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Lee SU, Kim SY, Lee JH, Baek JH, Lee JW, Jang HW, Park NG. Artificial Synapse Based on a δ-FAPbI 3/Atomic-Layer-Deposited SnO 2 Bilayer Memristor. Nano Lett 2024. [PMID: 38619226 DOI: 10.1021/acs.nanolett.4c00253] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/16/2024]
Abstract
Halide perovskite-based resistive switching memory (memristor) has potential in an artificial synapse. However, an abrupt switch behavior observed for a formamidinium lead triiodide (FAPbI3)-based memristor is undesirable for an artificial synapse. Here, we report on the δ-FAPbI3/atomic-layer-deposited (ALD)-SnO2 bilayer memristor for gradual analogue resistive switching. In comparison to a single-layer δ-FAPbI3 memristor, the heterojunction δ-FAPbI3/ALD-SnO2 bilayer effectively reduces the current level in the high-resistance state. The analog resistive switching characteristics of δ-FAPbI3/ALD-SnO2 demonstrate exceptional linearity and potentiation/depression performance, resembling an artificial synapse for neuromorphic computing. The nonlinearity of long-term potentiation and long-term depression is notably decreased from 12.26 to 0.60 and from -8.79 to -3.47, respectively. Moreover, the δ-FAPbI3/ALD-SnO2 bilayer achieves a recognition rate of ≤94.04% based on the modified National Institute of Standards and Technology database (MNIST), establishing its potential in an efficient artificial synapse.
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Affiliation(s)
- Sang-Uk Lee
- School of Chemical Engineering, Center for Antibonding Regulated Crystals, Sungkyunkwan University, Suwon 16419, Republic of Korea
| | - So-Yeon Kim
- School of Chemical Engineering, Center for Antibonding Regulated Crystals, Sungkyunkwan University, Suwon 16419, Republic of Korea
| | - Joo-Hong Lee
- Department of Nano Science and Technology and Department of Nanoengineering, SKKU Advanced Institute of Nanotechnology (SAINT), Sungkyunkwan University, Suwon 16419, Republic of Korea
| | - Ji Hyun Baek
- Department of Materials Science and Engineering, Research Institute of Advanced Materials, Seoul National University, Seoul 08826, Republic of Korea
| | - Jin-Wook Lee
- Department of Nano Science and Technology and Department of Nanoengineering, SKKU Advanced Institute of Nanotechnology (SAINT), Sungkyunkwan University, Suwon 16419, Republic of Korea
- SKKU Institute of Energy Science and Technology (SIEST), Sungkyunkwan University, Suwon 16419, Republic of Korea
| | - Ho Won Jang
- Department of Materials Science and Engineering, Research Institute of Advanced Materials, Seoul National University, Seoul 08826, Republic of Korea
- Advanced Institute of Convergence Technology, Seoul National University, Suwon 16229, Republic of Korea
| | - Nam-Gyu Park
- School of Chemical Engineering, Center for Antibonding Regulated Crystals, Sungkyunkwan University, Suwon 16419, Republic of Korea
- SKKU Institute of Energy Science and Technology (SIEST), Sungkyunkwan University, Suwon 16419, Republic of Korea
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Sung J, Chung S, Jang Y, Jang H, Kim J, Lee C, Lee D, Jeong D, Cho K, Kim YS, Kang J, Lee W, Lee E. Unveiling the Role of Side Chain for Improving Nonvolatile Characteristics of Conjugated Polymers-Based Artificial Synapse. Adv Sci (Weinh) 2024; 11:e2400304. [PMID: 38408158 DOI: 10.1002/advs.202400304] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/09/2024] [Indexed: 02/28/2024]
Abstract
Interest has grown in services that consume a significant amount of energy, such as large language models (LLMs), and research is being conducted worldwide on synaptic devices for neuromorphic hardware. However, various complex processes are problematic for the implementation of synaptic properties. Here, synaptic characteristics are implemented through a novel method, namely side chain control of conjugated polymers. The developed devices exhibit the characteristics of the biological brain, especially spike-timing-dependent plasticity (STDP), high-pass filtering, and long-term potentiation/depression (LTP/D). Moreover, the fabricated synaptic devices show enhanced nonvolatile characteristics, such as long retention time (≈102 s), high ratio of Gmax/Gmin, high linearity, and reliable cyclic endurance (≈103 pulses). This study presents a new pathway for next-generation neuromorphic computing by modulating conjugated polymers with side chain control, thereby achieving high-performance synaptic properties.
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Affiliation(s)
- Junho Sung
- Department of Chemical Engineering, Kumoh National Institute of Technology, Gumi, 39177, Republic of Korea
| | - Sein Chung
- Department of Chemical Engineering, Pohang University of Science and Technology, Pohang, 37673, Republic of Korea
| | - Yongchan Jang
- Department of Polymer Science and Engineering, Department of Energy Engineering Convergence, Kumoh National Institute of Technology, Gumi, 39177, Republic of Korea
| | - Hyoik Jang
- Department of Chemical Engineering, Kumoh National Institute of Technology, Gumi, 39177, Republic of Korea
| | - Jiyeon Kim
- Department of Applied Bioengineering, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, 08826, Republic of Korea
| | - Chan Lee
- Department of Chemical and Biological Engineering, and Institute of Chemical Processes, College of Engineering, Seoul National University, Seoul, 08826, Republic of Korea
| | - Donghwa Lee
- Department of Chemical Engineering, Kumoh National Institute of Technology, Gumi, 39177, Republic of Korea
| | - Dongyeong Jeong
- Department of Chemical Engineering, Kumoh National Institute of Technology, Gumi, 39177, Republic of Korea
| | - Kilwon Cho
- Department of Chemical Engineering, Pohang University of Science and Technology, Pohang, 37673, Republic of Korea
| | - Youn Sang Kim
- Department of Applied Bioengineering, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, 08826, Republic of Korea
- Department of Chemical and Biological Engineering, and Institute of Chemical Processes, College of Engineering, Seoul National University, Seoul, 08826, Republic of Korea
- Advanced Institute of Convergence Technology, Suwon, 16229, Republic of Korea
| | - Joonhee Kang
- Department of Nanoenergy Engineering, Pusan National University, Busan, 46241, Republic of Korea
| | - Wonho Lee
- Department of Polymer Science and Engineering, Department of Energy Engineering Convergence, Kumoh National Institute of Technology, Gumi, 39177, Republic of Korea
| | - Eunho Lee
- Department of Chemical and Biomolecular Engineering, Seoul National University of Science and Technology, Seoul, 01811, Republic of Korea
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Zhou X, Wang Z, Xiong T, He B, Wang Z, Zhang H, Hu D, Liu Y, Yang C, Li Q, Chen M, Zhang Q, Wei L. Fiber Crossbars: An Emerging Architecture of Smart Electronic Textiles. Adv Mater 2023; 35:e2300576. [PMID: 37042804 DOI: 10.1002/adma.202300576] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Revised: 03/18/2023] [Indexed: 06/19/2023]
Abstract
Smart wearables have a significant impact on people's daily lives, enabling personalized motion monitoring, realizing the Internet of Things, and even reshaping the next generation of telemedicine systems. Fiber crossbars (FCs), constructed by crossing two fibers, have become an emerging architecture among the accessible structures of state-of-the-art smart electronic textiles. The mechanical, chemical, and electrical interactions between crossing fibers result in extensive functionalities, leading to the significant development of innovative electronic textiles employing FCs as their basic units. This review provides a timely and comprehensive overview of the structure designs, material selections, and assembly techniques of FC-based devices. The recent advances in FC-based devices are summarized, including multipurpose sensing, multiple-mode computing, high-resolution display, high-efficient power supply, and large-scale textile systems. Finally, current challenges, potential solutions, and future perspectives for FC-based systems are discussed for their further development in scale-up production and commercial applications.
