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Tzouvadaki I, Gkoupidenis P, Vassanelli S, Wang S, Prodromakis T. Interfacing Biology and Electronics with Memristive Materials. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2023; 35:e2210035. [PMID: 36829290 DOI: 10.1002/adma.202210035] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/30/2022] [Revised: 01/31/2023] [Indexed: 06/18/2023]
Abstract
Memristive technologies promise to have a large impact on modern electronics, particularly in the areas of reconfigurable computing and artificial intelligence (AI) hardware. Meanwhile, the evolution of memristive materials alongside the technological progress is opening application perspectives also in the biomedical field, particularly for implantable and lab-on-a-chip devices where advanced sensing technologies generate a large amount of data. Memristive devices are emerging as bioelectronic links merging biosensing with computation, acting as physical processors of analog signals or in the framework of advanced digital computing architectures. Recent developments in the processing of electrical neural signals, as well as on transduction and processing of chemical biomarkers of neural and endocrine functions, are reviewed. It is concluded with a critical perspective on the future applicability of memristive devices as pivotal building blocks in bio-AI fusion concepts and bionic schemes.
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Affiliation(s)
- Ioulia Tzouvadaki
- Centre for Microsystems Technology, Ghent University-IMEC, Ghent, 9052, Belgium
| | | | - Stefano Vassanelli
- NeuroChip Laboratory and Padova Neuroscience Centre, University of Padova, Padova, 35129, Italy
| | - Shiwei Wang
- Centre for Electronics Frontiers, The University of Edinburgh, Edinburgh, EH9 3JL, UK
| | - Themis Prodromakis
- Centre for Electronics Frontiers, The University of Edinburgh, Edinburgh, EH9 3JL, UK
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2
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Zahoor F, Hussin FA, Isyaku UB, Gupta S, Khanday FA, Chattopadhyay A, Abbas H. Resistive random access memory: introduction to device mechanism, materials and application to neuromorphic computing. DISCOVER NANO 2023; 18:36. [PMID: 37382679 PMCID: PMC10409712 DOI: 10.1186/s11671-023-03775-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/25/2022] [Accepted: 01/17/2023] [Indexed: 06/30/2023]
Abstract
The modern-day computing technologies are continuously undergoing a rapid changing landscape; thus, the demands of new memory types are growing that will be fast, energy efficient and durable. The limited scaling capabilities of the conventional memory technologies are pushing the limits of data-intense applications beyond the scope of silicon-based complementary metal oxide semiconductors (CMOS). Resistive random access memory (RRAM) is one of the most suitable emerging memory technologies candidates that have demonstrated potential to replace state-of-the-art integrated electronic devices for advanced computing and digital and analog circuit applications including neuromorphic networks. RRAM has grown in prominence in the recent years due to its simple structure, long retention, high operating speed, ultra-low-power operation capabilities, ability to scale to lower dimensions without affecting the device performance and the possibility of three-dimensional integration for high-density applications. Over the past few years, research has shown RRAM as one of the most suitable candidates for designing efficient, intelligent and secure computing system in the post-CMOS era. In this manuscript, the journey and the device engineering of RRAM with a special focus on the resistive switching mechanism are detailed. This review also focuses on the RRAM based on two-dimensional (2D) materials, as 2D materials offer unique electrical, chemical, mechanical and physical properties owing to their ultrathin, flexible and multilayer structure. Finally, the applications of RRAM in the field of neuromorphic computing are presented.
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Affiliation(s)
- Furqan Zahoor
- School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore
| | - Fawnizu Azmadi Hussin
- Department of Electrical and Electronics Engineering, Universiti Teknologi Petronas, Seri Iskandar, Malaysia
| | - Usman Bature Isyaku
- Department of Electrical and Electronics Engineering, Universiti Teknologi Petronas, Seri Iskandar, Malaysia
| | - Shagun Gupta
- School of Electronics and Communication Engineering, Shri Mata Vaishno Devi University, Katra, India
| | - Farooq Ahmad Khanday
- Department of Electronics & Instrumentation Technology, University of Kashmir, Srinagar, India
| | - Anupam Chattopadhyay
- School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore
| | - Haider Abbas
- Division of Material Science and Engineering, Hanyang University, Seoul, South Korea
- School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore
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3
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Masaev DN, Suleimanova AA, Prudnikov NV, Serenko MV, Emelyanov AV, Demin VA, Lavrov IA, Talanov MO, Erokhin VV. Memristive circuit-based model of central pattern generator to reproduce spinal neuronal activity in walking pattern. Front Neurosci 2023; 17:1124950. [PMID: 36925742 PMCID: PMC10011148 DOI: 10.3389/fnins.2023.1124950] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Accepted: 02/03/2023] [Indexed: 03/08/2023] Open
Abstract
Existing methods of neurorehabilitation include invasive or non-invasive stimulators that are usually simple digital generators with manually set parameters like pulse width, period, burst duration, and frequency of stimulation series. An obvious lack of adaptation capability of stimulators, as well as poor biocompatibility and high power consumption of prosthetic devices, highlights the need for medical usage of neuromorphic systems including memristive devices. The latter are electrical devices providing a wide range of complex synaptic functionality within a single element. In this study, we propose the memristive schematic capable of self-learning according to bio-plausible spike-timing-dependant plasticity to organize the electrical activity of the walking pattern generated by the central pattern generator.
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Affiliation(s)
- Dinar N. Masaev
- ITIS, IFMB, Kazan Federal University, Kazan, Russia
- B-Rain Labs LLC, Kazan, Russia
| | | | - Nikita V. Prudnikov
- National Research Centre Kurchatov Institute, Moscow, Russia
- Moscow Institute of Physics and Technology, National Research University, Moscow, Russia
| | - Mariia V. Serenko
- National Research Centre Kurchatov Institute, Moscow, Russia
- Moscow Institute of Physics and Technology, National Research University, Moscow, Russia
| | - Andrey V. Emelyanov
- National Research Centre Kurchatov Institute, Moscow, Russia
- Moscow Institute of Physics and Technology, National Research University, Moscow, Russia
| | | | - Igor A. Lavrov
- ITIS, IFMB, Kazan Federal University, Kazan, Russia
- Department of Neurology, Mayo Clinic, Rochester, MN, United States
- Skolkovo Institute of Science and Technology, Moscow, Russia
| | - Max O. Talanov
- ITIS, IFMB, Kazan Federal University, Kazan, Russia
- Institute for Artificial Intelligence R&D, Novi Sad, Serbia
| | - Victor V. Erokhin
- Consiglio Nazionale delle Ricerche at Istituto dei Materiali per l'Elettronica ed il Magnetismo, Rome, Italy
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4
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Pyo J, Ha H, Kim S. Enhanced Short-Term Memory Plasticity of WOx-Based Memristors by Inserting AlO x Thin Layer. MATERIALS (BASEL, SWITZERLAND) 2022; 15:9081. [PMID: 36556886 PMCID: PMC9786020 DOI: 10.3390/ma15249081] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Revised: 12/12/2022] [Accepted: 12/15/2022] [Indexed: 06/17/2023]
Abstract
ITO/WOx/TaN and ITO/WOx/AlOx/TaN memory cells were fabricated as a neuromorphic device that is compatible with CMOS. They are suitable for the information age, which requires a large amount of data as next-generation memory. The device with a thin AlOx layer deposited by atomic layer deposition (ALD) has different electrical characteristics from the device without an AlOx layer. The low current is achieved by inserting an ultra-thin AlOx layer between the switching layer and the bottom electrode due to the tunneling barrier effect. Moreover, the short-term memory characteristics in bilayer devices are enhanced. The WOx/AlOx device returns to the HRS without a separate reset process or energy consumption. The amount of gradual current reduction could be controlled by interval time. In addition, it is possible to maintain LRS for a longer time by forming it to implement long-term memory.
