skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

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


Title: Multi-Level Neuromorphic Devices Built on Emerging Ferroic Materials: A Review
Achieving multi-level devices is crucial to efficiently emulate key bio-plausible functionalities such as synaptic plasticity and neuronal activity, and has become an important aspect of neuromorphic hardware development. In this review article, we focus on various ferromagnetic (FM) and ferroelectric (FE) devices capable of representing multiple states, and discuss the usage of such multi-level devices for implementing neuromorphic functionalities. We will elaborate that the analog-like resistive states in ferromagnetic or ferroelectric thin films are due to the non-coherent multi-domain switching dynamics, which is fundamentally different from most memristive materials involving electroforming processes or significant ion motion. Both device fundamentals related to the mechanism of introducing multilevel states and exemplary implementations of neural functionalities built on various device structures are highlighted. In light of the non-destructive nature and the relatively simple physical process of multi-domain switching, we envision that ferroic-based multi-state devices provide an alternative pathway toward energy efficient implementation of neuro-inspired computing hardware with potential advantages of high endurance and controllability.  more » « less
Award ID(s):
2008412
PAR ID:
10285986
Author(s) / Creator(s):
; ; ;
Date Published:
Journal Name:
Frontiers in Neuroscience
Volume:
15
ISSN:
1662-453X
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. ; ; ; ; ; ; (, Advanced Intelligent Systems)
    Despite the promise of superior efficiency and scalability, real‐world deployment of emerging nanoelectronic platforms for brain‐inspired computing have been limited thus far, primarily because of inter‐device variations and intrinsic non‐idealities. In this work, mitigation of these issues is demonstrated by performing learning directly on practical devices through a hardware‐in‐loop approach, utilizing stochastic neurons based on heavy metal/ferromagnetic spin–orbit torque heterostructures. The probabilistic switching and device‐to‐device variability of the fabricated devices of various sizes is characterized to showcase the effect of device dimension on the neuronal dynamics and its consequent impact on network‐level performance. The efficacy of the hardware‐in‐loop scheme is illustrated in a deep learning scenario achieving equivalent software performance. This work paves the way for future large‐scale implementations of neuromorphic hardware and realization of truly autonomous edge‐intelligent devices. 
    more » « less
  2. ; ; ; ; ; ; ; ; ; (, Nanoscale Advances)
    Multiferroic materials are an interesting functional material family combining two ferroic orderings, e.g. , ferroelectric and ferromagnetic orderings, or ferroelectric and antiferromagnetic orderings, and find various device applications, such as spintronics, multiferroic tunnel junctions, etc. Coupling multiferroic materials with plasmonic nanostructures offers great potential for optical-based switching in these devices. Here, we report a novel nanocomposite system consisting of layered Bi 1.25 AlMnO 3.25 (BAMO) as a multiferroic matrix and well dispersed plasmonic Au nanoparticles (NPs) and demonstrate that the Au nanoparticle morphology and the nanocomposite properties can be effectively tuned. Specifically, the Au particle size can be tuned from 6.82 nm to 31.59 nm and the 6.82 nm one presents the optimum ferroelectric and ferromagnetic properties and plasmonic properties. Besides the room temperature multiferroic properties, the BAMO-Au nanocomposite system presents other unique functionalities including localized surface plasmon resonance (LSPR), hyperbolicity in the visible region, and magneto-optical coupling, which can all be effectively tailored through morphology tuning. This study demonstrates the feasibility of coupling single phase multiferroic oxides with plasmonic metals for complex nanocomposite designs towards optically switchable spintronics and other memory devices. 
    more » « less
  3. ; ; ; ; ; (, Journal of Applied Physics)
    In this work, we investigate the device-to-device variations in the remanent polarization of metal–ferroelectric–insulator–metal stacks based on ferroelectric hafnium–zirconium–oxide (HZO). Our study employs a 3D dynamic multi-grain phase-field model to consider the effects of the polycrystalline nature of HZO in conjunction with the multi-domain polarization switching. We explore the dependence of variations on various design factors, such as the ferroelectric thickness and voltage stimuli (set voltage, pulse amplitude, and width), and correlate the trends to the underlying polarization switching mechanisms. Our analysis reveals a non-monotonic dependence of variations on the set voltage due to the coupled effect of the underlying polycrystalline structure variations and the voltage dependence of polarization switching mechanisms. We further report that collapsing of oppositely polarized domains at higher set voltages can lead to an increase in variations, while ferroelectric thickness scaling lowers the overall device-to-device variations. Considering the dynamics of polarization switching, we highlight the key role of voltage and temporal dependence of domain nucleation in dictating the trends in variations. Finally, we show that using a lower amplitude pulse for longer duration to reach a target mean polarization state results in lower variations compared to using a higher amplitude pulse for shorter duration. 
    more » « less
  4. ; (, Advanced Functional Materials)
    Abstract Ferroelectric memristors represent a promising new generation of devices that have a wide range of applications in memory, digital information processing, and neuromorphic computing. Recently, van der Waals ferroelectric In2Se3with unique interlinked out‐of‐plane and in‐plane polarizations has enabled multidirectional resistance switching, providing unprecedented flexibility in planar and vertical device integrations. However, the operating mechanisms of these devices have remained unclear. Here, through the demonstration of van der Waals In2Se3‐based planar ferroelectric memristors with the device resistance continuously tunable over three orders of magnitude, and by correlating device resistance states, ferroelectric domain configurations, and surface electric potential, the studies reveal that the resistive switching is controlled by the multidomain formations and the associated energy barriers between domains, as opposed to the commonly assumed Schottky barrier modulations at the metal‐ferroelectric interface. The findings reveal new device physics through elucidating the microscopic operating mechanisms of this new generation of devices, and provide a critical guide for future device development and integration efforts. 
    more » « less
  5. ; (, Journal of Physics D: Applied Physics)
    Abstract Spiking neural network (SNN) in future neuromorphic architectures requires hardware devices to be not only capable of emulating fundamental functionalities of biological synapse such as spike-timing dependent plasticity (STDP) and spike-rate dependent plasticity (SRDP), but also biodegradable to address current ecological challenges of electronic waste. Among different device technologies and materials, memristive synaptic devices based on natural organic materials have emerged as the favourable candidate to meet these demands. The metal–insulator-metal structure is analogous to biological synapse with low power consumption, fast switching speed and simulation of synaptic plasticity, while natural organic materials are water soluble, renewable and environmental friendly. In this study, the potential of a natural organic material—honey-based memristor for SNNs was demonstrated. The device exhibited forming-free bipolar resistive switching, a high switching speed of 100 ns set time and 500 ns reset time, STDP and SRDP learning behaviours, and dissolving in water. The intuitive conduction models for STDP and SRDP were proposed. These results testified that honey-based memristive synaptic devices are promising for SNN implementation in green electronics and biodegradable neuromorphic systems. 
    more » « less
  • Citation Formats
  • MLA
  • APA
  • Chicago
  • BibTeX
  • Save / Share this Record
  • Send to Email