The ultimate aim of artificial synaptic devices is to mimic the features of biological synapses as closely as possible, in particular, its ability of self-adjusting the synaptic weight responding to the external stimulus. In this work, memristors, based on trilayer oxides with a stack structure of TiN/TiON/HfO/HfO/TiN, are designed to function as the artificial synapses where intrinsically designed oxygen-deficient HfO layer, less oxygen-deficient HfO layer, and TiON layer, imitating the corresponding biological functionality of the pre-synapse, synaptic cleft, and post-synapse, respectively, resemble the features of bio-synapses most closely. Thus, diverse bio-synaptic functions and plasticity, including long-term potentiation and depression, spike-rate-dependent plasticity, spike-timing-dependent plasticity, and metaplasticity, are fulfilled in these devices. Moreover, they exhibit analogue plasticity in both potentiating and depressing, fully emulating the learning protocols of excitation and inhibition in the bio-synapses. The structure and Hf/O distribution of these devices, mimicking the structure and Ca deployment of bio-synapses, are consolidated by the high-resolution transmission electron microscopy and energy-dispersive X-ray spectroscopy, respectively. Powerful bio-realistic behavior, implemented in these simple artificial synaptic devices, make them tailored for neuromorphic hardware applications.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1021/acsami.9b17026 | DOI Listing |
ACS Appl Mater Interfaces
January 2025
School of Materials and Energy, Lanzhou University (LZU), Lanzhou 730000, China.
Complementary neural network circuits combining multifunctional high-performance p-type with n-type organic artificial synapses satisfy sophisticated applications such as image cognition and prosthesis control. However, implementing the dual-modal memory features that are both volatile and nonvolatile in a synaptic transistor is challenging. Herein, for the first time, we propose a single vertical n-type organic synaptic transistor (VNOST) with a novel polymeric organic mixed ionic-electronic conductor as the core channel material to achieve dual-modal synaptic learning/memory behaviors at different operating current densities via the formation of an electric double layer and the reversible ion doping.
View Article and Find Full Text PDFACS Appl Mater Interfaces
January 2025
School of Information Science and Technology, Fudan University, Shanghai 200433, China.
To date, various kinds of memristors have been proposed as artificial neurons and synapses for neuromorphic computing to overcome the so-called von Neumann bottleneck in conventional computing architectures. However, related working principles are mostly ascribed to randomly distributed conductive filaments or traps, which usually lead to high stochasticity and poor uniformity. In this work, a heterostructure with a two-dimensional WS monolayer and a ferroelectric PZT film were demonstrated for memristors and artificial synapses, triggered by in-plane ferroelectric polarization.
View Article and Find Full Text PDFACS Nano
January 2025
Chandra Family Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, Texas 78712, United States.
Spiking neural networks seek to emulate biological computation through interconnected artificial neuron and synapse devices. Spintronic neurons can leverage magnetization physics to mimic biological neuron functions, such as integration tied to magnetic domain wall (DW) propagation in a patterned nanotrack and firing tied to the resistance change of a magnetic tunnel junction (MTJ), captured in the domain wall-magnetic tunnel junction (DW-MTJ) device. Leaking, relaxation of a neuron when it is not under stimulation, is also predicted to be implemented based on DW drift as a DW relaxes to a low energy position, but it has not been well explored or demonstrated in device prototypes.
View Article and Find Full Text PDFMicrosyst Nanoeng
January 2025
State Key Laboratory of Explosion Science and Safety Protection, Beijing Institute of Technology, Ministry of Education, 100081, Beijing, China.
Recently, the biologically inspired intelligent artificial visual neural system has aroused enormous interest. However, there are still significant obstacles in pursuing large-scale parallel and efficient visual memory and recognition. In this study, we demonstrate a 28 × 28 synaptic devices array for the artificial visual neuromorphic system, within the size of 0.
View Article and Find Full Text PDFACS Appl Mater Interfaces
January 2025
School of Materials Science and Engineering, Gyeongsang National University, Jinju, Gyeongsangnam-do 52828, Republic of Korea.
Advances in the semiconductor industry have been limited owing to the constraints imposed by silicon-based CMOS technology; hence, innovative device design approaches are necessary. This study focuses on "more than Moore" approaches, specifically in neuromorphic computing. Although MoS devices have attracted attention as neuromorphic computing candidates, their performances have been limited due to environment-induced perturbations to carrier dynamics and the formation of defect states.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!