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Chiappalone M, Cota VR, Carè M, Di Florio M, Beaubois R, Buccelli S, Barban F, Brofiga M, Averna A, Bonacini F, Guggenmos DJ, Bornat Y, Massobrio P, Bonifazi P, Levi T. Neuromorphic-Based Neuroprostheses for Brain Rewiring: State-of-the-Art and Perspectives in Neuroengineering. Brain Sci 2022; 12:1578. [PMID: 36421904 PMCID: PMC9688667 DOI: 10.3390/brainsci12111578] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Revised: 11/09/2022] [Accepted: 11/17/2022] [Indexed: 08/27/2023] Open
Abstract
Neuroprostheses are neuroengineering devices that have an interface with the nervous system and supplement or substitute functionality in people with disabilities. In the collective imagination, neuroprostheses are mostly used to restore sensory or motor capabilities, but in recent years, new devices directly acting at the brain level have been proposed. In order to design the next-generation of neuroprosthetic devices for brain repair, we foresee the increasing exploitation of closed-loop systems enabled with neuromorphic elements due to their intrinsic energy efficiency, their capability to perform real-time data processing, and of mimicking neurobiological computation for an improved synergy between the technological and biological counterparts. In this manuscript, after providing definitions of key concepts, we reviewed the first exploitation of a real-time hardware neuromorphic prosthesis to restore the bidirectional communication between two neuronal populations in vitro. Starting from that 'case-study', we provide perspectives on the technological improvements for real-time interfacing and processing of neural signals and their potential usage for novel in vitro and in vivo experimental designs. The development of innovative neuroprosthetics for translational purposes is also presented and discussed. In our understanding, the pursuit of neuromorphic-based closed-loop neuroprostheses may spur the development of novel powerful technologies, such as 'brain-prostheses', capable of rewiring and/or substituting the injured nervous system.
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Affiliation(s)
- Michela Chiappalone
- Department of Informatics, Bioengineering, Robotics System Engineering (DIBRIS), University of Genova, 16145 Genova, Italy
- Rehab Technologies, Istituto Italiano di Tecnologia, 16163 Genova, Italy
| | - Vinicius R. Cota
- Rehab Technologies, Istituto Italiano di Tecnologia, 16163 Genova, Italy
| | - Marta Carè
- Department of Informatics, Bioengineering, Robotics System Engineering (DIBRIS), University of Genova, 16145 Genova, Italy
- Rehab Technologies, Istituto Italiano di Tecnologia, 16163 Genova, Italy
| | - Mattia Di Florio
- Department of Informatics, Bioengineering, Robotics System Engineering (DIBRIS), University of Genova, 16145 Genova, Italy
| | - Romain Beaubois
- IMS Laboratory, CNRS UMR 5218, University of Bordeaux, 33405 Talence, France
| | - Stefano Buccelli
- Rehab Technologies, Istituto Italiano di Tecnologia, 16163 Genova, Italy
| | - Federico Barban
- Department of Informatics, Bioengineering, Robotics System Engineering (DIBRIS), University of Genova, 16145 Genova, Italy
- Rehab Technologies, Istituto Italiano di Tecnologia, 16163 Genova, Italy
| | - Martina Brofiga
- Department of Informatics, Bioengineering, Robotics System Engineering (DIBRIS), University of Genova, 16145 Genova, Italy
| | - Alberto Averna
- Department of Neurology, Bern University Hospital, University of Bern, 3012 Bern, Switzerland
| | - Francesco Bonacini
- Department of Informatics, Bioengineering, Robotics System Engineering (DIBRIS), University of Genova, 16145 Genova, Italy
| | - David J. Guggenmos
- Department of Rehabilitation Medicine, University of Kansas Medical Center, Kansas City, KS 66103, USA
- Landon Center on Aging, University of Kansas Medical Center, Kansas City, KS 66103, USA
| | - Yannick Bornat
- IMS Laboratory, CNRS UMR 5218, University of Bordeaux, 33405 Talence, France
| | - Paolo Massobrio
- Department of Informatics, Bioengineering, Robotics System Engineering (DIBRIS), University of Genova, 16145 Genova, Italy
- National Institute for Nuclear Physics (INFN), 16146 Genova, Italy
| | - Paolo Bonifazi
- IKERBASQUE, The Basque Fundation, 48009 Bilbao, Spain
- Biocruces Health Research Institute, 48903 Barakaldo, Spain
| | - Timothée Levi
- IMS Laboratory, CNRS UMR 5218, University of Bordeaux, 33405 Talence, France
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6
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Stationary Probability Density Analysis for the Randomly Forced Phytoplankton–Zooplankton Model with Correlated Colored Noises. MATHEMATICS 2022. [DOI: 10.3390/math10142383] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
In this paper, we propose a stochastic phytoplankton–zooplankton model driven by correlated colored noises, which contains both anthropogenic and natural toxins. Using Khasminskii transformation and the stochastic averaging method, we first transform the original system into an Itô diffusion system. Afterwards, we derive the stationary probability density of the averaging amplitude equation by utilizing the corresponding Fokker–Planck–Kolmogorov equation. Then, the stability of the averaging amplitude is studied and the joint probability density of the original two-dimensional system is given. Finally, the theoretical results are verified by numerical simulations, and the effects of noise characteristics and toxins on system dynamics are further illustrated.
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7
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Makarov VA, Lobov SA, Shchanikov S, Mikhaylov A, Kazantsev VB. Toward Reflective Spiking Neural Networks Exploiting Memristive Devices. Front Comput Neurosci 2022; 16:859874. [PMID: 35782090 PMCID: PMC9243340 DOI: 10.3389/fncom.2022.859874] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Accepted: 05/10/2022] [Indexed: 11/29/2022] Open
Abstract
The design of modern convolutional artificial neural networks (ANNs) composed of formal neurons copies the architecture of the visual cortex. Signals proceed through a hierarchy, where receptive fields become increasingly more complex and coding sparse. Nowadays, ANNs outperform humans in controlled pattern recognition tasks yet remain far behind in cognition. In part, it happens due to limited knowledge about the higher echelons of the brain hierarchy, where neurons actively generate predictions about what will happen next, i.e., the information processing jumps from reflex to reflection. In this study, we forecast that spiking neural networks (SNNs) can achieve the next qualitative leap. Reflective SNNs may take advantage of their intrinsic dynamics and mimic complex, not reflex-based, brain actions. They also enable a significant reduction in energy consumption. However, the training of SNNs is a challenging problem, strongly limiting their deployment. We then briefly overview new insights provided by the concept of a high-dimensional brain, which has been put forward to explain the potential power of single neurons in higher brain stations and deep SNN layers. Finally, we discuss the prospect of implementing neural networks in memristive systems. Such systems can densely pack on a chip 2D or 3D arrays of plastic synaptic contacts directly processing analog information. Thus, memristive devices are a good candidate for implementing in-memory and in-sensor computing. Then, memristive SNNs can diverge from the development of ANNs and build their niche, cognitive, or reflective computations.
