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Defferriere T, Wang B, Klein J, Ross FM, Tuller HL. Field-Driven Solid-State Defect Control of Bilayer Switching Devices. ACS APPLIED MATERIALS & INTERFACES 2024; 16:46461-46472. [PMID: 39163521 DOI: 10.1021/acsami.4c09826] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/22/2024]
Abstract
We develop a framework for controlling and investigating reversible ionic transfer between two solid metal oxides layers by examining field-driven changes in electrical properties of the thin film bilayer oxide system Pr0.1Ce0.9O2/La1.85Ce0.15CuO4 (PCO/LCCO). We show that we can reversibly redistribute oxygen ions by applied voltage in a highly controlled and reversible fashion near ambient temperatures over large oxygen ion activity limits, which, for the first time, is directly interpretable by defect chemical models. This allowed us to determine how defect concentrations in each layer systematically varied with voltage and the subsequent impact on each film's conductance. These results showcase the relevance and applicability of defect chemical models, traditionally considered only at elevated temperatures, to the development of bilayer devices of importance to neuromorphic memory applications. This allows for a more systematic approach for studying and understanding the solid-solid exchange process in electrochemically controlled microelectronic devices. Moreover, our work sets the foundation for the development of large-area field-driven defect-controlled bilayer switching devices with potential application to a broad array of functionally modulated devices.
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Affiliation(s)
- Thomas Defferriere
- Department of Materials Science and Engineering, MIT, Cambridge, Massachusetts 02139, United States
| | - Baoming Wang
- Department of Materials Science and Engineering, MIT, Cambridge, Massachusetts 02139, United States
| | - Julian Klein
- Department of Materials Science and Engineering, MIT, Cambridge, Massachusetts 02139, United States
| | - Frances M Ross
- Department of Materials Science and Engineering, MIT, Cambridge, Massachusetts 02139, United States
| | - Harry L Tuller
- Department of Materials Science and Engineering, MIT, Cambridge, Massachusetts 02139, United States
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2
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Deng X, Liu YX, Yang ZZ, Zhao YF, Xu YT, Fu MY, Shen Y, Qu K, Guan Z, Tong WY, Zhang YY, Chen BB, Zhong N, Xiang PH, Duan CG. Spatial evolution of the proton-coupled Mott transition in correlated oxides for neuromorphic computing. SCIENCE ADVANCES 2024; 10:eadk9928. [PMID: 38820158 PMCID: PMC11141630 DOI: 10.1126/sciadv.adk9928] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/22/2023] [Accepted: 04/29/2024] [Indexed: 06/02/2024]
Abstract
The proton-electron coupling effect induces rich spectrums of electronic states in correlated oxides, opening tempting opportunities for exploring novel devices with multifunctions. Here, via modest Pt-aided hydrogen spillover at room temperature, amounts of protons are introduced into SmNiO3-based devices. In situ structural characterizations together with first-principles calculation reveal that the local Mott transition is reversibly driven by migration and redistribution of the predoped protons. The accompanying giant resistance change results in excellent memristive behaviors under ultralow electric fields. Hierarchical tree-like memory states, an instinct displayed in bio-synapses, are further realized in the devices by spatially varying the proton concentration with electric pulses, showing great promise in artificial neural networks for solving intricate problems. Our research demonstrates the direct and effective control of proton evolution using extremely low electric field, offering an alternative pathway for modifying the functionalities of correlated oxides and constructing low-power consumption intelligent devices and neural network circuits.
