Exploring FPGA Implementation and Emulation of Memristor Devices DOI Creative Commons
Zeyad Aklah, Amean Al-Safi,

Hussein T. Hassan

et al.

International Journal of Computational Methods and Experimental Measurements, Journal Year: 2024, Volume and Issue: 12(2), P. 135 - 146

Published: June 30, 2024

This paper explores the field of FPGA implementation and emulation memristor devices, providing insights into advancements, challenges, future directions.The discusses various techniques used for FPGA-based emulation, emphasizing importance accurate modeling performance evaluation.It identifies challenges in field, including improving accuracy, scalability, real-time adaptation, standardization, integration with design tools, exploring novel applications.Additionally, results study show that FPGAs are one viable solutions emulating memristors.The concludes based holds a promise studying memristor-based circuits systems, potential applications neuromorphic computing, machine learning accelerators, analog/mixed-signal circuit design.

Language: Английский

Covalent Organic Frameworks for Neuromorphic Devices DOI
Kui Zhou, Ziqi Jia,

Yao Zhou

et al.

The Journal of Physical Chemistry Letters, Journal Year: 2023, Volume and Issue: 14(32), P. 7173 - 7192

Published: Aug. 4, 2023

Neuromorphic computing could enable the potential to break inherent limitations of conventional von Neumann architectures, which has led widespread research interest in developing novel neuromorphic memory devices, such as memristors and bioinspired artificial synaptic devices. Covalent organic frameworks (COFs), crystalline porous polymers, have tailorable skeletons pores, providing unique platforms for interplay with photons, excitons, electrons, holes, ions, spins, molecules. Such features encourage rising COF materials electronics. To develop high-performance COF-based it is necessary comprehensively understand materials, applications. Therefore, this Perspective focuses on discussing use devices terms molecular design, thin-film processing, Finally, we provide an outlook future directions applications

Language: Английский

Citations

31
Towards on-receptor computing: Electronic nociceptor embedded neuromorphic functionalities at nanoscale DOI
Rupam Mandal, Aparajita Mandal, T. Som

et al.

Applied Materials Today, Journal Year: 2024, Volume and Issue: 37, P. 102103 - 102103

Published: Feb. 15, 2024

Language: Английский

Citations

15
A Reconfigurable All-Optical-Controlled Synaptic Device for Neuromorphic Computing Applications DOI
Tao Zhang, Chao Fan, Lingxiang Hu

et al.

ACS Nano, Journal Year: 2024, Volume and Issue: 18(25), P. 16236 - 16247

Published: June 13, 2024

Retina-inspired visual sensors play a crucial role in the realization of neuromorphic systems. Nevertheless, significant obstacles persist pursuit achieving bidirectional synaptic behavior and attaining high performance context photostimulation. In this study, we propose reconfigurable all-optical controlled device based on IGZO/SnO/SnS heterostructure, which integrates sensing, storage processing functions. Relying simple heterojunction stack structure energy band engineering, excitatory inhibitory behaviors can be observed under light stimulation ultraviolet (266 nm) visible (405, 520 658 without additional voltage modulation. particular, junction field-effect transistors heterostructure were fabricated to elucidate underlying photoresponse mechanism. addition optical signal processing, an artificial neural network simulator optoelectrical synapse was trained recognized handwritten numerals with recognition rate 91%. Furthermore, prepared 8 × array successfully demonstrated process perception memory for image human brain, as well simulated situation damage retina by light. This work provides effective strategy development high-performance optoelectronic synapses practical approach design multifunctional vision

Language: Английский

Citations

15
Atomistic description of conductive bridge formation in two-dimensional material based memristor DOI Creative Commons
Sanchali Mitra, Santanu Mahapatra

npj 2D Materials and Applications, Journal Year: 2024, Volume and Issue: 8(1)

Published: March 27, 2024

Abstract In-memory computing technology built on 2D material-based nonvolatile resistive switches (aka memristors) has made great progress in recent years. It however been debated whether such remarkable switching is an inherent property of the materials or if metal electrode plays any role? Can atoms penetrate through crystalline to form conductive filaments as observed amorphous oxide-based memristors? To find answers, here we investigate MoS 2 and h-BN-based devices with electrochemically passive active (metal) electrodes using reactive molecular dynamics a charge equilibration approach. We that SET RESET processes electrode-based multilayer involve formation disruption linking two exclusively grain boundaries, configuration which affects volatility switching. Whereas mechanisms require interlayer B-N bonds popping S atom Mo plane at point defects. also show adsorption defects causes monolayer . Our atomic-level understanding provides explanations apparently contradictory experimental findings enables defect-engineering guidelines for disruptive technology.

Language: Английский

Citations

10
Memristor-based spiking neural networks: cooperative development of neural network architecture/algorithms and memristors DOI Creative Commons

Huihui Peng,

Lin Gan, Xin Guo

et al.