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Affiliation(s)
- Xuhui Zhou
- School of Electrical and Electronic Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798, Singapore
| | - Zhe Wang
- School of Electrical and Electronic Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798, Singapore
| | - Ting Xiong
- School of Electrical and Electronic Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798, Singapore
| | - Bing He
- School of Electrical and Electronic Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798, Singapore
| | - Zhixun Wang
- School of Electrical and Electronic Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798, Singapore
| | - Haozhe Zhang
- School of Electrical and Electronic Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798, Singapore
| | - Dongmei Hu
- Key Laboratory of Multifunctional Nanomaterials and Smart Systems, Suzhou Institute of Nano-Tech and Nano-Bionics, Chinese Academy of Sciences, Suzhou, 215123, P. R. China
| | - Yanting Liu
- School of Electrical and Electronic Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798, Singapore
| | - Chunlei Yang
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, P. R. China
| | - Qingwen Li
- Key Laboratory of Multifunctional Nanomaterials and Smart Systems, Suzhou Institute of Nano-Tech and Nano-Bionics, Chinese Academy of Sciences, Suzhou, 215123, P. R. China
| | - Ming Chen
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, P. R. China
| | - Qichong Zhang
- Key Laboratory of Multifunctional Nanomaterials and Smart Systems, Suzhou Institute of Nano-Tech and Nano-Bionics, Chinese Academy of Sciences, Suzhou, 215123, P. R. China
| | - Lei Wei
- School of Electrical and Electronic Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798, Singapore
- The Institute for Digital Molecular Analytics and Science (IDMxS), Nanyang Technological University, 59 Nanyang Drive, Singapore, 636921, Singapore
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Kim D, Kim IJ, Lee JS. Demonstration of the threshold-switching memory devices using EMIm(AlCl 3)Cl and ZnO for neuromorphic applications. Nanotechnology 2023; 35:015203. [PMID: 37830748 DOI: 10.1088/1361-6528/acf93d] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Accepted: 09/13/2023] [Indexed: 10/14/2023]
Abstract
The threshold-switching behaviors of the synapses lead to energy-efficient operation in the neural computing system. Here, we demonstrated the threshold-switching memory devices by inserting the ZnO layer into the ionic synaptic devices. The EMIm(AlCl3)Cl is utilized as the electrolyte because its conductance can be tuned by the charge states of the Al-based ions. The redox reactions of the Al ions in the electrolyte can lead to the analog resistive switching characteristics, such as excitatory postsynaptic current, paired-pulse facilitation, potentiation, and depression. By inserting the ZnO layer into the EMIm(AlCl3)-based ionic synaptic devices, the threshold switching behaviors are demonstrated. Using the resistivity difference between ZnO and EMIm(AlCl3)Cl, the analog resistive switching behaviors are tunned as the threshold-switching behaviors. The threshold-switching behaviors are achieved by applying the spike stimuli to the device. Demonstration of the threshold-switching behaviors of the ionic synaptic devices has a possibility to achieve high energy-efficiency for the ion-based artificial synapses.
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Affiliation(s)
- Dongshin Kim
- Department of Materials Science and Engineering, Pohang University of Science and Technology (POSTECH), Pohang 37673, Republic of Korea
| | - Ik-Jyae Kim
- Department of Materials Science and Engineering, Pohang University of Science and Technology (POSTECH), Pohang 37673, Republic of Korea
| | - Jang-Sik Lee
- Department of Materials Science and Engineering, Pohang University of Science and Technology (POSTECH), Pohang 37673, Republic of Korea
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5
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Byun J, Kho W, Hwang H, Kang Y, Kang M, Noh T, Kim H, Lee J, Kim HB, Ahn JH, Ahn SE. Spike Optimization to Improve Properties of Ferroelectric Tunnel Junction Synaptic Devices for Neuromorphic Computing System Applications. Nanomaterials (Basel) 2023; 13:2704. [PMID: 37836345 PMCID: PMC10574482 DOI: 10.3390/nano13192704] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Revised: 10/02/2023] [Accepted: 10/03/2023] [Indexed: 10/15/2023]
Abstract
The continuous advancement of Artificial Intelligence (AI) technology depends on the efficient processing of unstructured data, encompassing text, speech, and video. Traditional serial computing systems based on the von Neumann architecture, employed in information and communication technology development for decades, are not suitable for the concurrent processing of massive unstructured data tasks with relatively low-level operations. As a result, there arises a pressing need to develop novel parallel computing systems. Recently, there has been a burgeoning interest among developers in emulating the intricate operations of the human brain, which efficiently processes vast datasets with remarkable energy efficiency. This has led to the proposal of neuromorphic computing systems. Of these, Spiking Neural Networks (SNNs), designed to closely resemble the information processing mechanisms of biological neural networks, are subjects of intense research activity. Nevertheless, a comprehensive investigation into the relationship between spike shapes and Spike-Timing-Dependent Plasticity (STDP) to ensure efficient synaptic behavior remains insufficiently explored. In this study, we systematically explore various input spike types to optimize the resistive memory characteristics of Hafnium-based Ferroelectric Tunnel Junction (FTJ) devices. Among the various spike shapes investigated, the square-triangle (RT) spike exhibited good linearity and symmetry, and a wide range of weight values could be realized depending on the offset of the RT spike. These results indicate that the spike shape serves as a crucial indicator in the alteration of synaptic connections, representing the strength of the signals.
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Affiliation(s)
- Jisu Byun
- Department of IT ∙ Semiconductor Convergence Eng, Tech University of Korea, Siheung 05073, Republic of Korea; (J.B.); (W.K.); (H.H.); (Y.K.); (M.K.); (T.N.); (H.K.); (J.L.)
| | - Wonwoo Kho
- Department of IT ∙ Semiconductor Convergence Eng, Tech University of Korea, Siheung 05073, Republic of Korea; (J.B.); (W.K.); (H.H.); (Y.K.); (M.K.); (T.N.); (H.K.); (J.L.)
| | - Hyunjoo Hwang
- Department of IT ∙ Semiconductor Convergence Eng, Tech University of Korea, Siheung 05073, Republic of Korea; (J.B.); (W.K.); (H.H.); (Y.K.); (M.K.); (T.N.); (H.K.); (J.L.)
| | - Yoomi Kang
- Department of IT ∙ Semiconductor Convergence Eng, Tech University of Korea, Siheung 05073, Republic of Korea; (J.B.); (W.K.); (H.H.); (Y.K.); (M.K.); (T.N.); (H.K.); (J.L.)
| | - Minjeong Kang
- Department of IT ∙ Semiconductor Convergence Eng, Tech University of Korea, Siheung 05073, Republic of Korea; (J.B.); (W.K.); (H.H.); (Y.K.); (M.K.); (T.N.); (H.K.); (J.L.)
| | - Taewan Noh
- Department of IT ∙ Semiconductor Convergence Eng, Tech University of Korea, Siheung 05073, Republic of Korea; (J.B.); (W.K.); (H.H.); (Y.K.); (M.K.); (T.N.); (H.K.); (J.L.)
| | - Hoseong Kim
- Department of IT ∙ Semiconductor Convergence Eng, Tech University of Korea, Siheung 05073, Republic of Korea; (J.B.); (W.K.); (H.H.); (Y.K.); (M.K.); (T.N.); (H.K.); (J.L.)
| | - Jimin Lee
- Department of IT ∙ Semiconductor Convergence Eng, Tech University of Korea, Siheung 05073, Republic of Korea; (J.B.); (W.K.); (H.H.); (Y.K.); (M.K.); (T.N.); (H.K.); (J.L.)
| | - Hyo-Bae Kim
- Department of Materials Science and Chemical Engineering, Hanyang University, Ansan 15588, Republic of Korea; (H.-B.K.); (J.-H.A.)
| | - Ji-Hoon Ahn
- Department of Materials Science and Chemical Engineering, Hanyang University, Ansan 15588, Republic of Korea; (H.-B.K.); (J.-H.A.)
| | - Seung-Eon Ahn
- Department of IT ∙ Semiconductor Convergence Eng, Tech University of Korea, Siheung 05073, Republic of Korea; (J.B.); (W.K.); (H.H.); (Y.K.); (M.K.); (T.N.); (H.K.); (J.L.)
- Department of Nano & Semiconductor Eng, Tech University of Korea, Siheung 05073, Republic of Korea
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6
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Hellenbrand M, MacManus-Driscoll J. Multi-level resistive switching in hafnium-oxide-based devices for neuromorphic computing. Nano Converg 2023; 10:44. [PMID: 37710080 PMCID: PMC10501996 DOI: 10.1186/s40580-023-00392-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Accepted: 08/30/2023] [Indexed: 09/16/2023]
Abstract
In the growing area of neuromorphic and in-memory computing, there are multiple reviews available. Most of them cover a broad range of topics, which naturally comes at the cost of details in specific areas. Here, we address the specific area of multi-level resistive switching in hafnium-oxide-based devices for neuromorphic applications and summarize the progress of the most recent years. While the general approach of resistive switching based on hafnium oxide thin films has been very busy over the last decade or so, the development of hafnium oxide with a continuous range of programmable states per device is still at a very early stage and demonstrations are mostly at the level of individual devices with limited data provided. On the other hand, it is positive that there are a few demonstrations of full network implementations. We summarize the general status of the field, point out open questions, and provide recommendations for future work.
