In this paper, we demonstrate a device using a Ni/SiN/BN/p-Si structure with improved performance in terms of a good ON/OFF ratio, excellent stability, and low power consumption when compared with single-layer Ni/SiN/p-Si and Ni/BN/p-Si devices. Its switching mechanism can be explained by trapping and de-trapping via nitride-related vacancies. We also reveal how higher nonlinearity and rectification ratio in a bilayer device is beneficial for enlarging the read margin in a cross-point array structure. In addition, we conduct a theoretical investigation for the interface charge accumulation/depletion in the SiN/BN layers that are responsible for defect creation at the interface and how this accounts for the improved switching characteristics.
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http://dx.doi.org/10.3390/mi13091498 | DOI Listing |
ACS Nano
December 2024
Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore 117583, Singapore.
Two-dimensional (2D) materials hold significant potential for the development of neuromorphic computing architectures owing to their exceptional electrical tunability, mechanical flexibility, and compatibility with heterointegration. However, the practical implementation of 2D memristors in neuromorphic computing is often hindered by the challenges of simultaneously achieving low latency and low energy consumption. Here, we demonstrate memristors based on 2D cobalt phosphorus trisulfide (CoPS), which achieve impressive performance metrics including high switching speed (20 ns), low switching energy (1.
View Article and Find Full Text PDFChem Rev
December 2024
Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, California 90089, United States.
Conventional artificial intelligence (AI) systems are facing bottlenecks due to the fundamental mismatches between AI models, which rely on parallel, in-memory, and dynamic computation, and traditional transistors, which have been designed and optimized for sequential logic operations. This calls for the development of novel computing units beyond transistors. Inspired by the high efficiency and adaptability of biological neural networks, computing systems mimicking the capabilities of biological structures are gaining more attention.
View Article and Find Full Text PDFSmall Methods
December 2024
State Key Laboratory of Powder Metallurgy, Central South University, Changsha, 410083, China.
Memristors and magnetic tunnel junctions are showing great potential in data storage and computing applications. A magnetoelectrically coupled memristor utilizing electron spin and electric field-induced ion migration can facilitate their operation, uncover new phenomena, and expand applications. In this study, devices consisting of Pt/(LaCoO/SrTiO)/LaCoO/Nb:SrTiO (Pt/(LCO/STO)/LCO/NSTO) are engineered using pulsed laser deposition to form the LCO/STO superlattice layer, with Pt and NSTO serving as the top and bottom electrodes, respectively.
View Article and Find Full Text PDFNanotechnology
December 2024
Instituto de Nanociencia y Nanotecnología (CONICET-CNEA), Gral. Paz 1499 - San Martín - Argentina, BUENOS AIRES, 1650, ARGENTINA.
Our study demonstrates that strong cationic segregation can occur in amorphous complex oxide memristors during electrical operation. With the help of analytic techniques, we observed that switching the electrical stimulation from voltage to current significantly prevents structural changes and cation segregation at the nanoscale, improving also the device cycle-to-cycle variability. These findings could contribute to the design of more reliable oxide-based memristors and underscore the crucial effect that has the type of electrical stimulation applied to the devices on their integrity and reliability.
View Article and Find Full Text PDFJ Phys Chem Lett
December 2024
School of Integrated Circuit Science and Engineering, Tianjin Key Laboratory of Film Electronic and Communication Devices, Tianjin University of Technology, Tianjin 300384, China.
Advancing the development of novel materials or architectures for random access memories, coupled with an in-depth understanding of their intrinsic conduction mechanisms, holds the potential to transcend the conventional von Neumann bottleneck. In this work, a novel memristor based on the Sb(S,Se) material with an alloy of S and Se was fabricated. A systematic investigation of the correlation between the Se/(S + Se) ratio and memristive performance revealed that Ag/Sb(S,Se)/FTO memristive behavior is uniquely associated with the formation and disruption of anion vacancies and silver filaments.
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