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Zhang L, Lorut F, Gruel K, Hÿtch MJ, Gatel C. Measuring Electrical Resistivity at the Nanoscale in Phase-Change Materials. NANO LETTERS 2024; 24:5913-5919. [PMID: 38710045 DOI: 10.1021/acs.nanolett.4c01462] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2024]
Abstract
Electrical resistivity is the key parameter in the active regions of many current nanoscale devices, from memristors to resistive random-access memory and phase-change memories. The local resistivity of the materials is engineered on the nanoscale to fit the performance requirements. Phase-change memories, for example, rely on materials whose electrical resistance increases dramatically with a change from a crystalline to an amorphous phase. Electrical characterization methods have been developed to measure the response of individual devices, but they cannot map the local resistance across the active area. Here, we propose a method based on operando electron holography to determine the local resistance within working devices. Upon switching the device, we show that electrical resistance is inhomogeneous on the scale of only a few nanometers.
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Affiliation(s)
- Leifeng Zhang
- CEMES-CNRS, Université Paul Sabatier, 29 rue Jeanne Marvig, 31055 Toulouse, France
| | - Frédéric Lorut
- STMicroelectronics, 820 rue Jean Monnet, 38920 Crolles, France
| | - Kilian Gruel
- CEMES-CNRS, Université Paul Sabatier, 29 rue Jeanne Marvig, 31055 Toulouse, France
| | - Martin J Hÿtch
- CEMES-CNRS, Université Paul Sabatier, 29 rue Jeanne Marvig, 31055 Toulouse, France
| | - Christophe Gatel
- CEMES-CNRS, Université Paul Sabatier, 29 rue Jeanne Marvig, 31055 Toulouse, France
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2
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Li F, Li D, Wang C, Liu G, Wang R, Ren H, Tang Y, Wang Y, Chen Y, Liang K, Huang Q, Sawan M, Qiu M, Wang H, Zhu B. An artificial visual neuron with multiplexed rate and time-to-first-spike coding. Nat Commun 2024; 15:3689. [PMID: 38693165 PMCID: PMC11063071 DOI: 10.1038/s41467-024-48103-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2023] [Accepted: 04/17/2024] [Indexed: 05/03/2024] Open
Abstract
Human visual neurons rely on event-driven, energy-efficient spikes for communication, while silicon image sensors do not. The energy-budget mismatch between biological systems and machine vision technology has inspired the development of artificial visual neurons for use in spiking neural network (SNN). However, the lack of multiplexed data coding schemes reduces the ability of artificial visual neurons in SNN to emulate the visual perception ability of biological systems. Here, we present an artificial visual spiking neuron that enables rate and temporal fusion (RTF) coding of external visual information. The artificial neuron can code visual information at different spiking frequencies (rate coding) and enables precise and energy-efficient time-to-first-spike (TTFS) coding. This multiplexed sensory coding scheme could improve the computing capability and efficacy of artificial visual neurons. A hardware-based SNN with the RTF coding scheme exhibits good consistency with real-world ground truth data and achieves highly accurate steering and speed predictions for self-driving vehicles in complex conditions. The multiplexed RTF coding scheme demonstrates the feasibility of developing highly efficient spike-based neuromorphic hardware.
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Affiliation(s)
- Fanfan Li
- School of Materials Science and Engineering, Zhejiang University, Hangzhou, China
- Key Laboratory of 3D Micro/Nano Fabrication and Characterization of Zhejiang Province, School of Engineering, Westlake University, Hangzhou, China
| | - Dingwei Li
- Key Laboratory of 3D Micro/Nano Fabrication and Characterization of Zhejiang Province, School of Engineering, Westlake University, Hangzhou, China
| | - Chuanqing Wang
- CenBRAIN Neurotech, School of Engineering, Westlake University, Hangzhou, China
| | - Guolei Liu
- Key Laboratory of 3D Micro/Nano Fabrication and Characterization of Zhejiang Province, School of Engineering, Westlake University, Hangzhou, China
| | - Rui Wang
- Key Laboratory of Wide Band Gap Semiconductor Technology, School of Microelectronics, Xidian University, Xi'an, China
| | - Huihui Ren
- Key Laboratory of 3D Micro/Nano Fabrication and Characterization of Zhejiang Province, School of Engineering, Westlake University, Hangzhou, China
| | - Yingjie Tang
- Key Laboratory of 3D Micro/Nano Fabrication and Characterization of Zhejiang Province, School of Engineering, Westlake University, Hangzhou, China
| | - Yan Wang
- Key Laboratory of 3D Micro/Nano Fabrication and Characterization of Zhejiang Province, School of Engineering, Westlake University, Hangzhou, China
| | - Yitong Chen
- Key Laboratory of 3D Micro/Nano Fabrication and Characterization of Zhejiang Province, School of Engineering, Westlake University, Hangzhou, China
| | - Kun Liang
- Key Laboratory of 3D Micro/Nano Fabrication and Characterization of Zhejiang Province, School of Engineering, Westlake University, Hangzhou, China
| | - Qi Huang
- Westlake Institute for Optoelectronics, Westlake University, Hangzhou, China
| | - Mohamad Sawan
- CenBRAIN Neurotech, School of Engineering, Westlake University, Hangzhou, China
- Westlake Institute for Optoelectronics, Westlake University, Hangzhou, China
- Institute of Advanced Technology, Westlake Institute for Advanced Study, Hangzhou, China
| | - Min Qiu
- Key Laboratory of 3D Micro/Nano Fabrication and Characterization of Zhejiang Province, School of Engineering, Westlake University, Hangzhou, China
- Westlake Institute for Optoelectronics, Westlake University, Hangzhou, China
- Institute of Advanced Technology, Westlake Institute for Advanced Study, Hangzhou, China
| | - Hong Wang
- Key Laboratory of Wide Band Gap Semiconductor Technology, School of Microelectronics, Xidian University, Xi'an, China.
| | - Bowen Zhu
- Key Laboratory of 3D Micro/Nano Fabrication and Characterization of Zhejiang Province, School of Engineering, Westlake University, Hangzhou, China.
- Westlake Institute for Optoelectronics, Westlake University, Hangzhou, China.
- Institute of Advanced Technology, Westlake Institute for Advanced Study, Hangzhou, China.
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3
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Li J, Fan F, Fu X, Liu M, Chen Y, Zhang B. Building Uniformly Structured Polymer Memristors via a 2D Conjugation Strategy for Neuromorphic Computing. Macromol Rapid Commun 2024:e2400172. [PMID: 38627960 DOI: 10.1002/marc.202400172] [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: 03/22/2024] [Revised: 04/15/2024] [Indexed: 04/23/2024]
Abstract
Polymer memristors represent a highly promising avenue for the advancement of next-generation computing systems. However, the intrinsic structural heterogeneity characteristic of most polymers often results in organic polymer memristors displaying erratic resistive switching phenomena, which in turn lead to diminished production yields and compromised reliability. In this study, a 2D conjugated polymer, named PBDTT-BPQTPA, is synthesized by integrating the coplanar bis(thiophene)-4,8-dihydrobenzo[1,2-b:4,5-b]dithiophene (BDTT) as an electron-donating unit with a quinoxaline derivative serving as an electron-accepting unit. The incorporation of triphenylamine groups at the quinoxaline termini significantly enhances the polymer's conjugation and planarity, thereby facilitating more efficient charge transport. The fabricated polymer memristor with the structure of Al/PBDTT-BPQTPA/ITO exhibits typical non-volatile resistive switching behavior under high voltage conditions, along with history-dependent memristive properties at lower voltages. The unique memristive behavior of the device enables the simulation of synaptic enhancement/inhibition, learning algorithms, and memory operations. Additionally, the memristor demonstrates its capability for executing logical operations and handling decimal calculations. This study offers a promising and innovative approach for the development of artificial neuromorphic computing systems.