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Affiliation(s)
- Valeri A. Makarov
- Instituto de Matemática Interdisciplinar, Universidad Complutense de Madrid, Madrid, Spain
- Department of Neurotechnologies, Research Institute of Physics and Technology, Laboratory of Stochastic Multistable Systems, Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia
- *Correspondence: Valeri A. Makarov,
| | - Sergey A. Lobov
- Department of Neurotechnologies, Research Institute of Physics and Technology, Laboratory of Stochastic Multistable Systems, Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia
- Neuroscience and Cognitive Technology Laboratory, Center for Technologies in Robotics and Mechatronics Components, Innopolis University, Innopolis, Russia
- Center For Neurotechnology and Machine Learning, Immanuel Kant Baltic Federal University, Kaliningrad, Russia
| | - Sergey Shchanikov
- Department of Neurotechnologies, Research Institute of Physics and Technology, Laboratory of Stochastic Multistable Systems, Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia
- Department of Information Technologies, Vladimir State University, Vladimir, Russia
| | - Alexey Mikhaylov
- Department of Neurotechnologies, Research Institute of Physics and Technology, Laboratory of Stochastic Multistable Systems, Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia
| | - Viktor B. Kazantsev
- Department of Neurotechnologies, Research Institute of Physics and Technology, Laboratory of Stochastic Multistable Systems, Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia
- Neuroscience and Cognitive Technology Laboratory, Center for Technologies in Robotics and Mechatronics Components, Innopolis University, Innopolis, Russia
- Center For Neurotechnology and Machine Learning, Immanuel Kant Baltic Federal University, Kaliningrad, Russia
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8
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Sabbar Y, Kiouach D, Rajasekar SP, El-Idrissi SEA. The influence of quadratic Lévy noise on the dynamic of an SIC contagious illness model: New framework, critical comparison and an application to COVID-19 (SARS-CoV-2) case. CHAOS, SOLITONS, AND FRACTALS 2022; 159:112110. [PMID: 35502416 PMCID: PMC9035369 DOI: 10.1016/j.chaos.2022.112110] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Revised: 04/08/2022] [Accepted: 04/10/2022] [Indexed: 06/14/2023]
Abstract
This study concentrates on the analysis of a stochastic SIC epidemic system with an enhanced and general perturbation. Given the intricacy of some impulses caused by external disturbances, we integrate the quadratic Lévy noise into our model. We assort the long-run behavior of a perturbed SIC epidemic model presented in the form of a system of stochastic differential equations driven by second-order jumps. By ameliorating the hypotheses and using some new analytical techniques, we find the exact threshold value between extinction and ergodicity (persistence) of our system. The idea and analysis used in this paper generalize the work of N. T. Dieu et al. (2020), and offer an innovative approach to dealing with other random population models. Comparing our results with those of previous studies reveals that quadratic jump-diffusion has no impact on the threshold value, but it remarkably influences the dynamics of the infection and may worsen the pandemic situation. In order to illustrate this comparison and confirm our analysis, we perform numerical simulations with some real data of COVID-19 in Morocco. Furthermore, we arrive at the following results: (i) the time average of the different classes depends on the intensity of the noise (ii) the quadratic noise has a negative effect on disease duration (iii) the stationary density function of the population abruptly changes its shape at some values of the noise intensity. Mathematics Subject Classification 2020: 34A26; 34A12; 92D30; 37C10; 60H30; 60H10.
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Affiliation(s)
- Yassine Sabbar
- LPAIS Laboratory, Faculty of Sciences Dhar El Mahraz, Sidi Mohamed Ben Abdellah University, Fez, Morocco
| | - Driss Kiouach
- LPAIS Laboratory, Faculty of Sciences Dhar El Mahraz, Sidi Mohamed Ben Abdellah University, Fez, Morocco
| | - S P Rajasekar
- Department of Mathematics, Government Arts College for Women, Nilakottai 624202, Tamilnadu, India
| | - Salim El Azami El-Idrissi
- LPAIS Laboratory, Faculty of Sciences Dhar El Mahraz, Sidi Mohamed Ben Abdellah University, Fez, Morocco
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9
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Dias C, Castro D, Aroso M, Ventura J, Aguiar P. Memristor-Based Neuromodulation Device for Real-Time Monitoring and Adaptive Control of Neuronal Populations. ACS APPLIED ELECTRONIC MATERIALS 2022; 4:2380-2387. [PMID: 36571090 PMCID: PMC9778128 DOI: 10.1021/acsaelm.2c00198] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
Neurons are specialized cells for information transmission and information processing. In fact, many neurologic disorders are directly linked not to cellular viability/homeostasis issues but rather to specific anomalies in electrical activity dynamics. Consequently, therapeutic strategies based on the direct modulation of neuronal electrical activity have been producing remarkable results, with successful examples ranging from cochlear implants to deep brain stimulation. Developments in these implantable devices are hindered, however, by important challenges such as power requirements, size factor, signal transduction, and adaptability/computational capabilities. Memristors, neuromorphic nanoscale electronic components able to emulate natural synapses, provide unique properties to address these constraints, and their use in neuroprosthetic devices is being actively explored. Here, we demonstrate, for the first time, the use of memristive devices in a clinically relevant setting where communication between two neuronal populations is conditioned to specific activity patterns in the source population. In our approach, the memristor device performs a pattern detection computation and acts as an artificial synapse capable of reversible short-term plasticity. Using in vitro hippocampal neuronal cultures, we show real-time adaptive control with a high degree of reproducibility using our monitor-compute-actuate paradigm. We envision very similar systems being used for the automatic detection and suppression of seizures in epileptic patients.
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Affiliation(s)
- Catarina Dias
- IFIMUP,
Departamento de Física e Astronomia, Faculdade de Ciências, Universidade do Porto, Rua do Campo Alegre s/n, Porto 4169-007, Portugal
| | - Domingos Castro
- Neuroengineering
and Computational Neuroscience Lab, INEB - Instituto de Engenharia
Biomédica, Universidade do Porto, Rua Alfredo Allen, 208, Porto 4200-135, Portugal
- i3S—Instituto
de Investigação e Inovação em Saúde, Universidade do Porto, Rua Alfredo Allen, 208, Porto 4200-135, Portugal
| | - Miguel Aroso
- Neuroengineering
and Computational Neuroscience Lab, INEB - Instituto de Engenharia
Biomédica, Universidade do Porto, Rua Alfredo Allen, 208, Porto 4200-135, Portugal
- i3S—Instituto
de Investigação e Inovação em Saúde, Universidade do Porto, Rua Alfredo Allen, 208, Porto 4200-135, Portugal
| | - João Ventura
- IFIMUP,
Departamento de Física e Astronomia, Faculdade de Ciências, Universidade do Porto, Rua do Campo Alegre s/n, Porto 4169-007, Portugal
| | - Paulo Aguiar
- Neuroengineering
and Computational Neuroscience Lab, INEB - Instituto de Engenharia
Biomédica, Universidade do Porto, Rua Alfredo Allen, 208, Porto 4200-135, Portugal
- i3S—Instituto
de Investigação e Inovação em Saúde, Universidade do Porto, Rua Alfredo Allen, 208, Porto 4200-135, Portugal
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10
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Extreme Learning Machine Approach to Modeling the Superconducting Critical Temperature of Doped MgB2 Superconductor. CRYSTALS 2022. [DOI: 10.3390/cryst12020228] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Magnesium diboride (MgB2) superconductor combines many unique features such as transparency of its grain boundaries to super-current flow, large coherence length, absence of weak links and small anisotropy. Doping is one of the mechanisms for enhancing these features, as well as the superconducting critical temperature, of the compound. During the process of doping, the MgB2 superconductor structural lattice is often distorted while the room temperature resistivity, as well as residual resistivity ratio, contributes to the impurity scattering in the lattice of doped samples. This work develops three extreme learning machine (ELM)-based empirical models for determining MgB2 superconducting critical temperature (TC) using structural distortion as contained in lattice parameters (LP) of doped superconductor, room temperature resistivity (RTR) and residual resistivity ratio (RRR) as descriptors. The developed models are compared with nine different existing models in the literature using different performance metrics and show superior performance over the existing models. The developed SINE-ELM-RTR model performs better than Intikhab et al. (2021)_linear model, Intikhab et al. (2021)_Exponential model, Intikhab et al. (2021)_Quadratic model, HGA-SVR-RRR(2021) model and HGA-SVR-CLD(2021) model with a performance improvement of 32.67%, 29.56%, 20.04%, 8.82% and 13.51%, respectively, on the basis of the coefficient of correlation. The established empirical relationships in this contribution will be of immense significance for quick estimation of the influence of dopants on superconducting transition temperature of MgB2 superconductor without the need for sophisticated equipment while preserving the experimental precision.
