1
|
Park J, Kim JO, Kang SW. Lateral heterostructures of WS 2 and MoS 2 monolayers for photo-synaptic transistor. Sci Rep 2024; 14:6922. [PMID: 38519613 PMCID: PMC10959970 DOI: 10.1038/s41598-024-57642-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Accepted: 03/20/2024] [Indexed: 03/25/2024] Open
Abstract
Von Neumann architecture-based computing, while widely successful in personal computers and embedded systems, faces inherent challenges including the von Neumann bottleneck, particularly amidst the ongoing surge of data-intensive tasks. Neuromorphic computing, designed to integrate arithmetic, logic, and memory operations, has emerged as a promising solution for improving energy efficiency and performance. This approach requires the construction of an artificial synaptic device that can simultaneously perform signal processing, learning, and memory operations. We present a photo-synaptic device with 32 analog multi-states by exploiting field-effect transistors based on the lateral heterostructures of two-dimensional (2D) WS2 and MoS2 monolayers, formed through a two-step metal-organic chemical vapor deposition process. These lateral heterostructures offer high photoresponsivity and enhanced efficiency of charge trapping at the interface between the heterostructures and SiO2 due to the presence of the WS2 monolayer with large trap densities. As a result, it enables the photo-synaptic transistor to implement synaptic behaviors of long-term plasticity and high recognition accuracy. To confirm the feasibility of the photo-synapse, we investigated its synaptic characteristics under optical and electrical stimuli, including the retention of excitatory post-synaptic currents, potentiation, habituation, nonlinearity factor, and paired-pulse facilitation. Our findings suggest the potential of versatile 2D material-synapse with a high density of device integration.
Collapse
Affiliation(s)
- Jaeseo Park
- Strategic Technology Research Institute, Korea Research Institute of Standards and Science, Daejeon, 34113, Republic of Korea
| | - Jun Oh Kim
- Strategic Technology Research Institute, Korea Research Institute of Standards and Science, Daejeon, 34113, Republic of Korea
| | - Sang-Woo Kang
- Strategic Technology Research Institute, Korea Research Institute of Standards and Science, Daejeon, 34113, Republic of Korea.
- Precision Measurement, University of Science and Technology, Daejeon, 34113, Republic of Korea.
| |
Collapse
|
2
|
Zhu R, Lilak S, Loeffler A, Lizier J, Stieg A, Gimzewski J, Kuncic Z. Online dynamical learning and sequence memory with neuromorphic nanowire networks. Nat Commun 2023; 14:6697. [PMID: 37914696 PMCID: PMC10620219 DOI: 10.1038/s41467-023-42470-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Accepted: 10/11/2023] [Indexed: 11/03/2023] Open
Abstract
Nanowire Networks (NWNs) belong to an emerging class of neuromorphic systems that exploit the unique physical properties of nanostructured materials. In addition to their neural network-like physical structure, NWNs also exhibit resistive memory switching in response to electrical inputs due to synapse-like changes in conductance at nanowire-nanowire cross-point junctions. Previous studies have demonstrated how the neuromorphic dynamics generated by NWNs can be harnessed for temporal learning tasks. This study extends these findings further by demonstrating online learning from spatiotemporal dynamical features using image classification and sequence memory recall tasks implemented on an NWN device. Applied to the MNIST handwritten digit classification task, online dynamical learning with the NWN device achieves an overall accuracy of 93.4%. Additionally, we find a correlation between the classification accuracy of individual digit classes and mutual information. The sequence memory task reveals how memory patterns embedded in the dynamical features enable online learning and recall of a spatiotemporal sequence pattern. Overall, these results provide proof-of-concept of online learning from spatiotemporal dynamics using NWNs and further elucidate how memory can enhance learning.
Collapse
Affiliation(s)
- Ruomin Zhu
- School of Physics, The University of Sydney, Sydney, NSW, Australia.
| | - Sam Lilak
- Department of Chemistry and Biochemistry, University of California, Los Angeles, Los Angeles, CA, US
| | - Alon Loeffler
- School of Physics, The University of Sydney, Sydney, NSW, Australia
| | - Joseph Lizier
- School of Computer Science, The University of Sydney, Sydney, NSW, Australia
- Centre for Complex Systems, The University of Sydney, Sydney, NSW, Australia
| | - Adam Stieg
- California NanoSystems Institute, University of California, Los Angeles, Los Angeles, CA, US.
- WPI Center for Materials Nanoarchitectonics (MANA), National Institute for Materials Science (NIMS), Tsukuba, Japan.
| | - James Gimzewski
- Department of Chemistry and Biochemistry, University of California, Los Angeles, Los Angeles, CA, US.
- California NanoSystems Institute, University of California, Los Angeles, Los Angeles, CA, US.
- WPI Center for Materials Nanoarchitectonics (MANA), National Institute for Materials Science (NIMS), Tsukuba, Japan.
- Research Center for Neuromorphic AI Hardware, Kyutech, Kitakyushu, Japan.
| | - Zdenka Kuncic
- School of Physics, The University of Sydney, Sydney, NSW, Australia.
- Centre for Complex Systems, The University of Sydney, Sydney, NSW, Australia.
- The University of Sydney Nano Institute, Sydney, NSW, Australia.
| |
Collapse
|
3
|
Pei M, Zhu Y, Liu S, Cui H, Li Y, Yan Y, Li Y, Wan C, Wan Q. Power-Efficient Multisensory Reservoir Computing Based on Zr-Doped HfO 2 Memcapacitive Synapse Arrays. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2023; 35:e2305609. [PMID: 37572299 DOI: 10.1002/adma.202305609] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Revised: 08/10/2023] [Indexed: 08/14/2023]
Abstract
Hardware implementation tailored to requirements in reservoir computing would facilitate lightweight and powerful temporal processing. Capacitive reservoirs would boost power efficiency due to their ultralow static power consumption but have not been experimentally exploited yet. Here, this work reports an oxide-based memcapacitive synapse (OMC) based on Zr-doped HfO2 (HZO) for a power-efficient and multisensory processing reservoir computing system. The nonlinearity and state richness required for reservoir computing could originate from the capacitively coupled polarization switching and charge trapping of hafnium-oxide-based devices. The power consumption (≈113.4 fJ per spike) and temporal processing versatility outperform most resistive reservoirs. This system is verified by common benchmark tasks, and it exhibits high accuracy (>94%) in recognizing multisensory information, including acoustic, electrophysiological, and mechanic modalities. As a proof-of-concept, a touchless user interface for virtual shopping based on the OMC-based reservoir computing system is demonstrated, benefiting from its interference-robust acoustic and electrophysiological perception. These results shed light on the development of highly power-efficient human-machine interfaces and machine-learning platforms.
Collapse
Affiliation(s)
- Mengjiao Pei
- National Laboratory of Solid-State Microstructures, School of Electronic Science and Engineering, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, 210093, P. R. China
| | - Ying Zhu
- National Laboratory of Solid-State Microstructures, School of Electronic Science and Engineering, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, 210093, P. R. China
| | - Siyao Liu
- National Laboratory of Solid-State Microstructures, School of Electronic Science and Engineering, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, 210093, P. R. China
| | - Hangyuan Cui
- National Laboratory of Solid-State Microstructures, School of Electronic Science and Engineering, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, 210093, P. R. China
| | - Yating Li
- National Laboratory of Solid-State Microstructures, School of Electronic Science and Engineering, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, 210093, P. R. China
| | - Yang Yan
- National Laboratory of Solid-State Microstructures, School of Electronic Science and Engineering, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, 210093, P. R. China
| | - Yun Li
- National Laboratory of Solid-State Microstructures, School of Electronic Science and Engineering, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, 210093, P. R. China
| | - Changjin Wan
- National Laboratory of Solid-State Microstructures, School of Electronic Science and Engineering, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, 210093, P. R. China
| | - Qing Wan
- National Laboratory of Solid-State Microstructures, School of Electronic Science and Engineering, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, 210093, P. R. China
- Yongjiang Laboratory (Y-LAB), Ningbo, Zhejiang, 315202, P. R. China
| |
Collapse
|
4
|
Milano G, Cultrera A, Boarino L, Callegaro L, Ricciardi C. Tomography of memory engrams in self-organizing nanowire connectomes. Nat Commun 2023; 14:5723. [PMID: 37758693 PMCID: PMC10533552 DOI: 10.1038/s41467-023-40939-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Accepted: 08/11/2023] [Indexed: 09/29/2023] Open
Abstract
Self-organizing memristive nanowire connectomes have been exploited for physical (in materia) implementation of brain-inspired computing paradigms. Despite having been shown that the emergent behavior relies on weight plasticity at single junction/synapse level and on wiring plasticity involving topological changes, a shift to multiterminal paradigms is needed to unveil dynamics at the network level. Here, we report on tomographical evidence of memory engrams (or memory traces) in nanowire connectomes, i.e., physicochemical changes in biological neural substrates supposed to endow the representation of experience stored in the brain. An experimental/modeling approach shows that spatially correlated short-term plasticity effects can turn into long-lasting engram memory patterns inherently related to network topology inhomogeneities. The ability to exploit both encoding and consolidation of information on the same physical substrate would open radically new perspectives for in materia computing, while offering to neuroscientists an alternative platform to understand the role of memory in learning and knowledge.
Collapse
Affiliation(s)
- Gianluca Milano
- Advanced Materials Metrology and Life Sciences Division, INRiM (Istituto Nazionale di Ricerca Metrologica), Strada delle Cacce 91, 10135, Torino, Italy.
| | - Alessandro Cultrera
- Quantum Metrology and Nanotechnologies Division, INRiM (Istituto Nazionale di Ricerca Metrologica), Strada delle Cacce 91, 10135, Torino, Italy
| | - Luca Boarino
- Advanced Materials Metrology and Life Sciences Division, INRiM (Istituto Nazionale di Ricerca Metrologica), Strada delle Cacce 91, 10135, Torino, Italy
| | - Luca Callegaro
- Quantum Metrology and Nanotechnologies Division, INRiM (Istituto Nazionale di Ricerca Metrologica), Strada delle Cacce 91, 10135, Torino, Italy
| | - Carlo Ricciardi
- Department of Applied Science and Technology, Politecnico di Torino, C.so Duca degli Abruzzi 24, 10129, Torino, Italy.
| |
Collapse
|
5
|
Tian B, Xie Z, Chen L, Hao S, Liu Y, Feng G, Liu X, Liu H, Yang J, Zhang Y, Bai W, Lin T, Shen H, Meng X, Zhong N, Peng H, Yue F, Tang X, Wang J, Zhu Q, Ivry Y, Dkhil B, Chu J, Duan C. Ultralow-power in-memory computing based on ferroelectric memcapacitor network. EXPLORATION (BEIJING, CHINA) 2023; 3:20220126. [PMID: 37933380 PMCID: PMC10624373 DOI: 10.1002/exp.20220126] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Accepted: 04/21/2023] [Indexed: 11/08/2023]
Abstract
Analog storage through synaptic weights using conductance in resistive neuromorphic systems and devices inevitably generates harmful heat dissipation. This thermal issue not only limits the energy efficiency but also hampers the very-large-scale and highly complicated hardware integration as in the human brain. Here we demonstrate that the synaptic weights can be simulated by reconfigurable non-volatile capacitances of a ferroelectric-based memcapacitor with ultralow-power consumption. The as-designed metal/ferroelectric/metal/insulator/semiconductor memcapacitor shows distinct 3-bit capacitance states controlled by the ferroelectric domain dynamics. These robust memcapacitive states exhibit uniform maintenance of more than 104 s and well endurance of 109 cycles. In a wired memcapacitor crossbar network hardware, analog vector-matrix multiplication is successfully implemented to classify 9-pixel images by collecting the sum of displacement currents (I = C × dV/dt) in each column, which intrinsically consumes zero energy in memcapacitors themselves. Our work sheds light on an ultralow-power neural hardware based on ferroelectric memcapacitors.