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Affiliation(s)
- Jinyong Li
- Key Laboratory for Advanced Materials and Joint International Research Laboratory of Precision Chemistry and Molecular Engineering, School of Chemistry and Molecular Engineering, East China University of Science and Technology, Shanghai, 200237, China
| | - Fei Fan
- Shanghai Key Laboratory of Crime Scene Evidence, Shanghai Research Institute of Criminal Science and Technology, Shanghai, 200083, China
| | - Xin Fu
- Key Laboratory for Advanced Materials and Joint International Research Laboratory of Precision Chemistry and Molecular Engineering, School of Chemistry and Molecular Engineering, East China University of Science and Technology, Shanghai, 200237, China
| | - Mingxing Liu
- Key Laboratory for Advanced Materials and Joint International Research Laboratory of Precision Chemistry and Molecular Engineering, School of Chemistry and Molecular Engineering, East China University of Science and Technology, Shanghai, 200237, China
| | - Yu Chen
- Key Laboratory for Advanced Materials and Joint International Research Laboratory of Precision Chemistry and Molecular Engineering, School of Chemistry and Molecular Engineering, East China University of Science and Technology, Shanghai, 200237, China
| | - Bin Zhang
- Key Laboratory for Advanced Materials and Joint International Research Laboratory of Precision Chemistry and Molecular Engineering, School of Chemistry and Molecular Engineering, East China University of Science and Technology, Shanghai, 200237, China
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Kim J, Lee S, Seo Y, Kim S. Emulating biological synaptic characteristics of HfOx/AlN-based 3D vertical resistive memory for neuromorphic systems. J Chem Phys 2024; 160:144703. [PMID: 38587228 DOI: 10.1063/5.0202610] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2024] [Accepted: 03/21/2024] [Indexed: 04/09/2024] Open
Abstract
Here, we demonstrate double-layer 3D vertical resistive random-access memory with a hole-type structure embedding Pt/HfOx/AlN/TiN memory cells, conduct analog resistive switching, and examine the potential of memristors for use in neuromorphic systems. The electrical characteristics, including resistive switching, retention, and endurance, of each layer are also obtained. Additionally, we investigate various synaptic characteristics, such as spike-timing dependent plasticity, spike-amplitude dependent plasticity, spike-rate dependent plasticity, spike-duration dependent plasticity, and spike-number dependent plasticity. This synapse emulation holds great potential for neuromorphic computing applications. Furthermore, potentiation and depression are manifested through identical pulses based on DC resistive switching. The pattern recognition rates within the neural network are evaluated, and based on the conductance changing linearly with incremental pulses, we achieve a pattern recognition accuracy of over 95%. Finally, the device's stability and synapse characteristics exhibit excellent potential for use in neuromorphic systems.
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Affiliation(s)
- Juri Kim
- Division of Electronics and Electrical Engineering, Dongguk University, Seoul 04620, South Korea
| | - Subaek Lee
- Division of Electronics and Electrical Engineering, Dongguk University, Seoul 04620, South Korea
| | - Yeongkyo Seo
- Department of Information and Communication Engineering, Inha University, Incheon 22212, Republic of Korea
| | - Sungjun Kim
- Division of Electronics and Electrical Engineering, Dongguk University, Seoul 04620, South Korea
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Kim M, Lee S, Kim SJ, Lim BM, Kang BS, Lee HS. Study on the Sodium-Doped Titania Interface-Type Memristor. ACS APPLIED MATERIALS & INTERFACES 2024; 16:16453-16461. [PMID: 38516695 DOI: 10.1021/acsami.3c19531] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/23/2024]
Abstract
Memristors integrated into a crossbar-array architecture (CAA) are promising candidates for analog in-memory computing accelerators. However, the relatively low reliability of the memristor device and sneak current issues in CAA remain the main obstacles. Alkali ion-based interface-type memristors are promising solutions for implementing highly reliable memristor devices and neuromorphic hardware. This interface-type device benefits from self-rectifying and forming-free resistive switching (RS), and exhibits relatively low variation from device to device and cycle to cycle. In a previous report, we introduced an in situ grown Na/TiO2 memristor using atomic layer deposition (ALD) and proposed a RS mechanism from experimentally measured Schottky barrier modulation. Self-rectifying RS characteristics were observed by the asymmetric distribution of Na dopants and oxygen vacancies as the Ti metal used as the adhesion layer for the bottom electrode diffuses over the Pt electrode at 250 °C during the ALD process and is doped into the TiO2 layer. Here, we theoretically verify the modulation of the Schottky barrier at the TiO2/Pt electrode interface by Na ions. This study fabricated a Pt/Na/TiO2/Pt memristor device and confirmed its self-rectifying RS characteristics and stable retention characteristics for 24 h at 85 °C. Additionally, this device exhibited relative standard deviations of 27 and 7% in the high and low resistance states, respectively, in terms of cycle-to-cycle variation. To verify the RS mechanism, we conducted density functional theory simulations to analyze the impact of Na cations at interstitial sites on the Schottky barrier. Our findings can contribute to both fundamental understanding and the design of high-performance memristor devices for neuromorphic computing.
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Affiliation(s)
- Minjae Kim
- Department of Electrical and Computer Engineering, University of Southern California Los Angeles, Los Angeles, California 90089, United States
| | - Sangjun Lee
- Institute of Industrial Science, The University of Tokyo, 4-6-1, Meguro-ku, Tokyo 135-8505, Japan
| | - Seung Ju Kim
- Department of Electrical and Computer Engineering, University of Southern California Los Angeles, Los Angeles, California 90089, United States
| | - Byeong Min Lim
- Department of Advanced Materials Engineering for Information and Electronics, Kyung Hee University, Yongin 17104, Republic of Korea
- Integrated Education Institute for Frontier Science & Technology (BK21 Four), Kyung Hee University, Yongin 17104, Republic of Korea
| | - Byeong-Soo Kang
- Department of Electrical and Computer Engineering, University of Southern California Los Angeles, Los Angeles, California 90089, United States
| | - Hong-Sub Lee
- Department of Advanced Materials Engineering for Information and Electronics, Kyung Hee University, Yongin 17104, Republic of Korea
- Integrated Education Institute for Frontier Science & Technology (BK21 Four), Kyung Hee University, Yongin 17104, Republic of Korea
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Carrara S, Chen J, Bhardwaj K, Golparvar A, Barbruni GL. In-Memory Sensing and Computing for Cancer Diagnostics: A Perspective Paper. IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS 2024; 18:361-368. [PMID: 38015674 DOI: 10.1109/tbcas.2023.3334144] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/30/2023]
Abstract
During the past two decades, a number of two-terminal switching devices have been demonstrated in the literature. They typically exhibit hysteric behavior in the current-to-voltage characteristics. These devices have often been also referred to as memristive devices. Their capacity to switch and exhibit electrical hysteresis has made them well-suited for applications such as data storage, in-memory computing, and in-sensor computing or in-memory sensing. The aim of this perspective paper is to is twofold. Firstly, it seeks to provide a comprehensive examination of the existing research findings in the field and engage in a critical discussion regarding the potential for the development of new non-Von-Neumann computing machines that can seamlessly integrate sensing and computing within memory units. Secondly, this paper aims to demonstrate the practical application of such an innovative approach in the realm of cancer medicine. Specifically, it explores the modern concept of employing multiple cancer markers simultaneously to enhance the efficiency of diagnostic processes in cancer medicine.
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Ko JH, Seo DH, Jeong HH, Kim S, Song YM. Sub-1-Volt Electrically Programmable Optical Modulator Based on Active Tamm Plasmon. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024; 36:e2310556. [PMID: 38174820 DOI: 10.1002/adma.202310556] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Revised: 11/26/2023] [Indexed: 01/05/2024]
Abstract
Reconfigurable optical devices hold great promise for advancing high-density optical interconnects, photonic switching, and memory applications. While many optical modulators based on active materials have been demonstrated, it is challenging to achieve a high modulation depth with a low operation voltage in the near-infrared (NIR) range, which is a highly sought-after wavelength window for free-space communication and imaging applications. Here, electrically switchable Tamm plasmon coupled with poly(3,4-ethylenedioxythiophene):polystyrene sulfonate (PEDOT:PSS) is introduced. The device allows for a high modulation depth across the entire NIR range by fully absorbing incident light even under epsilon near zero conditions. Optical modulation exceeding 88% is achieved using a CMOS-compatible voltage of ±1 V. This modulation is facilitated by precise electrical control of the charge carrier density through an electrochemical doping/dedoping process. Additionally, the potential applications of the device are extended for a non-volatile multi-memory state optical device, capable of rewritable optical memory storage and exhibiting long-term potentiation/depression properties with neuromorphic behavior.
