4.7 Article

Spike Optimization to Improve Properties of Ferroelectric Tunnel Junction Synaptic Devices for Neuromorphic Computing System Applications

Related references

Note: Only part of the references are listed.
Article Chemistry, Multidisciplinary

Improvement of Resistance Change Memory Characteristics in Ferroelectric and Antiferroelectric (like) Parallel Structures

Wonwoo Kho et al.

Summary: Recently, there has been a significant focus on the development of advanced technologies like AI and big data, and extensive research is being conducted on high-density, high-speed storage devices. Ferroelectrics have emerged as potential non-volatile memory materials due to their ability to maintain polarization. Ferroelectric tunnel junctions (FTJs) are gaining attention as ideal devices for integration and miniaturization, and the memory features of FTJs can be improved through the utilization of switching characteristics of both ferroelectric and antiferroelectric materials.

NANOMATERIALS (2023)

Article Chemistry, Multidisciplinary

Synaptic Characteristic of Hafnia-Based Ferroelectric Tunnel Junction Device for Neuromorphic Computing Application

Wonwoo Kho et al.

Summary: In this study, the implementation of spike timing-dependent plasticity (STDP) rule in the FTJ device was successful. Based on the simulation of handwriting image classification, it was demonstrated that the FTJ device can be used as a synaptic device for implementing an SNN.

NANOMATERIALS (2023)

Article Materials Science, Multidisciplinary

Highly Stable Artificial Synapses Based on Ferroelectric Tunnel Junctions for Neuromorphic Computing Applications

Sungmun Song et al.

Summary: This study investigates the potential application of artificial synapses composed of ferroelectric tunnel junctions based on a metal-hafnium zirconium oxide-metal structure for neuromorphic computing. Multiple resistance levels are implemented through partial polarization switching control, and synaptic plasticity is successfully imitated based on a high level of device stability and reproducibility. In addition, this device exhibits linear symmetric long-term potentiation and long-term depression using a highly variable pulse driving scheme. Finally, the artificial neural network applied with this synaptic device shows high classification accuracy (95.95%) for the Mixed National Institute of Standards and Technology handwritten digits.

ADVANCED MATERIALS TECHNOLOGIES (2022)

Article Engineering, Electrical & Electronic

Spike-time-dependent plasticity rule in memristor models for circuit design

Mouna Elhamdaoui et al.

Summary: This article analyzes and simulates three popular memristor models, effectively mimicking the plasticity rule of biological synapses. By comparing the characteristics of different models, the most suitable model as a synapse component for neuromorphic circuits is identified.

JOURNAL OF COMPUTATIONAL ELECTRONICS (2022)

Article Chemistry, Multidisciplinary

High-Performance Neuromorphic Computing Based on Ferroelectric Synapses with Excellent Conductance Linearity and Symmetry

Shu-Ting Yang et al.

Summary: This study successfully tackles the issues of repeated synaptic weight update and achieves linear and symmetric variation by controlling the ionic migration in ferroelectric materials. The artificial synapse based on this technique shows high classification accuracy and stable unsupervised learning in a noisy environment. This research paves the way for reliable and reproducible supervised and unsupervised learning strategies.

ADVANCED FUNCTIONAL MATERIALS (2022)

Article Physics, Applied

Stochastic artificial synapses based on nanoscale magnetic tunnel junction for neuromorphic applications

Wenxing Lv et al.

Summary: Bio-inspired neuromorphic computing shows potential in achieving on-chip learning with bio-plausibility and energy efficiency. In this study, stochastic artificial synapses based on nanoscale magnetic tunnel junctions are used to implement spike-timing-dependent plasticity (STDP). The magnitude and temporal requirements for STDP can be modulated by engineering the pre- and post-synaptic voltage pulses. Furthermore, unsupervised learning for tasks like pattern recognition can be achieved using arrays of binary magnetic synapses.

APPLIED PHYSICS LETTERS (2022)

Article Engineering, Electrical & Electronic

An efficient deep neural network accelerator using controlled ferroelectric domain dynamics

Sayani Majumdar

Summary: This paper presents an efficient deep neural network (DNN) accelerator that uses ferroelectric domain dynamics to control analog synaptic weight elements. A device-to-algorithm framework is used to evaluate novel synaptic devices, and simulations are performed to optimize the control circuits and array architectures for DNN training. The results demonstrate that precise control of polarization switching dynamics in multi-domain polycrystalline ferroelectric films can greatly improve the linearity of weight updates in metal-ferroelectric-semiconductor tunnel junctions, leading to energy-efficient operation.

NEUROMORPHIC COMPUTING AND ENGINEERING (2022)

Article Engineering, Electrical & Electronic

Molecular ferroelectric/semiconductor interfacial memristors for artificial synapses

Yichen Cai et al.