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Affiliation(s)
- Xing Deng
- Key Laboratory of Polar Materials and Devices (Ministry of Education), Shanghai Center of Brain-Inspired Intelligent Materials and Devices, Department of Electronics, East China Normal University, Shanghai 200241, China
| | - Yu-Xiang Liu
- Key Laboratory of Polar Materials and Devices (Ministry of Education), Shanghai Center of Brain-Inspired Intelligent Materials and Devices, Department of Electronics, East China Normal University, Shanghai 200241, China
| | - Zhen-Zhong Yang
- Key Laboratory of Polar Materials and Devices (Ministry of Education), Shanghai Center of Brain-Inspired Intelligent Materials and Devices, Department of Electronics, East China Normal University, Shanghai 200241, China
| | - Yi-Feng Zhao
- Key Laboratory of Polar Materials and Devices (Ministry of Education), Shanghai Center of Brain-Inspired Intelligent Materials and Devices, Department of Electronics, East China Normal University, Shanghai 200241, China
| | - Ya-Ting Xu
- Key Laboratory of Polar Materials and Devices (Ministry of Education), Shanghai Center of Brain-Inspired Intelligent Materials and Devices, Department of Electronics, East China Normal University, Shanghai 200241, China
| | - Meng-Yao Fu
- Key Laboratory of Polar Materials and Devices (Ministry of Education), Shanghai Center of Brain-Inspired Intelligent Materials and Devices, Department of Electronics, East China Normal University, Shanghai 200241, China
| | - Yu Shen
- Key Laboratory of Polar Materials and Devices (Ministry of Education), Shanghai Center of Brain-Inspired Intelligent Materials and Devices, Department of Electronics, East China Normal University, Shanghai 200241, China
| | - Ke Qu
- Key Laboratory of Polar Materials and Devices (Ministry of Education), Shanghai Center of Brain-Inspired Intelligent Materials and Devices, Department of Electronics, East China Normal University, Shanghai 200241, China
| | - Zhao Guan
- Key Laboratory of Polar Materials and Devices (Ministry of Education), Shanghai Center of Brain-Inspired Intelligent Materials and Devices, Department of Electronics, East China Normal University, Shanghai 200241, China
| | - Wen-Yi Tong
- Key Laboratory of Polar Materials and Devices (Ministry of Education), Shanghai Center of Brain-Inspired Intelligent Materials and Devices, Department of Electronics, East China Normal University, Shanghai 200241, China
| | - Yuan-Yuan Zhang
- Key Laboratory of Polar Materials and Devices (Ministry of Education), Shanghai Center of Brain-Inspired Intelligent Materials and Devices, Department of Electronics, East China Normal University, Shanghai 200241, China
| | - Bin-Bin Chen
- Key Laboratory of Polar Materials and Devices (Ministry of Education), Shanghai Center of Brain-Inspired Intelligent Materials and Devices, Department of Electronics, East China Normal University, Shanghai 200241, China
| | - Ni Zhong
- Key Laboratory of Polar Materials and Devices (Ministry of Education), Shanghai Center of Brain-Inspired Intelligent Materials and Devices, Department of Electronics, East China Normal University, Shanghai 200241, China
- Collaborative Innovation Center of Extreme Optics, Shanxi University, Taiyuan, Shanxi 030006, China
| | - Ping-Hua Xiang
- Key Laboratory of Polar Materials and Devices (Ministry of Education), Shanghai Center of Brain-Inspired Intelligent Materials and Devices, Department of Electronics, East China Normal University, Shanghai 200241, China
- Collaborative Innovation Center of Extreme Optics, Shanxi University, Taiyuan, Shanxi 030006, China
| | - Chun-Gang Duan
- Key Laboratory of Polar Materials and Devices (Ministry of Education), Shanghai Center of Brain-Inspired Intelligent Materials and Devices, Department of Electronics, East China Normal University, Shanghai 200241, China
- Collaborative Innovation Center of Extreme Optics, Shanxi University, Taiyuan, Shanxi 030006, China
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3
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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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4
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Xu M, Chen X, Guo Y, Wang Y, Qiu D, Du X, Cui Y, Wang X, Xiong J. Reconfigurable Neuromorphic Computing: Materials, Devices, and Integration. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2023; 35:e2301063. [PMID: 37285592 DOI: 10.1002/adma.202301063] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Revised: 05/15/2023] [Indexed: 06/09/2023]
Abstract
Neuromorphic computing has been attracting ever-increasing attention due to superior energy efficiency, with great promise to promote the next wave of artificial general intelligence in the post-Moore era. Current approaches are, however, broadly designed for stationary and unitary assignments, thus encountering reluctant interconnections, power consumption, and data-intensive computing in that domain. Reconfigurable neuromorphic computing, an on-demand paradigm inspired by the inherent programmability of brain, can maximally reallocate finite resources to perform the proliferation of reproducibly brain-inspired functions, highlighting a disruptive framework for bridging the gap between different primitives. Although relevant research has flourished in diverse materials and devices with novel mechanisms and architectures, a precise overview remains blank and urgently desirable. Herein, the recent strides along this pursuit are systematically reviewed from material, device, and integration perspectives. At the material and device level, one comprehensively conclude the dominant mechanisms for reconfigurability, categorized into ion migration, carrier migration, phase transition, spintronics, and photonics. Integration-level developments for reconfigurable neuromorphic computing are also exhibited. Finally, a perspective on the future challenges for reconfigurable neuromorphic computing is discussed, definitely expanding its horizon for scientific communities.