Collapse
Affiliation(s)
- Bobo Tian
- Key Laboratory of Polar Materials and Devices, Ministry of Education, Shanghai Center of Brain‐inspired Intelligent Materials and Devices, Department of ElectronicsEast China Normal UniversityShanghaiChina
- Zhejiang LabHangzhouChina
| | - Zhuozhuang Xie
- Key Laboratory of Polar Materials and Devices, Ministry of Education, Shanghai Center of Brain‐inspired Intelligent Materials and Devices, Department of ElectronicsEast China Normal UniversityShanghaiChina
- School of Materials Science and EngineeringShanghai University of Engineering ScienceShanghaiChina
| | - Luqiu Chen
- Key Laboratory of Polar Materials and Devices, Ministry of Education, Shanghai Center of Brain‐inspired Intelligent Materials and Devices, Department of ElectronicsEast China Normal UniversityShanghaiChina
| | - Shenglan Hao
- Key Laboratory of Polar Materials and Devices, Ministry of Education, Shanghai Center of Brain‐inspired Intelligent Materials and Devices, Department of ElectronicsEast China Normal UniversityShanghaiChina
- CentraleSupélec, CNRS‐UMR8580, Laboratoire SPMSUniversité Paris‐SaclayGif‐sur‐YvetteFrance
| | - Yifei Liu
- Key Laboratory of Polar Materials and Devices, Ministry of Education, Shanghai Center of Brain‐inspired Intelligent Materials and Devices, Department of ElectronicsEast China Normal UniversityShanghaiChina
| | - Guangdi Feng
- Key Laboratory of Polar Materials and Devices, Ministry of Education, Shanghai Center of Brain‐inspired Intelligent Materials and Devices, Department of ElectronicsEast China Normal UniversityShanghaiChina
| | - Xuefeng Liu
- Key Laboratory of Polar Materials and Devices, Ministry of Education, Shanghai Center of Brain‐inspired Intelligent Materials and Devices, Department of ElectronicsEast China Normal UniversityShanghaiChina
| | - Hongbo Liu
- School of Materials Science and EngineeringShanghai University of Engineering ScienceShanghaiChina
| | - Jing Yang
- Key Laboratory of Polar Materials and Devices, Ministry of Education, Shanghai Center of Brain‐inspired Intelligent Materials and Devices, Department of ElectronicsEast China Normal UniversityShanghaiChina
| | - Yuanyuan Zhang
- Key Laboratory of Polar Materials and Devices, Ministry of Education, Shanghai Center of Brain‐inspired Intelligent Materials and Devices, Department of ElectronicsEast China Normal UniversityShanghaiChina
| | - Wei Bai
- Key Laboratory of Polar Materials and Devices, Ministry of Education, Shanghai Center of Brain‐inspired Intelligent Materials and Devices, Department of ElectronicsEast China Normal UniversityShanghaiChina
| | - Tie Lin
- State Key Laboratory of Infrared Physics, Chinese Academy of SciencesShanghai Institute of Technical PhysicsShanghaiChina
| | - Hong Shen
- State Key Laboratory of Infrared Physics, Chinese Academy of SciencesShanghai Institute of Technical PhysicsShanghaiChina
| | - Xiangjian Meng
- State Key Laboratory of Infrared Physics, Chinese Academy of SciencesShanghai Institute of Technical PhysicsShanghaiChina
| | - Ni Zhong
- Key Laboratory of Polar Materials and Devices, Ministry of Education, Shanghai Center of Brain‐inspired Intelligent Materials and Devices, Department of ElectronicsEast China Normal UniversityShanghaiChina
| | - Hui Peng
- Key Laboratory of Polar Materials and Devices, Ministry of Education, Shanghai Center of Brain‐inspired Intelligent Materials and Devices, Department of ElectronicsEast China Normal UniversityShanghaiChina
| | - Fangyu Yue
- Key Laboratory of Polar Materials and Devices, Ministry of Education, Shanghai Center of Brain‐inspired Intelligent Materials and Devices, Department of ElectronicsEast China Normal UniversityShanghaiChina
| | - Xiaodong Tang
- Key Laboratory of Polar Materials and Devices, Ministry of Education, Shanghai Center of Brain‐inspired Intelligent Materials and Devices, Department of ElectronicsEast China Normal UniversityShanghaiChina
| | - Jianlu Wang
- Frontier Institute of Chip and SystemFudan UniversityShanghaiChina
| | - Qiuxiang Zhu
- Key Laboratory of Polar Materials and Devices, Ministry of Education, Shanghai Center of Brain‐inspired Intelligent Materials and Devices, Department of ElectronicsEast China Normal UniversityShanghaiChina
- Zhejiang LabHangzhouChina
- Guangdong Provisional Key Laboratory of Functional Oxide Materials and DevicesSouthern University of Science and TechnologyShenzhenChina
| | - Yachin Ivry
- Department of Materials Science and EngineeringSolid‐State InstituteTechnion‐Israel Institute of TechnologyHaifaIsrael
| | - Brahim Dkhil
- CentraleSupélec, CNRS‐UMR8580, Laboratoire SPMSUniversité Paris‐SaclayGif‐sur‐YvetteFrance
| | - Junhao Chu
- Key Laboratory of Polar Materials and Devices, Ministry of Education, Shanghai Center of Brain‐inspired Intelligent Materials and Devices, Department of ElectronicsEast China Normal UniversityShanghaiChina
- State Key Laboratory of Infrared Physics, Chinese Academy of SciencesShanghai Institute of Technical PhysicsShanghaiChina
- Institute of OptoelectronicsFudan UniversityShanghaiChina
| | - Chungang Duan
- Key Laboratory of Polar Materials and Devices, Ministry of Education, Shanghai Center of Brain‐inspired Intelligent Materials and Devices, Department of ElectronicsEast China Normal UniversityShanghaiChina
- Collaborative Innovation Center of Extreme OpticsShanxi UniversityShanxiChina
| |
Collapse
|
6
|
Loeffler A, Diaz-Alvarez A, Zhu R, Ganesh N, Shine JM, Nakayama T, Kuncic Z. Neuromorphic learning, working memory, and metaplasticity in nanowire networks. SCIENCE ADVANCES 2023; 9:eadg3289. [PMID: 37083527 PMCID: PMC10121165 DOI: 10.1126/sciadv.adg3289] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Abstract
Nanowire networks (NWNs) mimic the brain's neurosynaptic connectivity and emergent dynamics. Consequently, NWNs may also emulate the synaptic processes that enable higher-order cognitive functions such as learning and memory. A quintessential cognitive task used to measure human working memory is the n-back task. In this study, task variations inspired by the n-back task are implemented in a NWN device, and external feedback is applied to emulate brain-like supervised and reinforcement learning. NWNs are found to retain information in working memory to at least n = 7 steps back, remarkably similar to the originally proposed "seven plus or minus two" rule for human subjects. Simulations elucidate how synapse-like NWN junction plasticity depends on previous synaptic modifications, analogous to "synaptic metaplasticity" in the brain, and how memory is consolidated via strengthening and pruning of synaptic conductance pathways.
Collapse
Affiliation(s)
- Alon Loeffler
- The University of Sydney, School of Physics, Sydney, Australia
- Corresponding author. (A.L.); (A.D.-A.); (Z.K.)
| | - Adrian Diaz-Alvarez
- International Center for Young Scientist (ICYS), National Institute for Materials Science (NIMS), Tsukuba, Japan
- International Center for Materials Nanoarchitectonics (WPI-MANA), National Institute for Materials Science (NIMS), Tsukuba, Japan
- Corresponding author. (A.L.); (A.D.-A.); (Z.K.)
| | - Ruomin Zhu
- The University of Sydney, School of Physics, Sydney, Australia
| | - Natesh Ganesh
- National Institute of Standards and Technology (NIST), Boulder, CO, USA
- University of Colorado, Boulder, CO, USA
| | - James M. Shine
- The University of Sydney, School of Physics, Sydney, Australia
- Brain and Mind Centre, The University of Sydney, Sydney, Australia
- The University of Sydney, School of Medical Sciences, Sydney, Australia
| | - Tomonobu Nakayama
- The University of Sydney, School of Physics, Sydney, Australia
- International Center for Materials Nanoarchitectonics (WPI-MANA), National Institute for Materials Science (NIMS), Tsukuba, Japan
- Graduate School of Pure and Applied Sciences, University of Tsukuba, Tsukuba, Japan
| | - Zdenka Kuncic
- The University of Sydney, School of Physics, Sydney, Australia
- International Center for Materials Nanoarchitectonics (WPI-MANA), National Institute for Materials Science (NIMS), Tsukuba, Japan
- The University of Sydney Nano Institute, Sydney, Australia
- Corresponding author. (A.L.); (A.D.-A.); (Z.K.)
| |
Collapse
|
7
|
Chopin C, de Wergifosse S, Marchal N, Van Velthem P, Piraux L, Abreu Araujo F. Memristive and Tunneling Effects in 3D Interconnected Silver Nanowires. ACS OMEGA 2023; 8:6663-6668. [PMID: 36844586 PMCID: PMC9948158 DOI: 10.1021/acsomega.2c07171] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Accepted: 01/20/2023] [Indexed: 06/18/2023]
Abstract
A network of silver nanowires (Ag-NWs) is grown by electrodeposition in a nanoporous membrane with interconnected nanopores. This bottom-up approach fabrication method gives a conducting network with a 3D architecture and a high density of Ag-NWs. The network is then functionalized during the etching process, which leads to a high initial resistance as well as memristive behavior. The latter is expected to arise from the creation and the destruction of conducting silver filaments in the functionalized Ag-NW network. Moreover, after several cycles of measurement, the resistance of the network switches from a high-resistance regime in the GΩ range with tunnel conduction to a low-resistance regime presenting negative differential resistance in the kΩ range.
Collapse
|
8
|
Mambretti F, Mirigliano M, Tentori E, Pedrani N, Martini G, Milani P, Galli DE. Dynamical stochastic simulation of complex electrical behavior in neuromorphic networks of metallic nanojunctions. Sci Rep 2022; 12:12234. [PMID: 35851078 PMCID: PMC9294002 DOI: 10.1038/s41598-022-15996-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Accepted: 07/04/2022] [Indexed: 11/23/2022] Open
Abstract
Nanostructured Au films fabricated by the assembling of nanoparticles produced in the gas phase have shown properties suitable for neuromorphic computing applications: they are characterized by a non-linear and non-local electrical behavior, featuring switches of the electric resistance whose activation is typically triggered by an applied voltage over a certain threshold. These systems can be considered as complex networks of metallic nanojunctions where thermal effects at the nanoscale cause the continuous rearrangement of regions with low and high electrical resistance. In order to gain a deeper understanding of the electrical properties of this nano granular system, we developed a model based on a large three dimensional regular resistor network with non-linear conduction mechanisms and stochastic updates of conductances. Remarkably, by increasing enough the number of nodes in the network, the features experimentally observed in the electrical conduction properties of nanostructured gold films are qualitatively reproduced in the dynamical behavior of the system. In the activated non-linear conduction regime, our model reproduces also the growing trend, as a function of the subsystem size, of quantities like Mutual and Integrated Information, which have been extracted from the experimental resistance series data via an information theoretic analysis. This indicates that nanostructured Au films (and our model) possess a certain degree of activated interconnection among different areas which, in principle, could be exploited for neuromorphic computing applications.
