Fabrication and Investigation of Ferroelectric Memristors with Various Synaptic Plasticities

2021 ◽  
Author(s):  
Qi Qin ◽  
Miaocheng Zhang ◽  
Suhao Yao ◽  
Xingyu Chen ◽  
Aoze Han ◽  
...  
Keyword(s):  
Resistive Switching ◽  
First Principles Calculation ◽  
Neuromorphic Computing ◽  
Computing Systems ◽  
Von Neumann ◽  
Paired Pulse ◽  
Interface Barrier ◽  
Spike Time Dependent Plasticity ◽  
The Brain ◽  
Paired Pulse Depression

Abstract In the Post-Moore Era, the neuromorphic computing has been mainly focused on breaking the von Neumann bottlenecks. Memristor has been proposed as a key part for the neuromorphic computing architectures, which can be used to emulate the synaptic plasticities of human brain. Ferroelectric memristor is a breakthrough for memristive devices on account of its reliable-nonvolatile storage, low-write/read latency, and tunable-conductive states. However, among the reported ferroelectric memristors, the mechanisms of resistive-switching are still under debate. In addition, the research of emulation of the brain synapses using ferroelectric memristors needs to be further investigated. Herein, the Cu/PbZr0.52Ti0.48O3 (PZT)/Pt ferroelectric memristors have been fabricated. The devices are able to realize the transformation from threshold switching behaviors to resistive switching behaviors. The synaptic plasticities, including excitatory post-synaptic current (EPSC), paired-pulse facilitation (PPF), paired-pulse depression (PPD), and spike time-dependent plasticity (STDP) have been mimicked by the PZT devices. Furthermore, the mechanisms of PZT devices based on the interface barrier and conductive filament models have been investigated by first-principles calculation. This work may contribute to the applications of ferroelectric memristors in neuromorphic computing systems.

Reliable analog resistive switching behaviors achieved using memristive devices in AlOx/HfOx bilayer structure for neuromorphic systems

2021 ◽  
Author(s):  
Meng Qi ◽  
Tianquan Fu ◽  
Huadong Yang ◽  
ye tao ◽  
Chunran Li ◽  
...  
Keyword(s):  
Resistive Switching ◽  
Learning Experience ◽  
Random Access ◽  
Resistive Random Access Memory ◽  
Spike Timing ◽  
Information Storage ◽  
Bilayer Structure ◽  
Neuromorphic Computing ◽  
Von Neumann ◽  
Simulation Based

Abstract Human brain synaptic memory simulation based on resistive random access memory (RRAM) has an enormous potential to replace traditional Von Neumann digital computer thanks to several advantages, including its simple structure, high-density integration, and the capability to information storage and neuromorphic computing. Herein, the reliable resistive switching (RS) behaviors of RRAM are demonstrated by engineering AlOx/HfOx bilayer structure. This allows for uniform multibit information storage. Further, the analog switching behaviors are capable of imitate several synaptic learning functions, including learning experience behaviors, short-term plasticity-long-term plasticity transition, and spike-timing-dependent-plasticity (STDP). In addition, the memristor based on STDP learning rules are implemented in image pattern recognition. These results may offer a promising potential of HfOx-based memristors for future information storage and neuromorphic computing applications.

scholarly journals Memristive Spiking Neural Networks Trained with Unsupervised STDP

Electronics ◽  
2018 ◽  
Vol 7 (12) ◽  
pp. 396 ◽  
Author(s):  
Errui Zhou ◽  
Liang Fang ◽  
Binbin Yang
Keyword(s):  
Neural Networks ◽  
Feature Learning ◽  
Spike Timing ◽  
Spiking Neural Networks ◽  
Learning Method ◽  
Hardware Complexity ◽  
Neuromorphic Computing ◽  
Computing Systems ◽  
Von Neumann ◽  
Complex Architectures

Neuromorphic computing systems are promising alternatives in the fields of pattern recognition, image processing, etc. especially when conventional von Neumann architectures face several bottlenecks. Memristors play vital roles in neuromorphic computing systems and are usually used as synaptic devices. Memristive spiking neural networks (MSNNs) are considered to be more efficient and biologically plausible than other systems due to their spike-based working mechanism. In contrast to previous SNNs with complex architectures, we propose a hardware-friendly architecture and an unsupervised spike-timing dependent plasticity (STDP) learning method for MSNNs in this paper. The architecture, which is friendly to hardware implementation, includes an input layer, a feature learning layer and a voting circuit. To reduce hardware complexity, some constraints are enforced: the proposed architecture has no lateral inhibition and is purely feedforward; it uses the voting circuit as a classifier and does not use additional classifiers; all neurons can generate at most one spike and do not need to consider firing rates and refractory periods; all neurons have the same fixed threshold voltage for classification. The presented unsupervised STDP learning method is time-dependent and uses no homeostatic mechanism. The MNIST dataset is used to demonstrate our proposed architecture and learning method. Simulation results show that our proposed architecture with the learning method achieves a classification accuracy of 94.6%, which outperforms other unsupervised SNNs that use time-based encoding schemes.