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Affiliation(s)
- Minyi Xu
- State Key Laboratory of Electronic Thin Film and Integrated Devices, School of Physics, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Xinrui Chen
- State Key Laboratory of Electronic Thin Film and Integrated Devices, School of Physics, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Yehao Guo
- State Key Laboratory of Electronic Thin Film and Integrated Devices, School of Physics, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Yang Wang
- State Key Laboratory of Electronic Thin Film and Integrated Devices, School of Physics, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Dong Qiu
- State Key Laboratory of Electronic Thin Film and Integrated Devices, School of Physics, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Xinchuan Du
- State Key Laboratory of Electronic Thin Film and Integrated Devices, School of Physics, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Yi Cui
- State Key Laboratory of Electronic Thin Film and Integrated Devices, School of Physics, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Xianfu Wang
- State Key Laboratory of Electronic Thin Film and Integrated Devices, School of Physics, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Jie Xiong
- State Key Laboratory of Electronic Thin Film and Integrated Devices, School of Physics, University of Electronic Science and Technology of China, Chengdu, 610054, China
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5
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Nikam RD, Lee J, Lee K, Hwang H. Exploring the Cutting-Edge Frontiers of Electrochemical Random Access Memories (ECRAMs) for Neuromorphic Computing: Revolutionary Advances in Material-to-Device Engineering. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2023; 19:e2302593. [PMID: 37300356 DOI: 10.1002/smll.202302593] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Revised: 05/23/2023] [Indexed: 06/12/2023]
Abstract
Advanced materials and device engineering has played a crucial role in improving the performance of electrochemical random access memory (ECRAM) devices. ECRAM technology has been identified as a promising candidate for implementing artificial synapses in neuromorphic computing systems due to its ability to store analog values and its ease of programmability. ECRAM devices consist of an electrolyte and a channel material sandwiched between two electrodes, and the performance of these devices depends on the properties of the materials used. This review provides a comprehensive overview of material engineering strategies to optimize the electrolyte and channel materials' ionic conductivity, stability, and ionic diffusivity to improve the performance and reliability of ECRAM devices. Device engineering and scaling strategies are further discussed to enhance ECRAM performance. Last, perspectives on the current challenges and future directions in developing ECRAM-based artificial synapses in neuromorphic computing systems are provided.
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Affiliation(s)
- Revannath Dnyandeo Nikam
- Center for Single Atom-based Semiconductor Device, Pohang University of Science and Technology (POSTECH), Pohang, 790-784, Republic of Korea
- Department of Material Science and Engineering, Pohang University of Science and Technology (POSTECH), Pohang, 790-784, Republic of Korea
| | - Jongwon Lee
- Center for Single Atom-based Semiconductor Device, Pohang University of Science and Technology (POSTECH), Pohang, 790-784, Republic of Korea
- Department of Material Science and Engineering, Pohang University of Science and Technology (POSTECH), Pohang, 790-784, Republic of Korea
| | - Kyumin Lee
- Center for Single Atom-based Semiconductor Device, Pohang University of Science and Technology (POSTECH), Pohang, 790-784, Republic of Korea
- Department of Material Science and Engineering, Pohang University of Science and Technology (POSTECH), Pohang, 790-784, Republic of Korea
| | - Hyunsang Hwang
- Center for Single Atom-based Semiconductor Device, Pohang University of Science and Technology (POSTECH), Pohang, 790-784, Republic of Korea
- Department of Material Science and Engineering, Pohang University of Science and Technology (POSTECH), Pohang, 790-784, Republic of Korea
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6
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Talin AA, Li Y, Robinson DA, Fuller EJ, Kumar S. ECRAM Materials, Devices, Circuits and Architectures: A Perspective. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2023; 35:e2204771. [PMID: 36354177 DOI: 10.1002/adma.202204771] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Revised: 07/09/2022] [Indexed: 06/16/2023]
Abstract
Non-von-Neumann computing using neuromorphic systems based on two-terminal resistive nonvolatile memory elements has emerged as a promising approach, but its full potential has not been realized due to the lack of materials and devices with the appropriate attributes. Unlike memristors, which require large write currents to drive phase transformations or filament growth, electrochemical random access memory (ECRAM) decouples the "write" and "read" operations using a "gate" electrode to tune the conductance state through charge-transfer reactions, and every electron transferred through the external circuit in ECRAM corresponds to the migration of ≈1 ion used to store analogue information. Like static dopants in traditional semiconductors, electrochemically inserted ions modulate the conductivity by locally perturbing a host's electronic structure; however, ECRAM does so in a dynamic and reversible manner. The resulting change in conductance can span orders of magnitude, from gradual increments needed for analog elements, to large, abrupt changes for dynamically reconfigurable adaptive architectures. In this in-depth perspective, the history of ECRAM, the recent progress in devices spanning organic, inorganic, and 2D materials, circuits, architectures, the rich portfolio of challenging, fundamental questions, and how ECRAM can be harnessed to realize a new paradigm for low-power neuromorphic computing are discussed.