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Affiliation(s)
- Joo Hwan Ko
- School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Gwangju, 61005, Republic of Korea
| | - Dong Hyun Seo
- School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Gwangju, 61005, Republic of Korea
| | - Hyeon-Ho Jeong
- School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Gwangju, 61005, Republic of Korea
- Department of Semiconductor Engineering, Gwangju Institute of Science AND Technology, Gwangju, 61005, Republic of Korea
| | - Sejeong Kim
- Department of Electrical and Electronic Engineering, University of Melbourne, Victoria, 3000, Australia
| | - Young Min Song
- School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Gwangju, 61005, Republic of Korea
- Department of Semiconductor Engineering, Gwangju Institute of Science AND Technology, Gwangju, 61005, Republic of Korea
- AI Graduate School, Gwangju Institute of Science and Technology, Gwangju, 61005, Republic of Korea
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8
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Alquliah A, Ha J, Ndao A. Multi-channel broadband nonvolatile programmable modal switch. OPTICS EXPRESS 2024; 32:10979-10999. [PMID: 38570958 DOI: 10.1364/oe.517313] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/28/2023] [Accepted: 02/20/2024] [Indexed: 04/05/2024]
Abstract
Mode-division multiplexing (MDM) in chip-scale photonics is paramount to sustain data capacity growth and reduce power consumption. However, its scalability hinges on developing efficient and dynamic modal switches. Existing active modal switches suffer from substantial static power consumption, large footprints, and narrow bandwidth. Here, we present, for the first time, to the best of our knowledge, a novel multiport, broadband, non-volatile, and programmable modal switch designed for on-chip MDM systems. Our design leverages the unique properties of integrating nanoscale phase-change materials (PCM) within a silicon photonic architecture. This enables independent manipulation of spatial modes, allowing for dynamic, non-volatile, and selective routing to six distinct output ports. Crucially, our switch outperforms current dynamic modal switches by offering non-volatile, energy-efficient multiport functionality and excels in performance metrics. Our switch exhibits exceptional broadband operating bandwidth exceeding 70 nm, with low loss (< 1 dB), and a high extinction ratio (> 10 dB). Our framework provides a step forward in chip-scale MDM, paving the way for future green and scalable data centers and high-performance computers.
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Winkler R, Zintler A, Recalde-Benitez O, Jiang T, Nasiou D, Adabifiroozjaei E, Schreyer P, Kim T, Piros E, Kaiser N, Vogel T, Petzold S, Alff L, Molina-Luna L. Texture Transfer in Dielectric Layers via Nanocrystalline Networks: Insights from in Situ 4D-STEM. NANO LETTERS 2024; 24:2998-3004. [PMID: 38319977 DOI: 10.1021/acs.nanolett.3c03941] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/08/2024]
Abstract
Transition metal oxide dielectric layers have emerged as promising candidates for various relevant applications, such as supercapacitors or memory applications. However, the performance and reliability of these devices can critically depend on their microstructure, which can be strongly influenced by thermal processing and substrate-induced strain. To gain a more in-depth understanding of the microstructural changes, we conducted in situ transmission electron microscopy (TEM) studies of amorphous HfO2 dielectric layers grown on highly textured (111) substrates. Our results indicate that the minimum required phase transition temperature is 180 °C and that the developed crystallinity is affected by texture transfer. Using in situ TEM and 4D-STEM can provide valuable insights into the fundamental mechanisms underlying the microstructural evolution of dielectric layers and could pave the way for the development of more reliable and efficient devices for future applications.
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Affiliation(s)
- Robert Winkler
- Advanced Electron Microscopy Division, Institute of Materials Science, Technische Universität Darmstadt, Alarich-Weiss-Straße 2, 64287 Darmstadt, Germany
| | - Alexander Zintler
- Advanced Electron Microscopy Division, Institute of Materials Science, Technische Universität Darmstadt, Alarich-Weiss-Straße 2, 64287 Darmstadt, Germany
| | - Oscar Recalde-Benitez
- Advanced Electron Microscopy Division, Institute of Materials Science, Technische Universität Darmstadt, Alarich-Weiss-Straße 2, 64287 Darmstadt, Germany
| | - Tianshu Jiang
- Advanced Electron Microscopy Division, Institute of Materials Science, Technische Universität Darmstadt, Alarich-Weiss-Straße 2, 64287 Darmstadt, Germany
| | - Déspina Nasiou
- Advanced Electron Microscopy Division, Institute of Materials Science, Technische Universität Darmstadt, Alarich-Weiss-Straße 2, 64287 Darmstadt, Germany
| | - Esmaeil Adabifiroozjaei
- Advanced Electron Microscopy Division, Institute of Materials Science, Technische Universität Darmstadt, Alarich-Weiss-Straße 2, 64287 Darmstadt, Germany
| | - Philipp Schreyer
- Advanced Thin Film Technology Division, Institute of Materials Science, Technische Universität Darmstadt, Alarich-Weiss-Straße 2, 64287 Darmstadt, Germany
| | - Taewook Kim
- Advanced Thin Film Technology Division, Institute of Materials Science, Technische Universität Darmstadt, Alarich-Weiss-Straße 2, 64287 Darmstadt, Germany
| | - Eszter Piros
- Advanced Thin Film Technology Division, Institute of Materials Science, Technische Universität Darmstadt, Alarich-Weiss-Straße 2, 64287 Darmstadt, Germany
| | - Nico Kaiser
- Advanced Thin Film Technology Division, Institute of Materials Science, Technische Universität Darmstadt, Alarich-Weiss-Straße 2, 64287 Darmstadt, Germany
| | - Tobias Vogel
- Advanced Thin Film Technology Division, Institute of Materials Science, Technische Universität Darmstadt, Alarich-Weiss-Straße 2, 64287 Darmstadt, Germany
| | - Stefan Petzold
- Advanced Thin Film Technology Division, Institute of Materials Science, Technische Universität Darmstadt, Alarich-Weiss-Straße 2, 64287 Darmstadt, Germany
| | - Lambert Alff
- Advanced Thin Film Technology Division, Institute of Materials Science, Technische Universität Darmstadt, Alarich-Weiss-Straße 2, 64287 Darmstadt, Germany
| | - Leopoldo Molina-Luna
- Advanced Electron Microscopy Division, Institute of Materials Science, Technische Universität Darmstadt, Alarich-Weiss-Straße 2, 64287 Darmstadt, Germany
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Liu X, Sun C, Ye X, Zhu X, Hu C, Tan H, He S, Shao M, Li RW. Neuromorphic Nanoionics for Human-Machine Interaction: From Materials to Applications. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024:e2311472. [PMID: 38421081 DOI: 10.1002/adma.202311472] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Revised: 02/06/2024] [Indexed: 03/02/2024]
Abstract
Human-machine interaction (HMI) technology has undergone significant advancements in recent years, enabling seamless communication between humans and machines. Its expansion has extended into various emerging domains, including human healthcare, machine perception, and biointerfaces, thereby magnifying the demand for advanced intelligent technologies. Neuromorphic computing, a paradigm rooted in nanoionic devices that emulate the operations and architecture of the human brain, has emerged as a powerful tool for highly efficient information processing. This paper delivers a comprehensive review of recent developments in nanoionic device-based neuromorphic computing technologies and their pivotal role in shaping the next-generation of HMI. Through a detailed examination of fundamental mechanisms and behaviors, the paper explores the ability of nanoionic memristors and ion-gated transistors to emulate the intricate functions of neurons and synapses. Crucial performance metrics, such as reliability, energy efficiency, flexibility, and biocompatibility, are rigorously evaluated. Potential applications, challenges, and opportunities of using the neuromorphic computing technologies in emerging HMI technologies, are discussed and outlooked, shedding light on the fusion of humans with machines.
