Explore Research Products in the PAR
It may take a few hours for recently added research products to appear in PAR search results.


Title: (Digital Presentation) Oxide Memristors Based on SiO 2 with Cu/Ag Alloy Metallization for Neuromorphic Computing
By mimicking biomimetic synaptic processes, the success of artificial intelligence (AI) has been astounding with various applications such as driving automation, big data analysis, and natural-language processing.[1-4] Due to a large quantity of data transmission between the separated memory unit and the logic unit, the classical computing system with von Neumann architecture consumes excessive energy and has a significant processing delay.[5] Furthermore, the speed difference between the two units also causes extra delay, which is referred to as the memory wall.[6, 7] To keep pace with the rapid growth of AI applications, enhanced hardware systems that particularly feature an energy-efficient and high-speed hardware system need to be secured. The novel neuromorphic computing system, an in-memory architecture with low power consumption, has been suggested as an alternative to the conventional system. Memristors with analog-type resistive switching behavior are a promising candidate for implementing the neuromorphic computing system since the devices can modulate the conductance with cycles that act as synaptic weights to process input signals and store information.[8, 9] The memristor has sparked tremendous interest due to its simple two-terminal structure, including top electrode (TE), bottom electrode (BE), and an intermediate resistive switching (RS) layer. Many oxide materials, including HfO2, Ta2O5, and IGZO, have extensively been studied as an RS layer of memristors. Silicon dioxide (SiO2) features 3D structural conformity with the conventional CMOS technology and high wafer-scale homogeneity, which has benefited modern microelectronic devices as dielectric and/or passivation layers. Therefore, the use of SiO2as a memristor RS layer for neuromorphic computing is expected to be compatible with current Si technology with minimal processing and material-related complexities. In this work, we proposed SiO2-based memristor and investigated switching behaviors metallized with different reduction potentials by applying pure Cu and Ag, and their alloys with varied ratios. Heavily doped p-type silicon was chosen as BE in order to exclude any effects of the BE ions on the memristor performance. We previously reported that the selection of TE is crucial for achieving a high memory window and stable switching performance. According to the study which compares the roles of Cu (switching stabilizer) and Ag (large switching window performer) TEs for oxide memristors, we have selected the TE materials and their alloys to engineer the SiO2-based memristor characteristics. The Ag TE leads to a larger memory window of the SiO2memristor, but the device shows relatively large variation and less reliability. On the other hand, the Cu TE device presents uniform gradual switching behavior which is in line with our previous report that Cu can be served as a stabilizer, but with small on/off ratio.[9] These distinct performances with Cu and Ag metallization leads us to utilize a Cu/Ag alloy as the TE. Various compositions of Cu/Ag were examined for the optimization of the memristor TEs. With a Cu/Ag alloying TE with optimized ratio, our SiO2based memristor demonstrates uniform switching behavior and memory window for analog switching applications. Also, it shows ideal potentiation and depression synaptic behavior under the positive/negative spikes (pulse train). In conclusion, the SiO2memristors with different metallization were established. To tune the property of RS layer, the sputtering conditions of RS were varied. To investigate the influence of TE selections on switching performance of memristor, we integrated Cu, Ag and Cu/Ag alloy as TEs and compared the switch characteristics. Our encouraging results clearly demonstrate that SiO2with Cu/Ag is a promising memristor device with synaptic switching behavior in neuromorphic computing applications. Acknowledgement This work was supported by the U.S. National Science Foundation (NSF) Award No. ECCS-1931088. S.L. and H.W.S. acknowledge the support from the Improvement of Measurement Standards and Technology for Mechanical Metrology (Grant No. 22011044) by KRISS. References [1] Younget al.,IEEE Computational Intelligence Magazine,vol. 13, no. 3, pp. 55-75, 2018. [2] Hadsellet al.,Journal of Field Robotics,vol. 26, no. 2, pp. 120-144, 2009. [3] Najafabadiet al.,Journal of Big Data,vol. 2, no. 1, p. 1, 2015. [4] Zhaoet al.,Applied Physics Reviews,vol. 7, no. 1, 2020. [5] Zidanet al.,Nature Electronics,vol. 1, no. 1, pp. 22-29, 2018. [6] Wulfet al.,SIGARCH Comput. Archit. News,vol. 23, no. 1, pp. 20–24, 1995. [7] Wilkes,SIGARCH Comput. Archit. News,vol. 23, no. 4, pp. 4–6, 1995. [8] Ielminiet al.,Nature Electronics,vol. 1, no. 6, pp. 333-343, 2018. [9] Changet al.,Nano Letters,vol. 10, no. 4, pp. 1297-1301, 2010. [10] Qinet al., Physica Status Solidi (RRL) - Rapid Research Letters, pssr.202200075R1, In press, 2022.  more » « less
Award ID(s):
1931088
PAR ID:
10482458
Author(s) / Creator(s):
; ;
Publisher / Repository:
Institue of Physics
Date Published:
Journal Name:
ECS Meeting Abstracts
Volume:
MA2022-02
Issue:
15
ISSN:
2151-2043
Page Range / eLocation ID:
808 to 808
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. ; ; ; ; (, npj 2D Materials and Applications)
    Abstract A deeper understanding of resistive switching (RS) in 2D materials is essential for advancing neuromorphic computing. The Dissociation-Diffusion-Adsorption (DDA) model offers a useful framework for probing RS mechanisms in non-volatile memory (NVM) and in-memory computing. We have employed first-principles density functional theory (DFT) to explore dissociation, diffusion, and adsorption phenomena within the DDA model, focusing on the interactions between exemplary metal atoms (Au, Ag, Cu) and monolayer MoS2. Nudged elastic band (NEB) calculations evaluated diffusion barriers in pristine and sulfur-vacancy MoS2. Charged systems were modeled to assess the impact of applied bias on migration pathways. We also examined metal dissociation from bulk electrodes and adsorption at S vacancies. Ag/MoS2shows the lowest dissociation barrier (~0.034 eV), while Au and Cu exhibit similar values (~0.32 eV). These insights highlight Ag as a promising candidate for low-energy RS applications and provide guidance for optimizing switching efficiency in 2D memory devices. 