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Affiliation(s)
- A Alec Talin
- Sandia National Laboratories, Livermore, CA, 94551, USA
| | - Yiyang Li
- Department of Materials Science and Engineering, University of Michigan, Ann Arbor, MI, 48109, USA
| | | | | | - Suhas Kumar
- Sandia National Laboratories, Livermore, CA, 94551, USA
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7
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Huang M, Schwacke M, Onen M, Del Alamo J, Li J, Yildiz B. Electrochemical Ionic Synapses: Progress and Perspectives. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2023; 35:e2205169. [PMID: 36300807 DOI: 10.1002/adma.202205169] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Revised: 09/13/2022] [Indexed: 06/16/2023]
Abstract
Artificial neural networks based on crossbar arrays of analog programmable resistors can address the high energy challenge of conventional hardware in artificial intelligence applications. However, state-of-the-art two-terminal resistive switching devices based on conductive filament formation suffer from high variability and poor controllability. Electrochemical ionic synapses are three-terminal devices that operate by electrochemical and dynamic insertion/extraction of ions that control the electronic conductivity of a channel in a single solid-solution phase. They are promising candidates for programmable resistors in crossbar arrays because they have shown uniform and deterministic control of electronic conductivity based on ion doping, with very low energy consumption. Here, the desirable specifications of these programmable resistors are presented. Then, an overview of the current progress of devices based on Li+ , O2- , and H+ ions and material systems is provided. Achieving nanosecond speed, low operation voltage (≈1 V), low energy consumption, with complementary metal-oxide-semiconductor compatibility all simultaneously remains a challenge. Toward this goal, a physical model of the device is constructed to provide guidelines for the desired material properties to overcome the remaining challenges. Finally, an outlook is provided, including strategies to advance materials toward the desirable properties and the future opportunities for electrochemical ionic synapses.
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Affiliation(s)
- Mantao Huang
- Department of Nuclear Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Miranda Schwacke
- Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Murat Onen
- Microsystems Technology Laboratories, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Jesús Del Alamo
- Microsystems Technology Laboratories, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Ju Li
- Department of Nuclear Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
- Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Bilge Yildiz
- Department of Nuclear Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
- Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
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8
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Onen M, Emond N, Wang B, Zhang D, Ross FM, Li J, Yildiz B, Del Alamo JA. Nanosecond protonic programmable resistors for analog deep learning. Science 2022; 377:539-543. [PMID: 35901152 DOI: 10.1126/science.abp8064] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
Abstract
Nanoscale ionic programmable resistors for analog deep learning are 1000 times smaller than biological cells, but it is not yet clear how much faster they can be relative to neurons and synapses. Scaling analyses of ionic transport and charge-transfer reaction rates point to operation in the nonlinear regime, where extreme electric fields are present within the solid electrolyte and its interfaces. In this work, we generated silicon-compatible nanoscale protonic programmable resistors with highly desirable characteristics under extreme electric fields. This operation regime enabled controlled shuttling and intercalation of protons in nanoseconds at room temperature in an energy-efficient manner. The devices showed symmetric, linear, and reversible modulation characteristics with many conductance states covering a 20× dynamic range. Thus, the space-time-energy performance of the all-solid-state artificial synapses can greatly exceed that of their biological counterparts.