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Affiliation(s)
- Markus Hellenbrand
- Department of Materials Science & Metallurgy, University of Cambridge, 27 Charles Babbage Rd, Cambridge, CB3 0FS, UK.
| | - Judith MacManus-Driscoll
- Department of Materials Science & Metallurgy, University of Cambridge, 27 Charles Babbage Rd, Cambridge, CB3 0FS, UK
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Dai S, Liu X, Liu Y, Xu Y, Zhang J, Wu Y, Cheng P, Xiong L, Huang J. Emerging Iontronic Neural Devices for Neuromorphic Sensory Computing. Adv Mater 2023; 35:e2300329. [PMID: 36891745 DOI: 10.1002/adma.202300329] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Revised: 02/22/2023] [Indexed: 06/18/2023]
Abstract
Living organisms have a very mysterious and powerful sensory computing system based on ion activity. Interestingly, studies on iontronic devices in the past few years have proposed a promising platform for simulating the sensing and computing functions of living organisms, because: 1) iontronic devices can generate, store, and transmit a variety of signals by adjusting the concentration and spatiotemporal distribution of ions, which analogs to how the brain performs intelligent functions by alternating ion flux and polarization; 2) through ionic-electronic coupling, iontronic devices can bridge the biosystem with electronics and offer profound implications for soft electronics; 3) with the diversity of ions, iontronic devices can be designed to recognize specific ions or molecules by customizing the charge selectivity, and the ionic conductivity and capacitance can be adjusted to respond to external stimuli for a variety of sensing schemes, which can be more difficult for electron-based devices. This review provides a comprehensive overview of emerging neuromorphic sensory computing by iontronic devices, highlighting representative concepts of both low-level and high-level sensory computing and introducing important material and device breakthroughs. Moreover, iontronic devices as a means of neuromorphic sensing and computing are discussed regarding the pending challenges and future directions.
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Affiliation(s)
- Shilei Dai
- Shanghai Key Laboratory of Anesthesiology and Brain Functional Modulation, Translational Research Institute of Brain and Brain-Like Intelligence, Shanghai Fourth People's Hospital, Tongji University, Shanghai, 200434, P. R. China
- Interdisciplinary Materials Research Center, School of Materials Science and Engineering, Tongji University, Shanghai, 201804, P. R. China
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Pokfulam, Hong Kong, 999077, China
| | - Xu Liu
- Interdisciplinary Materials Research Center, School of Materials Science and Engineering, Tongji University, Shanghai, 201804, P. R. China
| | - Youdi Liu
- Department of Engineering Science and Mechanics, Pennsylvania State University, University Park, State College, PA, 16802, USA
| | - Yutong Xu
- Interdisciplinary Materials Research Center, School of Materials Science and Engineering, Tongji University, Shanghai, 201804, P. R. China
| | - Junyao Zhang
- Interdisciplinary Materials Research Center, School of Materials Science and Engineering, Tongji University, Shanghai, 201804, P. R. China
| | - Yue Wu
- Interdisciplinary Materials Research Center, School of Materials Science and Engineering, Tongji University, Shanghai, 201804, P. R. China
| | - Ping Cheng
- Pritzker School of Molecular Engineering, The University of Chicago, Chicago, IL, 60637, USA
| | - Lize Xiong
- Shanghai Key Laboratory of Anesthesiology and Brain Functional Modulation, Translational Research Institute of Brain and Brain-Like Intelligence, Shanghai Fourth People's Hospital, Tongji University, Shanghai, 200434, P. R. China
| | - Jia Huang
- Shanghai Key Laboratory of Anesthesiology and Brain Functional Modulation, Translational Research Institute of Brain and Brain-Like Intelligence, Shanghai Fourth People's Hospital, Tongji University, Shanghai, 200434, P. R. China
- Interdisciplinary Materials Research Center, School of Materials Science and Engineering, Tongji University, Shanghai, 201804, P. R. China
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Li Z, Wang T, Meng J, Zhu H, Sun Q, Zhang DW, Chen L. Flexible aluminum-doped hafnium oxide ferroelectric synapse devices for neuromorphic computing. Mater Horiz 2023; 10:3643-3650. [PMID: 37340846 DOI: 10.1039/d3mh00645j] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/22/2023]
Abstract
The HfO2-based ferroelectric tunnel junction has received outstanding attention owing to its high-speed and low-power characteristics. In this work, aluminum-doped HfO2 (HfAlO) ferroelectric thin films are deposited on a muscovite substrate (Mica). We investigate the bending effect on the ferroelectric characteristics of the Au/Ti/HfAlO/Pt/Ti/Mica device. After 1000 bending times, the ferroelectric properties and the fatigue characteristics are largely degraded. The finite element analysis indicates that crack formation is the main reason for the fatigue damage under threshold bending diameters. Moreover, the HfAlO-based ferroelectric synaptic device exhibits excellent performance of neuromorphic computing. The artificial synapse can mimic the paired-pulse facilitation and long-term potentiation/depression of biological synapses. Meanwhile, the accuracy of digit recognition is 88.8%. This research provides a new research idea for the further development of hafnium-based ferroelectric devices.
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Affiliation(s)
- Zhenhai Li
- School of Microelectronics, Fudan University, Shanghai 200433, P. R. China.
- Zhangjiang Fudan International Innovation Center, Shanghai 201203, China
| | - Tianyu Wang
- School of Microelectronics, Fudan University, Shanghai 200433, P. R. China.
- Zhangjiang Fudan International Innovation Center, Shanghai 201203, China
| | - Jialin Meng
- School of Microelectronics, Fudan University, Shanghai 200433, P. R. China.
- Zhangjiang Fudan International Innovation Center, Shanghai 201203, China
| | - Hao Zhu
- School of Microelectronics, Fudan University, Shanghai 200433, P. R. China.
- Zhangjiang Fudan International Innovation Center, Shanghai 201203, China
| | - Qingqing Sun
- School of Microelectronics, Fudan University, Shanghai 200433, P. R. China.
- Zhangjiang Fudan International Innovation Center, Shanghai 201203, China
| | - David Wei Zhang
- School of Microelectronics, Fudan University, Shanghai 200433, P. R. China.
- Zhangjiang Fudan International Innovation Center, Shanghai 201203, China
| | - Lin Chen
- School of Microelectronics, Fudan University, Shanghai 200433, P. R. China.
- Zhangjiang Fudan International Innovation Center, Shanghai 201203, China
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Zhou K, Jia Z, Zhou Y, Ding G, Ma XQ, Niu W, Han ST, Zhao J, Zhou Y. Covalent Organic Frameworks for Neuromorphic Devices. J Phys Chem Lett 2023; 14:7173-7192. [PMID: 37540588 DOI: 10.1021/acs.jpclett.3c01711] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/06/2023]
Abstract
Neuromorphic computing could enable the potential to break the inherent limitations of conventional von Neumann architectures, which has led to widespread research interest in developing novel neuromorphic memory devices, such as memristors and bioinspired artificial synaptic devices. Covalent organic frameworks (COFs), as crystalline porous polymers, have tailorable skeletons and pores, providing unique platforms for the interplay with photons, excitons, electrons, holes, ions, spins, and molecules. Such features encourage the rising research interest in COF materials in neuromorphic electronics. To develop high-performance COF-based neuromorphic memory devices, it is necessary to comprehensively understand materials, devices, and applications. Therefore, this Perspective focuses on discussing the use of COF materials for neuromorphic memory devices in terms of molecular design, thin-film processing, and neuromorphic applications. Finally, we provide an outlook for future directions and potential applications of COF-based neuromorphic electronics.
