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Ahsan R, Chae HU, Jalal SAA, Wu Z, Tao J, Das S, Liu H, Wu JB, Cronin SB, Wang H, Sideris C, Kapadia R. Ultralow Power In-Sensor Neuronal Computing with Oscillatory Retinal Neurons for Frequency-Multiplexed, Parallel Machine Vision. ACS NANO 2024. [PMID: 39140995 DOI: 10.1021/acsnano.4c09055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/15/2024]
Abstract
In-sensor and near-sensor computing architectures enable multiply accumulate operations to be carried out directly at the point of sensing. In-sensor architectures offer dramatic power and speed improvements over traditional von Neumann architectures by eliminating multiple analog-to-digital conversions, data storage, and data movement operations. Current in-sensor processing approaches rely on tunable sensors or additional weighting elements to perform linear functions such as multiply accumulate operations as the sensor acquires data. This work implements in-sensor computing with an oscillatory retinal neuron device that converts incident optical signals into voltage oscillations. A computing scheme is introduced based on the frequency shift of coupled oscillators that enables parallel, frequency multiplexed, nonlinear operations on the inputs. An experimentally implemented 3 × 3 focal plane array of coupled neurons shows that functions approximating edge detection, thresholding, and segmentation occur in parallel. An example of inference on handwritten digits from the MNIST database is also experimentally demonstrated with a 3 × 3 array of coupled neurons feeding into a single hidden layer neural network, approximating a liquid-state machine. Finally, the equivalent energy consumption to carry out image processing operations, including peripherals such as the Fourier transform circuits, is projected to be <20 fJ/OP, possibly reaching as low as 15 aJ/OP.
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Affiliation(s)
- Ragib Ahsan
- Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, California 90089, United States
| | - Hyun Uk Chae
- Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, California 90089, United States
| | - Seyedeh Atiyeh Abbasi Jalal
- Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, California 90089, United States
| | - Zezhi Wu
- Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, California 90089, United States
| | - Jun Tao
- Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, California 90089, United States
| | - Subrata Das
- Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, California 90089, United States
| | - Hefei Liu
- Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, California 90089, United States
| | - Jiang-Bin Wu
- Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, California 90089, United States
| | - Stephen B Cronin
- Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, California 90089, United States
| | - Han Wang
- Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, California 90089, United States
| | - Constantine Sideris
- Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, California 90089, United States
| | - Rehan Kapadia
- Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, California 90089, United States
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2
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Yao J, Wang Q, Zhang Y, Teng Y, Li J, Zhao P, Zhao C, Hu Z, Shen Z, Liu L, Tian D, Qiu S, Wang Z, Kang L, Li Q. Ultra-low power carbon nanotube/porphyrin synaptic arrays for persistent photoconductivity and neuromorphic computing. Nat Commun 2024; 15:6147. [PMID: 39034334 PMCID: PMC11271480 DOI: 10.1038/s41467-024-50490-y] [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: 01/23/2024] [Accepted: 07/10/2024] [Indexed: 07/23/2024] Open
Abstract
Developing devices with a wide-temperature range persistent photoconductivity (PPC) and ultra-low power consumption remains a significant challenge for optical synaptic devices used in neuromorphic computing. By harnessing the PPC properties in materials, it can achieve optical storage and neuromorphic computing, surpassing the von Neuman architecture-based systems. However, previous research implemented PPC required additional gate voltages and low temperatures, which need additional energy consumption and PPC cannot be achieved across a wide temperature range. Here, we fabricated a simple heterojunctions using zinc(II)-meso-tetraphenyl porphyrin (ZnTPP) and single-walled carbon nanotubes (SWCNTs). By leveraging the strong binding energy at the heterojunction interface and the unique band structure, the heterojunction achieved PPC over an exceptionally wide temperature range (77 K-400 K). Remarkably, it demonstrated nonvolatile storage for up to 2×104 s, without additional gate voltage. The minimum energy consumption for each synaptic event is as low as 6.5 aJ. Furthermore, we successfully demonstrate the feasibility to manufacture a flexible wafer-scale array utilizing this heterojunction. We applied it to autonomous driving under extreme temperatures and achieved as a high impressive accuracy rate as 94.5%. This tunable and stable wide-temperature PPC capability holds promise for ultra-low-power neuromorphic computing.
