Self-Rectifying and Forming-Free Resistive-Switching Device for Embedded Memory Application

Xiong

et al. 2020

Adv Elect Materials

using artificial synapses is an essential step to accomplish the neuromorphic computing system. [9,10] Formerly, artificial synapses were realized by complementary metal-oxide-semiconductor (CMOS) circuitry containing dozens of electronic components. [11] However, many electronic components result in complicated architecture and high energy consumption. As comparison, two-terminal memristors, especially resistive random access memory and phase change random access memory, that recently entered our field of vision have been widely discussed as artificial synapses owing to their structures which is similar to that of synapses and the reproducible tuning of resistance. [12][13][14][15][16] Also, for an ideal synapse device it is better to meet these requirements, such as symmetric potentiation-depression characteristics, 5-bit/cell analog levels, and high non-volatility with ≈100 conductance ON/OFF ratio. They are the key points we need to take into account. [17,18] In particular, HfO 2 -based memristors have been demonstrated as a leading alternative as synapse in virtue of its distinctive superiority, such as simple structure, <10 ns switching speed, <10 pJ power consumption, multilevel ability, and compatibility with CMOS fabrication process. [19][20][21] However, the resistance contrast (ON/OFF ratio) of HfO 2 -based memristors ranges from ≈40 to ≈150. [22] Considering the resistance fluctuation of these memristors across a silicon wafer, larger ON/OFF ratio is needed to guarantee high recognition accuracy (>97%). [23] Recently, several works were reported on improvement of the memristors' performance with thin interfacial layer. [24][25][26] As one of the most important multiferroic materials, bismuth iron oxide with perovskite structure has come into notice for its potential in multifunctional device applications. [27] Moreover, BiFeO 3 (BFO) has attracted much attention because it possesses superior characteristics of resistance switching (RS) such as large ON/OFF ratio in some researches. [28] It is also confirmed that BFO can be applied in bi-layer design memristor to significantly improve RS characteristics, [29] which gives inspiration to insert BFO thin film as the goal of high device performances.In this work, ultrathin BFO film was inserted to fabricate Pt/BFO/HfO 2 /TiN memristor to improve the RS characteristic of HfO 2 -based memristor. The material characterization and RS behavior were systemically analyzed. The role of the inserting BFO layer on the RS behavior was evaluated and the RS mechanism triggered by inserting BFO film was explored.HfO 2 -based memristors that remembers the history of the current that has passed through them have attracted great interest for use as artificial synapses in neuromorphic systems. However, the low resistance contrast exhibited by HfO 2 -based memristors seriously decreases their recognition accuracy. By inserting a 2 nm BiFeO 3 layer a large memory window of 10 4 and remarkable pulse endurance of 10 8 cycles are achieved. Multilevel storage capability is also d...

Section: Resultsmentioning

confidence: 99%

Designing High‐Performance Storage in HfO₂/BiFeO₃ Memristor for Artificial Synapse Applications

Xiong

et al. 2020

Adv Elect Materials

“…There are also other self-rectifying memristors associating memristive mechanisms such as interaction of composition dependent thermal conductivity and oxygen-ion migration, 58,[68][69][70] electron tunneling controlled by ferroelectric surfaces, 71,72 and interfacial trap site engineering. 73 Most reported self-rectifying memristors do not show an abrupt increase and decrease of conductance.…”

Section: Self-rectifying Memristormentioning

confidence: 99%

Research progress on solutions to the sneak path issue in memristor crossbar arrays

et al. 2020

IEICE Electron. Express

“…Then, according to Eqs. (4) and (11), the average R 3 M ðt; 0 ; ħÞ over a cycle is given by Fig. 2(i), ( j), (k), and (l), based on Eqs.…”

Section: Effects Of Initial Phase On X-controlled Dynamic Parametersmentioning

confidence: 99%

“…Since the successful development of the Hewlett-Packard (HP) prototype device [1], memristor has drawn a great deal of research interests [2]. It has been widely used in embedded memory [3,4], neurobiology [5,6], artificial intelligence [7], neural networks [8,9], etc., due to the following advantages: fast speed [10], high density [11], and low power consumption [12,13]. Memristor is a category of nano-devices with nonlinear input-output dynamics.…”

Section: Introductionmentioning

confidence: 99%

Analytic analysis for effects of input initial phase on input-output dynamics of memristor

Wei

Lin

2019

The analytic expressions of input-output dynamic parameters of memristor (i.e., state variable, memristance, and output response) with respect to initial phase of sinusoidal input are obtained by using Homotopy Analysis Method (HAM). Furthermore, a new exponent, called Response Time Delay (difference) between the Maximum Values of input and output-RDMV-IO, is proposed to rapidly analyze the memristor input-output dynamics, under different initial phases. Employing RDMV-IO, we further reveal the intrinsic relationship between initial phase and dynamics. The studies are verified by using Mathematica simulations adopting nonlinear dopant drift memristor model coupled with window function.