Summary: A two-terminal ferroelectric synaptic device was demonstrated using a molecular ferroelectric/semiconductor interface. The interfacial resistance can be tuned via polarization-controlled blocking effect, and exhibits typical synaptic features. The introduction of the semiconductor enables the attributes of optoelectronic synapse and in-sensor computing.

NPJ FLEXIBLE ELECTRONICS (2022)

Editorial Material Materials Science, Ceramics

Implementing ceramic materials into neuromorphic memory devices through oxide-based memristors

Bethany X. Rutherford

AMERICAN CERAMIC SOCIETY BULLETIN (2021)

Article Nanoscience & Nanotechnology

Self-adaptive STDP-based learning of a spiking neuron with nanocomposite memristive weights

A. Emelyanov et al.

NANOTECHNOLOGY (2020)

Article Multidisciplinary Sciences

A highly CMOS compatible hafnia-based ferroelectric diode

Qing Luo et al.

NATURE COMMUNICATIONS (2020)

Article Multidisciplinary Sciences

Sub-nanosecond memristor based on ferroelectric tunnel junction

Chao Ma et al.

NATURE COMMUNICATIONS (2020)

Article Multidisciplinary Sciences

Experimental Demonstration of Supervised Learning in Spiking Neural Networks with Phase-Change Memory Synapses

S. R. Nandakumar et al.

SCIENTIFIC REPORTS (2020)

Article Chemistry, Multidisciplinary

Blocking of Conducting Channels Widens Window for Ferroelectric Resistive Switching in Interface-Engineered Hf0.5Zr0.5O2Tunnel Devices

Milena Cervo Sulzbach et al.

ADVANCED FUNCTIONAL MATERIALS (2020)

Review Multidisciplinary Sciences

Emerging Materials for Neuromorphic Devices and Systems

Min-Kyu Kim et al.

ISCIENCE (2020)

Article Engineering, Electrical & Electronic

Hafnia-Based Double-Layer Ferroelectric Tunnel Junctions as Artificial Synapses for Neuromorphic Computing

Benjamin Max et al.

ACS APPLIED ELECTRONIC MATERIALS (2020)

Article Nanoscience & Nanotechnology

Energy-Efficient Organic Ferroelectric Tunnel Junction Memristors for Neuromorphic Computing

Sayani Majumdar et al.

ADVANCED ELECTRONIC MATERIALS (2019)

Article Engineering, Electrical & Electronic

Low power, ultrafast synaptic plasticity in 1R-ferroelectric tunnel memristive structure for spiking neural networks

F. Zayer et al.

AEU-INTERNATIONAL JOURNAL OF ELECTRONICS AND COMMUNICATIONS (2019)

Article Chemistry, Physical

RRAM-based synapse devices for neuromorphic systems

K. Moon et al.

FARADAY DISCUSSIONS (2019)

Article Automation & Control Systems

CMOS Compatible Hf0.5Zr0.5O2 Ferroelectric Tunnel Junctions for Neuromorphic Devices

Bernhard Mittermeier et al.

ADVANCED INTELLIGENT SYSTEMS (2019)

Article Engineering, Electrical & Electronic

Multiscale Co-Design Analysis of Energy, Latency, Area, and Accuracy of a ReRAM Analog Neural Training Accelerator

Matthew J. Marinella et al.

IEEE JOURNAL ON EMERGING AND SELECTED TOPICS IN CIRCUITS AND SYSTEMS (2018)

Article Physics, Applied

A phase-change memory model for neuromorphic computing

S. R. Nandakumar et al.

JOURNAL OF APPLIED PHYSICS (2018)

Article Chemistry, Multidisciplinary

Threshold Switching of Ag or Cu in Dielectrics: Materials, Mechanism, and Applications

Zhongrui Wang et al.

ADVANCED FUNCTIONAL MATERIALS (2018)

Review Materials Science, Multidisciplinary

Oxide-based RRAM materials for neuromorphic computing

XiaoLiang Hong et al.

JOURNAL OF MATERIALS SCIENCE (2018)

Article Engineering, Electrical & Electronic

PCMO-Based RRAM and NPN Bipolar Selector as Synapse for Energy Efficient STDP

S. Lashkare et al.

IEEE ELECTRON DEVICE LETTERS (2017)

Article Physics, Applied

Ferroelectricity in hafnium oxide thin films

T. S. Boescke et al.

APPLIED PHYSICS LETTERS (2011)

Article Nanoscience & Nanotechnology

Self-organized computation with unreliable, memristive nanodevices

G. S. Snider

NANOTECHNOLOGY (2007)