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Affiliation(s)
- Jian Yao
- School of Nano-Tech and Nano-Bionics, University of Science and Technology of China, Hefei, 230026, China
- Advanced Materials Division, Suzhou Institute of Nano-Tech and Nano-Bionics, Chinese Academy of Sciences, Suzhou, 215123, China
| | - Qinan Wang
- Advanced Materials Division, Suzhou Institute of Nano-Tech and Nano-Bionics, Chinese Academy of Sciences, Suzhou, 215123, China
- School of Advanced Technology, Xi'an Jiaotong-Liverpool University, Suzhou, 215123, China
| | - Yong Zhang
- Advanced Materials Division, Suzhou Institute of Nano-Tech and Nano-Bionics, Chinese Academy of Sciences, Suzhou, 215123, China
| | - Yu Teng
- Advanced Materials Division, Suzhou Institute of Nano-Tech and Nano-Bionics, Chinese Academy of Sciences, Suzhou, 215123, China
| | - Jing Li
- Advanced Materials Division, Suzhou Institute of Nano-Tech and Nano-Bionics, Chinese Academy of Sciences, Suzhou, 215123, China
| | - Pin Zhao
- Advanced Materials Division, Suzhou Institute of Nano-Tech and Nano-Bionics, Chinese Academy of Sciences, Suzhou, 215123, China
| | - Chun Zhao
- School of Advanced Technology, Xi'an Jiaotong-Liverpool University, Suzhou, 215123, China.
| | - Ziyi Hu
- Advanced Materials Division, Suzhou Institute of Nano-Tech and Nano-Bionics, Chinese Academy of Sciences, Suzhou, 215123, China
| | - Zongjie Shen
- Advanced Materials Division, Suzhou Institute of Nano-Tech and Nano-Bionics, Chinese Academy of Sciences, Suzhou, 215123, China
| | - Liwei Liu
- School of Nano-Tech and Nano-Bionics, University of Science and Technology of China, Hefei, 230026, China
- Advanced Materials Division, Suzhou Institute of Nano-Tech and Nano-Bionics, Chinese Academy of Sciences, Suzhou, 215123, China
| | - Dan Tian
- College of Materials Science and Engineering, Co-Innovation Center of Effiicient Processing and Utilization of Forest Resources, Nanjing Forestry University, Nanjing, 210037, China
| | - Song Qiu
- School of Nano-Tech and Nano-Bionics, University of Science and Technology of China, Hefei, 230026, China
- Advanced Materials Division, Suzhou Institute of Nano-Tech and Nano-Bionics, Chinese Academy of Sciences, Suzhou, 215123, China
| | - Zhongrui Wang
- Department of Electrical and Electronic Engineering, University of Hong Kong, Pokfulam Road, Hong Kong SAR, 999077, China
| | - Lixing Kang
- School of Nano-Tech and Nano-Bionics, University of Science and Technology of China, Hefei, 230026, China.
- Advanced Materials Division, Suzhou Institute of Nano-Tech and Nano-Bionics, Chinese Academy of Sciences, Suzhou, 215123, China.
| | - Qingwen Li
- School of Nano-Tech and Nano-Bionics, University of Science and Technology of China, Hefei, 230026, China.
- Advanced Materials Division, Suzhou Institute of Nano-Tech and Nano-Bionics, Chinese Academy of Sciences, Suzhou, 215123, China.