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Affiliation(s)
- Xuerong Liu
- CAS Key Laboratory of Magnetic Materials and Devices, and Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China
- Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China
- College of Materials Sciences and Opto-Electronic Technology, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Cui Sun
- CAS Key Laboratory of Magnetic Materials and Devices, and Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China
- Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China
| | - Xiaoyu Ye
- CAS Key Laboratory of Magnetic Materials and Devices, and Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China
- Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China
| | - Xiaojian Zhu
- CAS Key Laboratory of Magnetic Materials and Devices, and Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China
- Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China
| | - Cong Hu
- CAS Key Laboratory of Magnetic Materials and Devices, and Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China
- Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China
| | - Hongwei Tan
- Department of Applied Physics, Aalto University, Aalto, FI-00076, Finland
| | - Shang He
- CAS Key Laboratory of Magnetic Materials and Devices, and Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China
- Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China
| | - Mengjie Shao
- CAS Key Laboratory of Magnetic Materials and Devices, and Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China
- Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China
| | - Run-Wei Li
- CAS Key Laboratory of Magnetic Materials and Devices, and Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China
- Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China
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11
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Milano G, Raffone F, Bejtka K, De Carlo I, Fretto M, Pirri FC, Cicero G, Ricciardi C, Valov I. Electrochemical rewiring through quantum conductance effects in single metallic memristive nanowires. NANOSCALE HORIZONS 2024; 9:416-426. [PMID: 38224292 DOI: 10.1039/d3nh00476g] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/16/2024]
Abstract
Memristive devices have been demonstrated to exhibit quantum conductance effects at room temperature. In these devices, a detailed understanding of the relationship between electrochemical processes and ionic dynamic underlying the formation of atomic-sized conductive filaments and corresponding electronic transport properties in the quantum regime still represents a challenge. In this work, we report on quantum conductance effects in single memristive Ag nanowires (NWs) through a combined experimental and simulation approach that combines advanced classical molecular dynamics (MD) algorithms and quantum transport simulations (DFT). This approach provides new insights on quantum conductance effects in memristive devices by unravelling the intrinsic relationship between electronic transport and atomic dynamic reconfiguration of the nanofilment, by shedding light on deviations from integer multiples of the fundamental quantum of conductance depending on peculiar dynamic trajectories of nanofilament reconfiguration and on conductance fluctuations relying on atomic rearrangement due to thermal fluctuations.
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Affiliation(s)
- Gianluca Milano
- Advanced Materials Metrology and Life Sciences Division, INRiM (Istituto Nazionale di Ricerca Metrologica), Strada delle Cacce 91, 10135 Torino, Italy.
| | - Federico Raffone
- Department of Applied Science and Technology, Politecnico di Torino, C.so Duca degli Abruzzi 24, 10129 Torino, Italy.
| | - Katarzyna Bejtka
- Department of Applied Science and Technology, Politecnico di Torino, C.so Duca degli Abruzzi 24, 10129 Torino, Italy.
- Centre for Sustainable Future Technologies, Istituto Italiano di Tecnologia, Via Livorno 60, 10144 Torino, Italy
| | - Ivan De Carlo
- Advanced Materials Metrology and Life Sciences Division, INRiM (Istituto Nazionale di Ricerca Metrologica), Strada delle Cacce 91, 10135 Torino, Italy.
- Department of Electronics and Telecommunications, Politecnico di Torino, C.so Duca degli Abruzzi 24, 10129 Torino, Italy
| | - Matteo Fretto
- Advanced Materials Metrology and Life Sciences Division, INRiM (Istituto Nazionale di Ricerca Metrologica), Strada delle Cacce 91, 10135 Torino, Italy.
| | - Fabrizio Candido Pirri
- Department of Applied Science and Technology, Politecnico di Torino, C.so Duca degli Abruzzi 24, 10129 Torino, Italy.
- Centre for Sustainable Future Technologies, Istituto Italiano di Tecnologia, Via Livorno 60, 10144 Torino, Italy
| | - Giancarlo Cicero
- Department of Applied Science and Technology, Politecnico di Torino, C.so Duca degli Abruzzi 24, 10129 Torino, Italy.
| | - Carlo Ricciardi
- Department of Applied Science and Technology, Politecnico di Torino, C.so Duca degli Abruzzi 24, 10129 Torino, Italy.
| | - Ilia Valov
- Forschungszentrum Jülich, Institute of Electrochemistry and Energy System, WilhelmJohnen-Straße, 52428, Jülich, Germany
- "Acad. Evgeni Budevski" (IEE-BAS), Bulgarian Academy of Sciences (BAS), Acad. G. Bonchev Str., Block 10, 1113 Sofia, Bulgaria
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12
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Wan C, Pei M, Shi K, Cui H, Long H, Qiao L, Xing Q, Wan Q. Toward a Brain-Neuromorphics Interface. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024:e2311288. [PMID: 38339866 DOI: 10.1002/adma.202311288] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Revised: 01/17/2024] [Indexed: 02/12/2024]
Abstract
Brain-computer interfaces (BCIs) that enable human-machine interaction have immense potential in restoring or augmenting human capabilities. Traditional BCIs are realized based on complementary metal-oxide-semiconductor (CMOS) technologies with complex, bulky, and low biocompatible circuits, and suffer with the low energy efficiency of the von Neumann architecture. The brain-neuromorphics interface (BNI) would offer a promising solution to advance the BCI technologies and shape the interactions with machineries. Neuromorphic devices and systems are able to provide substantial computation power with extremely high energy-efficiency by implementing in-materia computing such as in situ vector-matrix multiplication (VMM) and physical reservoir computing. Recent progresses on integrating neuromorphic components with sensing and/or actuating modules, give birth to the neuromorphic afferent nerve, efferent nerve, sensorimotor loop, and so on, which has advanced the technologies for future neurorobotics by achieving sophisticated sensorimotor capabilities as the biological system. With the development on the compact artificial spiking neuron and bioelectronic interfaces, the seamless communication between a BNI and a bioentity is reasonably expectable. In this review, the upcoming BNIs are profiled by introducing the brief history of neuromorphics, reviewing the recent progresses on related areas, and discussing the future advances and challenges that lie ahead.
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Affiliation(s)
- Changjin Wan
- Yongjiang Laboratory (Y-LAB), Ningbo, Zhejiang, 315202, China
- School of Electronic Science and Engineering, National Laboratory of Solid-State Microstructures, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, 210093, China
- Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China
| | - Mengjiao Pei
- School of Electronic Science and Engineering, National Laboratory of Solid-State Microstructures, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, 210093, China
| | - Kailu Shi
- School of Electronic Science and Engineering, National Laboratory of Solid-State Microstructures, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, 210093, China
| | - Hangyuan Cui
- School of Electronic Science and Engineering, National Laboratory of Solid-State Microstructures, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, 210093, China
| | - Haotian Long
- School of Electronic Science and Engineering, National Laboratory of Solid-State Microstructures, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, 210093, China
| | - Lesheng Qiao
- School of Electronic Science and Engineering, National Laboratory of Solid-State Microstructures, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, 210093, China
| | - Qianye Xing
- School of Electronic Science and Engineering, National Laboratory of Solid-State Microstructures, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, 210093, China
| | - Qing Wan
- Yongjiang Laboratory (Y-LAB), Ningbo, Zhejiang, 315202, China
- School of Electronic Science and Engineering, National Laboratory of Solid-State Microstructures, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, 210093, China
- Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China
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13
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Chen W, Mou Z, Xin Y, Li H, Wang T, Chen Y, Chen L, Yang BR, Chen Z, Luo Y, Liu GS. Self-Assembled Monolayer and Nanoparticles Coenhanced Fragmented Silver Nanowire Network Memristor. ACS APPLIED MATERIALS & INTERFACES 2024; 16:6057-6067. [PMID: 38285926 DOI: 10.1021/acsami.3c15351] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/31/2024]
Abstract
Silver nanowire (AgNW) networks with self-assembled structures and synaptic connectivity have been recently reported for constructing neuromorphic memristors. However, resistive switching at the cross-point junctions of the network is unstable due to locally enhanced Joule heating and the Gibbs-Thomson effect, which poses an obstacle to the integration of threshold switching and memory function in the same AgNW memristor. Here, fragmented AgNW networks combined with Ag nanoparticles (AgNPs) and mercapto self-assembled monolayers (SAMs) are devised to construct memristors with stable threshold switching and memory behavior. In the above design, the planar gaps between NW segments are for resistive switching, the AgNPs act as metal islands in the gaps to reduce threshold voltage (Vth) and holding voltage (Vhold), and the SAMs suppress surface atom diffusion to avoid Oswald ripening of the AgNPs, which improves switching stability. The fragmented NW-NP/SAM memristors not only circumvent the side effects of conventional NW-stacked junctions to provide durable threshold switching at >Vth but also exhibit synaptic characteristics such as long-term potentiation at ultralow voltage (≪Vth). The combination of NW segments, nanoparticles, and SAMs blazes a new trail for integrating artificial neurons and synapses in AgNW network memristors.