Collapse
Affiliation(s)
- F Mambretti
- CIMAINA and Dipartimento di Fisica, Università degli Studi di Milano, via Celoria 16, 20133, Milano, Italy
- Dipartimento di Fisica e Astronomia, and INFN - Sezione di Padova, Università degli Studi di Padova, via Marzolo 8, 35131, Padova, Italy
| | - M Mirigliano
- CIMAINA and Dipartimento di Fisica, Università degli Studi di Milano, via Celoria 16, 20133, Milano, Italy
| | - E Tentori
- CIMAINA and Dipartimento di Fisica, Università degli Studi di Milano, via Celoria 16, 20133, Milano, Italy
| | - N Pedrani
- CIMAINA and Dipartimento di Fisica, Università degli Studi di Milano, via Celoria 16, 20133, Milano, Italy
| | - G Martini
- CIMAINA and Dipartimento di Fisica, Università degli Studi di Milano, via Celoria 16, 20133, Milano, Italy
| | - P Milani
- CIMAINA and Dipartimento di Fisica, Università degli Studi di Milano, via Celoria 16, 20133, Milano, Italy.
| | - D E Galli
- CIMAINA and Dipartimento di Fisica, Università degli Studi di Milano, via Celoria 16, 20133, Milano, Italy.
| |
Collapse
|
9
|
Daniels RK, Mallinson JB, Heywood ZE, Bones PJ, Arnold MD, Brown SA. Reservoir computing with 3D nanowire networks. Neural Netw 2022; 154:122-130. [PMID: 35882080 DOI: 10.1016/j.neunet.2022.07.001] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Revised: 04/27/2022] [Accepted: 07/06/2022] [Indexed: 10/17/2022]
Abstract
Networks of nanowires are currently being explored for a range of applications in brain-like (or neuromorphic) computing, and especially in reservoir computing (RC). Fabrication of real-world computing devices requires that the nanowires are deposited sequentially, leading to stacking of the wires on top of each other. However, most simulations of computational tasks using these systems treat the nanowires as 1D objects lying in a perfectly 2D plane - the effect of stacking on RC performance has not yet been established. Here we use detailed simulations to compare the performance of perfectly 2D and quasi-3D (stacked) networks of nanowires in two tasks: memory capacity and nonlinear transformation. We also show that our model of the junctions between nanowires is general enough to describe a wide range of memristive networks, and consider the impact of physically realistic electrode configurations on performance. We show that the various networks and configurations have a strikingly similar performance in RC tasks, which is surprising given their radically different topologies. Our results show that networks with an experimentally achievable number of electrodes perform close to the upper bounds achievable when using the information from every wire. However, we also show important differences, in particular that the quasi-3D networks are more resilient to changes in the input parameters, generalizing better to noisy training data. Since previous literature suggests that topology plays an important role in computing performance, these results may have important implications for future applications of nanowire networks in neuromorphic computing.
Collapse
Affiliation(s)
- R K Daniels
- The MacDiarmid Institute for Advanced Materials and Nanotechnology, School of Physical and Chemical Sciences, Te Kura Matū, University of Canterbury, Private Bag 4800, Christchurch 8140, New Zealand
| | - J B Mallinson
- The MacDiarmid Institute for Advanced Materials and Nanotechnology, School of Physical and Chemical Sciences, Te Kura Matū, University of Canterbury, Private Bag 4800, Christchurch 8140, New Zealand
| | - Z E Heywood
- Electrical and Computer Engineering, University of Canterbury, Private Bag 4800, Christchurch 8140, New Zealand
| | - P J Bones
- Electrical and Computer Engineering, University of Canterbury, Private Bag 4800, Christchurch 8140, New Zealand
| | - M D Arnold
- School of Mathematical and Physical Sciences, University of Technology Sydney, PO Box 123 Broadway NSW 2007, Australia
| | - S A Brown
- The MacDiarmid Institute for Advanced Materials and Nanotechnology, School of Physical and Chemical Sciences, Te Kura Matū, University of Canterbury, Private Bag 4800, Christchurch 8140, New Zealand.
| |
Collapse
|
10
|
Nakajima M, Minegishi K, Shimizu Y, Usami Y, Tanaka H, Hasegawa T. In-materio reservoir working at low frequencies in a Ag 2S-island network. NANOSCALE 2022; 14:7634-7640. [PMID: 35545216 DOI: 10.1039/d2nr01439d] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
A Ag2S-island network is fabricated with surrounding electrodes to enable it to be used as a reservoir for unconventional computing. Local conductance change occurs due to the growth/shrinkage of Ag filaments from/into each Ag2S island in the reservoir. The growth/shrinkage of Ag filaments is caused by the drift of Ag+ cations in each Ag2S island, which results in a unique non-linear response as a reservoir, especially at lower frequencies. The response of the reservoir is shown to depend on the frequency and amplitude of the input signals. So as to evaluate its capability as a reservoir, logical operations were performed using the subject Ag2S-island network, with the results showing an accuracy of greater than 99%.
Collapse
Affiliation(s)
- Motoharu Nakajima
- Department of Pure and Applied Physics, Graduate School of Advanced Science and Engineering, Waseda University, 3-4-1 Okubo, Shinjuku-ku, Tokyo 169-8555, Japan.
| | - Kazuki Minegishi
- Department of Pure and Applied Physics, Graduate School of Advanced Science and Engineering, Waseda University, 3-4-1 Okubo, Shinjuku-ku, Tokyo 169-8555, Japan.
| | - Yosuke Shimizu
- Department of Pure and Applied Physics, Graduate School of Advanced Science and Engineering, Waseda University, 3-4-1 Okubo, Shinjuku-ku, Tokyo 169-8555, Japan.
| | - Yuki Usami
- Department of Human Intelligence Systems, Graduate School of Life Science and Systems Engineering, Kyushu Institute of Technology, 2-4 Hibikino, Wakamatsu, Kitakyushu 808-0196, Japan
- Research Center for Neuromorphic AI Hardware, Kyushu Institute of Technology, Japan
| | - Hirofumi Tanaka
- Department of Human Intelligence Systems, Graduate School of Life Science and Systems Engineering, Kyushu Institute of Technology, 2-4 Hibikino, Wakamatsu, Kitakyushu 808-0196, Japan
- Research Center for Neuromorphic AI Hardware, Kyushu Institute of Technology, Japan
| | - Tsuyoshi Hasegawa
- Department of Pure and Applied Physics, Graduate School of Advanced Science and Engineering, Waseda University, 3-4-1 Okubo, Shinjuku-ku, Tokyo 169-8555, Japan.
| |
Collapse
|
11
|
Lin C, Stewart LA, Zhao S, Akopov G, Mohammadi R, Yeung MT, Weiss PS, Kaner RB. Effective Liquid Metal Seeds for Silver Nanovines. Z Anorg Allg Chem 2022. [DOI: 10.1002/zaac.202200067] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- Cheng‐Wei Lin
- Department of Chemistry and Biochemistry and California NanoSystems Institute University of California Los Angeles CA 90095 United States
| | - Logan A. Stewart
- Department of Chemistry and Biochemistry and California NanoSystems Institute University of California Los Angeles CA 90095 United States
| | - Sichen Zhao
- Department of Chemistry and Biochemistry and California NanoSystems Institute University of California Los Angeles CA 90095 United States
| | - Georgiy Akopov
- Ames Laboratory U.S. Department of Energy Ames IA 50011 United States / Department of Chemistry Iowa State University Ames IA 50011 United States
| | - Reza Mohammadi
- Department of Mechanical and Nuclear Engineering Virginia Commonwealth University Richmond VA 23284 United States
| | - Michael T. Yeung
- Department of Chemistry University at Albany – State University of New York Albany NY 12222 United States
| | - Paul S. Weiss
- Department of Chemistry and Biochemistry and California NanoSystems Institute University of California Los Angeles CA 90095 United States
- Department of Materials Science University of California Los Angeles CA 90095 United States
- Department of Bioengineering University of California Los Angeles CA 90095 United States
| | - Richard B. Kaner
- Department of Chemistry and Biochemistry and California NanoSystems Institute University of California Los Angeles CA 90095 United States
- Department of Materials Science University of California Los Angeles CA 90095 United States
| |
Collapse
|
12
|
Wu W, Pavloudis T, Verkhovtsev AV, Solov'yov AV, Palmer RE. Molecular dynamics simulation of nanofilament breakage in neuromorphic nanoparticle networks. NANOTECHNOLOGY 2022; 33:275602. [PMID: 35412471 DOI: 10.1088/1361-6528/ac5e6d] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Accepted: 03/15/2022] [Indexed: 06/14/2023]
Abstract
Neuromorphic computing systems may be the future of computing and cluster-based networks are a promising architecture for the realization of these systems. The creation and dissolution of synapses between the clusters are of great importance for their function. In this work, we model the thermal breakage of a gold nanofilament located between two gold nanoparticles via molecular dynamics simulations to study on the mechanisms of neuromorphic nanoparticle-based devices. We employ simulations of Au nanowires of different lengths (20-80 Å), widths (4-8 Å) and shapes connecting two Au1415nanoparticles (NPs) and monitor the evolution of the system via a detailed structural identification analysis. We found that atoms of the nanofilament gradually aggregate towards the clusters, causing the middle of wire to gradually thin and then break. Most of the system remains crystalline during this process but the center is molten. The terminal NPs increase the melting point of the NWs by fixing the middle wire and act as recrystallization areas. We report a strong dependence on the width of the NWs, but also their length and structure. These results may serve as guidelines for the realization of cluster-based neuromorphic computing systems.
Collapse
Affiliation(s)
- Wenkai Wu
- Nanomaterials Lab, College of Engineering, Swansea University, Fabian Way, SA1 8EN, Swansea, United Kingdom
| | - Theodoros Pavloudis
- Nanomaterials Lab, College of Engineering, Swansea University, Fabian Way, SA1 8EN, Swansea, United Kingdom
- School of Physics, Faculty of Sciences, Aristotle University of Thessaloniki, GR-54124 Thessaloniki, Greece
| | - Alexey V Verkhovtsev
- MBN Research Center gGmbH, Frankfurter Innovationszentrum Biotechnologie, Altenhöferallee 3, D-60438 Frankfurt am Main, Germany
| | - Andrey V Solov'yov
- MBN Research Center gGmbH, Frankfurter Innovationszentrum Biotechnologie, Altenhöferallee 3, D-60438 Frankfurt am Main, Germany
| | - Richard E Palmer
- Nanomaterials Lab, College of Engineering, Swansea University, Fabian Way, SA1 8EN, Swansea, United Kingdom
| |
Collapse
|
13
|
Bose SK, Mallinson JB, Galli E, Acharya SK, Minnai C, Bones PJ, Brown SA. Neuromorphic behaviour in discontinuous metal films. NANOSCALE HORIZONS 2022; 7:437-445. [PMID: 35262143 DOI: 10.1039/d1nh00620g] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Physical systems that exhibit brain-like behaviour are currently under intense investigation as platforms for neuromorphic computing. We show that discontinuous metal films, comprising irregular flat islands on a substrate and formed using simple evaporation processes, exhibit correlated avalanches of electrical signals that mimic those observed in the cortex. We further demonstrate that these signals meet established criteria for criticality. We perform a detailed experimental investigation of the atomic-scale switching processes that are responsible for these signals, and show that they mimic the integrate-and-fire mechanism of biological neurons. Using numerical simulations and a simple circuit model, we show that the characteristic features of the switching events are dependent on the network state and the local position of the switch within the complex network. We conclude that discontinuous films provide an interesting potential platform for brain-inspired computing.
