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Xue G, Qin B, Ma C, Yin P, Liu C, Liu K. Large-Area Epitaxial Growth of Transition Metal Dichalcogenides. Chem Rev 2024. [PMID: 39132950 DOI: 10.1021/acs.chemrev.3c00851] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/13/2024]
Abstract
Over the past decade, research on atomically thin two-dimensional (2D) transition metal dichalcogenides (TMDs) has expanded rapidly due to their unique properties such as high carrier mobility, significant excitonic effects, and strong spin-orbit couplings. Considerable attention from both scientific and industrial communities has fully fueled the exploration of TMDs toward practical applications. Proposed scenarios, such as ultrascaled transistors, on-chip photonics, flexible optoelectronics, and efficient electrocatalysis, critically depend on the scalable production of large-area TMD films. Correspondingly, substantial efforts have been devoted to refining the synthesizing methodology of 2D TMDs, which brought the field to a stage that necessitates a comprehensive summary. In this Review, we give a systematic overview of the basic designs and significant advancements in large-area epitaxial growth of TMDs. We first sketch out their fundamental structures and diverse properties. Subsequent discussion encompasses the state-of-the-art wafer-scale production designs, single-crystal epitaxial strategies, and techniques for structure modification and postprocessing. Additionally, we highlight the future directions for application-driven material fabrication and persistent challenges, aiming to inspire ongoing exploration along a revolution in the modern semiconductor industry.
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Affiliation(s)
- Guodong Xue
- State Key Laboratory for Mesoscopic Physics, Frontiers Science Center for Nano-optoelectronics, School of Physics, Peking University, Beijing 100871, China
- Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China
| | - Biao Qin
- State Key Laboratory for Mesoscopic Physics, Frontiers Science Center for Nano-optoelectronics, School of Physics, Peking University, Beijing 100871, China
| | - Chaojie Ma
- State Key Laboratory for Mesoscopic Physics, Frontiers Science Center for Nano-optoelectronics, School of Physics, Peking University, Beijing 100871, China
| | - Peng Yin
- Key Laboratory of Quantum State Construction and Manipulation (Ministry of Education), Department of Physics, Renmin University of China, Beijing 100872, China
| | - Can Liu
- Key Laboratory of Quantum State Construction and Manipulation (Ministry of Education), Department of Physics, Renmin University of China, Beijing 100872, China
| | - Kaihui Liu
- State Key Laboratory for Mesoscopic Physics, Frontiers Science Center for Nano-optoelectronics, School of Physics, Peking University, Beijing 100871, China
- International Centre for Quantum Materials, Collaborative Innovation Centre of Quantum Matter, Peking University, Beijing 100871, China
- Songshan Lake Materials Laboratory, Dongguan 523808, China
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2
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R M, Raina G. Compositional effects of hybrid MoS 2-GO active layer on the performance of unipolar, low-power and multistate RRAM device. NANOTECHNOLOGY 2024; 35:405701. [PMID: 38955133 DOI: 10.1088/1361-6528/ad5db6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/07/2024] [Accepted: 07/02/2024] [Indexed: 07/04/2024]
Abstract
Currently, 2D nanomaterials-based resistive random access memory (RRAMs) are explored on account of their tunable material properties enabling fabrication of low power and flexible RRAM devices. In this work, hybrid MoS2-GO based active layer RRAM devices are investigated. A facile hydrothermal co-synthesis approach is used to obtain the hybrid materials and a cost-effective spin coating method adopted for the fabrication of Ag/MoS2-GO/ITO RRAM devices. The performance of the fabricated hybrid active layer RRAM device is analysed with respect to change in material properties of the synthesized hybrid material. The progressive addition of 0.5, 1.5, 2.5 and 4.5 weight % of GO to MoS2, results in a hybrid active layer with higher intermolecular interaction, in the case of Ag/MoS2-GO4.5/ITO RRAM device, resulting in a unipolar resistive switching RRAM behavior with low SET voltage of 1.37 V and highIon/Ioffof 200 with multilevel resistance states. A space charge limited conduction mechanism is obtained during switching, which may be attributed to the trap states present due to functional groups of GO. The increased number of conduction pathways on account of both Ag+ions and oxygen vacancies (Vo2+), participating in the formation of conducting filament, results in higherIon/Ioff. This is the first report of unipolar Ag/MoS2-GO/ITO RRAM devices, which are particularly important in realizing high density crossbar memories for neuromorphic and in-memory computing as well as enabling flexible 2D nanomaterials-based memristor applications.
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Affiliation(s)
- Manikandan R
- School of Electronics Engineering (SENSE), Vellore Institute of Technology, Chennai, India
| | - Gargi Raina
- School of Electronics Engineering (SENSE), Vellore Institute of Technology, Chennai, India
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3
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Wang X, Qiao R, Lu H, He W, Liu Y, Zhou T, Wan D, Wang Q, Liu Y, Guo W. 2D Memory Selectors with Giant Nonlinearity Enabled by Van der Waals Heterostructures. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2024; 20:e2310158. [PMID: 38573962 DOI: 10.1002/smll.202310158] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Revised: 03/15/2024] [Indexed: 04/06/2024]
Abstract
The integration of one-selector-one-resistor crossbar arrays requires the selectors featured with high nonlinearity and bipolarity to prevent leakage currents and any crosstalk among distinct cells. However, a selector with sufficient nonlinearity especially in the frame of device miniaturization remains scarce, restricting the advance of high-density storage devices. Herein, a high-performance memory selector is reported by constructing a graphene/hBN/WSe2 heterostructure. Within the temperature range of 300-80 K, the nonlinearity of this selector varies from ≈103 - ≈104 under forward bias, and increases from ≈300 - ≈105 under reverse bias, the highest reported nonlinearity among 2D selectors. This improvement is ascribed to direct tunneling at low bias and Fowler-Nordheim tunneling at high bias. The tunneling current versus voltage curves exhibit excellent bipolarity behavior because of the comparable hole and electron tunneling barriers, and the charge transport polarity can be effectively tuned from N-type or P-type to bipolar by simply changing source-drain bias. In addition, the conceptual memory selector exhibits no sign of deterioration after 70 000 switching cycles, paving the way for assembling 2D selectors into modern memory devices.
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Affiliation(s)
- Xiaofan Wang
- Key Laboratory for Intelligent Nano Materials and Devices of Ministry of Education, State Key Laboratory of Mechanics and Control of Mechanical Structures, and Institute for Frontier Science, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, China
| | - Ruixi Qiao
- Key Laboratory for Intelligent Nano Materials and Devices of Ministry of Education, State Key Laboratory of Mechanics and Control of Mechanical Structures, and Institute for Frontier Science, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, China
| | - Huan Lu
- Key Laboratory for Intelligent Nano Materials and Devices of Ministry of Education, State Key Laboratory of Mechanics and Control of Mechanical Structures, and Institute for Frontier Science, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, China
| | - Weiwei He
- Key Laboratory for Intelligent Nano Materials and Devices of Ministry of Education, State Key Laboratory of Mechanics and Control of Mechanical Structures, and Institute for Frontier Science, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, China
| | - Ying Liu
- Key Laboratory for Intelligent Nano Materials and Devices of Ministry of Education, State Key Laboratory of Mechanics and Control of Mechanical Structures, and Institute for Frontier Science, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, China
| | - Tao Zhou
- School of Physics, Southeast University, Nanjing, 211189, China
| | - Dongyang Wan
- School of Physics, Southeast University, Nanjing, 211189, China
| | - Qin Wang
- Key Laboratory for Intelligent Nano Materials and Devices of Ministry of Education, State Key Laboratory of Mechanics and Control of Mechanical Structures, and Institute for Frontier Science, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, China
| | - Yanpeng Liu
- Key Laboratory for Intelligent Nano Materials and Devices of Ministry of Education, State Key Laboratory of Mechanics and Control of Mechanical Structures, and Institute for Frontier Science, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, China
| | - Wanlin Guo
- Key Laboratory for Intelligent Nano Materials and Devices of Ministry of Education, State Key Laboratory of Mechanics and Control of Mechanical Structures, and Institute for Frontier Science, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, China
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4
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Tan T, Guo H, Li Y, Wang Y, Cai W, Bao W, Zhou P, Feng X. Integration of MoS 2 Memtransistor Devices and Analogue Circuits for Sensor Fusion in Autonomous Vehicle Target Localization. ACS NANO 2024; 18:13652-13661. [PMID: 38751043 DOI: 10.1021/acsnano.4c00456] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
In contemporary autonomous driving systems relying on sensor fusion, traditional digital processors encounter challenges associated with analogue-to-digital conversion and iterative vector-matrix operations, which are encumbered by limitations in terms of response time and energy consumption. In this study, we present an analogue Kalman filter circuit based on molybdenum disulfide (MoS2) memtransistor, designed to accelerate sensor fusion for precise localization in autonomous vehicle applications. The nonvolatile memory characteristics of the memtransistor allow for the storage of a fixed Kalman gain, which eliminates the data convergence and thus accelerates the processing speeds. Additionally, the modulation of multiple conductance states by the gate terminal enables fast adaptability to diverse autonomous driving scenarios by tuning multiple Kalman filter gains. Our proposed analogue Kalman filter circuit accurately estimates the position coordinates of target vehicles by fusing sensor data from light detection and ranging (LiDAR), millimeter-wave radar (Radar), and camera, and it successfully solves real-word problems in a signal-free crossroad intersection. Notably, our system achieves a 1000-fold improvement in energy efficiency compared to that of digital circuits. This work underscores the viability of a memtransistor for achieving fast, energy-efficient real-time sensing, and continuous signal processing in advanced sensor fusion technology.
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Affiliation(s)
- Tian Tan
- School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Haoyue Guo
- School of Microelectronics, Southern University of Science and Technology, Shenzhen 518055, China
| | - Yida Li
- School of Microelectronics, Southern University of Science and Technology, Shenzhen 518055, China
| | - Yafei Wang
- School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Weiwei Cai
- School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Wenzhong Bao
- School of Microelectronics, Fudan University, Shanghai 200433, China
- Shaoxing Laboratory, Shaoxing 312300, China
| | - Peng Zhou
- School of Microelectronics, Fudan University, Shanghai 200433, China
| | - Xuewei Feng
- School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
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5
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Li L, Xiang H, Zheng H, Chien YC, Duong NT, Gao J, Ang KW. Physical reservoirs based on MoS 2-HZO integrated ferroelectric field-effect transistors for reservoir computing systems. NANOSCALE HORIZONS 2024; 9:752-763. [PMID: 38465422 DOI: 10.1039/d3nh00524k] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/12/2024]
Abstract
Reservoir computing (RC), a variant of recurrent neural networks (RNNs), is well-known for its reduced energy consumption through exclusive focus on training the output weight and its superior performance in handling spatiotemporal information. Implementing these networks in hardware requires devices with superior fading memory behavior. Unlike filament-based two-terminal devices, those relying on ferroelectric switching demonstrate improved voltage reliability, while three-terminal transistors provide additional active control. HfO2-based ferroelectric materials such as Hf0.5Zr0.5O2 (HZO), have garnered attention for their scalability and seamless integration with CMOS technology. This study implements a RC hardware based on MoS2-HZO integrated device structure with enhanced spontaneous polarization field. By adjusting the oxygen vacancy concentration, the devices exhibit consistent responses to both identical and nonidentical voltages, making them suitable for diverse RC applications. The high accuracy of MNIST handwritten digits recognition highlights the rich reservoir states of the traditional RC architecture. Additionally, the impact of masks on RC implementation is assessed, showcasing the device's capability for spatiotemporal signal analysis. This development paves the way for implementing energy-efficient and high-performance computing solutions.
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Affiliation(s)
- Lingqi Li
- Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore, 117583.
| | - Heng Xiang
- Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore, 117583.
| | - Haofei Zheng
- Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore, 117583.
| | - Yu-Chieh Chien
- Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore, 117583.
| | - Ngoc Thanh Duong
- Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore, 117583.
| | - Jing Gao
- Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore, 117583.
| | - Kah-Wee Ang
- Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore, 117583.
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6
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Lewerenz M, Passerini E, Cheng B, Fischer M, Emboras A, Luisier M, Koch U, Leuthold J. Versatile Nanoscale Three-Terminal Memristive Switch Enabled by Gating. ACS NANO 2024; 18:10798-10806. [PMID: 38593383 PMCID: PMC11044582 DOI: 10.1021/acsnano.3c11373] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Revised: 02/23/2024] [Accepted: 02/26/2024] [Indexed: 04/11/2024]
Abstract
A three-terminal memristor with an ultrasmall footprint of only 0.07 μm2 and critical dimensions of 70 nm × 10 nm × 6 nm is introduced. The device's feature is the presence of a gate contact, which enables two operation modes: either tuning the set voltage or directly inducing a resistance change. In I-V mode, we demonstrate that by changing the gate voltages between ±1 V one can shift the set voltage by 69%. In pulsing mode, we show that resistance change can be triggered by a gate pulse. Furthermore, we tested the device endurance under a 1 kHz operation. In an experiment with 2.6 million voltage pulses, we found two distinct resistance states. The device response to a pseudorandom bit sequence displays an open eye diagram and a success ratio of 97%. Our results suggest that this device concept is a promising candidate for a variety of applications ranging from Internet-of-Things to neuromorphic computing.