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Affiliation(s)
- Weizhen Chen
- Guangdong Provincial Key Laboratory of Optical Fiber Sensing and Communications, Key Laboratory of Visible Light Communications of Guangzhou, Key Laboratory of Optoelectronic Information and Sensing Technologies of Guangdong Higher Education Institutes, College of Physics & Optoelectronic Engineering, Jinan University, Guangzhou 510632, China
| | - Zongxia Mou
- Key Laboratory of Biomaterials of Guangdong Higher Education Institutes, Department of Biomedical Engineering, Jinan University, Guangzhou 510632, China
| | - Yijia Xin
- Department of Physics, Jinan University, Guangzhou 510632, China
| | - Haichuan Li
- Guangdong Provincial Key Laboratory of Optical Fiber Sensing and Communications, Key Laboratory of Visible Light Communications of Guangzhou, Key Laboratory of Optoelectronic Information and Sensing Technologies of Guangdong Higher Education Institutes, College of Physics & Optoelectronic Engineering, Jinan University, Guangzhou 510632, China
| | - Tianqi Wang
- Guangdong Provincial Key Laboratory of Optical Fiber Sensing and Communications, Key Laboratory of Visible Light Communications of Guangzhou, Key Laboratory of Optoelectronic Information and Sensing Technologies of Guangdong Higher Education Institutes, College of Physics & Optoelectronic Engineering, Jinan University, Guangzhou 510632, China
| | - Yaofei Chen
- Guangdong Provincial Key Laboratory of Optical Fiber Sensing and Communications, Key Laboratory of Visible Light Communications of Guangzhou, Key Laboratory of Optoelectronic Information and Sensing Technologies of Guangdong Higher Education Institutes, College of Physics & Optoelectronic Engineering, Jinan University, Guangzhou 510632, China
| | - Lei Chen
- Guangdong Provincial Key Laboratory of Optical Fiber Sensing and Communications, Key Laboratory of Visible Light Communications of Guangzhou, Key Laboratory of Optoelectronic Information and Sensing Technologies of Guangdong Higher Education Institutes, College of Physics & Optoelectronic Engineering, Jinan University, Guangzhou 510632, China
| | - Bo-Ru Yang
- State Key Laboratory of Optoelectronic Materials and Technologies, School of Electronics and Information Technology, Sun Yat-Sen University, Guangzhou 510006, China
| | - Zhe Chen
- Guangdong Provincial Key Laboratory of Optical Fiber Sensing and Communications, Key Laboratory of Visible Light Communications of Guangzhou, Key Laboratory of Optoelectronic Information and Sensing Technologies of Guangdong Higher Education Institutes, College of Physics & Optoelectronic Engineering, Jinan University, Guangzhou 510632, China
| | - Yunhan Luo
- Guangdong Provincial Key Laboratory of Optical Fiber Sensing and Communications, Key Laboratory of Visible Light Communications of Guangzhou, Key Laboratory of Optoelectronic Information and Sensing Technologies of Guangdong Higher Education Institutes, College of Physics & Optoelectronic Engineering, Jinan University, Guangzhou 510632, China
| | - Gui-Shi Liu
- Guangdong Provincial Key Laboratory of Optical Fiber Sensing and Communications, Key Laboratory of Visible Light Communications of Guangzhou, Key Laboratory of Optoelectronic Information and Sensing Technologies of Guangdong Higher Education Institutes, College of Physics & Optoelectronic Engineering, Jinan University, Guangzhou 510632, China
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14
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Attri R, Mondal I, Yadav B, Kulkarni GU, Rao CNR. Neuromorphic devices realised using self-forming hierarchical Al and Ag nanostructures: towards energy-efficient and wide ranging synaptic plasticity. MATERIALS HORIZONS 2024; 11:737-746. [PMID: 38018415 DOI: 10.1039/d3mh01367g] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/30/2023]
Abstract
Closely mimicking the hierarchical structural topology with emerging behavioral functionalities of biological neural networks in neuromorphic devices is considered of prime importance for the realization of energy-efficient intelligent systems. In this article, we report an artificial synaptic network (ASN) comprising of hierarchical structures of isolated Al and Ag micro-nano structures developed via the utilization of a desiccated crack pattern, anisotropic dewetting, and self-formation. The strategically designed ASN, despite having multiple synaptic junctions between electrodes, exhibits a threshold switching (Vth ∼ 1-2 V) with an ultra-low energy requirement of ∼1.3 fJ per synaptic event. Several configurations of the order of hierarchy in the device architecture are studied comprehensively to identify the importance of the individual metallic components in contributing to the threshold switching and energy-minimization. The emerging potentiation behavior of the conductance (G) profile under electrical stimulation and its permanence beyond are realized over a wide current compliance range of 0.25 to 300 μA, broadly classifying the short- and long-term potentiation grounded on the characteristics of filamentary structures. The scale-free correlation of potentiation in the device hosting metallic filaments of diverse shapes and strengths could provide an ideal platform for understanding and replicating the complex behavior of the brain for neuromorphic computing.
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Affiliation(s)
- Rohit Attri
- New Chemistry Unit and School of Advanced Materials (SAMat), Jawaharlal Nehru Centre for Advanced Scientific Research, Bangalore 560064, India.
| | - Indrajit Mondal
- Chemistry and Physics of Materials Unit and School of Advanced Materials (SAMat), Jawaharlal Nehru Centre for Advanced Scientific Research, Bangalore 560064, India
| | - Bhupesh Yadav
- Chemistry and Physics of Materials Unit and School of Advanced Materials (SAMat), Jawaharlal Nehru Centre for Advanced Scientific Research, Bangalore 560064, India
| | - Giridhar U Kulkarni
- Chemistry and Physics of Materials Unit and School of Advanced Materials (SAMat), Jawaharlal Nehru Centre for Advanced Scientific Research, Bangalore 560064, India
| | - C N R Rao
- New Chemistry Unit and School of Advanced Materials (SAMat), Jawaharlal Nehru Centre for Advanced Scientific Research, Bangalore 560064, India.