Collapse
Affiliation(s)
- Saurabh K Bose
- The MacDiarmid Institute for Advanced Materials and Nanotechnology, School of Physical and Chemical Sciences, University of Canterbury, Christchurch, New Zealand.
| | - Joshua B Mallinson
- The MacDiarmid Institute for Advanced Materials and Nanotechnology, School of Physical and Chemical Sciences, University of Canterbury, Christchurch, New Zealand.
| | - Edoardo Galli
- The MacDiarmid Institute for Advanced Materials and Nanotechnology, School of Physical and Chemical Sciences, University of Canterbury, Christchurch, New Zealand.
| | - Susant K Acharya
- The MacDiarmid Institute for Advanced Materials and Nanotechnology, School of Physical and Chemical Sciences, University of Canterbury, Christchurch, New Zealand.
| | - Chloé Minnai
- Molecular Cryo-Electron Microscopy Unit, Okinawa Institute of Science and Technology Graduate University, 1919-1 Tancha, Onna-son, Kunigami-gun, Okinawa, Japan
| | - Philip J Bones
- Electrical and Computer Engineering, University of Canterbury, Private Bag 4800, Christchurch 8140, New Zealand
| | - Simon A Brown
- The MacDiarmid Institute for Advanced Materials and Nanotechnology, School of Physical and Chemical Sciences, University of Canterbury, Christchurch, New Zealand.
| |
Collapse
|
14
|
Ariga K, Fakhrullin R. Materials Nanoarchitectonics from Atom to Living Cell: A Method for Everything. BULLETIN OF THE CHEMICAL SOCIETY OF JAPAN 2022. [DOI: 10.1246/bcsj.20220071] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Affiliation(s)
- Katsuhiko Ariga
- WPI Research Center for Materials Nanoarchitectonics (MANA), National Institute for Materials Science (NIMS), 1-1 Namiki, Tsukuba, Ibaraki 305-0044, Japan
- Graduate School of Frontier Sciences, The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba 277-8561, Japan
| | - Rawil Fakhrullin
- Institute of Fundamental Medicine and Biology, Kazan Federal University, Kreml uramı 18, Kazan, 42000, Republic of Tatarstan, Russian Federation
| |
Collapse
|
15
|
Chaikittisilp W, Yamauchi Y, Ariga K. Material Evolution with Nanotechnology, Nanoarchitectonics, and Materials Informatics: What will be the Next Paradigm Shift in Nanoporous Materials? ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2022; 34:e2107212. [PMID: 34637159 DOI: 10.1002/adma.202107212] [Citation(s) in RCA: 45] [Impact Index Per Article: 22.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/10/2021] [Revised: 10/05/2021] [Indexed: 05/27/2023]
Abstract
Materials science and chemistry have played a central and significant role in advancing society. With the shift toward sustainable living, it is anticipated that the development of functional materials will continue to be vital for sustaining life on our planet. In the recent decades, rapid progress has been made in materials science and chemistry owing to the advances in experimental, analytical, and computational methods, thereby producing several novel and useful materials. However, most problems in material development are highly complex. Here, the best strategy for the development of functional materials via the implementation of three key concepts is discussed: nanotechnology as a game changer, nanoarchitectonics as an integrator, and materials informatics as a super-accelerator. Discussions from conceptual viewpoints and example recent developments, chiefly focused on nanoporous materials, are presented. It is anticipated that coupling these three strategies together will open advanced routes for the swift design and exploratory search of functional materials truly useful for solving real-world problems. These novel strategies will result in the evolution of nanoporous functional materials.
Collapse
Affiliation(s)
- Watcharop Chaikittisilp
- JST-ERATO Yamauchi Materials Space-Tectonics Project, National Institute for Materials Science (NIMS), 1-1 Namiki, Tsukuba, Ibaraki, 305-0044, Japan
- Research and Services Division of Materials Data and Integrated System (MaDIS), National Institute for Materials Science (NIMS), 1-1 Namiki, Tsukuba, Ibaraki, 305-0044, Japan
| | - Yusuke Yamauchi
- JST-ERATO Yamauchi Materials Space-Tectonics Project, National Institute for Materials Science (NIMS), 1-1 Namiki, Tsukuba, Ibaraki, 305-0044, Japan
- International Center for Materials Nanoarchitectonics (WPI-MANA), National Institute for Materials Science (NIMS), 1-1 Namiki, Tsukuba, Ibaraki, 305-0044, Japan
- Australian Institute for Bioengineering and Nanotechnology (AIBN) and School of Chemical Engineering, The University of Queensland, Brisbane, QLD 4072, Australia
| | - Katsuhiko Ariga
- JST-ERATO Yamauchi Materials Space-Tectonics Project, National Institute for Materials Science (NIMS), 1-1 Namiki, Tsukuba, Ibaraki, 305-0044, Japan
- International Center for Materials Nanoarchitectonics (WPI-MANA), National Institute for Materials Science (NIMS), 1-1 Namiki, Tsukuba, Ibaraki, 305-0044, Japan
- Graduate School of Frontier Sciences, The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba, 277-8561, Japan
| |
Collapse
|
16
|
Hochstetter J, Zhu R, Loeffler A, Diaz-Alvarez A, Nakayama T, Kuncic Z. Avalanches and edge-of-chaos learning in neuromorphic nanowire networks. Nat Commun 2021; 12:4008. [PMID: 34188085 PMCID: PMC8242064 DOI: 10.1038/s41467-021-24260-z] [Citation(s) in RCA: 43] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2020] [Accepted: 06/10/2021] [Indexed: 02/06/2023] Open
Abstract
The brain's efficient information processing is enabled by the interplay between its neuro-synaptic elements and complex network structure. This work reports on the neuromorphic dynamics of nanowire networks (NWNs), a unique brain-inspired system with synapse-like memristive junctions embedded within a recurrent neural network-like structure. Simulation and experiment elucidate how collective memristive switching gives rise to long-range transport pathways, drastically altering the network's global state via a discontinuous phase transition. The spatio-temporal properties of switching dynamics are found to be consistent with avalanches displaying power-law size and life-time distributions, with exponents obeying the crackling noise relationship, thus satisfying criteria for criticality, as observed in cortical neuronal cultures. Furthermore, NWNs adaptively respond to time varying stimuli, exhibiting diverse dynamics tunable from order to chaos. Dynamical states at the edge-of-chaos are found to optimise information processing for increasingly complex learning tasks. Overall, these results reveal a rich repertoire of emergent, collective neural-like dynamics in NWNs, thus demonstrating the potential for a neuromorphic advantage in information processing.
Collapse
Affiliation(s)
- Joel Hochstetter
- grid.1013.30000 0004 1936 834XSchool of Physics, University of Sydney, Sydney, NSW Australia
| | - Ruomin Zhu
- grid.1013.30000 0004 1936 834XSchool of Physics, University of Sydney, Sydney, NSW Australia
| | - Alon Loeffler
- grid.1013.30000 0004 1936 834XSchool of Physics, University of Sydney, Sydney, NSW Australia
| | - Adrian Diaz-Alvarez
- grid.21941.3f0000 0001 0789 6880International Center for Materials Nanoarchitectonics (WPI-MANA), National Institute for Materials Science (NIMS), Tsukuba, Ibaraki Japan
| | - Tomonobu Nakayama
- grid.1013.30000 0004 1936 834XSchool of Physics, University of Sydney, Sydney, NSW Australia ,grid.21941.3f0000 0001 0789 6880International Center for Materials Nanoarchitectonics (WPI-MANA), National Institute for Materials Science (NIMS), Tsukuba, Ibaraki Japan ,grid.20515.330000 0001 2369 4728Graduate School of Pure and Applied Sciences, University of Tsukuba, Tsukuba, Ibaraki Japan
| | - Zdenka Kuncic
- grid.1013.30000 0004 1936 834XSchool of Physics, University of Sydney, Sydney, NSW Australia ,grid.21941.3f0000 0001 0789 6880International Center for Materials Nanoarchitectonics (WPI-MANA), National Institute for Materials Science (NIMS), Tsukuba, Ibaraki Japan ,grid.1013.30000 0004 1936 834XThe University of Sydney Nano Institute, Sydney, NSW Australia
| |
Collapse
|
17
|
Zhu R, Hochstetter J, Loeffler A, Diaz-Alvarez A, Nakayama T, Lizier JT, Kuncic Z. Information dynamics in neuromorphic nanowire networks. Sci Rep 2021; 11:13047. [PMID: 34158521 PMCID: PMC8219687 DOI: 10.1038/s41598-021-92170-7] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2020] [Accepted: 05/31/2021] [Indexed: 12/18/2022] Open
Abstract
Neuromorphic systems comprised of self-assembled nanowires exhibit a range of neural-like dynamics arising from the interplay of their synapse-like electrical junctions and their complex network topology. Additionally, various information processing tasks have been demonstrated with neuromorphic nanowire networks. Here, we investigate the dynamics of how these unique systems process information through information-theoretic metrics. In particular, Transfer Entropy (TE) and Active Information Storage (AIS) are employed to investigate dynamical information flow and short-term memory in nanowire networks. In addition to finding that the topologically central parts of networks contribute the most to the information flow, our results also reveal TE and AIS are maximized when the networks transitions from a quiescent to an active state. The performance of neuromorphic networks in memory and learning tasks is demonstrated to be dependent on their internal dynamical states as well as topological structure. Optimal performance is found when these networks are pre-initialised to the transition state where TE and AIS are maximal. Furthermore, an optimal range of information processing resources (i.e. connectivity density) is identified for performance. Overall, our results demonstrate information dynamics is a valuable tool to study and benchmark neuromorphic systems.
Collapse
Affiliation(s)
- Ruomin Zhu
- School of Physics, The University of Sydney, Sydney, NSW, 2006, Australia.
| | - Joel Hochstetter
- School of Physics, The University of Sydney, Sydney, NSW, 2006, Australia
| | - Alon Loeffler
- School of Physics, The University of Sydney, Sydney, NSW, 2006, Australia
| | - Adrian Diaz-Alvarez
- International Center for Materials Nanoarchitectonics (WPI-MANA), National Institute for Materials Science (NIMS), 1-1 Namiki, Tsukuba, Ibaraki, 305-0044, Japan
| | - Tomonobu Nakayama
- School of Physics, The University of Sydney, Sydney, NSW, 2006, Australia
- International Center for Materials Nanoarchitectonics (WPI-MANA), National Institute for Materials Science (NIMS), 1-1 Namiki, Tsukuba, Ibaraki, 305-0044, Japan
- Graduate School of Pure and Applied Sciences, University of Tsukuba, Tsukuba, Japan
| | - Joseph T Lizier
- Centre for Complex Systems, Faculty of Engineering, The University of Sydney, Sydney, NSW, 2006, Australia
| | - Zdenka Kuncic
- School of Physics, The University of Sydney, Sydney, NSW, 2006, Australia.
- International Center for Materials Nanoarchitectonics (WPI-MANA), National Institute for Materials Science (NIMS), 1-1 Namiki, Tsukuba, Ibaraki, 305-0044, Japan.
- Centre for Complex Systems, Faculty of Engineering, The University of Sydney, Sydney, NSW, 2006, Australia.
- Sydney Nano Institute, The University of Sydney, Sydney, NSW, 2006, Australia.
| |
Collapse
|
18
|
Daniels RK, Brown SA. Nanowire networks: how does small-world character evolve with dimensionality? NANOSCALE HORIZONS 2021; 6:482-488. [PMID: 33982039 DOI: 10.1039/d0nh00693a] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Networks of nanowires are currently under consideration for a wide range of electronic and optoelectronic applications. Nanowire devices are usually made by sequential deposition, which inevitably leads to stacking of the wires on top of one another. Here we demonstrate the effect of stacking on the topology of the resulting networks. We compare perfectly 2D networks with quasi-3D networks, and compare both nanowire networks to the corresponding Watts Strogatz networks, which are standard benchmark systems. By investigating quantities such as clustering, path length, modularity, and small world propensity we show that the connectivity of the quasi-3D networks is significantly different to that of the 2D networks, a result which may have important implications for applications of nanowire networks.
Collapse
Affiliation(s)
- Ryan K Daniels
- The MacDiarmid Institute for Advanced Materials and Nanotechnology, School of Physical and Chemical Sciences, University of Canterbury, Christchurch, New Zealand.
| | - Simon A Brown
- The MacDiarmid Institute for Advanced Materials and Nanotechnology, School of Physical and Chemical Sciences, University of Canterbury, Christchurch, New Zealand.
| |
Collapse
|
19
|
Lilak S, Woods W, Scharnhorst K, Dunham C, Teuscher C, Stieg AZ, Gimzewski JK. Spoken Digit Classification by In-Materio Reservoir Computing With Neuromorphic Atomic Switch Networks. FRONTIERS IN NANOTECHNOLOGY 2021. [DOI: 10.3389/fnano.2021.675792] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Atomic Switch Networks comprising silver iodide (AgI) junctions, a material previously unexplored as functional memristive elements within highly interconnected nanowire networks, were employed as a neuromorphic substrate for physical Reservoir Computing This new class of ASN-based devices has been physically characterized and utilized to classify spoken digit audio data, demonstrating the utility of substrate-based device architectures where intrinsic material properties can be exploited to perform computation in-materio. This work demonstrates high accuracy in the classification of temporally analyzed Free-Spoken Digit Data These results expand upon the class of viable memristive materials available for the production of functional nanowire networks and bolster the utility of ASN-based devices as unique hardware platforms for neuromorphic computing applications involving memory, adaptation and learning.
