The quantum conductance (QC) behaviors in synaptic devices with stable and tunable conductance states are essential for high-density storage and brain-like neurocomputing (NC). In this work, inspired by the discontinuous transport of fluid in spider silk, a synaptic device composed of a silicon oxide nanowire network embedded with silicon quantum dots (Si-QDs@SiO) is designed. The tunable QC behaviors are achieved in both the SET and RESET processes, and the QC states exhibit stable retention time exceeding 10 s in the synaptic device and show stable reproducibility after an interval of two months. The synaptic plasticity, including long-term potentiation/depression and Pavlovian conditioning function, is simulated based on the tunable conductance. The mechanism of stable and tunable QC behaviors is analyzed and clarified by beading effect of spider silk in Si-QDs@SiO nanowires structure. The digit recognition capability of the device is evaluated by simulation using an artificial neural network consisting of the Si-QDs@SiO-based synaptic device. These results provide insights into the development of neurocomputing systems with high classification accuracy.

Download full-text PDF

Source
http://dx.doi.org/10.1021/acsami.4c06328DOI Listing

Publication Analysis

Top Keywords

synaptic device
16
stable tunable
12
quantum conductance
8
tunable conductance
8
spider silk
8
tunable behaviors
8
synaptic
6
stable
5
device
5
tunable quantum
4

Similar Publications

Large-scale high uniform optoelectronic synapses array for artificial visual neural network.

Microsyst 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 PDF

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 PDF

Multifunctional hardware technologies for neuromorphic computing are essential for replicating the complexity of biological neural systems, thereby improving the performance of artificial synapses and neurons. Integrating ionic and spintronic technologies offers new degrees of freedom to modulate synaptic potentiation and depression, introducing novel magnetic functionalities alongside the established ionic analogue behavior. We demonstrate that magneto-ionic devices can perform as synaptic elements with dynamically tunable depression linearity controlled by an external magnetic field, a functionality reminiscent of neuromodulation in biological systems.

View Article and Find Full Text PDF

Side-Gated Iontronic Memtransistor: A Fast and Energy-Efficient Neuromorphic Building Block.

Small

January 2025

eNDR Laboratory, School of Physics, IISER Thiruvananthapuram, Trivandrum, Kerala, 695551, India.

Iontronic memtransistors have emerged as technologically superior to conventional memristors for neuromorphic applications due to their low operating voltage, additional gate control, and enhanced energy efficiency. In this study, a side-gated iontronic organic memtransistor (SG-IOMT) device is explored as a potential energy-efficient hardware building block for fast neuromorphic computing. Its operational flexibility, which encompasses the complex integration of redox activities, ion dynamics, and polaron generation, makes this device intriguing for simultaneous information storage and processing, as it effectively overcomes the von Neumann bottleneck of conventional computing.

View Article and Find Full Text PDF

Emerging Artificial Synaptic Devices Based on Organic Semiconductors: Molecular Design, Structure and Applications.

ACS Appl Mater Interfaces

January 2025

The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University/Hunan Cancer Hospital, Changsha 410013, Hunan, People's Republic of China.

In modern computing, the Von Neumann architecture faces challenges such as the memory bottleneck, hindering efficient processing of large datasets and concurrent programs. Neuromorphic computing, inspired by the brain's architecture, emerges as a promising alternative, offering unparalleled computational power while consuming less energy. Artificial synaptic devices play a crucial role in this paradigm shift.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!