- Chemistry and Physics of Materials Unit and School of Advanced Materials (SAMat), Jawaharlal Nehru Centre for Advanced Scientific Research, Bangalore 560064, India
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15
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Weng Z, Zheng H, Li L, Lei W, Jiang H, Ang KW, Zhao Z. Reliable Memristor Crossbar Array Based on 2D Layered Nickel Phosphorus Trisulfide for Energy-Efficient Neuromorphic Hardware. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2024; 20:e2304518. [PMID: 37752744 DOI: 10.1002/smll.202304518] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Revised: 08/04/2023] [Indexed: 09/28/2023]
Abstract
Designing reliable and energy-efficient memristors for artificial synaptic arrays in neuromorphic computing beyond von Neumann architecture remains a challenge. Here, memristors based on emerging layered nickel phosphorus trisulfide (NiPS3 ) are reported that exhibit several favorable characteristics, including uniform bipolar nonvolatile switching with small operating voltage (<1 V), fast switching speed (< 20 ns), high On/Off ratio (>102 ), and the ability to achieve programmable multilevel resistance states. Through direct experimental evidence using transmission electron microscopy and energy dispersive X-ray spectroscopy, it is revealed that the resistive switching mechanism in the Ti/NiPS3 /Au device is related to the formation and dissolution of Ti conductive filaments. Intriguingly, further investigation into the microstructural and chemical properties of NiPS3 suggests that the penetration of Ti ions is accompanied by the drift of phosphorus-sulfur ions, leading to induced P/S vacancies that facilitate the formation of conductive filaments. Furthermore, it is demonstrated that the memristor, when operating in quasi-reset mode, effectively emulates long-term synaptic weight plasticity. By utilizing a crossbar array, multipattern memorization and multiply-and-accumulate (MAC) operations are successfully implemented. Moreover, owing to the highly linear and symmetric multiple conductance states, a high pattern recognition accuracy of ≈96.4% is demonstrated in artificial neural network simulation for neuromorphic systems.
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Affiliation(s)
- Zhengjin Weng
- Joint International Research Laboratory of Information Display and Visualization, School of Electronic Science and Engineering, Southeast University, Nanjing, 210096, China
| | - Haofei Zheng
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, 117583, Singapore
| | - Lingqi Li
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, 117583, Singapore
| | - Wei Lei
- Joint International Research Laboratory of Information Display and Visualization, School of Electronic Science and Engineering, Southeast University, Nanjing, 210096, China
| | - Helong Jiang
- State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology Chinese Academy of Sciences, Nanjing, 210008, China
| | - Kah-Wee Ang
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, 117583, Singapore
- Institute of Materials Research and Engineering, A*STAR, Singapore, 138634, Singapore
| | - Zhiwei Zhao
- Joint International Research Laboratory of Information Display and Visualization, School of Electronic Science and Engineering, Southeast University, Nanjing, 210096, China
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16
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Moors M, Werner I, Bauer J, Lorenz J, Monakhov KY. Multistate switching of scanning tunnelling microscopy machined polyoxovanadate-dysprosium-phthalocyanine nanopatterns on graphite. NANOSCALE HORIZONS 2024; 9:233-237. [PMID: 38115762 DOI: 10.1039/d3nh00345k] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/21/2023]
Abstract
We demonstrate the first formation of stable, multistate switchable monolayers of polyoxometalates (POMs), which can be electronically triggered to higher charged states with increased conductance in the current-voltage profile at room temperature. These responsive two-dimensional monolayers are based on a fully oxidised dodecavanadate cage (POV12) equipped with Dy(III)-doped phthalocyanine (Pc) macrocycles adopting the face-on orientation on highly oriented pyrolytic graphite (HOPG). The layers can be lithographically processed by the tip of a scanning tunnelling microscope (STM) to machine patterns with diameters ranging from 30 to 150 nm2.
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Affiliation(s)
- Marco Moors
- Leibniz Institute of Surface Engineering (IOM), Permoserstraße 15, Leipzig 04318, Germany.
| | - Irina Werner
- Leibniz Institute of Surface Engineering (IOM), Permoserstraße 15, Leipzig 04318, Germany.
| | - Jens Bauer
- Leibniz Institute of Surface Engineering (IOM), Permoserstraße 15, Leipzig 04318, Germany.
| | - Jonas Lorenz
- Leibniz Institute of Surface Engineering (IOM), Permoserstraße 15, Leipzig 04318, Germany.
| | - Kirill Yu Monakhov
- Leibniz Institute of Surface Engineering (IOM), Permoserstraße 15, Leipzig 04318, Germany.
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17
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Mah DG, Park H, Cho WJ. Synaptic Plasticity Modulation of Neuromorphic Transistors through Phosphorus Concentration in Phosphosilicate Glass Electrolyte Gate. NANOMATERIALS (BASEL, SWITZERLAND) 2024; 14:203. [PMID: 38251166 PMCID: PMC10820041 DOI: 10.3390/nano14020203] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Revised: 01/05/2024] [Accepted: 01/15/2024] [Indexed: 01/23/2024]
Abstract
This study proposes a phosphosilicate glass (PSG)-based electrolyte gate synaptic transistor with varying phosphorus (P) concentrations. A metal oxide semiconductor capacitor structure device was employed to measure the frequency-dependent (C-f) capacitance curve, demonstrating that the PSG electric double-layer capacitance increased at 103 Hz with rising P concentration. Fourier transform infrared spectroscopy spectra analysis facilitated a theoretical understanding of the C-f curve results, examining peak differences in the P-OH structure based on P concentration. Using the proposed synaptic transistors with different P concentrations, changes in the hysteresis window were investigated by measuring the double-sweep transfer curves. Subsequently, alterations in proton movement within the PSG and charge characteristics at the channel/PSG electrolyte interface were observed through excitatory post-synaptic currents, paired-pulse facilitation, signal-filtering functions, resting current levels, and potentiation and depression characteristics. Finally, we demonstrated the proposed neuromorphic system's feasibility based on P concentration using the Modified National Institute of Standards and Technology learning simulations. The study findings suggest that, by adjusting the PSG film's P concentration for the same electrical stimulus, it is possible to selectively mimic the synaptic signal strength of human synapses. Therefore, this approach can positively contribute to the implementation of various neuromorphic systems.
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Affiliation(s)
- Dong-Gyun Mah
- Department of Electronic Materials Engineering, Kwangwoon University, Gwangun-ro 20, Nowon-gu, Seoul 01897, Republic of Korea
| | - Hamin Park
- Department of Electronic Engineering, Kwangwoon University, Gwangun-ro 20, Nowon-gu, Seoul 01897, Republic of Korea
| | - Won-Ju Cho
- Department of Electronic Materials Engineering, Kwangwoon University, Gwangun-ro 20, Nowon-gu, Seoul 01897, Republic of Korea
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18
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Sun B, Chen Y, Zhou G, Cao Z, Yang C, Du J, Chen X, Shao J. Memristor-Based Artificial Chips. ACS NANO 2024; 18:14-27. [PMID: 38153841 DOI: 10.1021/acsnano.3c07384] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/30/2023]
Abstract
Memristors, promising nanoelectronic devices with in-memory resistive switching behavior that is assembled with a physically integrated core processing unit (CPU) and memory unit and even possesses highly possible multistate electrical behavior, could avoid the von Neumann bottleneck of traditional computing devices and show a highly efficient ability of parallel computation and high information storage. These advantages position them as potential candidates for future data-centric computing requirements and add remarkable vigor to the research of next-generation artificial intelligence (AI) systems, particularly those that involve brain-like intelligence applications. This work provides an overview of the evolution of memristor-based devices, from their initial use in creating artificial synapses and neural networks to their application in developing advanced AI systems and brain-like chips. It offers a broad perspective of the key device primitives enabling their special applications from the view of materials, nanostructure, and mechanism models. We highlight these demonstrations of memristor-based nanoelectronic devices that have potential for use in the field of brain-like AI, point out the existing challenges of memristor-based nanodevices toward brain-like chips, and propose the guiding principle and promising outlook for future device promotion and system optimization in the biomedical AI field.