    more » « less
  2. ; ; ; ; ; ; ; ; ; ; et al (, Advanced Functional Materials)
    Abstract Oxide‐based memristors are considered promising candidates for next‐generation memory and neuromorphic computing applications owing to their intrinsic resistive switching properties, non‐volatile storage, excellent device plasticity, and compatibility with current complementary metal‐oxide‐semiconductor (CMOS) technology. For the physical implementation of neuromorphic microchips, memristors with high power efficiency, reasonable on/off ratios, excellent device reliability, and enhanced plasticity are essential. In this work, a novel memristor design based on SrTiO3‐CeO2(S‐C) vertically aligned nanocomposite (VAN) is proposed, utilizing the highly defective vertical interfaces as migration channels for oxygen vacancies (). More interestingly the interface density (σint) and strain can be effectively tuned by the deposition parameters. Comprehensive analyses, including microstructural analysis, electrical characterization, and finite element modeling, reveal that S‐C VAN memristors with high interface density (HID) exhibit superior power efficiency and reduced device‐to‐device (D2D) variation. Additionally, the S‐C HID memristor demonstrates endurance up to 1012cycles and retains its resistance states for up to 105s at room temperature. The improved plasticity of the S‐C HID memristor enables better linear modulation of conductance with minimal fluctuation over long cycles and across different devices. Furthermore, artificial neural networks (ANNs) incorporating the S‐C HID memristor achieve high accuracy (>95%) on multiple datasets, underscoring its potential for neuromorphic computing applications. 
    more » « less
  3. ; ; ; ; ; ; ; ; ; ; et al (, Advanced Electronic Materials)
    Abstract 2D materials have been of considerable interest as new materials for device applications. Non‐volatile resistive switching applications of MoS2and WS2have been previously demonstrated; however, these applications are dramatically limited by high temperatures and extended times needed for the large‐area synthesis of 2D materials on crystalline substrates. The experimental results demonstrate a one‐step sulfurization method to synthesize MoS2and WS2at 550 °C in 15 min on sapphire wafers. Furthermore, a large area transfer of the synthesized thin films to SiO2/Si substrates is achieved. Following this, MoS2and WS2memristors are fabricated that exhibit stable non‐volatile switching and a satisfactory large on/off current ratio (103–105) with good uniformity. Tuning the sulfurization parameters (temperature and metal precursor thickness) is found to be a straightforward and effective strategy to improve the performance of the memristors. The demonstration of large‐scale MoS2and WS2memristors with a one‐step low‐temperature sulfurization method with simple strategy to tuning can lead to potential applications such as flexible memory and neuromorphic computing. 
    more » « less
  4. ; ; ; ; ; ; ; (, Nano Express)
    Abstract Ultrathin (sub-2 nm) Al2O3/MgO memristors were recently developed using anin vacuoatomic layer deposition (ALD) process that minimizes unintended defects and prevents undesirable leakage current. These memristors provide a unique platform that allows oxygen vacancies (VO) to be inserted into the memristor with atomic precision and study how this affects the formation and rupture of conductive filaments (CFs) during memristive switching. Herein, we present a systematic study on three sets of ultrathin Al2O3/MgO memristors with VO-doping via modular MgO atomic layer insertion into an otherwise pristine insulating Al2O3atomic layer stack (ALS) using anin vacuoALD. At a fixed memristor thickness of 17 Al2O3/MgO atomic layers (∼1.9 nm), the properties of the memristors were found to be affected by the number and stacking pattern of the MgO atomic layers in the Al2O3/MgO ALS. Importantly, the trend of reduced low-state resistance and the increasing appearance of multi-step switches with an increasing number of MgO atomic layers suggests a direct correlation between the dimension and dynamic evolution of the conducting filaments and the VOconcentration and distribution. Understanding such a correlation is critical to an atomic-scale control of the switching behavior of ultrathin memristors. 
    more » « less
  5. ; ; ; (, International Journal of High Speed Electronics and Systems)
    Co-localized memory and processing components are necessary for the creation of scalable neuromorphic hardware, and these components can be produced using inexpensive techniques. While memristors have emerged as promising candidates for synaptic emulation, most devices rely on complex fabrication processes that hinder large-scale deployment. This work presents a fully inkjet-printed memristor utilizing hexagonal boron nitride (hBN) and graphene inks, demonstrating a manufacturable approach to neuromorphic systems. The devices exhibit nonvolatile resistive switching with symmetric pinched hysteresis loops [Formula: see text] and tunable conductance states. Furthermore, a piece-wise nonlinear memristor model was developed and implemented in Simscape to enable system-level simulations. A [Formula: see text] reservoir computing architecture, constructed using these memristive devices, successfully demonstrated temporal pattern processing and feature extraction capabilities. The results establish printed memristors as viable building blocks for next-generation edge computing applications, combining the advantages of solution-processable materials with neuromorphic functionality. 
    more » « less
  • Citation Formats
  • MLA
  • APA
  • Chicago
  • BibTeX
  • Text Encoded Conversion
  • XML
  • Save / Share this Record
  • Send to Email