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Affiliation(s)
- Mila Lewerenz
- TH
Zurich, Institute of Electromagnetic Fields (IEF), 8092 Zürich, Switzerland
| | - Elias Passerini
- TH
Zurich, Institute of Electromagnetic Fields (IEF), 8092 Zürich, Switzerland
| | - Bojun Cheng
- The
Hong Kong University of Science and Technology, Thrust of Microelectronics, Guangzhou 529200, China
| | - Markus Fischer
- TH
Zurich, Institute of Electromagnetic Fields (IEF), 8092 Zürich, Switzerland
| | - Alexandros Emboras
- ETH
Zurich, Integrated Systems Laboratory (IIS), 8092 Zürich, Switzerland
| | - Mathieu Luisier
- ETH
Zurich, Integrated Systems Laboratory (IIS), 8092 Zürich, Switzerland
| | - Ueli Koch
- TH
Zurich, Institute of Electromagnetic Fields (IEF), 8092 Zürich, Switzerland
| | - Juerg Leuthold
- TH
Zurich, Institute of Electromagnetic Fields (IEF), 8092 Zürich, Switzerland
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7
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Aguirre F, Sebastian A, Le Gallo M, Song W, Wang T, Yang JJ, Lu W, Chang MF, Ielmini D, Yang Y, Mehonic A, Kenyon A, Villena MA, Roldán JB, Wu Y, Hsu HH, Raghavan N, Suñé J, Miranda E, Eltawil A, Setti G, Smagulova K, Salama KN, Krestinskaya O, Yan X, Ang KW, Jain S, Li S, Alharbi O, Pazos S, Lanza M. Hardware implementation of memristor-based artificial neural networks. Nat Commun 2024; 15:1974. [PMID: 38438350 PMCID: PMC10912231 DOI: 10.1038/s41467-024-45670-9] [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: 06/08/2023] [Accepted: 02/01/2024] [Indexed: 03/06/2024] Open
Abstract
Artificial Intelligence (AI) is currently experiencing a bloom driven by deep learning (DL) techniques, which rely on networks of connected simple computing units operating in parallel. The low communication bandwidth between memory and processing units in conventional von Neumann machines does not support the requirements of emerging applications that rely extensively on large sets of data. More recent computing paradigms, such as high parallelization and near-memory computing, help alleviate the data communication bottleneck to some extent, but paradigm- shifting concepts are required. Memristors, a novel beyond-complementary metal-oxide-semiconductor (CMOS) technology, are a promising choice for memory devices due to their unique intrinsic device-level properties, enabling both storing and computing with a small, massively-parallel footprint at low power. Theoretically, this directly translates to a major boost in energy efficiency and computational throughput, but various practical challenges remain. In this work we review the latest efforts for achieving hardware-based memristive artificial neural networks (ANNs), describing with detail the working principia of each block and the different design alternatives with their own advantages and disadvantages, as well as the tools required for accurate estimation of performance metrics. Ultimately, we aim to provide a comprehensive protocol of the materials and methods involved in memristive neural networks to those aiming to start working in this field and the experts looking for a holistic approach.
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Affiliation(s)
- Fernando Aguirre
- Physical Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
- Departament d'Enginyeria Electrònica, Universitat Autònoma de Barcelona (UAB), 08193, Barcelona, Spain
| | | | | | - Wenhao Song
- Department of Electrical and Computer Engineering, University of Southern California (USC), Los Angeles, CA, 90089, USA
| | - Tong Wang
- Department of Electrical and Computer Engineering, University of Southern California (USC), Los Angeles, CA, 90089, USA
| | - J Joshua Yang
- Department of Electrical and Computer Engineering, University of Southern California (USC), Los Angeles, CA, 90089, USA
| | - Wei Lu
- Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Meng-Fan Chang
- Department of Electrical Engineering, National Tsing Hua University, Hsinchu, 30013, Taiwan
| | - Daniele Ielmini
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano and IUNET, Piazza L. da Vinci 32, 20133, Milano, Italy
| | - Yuchao Yang
- School of Electronic and Computer Engineering, Peking University, Shenzhen, China
| | - Adnan Mehonic
- Department of Electronic and Electrical Engineering, University College London (UCL), Torrington Place, WC1E 7JE, London, UK
| | - Anthony Kenyon
- Department of Electronic and Electrical Engineering, University College London (UCL), Torrington Place, WC1E 7JE, London, UK
| | - Marco A Villena
- Physical Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Juan B Roldán
- Departamento de Electrónica y Tecnología de Computadores, Facultad de Ciencias, Universidad de Granada, Avenida Fuentenueva s/n, 18071, Granada, Spain
| | - Yuting Wu
- Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Hung-Hsi Hsu
- Department of Electrical Engineering, National Tsing Hua University, Hsinchu, 30013, Taiwan
| | - Nagarajan Raghavan
- Engineering Product Development (EPD) Pillar, Singapore University of Technology & Design, 8 Somapah Road, 487372, Singapore, Singapore
| | - Jordi Suñé
- Departament d'Enginyeria Electrònica, Universitat Autònoma de Barcelona (UAB), 08193, Barcelona, Spain
| | - Enrique Miranda
- Departament d'Enginyeria Electrònica, Universitat Autònoma de Barcelona (UAB), 08193, Barcelona, Spain
| | - Ahmed Eltawil
- Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Gianluca Setti
- Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Kamilya Smagulova
- Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Khaled N Salama
- Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Olga Krestinskaya
- Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Xiaobing Yan
- Key Laboratory of Brain-Like Neuromorphic Devices and Systems of Hebei Province, Hebei University, Baoding, 071002, China
| | - Kah-Wee Ang
- Department of Electrical and Computer Engineering, College of Design and Engineering, National University of Singapore (NUS), Singapore, Singapore
| | - Samarth Jain
- Department of Electrical and Computer Engineering, College of Design and Engineering, National University of Singapore (NUS), Singapore, Singapore
| | - Sifan Li
- Department of Electrical and Computer Engineering, College of Design and Engineering, National University of Singapore (NUS), Singapore, Singapore
| | - Osamah Alharbi
- Physical Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Sebastian Pazos
- Physical Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Mario Lanza
- Physical Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia.
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8
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Xie J, Patoary MN, Rahman Laskar MA, Ignacio ND, Zhan X, Celano U, Akinwande D, Sanchez Esqueda I. Quantum Conductance in Vertical Hexagonal Boron Nitride Memristors with Graphene-Edge Contacts. NANO LETTERS 2024; 24:2473-2480. [PMID: 38252466 DOI: 10.1021/acs.nanolett.3c04057] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/23/2024]
Abstract
Two-dimensional materials (2DMs) have gained significant interest for resistive-switching memory toward neuromorphic and in-memory computing (IMC). To achieve atomic-level miniaturization, we introduce vertical hexagonal boron nitride (h-BN) memristors with graphene edge contacts. In addition to enabling three-dimensional (3D) integration (i.e., vertical stacking) for ultimate scalability, the proposed structure delivers ultralow power by isolating single conductive nanofilaments (CNFs) in ultrasmall active areas with negligible leakage thanks to atomically thin (∼0.3 nm) graphene edge contacts. Moreover, it facilitates studying fundamental resistive-switching behavior of single CNFs in CVD-grown 2DMs that was previously unattainable with planar devices. This way, we studied their programming characteristics and observed a consistent single quantum step in conductance attributed to unique atomically constrained nanofilament behavior in CVD-grown 2DMs. This resistive-switching property was previously suggested for h-BN memristors and linked to potential improvements in stability (robustness of CNFs), and now we show experimental evidence including superior retention of quantized conductance.
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Affiliation(s)
- Jing Xie
- Arizona State University, School of Electrical, Computer, and Energy Engineering, Tempe Arizona 85281, United States
| | - Md Naim Patoary
- Arizona State University, School of Electrical, Computer, and Energy Engineering, Tempe Arizona 85281, United States
| | - Md Ashiqur Rahman Laskar
- Arizona State University, School of Electrical, Computer, and Energy Engineering, Tempe Arizona 85281, United States
| | - Nicholas D Ignacio
- The University of Texas at Austin, Texas Materials Institute, Austin Texas 78712, United States
| | - Xun Zhan
- The University of Texas at Austin, Texas Materials Institute, Austin Texas 78712, United States
| | - Umberto Celano
- Arizona State University, School of Electrical, Computer, and Energy Engineering, Tempe Arizona 85281, United States
| | - Deji Akinwande
- The University of Texas at Austin, Texas Materials Institute, Austin Texas 78712, United States
- The University of Texas at Austin, Chandra Department of Electrical and Computer Engineering, Austin Texas 78712, United States
| | - Ivan Sanchez Esqueda
- Arizona State University, School of Electrical, Computer, and Energy Engineering, Tempe Arizona 85281, United States
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9
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Zhou H, Li S, Ang KW, Zhang YW. Recent Advances in In-Memory Computing: Exploring Memristor and Memtransistor Arrays with 2D Materials. NANO-MICRO LETTERS 2024; 16:121. [PMID: 38372805 PMCID: PMC10876512 DOI: 10.1007/s40820-024-01335-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Accepted: 12/25/2023] [Indexed: 02/20/2024]
Abstract
The conventional computing architecture faces substantial challenges, including high latency and energy consumption between memory and processing units. In response, in-memory computing has emerged as a promising alternative architecture, enabling computing operations within memory arrays to overcome these limitations. Memristive devices have gained significant attention as key components for in-memory computing due to their high-density arrays, rapid response times, and ability to emulate biological synapses. Among these devices, two-dimensional (2D) material-based memristor and memtransistor arrays have emerged as particularly promising candidates for next-generation in-memory computing, thanks to their exceptional performance driven by the unique properties of 2D materials, such as layered structures, mechanical flexibility, and the capability to form heterojunctions. This review delves into the state-of-the-art research on 2D material-based memristive arrays, encompassing critical aspects such as material selection, device performance metrics, array structures, and potential applications. Furthermore, it provides a comprehensive overview of the current challenges and limitations associated with these arrays, along with potential solutions. The primary objective of this review is to serve as a significant milestone in realizing next-generation in-memory computing utilizing 2D materials and bridge the gap from single-device characterization to array-level and system-level implementations of neuromorphic computing, leveraging the potential of 2D material-based memristive devices.
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Affiliation(s)
- Hangbo Zhou
- Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR), 1 Fusionopolis Way, #16-16 Connexis, Singapore, 138632, Republic of Singapore
| | - Sifan Li
- Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore, 117583, Republic of Singapore
| | - Kah-Wee Ang
- Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore, 117583, Republic of Singapore.
- Institute of Materials Research and Engineering, Agency for Science, Technology and Research (A*STAR), 2 Fusionopolis Way, Singapore, 138634, Republic of Singapore.
| | - Yong-Wei Zhang
- Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR), 1 Fusionopolis Way, #16-16 Connexis, Singapore, 138632, Republic of Singapore.
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10
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Liu A, Zhang X, Liu Z, Li Y, Peng X, Li X, Qin Y, Hu C, Qiu Y, Jiang H, Wang Y, Li Y, Tang J, Liu J, Guo H, Deng T, Peng S, Tian H, Ren TL. The Roadmap of 2D Materials and Devices Toward Chips. NANO-MICRO LETTERS 2024; 16:119. [PMID: 38363512 PMCID: PMC10873265 DOI: 10.1007/s40820-023-01273-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Accepted: 10/30/2023] [Indexed: 02/17/2024]
Abstract
Due to the constraints imposed by physical effects and performance degradation, silicon-based chip technology is facing certain limitations in sustaining the advancement of Moore's law. Two-dimensional (2D) materials have emerged as highly promising candidates for the post-Moore era, offering significant potential in domains such as integrated circuits and next-generation computing. Here, in this review, the progress of 2D semiconductors in process engineering and various electronic applications are summarized. A careful introduction of material synthesis, transistor engineering focused on device configuration, dielectric engineering, contact engineering, and material integration are given first. Then 2D transistors for certain electronic applications including digital and analog circuits, heterogeneous integration chips, and sensing circuits are discussed. Moreover, several promising applications (artificial intelligence chips and quantum chips) based on specific mechanism devices are introduced. Finally, the challenges for 2D materials encountered in achieving circuit-level or system-level applications are analyzed, and potential development pathways or roadmaps are further speculated and outlooked.