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Affiliation(s)
- Murat Onen
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, 77 Massachusetts Ave., Cambridge, MA 02139, USA.,MIT-IBM Watson AI Lab, 75 Binney St., Cambridge, MA 02142, USA
| | - Nicolas Emond
- MIT-IBM Watson AI Lab, 75 Binney St., Cambridge, MA 02142, USA.,Department of Materials Science and Engineering, Massachusetts Institute of Technology, 77 Massachusetts Ave., Cambridge, MA 02139, USA
| | - Baoming Wang
- MIT-IBM Watson AI Lab, 75 Binney St., Cambridge, MA 02142, USA.,Department of Materials Science and Engineering, Massachusetts Institute of Technology, 77 Massachusetts Ave., Cambridge, MA 02139, USA
| | - Difei Zhang
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, 77 Massachusetts Ave., Cambridge, MA 02139, USA.,MIT-IBM Watson AI Lab, 75 Binney St., Cambridge, MA 02142, USA
| | - Frances M Ross
- MIT-IBM Watson AI Lab, 75 Binney St., Cambridge, MA 02142, USA.,Department of Materials Science and Engineering, Massachusetts Institute of Technology, 77 Massachusetts Ave., Cambridge, MA 02139, USA
| | - Ju Li
- MIT-IBM Watson AI Lab, 75 Binney St., Cambridge, MA 02142, USA.,Department of Materials Science and Engineering, Massachusetts Institute of Technology, 77 Massachusetts Ave., Cambridge, MA 02139, USA.,Department of Nuclear Science and Engineering, Massachusetts Institute of Technology, 77 Massachusetts Ave., Cambridge, MA 02139, USA
| | - Bilge Yildiz
- MIT-IBM Watson AI Lab, 75 Binney St., Cambridge, MA 02142, USA.,Department of Materials Science and Engineering, Massachusetts Institute of Technology, 77 Massachusetts Ave., Cambridge, MA 02139, USA.,Department of Nuclear Science and Engineering, Massachusetts Institute of Technology, 77 Massachusetts Ave., Cambridge, MA 02139, USA
| | - Jesús A Del Alamo
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, 77 Massachusetts Ave., Cambridge, MA 02139, USA.,MIT-IBM Watson AI Lab, 75 Binney St., Cambridge, MA 02142, USA
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9
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Tian H, Bazant MZ. Interfacial Resistive Switching by Multiphase Polarization in Ion-Intercalation Nanofilms. NANO LETTERS 2022; 22:5866-5873. [PMID: 35815943 DOI: 10.1021/acs.nanolett.2c01765] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Nonvolatile resistive-switching (RS) memories promise to revolutionize hardware architectures with in-memory computing. Recently, ion-interclation materials have attracted increasing attention as potential RS materials for their ion-modulated electronic conductivity. In this Letter, we propose RS by multiphase polarization (MP) of ion-intercalated thin films between ion-blocking electrodes, in which interfacial phase separation triggered by an applied voltage switches the electron-transfer resistance. We develop an electrochemical phase-field model for simulations of coupled ion-electron transport and ion-modulated electron-transfer rates and use it to analyze the MP switching current and time, resistance ratio, and current-voltage response. The model is able to reproduce the complex cyclic voltammograms of lithium titanate (LTO) memristors, which cannot be explained by existing models based on bulk dielectric breakdown. The theory predicts the achievable switching speeds for multiphase ion-intercalation materials and could be used to guide the design of high-performance MP-based RS memories.
