Control of Synaptic Plasticity Learning of Ferroelectric Tunnel Memristor by Nanoscale Interface Engineering

Guo, Rui; Zhou, Ya-Xiong; Wu, Lijun; Wang, Zhuorui; Lim, Zhishiuh; Yan, Xiaobing; Lin, Weinan; Wang, Han; Yoong, Herng Yau; Chen, Jingsheng; Ariando, Ariando; Venkatesan, T.; Wang, John; Chow, Gan Moog; Gruverman, Alexei; Miao, Xiangshui; Zhu, Yimei

doi:10.1021/acsami.8b01469

Cited by 115 publications

(87 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Thanks to the existence of such voltage thresholds, the STDP learning rule can be emulated in these ferroelectric tunnel junctions (see Figure c). Synaptic functions have also been implemented in ferroelectric tunnel memristors based on Pt/BaTiO 3 /Nb:SrTiO 3 and Ag/PbZr 0.52 Ti 0.48 O 3 /La 0.8 Sr 0.2 MnO 3 ferroelectric tunnel junctions . In addition, three‐terminal ferroelectric synapses have also been developed by using Pb(Zr,Ti)O 3 and Hf 0.5 Zr 0.5 O 2 based ferroelectric tunnel junctions …”

Section: Working Mechanisms Of Memristive Synapsesmentioning

confidence: 99%

Memristive Synapses for Brain‐Inspired Computing

Wang

Zhuge

2019

Adv Materials Technologies

View full text Add to dashboard Cite

be precisely regulated by the ionic flow, which is called synaptic plasticity. Generally, there are two types of synaptic plasticity: potentiation and depression. In the case of potentiation or depression, the synaptic weight increases or decreases upon repeated stimulation. Inspired by the human brain, novel non von Neumann computing architectures mainly composed of artificial synapses and artificial neurons have been proposed, which we can call "artificial neural networks," "brain-like computers," or "neuromorphic computers." [1,[3][4][5] However, the development of artificial brains that possess the efficiency of their biological counterparts in information processing remains a major challenge in computing. [6] One of the major challenges is the lack of suitable hardware to implement synapses, which are the main data processing elements in artificial brains. Generally, there are four essential requirements for an artificial synapse. [7,8] First, the analog weight should be nonvolatile in the absence of learning and weight updates should be bidirectional when the synapse is learning. Second, the synapse output is the product of the input signal and the synapse weight. Third, the weight updates vary with both the input signal and the stored weight value. Fourth, the synapse should be rather compact, and operate off a unidirectional information transfer with low power consumption.Mead and co-workers fabricated a four-terminal synaptic device based on a single floating gate silicon transistor in 1995. [8] It can simultaneously perform long term weight storage, compute the product of the input and floating gate value, and update the weight value according to a Hebbian or a backpropagation learning rule. In 1996, they developed a new floating-gate silicon transistor synapse with a threeterminal structure. [7] Hot-electron injection and electron tunneling make possible bidirectional weight updates. Given that these updates depend on both the stored weight value and the transistor terminal voltages, such synapse can implement a learning function. However, the integration density of transistor synapses is severely restricted to their three-or fourterminal structures. [6] The emergence of memristors endows artificial synapses with a new development opportunity. The memristor (short for memory resistor) is a two-terminal circuit element characterized by a relationship between the charge and the flux-linkage, which shows a distinctive "fingerprint" characterized by a pinched hysteresis loop confined to the first and the third quadrants of the Although the structure and function of the human brain are still far from being fully understood, brain-inspired computing architectures mainly consisting of artificial neurons and artificial synapses have been attracting more and more attentions due to their powerful computing capability and energy efficient operation. Synaptic plasticity is believed to be the origin of learning and memory. However, it is still a big challenge to realize artificial synapses with high reliability, go...

show abstract

Section: Working Mechanisms Of Memristive Synapsesmentioning

confidence: 99%

Memristive Synapses for Brain‐Inspired Computing

Wang

Zhuge

2019

Adv Materials Technologies

View full text Add to dashboard Cite

show abstract

“…Various types of memristors, for example, conductive filament memories, phase change memories (PCMs), resistive switching memories based on ion migration, and ferroelectric tunnel junctions (FTJs), have been proposed for achieving high performance such as nonvolatility, a gradual resistance change, a threshold feature, a simple structure, and energy efficiency . Among these devices, a promising candidate for mimicking artificial synaptic devices and performing neural network operations is FTJ, which is an ultrathin ferroelectric film sandwiched by two electrodes whose resistance depends on the polarization direction (Figure b) . The pioneering work by Chanthbouala et al demonstrated that the gradual switching of the ferroelectric domain and the resulting change of resistance states can ensure a memristor response in FTJs .…”