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Affiliation(s)
- Kui Zhou
- Institute for Advanced Study, Shenzhen University, 3688 Nanhai Avenue, Shenzhen 518060, P. R. China
| | - Ziqi Jia
- Institute for Advanced Study, Shenzhen University, 3688 Nanhai Avenue, Shenzhen 518060, P. R. China
| | - Yao Zhou
- College of Materials Science and Engineering, Shenzhen University, 3688 Nanhai Avenue, Shenzhen 518060, P. R. China
| | - Guanglong Ding
- Institute for Advanced Study, Shenzhen University, 3688 Nanhai Avenue, Shenzhen 518060, P. R. China
| | - Xin-Qi Ma
- Institute for Advanced Study, Shenzhen University, 3688 Nanhai Avenue, Shenzhen 518060, P. R. China
| | - Wenbiao Niu
- Institute for Advanced Study, Shenzhen University, 3688 Nanhai Avenue, Shenzhen 518060, P. R. China
| | - Su-Ting Han
- College of Electronics and Information Engineering, Shenzhen University, 3688 Nanhai Avenue, Shenzhen 518060, P. R. China
| | - Jiyu Zhao
- State Key Laboratory of Fine Chemicals, Dalian University of Technology, 2 Linggong Road, Dalian 116024, P. R. China
| | - Ye Zhou
- Institute for Advanced Study, Shenzhen University, 3688 Nanhai Avenue, Shenzhen 518060, P. R. China
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10
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Sahu DP, Park K, Chung PH, Han J, Yoon TS. Linear and symmetric synaptic weight update characteristics by controlling filament geometry in oxide/suboxide HfO x bilayer memristive device for neuromorphic computing. Sci Rep 2023; 13:9592. [PMID: 37311855 DOI: 10.1038/s41598-023-36784-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2023] [Accepted: 06/09/2023] [Indexed: 06/15/2023] Open
Abstract
Memristive devices have been explored as electronic synaptic devices to mimic biological synapses for developing hardware-based neuromorphic computing systems. However, typical oxide memristive devices suffered from abrupt switching between high and low resistance states, which limits access to achieve various conductance states for analog synaptic devices. Here, we proposed an oxide/suboxide hafnium oxide bilayer memristive device by altering oxygen stoichiometry to demonstrate analog filamentary switching behavior. The bilayer device with Ti/HfO2/HfO2-x(oxygen-deficient)/Pt structure exhibited analog conductance states under a low voltage operation through controlling filament geometry as well as superior retention and endurance characteristics thanks to the robust nature of filament. A narrow cycle-to-cycle and device-to-device distribution were also demonstrated by the filament confinement in a limited region. The different concentrations of oxygen vacancies at each layer played a significant role in switching phenomena, as confirmed through X-ray photoelectron spectroscopy analysis. The analog weight update characteristics were found to strongly depend on the various conditions of voltage pulse parameters including its amplitude, width, and interval time. In particular, linear and symmetric weight updates for accurate learning and pattern recognition could be achieved by adopting incremental step pulse programming (ISPP) operation scheme which rendered a high-resolution dynamic range with linear and symmetry weight updates as a consequence of precisely controlled filament geometry. A two-layer perceptron neural network simulation with HfO2/HfO2-x synapses provided an 80% recognition accuracy for handwritten digits. The development of oxide/suboxide hafnium oxide memristive devices has the capacity to drive forward the development of efficient neuromorphic computing systems.
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Affiliation(s)
- Dwipak Prasad Sahu
- Department of Materials Science and Engineering, Ulsan National Institute of Science and Technology, Ulsan, 44919, Republic of Korea
| | - Kitae Park
- Graduate School of Semiconductor Materials and Devices Engineering, Ulsan National Institute of Science and Technology, Ulsan, 44919, Republic of Korea
| | - Peter Hayoung Chung
- Graduate School of Semiconductor Materials and Devices Engineering, Ulsan National Institute of Science and Technology, Ulsan, 44919, Republic of Korea
| | - Jimin Han
- Department of Materials Science and Engineering, Ulsan National Institute of Science and Technology, Ulsan, 44919, Republic of Korea
| | - Tae-Sik Yoon
- Department of Materials Science and Engineering, Ulsan National Institute of Science and Technology, Ulsan, 44919, Republic of Korea.
- Graduate School of Semiconductor Materials and Devices Engineering, Ulsan National Institute of Science and Technology, Ulsan, 44919, Republic of Korea.
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11
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You T, Zhao M, Fan Z, Ju C. Emerging Memtransistors for Neuromorphic System Applications: A Review. Sensors (Basel) 2023; 23:5413. [PMID: 37420582 DOI: 10.3390/s23125413] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Revised: 03/10/2023] [Accepted: 05/30/2023] [Indexed: 07/09/2023]
Abstract
The von Neumann architecture with separate memory and processing presents a serious challenge in terms of device integration, power consumption, and real-time information processing. Inspired by the human brain that has highly parallel computing and adaptive learning capabilities, memtransistors are proposed to be developed in order to meet the requirement of artificial intelligence, which can continuously sense the objects, store and process the complex signal, and demonstrate an "all-in-one" low power array. The channel materials of memtransistors include a range of materials, such as two-dimensional (2D) materials, graphene, black phosphorus (BP), carbon nanotubes (CNT), and indium gallium zinc oxide (IGZO). Ferroelectric materials such as P(VDF-TrFE), chalcogenide (PZT), HfxZr1-xO2(HZO), In2Se3, and the electrolyte ion are used as the gate dielectric to mediate artificial synapses. In this review, emergent technology using memtransistors with different materials, diverse device fabrications to improve the integrated storage, and the calculation performance are demonstrated. The different neuromorphic behaviors and the corresponding mechanisms in various materials including organic materials and semiconductor materials are analyzed. Finally, the current challenges and future perspectives for the development of memtransistors in neuromorphic system applications are presented.
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Affiliation(s)
- Tao You
- High-Frequency High-Voltage Device and Integrated Circuits R&D Center, Institute of Microelectronics of the Chinese Academy of Sciences, 3 Beitucheng West Road, Beijing 100029, China
- University of Chinese Academy of Sciences, Beijing 100029, China
| | - Miao Zhao
- High-Frequency High-Voltage Device and Integrated Circuits R&D Center, Institute of Microelectronics of the Chinese Academy of Sciences, 3 Beitucheng West Road, Beijing 100029, China
- University of Chinese Academy of Sciences, Beijing 100029, China
| | - Zhikang Fan
- High-Frequency High-Voltage Device and Integrated Circuits R&D Center, Institute of Microelectronics of the Chinese Academy of Sciences, 3 Beitucheng West Road, Beijing 100029, China
- University of Chinese Academy of Sciences, Beijing 100029, China
| | - Chenwei Ju
- High-Frequency High-Voltage Device and Integrated Circuits R&D Center, Institute of Microelectronics of the Chinese Academy of Sciences, 3 Beitucheng West Road, Beijing 100029, China
- University of Chinese Academy of Sciences, Beijing 100029, China
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12
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Kim IJ, Lee JS. Ferroelectric Transistors for Memory and Neuromorphic Device Applications. Adv Mater 2023; 35:e2206864. [PMID: 36484488 DOI: 10.1002/adma.202206864] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Revised: 11/26/2022] [Indexed: 06/02/2023]
Abstract
Ferroelectric materials have been intensively investigated for high-performance nonvolatile memory devices in the past decades, owing to their nonvolatile polarization characteristics. Ferroelectric memory devices are expected to exhibit lower power consumption and higher speed than conventional memory devices. However, non-complementary metal-oxide-semiconductor (CMOS) compatibility and degradation due to fatigue of traditional perovskite-based ferroelectric materials have hindered the development of high-density and high-performance ferroelectric memories in the past. The recently developed hafnia-based ferroelectric materials have attracted immense attention in the development of advanced semiconductor devices. Because hafnia is typically used in CMOS processes, it can be directly incorporated into current semiconductor technologies. Additionally, hafnia-based ferroelectrics show high scalability and large coercive fields that are advantageous for high-density memory devices. This review summarizes the recent developments in ferroelectric devices, especially ferroelectric transistors, for next-generation memory and neuromorphic applications. First, the types of ferroelectric memories and their operation mechanisms are reviewed. Then, issues limiting the realization of high-performance ferroelectric transistors and possible solutions are discussed. The experimental demonstration of ferroelectric transistor arrays, including 3D ferroelectric NAND and its operation characteristics, are also reviewed. Finally, challenges and strategies toward the development of next-generation memory and neuromorphic applications based on ferroelectric transistors are outlined.