scholarly journals Мемристоры на основе поли-n-ксилилена с внедренными наночастицами серебра

2020 ◽  
Vol 46 (2) ◽  
pp. 25
Author(s):  
А.Н. Мацукатова ◽  
А.В. Емельянов ◽  
А.А. Миннеханов ◽  
Д.А. Сахарутов ◽  
А.Ю. Вдовиченко ◽  
...  
Keyword(s):  
Silver Nanoparticles ◽  
Resistive Switching ◽  
Retention Time ◽  
Switching Effect ◽  
Neuromorphic Computing ◽  
Computing Systems ◽  
Current Voltage ◽  
Current Voltage Characteristics ◽  
Resistive Switching Effect

The properties of parylene based memristors with embedded silver nanoparticles were studied: current–voltage characteristics and resistive switching effect, endurance and retention time. It was found that introduction of nanoparticles leads to a significant improvement of the main memristive characteristics. Obtained results could be used to create large memristor arrays with homogeneous characteristics that emulate synapses in neuromorphic computing systems.

scholarly journals Designing artificial sodium ion reservoirs to emulate biological synapses

2020 ◽  
Vol 12 (1) ◽  
Author(s):  
Dongshin Kim ◽  
Jang-Sik Lee
Keyword(s):  
Nervous System ◽  
Sodium Ion ◽  
Short Term ◽  
Computing Systems ◽  
Von Neumann ◽  
Synaptic Functions ◽  
Von Neumann Architecture ◽  
The Brain ◽  
Ionic Movements

Abstract Emulating neurons/synapses in the brain is an important step to realizing highly efficient computers. This fact makes neuromorphic devices important emerging solutions to the limitations imposed by the current computing architecture. To mimic synaptic functions in the brain, it is critical to replicate ionic movements in the nervous system. It is therefore important to note that ions move easily in liquids. In this study, we demonstrate a liquid-based neuromorphic device that is capable of mimicking the movement of ions in the nervous system by controlling Na+ movement in an aqueous solution. The concentration of Na+ in the solution can control the ionic conductivity of the device. The device shows short-term and long-term plasticity such as excitatory postsynaptic current, paired-pulse facilitation, potentiation, and depression, which are key properties for memorization and computation in the brain. This device has the potential to overcome the limitations of current von Neumann architecture-based computing systems and substantially advance the technology of neuromorphic computing.

scholarly journals Analysis of Neuromorphic Computing Systems and its Applications in Machine Learning

2021 ◽  
Vol 9 (VI) ◽  
pp. 5309-5312
Author(s):  
Pranava Bhat
Keyword(s):  
Machine Learning ◽  
Human Brain ◽  
Neural Systems ◽  
Neuromorphic Computing ◽  
Computing Systems ◽  
The Past ◽  
Computational Capability ◽  
Biological World ◽  
Areas Of Interest ◽  
The Brain

The domain of engineering has always taken inspiration from the biological world. Understanding the functionalities of the human brain is one of the key areas of interest over time and has caused many advancements in the field of computing systems. The computational capability per unit power per unit volume of the human brain exceeds the current best supercomputers. Mimicking the physics of computations used by the nervous system and the brain can bring a paradigm shift to the computing systems. The concept of bridging computing and neural systems can be termed as neuromorphic computing and it is bringing revolutionary changes in the computing hardware. Neuromorphic computing systems have seen swift progress in the past decades. Many organizations have introduced a variety of designs, implementation methodologies and prototype chips. This paper discusses the parameters that are considered in the advanced neuromorphic computing systems and the tradeoffs between them. There have been attempts made to make computer models of neurons. Advancements in the hardware implementation are fuelling the applications in the field of machine learning. This paper presents the applications of these modern computing systems in Machine Learning.