Collapse
|
20
|
Abstract
In science and technology today, the crucial importance of the regulation of nanoscale objects and structures is well recognized. The production of functional material systems using nanoscale units can be achieved via the fusion of nanotechnology with the other research disciplines. This task is a part of the emerging concept of nanoarchitectonics, which is a concept moving beyond the area of nanotechnology. The concept of nanoarchitectonics is supposed to involve the architecting of functional materials using nanoscale units based on the principles of nanotechnology. In this focus article, the essences of nanotechnology and nanoarchitectonics are first explained, together with their historical backgrounds. Then, several examples of material production based on the concept of nanoarchitectonics are introduced via several approaches: (i) from atomic switches to neuromorphic networks; (ii) from atomic nanostructure control to environmental and energy applications; (iii) from interfacial processes to devices; and (iv) from biomolecular assemblies to life science. Finally, perspectives relating to the final goals of the nanoarchitectonics approach are discussed.
Collapse
Affiliation(s)
- Katsuhiko Ariga
- WPI Research Center for Materials Nanoarchitectonics (MANA), National Institute for Materials Science (NIMS), 1-1 Namiki, Tsukuba, Ibaraki 305-0044, Japan. and Graduate School of Frontier Sciences, The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba 277-8561, Japan
| |
Collapse
|
21
|
Ariga K, Shionoya M. Nanoarchitectonics for Coordination Asymmetry and Related Chemistry. BULLETIN OF THE CHEMICAL SOCIETY OF JAPAN 2021. [DOI: 10.1246/bcsj.20200362] [Citation(s) in RCA: 56] [Impact Index Per Article: 18.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Affiliation(s)
- Katsuhiko Ariga
- World Premier International (WPI) Research Centre for Materials Nanoarchitectonics (MANA), National Institute for Materials Science (NIMS), 1-1 Namiki, Tsukuba, Ibaraki 305-0044, Japan
- Department of Advanced Materials Science, Graduate School of Frontier Sciences, The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba 277-8561, Japan
| | - Mitsuhiko Shionoya
- Department of Chemistry, Graduate School of Science, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan
| |
Collapse
|
22
|
Ariga K. Nanoarchitectonics Revolution and Evolution: From Small Science to Big Technology. SMALL SCIENCE 2020. [DOI: 10.1002/smsc.202000032] [Citation(s) in RCA: 40] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Affiliation(s)
- Katsuhiko Ariga
- World Premier International (WPI) Research Center for Materials Nanoarchitectonics (MANA) National Institute for Materials Science (NIMS) 1-1 Namiki Tsukuba 305-0044 Japan
- Department of Advanced Materials Science Graduate School of Frontier Sciences The University of Tokyo 5-1-5 Kashiwanoha Kashiwa Chiba 277-8561 Japan
| |
Collapse
|
23
|
Li Q, Diaz-Alvarez A, Tang D, Higuchi R, Shingaya Y, Nakayama T. Sleep-Dependent Memory Consolidation in a Neuromorphic Nanowire Network. ACS APPLIED MATERIALS & INTERFACES 2020; 12:50573-50580. [PMID: 33135880 DOI: 10.1021/acsami.0c11157] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
A neuromorphic network composed of silver nanowires coated with TiO2 is found to show certain parallels with neural networks in nature such as biological brains. Owing to the memristive properties emerging at nanowire-to-nanowire contacts, where the Ag/TiO2/Ag interface exists, the network can store information in the form of connectivity between nanowires in the network as electrically measured as an increase in conductance. The observed memory arises from an interplay between the topological constraints imposed by a complex network structure and the plasticity of its constituting memristive Ag/TiO2/Ag junctions. Regarding the long-term decay of the connectivity in the network, we further investigate the controllability of the established connectivity. Inspired by the regulated activity cycles of the human brain during sleep, a learning-sleep-recovery cycle was mimicked by applying voltage pulses, with controlling pulse heights and duty ratios, to the nanowire network. Interestingly, even when the conductance was lost during sleep, the network could quickly recover previous states of conductance in the recovery process after sleep. Comparison between results of experiments and theoretical simulations revealed that such a quick recovery of conductance can be realized by sparse voltage pulse application during sleep; in other words, sleep-dependent memory consolidation occurs and can be controlled. The present results provide clues to new learning designs in neuromorphic networks for achieving longer memory retention for future neuromorphic technology.
Collapse
Affiliation(s)
- Qiao Li
- Graduate School of Pure and Applied Sciences, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki 305-8577, Japan
- International Center for Materials Nanoarchitectonics (WPI-MANA), National Institute for Materials Science (NIMS), 1-1 Namiki, Tsukuba, Ibaraki 305-0044, Japan
| | - Adrian Diaz-Alvarez
- International Center for Materials Nanoarchitectonics (WPI-MANA), National Institute for Materials Science (NIMS), 1-1 Namiki, Tsukuba, Ibaraki 305-0044, Japan
| | - Daiming Tang
- International Center for Materials Nanoarchitectonics (WPI-MANA), National Institute for Materials Science (NIMS), 1-1 Namiki, Tsukuba, Ibaraki 305-0044, Japan
| | - Rintaro Higuchi
- International Center for Materials Nanoarchitectonics (WPI-MANA), National Institute for Materials Science (NIMS), 1-1 Namiki, Tsukuba, Ibaraki 305-0044, Japan
| | - Yoshitaka Shingaya
- International Center for Materials Nanoarchitectonics (WPI-MANA), National Institute for Materials Science (NIMS), 1-1 Namiki, Tsukuba, Ibaraki 305-0044, Japan
| | - Tomonobu Nakayama
- Graduate School of Pure and Applied Sciences, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki 305-8577, Japan
- International Center for Materials Nanoarchitectonics (WPI-MANA), National Institute for Materials Science (NIMS), 1-1 Namiki, Tsukuba, Ibaraki 305-0044, Japan
| |
Collapse
|
24
|
|
25
|
Mirigliano M, Decastri D, Pullia A, Dellasega D, Casu A, Falqui A, Milani P. Complex electrical spiking activity in resistive switching nanostructured Au two-terminal devices. NANOTECHNOLOGY 2020; 31:234001. [PMID: 32202254 DOI: 10.1088/1361-6528/ab76ec] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Networks of nanoscale objects are the subject of increasing interest as resistive switching systems for the fabrication of neuromorphic computing architectures. Nanostructured films of bare gold clusters produced in gas phase with thickness well beyond the electrical percolation threshold, show a non-ohmic electrical behavior and resistive switching, resulting in groups of current spikes with irregular temporal organization. Here we report the systematic characterization of the temporal correlations between single spikes and spiking rate power spectrum of nanostructured Au two-terminal devices consisting of a cluster-assembled film deposited between two planar electrodes. By varying the nanostructured film thickness we fabricated two different classes of devices with high and low initial resistance respectively. We show that the switching dynamics can be described by a power law distribution in low resistance devices whereas a bi-exponential behavior is observed in the high resistance ones. The measured resistance of cluster-assembled films shows a [Formula: see text] scaling behavior in the range of analyzed frequencies. Our results suggest the possibility of using cluster-assembled Au films as components for neuromorphic systems where a certain degree of stochasticity is required.
Collapse
Affiliation(s)
- M Mirigliano
- CIMAINA and Department of Physics, Università degli Studi di Milano, via Celoria 16, I-20133, Milano, Italy
| | | | | | | | | | | | | |
Collapse
|
26
|
Loeffler A, Zhu R, Hochstetter J, Li M, Fu K, Diaz-Alvarez A, Nakayama T, Shine JM, Kuncic Z. Topological Properties of Neuromorphic Nanowire Networks. Front Neurosci 2020; 14:184. [PMID: 32210754 PMCID: PMC7069063 DOI: 10.3389/fnins.2020.00184] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2019] [Accepted: 02/19/2020] [Indexed: 01/10/2023] Open
Abstract
Graph theory has been extensively applied to the topological mapping of complex networks, ranging from social networks to biological systems. Graph theory has increasingly been applied to neuroscience as a method to explore the fundamental structural and functional properties of human neural networks. Here, we apply graph theory to a model of a novel neuromorphic system constructed from self-assembled nanowires, whose structure and function may mimic that of human neural networks. Simulations of neuromorphic nanowire networks allow us to directly examine their topology at the individual nanowire–node scale. This type of investigation is currently extremely difficult experimentally. We then apply network cartographic approaches to compare neuromorphic nanowire networks with: random networks (including an untrained artificial neural network); grid-like networks and the structural network of C. elegans. Our results demonstrate that neuromorphic nanowire networks exhibit a small–world architecture similar to the biological system of C. elegans, and significantly different from random and grid-like networks. Furthermore, neuromorphic nanowire networks appear more segregated and modular than random, grid-like and simple biological networks and more clustered than artificial neural networks. Given the inextricable link between structure and function in neural networks, these results may have important implications for mimicking cognitive functions in neuromorphic nanowire networks.
Collapse
Affiliation(s)
- Alon Loeffler
- School of Physics, The University of Sydney, Sydney, NSW, Australia
| | - Ruomin Zhu
- School of Physics, The University of Sydney, Sydney, NSW, Australia
| | - Joel Hochstetter
- School of Physics, The University of Sydney, Sydney, NSW, Australia
| | - Mike Li
- Central Clinical School, The University of Sydney, Sydney, NSW, Australia
| | - Kaiwei Fu
- School of Physics, The University of Sydney, Sydney, NSW, Australia
| | - Adrian Diaz-Alvarez
- International Center for Materials Nanoarchitectonics (WPI-MANA), National Institute for Materials Science (NIMS), Tsukuba, Japan
| | - Tomonobu Nakayama
- International Center for Materials Nanoarchitectonics (WPI-MANA), National Institute for Materials Science (NIMS), Tsukuba, Japan
| | - James M Shine
- Central Clinical School, The University of Sydney, Sydney, NSW, Australia
| | - Zdenka Kuncic
- School of Physics, The University of Sydney, Sydney, NSW, Australia
| |
Collapse
|
27
|
|
28
|
Diaz-Alvarez A, Higuchi R, Sanz-Leon P, Marcus I, Shingaya Y, Stieg AZ, Gimzewski JK, Kuncic Z, Nakayama T. Emergent dynamics of neuromorphic nanowire networks. Sci Rep 2019; 9:14920. [PMID: 31624325 PMCID: PMC6797708 DOI: 10.1038/s41598-019-51330-6] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2019] [Accepted: 09/25/2019] [Indexed: 12/31/2022] Open
Abstract
Neuromorphic networks are formed by random self-assembly of silver nanowires. Silver nanowires are coated with a polymer layer after synthesis in which junctions between two nanowires act as resistive switches, often compared with neurosynapses. We analyze the role of single junction switching in the dynamical properties of the neuromorphic network. Network transitions to a high-conductance state under the application of a voltage bias higher than a threshold value. The stability and permanence of this state is studied by shifting the voltage bias in order to activate or deactivate the network. A model of the electrical network with atomic switches reproduces the relation between individual nanowire junctions switching events with current pathway formation or destruction. This relation is further manifested in changes in 1/f power-law scaling of the spectral distribution of current. The current fluctuations involved in this scaling shift are considered to arise from an essential equilibrium between formation, stochastic-mediated breakdown of individual nanowire-nanowire junctions and the onset of different current pathways that optimize power dissipation. This emergent dynamics shown by polymer-coated Ag nanowire networks places this system in the class of optimal transport networks, from which new fundamental parallels with neural dynamics and natural computing problem-solving can be drawn.