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Affiliation(s)
- Ik-Jyae Kim
- Department of Materials Science and Engineering, Pohang University of Science and Technology (POSTECH), Pohang, 37673, Republic of Korea
| | - Jang-Sik Lee
- Department of Materials Science and Engineering, Pohang University of Science and Technology (POSTECH), Pohang, 37673, Republic of Korea
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13
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Hwang Y, Park B, Hwang S, Choi SW, Kim HS, Kim AR, Choi JW, Yoon J, Kwon JD, Kim Y. A Bioinspired Ultra Flexible Artificial van der Waals 2D-MoS 2 Channel/LiSiO x Solid Electrolyte Synapse Arrays via Laser-Lift Off Process for Wearable Adaptive Neuromorphic Computing. Small Methods 2023:e2201719. [PMID: 36960927 DOI: 10.1002/smtd.202201719] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/01/2023] [Revised: 02/06/2023] [Indexed: 06/18/2023]
Abstract
Wearable electronic devices with next-generation biocompatible, mechanical, ultraflexible, and portable sensors are a fast-growing technology. Hardware systems enabling artificial neural networks while consuming low power and processing massive in situ personal data are essential for adaptive wearable neuromorphic edging computing. Herein, the development of an ultraflexible artificial-synaptic array device with concrete-mechanical cyclic endurance consisting of a novel heterostructure with an all-solid-state 2D MoS2 channel and LiSiOx (lithium silicate) is demonstrated. Enabled by the sequential fabrication process of all layers, by excluding the transfer process, artificial van der Waals devices combined with the 2D-MoS2 channel and LiSiOx solid electrolyte exhibit excellent neuromorphic synaptic characteristics with a nonlinearity of 0.55 and asymmetry ratio of 0.22. Based on the excellent flexibility of colorless polyimide substrates and thin-layered structures, the fabricated flexible neuromorphic synaptic devices exhibit superior long-term potentiation and long-term depression cyclic endurance performance, even when bent over 700 times or on curved surfaces with a diameter of 10 mm. Thus, a high classification accuracy of 95% is achieved without any noticeable performance degradation in the Modified National Institute of Standards and Technology. These results are promising for the development of personalized wearable artificial neural systems in the future.
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Affiliation(s)
- Yunjeong Hwang
- Department of Energy and Electronic Materials, Surface Materials Division, Korea Institute of Materials Science (KIMS), 797 Changwondaero, Sungsan-gu, Changwon, Gyeongnam, 51508, Republic of Korea
| | - Byeongjin Park
- Department of Energy and Electronic Materials, Surface Materials Division, Korea Institute of Materials Science (KIMS), 797 Changwondaero, Sungsan-gu, Changwon, Gyeongnam, 51508, Republic of Korea
- School of Materials Science and Engineering, Pusan National University, 2 Busandaehak-ro 63-beon-gil, Geumjeong-gu, Busan, 46241, Republic of Korea
| | - Seungkwon Hwang
- Department of Energy and Electronic Materials, Surface Materials Division, Korea Institute of Materials Science (KIMS), 797 Changwondaero, Sungsan-gu, Changwon, Gyeongnam, 51508, Republic of Korea
- School of Materials Science and Engineering, Pusan National University, 2 Busandaehak-ro 63-beon-gil, Geumjeong-gu, Busan, 46241, Republic of Korea
| | - Soo-Won Choi
- Department of Energy and Electronic Materials, Surface Materials Division, Korea Institute of Materials Science (KIMS), 797 Changwondaero, Sungsan-gu, Changwon, Gyeongnam, 51508, Republic of Korea
- School of Materials Science and Engineering, Pusan National University, 2 Busandaehak-ro 63-beon-gil, Geumjeong-gu, Busan, 46241, Republic of Korea
| | - Han Seul Kim
- Department of Advanced Materials Engineering, Chungbuk National University, 1 Chungdae-ro, Seowon-gu, Cheongju, 28644, Republic of Korea
| | - Ah Ra Kim
- Department of Energy and Electronic Materials, Surface Materials Division, Korea Institute of Materials Science (KIMS), 797 Changwondaero, Sungsan-gu, Changwon, Gyeongnam, 51508, Republic of Korea
| | - Jin Woo Choi
- Department of Data Information and Physics, Kongju National University, 56 Gongjudaehak-ro, Gongju, Chungcheongnam-do, 32588, Republic ofKorea
| | - Jongwon Yoon
- Department of Energy and Electronic Materials, Surface Materials Division, Korea Institute of Materials Science (KIMS), 797 Changwondaero, Sungsan-gu, Changwon, Gyeongnam, 51508, Republic of Korea
| | - Jung-Dae Kwon
- Department of Energy and Electronic Materials, Surface Materials Division, Korea Institute of Materials Science (KIMS), 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 (KIMS), 797 Changwondaero, Sungsan-gu, Changwon, Gyeongnam, 51508, Republic of Korea
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14
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Wang D, Tang R, Lin H, Liu L, Xu N, Sun Y, Zhao X, Wang Z, Wang D, Mai Z, Zhou Y, Gao N, Song C, Zhu L, Wu T, Liu M, Xing G. Spintronic leaky-integrate-fire spiking neurons with self-reset and winner-takes-all for neuromorphic computing. Nat Commun 2023; 14:1068. [PMID: 36828856 PMCID: PMC9957988 DOI: 10.1038/s41467-023-36728-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Accepted: 02/14/2023] [Indexed: 02/26/2023] Open
Abstract
Neuromorphic computing using nonvolatile memories is expected to tackle the memory wall and energy efficiency bottleneck in the von Neumann system and to mitigate the stagnation of Moore's law. However, an ideal artificial neuron possessing bio-inspired behaviors as exemplified by the requisite leaky-integrate-fire and self-reset (LIFT) functionalities within a single device is still lacking. Here, we report a new type of spiking neuron with LIFT characteristics by manipulating the magnetic domain wall motion in a synthetic antiferromagnetic (SAF) heterostructure. We validate the mechanism of Joule heating modulated competition between the Ruderman-Kittel-Kasuya-Yosida interaction and the built-in field in the SAF device, enabling it with a firing rate up to 17 MHz and energy consumption of 486 fJ/spike. A spiking neuron circuit is implemented with a latency of 170 ps and power consumption of 90.99 μW. Moreover, the winner-takes-all is executed with a current ratio >104 between activated and inhibited neurons. We further establish a two-layer spiking neural network based on the developed spintronic LIFT neurons. The architecture achieves 88.5% accuracy on the handwritten digit database benchmark. Our studies corroborate the circuit compatibility of the spintronic neurons and their great potential in the field of intelligent devices and neuromorphic computing.
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Affiliation(s)
- Di Wang
- Key Laboratory of Microelectronics Devices & Integration Technology, Institute of Microelectronics, Chinese Academy of Sciences, 100029, Beijing, China
- University of Chinese Academy of Sciences, 100049, Beijing, China
| | - Ruifeng Tang
- Key Laboratory of Microelectronics Devices & Integration Technology, Institute of Microelectronics, Chinese Academy of Sciences, 100029, Beijing, China
- University of Chinese Academy of Sciences, 100049, Beijing, China
| | - Huai Lin
- Key Laboratory of Microelectronics Devices & Integration Technology, Institute of Microelectronics, Chinese Academy of Sciences, 100029, Beijing, China
- University of Chinese Academy of Sciences, 100049, Beijing, China
| | - Long Liu
- Key Laboratory of Microelectronics Devices & Integration Technology, Institute of Microelectronics, Chinese Academy of Sciences, 100029, Beijing, China
- University of Chinese Academy of Sciences, 100049, Beijing, China
| | - Nuo Xu
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA, 94720, USA
| | - Yan Sun
- Institute of Metal Research, Chinese Academy of Sciences, Shenyang, 110016, China
| | - Xuefeng Zhao
- Key Laboratory of Microelectronics Devices & Integration Technology, Institute of Microelectronics, Chinese Academy of Sciences, 100029, Beijing, China
- School of Microelectronics, University of Science and Technology of China, Hefei, 230026, Anhui, China
| | - Ziwei Wang
- Key Laboratory of Microelectronics Devices & Integration Technology, Institute of Microelectronics, Chinese Academy of Sciences, 100029, Beijing, China
- University of Chinese Academy of Sciences, 100049, Beijing, China
| | - Dandan Wang
- Jiufengshan Laboratory, Wuhan, 430206, Hubei, China
| | - Zhihong Mai
- Jiufengshan Laboratory, Wuhan, 430206, Hubei, China
| | - Yongjian Zhou
- Key Laboratory of Advanced Materials (MOE), School of Materials Science and Engineering, Tsinghua University, 100084, Beijing, China
| | - Nan Gao
- School of Microelectronics, University of Science and Technology of China, Hefei, 230026, Anhui, China
| | - Cheng Song
- Key Laboratory of Advanced Materials (MOE), School of Materials Science and Engineering, Tsinghua University, 100084, Beijing, China
| | - Lijun Zhu
- State Key Laboratory of Superlattices and Microstructures, Institute of Semiconductors, Chinese Academy of Sciences, 100083, Beijing, China
| | - Tom Wu
- Department of Applied Physics, The Hong Kong Polytechnic University, Kowloon, Hong Kong, China
| | - Ming Liu
- Key Laboratory of Microelectronics Devices & Integration Technology, Institute of Microelectronics, Chinese Academy of Sciences, 100029, Beijing, China
- University of Chinese Academy of Sciences, 100049, Beijing, China
| | - Guozhong Xing
- Key Laboratory of Microelectronics Devices & Integration Technology, Institute of Microelectronics, Chinese Academy of Sciences, 100029, Beijing, China.