Chip, Journal Year: 2024, Volume and Issue: 3(2), P. 100093 - 100093

Published: April 6, 2024

Inspired by the structure and principles of human brain, spike neural networks (SNNs) appear as latest generation artificial networks, attracting significant universal attention due to their remarkable low-energy transmission pulse powerful capability for large-scale parallel computation. Current researches on gradually change from software simulation into hardware implementation. However, such a process is fraught with challenges. In particular, memristors are highly anticipated candidate considering fast-programming speed, low power consumption, compatibility CMOS technology. this review, we start basic SNNs, then introduce memristor-based technologies implementation further discuss feasibility integrating customized algorithm optimization promote efficient energy-saving SNN systems. Finally, based existing memristor technology, summarize current problems challenges in field.

Language: Английский

Citations

8
2D Ferroelectrics and ferroelectrics with 2D: Materials and device prospects DOI
Chloe Leblanc, Seunguk Song, Deep Jariwala

et al.

Current Opinion in Solid State and Materials Science, Journal Year: 2024, Volume and Issue: 32, P. 101178 - 101178

Published: July 30, 2024

Language: Английский

Citations

6
Design of Mixed-Dimensional QDs/MoS2/TiO2 Heterostructured Resistive Random-Access Memory with Interfacial Analog Switching Characteristics for Potential Neuromorphic Computing DOI

Shin‐Yi Tang,

Yu‐Chuan Shih,

Ying‐Chun Shen

et al.

ACS Applied Electronic Materials, Journal Year: 2024, Volume and Issue: 6(3), P. 1581 - 1589

Published: March 11, 2024

Resistive random-access memory (RRAM) is one of the most promising candidates for next-generation nanoscale nonvolatile devices and neuromorphic computing applications. In this study, we developed a novel mixed-dimensional design RRAM devices, incorporating zero-dimensional quantum dots (QDs), two-dimensional MoS2, TiO2 switching layer to achieve prominent interfacial behaviors. Compared with typical filamentary proposed heterostructure featured light-sensitive QDs/MoS2 that allowed bias-controllable resistive changes during set reset processes without abrupt switching. This was endowed by effective electron–hole pair separations upon excitation generation thin molybdenum oxide (MoOx) due accumulation oxygen ions at interface between MoS2 TiO2. The ITO/QDs/MoS2/TiO2/Pt device exhibited an on/off ratio 10 improved endurance under 515 nm laser illumination wavelength-dependent behavior, making it useful multilevel storage. Furthermore, heterostructured demonstrated synaptic characteristics enhanced potentiation depression nonlinearities asymmetry factors, revealing its potential future

Language: Английский

Citations

5
Nonvolatile and volatile resistive switching characteristics in MoS2 thin film for RRAM application DOI
Xiaoyi Lei,

Xiaoya Zhu,

Hao Wang

et al.

Journal of Alloys and Compounds, Journal Year: 2023, Volume and Issue: 969, P. 172443 - 172443

Published: Oct. 6, 2023

Language: Английский

Citations

11
Proton Conducting Neuromorphic Materials and Devices DOI
Yifan Yuan, Ranjan Kumar Patel, Suvo Banik

et al.

Chemical Reviews, Journal Year: 2024, Volume and Issue: 124(16), P. 9733 - 9784

Published: July 22, 2024

Neuromorphic computing and artificial intelligence hardware generally aims to emulate features found in biological neural circuit components enable the development of energy-efficient machines. In brain, ionic currents temporal concentration gradients control information flow storage. It is therefore interest examine materials devices for neuromorphic wherein electronic can propagate. Protons being mobile under an external electric field offers a compelling avenue facilitating functionalities synapses neurons. this review, we first highlight interesting analog protons as neurotransmitters various animals. We then discuss experimental approaches mechanisms proton doping classes inorganic organic proton-conducting advancement architectures. Since hydrogen among lightest elements, characterization solid matrix requires advanced techniques. review powerful synchrotron-based spectroscopic techniques characterizing well complementary scattering detect hydrogen. First-principles calculations are discussed they help provide understanding migration structure modification. Outstanding scientific challenges further our its use emerging electronics pointed out.

Language: Английский

Citations

4
Low-power artificial neuron networks with enhanced synaptic functionality using dual transistor and dual memristor DOI Creative Commons

Keerthi Nalliboyina,

R. Sakthivel

PLoS ONE, Journal Year: 2025, Volume and Issue: 20(1), P. e0318009 - e0318009

Published: Jan. 27, 2025

Artificial neurons with bio-inspired firing patterns have the potential to significantly improve performance of neural network computing. The most significant component an artificial neuron circuit is a large amount energy consumption. Recent literature has proposed memristors as promising option for synaptic implementation. In contrast, implementing memristive circuitry through hardware presents challenges and relevant research topic. This paper describes efficient circuit-level mixed CMOS memristor synapse model. From this perspective, design in standard technology low power utilization. response modified version Morris-Lecar theoretical suggested employs memristor-based Dual Transistor Memristor (DTDM) circuit. produces high spiking frequency According our research, Morris Lecar (ML) DTDM consumes 12.55 pW power, 22.72 kHz, 2.13 fJ per spike. simulations were carried out using Spectre tool 45 nm technology.

Language: Английский

Citations

0