Collapse
Affiliation(s)
- Adrian Diaz-Alvarez
- International Center for Material Nanoarchitectonics (WPI-MANA), National Institute for Materials Science (NIMS), 1-1 Namiki, Tsukuba, Ibaraki, 305-0044, Japan.
| | - Rintaro Higuchi
- International Center for Material Nanoarchitectonics (WPI-MANA), National Institute for Materials Science (NIMS), 1-1 Namiki, Tsukuba, Ibaraki, 305-0044, Japan
| | - Paula Sanz-Leon
- Sydney Nano Institute and School of Physics, University of Sydney, Sydney, NSW, 2006, Australia
| | - Ido Marcus
- Sydney Nano Institute and School of Physics, University of Sydney, Sydney, NSW, 2006, Australia
| | - Yoshitaka Shingaya
- International Center for Material Nanoarchitectonics (WPI-MANA), National Institute for Materials Science (NIMS), 1-1 Namiki, Tsukuba, Ibaraki, 305-0044, Japan
| | - Adam Z Stieg
- International Center for Material Nanoarchitectonics (WPI-MANA), National Institute for Materials Science (NIMS), 1-1 Namiki, Tsukuba, Ibaraki, 305-0044, Japan.,California NanoSystems Institute (CNSI), University of California Los Angeles, 570 Westwood Plaza, Los Angeles, California, 90095, USA
| | - James K Gimzewski
- International Center for Material Nanoarchitectonics (WPI-MANA), National Institute for Materials Science (NIMS), 1-1 Namiki, Tsukuba, Ibaraki, 305-0044, Japan.,California NanoSystems Institute (CNSI), University of California Los Angeles, 570 Westwood Plaza, Los Angeles, California, 90095, USA.,Department of Chemistry and Biochemistry, University of California Los Angeles, 607 Charles E. Young Drive East, Los Angeles, California, 90095, USA
| | - Zdenka Kuncic
- International Center for Material Nanoarchitectonics (WPI-MANA), National Institute for Materials Science (NIMS), 1-1 Namiki, Tsukuba, Ibaraki, 305-0044, Japan.,Sydney Nano Institute and School of Physics, University of Sydney, Sydney, NSW, 2006, Australia
| | - Tomonobu Nakayama
- International Center for Material Nanoarchitectonics (WPI-MANA), National Institute for Materials Science (NIMS), 1-1 Namiki, Tsukuba, Ibaraki, 305-0044, Japan. .,Sydney Nano Institute and School of Physics, University of Sydney, Sydney, NSW, 2006, Australia. .,Graduate School of Pure and Applied Sciences, University of Tsukuba, 1-1 Namiki, Tsukuba, Ibaraki, 305-0055, Japan.
| |
Collapse
|
29
|
Caravelli F. Asymptotic Behavior of Memristive Circuits. ENTROPY 2019; 21:e21080789. [PMID: 33267502 PMCID: PMC7515318 DOI: 10.3390/e21080789] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/16/2019] [Revised: 08/02/2019] [Accepted: 08/06/2019] [Indexed: 11/16/2022]
Abstract
The interest in memristors has risen due to their possible application both as memory units and as computational devices in combination with CMOS. This is in part due to their nonlinear dynamics, and a strong dependence on the circuit topology. We provide evidence that also purely memristive circuits can be employed for computational purposes. In the present paper we show that a polynomial Lyapunov function in the memory parameters exists for the case of DC controlled memristors. Such a Lyapunov function can be asymptotically approximated with binary variables, and mapped to quadratic combinatorial optimization problems. This also shows a direct parallel between memristive circuits and the Hopfield-Little model. In the case of Erdos-Renyi random circuits, we show numerically that the distribution of the matrix elements of the projectors can be roughly approximated with a Gaussian distribution, and that it scales with the inverse square root of the number of elements. This provides an approximated but direct connection with the physics of disordered system and, in particular, of mean field spin glasses. Using this and the fact that the interaction is controlled by a projector operator on the loop space of the circuit. We estimate the number of stationary points of the approximate Lyapunov function and provide a scaling formula as an upper bound in terms of the circuit topology only.
Collapse
Affiliation(s)
- Francesco Caravelli
- Theoretical Division and Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, NM 87545, USA
| |
Collapse
|
30
|
Mirigliano M, Borghi F, Podestà A, Antidormi A, Colombo L, Milani P. Non-ohmic behavior and resistive switching of Au cluster-assembled films beyond the percolation threshold. NANOSCALE ADVANCES 2019; 1:3119-3130. [PMID: 36133584 PMCID: PMC9417734 DOI: 10.1039/c9na00256a] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/19/2019] [Accepted: 07/01/2019] [Indexed: 05/27/2023]
Abstract
Networks based on nanoscale resistive switching junctions are considered promising for the fabrication of neuromorphic computing architectures. To date random networks of nanowires, nanoparticles, and metal clusters embedded in a polymeric matrix or passivated by shell of ligands or oxide layers have been used to produce resistive switching systems. The strategies applied to tailor resistive switching behavior are currently based on the careful control of the volume fraction of the nanoscale conducting phase that must be fixed close to the electrical percolation threshold. Here, by blending laboratory and computer experiments, we demonstrate that metallic nanostructured Au films fabricated by bare gold nanoparticles produced in the gas phase and with thickness well beyond the electrical percolation threshold, show a non-ohmic electrical behavior and complex and reproducible resistive switching. We observe that the nanogranular structure of the Au films does not evolve with thickness: this introduces a huge number of defects and junctions affecting the electrical transport and causing a dynamic evolution of the nanoscale electrical contacts under the current flow. To uncover the origin of the resistive switching behavior in Au cluster-assembled films, we developed a simple computational model for determining the evolution of a model granular film under bias conditions. The model exploits the information provided by experimental investigation about the nanoscale granular morphology of real films. Our results show that metallic nanogranular materials have functional properties radically different from their bulk counterparts, in particular nanostructured Au films can be fabricated by assembling bare gold clusters which retain their individuality to produce an all-metal resistive switching system.
Collapse
Affiliation(s)
- M Mirigliano
- CIMAINA and Department of Physics, Università degli Studi di Milano Via Celoria 16 20133 Milano Italy
| | - F Borghi
- CIMAINA and Department of Physics, Università degli Studi di Milano Via Celoria 16 20133 Milano Italy
| | - A Podestà
- CIMAINA and Department of Physics, Università degli Studi di Milano Via Celoria 16 20133 Milano Italy
| | - A Antidormi
- Department of Physics, University of Cagliari, Cittadella Universitaria 09042 Monserrato (Ca) Italy
| | - L Colombo
- Department of Physics, University of Cagliari, Cittadella Universitaria 09042 Monserrato (Ca) Italy
| | - P Milani
- CIMAINA and Department of Physics, Università degli Studi di Milano Via Celoria 16 20133 Milano Italy
| |
Collapse
|
31
|
Sánta B, Balogh Z, Gubicza A, Pósa L, Krisztián D, Mihály G, Csontos M, Halbritter A. Universal 1/f type current noise of Ag filaments in redox-based memristive nanojunctions. NANOSCALE 2019; 11:4719-4725. [PMID: 30839979 DOI: 10.1039/c8nr09985e] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
The microscopic origins and technological impact of 1/f type current fluctuations in Ag based, filamentary type resistive switching devices have been investigated upon downscaling toward the ultimate single atomic limit. The analysis of the low-frequency current noise spectra revealed that the main electronic noise contribution arises from the resistance fluctuations due to internal dynamical defects of Ag nanofilaments. The resulting 0.01-1% current noise ratio, i.e. the total noise level with respect to the mean value of the current, is found to be universal: its magnitude only depends on the total resistance of the device, irrespective of the materials aspects of the surrounding solid electrolyte and of the specific filament formation procedure. Moreover, the resistance dependence of the current noise ratio also displays the diffusive to ballistic crossover, confirming that stable resistive switching operation utilizing Ag nanofilaments is not compromised even in truly atomic scale junctions by technologically impeding noise levels.
Collapse
Affiliation(s)
- Botond Sánta
- Department of Physics, Budapest University of Technology and Economics, Budafoki ut 8, 1111 Budapest, Hungary. and MTA-BME Condensed Matter Research Group, Budafoki ut 8, 1111 Budapest, Hungary
| | - Zoltán Balogh
- Department of Physics, Budapest University of Technology and Economics, Budafoki ut 8, 1111 Budapest, Hungary. and MTA-BME Condensed Matter Research Group, Budafoki ut 8, 1111 Budapest, Hungary
| | - Agnes Gubicza
- Department of Physics, Budapest University of Technology and Economics, Budafoki ut 8, 1111 Budapest, Hungary. and Empa, Swiss Federal Laboratories for Materials Science and Technology, Transport at Nanoscale Interfaces Laboratory, Überlandstrasse 129, CH-8600 Dübendorf, Switzerland
| | - László Pósa
- Department of Physics, Budapest University of Technology and Economics, Budafoki ut 8, 1111 Budapest, Hungary. and Institute for Technical Physics and Materials Science, Centre for Energy Research, Hungarian Academy of Sciences, Konkoly Thege ut 29-33, 1121 Budapest, Hungary
| | - Dávid Krisztián
- Department of Physics, Budapest University of Technology and Economics, Budafoki ut 8, 1111 Budapest, Hungary.
| | - György Mihály
- Department of Physics, Budapest University of Technology and Economics, Budafoki ut 8, 1111 Budapest, Hungary. and MTA-BME Condensed Matter Research Group, Budafoki ut 8, 1111 Budapest, Hungary
| | - Miklós Csontos
- Department of Physics, Budapest University of Technology and Economics, Budafoki ut 8, 1111 Budapest, Hungary. and Empa, Swiss Federal Laboratories for Materials Science and Technology, Transport at Nanoscale Interfaces Laboratory, Überlandstrasse 129, CH-8600 Dübendorf, Switzerland
| | - András Halbritter
- Department of Physics, Budapest University of Technology and Economics, Budafoki ut 8, 1111 Budapest, Hungary. and MTA-BME Condensed Matter Research Group, Budafoki ut 8, 1111 Budapest, Hungary
| |
Collapse
|
32
|
Zegarac A, Caravelli F. Memristive networks: From graph theory to statistical physics. ACTA ACUST UNITED AC 2019. [DOI: 10.1209/0295-5075/125/10001] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
|
33
|
Bose SK, Shirai S, Mallinson JB, Brown SA. Synaptic dynamics in complex self-assembled nanoparticle networks. Faraday Discuss 2019; 213:471-485. [PMID: 30357187 DOI: 10.1039/c8fd00109j] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
We report a detailed study of neuromorphic switching behaviour in inherently complex percolating networks of self-assembled metal nanoparticles. We show that variation of the strength and duration of the electric field applied to this network of synapse-like atomic switches allows us to control the switching dynamics. Switching is observed for voltages above a well-defined threshold, with higher voltages leading to increased switching rates. We demonstrate two behavioral archetypes and show how the switching dynamics change as a function of duration and amplitude of the voltage stimulus. We show that the state of each synapse can influence the activity of the other synapses, leading to complex switching dynamics. We further demonstrate the influence of the morphology of the network on the measured device properties, and the constraints imposed by the overall network conductance. The correlated switching dynamics, device stability over long periods, and the simplicity of the device fabrication provide an attractive pathway to practical implementation of on-chip neuromorphic computing.
Collapse
Affiliation(s)
- S K Bose
- The MacDiarmid Institute for Advanced Materials and Nanotechnology, School of Physical and Chemical Sciences, University of Canterbury, Private Bag 4800, Christchurch 8140, New Zealand.
| | | | | | | |
Collapse
|
34
|
Abstract
We present both an overview and a perspective of recent experimental advances and proposed new approaches to performing computation using memristors. A memristor is a 2-terminal passive component with a dynamic resistance depending on an internal parameter. We provide an brief historical introduction, as well as an overview over the physical mechanism that lead to memristive behavior. This review is meant to guide nonpractitioners in the field of memristive circuits and their connection to machine learning and neural computation.