- University of Chinese Academy of Sciences, 100049, Beijing, China.
- School of Microelectronics, University of Science and Technology of China, Hefei, 230026, Anhui, China.
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15
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Tanim MMH, Templin Z, Zhao F. Natural Organic Materials Based Memristors and Transistors for Artificial Synaptic Devices in Sustainable Neuromorphic Computing Systems. Micromachines (Basel) 2023; 14:235. [PMID: 36837935 PMCID: PMC9963886 DOI: 10.3390/mi14020235] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/25/2022] [Revised: 01/15/2023] [Accepted: 01/16/2023] [Indexed: 06/18/2023]
Abstract
Natural organic materials such as protein and carbohydrates are abundant in nature, renewable, and biodegradable, desirable for the construction of artificial synaptic devices for emerging neuromorphic computing systems with energy efficient operation and environmentally friendly disposal. These artificial synaptic devices are based on memristors or transistors with the memristive layer or gate dielectric formed by natural organic materials. The fundamental requirement for these synaptic devices is the ability to mimic the memory and learning behaviors of biological synapses. This paper reviews the synaptic functions emulated by a variety of artificial synaptic devices based on natural organic materials and provides a useful guidance for testing and investigating more of such devices.
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16
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Lei Y, Zhang T, Lin YC, Granzier-Nakajima T, Bepete G, Kowalczyk DA, Lin Z, Zhou D, Schranghamer TF, Dodda A, Sebastian A, Chen Y, Liu Y, Pourtois G, Kempa TJ, Schuler B, Edmonds MT, Quek SY, Wurstbauer U, Wu SM, Glavin NR, Das S, Dash SP, Redwing JM, Robinson JA, Terrones M. Graphene and Beyond: Recent Advances in Two-Dimensional Materials Synthesis, Properties, and Devices. ACS Nanosci Au 2022; 2:450-485. [PMID: 36573124 PMCID: PMC9782807 DOI: 10.1021/acsnanoscienceau.2c00017] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Revised: 08/22/2022] [Accepted: 08/23/2022] [Indexed: 12/30/2022]
Abstract
Since the isolation of graphene in 2004, two-dimensional (2D) materials research has rapidly evolved into an entire subdiscipline in the physical sciences with a wide range of emergent applications. The unique 2D structure offers an open canvas to tailor and functionalize 2D materials through layer number, defects, morphology, moiré pattern, strain, and other control knobs. Through this review, we aim to highlight the most recent discoveries in the following topics: theory-guided synthesis for enhanced control of 2D morphologies, quality, yield, as well as insights toward novel 2D materials; defect engineering to control and understand the role of various defects, including in situ and ex situ methods; and properties and applications that are related to moiré engineering, strain engineering, and artificial intelligence. Finally, we also provide our perspective on the challenges and opportunities in this fascinating field.
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Affiliation(s)
- Yu Lei
- Department
of Physics, The Pennsylvania State University, University Park, Pennsylvania 16802, United States,Center
for Atomically Thin Multifunctional Coatings, The Pennsylvania State University, University Park, Pennsylvania 16802, United States,Institute
of Materials Research, Tsinghua Shenzhen
International Graduate School, Shenzhen, Guangdong 518055, China,Center
for 2-Dimensional and Layered Materials, The Pennsylvania State University, University Park, Pennsylvania 16802, United States
| | - Tianyi Zhang
- Center
for 2-Dimensional and Layered Materials, The Pennsylvania State University, University Park, Pennsylvania 16802, United States,Department
of Material Science and Engineering, The
Pennsylvania State University, University Park, Pennsylvania 16802, United States
| | - Yu-Chuan Lin
- Center
for Atomically Thin Multifunctional Coatings, The Pennsylvania State University, University Park, Pennsylvania 16802, United States,Center
for 2-Dimensional and Layered Materials, The Pennsylvania State University, University Park, Pennsylvania 16802, United States,Department
of Material Science and Engineering, The
Pennsylvania State University, University Park, Pennsylvania 16802, United States
| | - Tomotaroh Granzier-Nakajima
- Department
of Physics, The Pennsylvania State University, University Park, Pennsylvania 16802, United States,Center
for 2-Dimensional and Layered Materials, The Pennsylvania State University, University Park, Pennsylvania 16802, United States
| | - George Bepete
- Department
of Physics, The Pennsylvania State University, University Park, Pennsylvania 16802, United States,Center
for Atomically Thin Multifunctional Coatings, The Pennsylvania State University, University Park, Pennsylvania 16802, United States,Center
for 2-Dimensional and Layered Materials, The Pennsylvania State University, University Park, Pennsylvania 16802, United States,Department
of Chemistry, The Pennsylvania State University, University Park, Pennsylvania 16802, United States
| | - Dorota A. Kowalczyk
- Department
of Solid State Physics, Faculty of Physics and Applied Informatics, University of Lodz, Pomorska 149/153, Lodz 90-236, Poland
| | - Zhong Lin
- Department
of Physics, University of Washington, Seattle, Washington 98195, United States
| | - Da Zhou
- Department
of Physics, The Pennsylvania State University, University Park, Pennsylvania 16802, United States,Center
for 2-Dimensional and Layered Materials, The Pennsylvania State University, University Park, Pennsylvania 16802, United States
| | - Thomas F. Schranghamer
- Department
of Engineering Science and Mechanics, Pennsylvania
State University, University Park, Pennsylvania 16802, United States
| | - Akhil Dodda
- Department
of Engineering Science and Mechanics, Pennsylvania
State University, University Park, Pennsylvania 16802, United States
| | - Amritanand Sebastian
- Department
of Engineering Science and Mechanics, Pennsylvania
State University, University Park, Pennsylvania 16802, United States
| | - Yifeng Chen
- Department
of Materials Science and Engineering, National
University of Singapore, 9 Engineering Drive, Singapore 117456, Singapore
| | - Yuanyue Liu
- Texas
Materials Institute and Department of Mechanical Engineering, The University of Texas at Austin, Austin, Texas 78712, United States
| | | | - Thomas J. Kempa
- Department
of Chemistry, Johns Hopkins University, Baltimore, Maryland 21287, United States
| | - Bruno Schuler
- nanotech@surfaces
Laboratory, Empa − Swiss Federal
Laboratories for Materials Science and Technology, Dübendorf 8600, Switzerland
| | - Mark T. Edmonds
- School
of Physics and Astronomy, Monash University, Clayton, Victoria 3800, Australia
| | - Su Ying Quek
- Department
of Materials Science and Engineering, National
University of Singapore, 9 Engineering Drive, Singapore 117456, Singapore
| | - Ursula Wurstbauer
- Institute
of Physics, University of Münster, Wilhelm-Klemm-Str. 10, Münster 48149, Germany
| | - Stephen M. Wu
- Department
of Electrical and Computer Engineering & Department of Physics
and Astronomy, University of Rochester, Rochester, New York 14627, United States
| | - Nicholas R. Glavin
- Air
Force
Research Laboratory, Materials and Manufacturing Directorate, Wright-Patterson AFB, Dayton, Ohio 45433, United States
| | - Saptarshi Das
- Center
for Atomically Thin Multifunctional Coatings, The Pennsylvania State University, University Park, Pennsylvania 16802, United States,Center
for 2-Dimensional and Layered Materials, The Pennsylvania State University, University Park, Pennsylvania 16802, United States,Department
of Material Science and Engineering, The
Pennsylvania State University, University Park, Pennsylvania 16802, United States,Department
of Engineering Science and Mechanics, Pennsylvania
State University, University Park, Pennsylvania 16802, United States
| | - Saroj Prasad Dash
- Department
of Microtechnology and Nanoscience, Chalmers
University of Technology, Göteborg SE-412 96, Sweden
| | - Joan M. Redwing
- Center
for 2-Dimensional and Layered Materials, The Pennsylvania State University, University Park, Pennsylvania 16802, United States,Department
of Material Science and Engineering, The
Pennsylvania State University, University Park, Pennsylvania 16802, United States
| | - Joshua A. Robinson
- Center
for Atomically Thin Multifunctional Coatings, The Pennsylvania State University, University Park, Pennsylvania 16802, United States,Center
for 2-Dimensional and Layered Materials, The Pennsylvania State University, University Park, Pennsylvania 16802, United States,Department
of Material Science and Engineering, The
Pennsylvania State University, University Park, Pennsylvania 16802, United States,