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11
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Koelewijn AD, Audu M, del-Ama AJ, Colucci A, Font-Llagunes JM, Gogeascoechea A, Hnat SK, Makowski N, Moreno JC, Nandor M, Quinn R, Reichenbach M, Reyes RD, Sartori M, Soekadar S, Triolo RJ, Vermehren M, Wenger C, Yavuz US, Fey D, Beckerle P. Adaptation Strategies for Personalized Gait Neuroprosthetics. Front Neurorobot 2021; 15:750519. [PMID: 34975445 PMCID: PMC8716811 DOI: 10.3389/fnbot.2021.750519] [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: 07/30/2021] [Accepted: 11/18/2021] [Indexed: 11/13/2022] Open
Abstract
Personalization of gait neuroprosthetics is paramount to ensure their efficacy for users, who experience severe limitations in mobility without an assistive device. Our goal is to develop assistive devices that collaborate with and are tailored to their users, while allowing them to use as much of their existing capabilities as possible. Currently, personalization of devices is challenging, and technological advances are required to achieve this goal. Therefore, this paper presents an overview of challenges and research directions regarding an interface with the peripheral nervous system, an interface with the central nervous system, and the requirements of interface computing architectures. The interface should be modular and adaptable, such that it can provide assistance where it is needed. Novel data processing technology should be developed to allow for real-time processing while accounting for signal variations in the human. Personalized biomechanical models and simulation techniques should be developed to predict assisted walking motions and interactions between the user and the device. Furthermore, the advantages of interfacing with both the brain and the spinal cord or the periphery should be further explored. Technological advances of interface computing architecture should focus on learning on the chip to achieve further personalization. Furthermore, energy consumption should be low to allow for longer use of the neuroprosthesis. In-memory processing combined with resistive random access memory is a promising technology for both. This paper discusses the aforementioned aspects to highlight new directions for future research in gait neuroprosthetics.
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Affiliation(s)
- Anne D. Koelewijn
- Biomechanical Data Analysis and Creation (BIOMAC) Group, Machine Learning and Data Analytics Lab, Faculty of Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Musa Audu
- Department of Veterans Affairs, Louis Stokes Clevel and Veterans Affairs Medical Center, Advanced Platform Technology Center, Cleveland, OH, United States
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States
| | - Antonio J. del-Ama
- Applied Mathematics, Materials Science and Technology and Electronic Technology Department, Rey Juan Carlos University, Mostoles, Spain
| | - Annalisa Colucci
- Clinical Neurotechnology Lab, Neuroscience Research Center (NWFZ), Department of Psychiatry and Neurosciences, Charité - Universita¨tsmedizin Berlin, Berlin, Germany
| | - Josep M. Font-Llagunes
- Biomechanical Engineering Lab, Department of Mechanical Engineering and Research Centre for Biomedical Engineering, Universitat Politècnica de Catalunya, Barcelona, Spain
- Institut de Recerca Sant Joan de Déu, Esplugues de Llobregat, Spain
| | - Antonio Gogeascoechea
- Department of Biomechanical Engineering, Faculty of Engineering Technology, University of Twente, Enschede, Netherlands
| | - Sandra K. Hnat
- Department of Veterans Affairs, Louis Stokes Clevel and Veterans Affairs Medical Center, Advanced Platform Technology Center, Cleveland, OH, United States
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States
| | - Nathan Makowski
- Department of Veterans Affairs, Louis Stokes Clevel and Veterans Affairs Medical Center, Advanced Platform Technology Center, Cleveland, OH, United States
- Department of Physical Medicine and Rehabilitation, MetroHealth Medical Center, Cleveland, OH, United States
| | - Juan C. Moreno
- Neural Rehabilitation Group, Department of Translational Neuroscience, Cajal Institute, CSIC, Madrid, Spain
| | - Mark Nandor
- Department of Veterans Affairs, Louis Stokes Clevel and Veterans Affairs Medical Center, Advanced Platform Technology Center, Cleveland, OH, United States
- Department of Mechanical Engineering, Case Western Reserve University, Cleveland, OH, United States
| | - Roger Quinn
- Department of Veterans Affairs, Louis Stokes Clevel and Veterans Affairs Medical Center, Advanced Platform Technology Center, Cleveland, OH, United States
- Department of Mechanical Engineering, Case Western Reserve University, Cleveland, OH, United States
| | - Marc Reichenbach
- Chair of Computer Engineering, Brandenburg University of Technology Cottbus-Senftenberg, Cottbus, Germany
- Chair for Computer Architecture, Department of Computer Science, Faculty of Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Ryan-David Reyes
- Department of Veterans Affairs, Louis Stokes Clevel and Veterans Affairs Medical Center, Advanced Platform Technology Center, Cleveland, OH, United States
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States
| | - Massimo Sartori
- Department of Biomechanical Engineering, Faculty of Engineering Technology, University of Twente, Enschede, Netherlands
| | - Surjo Soekadar
- Clinical Neurotechnology Lab, Neuroscience Research Center (NWFZ), Department of Psychiatry and Neurosciences, Charité - Universita¨tsmedizin Berlin, Berlin, Germany
| | - Ronald J. Triolo
- Department of Veterans Affairs, Louis Stokes Clevel and Veterans Affairs Medical Center, Advanced Platform Technology Center, Cleveland, OH, United States
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States
| | - Mareike Vermehren
- Clinical Neurotechnology Lab, Neuroscience Research Center (NWFZ), Department of Psychiatry and Neurosciences, Charité - Universita¨tsmedizin Berlin, Berlin, Germany
| | - Christian Wenger
- IHP-Leibniz Institut Fuer Innovative Mikroelektronik, Frankfurt (Oder), Germany
| | - Utku S. Yavuz
- Biomedical Signals and Systems Group, University of Twente, Enschede, Netherlands
| | - Dietmar Fey
- Chair for Computer Architecture, Department of Computer Science, Faculty of Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Philipp Beckerle
- Chair of Autonomous Systems and Mechatronics, Department of Electrical Engineering, Artificial Intelligence in Biomedical Engineering, Faculty of Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
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12
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Conductance Quantization Behavior in Pt/SiN/TaN RRAM Device for Multilevel Cell. METALS 2021. [DOI: 10.3390/met11121918] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
In this work, we fabricated a Pt/SiN/TaN memristor device and characterized its resistive switching by controlling the compliance current and switching polarity. The chemical and material properties of SiN and TaN were investigated by X-ray photoelectron spectroscopy. Compared with the case of a high compliance current (5 mA), the resistive switching was more gradual in the set and reset processes when a low compliance current (1 mA) was applied by DC sweep and pulse train. In particular, low-power resistive switching was demonstrated in the first reset process, and was achieved by employing the negative differential resistance effect. Furthermore, conductance quantization was observed in the reset process upon decreasing the DC sweep speed. These results have the potential for multilevel cell (MLC) operation. Additionally, the conduction mechanism of the memristor device was investigated by I-V fitting.
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13
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Abstract
This work characterizes resistive switching and neuromorphic simulation of Pt/HfO2/TaN stack as an artificial synaptic device. A stable bipolar resistive switching operation is performed by repetitive DC sweep cycles. Furthermore, endurance (DC 100 cycles) and retention (5000 s) are demonstrated for reliable resistive operation. Low-resistance and high-resistance states follow the Ohmic conduction and Poole–Frenkel emission, respectively, which is verified through the fitting process. For practical operation, the set and reset processes are performed through pulses. Further, potentiation and depression are demonstrated for neuromorphic application. Finally, neuromorphic system simulation is performed through a neural network for pattern recognition accuracy of the Fashion Modified National Institute of Standards and Technology dataset.