scholarly journals Memristive Artificial Synapses for Neuromorphic Computing

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Wen Huang ◽  
Xuwen Xia ◽  
Chen Zhu ◽  
Parker Steichen ◽  
Weidong Quan ◽  
...  
Keyword(s):  
Brain Function ◽  
Neuromorphic Computing ◽  
Von Neumann ◽  
Optical Signals ◽  
Synaptic Functions ◽  
Neuromorphic Systems ◽  
Von Neumann Architecture ◽  
The Brain ◽  
Stimulation Of ◽  
Working Mechanisms

AbstractNeuromorphic computing simulates the operation of biological brain function for information processing and can potentially solve the bottleneck of the von Neumann architecture. This computing is realized based on memristive hardware neural networks in which synaptic devices that mimic biological synapses of the brain are the primary units. Mimicking synaptic functions with these devices is critical in neuromorphic systems. In the last decade, electrical and optical signals have been incorporated into the synaptic devices and promoted the simulation of various synaptic functions. In this review, these devices are discussed by categorizing them into electrically stimulated, optically stimulated, and photoelectric synergetic synaptic devices based on stimulation of electrical and optical signals. The working mechanisms of the devices are analyzed in detail. This is followed by a discussion of the progress in mimicking synaptic functions. In addition, existing application scenarios of various synaptic devices are outlined. Furthermore, the performances and future development of the synaptic devices that could be significant for building efficient neuromorphic systems are prospected.

scholarly journals Мемристоры на основе полипараксилилена на гибких подложках

2019 ◽  
Vol 45 (21) ◽  
pp. 40
Author(s):  
Б.С. Швецов ◽  
А.Н. Мацукатова ◽  
А.А. Миннеханов ◽  
А.А. Несмелов ◽  
Б.В. Гончаров ◽  
...  
Keyword(s):  
Resistive Switching ◽  
Limiting Current ◽  
Resistive State ◽  
Wearable Electronics ◽  
Neuromorphic Computing ◽  
Computing Systems

This work presents the results of the fabrication and investigation of flexible memristive structures based on parylene layers, which demonstrate stable resistive switching and are resistant to bends up to 10 mm radii. It is also proposed a two-step scheme for establishing the resistive state of the memristive structure, based on control of the value of the limiting current flowing through the structure. Obtained results open the possibility of using memristive structures based on parylene layers for neuromorphic computing systems and biocompatible "wearable" electronics.

Organic synaptic devices based on ionic gel with reduced leakage current

2021 ◽  
Vol 57 (15) ◽  
pp. 1907-1910
Author(s):  
Dapeng Liu ◽  
Yiwei Zhao ◽  
Qianqian Shi ◽  
Shilei Dai ◽  
Li Tian ◽  
...  
Keyword(s):  
Solid State ◽  
Leakage Current ◽  
Dielectric Film ◽  
Neuromorphic Computing ◽  
Computing Systems ◽  
Current Reduction

A solid-state hybrid electrolyte dielectric film was designed for leakage current reduction, synaptic simulation and neuromorphic computing systems.

scholarly journals Multistate resistive switching behaviors for neuromorphic computing in memristor

2021 ◽  
Vol 9 ◽  
pp. 100125
Author(s):  
B. Sun ◽  
S. Ranjan ◽  
G. Zhou ◽  
T. Guo ◽  
Y. Xia ◽  
...  
Keyword(s):  
Resistive Switching ◽  
Neuromorphic Computing

scholarly journals A Neural Network Decomposition Algorithm for Mapping on Crossbar-Based Computing Systems

Electronics ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 1526 ◽  
Author(s):  
Choongmin Kim ◽  
Jacob A. Abraham ◽  
Woochul Kang ◽  
Jaeyong Chung
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Predictive Performance ◽  
Fixed Number ◽  
Decomposition Algorithm ◽  
Computing Systems ◽  
Network Decomposition ◽  
Von Neumann ◽  
Analog Computing ◽  
The Neural Network

Crossbar-based neuromorphic computing to accelerate neural networks is a popular alternative to conventional von Neumann computing systems. It is also referred as processing-in-memory and in-situ analog computing. The crossbars have a fixed number of synapses per neuron and it is necessary to decompose neurons to map networks onto the crossbars. This paper proposes the k-spare decomposition algorithm that can trade off the predictive performance against the neuron usage during the mapping. The proposed algorithm performs a two-level hierarchical decomposition. In the first global decomposition, it decomposes the neural network such that each crossbar has k spare neurons. These neurons are used to improve the accuracy of the partially mapped network in the subsequent local decomposition. Our experimental results using modern convolutional neural networks show that the proposed method can improve the accuracy substantially within about 10% extra neurons.

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