skip to main content

Explore Research Products in the NSF-PAR

Title: Organic neuromorphic devices: Past, present, and future challenges
The main goal of the field of neuromorphic computing is to build machines that emulate aspects of the brain in its ability to perform complex tasks in parallel and with great energy efficiency. Thanks to new computing architectures, these machines could revolutionize high-performance computing and find applications to perform local, low-energy computing for sensors and robots. The use of organic and soft materials in neuromorphic computing is appealing in many respects, for instance, because it allows better integration with living matter to seamlessly meld sensing with signal processing, and ultimately, stimulation in a closed-feedback loop. Indeed, not only can the mechanical properties of organic materials match those of tissue, but also, the working mechanisms of these devices involving ions, in addition to electrons, are compatible with human physiology. Another advantage of organic materials is the potential to introduce novel fabrication techniques relying on additive manufacturing amenable to one-of-a-kind form factors. This field is still nascent, therefore many concepts are still being proposed, without a clear winner. Furthermore, the field of application of organic neuromorphics, where bioinspiration and biointegration are extremely appealing, calls for a co-design approach from materials to systems.  more » « less
Award ID(s):
1739795
NSF-PAR ID:
10196377
Author(s) / Creator(s):
; ; ; ; ; ; ; ; ;
Date Published:
Journal Name:
MRS Bulletin
Volume:
45
Issue:
8
ISSN:
0883-7694
Page Range / eLocation ID:
619 to 630
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. ; ; ( , ECS Meeting Abstracts)

    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
  2. ; ; ; ; ; ; ; ; ; ; et al ( , Nano Futures)
    Abstract

    In the ‘Beyond Moore’s Law’ era, with increasing edge intelligence, domain-specific computing embracing unconventional approaches will become increasingly prevalent. At the same time, adopting a variety of nanotechnologies will offer benefits in energy cost, computational speed, reduced footprint, cyber resilience, and processing power. The time is ripe for a roadmap for unconventional computing with nanotechnologies to guide future research, and this collection aims to fill that need. The authors provide a comprehensive roadmap for neuromorphic computing using electron spins, memristive devices, two-dimensional nanomaterials, nanomagnets, and various dynamical systems. They also address other paradigms such as Ising machines, Bayesian inference engines, probabilistic computing with p-bits, processing in memory, quantum memories and algorithms, computing with skyrmions and spin waves, and brain-inspired computing for incremental learning and problem-solving in severely resource-constrained environments. These approaches have advantages over traditional Boolean computing based on von Neumann architecture. As the computational requirements for artificial intelligence grow 50 times faster than Moore’s Law for electronics, more unconventional approaches to computing and signal processing will appear on the horizon, and this roadmap will help identify future needs and challenges. In a very fertile field, experts in the field aim to present some of the dominant and most promising technologies for unconventional computing that will be around for some time to come. Within a holistic approach, the goal is to provide pathways for solidifying the field and guiding future impactful discoveries.

     
    more » « less
  3. ; ; ; ; ; ; ; ; ; ; et al ( , Angewandte Chemie)
    Abstract

    Titanium metal–organic frameworks (Ti‐MOFs), as an appealing type of artificial photocatalyst, have shown great potential in the field of solar energy conversion due to their well‐studied photoredox activity (similar to TiO2) and good optical responsiveness of linkers, which serve as the antenna to absorb visible‐light. Although much effort has been dedicated to developing Ti‐MOFs with high photocatalytic activity, their solar energy conversion performances are still poor. Herein, we have implemented a covalent‐integration strategy to construct a series of multivariate Ti‐MOF/COF hybrid materials PdTCPP⊂PCN‐415(NH2)/TpPa (composites 1, 2, and 3), featuring excellent visible‐light utilization, a suitable band gap, and high surface area for photocatalytic H2production. Notably, the resulting composites demonstrated remarkably enhanced visible‐light‐driven photocatalytic H2evolution performance, especially for the composite 2 with a maximum H2evolution rate of 13.98 mmol g−1 h−1(turnover frequency (TOF)=227 h−1), which is much higher than that of PdTCPP⊂PCN‐415(NH2) (0.21 mmol g−1 h−1) and TpPa (6.51 mmol g−1 h−1). Our work thereby suggests a new approach to highly efficient photocatalysts for H2evolution and beyond.

     
    more » « less
  4. ; ; ; ; ; ; ; ; ; ; et al ( , Angewandte Chemie International Edition)
    Abstract

    Titanium metal–organic frameworks (Ti‐MOFs), as an appealing type of artificial photocatalyst, have shown great potential in the field of solar energy conversion due to their well‐studied photoredox activity (similar to TiO2) and good optical responsiveness of linkers, which serve as the antenna to absorb visible‐light. Although much effort has been dedicated to developing Ti‐MOFs with high photocatalytic activity, their solar energy conversion performances are still poor. Herein, we have implemented a covalent‐integration strategy to construct a series of multivariate Ti‐MOF/COF hybrid materials PdTCPP⊂PCN‐415(NH2)/TpPa (composites 1, 2, and 3), featuring excellent visible‐light utilization, a suitable band gap, and high surface area for photocatalytic H2production. Notably, the resulting composites demonstrated remarkably enhanced visible‐light‐driven photocatalytic H2evolution performance, especially for the composite 2 with a maximum H2evolution rate of 13.98 mmol g−1 h−1(turnover frequency (TOF)=227 h−1), which is much higher than that of PdTCPP⊂PCN‐415(NH2) (0.21 mmol g−1 h−1) and TpPa (6.51 mmol g−1 h−1). Our work thereby suggests a new approach to highly efficient photocatalysts for H2evolution and beyond.

     
    more » « less
  5. ; ; ( , Frontiers in Nanotechnology)

    Neuromorphic computing, commonly understood as a computing approach built upon neurons, synapses, and their dynamics, as opposed to Boolean gates, is gaining large mindshare due to its direct application in solving current and future computing technological problems, such as smart sensing, smart devices, self-hosted and self-contained devices, artificial intelligence (AI) applications, etc. In a largely software-defined implementation of neuromorphic computing, it is possible to throw enormous computational power or optimize models and networks depending on the specific nature of the computational tasks. However, a hardware-based approach needs the identification of well-suited neuronal and synaptic models to obtain high functional and energy efficiency, which is a prime concern in size, weight, and power (SWaP) constrained environments. In this work, we perform a study on the characteristics of hardware neuron models (namely, inference errors, generalizability and robustness, practical implementability, and memory capacity) that have been proposed and demonstrated using a plethora of emerging nano-materials technology-based physical devices, to quantify the performance of such neurons on certain classes of problems that are of great importance in real-time signal processing like tasks in the context of reservoir computing. We find that the answer on which neuron to use for what applications depends on the particulars of the application requirements and constraints themselves, i.e., we need not only a hammer but all sorts of tools in our tool chest for high efficiency and quality neuromorphic computing.

     
    more » « less
  • Citation Formats
  • MLA
  • APA
  • Chicago
  • BibTeX
  • Save / Share this Record
  • Send to Email