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14
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Siddiqui AM, Islam R, Cuellar CA, Silvernail JL, Knudsen B, Curley DE, Strickland T, Manske E, Suwan PT, Latypov T, Akhmetov N, Zhang S, Summer P, Nesbitt JJ, Chen BK, Grahn PJ, Madigan NN, Yaszemski MJ, Windebank AJ, Lavrov IA. Newly regenerated axons via scaffolds promote sub-lesional reorganization and motor recovery with epidural electrical stimulation. NPJ Regen Med 2021; 6:66. [PMID: 34671050 PMCID: PMC8528837 DOI: 10.1038/s41536-021-00176-6] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2021] [Accepted: 08/31/2021] [Indexed: 01/10/2023] Open
Abstract
Here, we report the effect of newly regenerated axons via scaffolds on reorganization of spinal circuitry and restoration of motor functions with epidural electrical stimulation (EES). Motor recovery was evaluated for 7 weeks after spinal transection and following implantation with scaffolds seeded with neurotrophin producing Schwann cell and with rapamycin microspheres. Combined treatment with scaffolds and EES-enabled stepping led to functional improvement compared to groups with scaffold or EES, although, the number of axons across scaffolds was not different between groups. Re-transection through the scaffold at week 6 reduced EES-enabled stepping, still demonstrating better performance compared to the other groups. Greater synaptic reorganization in the presence of regenerated axons was found in group with combined therapy. These findings suggest that newly regenerated axons through cell-containing scaffolds with EES-enabled motor training reorganize the sub-lesional circuitry improving motor recovery, demonstrating that neuroregenerative and neuromodulatory therapies cumulatively enhancing motor function after complete SCI.
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Affiliation(s)
| | - Riazul Islam
- Department of Neurology, Mayo Clinic, Rochester, MN, USA
| | - Carlos A Cuellar
- School of Sport Sciences, Universidad Anáhuac México, Campus Norte, Huixquilucan, State of Mexico, Mexico
| | | | - Bruce Knudsen
- Department of Neurologic Surgery, Mayo Clinic, Rochester, MN, USA
| | - Dallece E Curley
- Department of Neurology, Mayo Clinic, Rochester, MN, USA
- Department of Neuroscience, Brown University, Providence, Rhode Island, USA
| | | | - Emilee Manske
- Department of Neurology, Mayo Clinic, Rochester, MN, USA
- Department of Neuroscience, Scripps College, Claremont, CA, USA
| | - Parita T Suwan
- Paracelsus Medical Private University, Salzburg, Austria
| | - Timur Latypov
- Division of Brain, Imaging, and Behaviour - Systems Neuroscience, Krembil Research Institute, Toronto Western Hospital, University Health Network, Toronto, ON, Canada
- Institute of Medical Science, Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Nafis Akhmetov
- Institute of Fundamental Medicine and Biology, Kazan Federal University, Kazan, Russia
| | - Shuya Zhang
- Department of Neurology, Mayo Clinic, Rochester, MN, USA
| | - Priska Summer
- Paracelsus Medical Private University, Salzburg, Austria
| | | | - Bingkun K Chen
- Department of Neurology, Mayo Clinic, Rochester, MN, USA
| | - Peter J Grahn
- Department of Physical Medicine and Rehabilitation, Mayo Clinic, Rochester, MN, USA
| | | | | | | | - Igor A Lavrov
- Department of Neurology, Mayo Clinic, Rochester, MN, USA.
- Institute of Fundamental Medicine and Biology, Kazan Federal University, Kazan, Russia.
- Department of Biomedical Engineering, Mayo Clinic, Rochester, MN, USA.
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15
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Demonstration of Threshold Switching and Bipolar Resistive Switching in Ag/SnOx/TiN Memory Device. METALS 2021. [DOI: 10.3390/met11101605] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Abstract
In this work, we observed the duality of threshold switching and non-volatile memory switching of Ag/SnOx/TiN memory devices by controlling the compliance current (CC) or pulse amplitude. The insulator thickness and chemical analysis of the device stack were confirmed by transmission electron microscope (TEM) images of the Ag/SnOx/TiN stack and X-ray photoelectron spectroscopy (XPS) of the SnOx film. The threshold switching was achieved at low CC (50 μA), showing volatile resistive switching. Optimal CC (5 mA) for bipolar resistive switching conditions with a gradual transition was also found. An unstable low-resistance state (LRS) and negative-set behavior were observed at CCs of 1 mA and 30 mA, respectively. We also demonstrated the pulse operation for volatile switching, set, reset processes, and negative-set behaviors by controlling pulse amplitude and polarity. Finally, the potentiation and depression characteristics were mimicked by multiple pulses, and MNIST pattern recognition was calculated using a neural network, including the conductance update for a hardware-based neuromorphic system.
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16
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Gerasimova SA, Belov AI, Korolev DS, Guseinov DV, Lebedeva AV, Koryazhkina MN, Mikhaylov AN, Kazantsev VB, Pisarchik AN. Stochastic Memristive Interface for Neural Signal Processing. SENSORS 2021; 21:s21165587. [PMID: 34451027 PMCID: PMC8402302 DOI: 10.3390/s21165587] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/02/2021] [Revised: 08/09/2021] [Accepted: 08/16/2021] [Indexed: 11/16/2022]
Abstract
We propose a memristive interface consisting of two FitzHugh–Nagumo electronic neurons connected via a metal–oxide (Au/Zr/ZrO2(Y)/TiN/Ti) memristive synaptic device. We create a hardware–software complex based on a commercial data acquisition system, which records a signal generated by a presynaptic electronic neuron and transmits it to a postsynaptic neuron through the memristive device. We demonstrate, numerically and experimentally, complex dynamics, including chaos and different types of neural synchronization. The main advantages of our system over similar devices are its simplicity and real-time performance. A change in the amplitude of the presynaptic neurogenerator leads to the potentiation of the memristive device due to the self-tuning of its parameters. This provides an adaptive modulation of the postsynaptic neuron output. The developed memristive interface, due to its stochastic nature, simulates a real synaptic connection, which is very promising for neuroprosthetic applications.
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Affiliation(s)
- Svetlana A. Gerasimova
- Institute of Biology and Biomedicine, National Research Lobachevsky State University of Nizhny Novgorod, 603950 Nizhny Novgorod, Russia; (S.A.G.); (A.V.L.); (V.B.K.)
- Research Institute and Technology, National Research Lobachevsky State University of Nizhny Novgorod, 603950 Nizhny Novgorod, Russia; (A.I.B.); (D.S.K.); (D.V.G.); (A.N.M.)
| | - Alexey I. Belov
- Research Institute and Technology, National Research Lobachevsky State University of Nizhny Novgorod, 603950 Nizhny Novgorod, Russia; (A.I.B.); (D.S.K.); (D.V.G.); (A.N.M.)
| | - Dmitry S. Korolev
- Research Institute and Technology, National Research Lobachevsky State University of Nizhny Novgorod, 603950 Nizhny Novgorod, Russia; (A.I.B.); (D.S.K.); (D.V.G.); (A.N.M.)
| | - Davud V. Guseinov
- Research Institute and Technology, National Research Lobachevsky State University of Nizhny Novgorod, 603950 Nizhny Novgorod, Russia; (A.I.B.); (D.S.K.); (D.V.G.); (A.N.M.)
| | - Albina V. Lebedeva
- Institute of Biology and Biomedicine, National Research Lobachevsky State University of Nizhny Novgorod, 603950 Nizhny Novgorod, Russia; (S.A.G.); (A.V.L.); (V.B.K.)
| | - Maria N. Koryazhkina
- Research and Educational Center “Physics of Solid State Nanostructures”, National Research Lobachevsky State University of Nizhny Novgorod, 603950 Nizhny Novgorod, Russia;
| | - Alexey N. Mikhaylov
- Research Institute and Technology, National Research Lobachevsky State University of Nizhny Novgorod, 603950 Nizhny Novgorod, Russia; (A.I.B.); (D.S.K.); (D.V.G.); (A.N.M.)
| | - Victor B. Kazantsev
- Institute of Biology and Biomedicine, National Research Lobachevsky State University of Nizhny Novgorod, 603950 Nizhny Novgorod, Russia; (S.A.G.); (A.V.L.); (V.B.K.)