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3
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Zhao X, Zou H, Wang M, Wang J, Wang T, Wang L, Chen X. Conformal Neuromorphic Bioelectronics for Sense Digitalization. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024:e2403444. [PMID: 38934554 DOI: 10.1002/adma.202403444] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/07/2024] [Revised: 06/03/2024] [Indexed: 06/28/2024]
Abstract
Sense digitalization, the process of transforming sensory experiences into digital data, is an emerging research frontier that links the physical world with human perception and interaction. Inspired by the adaptability, fault tolerance, robustness, and energy efficiency of biological senses, this field drives the development of numerous innovative digitalization techniques. Neuromorphic bioelectronics, characterized by biomimetic adaptability, stand out for their seamless bidirectional interactions with biological entities through stimulus-response and feedback loops, incorporating bio-neuromorphic intelligence for information exchange. This review illustrates recent progress in sensory digitalization, encompassing not only the digital representation of physical sensations such as touch, light, and temperature, correlating to tactile, visual, and thermal perceptions, but also the detection of biochemical stimuli such as gases, ions, and neurotransmitters, mirroring olfactory, gustatory, and neural processes. It thoroughly examines the material design, device manufacturing, and system integration, offering detailed insights. However, the field faces significant challenges, including the development of new device/system paradigms, forging genuine connections with biological systems, ensuring compatibility with the semiconductor industry and overcoming the absence of standardization. Future ambition includes realization of biocompatible neural prosthetics, exoskeletons, soft humanoid robots, and cybernetic devices that integrate smoothly with both biological tissues and artificial components.
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Affiliation(s)
- Xiao Zhao
- State Key Laboratory of Organic Electronics and Information Displays, Jiangsu Key Laboratory of Smart Biomaterials and Theranostic Technology, Institute of Advanced Materials (IAM), Nanjing University of Posts and Telecommunications, Nanjing, 210023, China
| | - Haochen Zou
- State Key Laboratory of Organic Electronics and Information Displays, Jiangsu Key Laboratory of Smart Biomaterials and Theranostic Technology, Institute of Advanced Materials (IAM), Nanjing University of Posts and Telecommunications, Nanjing, 210023, China
| | - Ming Wang
- Frontier Institute of Chip and System, State Key Laboratory of Integrated Chips and Systems, Fudan University, Shanghai, 200433, China
| | - Jianwu Wang
- Institute for Digital Molecular Analytics and Science (IDMxS), Nanyang Technological University, 59 Nanyang Drive, Singapore, 636921, Singapore
- Innovative Centre for Flexible Devices (iFLEX) Max Planck-NTU Joint Lab for Artificial Senses, School of Materials Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798, Singapore
| | - Ting Wang
- State Key Laboratory of Organic Electronics and Information Displays, Jiangsu Key Laboratory of Smart Biomaterials and Theranostic Technology, Institute of Advanced Materials (IAM), Nanjing University of Posts and Telecommunications, Nanjing, 210023, China
| | - Lianhui Wang
- State Key Laboratory of Organic Electronics and Information Displays, Jiangsu Key Laboratory of Smart Biomaterials and Theranostic Technology, Institute of Advanced Materials (IAM), Nanjing University of Posts and Telecommunications, Nanjing, 210023, China
| | - Xiaodong Chen
- Institute for Digital Molecular Analytics and Science (IDMxS), Nanyang Technological University, 59 Nanyang Drive, Singapore, 636921, Singapore
- Innovative Centre for Flexible Devices (iFLEX) Max Planck-NTU Joint Lab for Artificial Senses, School of Materials Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798, Singapore
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4
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Li Y, Ren H, Chi C, Miao Y. Artificial Intelligence-Guided Gut-Microenvironment-Triggered Imaging Sensor Reveals Potential Indicators of Parkinson's Disease. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2307819. [PMID: 38569219 PMCID: PMC11187919 DOI: 10.1002/advs.202307819] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Revised: 03/16/2024] [Indexed: 04/05/2024]
Abstract
The gut-brain axis has recently emerged as a crucial link in the development and progression of Parkinson's disease (PD). Dysregulation of the gut microbiota has been implicated in the pathogenesis of this disease, sparking growing interest in the quest for non-invasive biomarkers derived from the gut for early PD diagnosis. Herein, an artificial intelligence-guided gut-microenvironment-triggered imaging sensor (Eu-MOF@Au-Aptmer) to achieve non-invasive, accurate screening for various stages of PD is presented. The sensor works by analyzing α-Syn in the gut using deep learning algorithms. By monitoring changes in α-Syn, the sensor can predict the onset of PD with high accuracy. This work has the potential to revolutionize the diagnosis and treatment of PD by allowing for early intervention and personalized treatment plans. Moreover, it exemplifies the promising prospects of integrating artificial intelligence (AI) and advanced sensors in the monitoring and prediction of a broad spectrum of diseases and health conditions.