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Affiliation(s)
- Bai Sun
- National Local Joint Engineering Research Center for Precision Surgery & Regenerative Medicine, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi 710049, People's Republic of China
- Department of Hepatobiliary Surgery, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi 710049, People's Republic of China
- Micro-and Nano-technology Research Center, State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, People's Republic of China
- Frontier Institute of Science and Technology (FIST), Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China
| | - Yuanzheng Chen
- School of Physical Science and Technology, Key Laboratory of Advanced Technology of Materials, Southwest Jiaotong University, Chengdu, Sichuan 610031, People's Republic of China
| | - Guangdong Zhou
- College of Artificial Intelligence, Brain-inspired Computing & Intelligent Control of Chongqing Key Lab, Southwest University, Chongqing 400715, People's Republic of China
| | - Zelin Cao
- National Local Joint Engineering Research Center for Precision Surgery & Regenerative Medicine, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi 710049, People's Republic of China
- Department of Hepatobiliary Surgery, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi 710049, People's Republic of China
- Micro-and Nano-technology Research Center, State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, People's Republic of China
- Frontier Institute of Science and Technology (FIST), Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China
| | - Chuan Yang
- School of Physical Science and Technology, Key Laboratory of Advanced Technology of Materials, Southwest Jiaotong University, Chengdu, Sichuan 610031, People's Republic of China
| | - Junmei Du
- School of Physical Science and Technology, Key Laboratory of Advanced Technology of Materials, Southwest Jiaotong University, Chengdu, Sichuan 610031, People's Republic of China
| | - Xiaoliang Chen
- Micro-and Nano-technology Research Center, State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, People's Republic of China
- Frontier Institute of Science and Technology (FIST), Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China
| | - Jinyou Shao
- Micro-and Nano-technology Research Center, State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, People's Republic of China
- Frontier Institute of Science and Technology (FIST), Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China
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Shaikh MTAS, Nguyen THV, Jeon HJ, Prasad CV, Kim KJ, Jo ES, Kim S, Rim YS. Multilevel Reset Dependent Set of a Biodegradable Memristor with Physically Transient. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2306206. [PMID: 38032140 PMCID: PMC10811477 DOI: 10.1002/advs.202306206] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Revised: 10/23/2023] [Indexed: 12/01/2023]
Abstract
The electronic device, with its biocompatibility, biodegradability, and ease of fabrication process, shows great potential to embed into health monitoring and hardware data security systems. Herein, polyvinylpyrrolidone (PVP) biopolymer is presented as an active layer, electrochemically active magnesium (Mg) as a metal electrode, and chitosan-based substrate (CHS) to fabricate biocompatible and biodegradable physically transient neuromorphic device (W/Mg/PVP/Mg/CHS). The I-V curve of device is non-volatile bipolar in nature and shows a unique compliance-induced multilevel RESET-dependent-SET behavior while sweeping the compliance current from a few microamperes to milliamperes. Non-volatile and stable switching properties are demonstrated with a long endurance test (100 sweeps) and retention time of over 104 s. The physically transient memristor (PTM) has remarkably high dynamic RON /ROFF (ON/OFF state resistance) ratio (106 Ω), and when placed in deionized (DI) water, the device is observed to completely dissolve within 10 min. The pulse transient measurements demonstrate the neuromorphic computation capabilities of the device in the form of excitatory post synaptic current (EPSC), potentiation, depression, and learning behavior, which resemble the biological function of neurons. The results demonstrate the potential of W/Mg/PVP/Mg/CHS device for use in future healthcare and physically transient electronics.
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Affiliation(s)
- Mohammad Tauquir Alam Shamim Shaikh
- Department of Semiconductor Systems Engineering and Institute of Semiconductor and System ICSejong UniversitySeoul05006Republic of Korea
- Department of Intelligent Mechatronics Engineering and Convergence Engineering for Intelligent DroneSejong UniversitySeoul05006Republic of Korea
| | - Tan Hoang Vu Nguyen
- Department of Intelligent Mechatronics Engineering and Convergence Engineering for Intelligent DroneSejong UniversitySeoul05006Republic of Korea
| | - Ho Jung Jeon
- Department of Semiconductor Systems Engineering and Institute of Semiconductor and System ICSejong UniversitySeoul05006Republic of Korea
- Department of Intelligent Mechatronics Engineering and Convergence Engineering for Intelligent DroneSejong UniversitySeoul05006Republic of Korea
| | - Chowdam Venkata Prasad
- Department of Intelligent Mechatronics Engineering and Convergence Engineering for Intelligent DroneSejong UniversitySeoul05006Republic of Korea
| | - Kyong Jae Kim
- Department of Intelligent Mechatronics Engineering and Convergence Engineering for Intelligent DroneSejong UniversitySeoul05006Republic of Korea
| | - Eun Seo Jo
- Department of Semiconductor Systems Engineering and Institute of Semiconductor and System ICSejong UniversitySeoul05006Republic of Korea
| | - Sangmo Kim
- Department of Intelligent Mechatronics Engineering and Convergence Engineering for Intelligent DroneSejong UniversitySeoul05006Republic of Korea
| | - You Seung Rim
- Department of Semiconductor Systems Engineering and Institute of Semiconductor and System ICSejong UniversitySeoul05006Republic of Korea
- Department of Intelligent Mechatronics Engineering and Convergence Engineering for Intelligent DroneSejong UniversitySeoul05006Republic of Korea
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20
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Minnekhanov A, Matsukatova A, Trofimov A, Nesmelov A, Zavyalov S, Demin V, Emelyanov A. Reliable Memristive Synapses Based on Parylene-MoO x Nanocomposites for Neuromorphic Applications. ACS APPLIED MATERIALS & INTERFACES 2023; 15:54996-55008. [PMID: 37962902 DOI: 10.1021/acsami.3c13956] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Memristive devices, known for their nonvolatile resistive switching, are promising components for next-generation neuromorphic computing systems, which mimic the brain's neural architecture. Specifically, these devices are well-suited for functioning as artificial synapses due to their analogue tunability and low energy consumption. However, the improvement of their performance and reliability remains a pressing challenge. In this study, we report the development and comprehensive characterization of memristive devices based on a parylene-MoOx (PPX-Mo) nanocomposite layer, which exhibit improved characteristics over their parylene-based counterparts: lower switching voltage and energy, smaller dispersion, and better resistive plasticity. A robust statistical analysis identified the optimal synthesis parameters for these devices, providing valuable insights for future device optimization. The most probable resistive switching mechanism of the devices is proposed. By successfully integrating these memristors into a neuromorphic computing model and showcasing their scalability in crossbar geometry, we demonstrate their potential as functional artificial synapses. The results obtained from this study can be useful for the development of hardware-brain-inspired computational systems.
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Affiliation(s)
| | - Anna Matsukatova
- National Research Centre Kurchatov Institute, Moscow 123182, Russia
- Lomonosov Moscow State University, Moscow 119991, Russia
| | - Andrey Trofimov
- National Research Centre Kurchatov Institute, Moscow 123182, Russia
- Moscow Institute of Physics and Technology (National Research University), Dolgoprudny, Moscow 141701, Russia
| | | | - Sergey Zavyalov
- National Research Centre Kurchatov Institute, Moscow 123182, Russia
| | - Vyacheslav Demin
- National Research Centre Kurchatov Institute, Moscow 123182, Russia
- Moscow Institute of Physics and Technology (National Research University), Dolgoprudny, Moscow 141701, Russia
| | - Andrey Emelyanov
- National Research Centre Kurchatov Institute, Moscow 123182, Russia
- Moscow Institute of Physics and Technology (National Research University), Dolgoprudny, Moscow 141701, Russia
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Kang JH, Shin H, Kim KS, Song MK, Lee D, Meng Y, Choi C, Suh JM, Kim BJ, Kim H, Hoang AT, Park BI, Zhou G, Sundaram S, Vuong P, Shin J, Choe J, Xu Z, Younas R, Kim JS, Han S, Lee S, Kim SO, Kang B, Seo S, Ahn H, Seo S, Reidy K, Park E, Mun S, Park MC, Lee S, Kim HJ, Kum HS, Lin P, Hinkle C, Ougazzaden A, Ahn JH, Kim J, Bae SH. Monolithic 3D integration of 2D materials-based electronics towards ultimate edge computing solutions. NATURE MATERIALS 2023:10.1038/s41563-023-01704-z. [PMID: 38012388 DOI: 10.1038/s41563-023-01704-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/06/2023] [Accepted: 09/27/2023] [Indexed: 11/29/2023]
Abstract
Three-dimensional (3D) hetero-integration technology is poised to revolutionize the field of electronics by stacking functional layers vertically, thereby creating novel 3D circuity architectures with high integration density and unparalleled multifunctionality. However, the conventional 3D integration technique involves complex wafer processing and intricate interlayer wiring. Here we demonstrate monolithic 3D integration of two-dimensional, material-based artificial intelligence (AI)-processing hardware with ultimate integrability and multifunctionality. A total of six layers of transistor and memristor arrays were vertically integrated into a 3D nanosystem to perform AI tasks, by peeling and stacking of AI processing layers made from bottom-up synthesized two-dimensional materials. This fully monolithic-3D-integrated AI system substantially reduces processing time, voltage drops, latency and footprint due to its densely packed AI processing layers with dense interlayer connectivity. The successful demonstration of this monolithic-3D-integrated AI system will not only provide a material-level solution for hetero-integration of electronics, but also pave the way for unprecedented multifunctional computing hardware with ultimate parallelism.