Collapse
|
35
|
Evaluation of the computational capabilities of a memristive random network (MN3) under the context of reservoir computing. Neural Netw 2018; 106:223-236. [DOI: 10.1016/j.neunet.2018.07.003] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2018] [Revised: 05/17/2018] [Accepted: 07/10/2018] [Indexed: 11/22/2022]
|
36
|
Manning HG, Niosi F, da Rocha CG, Bellew AT, O'Callaghan C, Biswas S, Flowers PF, Wiley BJ, Holmes JD, Ferreira MS, Boland JJ. Emergence of winner-takes-all connectivity paths in random nanowire networks. Nat Commun 2018; 9:3219. [PMID: 30104665 PMCID: PMC6089893 DOI: 10.1038/s41467-018-05517-6] [Citation(s) in RCA: 39] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2017] [Accepted: 07/10/2018] [Indexed: 11/09/2022] Open
Abstract
Nanowire networks are promising memristive architectures for neuromorphic applications due to their connectivity and neurosynaptic-like behaviours. Here, we demonstrate a self-similar scaling of the conductance of networks and the junctions that comprise them. We show this behavior is an emergent property of any junction-dominated network. A particular class of junctions naturally leads to the emergence of conductance plateaus and a "winner-takes-all" conducting path that spans the entire network, and which we show corresponds to the lowest-energy connectivity path. The memory stored in the conductance state is distributed across the network but encoded in specific connectivity pathways, similar to that found in biological systems. These results are expected to have important implications for development of neuromorphic devices based on reservoir computing.
Collapse
Affiliation(s)
- Hugh G Manning
- School of Chemistry, Trinity College Dublin, Dublin 2, Ireland
- Centre for Research on Adaptive Nanostructures and Nanodevices (CRANN) & Advanced Materials and Bioengineering Research (AMBER) Centre, Trinity College Dublin, Dublin 2, Ireland
| | - Fabio Niosi
- School of Chemistry, Trinity College Dublin, Dublin 2, Ireland
- Centre for Research on Adaptive Nanostructures and Nanodevices (CRANN) & Advanced Materials and Bioengineering Research (AMBER) Centre, Trinity College Dublin, Dublin 2, Ireland
| | - Claudia Gomes da Rocha
- Centre for Research on Adaptive Nanostructures and Nanodevices (CRANN) & Advanced Materials and Bioengineering Research (AMBER) Centre, Trinity College Dublin, Dublin 2, Ireland
- School of Physics, Trinity College Dublin, Dublin 2, Ireland
| | - Allen T Bellew
- School of Chemistry, Trinity College Dublin, Dublin 2, Ireland
- Centre for Research on Adaptive Nanostructures and Nanodevices (CRANN) & Advanced Materials and Bioengineering Research (AMBER) Centre, Trinity College Dublin, Dublin 2, Ireland
| | - Colin O'Callaghan
- Centre for Research on Adaptive Nanostructures and Nanodevices (CRANN) & Advanced Materials and Bioengineering Research (AMBER) Centre, Trinity College Dublin, Dublin 2, Ireland
- School of Physics, Trinity College Dublin, Dublin 2, Ireland
| | - Subhajit Biswas
- Materials Chemistry & Analysis Group, School of Chemistry and the Tyndall National Institute, University College Cork, Cork, Ireland
| | - Patrick F Flowers
- Department of Chemistry, Duke University, Durham, 27708, North Carolina, USA
| | - Benjamin J Wiley
- Department of Chemistry, Duke University, Durham, 27708, North Carolina, USA
| | - Justin D Holmes
- Materials Chemistry & Analysis Group, School of Chemistry and the Tyndall National Institute, University College Cork, Cork, Ireland
| | - Mauro S Ferreira
- Centre for Research on Adaptive Nanostructures and Nanodevices (CRANN) & Advanced Materials and Bioengineering Research (AMBER) Centre, Trinity College Dublin, Dublin 2, Ireland
- School of Physics, Trinity College Dublin, Dublin 2, Ireland
| | - John J Boland
- School of Chemistry, Trinity College Dublin, Dublin 2, Ireland.
- Centre for Research on Adaptive Nanostructures and Nanodevices (CRANN) & Advanced Materials and Bioengineering Research (AMBER) Centre, Trinity College Dublin, Dublin 2, Ireland.
| |
Collapse
|
37
|
Microbial nanowires - Electron transport and the role of synthetic analogues. Acta Biomater 2018; 69:1-30. [PMID: 29357319 DOI: 10.1016/j.actbio.2018.01.007] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2017] [Revised: 01/07/2018] [Accepted: 01/09/2018] [Indexed: 02/07/2023]
Abstract
Electron transfer is central to cellular life, from photosynthesis to respiration. In the case of anaerobic respiration, some microbes have extracellular appendages that can be utilised to transport electrons over great distances. Two model organisms heavily studied in this arena are Shewanella oneidensis and Geobacter sulfurreducens. There is some debate over how, in particular, the Geobacter sulfurreducens nanowires (formed from pilin nanofilaments) are capable of achieving the impressive feats of natural conductivity that they display. In this article, we outline the mechanisms of electron transfer through delocalised electron transport, quantum tunnelling, and hopping as they pertain to biomaterials. These are described along with existing examples of the different types of conductivity observed in natural systems such as DNA and proteins in order to provide context for understanding the complexities involved in studying the electron transport properties of these unique nanowires. We then introduce some synthetic analogues, made using peptides, which may assist in resolving this debate. Microbial nanowires and the synthetic analogues thereof are of particular interest, not just for biogeochemistry, but also for the exciting potential bioelectronic and clinical applications as covered in the final section of the review. STATEMENT OF SIGNIFICANCE Some microbes have extracellular appendages that transport electrons over vast distances in order to respire, such as the dissimilatory metal-reducing bacteria Geobacter sulfurreducens. There is significant debate over how G. sulfurreducens nanowires are capable of achieving the impressive feats of natural conductivity that they display: This mechanism is a fundamental scientific challenge, with important environmental and technological implications. Through outlining the techniques and outcomes of investigations into the mechanisms of such protein-based nanofibrils, we provide a platform for the general study of the electronic properties of biomaterials. The implications are broad-reaching, with fundamental investigations into electron transfer processes in natural and biomimetic materials underway. From these studies, applications in the medical, energy, and IT industries can be developed utilising bioelectronics.
Collapse
|
38
|
Caravelli F. Locality of interactions for planar memristive circuits. Phys Rev E 2017; 96:052206. [PMID: 29347736 DOI: 10.1103/physreve.96.052206] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2017] [Indexed: 11/07/2022]
Abstract
The dynamics of purely memristive circuits has been shown to depend on a projection operator which expresses the Kirchhoff constraints, is naturally non-local in nature, and does represent the interaction between memristors. In the present paper we show that for the case of planar circuits, for which a meaningful Hamming distance can be defined, the elements of such projector can be bounded by exponentially decreasing functions of the distance. We provide a geometrical interpretation of the projector elements in terms of determinants of Dirichlet Laplacian of the dual circuit. For the case of linearized dynamics of the circuit for which a solution is known, this can be shown to provide a light cone bound for the interaction between memristors. This result establishes a finite speed of propagation of signals across the network, despite the non-local nature of the system.
Collapse
Affiliation(s)
- F Caravelli
- Theoretical Division and Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA
| |
Collapse
|
39
|
Minnai C, Bellacicca A, Brown SA, Milani P. Facile fabrication of complex networks of memristive devices. Sci Rep 2017; 7:7955. [PMID: 28801572 PMCID: PMC5554187 DOI: 10.1038/s41598-017-08244-y] [Citation(s) in RCA: 38] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2017] [Accepted: 07/10/2017] [Indexed: 11/09/2022] Open
Abstract
We describe the memristive properties of cluster-assembled gold films. We show that resistive switching is observed in pure metallic nanostructured films at room temperature and atmospheric pressure, in response to applied voltage inputs. In particular, we observe resistance changes up to 400% and archetypal switching events that have remarkable symmetry with the applied voltage. We associated this symmetry with 'potentiation' and 'anti-potentiation' processes involving the activation of synapses and of pathways comprising multiple synapses. The stability and reproducibility of the resistance switching, which lasted over many hours, make these devices ideal test-beds for exploration of the basic mechanisms of the switching processes, and allow convenient fabrication of devices that may have neuromorphic properties.
Collapse
Affiliation(s)
- Chloé Minnai
- CIMAINA and Dipartimento di Fisica, Università degli Studi di Milano, via Celoria 16, 20133, Milano, Italy
| | - Andrea Bellacicca
- CIMAINA and Dipartimento di Fisica, Università degli Studi di Milano, via Celoria 16, 20133, Milano, Italy
| | - Simon A Brown
- The MacDiarmid Institute for Advanced Materials and Nanotechnology, Department of Physics and Astronomy, University of Canterbury, Private Bag 4800, Christchurch, 8140, New Zealand.
| | - Paolo Milani
- CIMAINA and Dipartimento di Fisica, Università degli Studi di Milano, via Celoria 16, 20133, Milano, Italy.
| |
Collapse
|
40
|
John RA, Ko J, Kulkarni MR, Tiwari N, Chien NA, Ing NG, Leong WL, Mathews N. Flexible Ionic-Electronic Hybrid Oxide Synaptic TFTs with Programmable Dynamic Plasticity for Brain-Inspired Neuromorphic Computing. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2017; 13:1701193. [PMID: 28656608 DOI: 10.1002/smll.201701193] [Citation(s) in RCA: 52] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/12/2017] [Indexed: 05/28/2023]
Abstract
Emulation of biological synapses is necessary for future brain-inspired neuromorphic computational systems that could look beyond the standard von Neuman architecture. Here, artificial synapses based on ionic-electronic hybrid oxide-based transistors on rigid and flexible substrates are demonstrated. The flexible transistors reported here depict a high field-effect mobility of ≈9 cm2 V-1 s-1 with good mechanical performance. Comprehensive learning abilities/synaptic rules like paired-pulse facilitation, excitatory and inhibitory postsynaptic currents, spike-time-dependent plasticity, consolidation, superlinear amplification, and dynamic logic are successfully established depicting concurrent processing and memory functionalities with spatiotemporal correlation. The results present a fully solution processable approach to fabricate artificial synapses for next-generation transparent neural circuits.
Collapse
Affiliation(s)
- Rohit Abraham John
- School of Materials Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798, Singapore
| | - Jieun Ko
- School of Electrical and Electronic Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798, Singapore
| | - Mohit R Kulkarni
- School of Materials Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798, Singapore
| | - Naveen Tiwari
- School of Materials Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798, Singapore
| | - Nguyen Anh Chien
- School of Materials Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798, Singapore
| | - Ng Geok Ing
- School of Electrical and Electronic Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798, Singapore
| | - Wei Lin Leong
- School of Electrical and Electronic Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798, Singapore
- School of Chemical and Biomedical Engineering, Nanyang Technological University, 50 Nanyang Drive, Singapore, 637459, Singapore
| | - Nripan Mathews
- School of Materials Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798, Singapore
- Energy Research Institute @ NTU (ERI@N), Nanyang Technological University, Singapore, 637553, Singapore
| |
Collapse
|
41
|
Caravelli F, Traversa FL, Di Ventra M. Complex dynamics of memristive circuits: Analytical results and universal slow relaxation. Phys Rev E 2017; 95:022140. [PMID: 28297937 DOI: 10.1103/physreve.95.022140] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2016] [Indexed: 11/07/2022]
Abstract
Networks with memristive elements (resistors with memory) are being explored for a variety of applications ranging from unconventional computing to models of the brain. However, analytical results that highlight the role of the graph connectivity on the memory dynamics are still few, thus limiting our understanding of these important dynamical systems. In this paper, we derive an exact matrix equation of motion that takes into account all the network constraints of a purely memristive circuit, and we employ it to derive analytical results regarding its relaxation properties. We are able to describe the memory evolution in terms of orthogonal projection operators onto the subspace of fundamental loop space of the underlying circuit. This orthogonal projection explicitly reveals the coupling between the spatial and temporal sectors of the memristive circuits and compactly describes the circuit topology. For the case of disordered graphs, we are able to explain the emergence of a power-law relaxation as a superposition of exponential relaxation times with a broad range of scales using random matrices. This power law is also universal, namely independent of the topology of the underlying graph but dependent only on the density of loops. In the case of circuits subject to alternating voltage instead, we are able to obtain an approximate solution of the dynamics, which is tested against a specific network topology. These results suggest a much richer dynamics of memristive networks than previously considered.