- Laboratory of Neuroscience and Cognitive Technology, Innopolis University, 420500 Innopolis, Russia
- Center for Neurotechnology and Machine Learning, Immanuel Kant Baltic Federal University, 236016 Kaliningrad, Russia
| | - Alexander N. Pisarchik
- Research and Educational Center “Physics of Solid State Nanostructures”, National Research Lobachevsky State University of Nizhny Novgorod, 603950 Nizhny Novgorod, Russia;
- Laboratory of Neuroscience and Cognitive Technology, Innopolis University, 420500 Innopolis, Russia
- Center for Biomedical Technology, Universidad Politécnica de Madrid, Pozuelo de Alarcón, 28223 Madrid, Spain
- Correspondence:
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17
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Gradually Modified Conductance in the Self-Compliance Region of an Atomic-Layer-Deposited Pt/TiO2/HfAlOx/TiN RRAM Device. METALS 2021. [DOI: 10.3390/met11081199] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
Abstract
This study presents conductance modulation in a Pt/TiO2/HfAlOx/TiN resistive memory device in the compliance region for neuromorphic system applications. First, the chemical and material characteristics of the atomic-layer-deposited films were verified by X-ray photoelectron spectroscopy depth profiling. The low-resistance state was effectively controlled by the compliance current, and the high-resistance state was adjusted by the reset stop voltage. Stable endurance and retention in bipolar resistive switching were achieved. When a compliance current of 1 mA was imposed, only gradual switching was observed in the reset process. Self-compliance was used after an abrupt set transition to achieve a gradual set process. Finally, 10 cycles of long-term potentiation and depression were obtained in the compliance current region for neuromorphic system applications.
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18
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Yang J, Cho H, Ryu H, Ismail M, Mahata C, Kim S. Tunable Synaptic Characteristics of a Ti/TiO 2/Si Memory Device for Reservoir Computing. ACS APPLIED MATERIALS & INTERFACES 2021; 13:33244-33252. [PMID: 34251796 DOI: 10.1021/acsami.1c06618] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
In this study, we fabricate and characterize a Ti/TiO2/Si device with different dopant concentrations on a silicon surface for neuromorphic systems. We verify the device stack using transmission electron microscopy (TEM). The Ti/TiO2/p++Si device exhibits interface-type bipolar resistive switching with long-term memory. The potentiation and depression by the pulses of various amplitudes are demonstrated using gradual resistive switching. Moreover, pattern-recognition accuracy (>85%) is obtained in the neuromorphic system simulation when conductance is used as the weight in the network. Next, we investigate the short-term memory characteristics of the Ti/TiO2/p+Si device. The dynamic range is well-controlled by the pulse amplitude, and the conductance decay depends on the interval between the pulses. Finally, we build a reservoir computing system using the short-term effect of the Ti/TiO2/p+Si device, in which 4 bits (16 states) are differentiated by various pulse streams through the device that can be used for pattern recognition.
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Affiliation(s)
- Jinwoong Yang
- Division of Electronics and Electrical Engineering, Dongguk University, Seoul 04620, South Korea
| | - Hyojong Cho
- Division of Electronics and Electrical Engineering, Dongguk University, Seoul 04620, South Korea
| | - Hojeong Ryu
- Division of Electronics and Electrical Engineering, Dongguk University, Seoul 04620, South Korea
| | - Muhammad Ismail
- Division of Electronics and Electrical Engineering, Dongguk University, Seoul 04620, South Korea
| | - Chandreswar Mahata
- Division of Electronics and Electrical Engineering, Dongguk University, Seoul 04620, South Korea
| | - Sungjun Kim
- Division of Electronics and Electrical Engineering, Dongguk University, Seoul 04620, South Korea
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19
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Irregular Resistive Switching Behaviors of Al2O3-Based Resistor with Cu Electrode. METALS 2021. [DOI: 10.3390/met11040653] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
Abstract
In this work, we examined the irregular resistive switching behaviors of a complementary metal–oxide–semiconductor (CMOS)-compatible Cu/Al2O3/Si resistor device. X-ray photoelectron spectroscopy (XPS) analysis confirmed the chemical and material compositions of a Al2O3 thin film layer and Si substrate. Bipolar resistive switching occurred in a more stable manner than the unipolar resistive switching in the device did. Five cells were verified over 50 endurance cycles in terms of bipolar resistive switching, and a good retention was confirmed for 10,000 s in the high-resistance state (HRS) and the low-resistance state (LRS). Both high reset current (~10 mA) and low reset current (<100 μA) coexisted in the bipolar resistive switching. We investigated nonideal resistive switching behaviors such as negative-set and current overshoot, which could lead to resistive switching failure.
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20
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Gradually Tunable Conductance in TiO2/Al2O3 Bilayer Resistors for Synaptic Device. METALS 2021. [DOI: 10.3390/met11030440] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
In this work, resistive switching and synaptic behaviors of a TiO2/Al2O3 bilayer device were studied. The deposition of Pt/Ti/TiO2/Al2O3/TiN stack was confirmed by transmission electron microscopy (TEM) and energy X-ray dispersive spectroscopy (EDS). The initial state before the forming process followed Fowler-Nordheim (FN) tunneling. A strong electric field was applied to Al2O3 with a large energy bandgap for FN tunneling, which was confirmed by the I-V fitting process. Bipolar resistive switching was conducted by the set process in a positive bias and the reset process in a negative bias. High-resistance state (HRS) followed the trap-assisted tunneling (TAT) model while low-resistance state (LRS) followed the Ohmic conduction model. Set and reset operations were verified by pulse. Moreover, potentiation and depression in the biological synapse were verified by repetitive set pulses and reset pulses. Finally, the device showed good pattern recognition accuracy (~88.8%) for a Modified National Institute of Standards and Technology (MNIST) handwritten digit database in a single layer neural network including the conductance update of the device.
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21
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Kilic Z, Sgouralis I, Pressé S. Generalizing HMMs to Continuous Time for Fast Kinetics: Hidden Markov Jump Processes. Biophys J 2021; 120:409-423. [PMID: 33421415 PMCID: PMC7896036 DOI: 10.1016/j.bpj.2020.12.022] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2020] [Revised: 12/25/2020] [Accepted: 12/30/2020] [Indexed: 12/18/2022] Open
Abstract
The hidden Markov model (HMM) is a framework for time series analysis widely applied to single-molecule experiments. Although initially developed for applications outside the natural sciences, the HMM has traditionally been used to interpret signals generated by physical systems, such as single molecules, evolving in a discrete state space observed at discrete time levels dictated by the data acquisition rate. Within the HMM framework, transitions between states are modeled as occurring at the end of each data acquisition period and are described using transition probabilities. Yet, whereas measurements are often performed at discrete time levels in the natural sciences, physical systems evolve in continuous time according to transition rates. It then follows that the modeling assumptions underlying the HMM are justified if the transition rates of a physical process from state to state are small as compared to the data acquisition rate. In other words, HMMs apply to slow kinetics. The problem is, because the transition rates are unknown in principle, it is unclear, a priori, whether the HMM applies to a particular system. For this reason, we must generalize HMMs for physical systems, such as single molecules, because these switch between discrete states in "continuous time". We do so by exploiting recent mathematical tools developed in the context of inferring Markov jump processes and propose the hidden Markov jump process. We explicitly show in what limit the hidden Markov jump process reduces to the HMM. Resolving the discrete time discrepancy of the HMM has clear implications: we no longer need to assume that processes, such as molecular events, must occur on timescales slower than data acquisition and can learn transition rates even if these are on the same timescale or otherwise exceed data acquisition rates.
