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: From memristive devices to neuromorphic systems
Progress in hardware and algorithms for artificial intelligence (AI) has ushered in large machine learning models and various applications impacting our everyday lives. However, today's AI, mainly artificial neural networks, still cannot compete with human brains because of two major issues: the high energy consumption of the hardware running AI models and the lack of ability to generalize knowledge and self-adapt to changes. Neuromorphic systems built upon emerging devices, for instance, memristors, provide a promising path to address these issues. Although innovative memristor devices and circuit designs have been proposed for neuromorphic computing and applied to different proof-of-concept applications, there is still a long way to go to build large-scale low-power memristor-based neuromorphic systems that can bridge the gap between AI and biological brains. This Perspective summarizes the progress and challenges from memristor devices to neuromorphic systems and proposes possible directions for neuromorphic system implementation based on memristive devices.  more » « less
Award ID(s):
2023752
PAR ID:
10440249
Author(s) / Creator(s):
; ; ;
Date Published:
Journal Name:
Applied Physics Letters
Volume:
122
Issue:
11
ISSN:
0003-6951
Page Range / eLocation ID:
110501
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. ; ; (, npj 2D Materials and Applications)
    Abstract Recent studies of resistive switching devices with hexagonal boron nitride (h-BN) as the switching layer have shown the potential of two-dimensional (2D) materials for memory and neuromorphic computing applications. The use of 2D materials allows scaling the resistive switching layer thickness to sub-nanometer dimensions enabling devices to operate with low switching voltages and high programming speeds, offering large improvements in efficiency and performance as well as ultra-dense integration. These characteristics are of interest for the implementation of neuromorphic computing and machine learning hardware based on memristor crossbars. However, existing demonstrations of h-BN memristors focus on single isolated device switching properties and lack attention to fundamental machine learning functions. This paper demonstrates the hardware implementation of dot product operations, a basic analog function ubiquitous in machine learning, using h-BN memristor arrays. Moreover, we demonstrate the hardware implementation of a linear regression algorithm on h-BN memristor arrays. 
    more » « less
  2. ; (, ECS Meeting Abstracts)
    Neuromorphic computing is considered to have the potential to overcome the limitations of traditional von Neumann architecture due to its high efficiency, low energy consumption, and fault-tolerance. Hardware components that can emulate the synaptic plasticity of neurons, i.e. artificial synaptic devices, are required by neuromorphic systems. New devices have been examined for such components, such as phase-change artificial synapse, ferroelectric artificial synapse, and memristor synapses. Among them, memristor, a two-terminal metal-insulator-metal structure that are analogous to a biological synapse with presynaptic neuron (top electrode), postsynapticneuron (bottom electrode), and synaptic cleft (memristive film), is a promising device technology because of its tunable resistance, scalability, 3D integration compatibility, low power consumption, and relatively high speed. In contrary to inorganic materials such as metal oxides, natural organic materials have attracted interest to form the memristive layer because they are renewable, biodegradable, sustainable, biocompatible, and environmentally friendly. In this paper, honey solution embedded with carbon nanotubes (CNTs) was processed into the memristive layer by a low cost solution-based process, with synaptic plasticity of the final honey-CNT memristors characterized, including forget and relearn, spike-rate-dependent plasticity, spike-voltage-dependent plasticity, short-term to long-term memory transition, paired pulse facilitation, and spatial supra-linear summation behaviors. The successful emulation of these essential biological synaptic behaviors demonstrates the potential of honey-CNT memristors as a viable hardware component in neuromorphic computing systems. 
    more » « less
  3. ; ; ; ; (, Advanced Materials Technologies)
    Abstract Artificial synaptic devices are the essential hardware component in emerging neuromorphic computing systems by mimicking biological synapse and brain functions. When made from natural organic materials such as protein and carbohydrate, they have potential to improve sustainability and reduce electronic waste by enabling environmentally‐friendly disposal. In this paper, a new natural organic memristor based artificial synaptic device is reported with the memristive film processed by a honey and carbon nanotube (CNT) admixture, that is, honey‐CNT memristor. Optical microscopy, scanning electron microscopy, and micro‐Raman spectroscopy are employed to analyze the morphology and chemical structure of the honey‐CNT film. The device demonstrates analog memristive potentiation and depression, with the mechanism governing these functions explained by the formation and dissolution of conductive paths due to the electrochemical metal filaments which are assisted by CNT clusters and bundles in the honey‐CNT film. The honey‐CNT memristor successfully emulates synaptic functionalities such as short‐term plasticity and its transition to long‐term plasticity for memory rehearsal, spatial summation, and shunting inhibition, and for the first time, the classical conditioning behavior for associative learning by mimicking the Pavlov's dog experiment. All these results testify that honey‐CNT memristor based artificial synaptic device is promising for energy‐efficient and eco‐friendly neuromorphic systems. 
    more » « less
  4. ; ; ; (, 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
  5. ; ; ; (, Chemical Reviews)
    Recent breakthroughs in brain-inspired computing promise to address a wide range of problems from security to healthcare. However, the current strategy of implementing artificial intelligence algorithms using conventional silicon hardware is leading to unsustainable energy consumption. Neuromorphic hardware based on electronic devices mimicking biological systems is emerging as a low-energy alternative, although further progress requires materials that can mimic biological function while maintaining scalability and speed. As a result of their diverse unique properties, atomically thin two-dimensional (2D) materials are promising building blocks for next-generation electronics including nonvolatile memory, in-memory and neuromorphic computing, and flexible edge-computing systems. Furthermore, 2D materials achieve biorealistic synaptic and neuronal responses that extend beyond conventional logic and memory systems. Here, we provide a comprehensive review of the growth, fabrication, and integration of 2D materials and van der Waals heterojunctions for neuromorphic electronic and optoelectronic devices, circuits, and systems. For each case, the relationship between physical properties and device responses is emphasized followed by a critical comparison of technologies for different applications. We conclude with a forward-looking perspective on the key remaining challenges and opportunities for neuromorphic applications that leverage the fundamental properties of 2D materials and heterojunctions. 
    more » « less
  • Citation Formats
  • MLA
  • APA
  • Chicago
  • BibTeX
  • Save / Share this Record
  • Send to Email