for continued device design, optimization, and applications. The proposed model reveals the roles of electric field, temperature, oxygen vacancy concentration gradient, and different material and device parameters on RS and allows accurate predictions of diverse set/reset, analog switching, and complementary RS behaviors using only material...

Comprehensive Physical Model of Dynamic Resistive Switching in an Oxide Memristor

Novel nonvolatile universal memory technology is essential for providing required storage for nanocomputing. As a potential contender for the next-generation memory, the recently found "the missing fourth circuit element," memristor, has drawn a great deal of research interests. In this paper, by starting from basic memristor device equations, we develop a comprehensive set of properties and design equations for memristor based memories. Our analyses are specifically targeting key electrical memristor device characteristics relevant to memory operations. Using our derived properties, we investigate the design of read and write circuits and analyze important data integrity and noise-tolerance is sues.

Dynamical Properties and Design Analysis for Nonvolatile Memristor Memories

In this paper we consider using memristors to implement the neighborhood connections of a CNN. First the benefits and drawbacks of using memristors as programmable CNN weights are described. Then, an existing memristor model is improved to allow full-scale simulation of the design. The new model is implemented in the SPICE simulation environment and is not restricted to CNN applications. Then, the CNN cell design is presented and simulations describing memristor programming are performed.

CNN using memristors for neighborhood connections

This paper describes how memristors could be used as an analog memory and computing elements. The key idea is to apply comparison and programming phases cyclically so that the memristor can be programmed to a given conductance level at a fixed voltage. It is further described how the cyclical programming could be used in computing. A configuration needed to copy the sum of conductances of two memristors into a third one is described. It is further shown how the devices could be configured so that addition and subtraction of positive and negative analog conductances could be performed. The presented memory structure requires a memristor model with a nonlinear programming sensitivity (programming threshold) for proper programming selectivity. A model of such a memristor is shown and key simulations are presented.

/pdf/arithmetic-operations-within-memristor-based-analog-memory-2pemx112df.pdf

E. Lehtonen

Papers

Arithmetic operations within memristor-based analog memory