Emerging memory devices for artificial synapses

  title={Emerging memory devices for artificial synapses},
  author={Youngjune Park and Min-Kyu Kim and Jang‐Sik Lee},
  journal={Journal of Materials Chemistry C},
Increasing demands for new computing systems to fulfill enormous information processing have driven the development of brain-inspired neuromorphic systems that can perform fast and energy-efficient computing. Neuromorphic computing requires efficient artificial synapses. Emerging memory devices to replace complementary metal-oxide semiconductors are being evaluated. This paper reviews recent developments in artificial synapses that exploit emerging memory devices for use in neuromorphic devices… 
Memory Devices for Flexible and Neuromorphic Device Applications
Recently, consumer electronics have moved toward data‐centric applications due to the development of smart electronic devices. Moreover, electronic devices have become highly portable, wearable, and
Towards engineering in memristors for emerging memory and neuromorphic computing: A review
This review discusses emergent memory technologies using memristors, together with its potential neuromorphic applications, by elucidating the different material engineering techniques used during device fabrication to improve the memory and neuromorphic performance of devices, in areas such as I ON/I OFF ratio, endurance, spike time-dependent plasticity (STDP), and paired-pulse facilitation (PPF).
An Artificial Synapse Based on CsPbI3 Thin Film
The artificial synapse with the structure of Au/CsPbI3/ITO exhibited learning and memory behavior similar to biological neurons, and the synaptic plasticity was proven, including short-term synaptic Plasticity (STSP) and long-termaptic plasticity (LTSP).
Two-terminal Resistive-switching Memories based on Liquid AgNO3 as Artificial Synapses
A two-terminal structure that works based on a solution of silver nitrate (AgNO3) and a silver (Ag) electrode to mimic the behavior of synapses and the dynamical properties of resistive switching, endurance and data retention were studied.
CMOS-compatible compute-in-memory accelerators based on integrated ferroelectric synaptic arrays for convolution neural networks
Convolutional neural networks (CNNs) have gained much attention because they can provide superior complex image recognition through convolution operations. Convolution processes require repeated
Adaptive Spiking Sensor System Based on CMOS Memristors Emulating Long and Short-Term Plasticity of Biological Synapses for Industry 4.0 Applications
The proposed adaptive spike-to-rank coding (ASRC), which is the main part of the spiking neural ADC, is based on CMOS memristors emulating short-term plasticity (STP) and long-term Plasticity (LTP) biological synapses, and compensates deviations by adapting the weights of the synapses.
Low Power MoS2/Nb2O5 Memtransistor Device with Highly Reliable Heterosynaptic Plasticity
Artificial synapses based on 2D MoS2 memtransistors have recently attracted considerable attention as a promising device architecture for complex neuromorphic systems. However, previous memtransistor
Towards peptide-based tunable multistate memristive materials.
The unique potential of biomolecules for the design of multistate memristors with a controlled- and indeed, programmable-nanostructure, allowing going beyond anything that is conceivable by employing conventional coordination chemistry.
Ferroelectric field effect transistors: Progress and perspective
Ferroelectric field effect transistors (FeFETs) have attracted attention as next-generation devices as they can serve as a synaptic device for neuromorphic implementation and a one-transistor (1T)
A perspective on electrode engineering in ultrathin ferroelectric heterostructures for enhanced tunneling electroresistance
The combination of ferroelectricity and quantum tunneling enables the tantalizing possibility of next-generation nonvolatile memories based on ferroelectric tunnel junctions (FTJs). In the last two


Nanoelectronic programmable synapses based on phase change materials for brain-inspired computing.
A new nanoscale electronic synapse based on technologically mature phase change materials employed in optical data storage and nonvolatile memory applications is reported, utilizing continuous resistance transitions in phase change material to mimic the analog nature of biological synapses, enabling the implementation of a synaptic learning rule.
Carbon Nanotube Synaptic Transistor Network for Pattern Recognition.
It is reported that a three-terminal synaptic transistor based on carbon nanotubes can provide reliable synaptic functions that encode relative timing and regulate weight change and the developed synaptic transistor network associated with CMOS circuits can perform unsupervised learning for pattern recognition using a simplified spike-timing-dependent plasticity scheme.
Mimicking Biological Synaptic Functionality with an Indium Phosphide Synaptic Device on Silicon for Scalable Neuromorphic Computing.
A crystalline indium phosphide (InP)-based artificial synapse for spiking neural networks that exhibits elasticity, short-term plasticity, long-term Plasticity, metaplasticity, and spike timing-dependent plasticity , emulating the critical behaviors exhibited by biological synapses is demonstrated.
Ferroelectric Analog Synaptic Transistors.
The analog conductance modulation behavior in the ferroElectric thin-film transistors (FeTFT) that have the nanoscale ferroelectric material and oxide semiconductors is demonstrated to demonstrate linear potentiation and depression characteristics of FeTFTs.
Organic electronics for neuromorphic computing
This Review Article examines the development of organic neuromorphic devices, considering the different switching mechanisms used in the devices and the challenges the field faces in delivering neuromorphic computing applications.
Activity-Dependent Synaptic Plasticity of a Chalcogenide Electronic Synapse for Neuromorphic Systems
A Ag/AgInSbTe/Ag structure for chalcogenide memristor-based electronic synapses is demonstrated, using the memristive characteristics with reproducible gradual resistance tuning to mimic the activity-dependent synaptic plasticity that serves as the basis of memory and learning.
Physical aspects of low power synapses based on phase change memory devices
This work proposes a unique low-power methodology named the 2-PCM Synapse, to use PCM devices as synapses in large scale neuromorphic systems, and efficiently simulated fully connected feed-forward spiking neural network capable of complex visual pattern extraction from real world data.
Neuromorphic computing with multi-memristive synapses
A multi-memristive synaptic architecture with an efficient global counter-based arbitration scheme to address challenges associated with the non-ideal memristive device behavior is proposed.
Synaptic electronics: materials, devices and applications.
In this paper, the recent progress of synaptic electronics is reviewed, with a focus on the use of synaptic devices for neuromorphic or brain-inspired computing.
Artificial Synapses with Short- and Long-Term Memory for Spiking Neural Networks Based on Renewable Materials.
Flexible biomemristor devices based on lignin can be a promising key component for artificial synapses and flexible electronic devices.