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Affiliation(s)
- Huanhuan Tian
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Martin Z Bazant
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
- Department of Mathematics, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
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10
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Wang S, Jiang H, Dong Y, Clarkson D, Zhu H, Settens CM, Ren Y, Nguyen T, Han F, Fan W, Kim SY, Zhang J, Xue W, Sandstrom SK, Xu G, Tekoglu E, Li M, Deng S, Liu Q, Greenbaum SG, Ji X, Gao T, Li J. Acid-in-Clay Electrolyte for Wide-Temperature-Range and Long-Cycle Proton Batteries. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2022; 34:e2202063. [PMID: 35443084 DOI: 10.1002/adma.202202063] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Revised: 03/30/2022] [Indexed: 06/14/2023]
Abstract
Proton conduction underlies many important electrochemical technologies. A family of new proton electrolytes is reported: acid-in-clay electrolyte (AiCE) prepared by integrating fast proton carriers in a natural phyllosilicate clay network, which can be made into thin-film (tens of micrometers) fluid-impervious membranes. The chosen example systems (sepiolite-phosphoric acid) rank top among the solid proton conductors in terms of proton conductivities (15 mS cm-1 at 25 °C, 0.023 mS cm-1 at -82 °C), electrochemical stability window (3.35 V), and reduced chemical reactivity. A proton battery is assembled using AiCE as the solid electrolyte membrane. Benefitting from the wider electrochemical stability window, reduced corrosivity, and excellent ionic selectivity of AiCE, the two main problems (gassing and cyclability) of proton batteries are successfully solved. This work draws attention to the element cross-over problem in proton batteries and the generic "acid-in-clay" solid electrolyte approach with superfast proton transport, outstanding selectivity, and improved stability for room- to cryogenic-temperature protonic applications.
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Affiliation(s)
- Shitong Wang
- Department of Nuclear Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
- Department of Chemical Engineering, The University of Utah, Salt Lake City, UT, 84112, USA
| | - Heng Jiang
- Department of Chemistry, Oregon State University, Corvallis, OR, 97331, USA
| | - Yanhao Dong
- Department of Nuclear Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - David Clarkson
- Department of Physics and Astronomy, Hunter College, City University of New York, New York, NY, 10065, USA
| | - He Zhu
- Department of Physics, City University of Hong Kong, Kowloon, Hong Kong, 999077, China
| | - Charles M Settens
- Materials Research Laboratory, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Yang Ren
- Department of Physics, City University of Hong Kong, Kowloon, Hong Kong, 999077, China
| | - Thanh Nguyen
- Department of Nuclear Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Fei Han
- Department of Nuclear Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Weiwei Fan
- Department of Nuclear Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - So Yeon Kim
- Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Jianan Zhang
- Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Weijiang Xue
- Department of Nuclear Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Sean K Sandstrom
- Department of Chemistry, Oregon State University, Corvallis, OR, 97331, USA
| | - Guiyin Xu
- Department of Nuclear Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Emre Tekoglu
- Department of Nuclear Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Mingda Li
- Department of Nuclear Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Sili Deng
- Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Qi Liu
- Department of Physics, City University of Hong Kong, Kowloon, Hong Kong, 999077, China
| | - Steven G Greenbaum
- Department of Physics and Astronomy, Hunter College, City University of New York, New York, NY, 10065, USA
| | - Xiulei Ji
- Department of Chemistry, Oregon State University, Corvallis, OR, 97331, USA
| | - Tao Gao
- Department of Chemical Engineering, The University of Utah, Salt Lake City, UT, 84112, USA
| | - Ju Li
- Department of Nuclear Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
- Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
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11
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Yoon C, Oh G, Park BH. Ion-Movement-Based Synaptic Device for Brain-Inspired Computing. NANOMATERIALS (BASEL, SWITZERLAND) 2022; 12:1728. [PMID: 35630952 PMCID: PMC9148095 DOI: 10.3390/nano12101728] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Revised: 05/13/2022] [Accepted: 05/16/2022] [Indexed: 02/04/2023]
Abstract
As the amount of data has grown exponentially with the advent of artificial intelligence and the Internet of Things, computing systems with high energy efficiency, high scalability, and high processing speed are urgently required. Unlike traditional digital computing, which suffers from the von Neumann bottleneck, brain-inspired computing can provide efficient, parallel, and low-power computation based on analog changes in synaptic connections between neurons. Synapse nodes in brain-inspired computing have been typically implemented with dozens of silicon transistors, which is an energy-intensive and non-scalable approach. Ion-movement-based synaptic devices for brain-inspired computing have attracted increasing attention for mimicking the performance of the biological synapse in the human brain due to their low area and low energy costs. This paper discusses the recent development of ion-movement-based synaptic devices for hardware implementation of brain-inspired computing and their principles of operation. From the perspective of the device-level requirements for brain-inspired computing, we address the advantages, challenges, and future prospects associated with different types of ion-movement-based synaptic devices.
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Affiliation(s)
| | | | - Bae Ho Park
- Division of Quantum Phases & Devices, Department of Physics, Konkuk University, Seoul 05029, Korea; (C.Y.); (G.O.)
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