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Affiliation(s)
- Anhan Liu
- School of Integrated Circuits and Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing, 100049, People's Republic of China
| | - Xiaowei Zhang
- School of Integrated Circuits and Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing, 100049, People's Republic of China
| | - Ziyu Liu
- School of Microelectronics, Fudan University, Shanghai, 200433, People's Republic of China
| | - Yuning Li
- School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing, 100044, People's Republic of China
| | - Xueyang Peng
- High-Frequency High-Voltage Device and Integrated Circuits R&D Center, Institute of Microelectronics, Chinese Academy of Sciences, Beijing, 100029, People's Republic of China
- School of Integrated Circuits, University of Chinese Academy of Sciences, Beijing, 100049, People's Republic of China
| | - Xin Li
- State Key Laboratory of Dynamic Measurement Technology, Shanxi Province Key Laboratory of Quantum Sensing and Precision Measurement, North University of China, Taiyuan, 030051, People's Republic of China
| | - Yue Qin
- State Key Laboratory of Dynamic Measurement Technology, Shanxi Province Key Laboratory of Quantum Sensing and Precision Measurement, North University of China, Taiyuan, 030051, People's Republic of China
| | - Chen Hu
- High-Frequency High-Voltage Device and Integrated Circuits R&D Center, Institute of Microelectronics, Chinese Academy of Sciences, Beijing, 100029, People's Republic of China
- School of Integrated Circuits, University of Chinese Academy of Sciences, Beijing, 100049, People's Republic of China
| | - Yanqing Qiu
- High-Frequency High-Voltage Device and Integrated Circuits R&D Center, Institute of Microelectronics, Chinese Academy of Sciences, Beijing, 100029, People's Republic of China
- School of Integrated Circuits, University of Chinese Academy of Sciences, Beijing, 100049, People's Republic of China
| | - Han Jiang
- School of Microelectronics, Fudan University, Shanghai, 200433, People's Republic of China
| | - Yang Wang
- School of Microelectronics, Fudan University, Shanghai, 200433, People's Republic of China
| | - Yifan Li
- School of Integrated Circuits and Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing, 100049, People's Republic of China
| | - Jun Tang
- State Key Laboratory of Dynamic Measurement Technology, Shanxi Province Key Laboratory of Quantum Sensing and Precision Measurement, North University of China, Taiyuan, 030051, People's Republic of China
| | - Jun Liu
- State Key Laboratory of Dynamic Measurement Technology, Shanxi Province Key Laboratory of Quantum Sensing and Precision Measurement, North University of China, Taiyuan, 030051, People's Republic of China
| | - Hao Guo
- State Key Laboratory of Dynamic Measurement Technology, Shanxi Province Key Laboratory of Quantum Sensing and Precision Measurement, North University of China, Taiyuan, 030051, People's Republic of China.
| | - Tao Deng
- School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing, 100044, People's Republic of China.
| | - Songang Peng
- High-Frequency High-Voltage Device and Integrated Circuits R&D Center, Institute of Microelectronics, Chinese Academy of Sciences, Beijing, 100029, People's Republic of China.
- IMECAS-HKUST-Joint Laboratory of Microelectronics, Beijing, 100029, People's Republic of China.
| | - He Tian
- School of Integrated Circuits and Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing, 100049, People's Republic of China.
| | - Tian-Ling Ren
- School of Integrated Circuits and Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing, 100049, People's Republic of China.
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11
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Hwang J, Joh H, Kim C, Ahn J, Jeon S. Monolithically Integrated Complementary Ferroelectric FET XNOR Synapse for the Binary Neural Network. ACS APPLIED MATERIALS & INTERFACES 2024; 16:2467-2476. [PMID: 38175955 DOI: 10.1021/acsami.3c13945] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/06/2024]
Abstract
Neuromorphic computing, which mimics the structure and principles of the human brain, has the potential to facilitate the hardware implementation of next-generation artificial intelligence systems and process large amounts of data with very low power consumption. Among them, the XNOR synapse-based Binary Neural Network (BNN) has been attracting attention due to its compact neural network parameter size and low hardware cost. The previous XNOR synapse has drawbacks, such as a trade-off between cell density and accuracy. In this work, we show nonvolatile XNOR synapses with high density and accuracy using a monolithically stacked complementary ferroelectric field-effect transistor (C-FeFET) composed of a p-type Si MFMIS-FeFET at the bottom and a 3D stackable n-type Al:IZTO MFS-FeTFT, achieving 60F2 per cell (2C-FeFET). For adjusting the threshold voltage and improving the switching speed (100 ns) of n-type ferroelectric TFT, we employed a dual-gate configuration and a unique operation scheme, making it comparable to those of Si-based FeFETs. We performed array-level simulation with a 512 × 512 subarray size and a 3-bit flash ADC, demonstrating that the image recognition accuracies using the MNIST and CIFAR-10 data sets were increased by 3.17 and 14.07%, respectively, in comparison to other nonvolatile XNOR synapses. In addition, we performed system-level analysis on a 512 × 512 XNOR C-FeFET, exhibiting an outstanding throughput of 717.37 GOPS and an energy efficiency of 196.7 TOPS/W. We expect that our approach would contribute to the high-density memory systems, logic-in-memory technology, and hardware implementation of neural networks.
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Affiliation(s)
- Junghyeon Hwang
- School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291, Daehak-ro, Yuseong-gu, Daejeon 34141, Korea
| | - Hongrae Joh
- School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291, Daehak-ro, Yuseong-gu, Daejeon 34141, Korea
| | - Chaeheon Kim
- School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291, Daehak-ro, Yuseong-gu, Daejeon 34141, Korea
| | - Jinho Ahn
- Division of Materials Science and Engineering, Hanyang University, 222, Wangsimni-ro, Seonhdong-gu, Seoul 04763, Korea
| | - Sanghun Jeon
- School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291, Daehak-ro, Yuseong-gu, Daejeon 34141, Korea
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12
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Ren SG, Dong AW, Yang L, Xue YB, Li JC, Yu YJ, Zhou HJ, Zuo WB, Li Y, Cheng WM, Miao XS. Self-Rectifying Memristors for Three-Dimensional In-Memory Computing. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024; 36:e2307218. [PMID: 37972344 DOI: 10.1002/adma.202307218] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Revised: 10/13/2023] [Indexed: 11/19/2023]
Abstract
Costly data movement in terms of time and energy in traditional von Neumann systems is exacerbated by emerging information technologies related to artificial intelligence. In-memory computing (IMC) architecture aims to address this problem. Although the IMC hardware prototype represented by a memristor is developed rapidly and performs well, the sneak path issue is a critical and unavoidable challenge prevalent in large-scale and high-density crossbar arrays, particularly in three-dimensional (3D) integration. As a perfect solution to the sneak-path issue, a self-rectifying memristor (SRM) is proposed for 3D integration because of its superior integration density. To date, SRMs have performed well in terms of power consumption (aJ level) and scalability (>102 Mbit). Moreover, SRM-configured 3D integration is considered an ideal hardware platform for 3D IMC. This review focuses on the progress in SRMs and their applications in 3D memory, IMC, neuromorphic computing, and hardware security. The advantages, disadvantages, and optimization strategies of SRMs in diverse application scenarios are illustrated. Challenges posed by physical mechanisms, fabrication processes, and peripheral circuits, as well as potential solutions at the device and system levels, are also discussed.
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Affiliation(s)
- Sheng-Guang Ren
- School of Integrated Circuits, Hubei Key Laboratory of Advanced Memories, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - A-Wei Dong
- School of Integrated Circuits, Hubei Key Laboratory of Advanced Memories, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Ling Yang
- School of Integrated Circuits, Hubei Key Laboratory of Advanced Memories, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Yi-Bai Xue
- School of Integrated Circuits, Hubei Key Laboratory of Advanced Memories, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Jian-Cong Li
- School of Integrated Circuits, Hubei Key Laboratory of Advanced Memories, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Yin-Jie Yu
- School of Integrated Circuits, Hubei Key Laboratory of Advanced Memories, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Hou-Ji Zhou
- School of Integrated Circuits, Hubei Key Laboratory of Advanced Memories, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Wen-Bin Zuo
- School of Integrated Circuits, Hubei Key Laboratory of Advanced Memories, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Yi Li
- School of Integrated Circuits, Hubei Key Laboratory of Advanced Memories, Huazhong University of Science and Technology, Wuhan, 430074, China
- Hubei Yangtze Memory Laboratories, Wuhan, 430205, China
| | - Wei-Ming Cheng
- School of Integrated Circuits, Hubei Key Laboratory of Advanced Memories, Huazhong University of Science and Technology, Wuhan, 430074, China
- Hubei Yangtze Memory Laboratories, Wuhan, 430205, China
| | - Xiang-Shui Miao
- School of Integrated Circuits, Hubei Key Laboratory of Advanced Memories, Huazhong University of Science and Technology, Wuhan, 430074, China
- Hubei Yangtze Memory Laboratories, Wuhan, 430205, China
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13
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Li J, Lei Y, Wang Z, Meng H, Zhang W, Li M, Tan Q, Li Z, Guo W, Wen S, Zhang J. High-Density Artificial Synapse Array Consisting of Homogeneous Electrolyte-Gated Transistors. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2305430. [PMID: 38018350 PMCID: PMC10797465 DOI: 10.1002/advs.202305430] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/06/2023] [Revised: 10/25/2023] [Indexed: 11/30/2023]
Abstract
The artificial synapse array with an electrolyte-gated transistor (EGT) as an array unit presents considerable potential for neuromorphic computation. However, the integration of EGTs faces the drawback of the conflict between the polymer electrolytes and photo-lithography. This study presents a scheme based on a lateral-gate structure to realize high-density integration of EGTs and proposes the integration of 100 × 100 EGTs into a 2.5 × 2.5 cm2 glass, with a unit density of up to 1600 devices cm-2 . Furthermore, an electrolyte framework is developed to enhance the array performance, with ionic conductivity of up to 2.87 × 10-3 S cm-1 owing to the porosity of zeolitic imidazolate frameworks-67. The artificial synapse array realizes image processing functions, and exhibits high performance and homogeneity. The handwriting recognition accuracy of a representative device reaches 92.80%, with the standard deviation of all the devices being limited to 9.69%. The integrated array and its high performance demonstrate the feasibility of the scheme and provide a solid reference for the integration of EGTs.
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Affiliation(s)
- Jun Li
- School of Material Science and EngineeringShanghai UniversityJiadingShanghai201800P. R. China
- Key Laboratory of Advanced Display and System ApplicationsMinistry of EducationShanghai UniversityShanghai200072P. R. China
- School of MicroelectronicsShanghai UniversityJiadingShanghai201800P. R. China
| | - Yuxing Lei
- School of Material Science and EngineeringShanghai UniversityJiadingShanghai201800P. R. China
| | - Zexin Wang
- School of Material Science and EngineeringShanghai UniversityJiadingShanghai201800P. R. China
| | - Hu Meng
- Central Research InstituteBOE Technology Group Company, Ltd.Beijing100176P. R. China
| | - Wenkui Zhang
- School of MicroelectronicsShanghai UniversityJiadingShanghai201800P. R. China
| | - Mengjiao Li
- School of MicroelectronicsShanghai UniversityJiadingShanghai201800P. R. China
| | - Qiuyun Tan
- Central Research InstituteBOE Technology Group Company, Ltd.Beijing100176P. R. China
| | - Zeyuan Li
- Central Research InstituteBOE Technology Group Company, Ltd.Beijing100176P. R. China
| | - Wei Guo
- Central Research InstituteBOE Technology Group Company, Ltd.Beijing100176P. R. China
| | - Shengkai Wen
- School of Material Science and EngineeringShanghai UniversityJiadingShanghai201800P. R. China
| | - Jianhua Zhang
- Key Laboratory of Advanced Display and System ApplicationsMinistry of EducationShanghai UniversityShanghai200072P. R. China
- School of MicroelectronicsShanghai UniversityJiadingShanghai201800P. R. China
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14
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Zhang H, Qiu P, Lu Y, Ju X, Chi D, Yew KS, Zhu M, Wang S, Wei R, Hu W. In-Sensor Computing Realization Using Fully CMOS-Compatible TiN/HfO x-Based Neuristor Array. ACS Sens 2023; 8:3873-3881. [PMID: 37707324 DOI: 10.1021/acssensors.3c01418] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/15/2023]
Abstract
With the evolution of artificial intelligence, the explosive growth of data from sensory terminals gives rise to severe energy-efficiency bottleneck issues due to cumbersome data interactions among sensory, memory, and computing modules. Heterogeneous integration methods such as chiplet technology can significantly reduce unnecessary data movement; however, they fail to address the fundamental issue of the substantial time and energy overheads resulting from the physical separation of computing and sensory components. Brain-inspired in-sensor neuromorphic computing (ISNC) has plenty of room for such data-intensive applications. However, one key obstacle in developing ISNC systems is the lack of compatibility between material systems and manufacturing processes deployed in sensors and computing units. This study successfully addresses this challenge by implementing fully CMOS-compatible TiN/HfOx-based neuristor array. The developed ISNC system demonstrates several advantageous features, including multilevel analogue modulation, minimal dispersion, and no significant degradation in conductance (@125 °C). These characteristics enable stable and reproducible neuromorphic computing. Additionally, the device exhibits modulatable sensory and multi-store memory processes. Furthermore, the system achieves information recognition with a high accuracy rate of 93%, along with frequency selectivity and notable activity-dependent plasticity. This work provides a promising route to affordable and highly efficient sensory neuromorphic systems.