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Affiliation(s)
- Yiwei Li
- Department of Haematology, Sichuan Academy of Medical Sciences & Sichuan Provincial People's HospitalSchool of Medicine of University of Electronic Science and Technology of ChinaNo. 32, West Section 2, First Ring Road, Qingyang DistrictChengdu610000China
- Institute of Communications Engineering & Department of Electrical EngineeringNational Tsing Hua UniversityHsinchu30013Taiwan
| | - Hong‐Xia Ren
- Sichuan Technology & Business CollegeChengdu611800China
| | - Chong‐Yung Chi
- Institute of Communications Engineering & Department of Electrical EngineeringNational Tsing Hua UniversityHsinchu30013Taiwan
| | - Yang‐Bao Miao
- Department of Haematology, Sichuan Academy of Medical Sciences & Sichuan Provincial People's HospitalSchool of Medicine of University of Electronic Science and Technology of ChinaNo. 32, West Section 2, First Ring Road, Qingyang DistrictChengdu610000China
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5
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Lee T, Jung KS, Seo S, Lee J, Park J, Kang S, Park J, Kang J, Ahn H, Kim S, Lee HW, Lee D, Kim KS, Kim H, Heo K, Kim S, Bae SH, Kang S, Kang K, Kim J, Park JH. Junctionless Negative-Differential-Resistance Device Using 2D Van-Der-Waals Layered Materials for Ternary Parallel Computing. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024; 36:e2310015. [PMID: 38450812 DOI: 10.1002/adma.202310015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Revised: 01/31/2024] [Indexed: 03/08/2024]
Abstract
Negative-differential-resistance (NDR) devices offer a promising pathway for developing future computing technologies characterized by exceptionally low energy consumption, especially multivalued logic computing. Nevertheless, conventional approaches aimed at attaining the NDR phenomenon involve intricate junction configurations and/or external doping processes in the channel region, impeding the progress of NDR devices to the circuit and system levels. Here, an NDR device is presented that incorporates a channel without junctions. The NDR phenomenon is achieved by introducing a metal-insulator-semiconductor capacitor to a portion of the channel area. This approach establishes partial potential barrier and well that effectively restrict the movement of hole and electron carriers within specific voltage ranges. Consequently, this facilitates the implementation of both a ternary inverter and a ternary static-random-access-memory, which are essential components in the development of multivalued logic computing technology.