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Affiliation(s)
- Zeliha Kilic
- Center for Biological Physics, Department of Physics, Arizona State University, Tempe, Arizona
| | - Ioannis Sgouralis
- Department of Mathematics, University of Tennessee, Knoxville, Tennessee
| | - Steve Pressé
- Center for Biological Physics, Department of Physics, Arizona State University, Tempe, Arizona; School of Molecular Sciences, Arizona State University, Tempe, Arizona.
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Bipolar and Complementary Resistive Switching Characteristics and Neuromorphic System Simulation in a Pt/ZnO/TiN Synaptic Device. NANOMATERIALS 2021; 11:nano11020315. [PMID: 33513672 PMCID: PMC7911158 DOI: 10.3390/nano11020315] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/12/2020] [Revised: 01/19/2021] [Accepted: 01/22/2021] [Indexed: 12/14/2022]
Abstract
In this work, a ZnO-based resistive switching memory device is characterized by using simplified electrical conduction models. The conventional bipolar resistive switching and complementary resistive switching modes are accomplished by tuning the bias voltage condition. The material and chemical information of the device stack including the interfacial layer of TiON is well confirmed by transmission electron microscopy (TEM) and X-ray photoelectron spectroscopy (XPS) analysis. The device exhibits uniform gradual bipolar resistive switching (BRS) with good endurance and self-compliance characteristics. Moreover, complementary resistive switching (CRS) is achieved by applying the compliance current at negative bias and increasing the voltage at positive bias. The synaptic behaviors such as long-term potentiation and long-term depression are emulated by applying consecutive pulse input to the device. The CRS mode has a higher array size in the cross-point array structure than the BRS mode due to more nonlinear I–V characteristics in the CRS mode. However, we reveal that the BRS mode shows a better pattern recognition rate than the CRS mode due to more uniform conductance update.
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Tominov RV, Vakulov ZE, Avilov VI, Khakhulin DA, Polupanov NV, Smirnov VA, Ageev OA. The Effect of Growth Parameters on Electrophysical and Memristive Properties of Vanadium Oxide Thin Films. Molecules 2020; 26:E118. [PMID: 33383898 PMCID: PMC7795261 DOI: 10.3390/molecules26010118] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2020] [Revised: 12/22/2020] [Accepted: 12/25/2020] [Indexed: 11/23/2022] Open
Abstract
We have experimentally studied the influence of pulsed laser deposition parameters on the morphological and electrophysical parameters of vanadium oxide films. It is shown that an increase in the number of laser pulses from 10,000 to 60,000 and an oxygen pressure from 3 × 10-4 Torr to 3 × 10-2 Torr makes it possible to form vanadium oxide films with a thickness from 22.3 ± 4.4 nm to 131.7 ± 14.4 nm, a surface roughness from 7.8 ± 1.1 nm to 37.1 ± 11.2 nm, electron concentration from (0.32 ± 0.07) × 1017 cm-3 to (42.64 ± 4.46) × 1017 cm-3, electron mobility from 0.25 ± 0.03 cm2/(V·s) to 7.12 ± 1.32 cm2/(V·s), and resistivity from 6.32 ± 2.21 Ω·cm to 723.74 ± 89.21 Ω·cm. The regimes at which vanadium oxide films with a thickness of 22.3 ± 4.4 nm, a roughness of 7.8 ± 1.1 nm, and a resistivity of 6.32 ± 2.21 Ω·cm are obtained for their potential use in the fabrication of ReRAM neuromorphic systems. It is shown that a 22.3 ± 4.4 nm thick vanadium oxide film has the bipolar effect of resistive switching. The resistance in the high state was (89.42 ± 32.37) × 106 Ω, the resistance in the low state was equal to (6.34 ± 2.34) × 103 Ω, and the ratio RHRS/RLRS was about 14,104. The results can be used in the manufacture of a new generation of micro- and nanoelectronics elements to create ReRAM of neuromorphic systems based on vanadium oxide thin films.
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Affiliation(s)
- Roman V. Tominov
- Institute of Nanotechnologies, Electronics and Electronic Equipment Engineering, Southern Federal University, 347922 Taganrog, Russia; (R.V.T.); (V.I.A.); (D.A.K.); (N.V.P.); (O.A.A.)
- Research and Education Center “Nanotechnologies” at the Southern Federal University, Southern Federal University, 347922 Taganrog, Russia
| | - Zakhar E. Vakulov
- Federal Research Centre The Southern Scientific Centre of the Russian Academy of Sciences, 344006 Rostov-on-Don, Russia;
| | - Vadim I. Avilov
- Institute of Nanotechnologies, Electronics and Electronic Equipment Engineering, Southern Federal University, 347922 Taganrog, Russia; (R.V.T.); (V.I.A.); (D.A.K.); (N.V.P.); (O.A.A.)
- Research and Education Center “Nanotechnologies” at the Southern Federal University, Southern Federal University, 347922 Taganrog, Russia
| | - Daniil A. Khakhulin
- Institute of Nanotechnologies, Electronics and Electronic Equipment Engineering, Southern Federal University, 347922 Taganrog, Russia; (R.V.T.); (V.I.A.); (D.A.K.); (N.V.P.); (O.A.A.)
- Research and Education Center “Nanotechnologies” at the Southern Federal University, Southern Federal University, 347922 Taganrog, Russia
| | - Nikita V. Polupanov
- Institute of Nanotechnologies, Electronics and Electronic Equipment Engineering, Southern Federal University, 347922 Taganrog, Russia; (R.V.T.); (V.I.A.); (D.A.K.); (N.V.P.); (O.A.A.)
- Research and Education Center “Nanotechnologies” at the Southern Federal University, Southern Federal University, 347922 Taganrog, Russia
| | - Vladimir A. Smirnov
- Institute of Nanotechnologies, Electronics and Electronic Equipment Engineering, Southern Federal University, 347922 Taganrog, Russia; (R.V.T.); (V.I.A.); (D.A.K.); (N.V.P.); (O.A.A.)
- Research and Education Center “Nanotechnologies” at the Southern Federal University, Southern Federal University, 347922 Taganrog, Russia
| | - Oleg A. Ageev
- Institute of Nanotechnologies, Electronics and Electronic Equipment Engineering, Southern Federal University, 347922 Taganrog, Russia; (R.V.T.); (V.I.A.); (D.A.K.); (N.V.P.); (O.A.A.)
- Research and Education Center “Nanotechnologies” at the Southern Federal University, Southern Federal University, 347922 Taganrog, Russia
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Ryu H, Kim S. Improved Pulse-Controlled Conductance Adjustment in Trilayer Resistors by Suppressing Current Overshoot. NANOMATERIALS 2020; 10:nano10122462. [PMID: 33317045 PMCID: PMC7763874 DOI: 10.3390/nano10122462] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/10/2020] [Revised: 12/06/2020] [Accepted: 12/08/2020] [Indexed: 01/13/2023]
Abstract
In this work, we demonstrate the enhanced synaptic behaviors in trilayer dielectrics (HfO2/Si3N4/SiO2) on highly doped n-type silicon substrate. First, the three dielectric layers were subjected to material and chemical analyses and thoroughly investigated via transmission electron microscopy and X-ray photoelectron spectroscopy. The resistive switching and synaptic behaviors were improved by inserting a Si3N4 layer between the HfO2 and SiO2 layers. The electric field within SiO2 was mitigated, thus reducing the current overshoot in the trilayer device. The reset current was considerably reduced in the trilayer device compared to the bilayer device without a Si3N4 layer. Moreover, the nonlinear characteristics in the low-resistance state are helpful for implementing high-density memory. The higher array size in the trilayer device was verified by cross-point array simulation. Finally, the multiple conductance adjustment was demonstrated in the trilayer device by controlling the gradual set and reset switching behavior.