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Affiliation(s)
- Haizhong Zhang
- College of Physics and Information Engineering, Fuzhou University, Fuzhou 350116, China
- FZU-Jinjiang Joint Institute of Microelectronics, Jinjiang Science and Education Park, Fuzhou University, Jinjiang 362200, China
| | - Peng Qiu
- College of Physics and Information Engineering, Fuzhou University, Fuzhou 350116, China
| | - Yaoping Lu
- College of Physics and Information Engineering, Fuzhou University, Fuzhou 350116, China
| | - Xin Ju
- Institute of Materials Research and Engineering, 2 Fusionopolis Way, Innovis, #08-03, Agency for Science, Technology and Research, Singapore 138634, Singapore
| | - Dongzhi Chi
- Institute of Materials Research and Engineering, 2 Fusionopolis Way, Innovis, #08-03, Agency for Science, Technology and Research, Singapore 138634, Singapore
| | - Kwang Sing Yew
- Global Foundries, 60 Woodlands Industrial Park D Street 2, Singapore 738406, Singapore
| | - Minmin Zhu
- College of Physics and Information Engineering, Fuzhou University, Fuzhou 350116, China
- FZU-Jinjiang Joint Institute of Microelectronics, Jinjiang Science and Education Park, Fuzhou University, Jinjiang 362200, China
| | - Shaohao Wang
- College of Physics and Information Engineering, Fuzhou University, Fuzhou 350116, China
- FZU-Jinjiang Joint Institute of Microelectronics, Jinjiang Science and Education Park, Fuzhou University, Jinjiang 362200, China
| | - Rongshan Wei
- College of Physics and Information Engineering, Fuzhou University, Fuzhou 350116, China
- FZU-Jinjiang Joint Institute of Microelectronics, Jinjiang Science and Education Park, Fuzhou University, Jinjiang 362200, China
| | - Wei Hu
- College of Physics and Information Engineering, Fuzhou University, Fuzhou 350116, China
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15
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Chen X, Sun YF, Wu X, Shi S, Wang Z, Zhang J, Fang WH, Huang W. Breaking the Trade-Off Between Polymer Dielectric Constant and Loss via Aluminum Oxo Macrocycle Dopants for High-Performance Neuromorphic Electronics. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2023:e2306260. [PMID: 37660306 DOI: 10.1002/adma.202306260] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Revised: 08/24/2023] [Indexed: 09/05/2023]
Abstract
The dielectric layer is crucial in regulating the overall performance of field-effect transistors (FETs), the key component in central processing units, sensors, and displays. Despite considerable efforts being devoted to developing high-permittivity (k) dielectrics, limited progress is made due to the inherent trade-off between dielectric constant and loss. Here, a solution is presented by designing a monodispersed disk-shaped Ce-Al-O-macrocycle as a dopant in polymer dielectrics. The molecule features a central Ce(III) core connected with eight Al atoms through sixteen bridging hydroxyls and eight 3-aminophenyl peripheries. The incorporation of this macrocycle in polymer dielectrics results in an up to sevenfold increase in dielectric constants and up to 89% reduction in dielectric loss at low frequencies. Moreover, the leakage-current densities decrease, and the breakdown strengths are improved by 63%. Relying on the above merits, FETs bearing cluster-doped polymer dielectrics give near three-orders source-drain current increments while maintaining low-level leakage/off currents, resulting in much higher charge-carrier mobilities (up to 2.45 cm2 V-1 s-1 ) and on/off ratios. This cluster-doping strategy is generalizable and shows great promise for ultralow-power photoelectric synapses and neuromorphic retinas. This work successfully breaks the trade-off between dielectric constant and loss and offers a unique design for polymer composite dielectrics.
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Affiliation(s)
- Xiaowei Chen
- State Key Laboratory of Structural Chemistry, Fujian Institute of Research on the Structure of Matter, Chinese Academy of Sciences, Fuzhou, Fujian, 350002, P. R. China
- University of Chinese Academy of Sciences, 19A Yuquan Road, Beijing, 100049, P. R. China
| | - Yi-Fan Sun
- State Key Laboratory of Structural Chemistry, Fujian Institute of Research on the Structure of Matter, Chinese Academy of Sciences, Fuzhou, Fujian, 350002, P. R. China
- University of Chinese Academy of Sciences, 19A Yuquan Road, Beijing, 100049, P. R. China
| | - Xiaosong Wu
- State Key Laboratory of Structural Chemistry, Fujian Institute of Research on the Structure of Matter, Chinese Academy of Sciences, Fuzhou, Fujian, 350002, P. R. China
- University of Chinese Academy of Sciences, 19A Yuquan Road, Beijing, 100049, P. R. China
| | - Shuhui Shi
- Department of Electrical and Electronic Engineering, University of Hong Kong, Pokfulam Road, Hong Kong SAR, Hong Kong
| | - Zhongrui Wang
- Department of Electrical and Electronic Engineering, University of Hong Kong, Pokfulam Road, Hong Kong SAR, Hong Kong
| | - Jian Zhang
- State Key Laboratory of Structural Chemistry, Fujian Institute of Research on the Structure of Matter, Chinese Academy of Sciences, Fuzhou, Fujian, 350002, P. R. China
- University of Chinese Academy of Sciences, 19A Yuquan Road, Beijing, 100049, P. R. China
| | - Wei-Hui Fang
- State Key Laboratory of Structural Chemistry, Fujian Institute of Research on the Structure of Matter, Chinese Academy of Sciences, Fuzhou, Fujian, 350002, P. R. China
- University of Chinese Academy of Sciences, 19A Yuquan Road, Beijing, 100049, P. R. China
| | - Weiguo Huang
- State Key Laboratory of Structural Chemistry, Fujian Institute of Research on the Structure of Matter, Chinese Academy of Sciences, Fuzhou, Fujian, 350002, P. R. China
- University of Chinese Academy of Sciences, 19A Yuquan Road, Beijing, 100049, P. R. China
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16
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Rehman S, Khan MF, Kim HD, Kim S. A self-tuning PID controller based on analog-digital hybrid computing with a double-gate SnS 2 memtransistor. NANOSCALE 2023; 15:13675-13684. [PMID: 37554054 DOI: 10.1039/d2nr06853b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/10/2023]
Abstract
Most commercial drones utilize a traditional proportional-integral-derivative (PID) controller because of its design simplicity. However, the traditional PID controller has certain limitations in terms of optimality and robustness; it is difficult to actively adjust the PID gains under some disturbances. In this study, we demonstrated an analog-digital hybrid computing platform based on double-gate SnS2 memtransistors to implement a self-tuning/energy-efficient PID controller in drones. The customized analog circuit with memtransistors executes the PID control algorithm with low power consumption; we experimentally verified that the energy consumption of the proposed hybrid computing-based PID controller is only 63% of that of the traditional PID controller. In addition, the precise tunability of analog conductance states in the memtransistor proved to be capable of reconfiguring the performance of the PID controller, where the developed self-tuning algorithm can automatically find the optimal PID control performance.
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Affiliation(s)
- Shania Rehman
- Department of Semiconductor Systems Engineering and Convergence Engineering for Intelligent Drone, Sejong University, Seoul, 05006, Korea.
| | | | - Hee-Dong Kim
- Department of Semiconductor Systems Engineering and Convergence Engineering for Intelligent Drone, Sejong University, Seoul, 05006, Korea.
| | - Sungho Kim
- Department of Semiconductor Systems Engineering and Convergence Engineering for Intelligent Drone, Sejong University, Seoul, 05006, Korea.
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17
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Fu S, Park JH, Gao H, Zhang T, Ji X, Fu T, Sun L, Kong J, Yao J. Two-Terminal MoS 2 Memristor and the Homogeneous Integration with a MoS 2 Transistor for Neural Networks. NANO LETTERS 2023. [PMID: 37338212 DOI: 10.1021/acs.nanolett.2c05007] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/21/2023]
Abstract
Memristors are promising candidates for constructing neural networks. However, their dissimilar working mechanism to that of the addressing transistors can result in a scaling mismatch, which may hinder efficient integration. Here, we demonstrate two-terminal MoS2 memristors that work with a charge-based mechanism similar to that in transistors, which enables the homogeneous integration with MoS2 transistors to realize one-transistor-one-memristor addressable cells for assembling programmable networks. The homogenously integrated cells are implemented in a 2 × 2 network array to demonstrate the enabled addressability and programmability. The potential for assembling a scalable network is evaluated in a simulated neural network using obtained realistic device parameters, which achieves over 91% pattern recognition accuracy. This study also reveals a generic mechanism and strategy that can be applied to other semiconducting devices for the engineering and homogeneous integration of memristive systems.
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Affiliation(s)
- Shuai Fu
- Department of Electrical Computer and Engineering, University of Massachusetts, Amherst, Massachusetts 01003, United States
| | - Ji-Hoon Park
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Hongyan Gao
- Department of Electrical Computer and Engineering, University of Massachusetts, Amherst, Massachusetts 01003, United States
| | - Tianyi Zhang
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Xiang Ji
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Tianda Fu
- Department of Electrical Computer and Engineering, University of Massachusetts, Amherst, Massachusetts 01003, United States
| | - Lu Sun
- Department of Electrical Computer and Engineering, University of Massachusetts, Amherst, Massachusetts 01003, United States
| | - Jing Kong
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Jun Yao
- Department of Electrical Computer and Engineering, University of Massachusetts, Amherst, Massachusetts 01003, United States
- Institute for Applied Life Sciences (IALS), University of Massachusetts, Amherst, Massachusetts 01003, United States
- Department of Biomedical Engineering, University of Massachusetts, Amherst, Massachusetts 01003, United States
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18
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Huh W, Lee D, Jang S, Kang JH, Yoon TH, So JP, Kim YH, Kim JC, Park HG, Jeong HY, Wang G, Lee CH. Heterosynaptic MoS 2 Memtransistors Emulating Biological Neuromodulation for Energy-Efficient Neuromorphic Electronics. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2023; 35:e2211525. [PMID: 36930856 DOI: 10.1002/adma.202211525] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Revised: 03/04/2023] [Indexed: 06/16/2023]
Abstract
Heterosynaptic neuromodulation is a key enabler for energy-efficient and high-level biological neural processing. However, such manifold synaptic modulation cannot be emulated using conventional memristors and synaptic transistors. Thus, reported herein is a three-terminal heterosynaptic memtransistor using an intentional-defect-generated molybdenum disulfide channel. Particularly, the defect-mediated space-charge-limited conduction in the ultrathin channel results in memristive switching characteristics between the source and drain terminals, which are further modulated using a gate terminal according to the gate-tuned filling of trap states. The device acts as an artificial synapse controlled by sub-femtojoule impulses from both the source and gate terminals, consuming lower energy than its biological counterpart. In particular, electrostatic gate modulation, corresponding to biological neuromodulation, additionally regulates the dynamic range and tuning rate of the synaptic weight, independent of the programming (source) impulses. Notably, this heterosynaptic modulation not only improves the learning accuracy and efficiency but also reduces energy consumption in the pattern recognition. Thus, the study presents a new route leading toward the realization of highly networked and energy-efficient neuromorphic electronics.
