Memristor-based neuromorphic computing is expected to overcome the bottleneck of von Neumann architecture. An artificial synaptic device with continuous conductance variation is essential for implementing bioinspired neuromorphic systems. In this work, a memristor based on Pt/LiSiO/TiN structure is developed to emulate an artificial synapse, which shows non-volatile multilevel resistance state memory behavior. Moreover, the high nonlinearity caused by abrupt changes in the set process is optimized by adjusting the initial resistance. 100 levels of continuously modulated conductance states are achieved and the nonlinearity factors are reduced to 1.31. The significant improvement is attributed to the decrease in the Schottky barrier height and the evolution of the conductive filaments. Finally, due to the improved linearity of the long-term potentiation/long-term depression behaviors in LiSiOmemristor, a robust recognition rate (∼94.58%) is achieved for pattern recognition with the modified National Institute of Standards and Technology handwriting database. The Pt/LiSiO/TiN memristor shows significant potential in high-performance multilevel data storage and neuromorphic computing systems.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1088/1361-6528/acf0c8 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Harnessing spatiotemporal transformation in magnetic domains for nonvolatile physical reservoir computing.
Sci Adv
January 2025
Institute of Materials Research and Engineering (IMRE), Agency for Science Technology and Research (A*STAR), 2 Fusionopolis Way, #08-03 Innovis, Singapore 138634, Republic of Singapore.
Combining physics with computational models is increasingly recognized for enhancing the performance and energy efficiency in neural networks. Physical reservoir computing uses material dynamics of physical substrates for temporal data processing. Despite the ease of training, building an efficient reservoir remains challenging.
View Article and Find Full Text PDFGuanine-based spin valve with spin rectification effect for an artificial memory element.
Heliyon
January 2025
National Institute of Materials Physics, 077125 Magurele, Ilfov, Romania.
Non-volatile electronic memory elements are very attractive for applications, not only for information storage but also in logic circuits, sensing devices and neuromorphic computing. Here, a ferroelectric film of guanine nucleobase is used in a resistive memory junction sandwiched between two different ferromagnetic films of Co and CoCr alloys. The magnetic films have an in-plane easy axis of magnetization and different coercive fields whereas the guanine film ensures a very long spin transport length, at 100 K.
View Article and Find Full Text PDFResistive memory-based zero-shot liquid state machine for multimodal event data learning.
Nat Comput Sci
January 2025
Key Lab of Fabrication Technologies for Integrated Circuits and Key Laboratory of Microelectronic Devices and Integrated Technology, Institute of Microelectronics of the Chinese Academy of Sciences, Beijing, China.
The human brain is a complex spiking neural network (SNN) capable of learning multimodal signals in a zero-shot manner by generalizing existing knowledge. Remarkably, it maintains minimal power consumption through event-based signal propagation. However, replicating the human brain in neuromorphic hardware presents both hardware and software challenges.
View Article and Find Full Text PDFImproving Recall Accuracy in Sparse Associative Memories That Use Neurogenesis.
Neural Comput
January 2025
Electronics and Computer Science, University of Southampton, Southampton SO17 1BJ, U.K.
The creation of future low-power neuromorphic solutions requires specialist spiking neural network (SNN) algorithms that are optimized for neuromorphic settings. One such algorithmic challenge is the ability to recall learned patterns from their noisy variants. Solutions to this problem may be required to memorize vast numbers of patterns based on limited training data and subsequently recall the patterns in the presence of noise.
View Article and Find Full Text PDFEmerging 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 PDFWant 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!