| | - Mauricio Terrones
- Department
of Physics, The Pennsylvania State University, University Park, Pennsylvania 16802, United States,Center
for Atomically Thin Multifunctional Coatings, The Pennsylvania State University, University Park, Pennsylvania 16802, United States,Center
for 2-Dimensional and Layered Materials, The Pennsylvania State University, University Park, Pennsylvania 16802, United States,Department
of Material Science and Engineering, The
Pennsylvania State University, University Park, Pennsylvania 16802, United States,Department
of Chemistry, The Pennsylvania State University, University Park, Pennsylvania 16802, United States,Research
Initiative for Supra-Materials and Global Aqua Innovation Center, Shinshu University, 4-17-1Wakasato, Nagano 380-8553, Japan,
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17
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Li P, Bräuniger Y, Kunigkeit J, Zhou H, Ortega Vega MR, Zhang E, Grothe J, Brunner E, Kaskel S. Bioactive Ion-Based Switchable Supercapacitors. Angew Chem Int Ed Engl 2022; 61:e202212250. [PMID: 36260635 PMCID: PMC10100445 DOI: 10.1002/anie.202212250] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Indexed: 11/16/2022]
Abstract
Switchable supercapacitors (SCs) enable a reversible electrically-driven uptake/release of bioactive ions by polarizing porous carbon electrodes. Herein we demonstrate the first example of a bioactive ion-based switchable supercapacitor. Based on choline chloride and porous carbons we unravel the mechanism of physisorption vs. electrosorption by nuclear magnetic resonance, Raman, and impedance spectroscopy. Weak physisorption facilitates electrically-driven electrolyte depletion enabling the controllable uptake/release of electrolyte ions. A new 4-terminal device is proposed, with a main capacitor and a detective capacitor for monitoring bioactive ion adsorption in situ. Ion-concentration control in printed choline-based switchable SCs realizes switching down to 8.3 % residual capacitance. The exploration of adsorption mechanisms in printable microdevices will open an avenue of manipulating bioactive ions for the application of drug delivery, neuromodulation, or neuromorphic devices.
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Affiliation(s)
- Panlong Li
- Inorganic Chemistry ITechnische Universität DresdenBergstrasse 6601069DresdenGermany
| | - Yannik Bräuniger
- Inorganic Chemistry ITechnische Universität DresdenBergstrasse 6601069DresdenGermany
| | - Jonas Kunigkeit
- Bioanalytical ChemistryTechnische Universität DresdenBergstrasse 6601069DresdenGermany
| | - Hanfeng Zhou
- Inorganic Chemistry ITechnische Universität DresdenBergstrasse 6601069DresdenGermany
| | | | - En Zhang
- Inorganic Chemistry ITechnische Universität DresdenBergstrasse 6601069DresdenGermany
| | - Julia Grothe
- Inorganic Chemistry ITechnische Universität DresdenBergstrasse 6601069DresdenGermany
| | - Eike Brunner
- Bioanalytical ChemistryTechnische Universität DresdenBergstrasse 6601069DresdenGermany
| | - Stefan Kaskel
- Inorganic Chemistry ITechnische Universität DresdenBergstrasse 6601069DresdenGermany
- Fraunhofer IWSWinterbergstrasse 2801277DresdenGermany
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18
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Wang D, Wang Z, Xu N, Liu L, Lin H, Zhao X, Jiang S, Lin W, Gao N, Liu M, Xing G. Synergy of Spin-Orbit Torque and Built-In Field in Magnetic Tunnel Junctions with Tilted Magnetic Anisotropy: Toward Tunable and Reliable Spintronic Neurons. Adv Sci (Weinh) 2022; 9:e2203006. [PMID: 35927016 PMCID: PMC9596820 DOI: 10.1002/advs.202203006] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/21/2022] [Revised: 07/09/2022] [Indexed: 06/15/2023]
Abstract
Owing to programmable nonlinear dynamics, magnetic domain wall (DW)-based devices can be configured to function as spintronic neurons, promising to execute sophisticated tasks as a human brain. Developing energy-efficient, CMOS compatible, reliable, and tunable spintronic neurons to emulate brain-inspired processes has been a key research goal for decades. Here, a new type of DW device is reported with biological neuron characteristics driven by the synergistic interaction between spin-orbit torque and built-in field (Hbuilt-in ) in magnetic tunnel junctions, enabling time- and energy-efficient leaky-integrate-and-fire and self-reset neuromorphic implementations. A tilted magnetic anisotropic free layer is proposed and further executed to mitigate the DW retrograde motion by suppressing the Walker breakdown. Complementary experiments and micromagnetic co-simulation results show that the integrating/leaking time of the developed spintronic neuron can be tuned to 12/15 ns with an integrating power consumption of 65 µW, which is 36× and 1.84× time and energy efficient than the state-of-the-art alternatives, respectively. Moreover, the spatial distribution of Hbuilt-in can be modulated by adjusting the width and compensation of the reference layer, facilitating tunable activation function generator exploration. Such architecture demonstrates great potential in both fundamental research and new trajectories of technology advancement for spintronic neuron hardware applications.
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Affiliation(s)
- Di Wang
- Key Laboratory of Microelectronic Devices and Integrated TechnologyInstitute of MicroelectronicsChinese Academy of SciencesBeijing100029China
- School of Integrated CircuitsUniversity of Chinese Academy of SciencesBeijing100049China
| | - Ziwei Wang
- Key Laboratory of Microelectronic Devices and Integrated TechnologyInstitute of MicroelectronicsChinese Academy of SciencesBeijing100029China
- School of Integrated CircuitsUniversity of Chinese Academy of SciencesBeijing100049China
| | - Nuo Xu
- Department of Electrical Engineering and Computer SciencesUniversity of CaliforniaBerkeleyCA94720USA
| | - Long Liu
- Key Laboratory of Microelectronic Devices and Integrated TechnologyInstitute of MicroelectronicsChinese Academy of SciencesBeijing100029China
- School of Integrated CircuitsUniversity of Chinese Academy of SciencesBeijing100049China
| | - Huai Lin
- Key Laboratory of Microelectronic Devices and Integrated TechnologyInstitute of MicroelectronicsChinese Academy of SciencesBeijing100029China
- School of Integrated CircuitsUniversity of Chinese Academy of SciencesBeijing100049China
| | - Xuefeng Zhao
- Key Laboratory of Microelectronic Devices and Integrated TechnologyInstitute of MicroelectronicsChinese Academy of SciencesBeijing100029China
- School of MicroelectronicsUniversity of Science and Technology of ChinaHefei230026China
| | - Sheng Jiang
- School of MicroelectronicsNorthwestern Polytechnical UniversityXi'an710072China
| | - Weinan Lin
- Department of PhysicsXiamen UniversityXiamen361005China
| | - Nan Gao
- School of MicroelectronicsUniversity of Science and Technology of ChinaHefei230026China
| | - Ming Liu
- School of Integrated CircuitsUniversity of Chinese Academy of SciencesBeijing100049China
| | - Guozhong Xing
- Key Laboratory of Microelectronic Devices and Integrated TechnologyInstitute of MicroelectronicsChinese Academy of SciencesBeijing100029China
- School of Integrated CircuitsUniversity of Chinese Academy of SciencesBeijing100049China
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19
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Paradezhenko GV, Pervishko AA, Swain N, Sengupta P, Yudin D. Spin-hedgehog-derived electromagnetic effects in itinerant magnets. Phys Chem Chem Phys 2022; 24:24317-24322. [PMID: 36173187 DOI: 10.1039/d2cp03486g] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
In itinerant magnets, the indirect exchange coupling of Ruderman-Kittel-Kasuya-Yosida type is known to stabilize incommensurate spin spirals, whereas an account of higher order spin interactions favors the formation of a noncoplanar magnetic texture. This is manifested by the finite Berry phase the conduction electrons accumulate when their spins follow this texture, leading thus to the topological Hall effect. We herein utilize the effective spin model with bilinear-biquadratic exchange interactions for studying the formation of the magnetic hedgehog lattice, that represents a periodic array of magnetic anti- and monopoles and has been recently observed in the B20-type compounds, in a three-dimensional itinerant magnet. As opposed to widely used Monte Carlo simulations, we employ a neural-network-based approach for exploring the ground state spin configuration in a noncentrosymmetric crystal structure. Further, we address the topological Hall conductivity, associated with nonzero scalar spin chirality, in the itinerant magnet due to the coupling to the spin hedgehog lattice, and provide the evidence of a magneto-optic Kerr effect.