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Affiliation(s)
- Woong Huh
- KU-KIST Graduate School of Converging Science and Technology, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul, 02841, Republic of Korea
| | - Donghun Lee
- KU-KIST Graduate School of Converging Science and Technology, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul, 02841, Republic of Korea
| | - Seonghoon Jang
- KU-KIST Graduate School of Converging Science and Technology, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul, 02841, Republic of Korea
| | - Jung Hoon Kang
- KU-KIST Graduate School of Converging Science and Technology, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul, 02841, Republic of Korea
| | - Tae Hyun Yoon
- KU-KIST Graduate School of Converging Science and Technology, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul, 02841, Republic of Korea
| | - Jae-Pil So
- Department of Physics, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul, 02841, Republic of Korea
| | - Yeon Ho Kim
- KU-KIST Graduate School of Converging Science and Technology, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul, 02841, Republic of Korea
| | - Jong Chan Kim
- School of Materials Science and Engineering, Ulsan National Institute of Science and Technology (UNIST), UNIST-gil 50, Ulsan, 44919, Republic of Korea
| | - Hong-Gyu Park
- Department of Physics, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul, 02841, Republic of Korea
| | - Hu Young Jeong
- UNIST Central Research Facilities (UCRF), Ulsan National Institute of Science and Technology (UNIST), UNIST-gil 50, Ulsan, 44919, Republic of Korea
| | - Gunuk Wang
- KU-KIST Graduate School of Converging Science and Technology, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul, 02841, Republic of Korea
- Department of Integrative Energy Engineering, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul, 02841, Republic of Korea
- Center for Neuromorphic Engineering, Korea Institute of Science and Technology, 5 Hwarang-ro 14-gil, Seongbuk-gu, Seoul, 02792, Republic of Korea
| | - Chul-Ho Lee
- Department of Electrical and Computer Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul, 08826, Republic of Korea
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Kim JY, Ju X, Ang KW, Chi D. Van der Waals Layer Transfer of 2D Materials for Monolithic 3D Electronic System Integration: Review and Outlook. ACS NANO 2023; 17:1831-1844. [PMID: 36655854 DOI: 10.1021/acsnano.2c10737] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
Two-dimensional materials (2DMs) have attracted a great deal of interest due to their immense potential for scientific breakthroughs and technological innovations. While some 2D transition metal dichalcogenides (TMDC) such as MoS2 and WS2 are considered as the ultimate channel materials in unltrascaled transistors as replacements for Si, there has also been increasing interest in the monolithic 3D integration of 2DMs on the Si CMOS platform or in flexible electronics as back-end-of-line transistors, memory devices/selectors, and sensors, taking advantage of 2DM properties such as a high current driving capability with low leakage current, nonvolatile switching characteristics, a large surface-to-volume ratio, and a tunable bandgap. However, the realization of both of these scenarios critically depends on the development of manufacturing-viable high-yield 2DM layers transfer from the growth substrate to the Si, since the growth of high-quality 2DM layers often requires a high-temperature growth process on template substrates. Motivated by this, extensive efforts have been made by the 2DM research community to develop various 2DM layer transfer methods, leveraging the van der Waals transfer capability of the layer-structured 2DMs. These efforts have led to a number of successful demonstrations of wafer-scale 2D TMDC layer transfer, while 2DM-enabled template growth/transfer of some functional bulk materials such as III-V, Ge, and AlN has also been demonstrated. This review surveys and compares different 2DM transfer methods developed recently, with a focus on large-area 2D TMDC film transfer along with an introduction of 2DM template-assisted van der Waals growth/transfer of non-2D thin films. We will also briefly present an outlook of our envisioned multifunctionalities in 3D integrated electronic systems enabled by monolithic 3D integration of 2DMs and III-V via van der Waals transfer and discuss possible technology options for overcoming remaining challenges.
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Affiliation(s)
- Jun-Young Kim
- Institute of Materials Research and Engineering, Agency for Science, Technology and Research, 2 Fusionopolis Way, Singapore 138634, Singapore
| | - Xin Ju
- Institute of Materials Research and Engineering, Agency for Science, Technology and Research, 2 Fusionopolis Way, Singapore 138634, Singapore
| | - Kah-Wee Ang
- Institute of Materials Research and Engineering, Agency for Science, Technology and Research, 2 Fusionopolis Way, Singapore 138634, Singapore
- Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore 117583, Singapore
| | - Dongzhi Chi
- Institute of Materials Research and Engineering, Agency for Science, Technology and Research, 2 Fusionopolis Way, Singapore 138634, Singapore
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20
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Wearable in-sensor reservoir computing using optoelectronic polymers with through-space charge-transport characteristics for multi-task learning. Nat Commun 2023; 14:468. [PMID: 36709349 PMCID: PMC9884246 DOI: 10.1038/s41467-023-36205-9] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Accepted: 01/17/2023] [Indexed: 01/30/2023] Open
Abstract
In-sensor multi-task learning is not only the key merit of biological visions but also a primary goal of artificial-general-intelligence. However, traditional silicon-vision-chips suffer from large time/energy overheads. Further, training conventional deep-learning models is neither scalable nor affordable on edge-devices. Here, a material-algorithm co-design is proposed to emulate human retina and the affordable learning paradigm. Relying on a bottle-brush-shaped semiconducting p-NDI with efficient exciton-dissociations and through-space charge-transport characteristics, a wearable transistor-based dynamic in-sensor Reservoir-Computing system manifesting excellent separability, fading memory, and echo state property on different tasks is developed. Paired with a 'readout function' on memristive organic diodes, the RC recognizes handwritten letters and numbers, and classifies diverse costumes with accuracies of 98.04%, 88.18%, and 91.76%, respectively (higher than all reported organic semiconductors). In addition to 2D images, the spatiotemporal dynamics of RC naturally extract features of event-based videos, classifying 3 types of hand gestures at an accuracy of 98.62%. Further, the computing cost is significantly lower than that of the conventional artificial-neural-networks. This work provides a promising material-algorithm co-design for affordable and highly efficient photonic neuromorphic systems.
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21
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Chien YC, Xiang H, Shi Y, Duong NT, Li S, Ang KW. A MoS 2 Hafnium Oxide Based Ferroelectric Encoder for Temporal-Efficient Spiking Neural Network. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2023; 35:e2204949. [PMID: 36366910 DOI: 10.1002/adma.202204949] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Revised: 07/26/2022] [Indexed: 06/16/2023]
Abstract
Spiking neural network (SNN), where the information is evaluated recurrently through spikes, has manifested significant promises to minimize the energy expenditure in data-intensive machine learning and artificial intelligence. Among these applications, the artificial neural encoders are essential to convert the external stimuli to a spiking format that can be subsequently fed to the neural network. Here, a molybdenum disulfide (MoS2 ) hafnium oxide-based ferroelectric encoder is demonstrated for temporal-efficient information processing in SNN. The fast domain switching attribute associated with the polycrystalline nature of hafnium oxide-based ferroelectric material is exploited for spike encoding, rendering it suitable for realizing biomimetic encoders. Accordingly, a high-performance ferroelectric encoder is achieved, featuring a superior switching efficiency, negligible charge trapping effect, and robust ferroelectric response, which successfully enable a broad dynamic range. Furthermore, an SNN is simulated to verify the precision of the encoded information, in which an average inference accuracy of 95.14% can be achieved, using the Modified National Insitute of Standards and Technology (MNIST) dataset for digit classification. Moreover, this ferroelectric encoder manifests prominent resilience against noise injection with an overall prediction accuracy of 94.73% under various Gaussian noise levels, showing practical promises to reduce the computational load for the neural network.
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Affiliation(s)
- Yu-Chieh Chien
- Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore, 117583, Singapore
| | - Heng Xiang
- Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore, 117583, Singapore
| | - Yufei Shi
- Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore, 117583, Singapore
| | - Ngoc Thanh Duong
- Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore, 117583, Singapore
| | - Sifan Li
- Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore, 117583, Singapore
| | - Kah-Wee Ang
- Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore, 117583, Singapore
- Institute of Materials Research and Engineering, A*STAR, 2 Fusionopolis Way, Singapore, 138634, Singapore
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22
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Liu S, Wang J, Shao J, Ouyang D, Zhang W, Liu S, Li Y, Zhai T. Nanopatterning Technologies of 2D Materials for Integrated Electronic and Optoelectronic Devices. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2022; 34:e2200734. [PMID: 35501143 DOI: 10.1002/adma.202200734] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/23/2022] [Revised: 04/12/2022] [Indexed: 06/14/2023]
Abstract
With the reduction of feature size and increase of integration density, traditional 3D semiconductors are unable to meet the future requirements of chip integration. The current semiconductor fabrication technologies are approaching their physical limits based on Moore's law. 2D materials such as graphene, transitional metal dichalcogenides, etc., are of great promise for future memory, logic, and photonic devices due to their unique and excellent properties. To prompt 2D materials and devices from the laboratory research stage to the industrial integrated circuit-level, it is necessary to develop advanced nanopatterning methods to obtain high-quality, wafer-scale, and patterned 2D products. Herein, the recent development of nanopatterning technologies, particularly toward realizing large-scale practical application of 2D materials is reviewed. Based on the technological progress, the unique requirement and advances of the 2D integration process for logic, memory, and optoelectronic devices are further summarized. Finally, the opportunities and challenges of nanopatterning technologies of 2D materials for future integrated chip devices are prospected.
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Affiliation(s)
- Shenghong Liu
- State Key Laboratory of Materials Processing and Die & Mould Technology, School of Materials Science and Engineering, Huazhong University of Science and Technology, Wuhan, 430074, P. R. China
| | - Jing Wang
- State Key Laboratory of Materials Processing and Die & Mould Technology, School of Materials Science and Engineering, Huazhong University of Science and Technology, Wuhan, 430074, P. R. China
| | - Jiefan Shao
- State Key Laboratory of Materials Processing and Die & Mould Technology, School of Materials Science and Engineering, Huazhong University of Science and Technology, Wuhan, 430074, P. R. China
| | - Decai Ouyang
- State Key Laboratory of Materials Processing and Die & Mould Technology, School of Materials Science and Engineering, Huazhong University of Science and Technology, Wuhan, 430074, P. R. China
| | - Wenjing Zhang
- International Collaborative Laboratory of 2D Materials for Optoelectronics Science and Technology of Ministry of Education, Institute of Microscale Optoelectronics, Shenzhen University, Shenzhen, 518060, P. R. China
| | - Shiyuan Liu
- State Key Laboratory of Digital Manufacturing Equipment and Technology, School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, 430074, P. R. China
| | - Yuan Li
- State Key Laboratory of Materials Processing and Die & Mould Technology, School of Materials Science and Engineering, Huazhong University of Science and Technology, Wuhan, 430074, P. R. China
| | - Tianyou Zhai
- State Key Laboratory of Materials Processing and Die & Mould Technology, School of Materials Science and Engineering, Huazhong University of Science and Technology, Wuhan, 430074, P. R. China
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23
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Shen Y, Dong Z, Sun Y, Guo H, Wu F, Li X, Tang J, Liu J, Wu X, Tian H, Ren TL. The Trend of 2D Transistors toward Integrated Circuits: Scaling Down and New Mechanisms. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2022; 34:e2201916. [PMID: 35535757 DOI: 10.1002/adma.202201916] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Revised: 04/12/2022] [Indexed: 06/14/2023]
Abstract
2D transition metal chalcogenide (TMDC) materials, such as MoS2 , have recently attracted considerable research interest in the context of their use in ultrascaled devices owing to their excellent electronic properties. Microprocessors and neural network circuits based on MoS2 have been developed at a large scale but still do not have an advantage over silicon in terms of their integrated density. In this study, the current structures, contact engineering, and doping methods for 2D TMDC materials for the scaling-down process and performance optimization are reviewed. Devices are introduced according to a new mechanism to provide the comprehensive prospects for the use of MoS2 beyond the traditional complementary-metal-oxide semiconductor in order to summarize obstacles to the goal of developing high-density and low-power integrated circuits (ICs). Finally, prospects for the use of MoS2 in large-scale ICs from the perspectives of the material, system performance, and application to nonlogic functionalities such as sensor circuits and analogous circuits, are briefly analyzed. The latter issue is along the direction of "more than Moore" research.