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Ryu H, Kim S. Self-Rectifying Resistive Switching and Short-Term Memory Characteristics in Pt/HfO 2/TaO x/TiN Artificial Synaptic Device. NANOMATERIALS 2020; 10:nano10112159. [PMID: 33138118 PMCID: PMC7693614 DOI: 10.3390/nano10112159] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/04/2020] [Revised: 10/21/2020] [Accepted: 10/26/2020] [Indexed: 11/16/2022]
Abstract
Here, we propose a Pt/HfO2/TaOx/TiN artificial synaptic device that is an excellent candidate for artificial synapses. First, XPS analysis is conducted to provide the dielectric (HfO2/TaOx/TiN) information deposited by DC sputtering and atomic layer deposition (ALD). The self-rectifying resistive switching characteristics are achieved by the asymmetric device stack, which is an advantage of the current suppression in the crossbar array structure. The results show that the programmed data are lost over time and that the decay rate, which is verified from the retention test, can be adjusted by controlling the compliance current (CC). Based on these properties, we emulate bio-synaptic characteristics, such as short-term plasticity (STP), long-term plasticity (LTP), and paired-pulse facilitation (PPF), in the self-rectifying I–V characteristics of the Pt/HfO2/TaOx/TiN bilayer memristor device. The PPF characteristics are mimicked by replacing the bio-stimulation with the interval time of paired pulse inputs. The typical potentiation and depression are also implemented by optimizing the set and reset pulse. Finally, we demonstrate the natural depression by varying the interval time between pulse inputs.
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Affiliation(s)
- Hojeong Ryu
- Division of Electronics and Electrical Engineering, Dongguk University, Seoul 04620, Korea
| | - Sungjun Kim
- Division of Electronics and Electrical Engineering, Dongguk University, Seoul 04620, Korea
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Voltage Amplitude-Controlled Synaptic Plasticity from Complementary Resistive Switching in Alloying HfOx with AlOx-Based RRAM. METALS 2020. [DOI: 10.3390/met10111410] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
Abstract
In this work, the synaptic plasticity from complementary resistive switching in a HfAlOx-based resistive memory device was emulated by a direct current (DC) voltage sweep, current sweep, and pulse transient. The alloyed HfAlOx dielectric was confirmed by X-ray photoelectron spectroscopy analysis. The negative differential resistance observed before the forming and set processes can be used for interface resistive switching with a low current level. Complementary resistive switching is obtained after the forming process at a negative bias. This unique resistive switching is also suitable for synaptic device applications in which the reset process occurs after an additional set process. The current sweep mode provides more clear information on the complementary resistive switching. Multiple current states are achieved by controlling the amplitude of the set and reset voltages under DC sweep mode. The potentiation and depression characteristics are mimicked by varying the pulse voltage amplitude for synaptic device application in a neuromorphic system. Finally, we demonstrate spike-timing-dependent plasticity by tuning the timing differences between pre-spikes and post-spikes.
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Krupa P, Siddiqui AM, Grahn PJ, Islam R, Chen BK, Madigan NN, Windebank AJ, Lavrov IA. The Translesional Spinal Network and Its Reorganization after Spinal Cord Injury. Neuroscientist 2020; 28:163-179. [PMID: 33089762 DOI: 10.1177/1073858420966276] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
Evidence from preclinical and clinical research suggest that neuromodulation technologies can facilitate the sublesional spinal networks, isolated from supraspinal commands after spinal cord injury (SCI), by reestablishing the levels of excitability and enabling descending motor signals via residual connections. Herein, we evaluate available evidence that sublesional and supralesional spinal circuits could form a translesional spinal network after SCI. We further discuss evidence of translesional network reorganization after SCI in the presence of sensory inputs during motor training. In this review, we evaluate potential mechanisms that underlie translesional circuitry reorganization during neuromodulation and rehabilitation in order to enable motor functions after SCI. We discuss the potential of neuromodulation technologies to engage various components that comprise the translesional network, their functional recovery after SCI, and the implications of the concept of translesional network in development of future neuromodulation, rehabilitation, and neuroprosthetics technologies.
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Affiliation(s)
- Petr Krupa
- Department of Neurosurgery, University Hospital Hradec Kralove, Charles University, Faculty of Medicine in Hradec Kralove, Czech Republic.,Institute of Experimental Medicine, Czech Academy of Sciences, Prague, Czech Republic
| | | | - Peter J Grahn
- Department of Physical Medicine and Rehabilitation, Mayo Clinic, Rochester, MN, USA.,Department of Neurologic Surgery, Mayo Clinic, Rochester, MN, USA
| | - Riazul Islam
- Department of Neurology, Mayo Clinic, Rochester, MN, USA
| | - Bingkun K Chen
- Department of Neurology, Mayo Clinic, Rochester, MN, USA
| | | | | | - Igor A Lavrov
- Department of Neurology, Mayo Clinic, Rochester, MN, USA.,Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN, USA.,Kazan Federal University, Kazan, Russia
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Synaptic Characteristics from Homogeneous Resistive Switching in Pt/Al 2O 3/TiN Stack. NANOMATERIALS 2020; 10:nano10102055. [PMID: 33080978 PMCID: PMC7603159 DOI: 10.3390/nano10102055] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/18/2020] [Revised: 10/05/2020] [Accepted: 10/16/2020] [Indexed: 11/27/2022]
Abstract
In this work, we propose three types of resistive switching behaviors by controlling operation conditions. We confirmed well-known filamentary switching in Al2O3-based resistive switching memory using the conventional device working operation with a forming process. Here, filamentary switching can be classified into two types depending on the compliance current. On top of that, the homogeneous switching is obtained by using a negative differential resistance effect before the forming or setting process in a negative bias. The variations of the low-resistance and high-resistance states in the homogeneous switching are comparable to the filamentary switching cases. However, the drift characteristics of the low-resistance and high-resistance states in the homogeneous switching are unstable with time. Therefore, the short-term plasticity effects, such as the current decay in repeated pulses and paired pulses facilitation, are demonstrated when using the resistance drift characteristics. Finally, the conductance can be increased and decreased by 50 consecutive potentiation pulses and 50 consecutive depression pulses, respectively. The linear conductance update in homogeneous switching is achieved compared to the filamentary switching, which ensures the high pattern-recognition accuracy.
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Cho H, Kim S. Emulation of Biological Synapse Characteristics from Cu/AlN/TiN Conductive Bridge Random Access Memory. NANOMATERIALS (BASEL, SWITZERLAND) 2020; 10:E1709. [PMID: 32872514 PMCID: PMC7557739 DOI: 10.3390/nano10091709] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/11/2020] [Revised: 08/26/2020] [Accepted: 08/27/2020] [Indexed: 02/07/2023]
Abstract
Here, we present the synaptic characteristics of AlN-based conductive bridge random access memory (CBRAM) as a synaptic device for neuromorphic systems. Both non-volatile and volatile memory are observed by simply controlling the strength of the Cu filament inside the AlN film. For non-volatile switching induced by high compliance current (CC), good retention with a strong Cu metallic filament is verified. Low-resistance state (LRS) and high-resistance state (HRS) conduction follow metallic Ohmic and trap-assisted tunneling (TAT), respectively, which are supported by I-V fitting and temperature dependence. The transition from long-term plasticity (LTP) to short-term plasticity (STP) is demonstrated by increasing the pulse interval time for synaptic device application. Also, paired-pulse facilitation (PPF) in the nervous system is mimicked by sending two identical pulses to the CBRAM device to induce STP. Finally, potentiation and depression are achieved by gradually increasing the set and reset voltage in pulse transient mode.
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Affiliation(s)
| | - Sungjun Kim
- Division of Electronics and Electrical Engineering, Dongguk University, Seoul 04620, Korea;
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Memristive Logic Design of Multifunctional Spiking Neural Network with Unsupervised Learning. BIONANOSCIENCE 2020. [DOI: 10.1007/s12668-020-00778-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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