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Affiliation(s)
- G V Paradezhenko
- Skolkovo Institute of Science and Technology, Moscow 121205, Russia.
| | - A A Pervishko
- Skolkovo Institute of Science and Technology, Moscow 121205, Russia.
| | - N Swain
- MajuLab, CNRS-UCA-SU-NUS-NTU International Joint Research Unit IRL, 3654, Singapore.,Centre for Quantum Technologies, National University of Singapore, 117543, Singapore
| | - P Sengupta
- School of Physical and Mathematical Sciences, Nanyang Technological University, 637371, Singapore
| | - D Yudin
- Skolkovo Institute of Science and Technology, Moscow 121205, Russia.
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20
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Kim D, Lee JS. Emulating the Signal Transmission in a Neural System Using Polymer Membranes. ACS Appl Mater Interfaces 2022; 14:42308-42316. [PMID: 36069456 DOI: 10.1021/acsami.2c12166] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Neurons are vital components of the brain. When stimulated by neurotransmitters at the dendrites, neurons deliver signals as changes in the membrane potential by ion movement. The signal transmission of a nervous system exhibits a high energy efficiency. These characteristics of neurons are being exploited to develop efficient neuromorphic computing systems. In this study, we develop chemical synapses for neuromorphic devices and emulate the signaling processes in a nervous system using a polymer membrane, in which the ionic permeability can be controlled. The polymer membrane comprises poly(diallyl-dimethylammonium chloride) and poly(3-sulfopropyl acrylate potassium salt), which have positive and negative charges, respectively. The ionic permeability of the polymer membrane is controlled by the injection of a neurotransmitter solution. This device emulates the signal transmission behavior of biological neurons depending on the concentration of the injected neurotransmitter solution. The proposed artificial neuronal signaling device can facilitate the development of bio-realistic neuromorphic devices.
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Affiliation(s)
- Dongshin Kim
- Department of Materials Science and Engineering, Pohang University of Science and Technology (POSTECH), Pohang 37673, Korea
| | - Jang-Sik Lee
- Department of Materials Science and Engineering, Pohang University of Science and Technology (POSTECH), Pohang 37673, Korea
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21
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Banerjee W, Kashir A, Kamba S. Hafnium Oxide (HfO 2 ) - A Multifunctional Oxide: A Review on the Prospect and Challenges of Hafnium Oxide in Resistive Switching and Ferroelectric Memories. Small 2022; 18:e2107575. [PMID: 35510954 DOI: 10.1002/smll.202107575] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/26/2022] [Revised: 03/24/2022] [Indexed: 06/14/2023]
Abstract
Hafnium oxide (HfO2 ) is one of the mature high-k dielectrics that has been standing strong in the memory arena over the last two decades. Its dielectric properties have been researched rigorously for the development of flash memory devices. In this review, the application of HfO2 in two main emerging nonvolatile memory technologies is surveyed, namely resistive random access memory and ferroelectric memory. How the properties of HfO2 equip the former to achieve superlative performance with high-speed reliable switching, excellent endurance, and retention is discussed. The parameters to control HfO2 domains are further discussed, which can unleash the ferroelectric properties in memory applications. Finally, the prospect of HfO2 materials in emerging applications, such as high-density memory and neuromorphic devices are examined, and the various challenges of HfO2 -based resistive random access memory and ferroelectric memory devices are addressed with a future outlook.
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Affiliation(s)
- Writam Banerjee
- Center for Single Atom-based Semiconductor Device, Department of Material Science and Engineering, Pohang University of Science and Technology (POSTECH), Pohang, 37673, Republic of Korea
| | - Alireza Kashir
- Institute of Physics of the Czech Academy of Sciences, Na Slovance 2, Prague 8, 182 21, Czech Republic
| | - Stanislav Kamba
- Institute of Physics of the Czech Academy of Sciences, Na Slovance 2, Prague 8, 182 21, Czech Republic
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22
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Kim MK, Kim IJ, Lee JS. CMOS-compatible compute-in-memory accelerators based on integrated ferroelectric synaptic arrays for convolution neural networks. Sci Adv 2022; 8:eabm8537. [PMID: 35394830 PMCID: PMC8993117 DOI: 10.1126/sciadv.abm8537] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/16/2021] [Accepted: 02/22/2022] [Indexed: 05/31/2023]
Abstract
Convolutional neural networks (CNNs) have gained much attention because they can provide superior complex image recognition through convolution operations. Convolution processes require repeated multiplication and accumulation operations, which are difficult tasks for conventional computing systems. Compute-in-memory (CIM) that uses parallel data processing is an ideal device structure for convolution operations. CIM based on two-terminal synaptic devices with a crossbar structure has been developed, but unwanted leakage current paths and the high-power consumption remain as the challenges. Here, we demonstrate integrated ferroelectric thin-film transistor (FeTFT) synaptic arrays that can provide efficient parallel programming and data processing for CNNs by the selective and accurate control of polarization in the ferroelectric layer. In addition, three-terminal FeTFTs can act as both nonvolatile memory and access device, which tackle issues from two-terminal devices. An integrated FeTFT synaptic array with parallel programming capabilities can perform convolution operations to extract image features with a high-recognition accuracy.
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23
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Xu Z, Bernussi AA, Fan Z. Voltage Pulse Driven VO2 Volatile Resistive Transition Devices as Leaky Integrate-and-Fire Artificial Neurons. Electronics 2022; 11:516. [DOI: 10.3390/electronics11040516] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
In a hardware-based neuromorphic computation system, using emerging nonvolatile memory devices as artificial synapses, which have an inelastic memory characteristic, has attracted considerable interest. In contrast, the elastic artificial neurons have received much less attention. An ideal material system that is suitable for mimicking biological neurons is the one with volatile (or mono-stable) resistive change property. Vanadium dioxide (VO2) is a well-known material that exhibits an abrupt and volatile insulator-to-metal transition property. In this work, we experimentally demonstrate that pulse-driven two-terminal VO2 devices behave in a leaky integrate-and-fire (LIF) manner, and they elastically relax back to their initial value after firing, thus, mimicking the behavior of biological neurons. The VO2 device with a channel length of 20 µm can be driven to fire by a single long-duration pulse (>83 µs) or multiple short-duration pulses. We further model the VO2 devices as resistive networks based on their granular domain structure, with resistivities corresponding to the insulator or metallic states. Simulation results confirm that the volatile resistive transition under voltage pulse driving is caused by the formation of a metallic filament in an avalanche-like process, while this volatile metallic filament will relax back to the insulating state at the end of driving pulses. The simulation offers a microscopic view of the dynamic and abrupt filament formation process to explain the experimentally observed LIF behavior. These results suggest that VO2 insulator–metal transition could be exploited for artificial neurons.
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Abstract
Analog computing from recycling principle for next circular economy scenario has been studied with an iron oxide-coupled graphite/Fe–Si steel structure which was built using recycled waste materials, such as lead pencil and 3% Si steel (Fe–Si steel) foils. Proximity phenomena, such as disordered structure of iron oxide and magnetostriction-induced conduction, inside graphite lattice resulted in functional properties to advance analog architectures. Thermal oxidation was the synthesis route to produce iron oxide as coating film on Fe–Si steel foil, whose structure properties were validated by Raman spectroscopy where phase formation of hematite, α-Fe2O3, resulted as iron oxide thin-film. Three graphite layers with different compositions were also analyzed by Raman spectroscopy and used for studying electrical conduction in Fe–Si steel/α-Fe2O3/graphite structure from current–voltage plots at room temperature.
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