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Affiliation(s)
- Yang Shen
- Institute of Microelectronics and Beijing National Research Center for Information Science and Technology (BNRist) School of Integrated Circuits and Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing, 100084, China
| | - Zuoyuan Dong
- Shanghai Key Laboratory of Multidimensional Information Processing, School of Communication and Electronic Engineering, East China Normal University, Shanghai, 200241, China
| | - Yabin Sun
- Shanghai Key Laboratory of Multidimensional Information Processing, School of Communication and Electronic Engineering, East China Normal University, Shanghai, 200241, China
| | - Hao Guo
- Shanxi Province Key Laboratory of Quantum Sensing and Precision Measurement, School of Instrument and Electronics, North University of China, Taiyuan, Shanxi, 030051, China
| | - Fan Wu
- Institute of Microelectronics and Beijing National Research Center for Information Science and Technology (BNRist) School of Integrated Circuits and Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing, 100084, China
| | - Xianglong Li
- Shanghai Key Laboratory of Multidimensional Information Processing, School of Communication and Electronic Engineering, East China Normal University, Shanghai, 200241, China
| | - Jun Tang
- Shanxi Province Key Laboratory of Quantum Sensing and Precision Measurement, School of Instrument and Electronics, North University of China, Taiyuan, Shanxi, 030051, China
| | - Jun Liu
- Shanxi Province Key Laboratory of Quantum Sensing and Precision Measurement, School of Instrument and Electronics, North University of China, Taiyuan, Shanxi, 030051, China
| | - Xing Wu
- Shanghai Key Laboratory of Multidimensional Information Processing, School of Communication and Electronic Engineering, East China Normal University, Shanghai, 200241, China
| | - He Tian
- Institute of Microelectronics and Beijing National Research Center for Information Science and Technology (BNRist) School of Integrated Circuits and Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing, 100084, China
| | - Tian-Ling Ren
- Institute of Microelectronics and Beijing National Research Center for Information Science and Technology (BNRist) School of Integrated Circuits and Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing, 100084, China
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24
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Yan X, Qian JH, Sangwan VK, Hersam MC. Progress and Challenges for Memtransistors in Neuromorphic Circuits and Systems. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2022; 34:e2108025. [PMID: 34813677 DOI: 10.1002/adma.202108025] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/07/2021] [Revised: 11/07/2021] [Indexed: 06/13/2023]
Abstract
Due to the increasing importance of artificial intelligence (AI), significant recent effort has been devoted to the development of neuromorphic circuits that seek to emulate the energy-efficient information processing of the brain. While non-volatile memory (NVM) based on resistive switches, phase-change memory, and magnetic tunnel junctions has shown potential for implementing neural networks, additional multi-terminal device concepts are required for more sophisticated bio-realistic functions. Of particular interest are memtransistors based on low-dimensional nanomaterials, which are capable of electrostatically tuning memory and learning behavior at the device level. Herein, a conceptual overview of the memtransistor is provided in the context of neuromorphic circuits. Recent progress is surveyed for memtransistors and related multi-terminal NVM devices including dual-gated floating-gate memories, dual-gated ferroelectric transistors, and dual-gated van der Waals heterojunctions. The different materials systems and device architectures are classified based on the degree of control and relative tunability of synaptic behavior, with an emphasis on device concepts that harness the reduced dimensionality, weak electrostatic screening, and phase-changes properties of nanomaterials. Finally, strategies for achieving wafer-scale integration of memtransistors and multi-terminal NVM devices are delineated, with specific attention given to the materials challenges for practical neuromorphic circuits.
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Affiliation(s)
- Xiaodong Yan
- Department of Materials Science and Engineering, Northwestern University, Evanston, IL, 60208, USA
| | - Justin H Qian
- Department of Materials Science and Engineering, Northwestern University, Evanston, IL, 60208, USA
| | - Vinod K Sangwan
- Department of Materials Science and Engineering, Northwestern University, Evanston, IL, 60208, USA
| | - Mark C Hersam
- Department of Materials Science and Engineering, Northwestern University, Evanston, IL, 60208, USA
- Department of Electrical and Computer Engineering, Northwestern University, Evanston, IL, 60208, USA
- Department of Chemistry, Northwestern University, Evanston, IL, 60208, USA
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25
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Xue F, Zhang C, Ma Y, Wen Y, He X, Yu B, Zhang X. Integrated Memory Devices Based on 2D Materials. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2022; 34:e2201880. [PMID: 35557021 DOI: 10.1002/adma.202201880] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/27/2022] [Revised: 05/07/2022] [Indexed: 06/15/2023]
Abstract
With the advent of the Internet of Things and big data, massive data must be rapidly processed and stored within a short timeframe. This imposes stringent requirements on memory hardware implementation in terms of operation speed, energy consumption, and integration density. To fulfill these demands, 2D materials, which are excellent electronic building blocks, provide numerous possibilities for developing advanced memory device arrays with high performance, smart computing architectures, and desirable downscaling. Over the past few years, 2D-material-based memory-device arrays with different working mechanisms, including defects, filaments, charges, ferroelectricity, and spins, have been increasingly developed. These arrays can be used to implement brain-inspired computing or sensing with extraordinary performance, architectures, and functionalities. Here, recent research into integrated, state-of-the-art memory devices made from 2D materials, as well as their implications for brain-inspired computing are surveyed. The existing challenges at the array level are discussed, and the scope for future research is presented.
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Affiliation(s)
- Fei Xue
- Physical Science and Engineering Division, King Abdullah University of Science and Technology, Thuwal, 23955-6900, Saudi Arabia
- ZJU-Hangzhou Global Scientific and Technological Innovation Center, Zhejiang University, Hangzhou, 310020, P. R. China
- School of Micro-Nano Electronics, Zhejiang University, Hangzhou, 311200, P. R. China
| | - Chenhui Zhang
- Physical Science and Engineering Division, King Abdullah University of Science and Technology, Thuwal, 23955-6900, Saudi Arabia
| | - Yinchang Ma
- Physical Science and Engineering Division, King Abdullah University of Science and Technology, Thuwal, 23955-6900, Saudi Arabia
| | - Yan Wen
- Physical Science and Engineering Division, King Abdullah University of Science and Technology, Thuwal, 23955-6900, Saudi Arabia
| | - Xin He
- Physical Science and Engineering Division, King Abdullah University of Science and Technology, Thuwal, 23955-6900, Saudi Arabia
| | - Bin Yu
- ZJU-Hangzhou Global Scientific and Technological Innovation Center, Zhejiang University, Hangzhou, 310020, P. R. China
- School of Micro-Nano Electronics, Zhejiang University, Hangzhou, 311200, P. R. China
| | - Xixiang Zhang
- Physical Science and Engineering Division, King Abdullah University of Science and Technology, Thuwal, 23955-6900, Saudi Arabia
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26
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Jang HY, Kwon O, Nam JH, Kwon JD, Kim Y, Park W, Cho B. Highly Reproducible Heterosynaptic Plasticity Enabled by MoS 2/ZrO 2-x Heterostructure Memtransistor. ACS APPLIED MATERIALS & INTERFACES 2022; 14:52173-52181. [PMID: 36368778 DOI: 10.1021/acsami.2c15497] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Electrically tunable resistive switching of a polycrystalline MoS2-based memtransistor has attracted a great deal of attention as an essential synaptic component of neuromorphic circuitry because its switching characteristics from the field-induced migration of sulfur defects in the MoS2 grain boundaries can realize multilevel conductance tunability and heterosynaptic functionality. However, reproducible switching properties in the memtransistor are usually disturbed by the considerable difficulty in controlling the concentration and distribution of the intrinsically existing sulfur defects. Herein, we demonstrate reliable heterosynaptic characteristics using a memtransistor device with a MoS2/ZrO2-x heterostructure. Compared to the control device with the MoS2 semiconducting channel, the Schottky barrier height was more effectively modulated by the insertion of the insulating ZrO2-x layer below the MoS2, confirmed by an ultraviolet photoelectron spectroscopy analysis and the corresponding energy-band structures. The MoS2/ZrO2-x memtransistor accomplishes dual-terminal (drain and gate electrode) stimulated multilevel conductance owing to the tunable resistive switching behavior under varying gate voltages. Furthermore, the memtransistor exhibits long-term potentiation/depression endurance cycling over 7000 pulses and stable pulse cycling behavior by the pulse stimulus from different terminal regions. The promising candidate as an essential synaptic component of the MoS2/ZrO2-x memtransistors for neuromorphic systems results from the high recognition accuracy (∼92%) of the deep neural network simulation test, based on the training and inference of handwritten numbers (0-9). The simple memtransistor structure facilitates the implementation of complex neural circuitry.
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Affiliation(s)
- Hye Yeon Jang
- Department of Advanced Material Engineering, Chungbuk National University, Chungdae-ro 1, Seowon-Gu, Cheongju, Chungbuk 28644, Republic of Korea
- Department of Urban, Energy, and Environmental Engineering, Chungbuk National University, Chungdae-ro 1, Seowon-Gu, Cheongju, Chungbuk 28644, Republic of Korea
| | - Ojun Kwon
- Department of Advanced Material Engineering, Chungbuk National University, Chungdae-ro 1, Seowon-Gu, Cheongju, Chungbuk 28644, Republic of Korea
- Department of Urban, Energy, and Environmental Engineering, Chungbuk National University, Chungdae-ro 1, Seowon-Gu, Cheongju, Chungbuk 28644, Republic of Korea
| | - Jae Hyeon Nam
- Department of Advanced Material Engineering, Chungbuk National University, Chungdae-ro 1, Seowon-Gu, Cheongju, Chungbuk 28644, Republic of Korea
- Department of Urban, Energy, and Environmental Engineering, Chungbuk National University, Chungdae-ro 1, Seowon-Gu, Cheongju, Chungbuk 28644, Republic of Korea
| | - Jung-Dae Kwon
- Department of Energy and Electronic Materials, Surface Materials Division, Korea Institute of Materials Science, 797 Changwondaero, Sungsan-gu, Changwon, Gyeongnam 51508, Republic of Korea
| | - Yonghun Kim
- Department of Energy and Electronic Materials, Surface Materials Division, Korea Institute of Materials Science, 797 Changwondaero, Sungsan-gu, Changwon, Gyeongnam 51508, Republic of Korea
| | - Woojin Park
- Department of Advanced Material Engineering, Chungbuk National University, Chungdae-ro 1, Seowon-Gu, Cheongju, Chungbuk 28644, Republic of Korea
- Department of Urban, Energy, and Environmental Engineering, Chungbuk National University, Chungdae-ro 1, Seowon-Gu, Cheongju, Chungbuk 28644, Republic of Korea
| | - Byungjin Cho
- Department of Advanced Material Engineering, Chungbuk National University, Chungdae-ro 1, Seowon-Gu, Cheongju, Chungbuk 28644, Republic of Korea
- Department of Urban, Energy, and Environmental Engineering, Chungbuk National University, Chungdae-ro 1, Seowon-Gu, Cheongju, Chungbuk 28644, Republic of Korea
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27
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Wang S, Liu X, Xu M, Liu L, Yang D, Zhou P. Two-dimensional devices and integration towards the silicon lines. NATURE MATERIALS 2022; 21:1225-1239. [PMID: 36284239 DOI: 10.1038/s41563-022-01383-2] [Citation(s) in RCA: 50] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Accepted: 09/14/2022] [Indexed: 06/16/2023]
Abstract
Despite technical efforts and upgrades, advances in complementary metal-oxide-semiconductor circuits have become unsustainable in the face of inherent silicon limits. New materials are being sought to compensate for silicon deficiencies, and two-dimensional materials are considered promising candidates due to their atomically thin structures and exotic physical properties. However, a potentially applicable method for incorporating two-dimensional materials into silicon platforms remains to be illustrated. Here we try to bridge two-dimensional materials and silicon technology, from integrated devices to monolithic 'on-silicon' (silicon as the substrate) and 'with-silicon' (silicon as a functional component) circuits, and discuss the corresponding requirements for material synthesis, device design and circuitry integration. Finally, we summarize the role played by two-dimensional materials in the silicon-dominated semiconductor industry and suggest the way forward, as well as the technologies that are expected to become mainstream in the near future.
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Affiliation(s)
- Shuiyuan Wang
- Shanghai Key Lab for Future Computing Hardware and System, School of Microelectronics, Fudan University, Shanghai, China
| | - Xiaoxian Liu
- Shanghai Key Lab for Future Computing Hardware and System, School of Microelectronics, Fudan University, Shanghai, China
| | - Mingsheng Xu
- State Key Laboratory of Silicon Materials, School of Micro-Nano Electronics & Materials Science and Engineering, Zhejiang University, Hangzhou, China
| | - Liwei Liu
- Frontier Institute of Chip and System & Qizhi Institute, Fudan University, Shanghai, China
| | - Deren Yang
- State Key Laboratory of Silicon Materials, School of Micro-Nano Electronics & Materials Science and Engineering, Zhejiang University, Hangzhou, China
| | - Peng Zhou
- Shanghai Key Lab for Future Computing Hardware and System, School of Microelectronics, Fudan University, Shanghai, China.
- Frontier Institute of Chip and System & Qizhi Institute, Fudan University, Shanghai, China.
- Hubei Yangtze Memory Laboratories, Wuhan, China.
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28
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Review on the Basic Circuit Elements and Memristor Interpretation: Analysis, Technology and Applications. JOURNAL OF LOW POWER ELECTRONICS AND APPLICATIONS 2022. [DOI: 10.3390/jlpea12030044] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/10/2022]
Abstract
Circuit or electronic components are useful elements allowing the realization of different circuit functionalities. The resistor, capacitor and inductor represent the three commonly known basic passive circuit elements owing to their fundamental nature relating them to the four circuit variables, namely voltage, magnetic flux, current and electric charge. The memory resistor (or memristor) was claimed to be the fourth basic passive circuit element, complementing the resistor, capacitor and inductor. This paper presents a review on the four basic passive circuit elements. After a brief recall on the first three known basic passive circuit elements, a thorough description of the memristor follows. Memristor sparks interest in the scientific community due to its interesting features, for example nano-scalability, memory capability, conductance modulation, connection flexibility and compatibility with CMOS technology, etc. These features among many others are currently in high demand on an industrial scale. For this reason, thousands of memristor-based applications are reported. Hence, the paper presents an in-depth overview of the philosophical argumentations of memristor, technologies and applications.