Collapse
Affiliation(s)
- F Caravelli
- Invenia Labs, 27 Parkside Place, Parkside, Cambridge CB1 1HQ, United Kingdom.,London Institute for Mathematical Sciences, 35a South Street, London W1K 2XF, United Kingdom
| | - F L Traversa
- Department of Physics, University of California, San Diego, La Jolla, California 92093, USA
| | - M Di Ventra
- Department of Physics, University of California, San Diego, La Jolla, California 92093, USA
| |
Collapse
|
42
|
Sheldon FC, Di Ventra M. Conducting-insulating transition in adiabatic memristive networks. Phys Rev E 2017; 95:012305. [PMID: 28208448 DOI: 10.1103/physreve.95.012305] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2016] [Indexed: 06/06/2023]
Abstract
The development of neuromorphic systems based on memristive elements-resistors with memory-requires a fundamental understanding of their collective dynamics when organized in networks. Here, we study an experimentally inspired model of two-dimensional disordered memristive networks subject to a slowly ramped voltage and show that they undergo a discontinuous transition in the conductivity for sufficiently high values of memory, as quantified by the memristive ON-OFF ratio. We investigate the consequences of this transition for the memristive current-voltage characteristics both through simulation and theory, and demonstrate the role of current-voltage duality in relating forward and reverse switching processes. Our work sheds considerable light on the statistical properties of memristive networks that are presently studied both for unconventional computing and as models of neural networks.
Collapse
Affiliation(s)
- Forrest C Sheldon
- Department of Physics, University of California San Diego, La Jolla, California 92093, USA
| | - Massimiliano Di Ventra
- Department of Physics, University of California San Diego, La Jolla, California 92093, USA
| |
Collapse
|
43
|
|
44
|
|
45
|
Ariga K, Minami K, Ebara M, Nakanishi J. What are the emerging concepts and challenges in NANO? Nanoarchitectonics, hand-operating nanotechnology and mechanobiology. Polym J 2016. [DOI: 10.1038/pj.2016.8] [Citation(s) in RCA: 155] [Impact Index Per Article: 19.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
|
46
|
Ariga K, Li J, Fei J, Ji Q, Hill JP. Nanoarchitectonics for Dynamic Functional Materials from Atomic-/Molecular-Level Manipulation to Macroscopic Action. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2016; 28:1251-86. [PMID: 26436552 DOI: 10.1002/adma.201502545] [Citation(s) in RCA: 291] [Impact Index Per Article: 36.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/27/2015] [Revised: 07/27/2015] [Indexed: 05/21/2023]
Abstract
Objects in all dimensions are subject to translational dynamism and dynamic mutual interactions, and the ability to exert control over these events is one of the keys to the synthesis of functional materials. For the development of materials with truly dynamic functionalities, a paradigm shift from "nanotechnology" to "nanoarchitectonics" is proposed, with the aim of design and preparation of functional materials through dynamic harmonization of atomic-/molecular-level manipulation and control, chemical nanofabrication, self-organization, and field-controlled organization. Here, various examples of dynamic functional materials are presented from the atom/molecular-level to macroscopic dimensions. These systems, including atomic switches, molecular machines, molecular shuttles, motional crystals, metal-organic frameworks, layered assemblies, gels, supramolecular assemblies of biomaterials, DNA origami, hollow silica capsules, and mesoporous materials, are described according to their various dynamic functions, which include short-term plasticity, long-term potentiation, molecular manipulation, switchable catalysis, self-healing properties, supramolecular chirality, morphological control, drug storage and release, light-harvesting, mechanochemical transduction, molecular tuning molecular recognition, hand-operated nanotechnology.
Collapse
Affiliation(s)
- Katsuhiko Ariga
- World Premier International (WPI) Research Center for Materials Nanoarchitectonics (MANA), National Institute for Materials Science (NIMS), 1-1 Namiki, Tsukuba, 305-0044, Japan
| | - Junbai Li
- Beijing National Laboratory for Molecular Science, CAS Key Lab of Colloid, Interface and Chemical Thermodynamics, Institute of Chemistry, Chinese Academy of Science, Beijing, 100190, P. R. China
| | - Jinbo Fei
- Beijing National Laboratory for Molecular Science, CAS Key Lab of Colloid, Interface and Chemical Thermodynamics, Institute of Chemistry, Chinese Academy of Science, Beijing, 100190, P. R. China
| | - Qingmin Ji
- World Premier International (WPI) Research Center for Materials Nanoarchitectonics (MANA), National Institute for Materials Science (NIMS), 1-1 Namiki, Tsukuba, 305-0044, Japan
| | - Jonathan P Hill
- World Premier International (WPI) Research Center for Materials Nanoarchitectonics (MANA), National Institute for Materials Science (NIMS), 1-1 Namiki, Tsukuba, 305-0044, Japan
| |
Collapse
|
47
|
Srinivasa N, Stepp ND, Cruz-Albrecht J. Criticality as a Set-Point for Adaptive Behavior in Neuromorphic Hardware. Front Neurosci 2015; 9:449. [PMID: 26648839 PMCID: PMC4664726 DOI: 10.3389/fnins.2015.00449] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2015] [Accepted: 11/13/2015] [Indexed: 11/13/2022] Open
Abstract
Neuromorphic hardware are designed by drawing inspiration from biology to overcome limitations of current computer architectures while forging the development of a new class of autonomous systems that can exhibit adaptive behaviors. Several designs in the recent past are capable of emulating large scale networks but avoid complexity in network dynamics by minimizing the number of dynamic variables that are supported and tunable in hardware. We believe that this is due to the lack of a clear understanding of how to design self-tuning complex systems. It has been widely demonstrated that criticality appears to be the default state of the brain and manifests in the form of spontaneous scale-invariant cascades of neural activity. Experiment, theory and recent models have shown that neuronal networks at criticality demonstrate optimal information transfer, learning and information processing capabilities that affect behavior. In this perspective article, we argue that understanding how large scale neuromorphic electronics can be designed to enable emergent adaptive behavior will require an understanding of how networks emulated by such hardware can self-tune local parameters to maintain criticality as a set-point. We believe that such capability will enable the design of truly scalable intelligent systems using neuromorphic hardware that embrace complexity in network dynamics rather than avoiding it.
Collapse
Affiliation(s)
- Narayan Srinivasa
- Information and System Sciences Lab, Center for Neural and Emergent Systems, HRL Laboratories LLC Malibu, CA, USA
| | - Nigel D Stepp
- Information and System Sciences Lab, Center for Neural and Emergent Systems, HRL Laboratories LLC Malibu, CA, USA
| | | |
Collapse
|
48
|
Fostner S, Brown SA. Neuromorphic behavior in percolating nanoparticle films. PHYSICAL REVIEW. E, STATISTICAL, NONLINEAR, AND SOFT MATTER PHYSICS 2015; 92:052134. [PMID: 26651673 DOI: 10.1103/physreve.92.052134] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/02/2015] [Indexed: 06/05/2023]
Abstract
We show that the complex connectivity of percolating networks of nanoparticles provides a natural solid-state system in which bottom-up assembly provides a route to realization of neuromorphic behavior. Below the percolation threshold the networks comprise groups of particles separated by tunnel gaps; an applied voltage causes atomic scale wires to form in the gaps, and we show that the avalanche of switching events that occurs is similar to potentiation in biological neural systems. We characterize the level of potentiation in the percolating system as a function of the surface coverage of nanoparticles and other experimentally relevant variables, and compare our results with those from biological systems. The complex percolating structure and the electric field driven switching mechanism provide several potential advantages in comparison to previously reported solid-state neuromorphic systems.
Collapse
Affiliation(s)
- Shawn Fostner
- The MacDiarmid Institute for Advanced Materials and Nanotechnology, University of Canterbury, Private Bag 4800, Christchurch 8140, New Zealand and Department of Physics and Astronomy, University of Canterbury, Private Bag 4800, Christchurch 8140, New Zealand
| | - Simon A Brown
- The MacDiarmid Institute for Advanced Materials and Nanotechnology, University of Canterbury, Private Bag 4800, Christchurch 8140, New Zealand and Department of Physics and Astronomy, University of Canterbury, Private Bag 4800, Christchurch 8140, New Zealand
| |
Collapse
|
49
|
Sandouk EJ, Gimzewski JK, Stieg AZ. Multistate resistive switching in silver nanoparticle films. SCIENCE AND TECHNOLOGY OF ADVANCED MATERIALS 2015; 16:045004. [PMID: 27877824 PMCID: PMC5090183 DOI: 10.1088/1468-6996/16/4/045004] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/02/2015] [Revised: 06/30/2015] [Accepted: 07/01/2015] [Indexed: 06/06/2023]
Abstract
Resistive switching devices have garnered significant consideration for their potential use in nanoelectronics and non-volatile memory applications. Here we investigate the nonlinear current-voltage behavior and resistive switching properties of composite nanoparticle films comprising a large collective of metal-insulator-metal junctions. Silver nanoparticles prepared via the polyol process and coated with an insulating polymer layer of tetraethylene glycol were deposited onto silicon oxide substrates. Activation required a forming step achieved through application of a bias voltage. Once activated, the nanoparticle films exhibited controllable resistive switching between multiple discrete low resistance states that depended on operational parameters including the applied bias voltage, temperature and sweep frequency. The films' resistance switching behavior is shown here to be the result of nanofilament formation due to formative electromigration effects. Because of their tunable and distinct resistance states, scalability and ease of fabrication, nanoparticle films have a potential place in memory technology as resistive random access memory cells.
Collapse
Affiliation(s)
- Eric J Sandouk
- Department of Physics and Astronomy, UCLA, USA
- Department of Chemistry and Biochemistry, UCLA, USA
| | - James K Gimzewski
- Department of Chemistry and Biochemistry, UCLA, USA
- California NanoSystems Institute (CNSI), UCLA, USA
- WPI Center for Materials Nanoarchitectonics (MANA), National Institute for Materials Science (NIMS), Japan
- School of Physics, Centre for Nanoscience and Quantum Information, University of Bristol, UK
| | - Adam Z Stieg
- California NanoSystems Institute (CNSI), UCLA, USA
- WPI Center for Materials Nanoarchitectonics (MANA), National Institute for Materials Science (NIMS), Japan
| |
Collapse
|
50
|
Demis EC, Aguilera R, Sillin HO, Scharnhorst K, Sandouk EJ, Aono M, Stieg AZ, Gimzewski JK. Atomic switch networks-nanoarchitectonic design of a complex system for natural computing. NANOTECHNOLOGY 2015; 26:204003. [PMID: 25912970 DOI: 10.1088/0957-4484/26/20/204003] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2023]
Abstract
Self-organized complex systems are ubiquitous in nature, and the structural complexity of these natural systems can be used as a model to design new classes of functional nanotechnology based on highly interconnected networks of interacting units. Conventional fabrication methods for electronic computing devices are subject to known scaling limits, confining the diversity of possible architectures. This work explores methods of fabricating a self-organized complex device known as an atomic switch network and discusses its potential utility in computing. Through a merger of top-down and bottom-up techniques guided by mathematical and nanoarchitectonic design principles, we have produced functional devices comprising nanoscale elements whose intrinsic nonlinear dynamics and memorization capabilities produce robust patterns of distributed activity and a capacity for nonlinear transformation of input signals when configured in the appropriate network architecture. Their operational characteristics represent a unique potential for hardware implementation of natural computation, specifically in the area of reservoir computing-a burgeoning field that investigates the computational aptitude of complex biologically inspired systems.
Collapse
Affiliation(s)
- E C Demis
- Department of Chemistry and Biochemistry, UCLA, USA
| | | | | | | | | | | | | | | |
Collapse
|