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Affiliation(s)
- Ji-Hoon Kang
- Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
- Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Electronic Engineering, Inha University, Incheon, Republic of Korea
| | - Heechang Shin
- School of Electrical and Electronic Engineering, Yonsei University, Seoul, Republic of Korea
| | - Ki Seok Kim
- Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
- Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Min-Kyu Song
- Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
- Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Doyoon Lee
- Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
- Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Yuan Meng
- Department of Mechanical Engineering and Materials Science, Washington University in Saint Louis, Saint Louis, MO, USA
| | - Chanyeol Choi
- Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Jun Min Suh
- Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
- Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Beom Jin Kim
- School of Electrical and Electronic Engineering, Yonsei University, Seoul, Republic of Korea
| | - Hyunseok Kim
- Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
- Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Anh Tuan Hoang
- School of Electrical and Electronic Engineering, Yonsei University, Seoul, Republic of Korea
| | - Bo-In Park
- Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
- Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Guanyu Zhou
- Department of Electrical Engineering, University of Notre Dame, Notre Dame, IN, USA
| | - Suresh Sundaram
- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA
- CNRS, Georgia Tech - CNRS IRL 2958, GT-Europe, Metz, France
| | - Phuong Vuong
- CNRS, Georgia Tech - CNRS IRL 2958, GT-Europe, Metz, France
| | - Jiho Shin
- Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
- Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Jinyeong Choe
- School of Electrical and Electronic Engineering, Yonsei University, Seoul, Republic of Korea
| | - Zhihao Xu
- Institute of Materials Science and Engineering, Washington University in Saint Louis, Saint Louis, MO, USA
| | - Rehan Younas
- Department of Electrical Engineering, University of Notre Dame, Notre Dame, IN, USA
| | - Justin S Kim
- Institute of Materials Science and Engineering, Washington University in Saint Louis, Saint Louis, MO, USA
| | - Sangmoon Han
- Department of Mechanical Engineering and Materials Science, Washington University in Saint Louis, Saint Louis, MO, USA
| | - Sangho Lee
- Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
- Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Sun Ok Kim
- Department of Mechanical Engineering and Materials Science, Washington University in Saint Louis, Saint Louis, MO, USA
| | - Beomseok Kang
- Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
- Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Seungju Seo
- Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
- Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Hyojung Ahn
- Future Innovation Research Center, Korea Aerospace Research Institute, Daejeon, Republic of Korea
- Aerospace System Engineering, University of Science and Technology, Daejeon, Republic of Korea
| | - Seunghwan Seo
- Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
- Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Kate Reidy
- Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Eugene Park
- Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Sungchul Mun
- Department of Industrial Engineering, Jeonju University, Jeonju, Republic of Korea
- Convergence Institute of Human Data Technology, Jeonju University, Jeonju, Republic of Korea
| | - Min-Chul Park
- Post-Silicon Semiconductor Institute, Korea Institute of Science and Technology, Seoul, Republic of Korea
| | - Suyoun Lee
- Post-Silicon Semiconductor Institute, Korea Institute of Science and Technology, Seoul, Republic of Korea
| | - Hyung-Jun Kim
- Post-Silicon Semiconductor Institute, Korea Institute of Science and Technology, Seoul, Republic of Korea
| | - Hyun S Kum
- School of Electrical and Electronic Engineering, Yonsei University, Seoul, Republic of Korea
| | - Peng Lin
- Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
- Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA, USA
- College of Computer Science and Technology, Zhejiang University, Hangzhou, China
| | - Christopher Hinkle
- Department of Electrical Engineering, University of Notre Dame, Notre Dame, IN, USA
| | - Abdallah Ougazzaden
- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA
- CNRS, Georgia Tech - CNRS IRL 2958, GT-Europe, Metz, France
| | - Jong-Hyun Ahn
- School of Electrical and Electronic Engineering, Yonsei University, Seoul, Republic of Korea.
| | - Jeehwan Kim
- Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.
- Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA, USA.
- Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.
| | - Sang-Hoon Bae
- Department of Mechanical Engineering and Materials Science, Washington University in Saint Louis, Saint Louis, MO, USA.
- Institute of Materials Science and Engineering, Washington University in Saint Louis, Saint Louis, MO, USA.
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22
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Leonetti G, Fretto M, Pirri FC, De Leo N, Valov I, Milano G. Effect of electrode materials on resistive switching behaviour of NbO x-based memristive devices. Sci Rep 2023; 13:17003. [PMID: 37813937 PMCID: PMC10562416 DOI: 10.1038/s41598-023-44110-w] [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: 06/19/2023] [Accepted: 10/03/2023] [Indexed: 10/11/2023] Open
Abstract
Memristive devices that rely on redox-based resistive switching mechanism have attracted great attention for the development of next-generation memory and computing architectures. However, a detailed understanding of the relationship between involved materials, interfaces, and device functionalities still represents a challenge. In this work, we analyse the effect of electrode metals on resistive switching functionalities of NbOx-based memristive cells. For this purpose, the effect of Au, Pt, Ir, TiN, and Nb top electrodes was investigated in devices based on amorphous NbOx grown by anodic oxidation on a Nb substrate exploited also as counter electrode. It is shown that the choice of the metal electrode regulates electronic transport properties of metal-insulator interfaces, strongly influences the electroforming process, and the following resistive switching characteristics. Results show that the electronic blocking character of Schottky interfaces provided by Au and Pt metal electrodes results in better resistive switching performances. It is shown that Pt represents the best choice for the realization of memristive cells when the NbOx thickness is reduced, making possible the realization of memristive cells characterised by low variability in operating voltages, resistance states and with low device-to-device variability. These results can provide new insights towards a rational design of redox-based memristive cells.
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Affiliation(s)
- Giuseppe Leonetti
- Department of Applied Science and Technology (DISAT), Politecnico di Torino, C.So Duca Degli Abruzzi 24, 10129, Turin, Italy
| | - Matteo Fretto
- Advanced Materials Metrology and Life Sciences Division, Istituto Nazionale Di Ricerca Metrologica (INRiM), Strada Delle Cacce 91, 10135, Turin, Italy
| | - Fabrizio Candido Pirri
- Department of Applied Science and Technology (DISAT), Politecnico di Torino, C.So Duca Degli Abruzzi 24, 10129, Turin, Italy
| | - Natascia De Leo
- Advanced Materials Metrology and Life Sciences Division, Istituto Nazionale Di Ricerca Metrologica (INRiM), Strada Delle Cacce 91, 10135, Turin, Italy
| | - Ilia Valov
- Institute of Electrochemistry and Energy System, Forschungszentrum Jülich, WilhelmJohnen-Straße, 52428, Jülich, Germany.
- "Acad. Evgeni Budevski" IEE-BAS, Bulgarian Academy of Sciences (BAS), Acad. G. Bonchev Str, Block 10, 1113, Sofia, Bulgaria.
| | - Gianluca Milano
- Advanced Materials Metrology and Life Sciences Division, Istituto Nazionale Di Ricerca Metrologica (INRiM), Strada Delle Cacce 91, 10135, Turin, Italy.
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