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Pam ME, Li S, Su T, Chien YC, Li Y, Ang YS, Ang KW. Interface-Modulated Resistive Switching in Mo-Irradiated ReS 2 for Neuromorphic Computing. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2022; 34:e2202722. [PMID: 35610176 DOI: 10.1002/adma.202202722] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Revised: 04/30/2022] [Indexed: 06/15/2023]
Abstract
Coupling charge impurity scattering effects and charge-carrier modulation by doping can offer intriguing opportunities for atomic-level control of resistive switching (RS). Nonetheless, such effects have remained unexplored for memristive applications based on 2D materials. Here a facile approach is reported to transform an RS-inactive rhenium disulfide (ReS2 ) into an effective switching material through interfacial modulation induced by molybdenum-irradiation (Mo-i) doping. Using ReS2 as a model system, this study unveils a unique RS mechanism based on the formation/dissolution of metallic β-ReO2 filament across the defective ReS2 interface during the set/reset process. Through simple interfacial modulation, ReS2 of various thicknesses are switchable by modulating the Mo-irradiation period. Besides, the Mo-irradiated ReS2 (Mo-ReS2 ) memristor further exhibits a bipolar non-volatile switching ratio of nearly two orders of magnitude, programmable multilevel resistance states, and long-term synaptic plasticity. Additionally, the fabricated device can achieve a high MNIST learning accuracy of 91% under a non-identical pulse train. The study's findings demonstrate the potential for modulating RS in RS-inactive 2D materials via the unique doping-induced charged impurity scattering property.
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Affiliation(s)
- Mei Er Pam
- Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore, 117583, Singapore
| | - Sifan Li
- Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore, 117583, Singapore
| | - Tong Su
- Science, Mathematics and Technology (SMT), Singapore University of Technology and Design (SUTD), 8 Somapah Road, Singapore, 487372, Singapore
| | - Yu-Chieh Chien
- Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore, 117583, Singapore
| | - Yesheng Li
- Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore, 117583, Singapore
| | - Yee Sin Ang
- Science, Mathematics and Technology (SMT), Singapore University of Technology and Design (SUTD), 8 Somapah Road, Singapore, 487372, Singapore
| | - Kah-Wee Ang
- Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore, 117583, Singapore
- Institute of Materials Research and Engineering, A*STAR, 2 Fusionopolis, Singapore, 138634, Singapore
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30
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Li S, Pam ME, Li Y, Chen L, Chien YC, Fong X, Chi D, Ang KW. Wafer-Scale 2D Hafnium Diselenide Based Memristor Crossbar Array for Energy-Efficient Neural Network Hardware. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2022; 34:e2103376. [PMID: 34510567 DOI: 10.1002/adma.202103376] [Citation(s) in RCA: 53] [Impact Index Per Article: 26.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/03/2021] [Revised: 07/28/2021] [Indexed: 06/13/2023]
Abstract
Memristor crossbar with programmable conductance could overcome the energy consumption and speed limitations of neural networks when executing core computing tasks in image processing. However, the implementation of crossbar array (CBA) based on ultrathin 2D materials is hindered by challenges associated with large-scale material synthesis and device integration. Here, a memristor CBA is demonstrated using wafer-scale (2-inch) polycrystalline hafnium diselenide (HfSe2 ) grown by molecular beam epitaxy, and a metal-assisted van der Waals transfer technique. The memristor exhibits small switching voltage (0.6 V), low switching energy (0.82 pJ), and simultaneously achieves emulation of synaptic weight plasticity. Furthermore, the CBA enables artificial neural network with a high recognition accuracy of 93.34%. Hardware multiply-and-accumulate (MAC) operation with a narrow error distribution of 0.29% is also demonstrated, and a high power efficiency of greater than 8-trillion operations per second per Watt is achieved. Based on the MAC results, hardware convolution image processing can be performed using programmable kernels (i.e., soft, horizontal, and vertical edge enhancement), which constitutes a vital function for neural network hardware.
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Affiliation(s)
- Sifan Li
- Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore, 117583, Singapore
| | - Mei-Er Pam
- Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore, 117583, Singapore
| | - Yesheng Li
- Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore, 117583, Singapore
| | - Li Chen
- Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore, 117583, Singapore
| | - Yu-Chieh Chien
- Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore, 117583, Singapore
| | - Xuanyao Fong
- Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore, 117583, Singapore
| | - Dongzhi Chi
- Institute of Materials Research and Engineering, Agency for Science, Technology and Research, 2 Fusionopolis Way, Singapore, 138634, Singapore
| | - Kah-Wee Ang
- Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore, 117583, Singapore
- Institute of Materials Research and Engineering, Agency for Science, Technology and Research, 2 Fusionopolis Way, Singapore, 138634, Singapore
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31
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Migliato Marega G, Wang Z, Paliy M, Giusi G, Strangio S, Castiglione F, Callegari C, Tripathi M, Radenovic A, Iannaccone G, Kis A. Low-Power Artificial Neural Network Perceptron Based on Monolayer MoS 2. ACS NANO 2022; 16:3684-3694. [PMID: 35167265 PMCID: PMC8945700 DOI: 10.1021/acsnano.1c07065] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Accepted: 02/07/2022] [Indexed: 06/14/2023]
Abstract
Machine learning and signal processing on the edge are poised to influence our everyday lives with devices that will learn and infer from data generated by smart sensors and other devices for the Internet of Things. The next leap toward ubiquitous electronics requires increased energy efficiency of processors for specialized data-driven applications. Here, we show how an in-memory processor fabricated using a two-dimensional materials platform can potentially outperform its silicon counterparts in both standard and nontraditional Von Neumann architectures for artificial neural networks. We have fabricated a flash memory array with a two-dimensional channel using wafer-scale MoS2. Simulations and experiments show that the device can be scaled down to sub-micrometer channel length without any significant impact on its memory performance and that in simulation a reasonable memory window still exists at sub-50 nm channel lengths. Each device conductance in our circuit can be tuned with a 4-bit precision by closed-loop programming. Using our physical circuit, we demonstrate seven-segment digit display classification with a 91.5% accuracy with training performed ex situ and transferred from a host. Further simulations project that at a system level, the large memory arrays can perform AlexNet classification with an upper limit of 50 000 TOpS/W, potentially outperforming neural network integrated circuits based on double-poly CMOS technology.
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Affiliation(s)
- Guilherme Migliato Marega
- Institute
of Electrical and Microengineering, École
Polytechnique Fédérale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland
- Institute
of Materials Science and Engineering, École
Polytechnique Fédérale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland
| | - Zhenyu Wang
- Institute
of Electrical and Microengineering, École
Polytechnique Fédérale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland
- Institute
of Materials Science and Engineering, École
Polytechnique Fédérale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland
| | - Maksym Paliy
- Department
of Information Engineering, University of
Pisa, I-56122 Pisa, Italy
| | - Gino Giusi
- Engineering
Department, University of Messina, I-98166 Messina, Italy
| | - Sebastiano Strangio
- Department
of Information Engineering, University of
Pisa, I-56122 Pisa, Italy
| | | | | | - Mukesh Tripathi
- Institute
of Electrical and Microengineering, École
Polytechnique Fédérale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland
- Institute
of Materials Science and Engineering, École
Polytechnique Fédérale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland
| | - Aleksandra Radenovic
- Institute
of Bioengineering, École Polytechnique
Fédérale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland
| | - Giuseppe Iannaccone
- Department
of Information Engineering, University of
Pisa, I-56122 Pisa, Italy
- Quantavis
s.r.l., Largo Padre Renzo Spadoni snc, I-56123 Pisa, Italy
| | - Andras Kis
- Institute
of Electrical and Microengineering, École
Polytechnique Fédérale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland
- Institute
of Materials Science and Engineering, École
Polytechnique Fédérale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland
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32
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Kwon O, Oh S, Park H, Jeong SH, Park W, Cho B. In-depth analysis on electrical parameters of floating gate IGZO synaptic transistor affecting pattern recognition accuracy. NANOTECHNOLOGY 2022; 33:215201. [PMID: 35147525 DOI: 10.1088/1361-6528/ac5444] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/29/2021] [Accepted: 02/09/2022] [Indexed: 06/14/2023]
Abstract
The reliable conductance modulation of synaptic devices is key when implementing high-performance neuromorphic systems. Herein, we propose a floating gate indium gallium zinc oxide (IGZO) synaptic device with an aluminum trapping layer to investigate the correlation between its diverse electrical parameters and pattern recognition accuracy. Basic synaptic properties such as excitatory postsynaptic current, paired pulse facilitation, long/short term memory, and long-term potentiation/depression are demonstrated in the IGZO synaptic transistor. The effects of pulse tuning conditions associated with the pulse voltage magnitude, interval, duration, and cycling number of the applied pulses on the conductance update are systematically investigated. It is discovered that both the nonlinearity of the conductance update and cycle-to-cycle variation should be critically considered using an artificial neural network simulator to ensure the high pattern recognition accuracy of Modified National Institute of Standards and Technology (MNIST) handwritten digit images. The highest recognition rate of the MNIST handwritten dataset is 94.06% for the most optimized pulse condition. Finally, a systematic study regarding the synaptic parameters must be performed to optimize the developed synapse device.
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Affiliation(s)
- Ojun Kwon
- Department of Advanced Material Engineering, Chungbuk National University, Chungdae-ro 1, Seowon-Gu, Cheongju, Chungbuk 28644, Republic of Korea
- Department of Urban, Energy, and Environmental Engineering, Chungbuk National University, Chungdae-ro 1, Seowon-Gu, Cheongju, Chungbuk 28644, Republic of Korea
| | - Seyoung Oh
- Department of Advanced Material Engineering, Chungbuk National University, Chungdae-ro 1, Seowon-Gu, Cheongju, Chungbuk 28644, Republic of Korea
- Department of Urban, Energy, and Environmental Engineering, Chungbuk National University, Chungdae-ro 1, Seowon-Gu, Cheongju, Chungbuk 28644, Republic of Korea
| | - Heejeong Park
- Department of Advanced Material Engineering, Chungbuk National University, Chungdae-ro 1, Seowon-Gu, Cheongju, Chungbuk 28644, Republic of Korea
- Department of Urban, Energy, and Environmental Engineering, Chungbuk National University, Chungdae-ro 1, Seowon-Gu, Cheongju, Chungbuk 28644, Republic of Korea
| | - Soo-Hong Jeong
- Department of Advanced Material Engineering, Chungbuk National University, Chungdae-ro 1, Seowon-Gu, Cheongju, Chungbuk 28644, Republic of Korea
- Department of Urban, Energy, and Environmental Engineering, Chungbuk National University, Chungdae-ro 1, Seowon-Gu, Cheongju, Chungbuk 28644, Republic of Korea
| | - Woojin Park
- Department of Advanced Material Engineering, Chungbuk National University, Chungdae-ro 1, Seowon-Gu, Cheongju, Chungbuk 28644, Republic of Korea
- Department of Urban, Energy, and Environmental Engineering, Chungbuk National University, Chungdae-ro 1, Seowon-Gu, Cheongju, Chungbuk 28644, Republic of Korea
| | - Byungjin Cho
- Department of Advanced Material Engineering, Chungbuk National University, Chungdae-ro 1, Seowon-Gu, Cheongju, Chungbuk 28644, Republic of Korea
- Department of Urban, Energy, and Environmental Engineering, Chungbuk National University, Chungdae-ro 1, Seowon-Gu, Cheongju, Chungbuk 28644, Republic of Korea
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33
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Chen P, Pan J, Gao W, Wan B, Kong X, Cheng Y, Liu K, Du S, Ji W, Pan C, Wang ZL. Anisotropic Carrier Mobility from 2H WSe 2. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2022; 34:e2108615. [PMID: 34859917 DOI: 10.1002/adma.202108615] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/26/2021] [Revised: 12/01/2021] [Indexed: 06/13/2023]
Abstract
Transition metal dichalcogenides (TMDCs) with 2H phase are expected to be building blocks in next-generation electronics; however, they suffer from electrical anisotropy, which is the basics for multi-terminal artificial synaptic devices, digital inverters, and anisotropic memtransistors, which are highly desired in neuromorphic computing. Herein, the anisotropic carrier mobility from 2H WSe2 is reported, where the anisotropic degree of carrier mobility spans from 0.16 to 0.95 for various WSe2 field-effect transistors under a gate voltage of -60 V. Phonon scattering, impurity ions scattering, and defect scattering are excluded for anisotropic mobility. An intrinsic screening layer is proposed and confirmed by Z-contrast scanning transmission electron microscopy (STEM) imaging to respond to the electrical anisotropy. Seven types of intrinsic screening layers are created and calculated by density functional theory to evaluate the modulated electronic structures, effective masses, and scattering intensities, resulting in anisotropic mobility. The discovery of anisotropic carrier mobility from 2H WSe2 provides a degree of freedom for adjusting the physical properties of 2H TMDCs and fertile ground for exploring and integrating TMDC electronic transistors with better performance along the direction of high mobility.