mentioning

confidence: 99%

Reproducible Ultrathin Ferroelectric Domain Switching for High‐Performance Neuromorphic Computing

et al. 2019

View full text Add to dashboard Cite

Neuromorphic computing consisting of artificial synapses and neural network algorithms provides a promising approach for overcoming the inherent limitations of current computing architecture. Developments in electronic devices that can accurately mimic the synaptic plasticity of biological synapses, have promoted the research boom of neuromorphic computing. It is reported that robust ferroelectric tunnel junctions can be employed to design high‐performance electronic synapses. These devices show an excellent memristor function with many reproducible states (≈200) through gradual ferroelectric domain switching. Both short‐ and long‐term plasticity can be emulated by finely tuning the applied pulse parameters in the electronic synapse. The analog conductance switching exhibits high linearity and symmetry with small switching variations. A simulated artificial neural network with supervised learning built from these synaptic devices exhibited high classification accuracy (96.4%) for the Mixed National Institute of Standards and Technology (MNIST) handwritten recognition data set.

show abstract

“…Moreover, the conductance of an NQD MD can be continuously tuned by changing the pulse parameters, thereby providing a crucial foundation for spike‐timing‐dependent plasticity (STDP). Furthermore, biosynaptic functions and plasticity, including long‐term potentiation and depression, STDP, and paired‐pulse facilitation, are successfully implemented in this type of memristor . In particular, applying pulses with widths of 200 nanoseconds facilitates fast learning and computing in neuromorphic chips.…”

mentioning

confidence: 99%

Self‐Assembled Networked PbS Distribution Quantum Dots for Resistive Switching and Artificial Synapse Performance Boost of Memristors

et al. 2018

Self Cite

View full text Add to dashboard Cite

Resistive switching (RS) is a promising emerging storage technology that has received much attention due to its many advantages, such as economy, fast operating speed, long retention, high density, and low energy consumption. [1] RS effects are widely applied in the fields of nonvolatile RS random access memory (RRAM), artificial neural computing, and reconfigurable logic operations and so on. [2] RRAM memories based on electrochemical metallization (ECM) and valance change mechanism (VCM) are commonly used for the memristor application. [3] Compared with conventional computing based on the von Neumann architecture, memristor (i.e., RS device) computing is proving to be superior for brain-inspired computing, such as image processing and speech recognition, [2] where the diffusion of the metal ions such as Ag + , Cu + , or oxygen vacancies are used to mimic the diffusion of Ca + in the neural cell. [4] However, the switching voltages in the memristor devices (MDs) showWith the advent of the era of big data, resistive random access memory (RRAM) has become one of the most promising nanoscale memristor devices (MDs) for storing huge amounts of information. However, the switching voltage of the RRAM MDs shows a very broad distribution due to the random formation of the conductive filaments. Here, selfassembled lead sulfide (PbS) quantum dots (QDs) are used to improve the uniformity of switching parameters of RRAM, which is very simple comparing with other methods. The resistive switching (RS) properties of the MD with the self-assembled PbS QDs exhibit better performance than those of MDs with pure-Ga 2 O 3 and randomly distributed PbS QDs, such as a reduced threshold voltage, uniformly distributed SET and RESET voltages, robust retention, fast response time, and low power consumption. This enhanced performance may be attributed to the ordered arrangement of the PbS QDs in the self-assembled PbS QDs which can efficiently guide the growth direction for the conducting filaments. Moreover, biosynaptic functions and plasticity, are implemented successfully in the MD with the self-assembled PbS QDs. This work offers a new method of improving memristor performance, which can significantly expand existing applications and facilitate the development of artificial neural systems. Data Storage

show abstract

Control of Synaptic Plasticity Learning of Ferroelectric Tunnel Memristor by Nanoscale Interface Engineering

Cited by 115 publications

References 47 publications

Memristive Synapses for Brain‐Inspired Computing

Memristive Synapses for Brain‐Inspired Computing

Reproducible Ultrathin Ferroelectric Domain Switching for High‐Performance Neuromorphic Computing

Self‐Assembled Networked PbS Distribution Quantum Dots for Resistive Switching and Artificial Synapse Performance Boost of Memristors

Contact Info

Product

Resources

About