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Affiliation(s)
- Ping Chen
- Center on Nanoenergy Research, School of Physical Science and Technology, Guangxi University, Nanning, 530004, China
- CAS Center for Excellence in Nanoscience, Beijing Key Laboratory of Micro-nano Energy and Sensor, Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing, 100083, China
| | - Jinbo Pan
- Institute of Physics & University of Chinese Academy of Sciences, Chinese Academy of Sciences, Beijing, 100190, China
| | - Wenchao Gao
- CAS Center for Excellence in Nanoscience, Beijing Key Laboratory of Micro-nano Energy and Sensor, Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing, 100083, China
| | - Bensong Wan
- CAS Center for Excellence in Nanoscience, Beijing Key Laboratory of Micro-nano Energy and Sensor, Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing, 100083, China
| | - Xianghua Kong
- Centre for the Physics of Materials and Department of Physics, McGill University, Montreal, QC, H3A 2T8, Canada
| | - Yang Cheng
- State Key Laboratory for Mesoscopic Physics, Academy for Advanced Interdisciplinary Studies, School of Physics, Peking University, Beijing, 100871, China
| | - Kaihui Liu
- State Key Laboratory for Mesoscopic Physics, Academy for Advanced Interdisciplinary Studies, School of Physics, Peking University, Beijing, 100871, China
| | - Shixuan Du
- Institute of Physics & University of Chinese Academy of Sciences, Chinese Academy of Sciences, Beijing, 100190, China
| | - Wei Ji
- Department of Physics and Beijing Key Laboratory of Optoelectronic Functional Materials & Micro-Nano Devices, Renmin University of China, Beijing, 100872, China
| | - Caofeng Pan
- Center on Nanoenergy Research, School of Physical Science and Technology, Guangxi University, Nanning, 530004, China
- CAS Center for Excellence in Nanoscience, Beijing Key Laboratory of Micro-nano Energy and Sensor, Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing, 100083, China
- College of Physics and Optoelectronic Engineering, Shenzhen University, Shenzhen, 518060, China
- School of Nanoscience and Technology, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Zhong Lin Wang
- Center on Nanoenergy Research, School of Physical Science and Technology, Guangxi University, Nanning, 530004, China
- CAS Center for Excellence in Nanoscience, Beijing Key Laboratory of Micro-nano Energy and Sensor, Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing, 100083, China
- School of Nanoscience and Technology, University of Chinese Academy of Sciences, Beijing, 100049, China
- School of Materials Science and Engineering, Georgia Institute of Technology, Atlanta, GA, 30332-0245, USA
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34
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Graphene/Ferroelectric (Ge-Doped HfO2) Adaptable Transistors Acting as Reconfigurable Logic Gates. NANOMATERIALS 2022; 12:nano12020279. [PMID: 35055296 PMCID: PMC8778263 DOI: 10.3390/nano12020279] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Revised: 01/07/2022] [Accepted: 01/15/2022] [Indexed: 02/04/2023]
Abstract
We present an array of 225 field-effect transistors (FETs), where each of them has a graphene monolayer channel grown on a 3-layer deposited stack of 22 nm control HfO2/5 nm Ge-HfO2 intermediate layer/8 nm tunnel HfO2/p-Si substrate. The intermediate layer is ferroelectric and acts as a floating gate. All transistors have two top gates, while the p-Si substrate is acting as a back gate. We show that these FETs are acting memtransistors, working as two-input reconfigurable logic gates with memory, the type of the logic gate depending only on the values of the applied gate voltages and the choice of a threshold current.
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35
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Ding G, Yang B, Chen RS, Mo WA, Zhou K, Liu Y, Shang G, Zhai Y, Han ST, Zhou Y. Reconfigurable 2D WSe 2 -Based Memtransistor for Mimicking Homosynaptic and Heterosynaptic Plasticity. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2021; 17:e2103175. [PMID: 34528382 DOI: 10.1002/smll.202103175] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/01/2021] [Revised: 07/30/2021] [Indexed: 06/13/2023]
Abstract
The mimicking of both homosynaptic and heterosynaptic plasticity using a high-performance synaptic device is important for developing human-brain-like neuromorphic computing systems to overcome the ever-increasing challenges caused by the conventional von Neumann architecture. However, the commonly used synaptic devices (e.g., memristors and transistors) require an extra modulate terminal to mimic heterosynaptic plasticity, and their capability of synaptic plasticity simulation is limited by the low weight adjustability. In this study, a WSe2 -based memtransistor for mimicking both homosynaptic and heterosynaptic plasticity is fabricated. By applying spikes on either the drain or gate terminal, the memtransistor can mimic common homosynaptic plasticity, including spiking rate dependent plasticity, paired pulse facilitation/depression, synaptic potentiation/depression, and filtering. Benefitting from the multi-terminal input and high adjustability, the resistance state number and linearity of the memtransistor can be improved by optimizing the conditions of the two inputs. Moreover, the device can successfully mimic heterosynaptic plasticity without introducing an extra terminal and can simultaneously offer versatile reconfigurability of excitatory and inhibitory plasticity. These highly adjustable and reconfigurable characteristics offer memtransistors more freedom of choice for tuning synaptic weight, optimizing circuit design, and building artificial neuromorphic computing systems.
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Affiliation(s)
- Guanglong Ding
- Institute for Advanced Study, Shenzhen University, Shenzhen, 518060, P. R. China
| | - Baidong Yang
- College of Electronics and Information Engineering, Shenzhen University, Shenzhen, 518060, P. R. China
- Shenzhen Key Laboratory of Flexible Memory Materials and Devices, Institute of Microscale Optoelectronics, Shenzhen University, Shenzhen, 518060, P. R. China
| | - Ruo-Si Chen
- College of Electronics and Information Engineering, Shenzhen University, Shenzhen, 518060, P. R. China
- Shenzhen Key Laboratory of Flexible Memory Materials and Devices, Institute of Microscale Optoelectronics, Shenzhen University, Shenzhen, 518060, P. R. China
| | - Wen-Ai Mo
- College of Electronics and Information Engineering, Shenzhen University, Shenzhen, 518060, P. R. China
- Shenzhen Key Laboratory of Flexible Memory Materials and Devices, Institute of Microscale Optoelectronics, Shenzhen University, Shenzhen, 518060, P. R. China
| | - Kui Zhou
- Institute for Advanced Study, Shenzhen University, Shenzhen, 518060, P. R. China
| | - Yang Liu
- Shenzhen Key Laboratory of Flexible Memory Materials and Devices, Institute of Microscale Optoelectronics, Shenzhen University, Shenzhen, 518060, P. R. China
| | - Gang Shang
- Shenzhen Key Laboratory of Flexible Memory Materials and Devices, Institute of Microscale Optoelectronics, Shenzhen University, Shenzhen, 518060, P. R. China
| | - Yongbiao Zhai
- Shenzhen Key Laboratory of Flexible Memory Materials and Devices, Institute of Microscale Optoelectronics, Shenzhen University, Shenzhen, 518060, P. R. China
| | - Su-Ting Han
- Shenzhen Key Laboratory of Flexible Memory Materials and Devices, Institute of Microscale Optoelectronics, Shenzhen University, Shenzhen, 518060, P. R. China
| | - Ye Zhou
- Institute for Advanced Study, Shenzhen University, Shenzhen, 518060, P. R. China
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36
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Yuan J, Liu SE, Shylendra A, Gaviria Rojas WA, Guo S, Bergeron H, Li S, Lee HS, Nasrin S, Sangwan VK, Trivedi AR, Hersam MC. Reconfigurable MoS 2 Memtransistors for Continuous Learning in Spiking Neural Networks. NANO LETTERS 2021; 21:6432-6440. [PMID: 34283622 DOI: 10.1021/acs.nanolett.1c00982] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Artificial intelligence and machine learning are growing computing paradigms, but current algorithms incur undesirable energy costs on conventional hardware platforms, thus motivating the exploration of more efficient neuromorphic architectures. Toward this end, we introduce here a memtransistor with gate-tunable dynamic learning behavior. By fabricating memtransistors from monolayer MoS2 grown on sapphire, the relative importance of the vertical field effect from the gate is enhanced, thereby heightening reconfigurability of the device response. Inspired by biological systems, gate pulses are used to modulate potentiation and depression, resulting in diverse learning curves and simplified spike-timing-dependent plasticity that facilitate unsupervised learning in simulated spiking neural networks. This capability also enables continuous learning, which is a previously underexplored cognitive concept in neuromorphic computing. Overall, this work demonstrates that the reconfigurability of memtransistors provides unique hardware accelerator opportunities for energy efficient artificial intelligence and machine learning.
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Affiliation(s)
- Jiangtan Yuan
- Department of Materials Science and Engineering, Northwestern University, Evanston, Illinois 60208, United States
| | - Stephanie E Liu
- Department of Materials Science and Engineering, Northwestern University, Evanston, Illinois 60208, United States
| | - Ahish Shylendra
- Department of Electrical and Computer Engineering, University of Illinois at Chicago, Chicago, Illinois 60607, United States
| | - William A Gaviria Rojas
- Department of Materials Science and Engineering, Northwestern University, Evanston, Illinois 60208, United States
| | - Silu Guo
- Department of Materials Science and Engineering, Northwestern University, Evanston, Illinois 60208, United States
| | - Hadallia Bergeron
- Department of Materials Science and Engineering, Northwestern University, Evanston, Illinois 60208, United States
| | - Shaowei Li
- Department of Materials Science and Engineering, Northwestern University, Evanston, Illinois 60208, United States
| | - Hong-Sub Lee
- Department of Materials Science and Engineering, Northwestern University, Evanston, Illinois 60208, United States
| | - Shamma Nasrin
- Department of Electrical and Computer Engineering, University of Illinois at Chicago, Chicago, Illinois 60607, United States
| | - Vinod K Sangwan
- Department of Materials Science and Engineering, Northwestern University, Evanston, Illinois 60208, United States
| | - Amit Ranjan Trivedi
- Department of Electrical and Computer Engineering, University of Illinois at Chicago, Chicago, Illinois 60607, United States
| | - Mark C Hersam
- Department of Materials Science and Engineering, Northwestern University, Evanston, Illinois 60208, United States
- Department of Chemistry, Northwestern University, Evanston, Illinois 60208, United States
- Department of Electrical and Computer Engineering, Northwestern University, Evanston, Illinois 60208, United States
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37
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Wang Y, Huang W, Zhang Z, Fan L, Huang Q, Wang J, Zhang Y, Zhang M. Ultralow-power flexible transparent carbon nanotube synaptic transistors for emotional memory. NANOSCALE 2021; 13:11360-11369. [PMID: 34096562 DOI: 10.1039/d1nr02099d] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/06/2023]
Abstract
Emulating the biological behavior of the human brain with artificial neuromorphic devices is essential for the future development of human-machine interactive systems, bionic sensing systems and intelligent robotic systems. In this paper, artificial flexible transparent carbon nanotube synaptic transistors (F-CNT-STs) with signal transmission and emotional learning functions are realized by adopting the poly(vinyl alcohol) (PVA)/SiO2 proton-conducting electrolyte. Synaptic functions of biological synapses including excitatory and inhibitory behaviors are successfully emulated in the F-CNT-STs. Besides, synaptic plasticity such as spike-duration-dependent plasticity, spike-number-dependent plasticity, spike-amplitude-dependent plasticity, paired-pulse facilitation, short-term plasticity, and long-term plasticity have all been systematically characterized. Moreover, the F-CNT-STs also closely imitate the behavior of human brain learning and emotional memory functions. After 1000 bending cycles at a radius of 3 mm, both the transistor characteristics and the synaptic functions can still be implemented correctly, showing outstanding mechanical capability. The realized F-CNT-STs possess low operating voltage, quick response, and ultra-low power consumption, indicating their high potential to work in low-power biological systems and artificial intelligence systems. The flexible artificial synaptic transistor enables its potential to be generally applicable to various flexible wearable biological and intelligent applications.
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Affiliation(s)
- Yarong Wang
- School of Electronic and Computer Engineering, Peking University, Shenzhen 518055, China.
| | - Weihong Huang
- School of Electronic and Computer Engineering, Peking University, Shenzhen 518055, China.
| | - Ziwei Zhang
- School of Electronic and Computer Engineering, Peking University, Shenzhen 518055, China.
| | - Lingchong Fan
- School of Electronic and Computer Engineering, Peking University, Shenzhen 518055, China.
| | - Qiuyue Huang
- School of Electronic and Computer Engineering, Peking University, Shenzhen 518055, China.
| | - Jiaxin Wang
- School of Electronic and Computer Engineering, Peking University, Shenzhen 518055, China.
| | - Yiming Zhang
- School of Electronic and Computer Engineering, Peking University, Shenzhen 518055, China.
| | - Min Zhang
- School of Electronic and Computer Engineering, Peking University, Shenzhen 518055, China.
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