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Affiliation(s)
- Taeran Lee
- Department of Electrical and Computer Engineering, Sungkyunkwan University (SKKU), Suwon, 16419, South Korea
| | - Kil-Su Jung
- Flash Memory Technology Design Team, Samsung Electronics Co. Ltd., Giheung, 17113, South Korea
- Department of Semiconductor and Display Engineering, Sungkyunkwan University (SKKU), Suwon, 16419, South Korea
| | - Seunghwan Seo
- Department of Electrical and Computer Engineering, Sungkyunkwan University (SKKU), Suwon, 16419, South Korea
- Department of Materials Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, South Korea
- Research Laboratory of Electronics, Massachusetts Institute of Technology (MIT), Cambridge, MA, 02138, USA
| | - Junseo Lee
- Department of Electrical and Computer Engineering, Sungkyunkwan University (SKKU), Suwon, 16419, South Korea
| | - Jihye Park
- Department of Electrical Engineering, Pohang University of Science and Technology (POSTECH), Pohang, 37673, South Korea
| | - Sumin Kang
- Department of Materials Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, South Korea
| | - Jeongwon Park
- Department of Materials Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, South Korea
| | - Juncheol Kang
- Department of Electrical and Computer Engineering, Sungkyunkwan University (SKKU), Suwon, 16419, South Korea
| | - Hogeun Ahn
- Department of Electrical and Computer Engineering, Sungkyunkwan University (SKKU), Suwon, 16419, South Korea
| | - Suhyun Kim
- Department of Electrical and Computer Engineering, Sungkyunkwan University (SKKU), Suwon, 16419, South Korea
| | - Hae Won Lee
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology (MIT), Cambridge, MA, 02138, USA
| | - Doyoon Lee
- Research Laboratory of Electronics, Massachusetts Institute of Technology (MIT), Cambridge, MA, 02138, USA
- Department of Mechanical Engineering, Massachusetts Institute of Technology (MIT), Cambridge, MA, 02138, USA
| | - Ki Seok Kim
- Research Laboratory of Electronics, Massachusetts Institute of Technology (MIT), Cambridge, MA, 02138, USA
- Department of Mechanical Engineering, Massachusetts Institute of Technology (MIT), Cambridge, MA, 02138, USA
| | - Hyunseok Kim
- Research Laboratory of Electronics, Massachusetts Institute of Technology (MIT), Cambridge, MA, 02138, USA
- Department of Mechanical Engineering, Massachusetts Institute of Technology (MIT), Cambridge, MA, 02138, USA
- Department of Electrical and Computer Engineering, University of Illinois Urbana-Champaign (UIUC), Urbana, IL, 61801, USA
- Nick Holonyak, Jr. Micro and Nanotechnology Laboratory, University of Illinois Urbana-Champaign (UIUC), Urbana, IL, 61801, USA
| | - Keun Heo
- School of Semiconductor Science & Technology, Jeonbuk National University, Jeonju, 54896, South Korea
| | - Sunmean Kim
- School of Electronics Engineering College of IT Engineering, Kyungpook National University, Daegu, 41566, South Korea
| | - Sang-Hoon Bae
- Department of Mechanical Engineering and Materials Science, Washington University in Saint Louis, Missouri, MO, 63130, USA
- Institute of Materials Science and Engineering, Washington University in Saint Louis, Missouri, MO, 63130, USA
| | - Seokhyeong Kang
- Department of Electrical Engineering, Pohang University of Science and Technology (POSTECH), Pohang, 37673, South Korea
| | - Kibum Kang
- Department of Materials Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, South Korea
- Graduate School of Semiconductor Technology, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, South Korea
| | - Jeehwan Kim
- Research Laboratory of Electronics, Massachusetts Institute of Technology (MIT), Cambridge, MA, 02138, USA
- Department of Mechanical Engineering, Massachusetts Institute of Technology (MIT), Cambridge, MA, 02138, USA
| | - Jin-Hong Park
- Department of Electrical and Computer Engineering, Sungkyunkwan University (SKKU), Suwon, 16419, South Korea
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Lee M, Kim Y, Mo SH, Kim S, Eom K, Lee H. Optoelectronic Synapse Based on 2D Electron Gas in Stoichiometry-Controlled Oxide Heterostructures. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2024; 20:e2309851. [PMID: 38214690 DOI: 10.1002/smll.202309851] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Revised: 12/17/2023] [Indexed: 01/13/2024]
Abstract
Emulating synaptic functionalities in optoelectronic devices is significant in developing artificial visual-perception systems and neuromorphic photonic computing. Persistent photoconductivity (PPC) in metal oxides provides a facile way to realize the optoelectronic synaptic devices, but the PPC performance is often limited due to the oxygen vacancy defects that release excess conduction electrons without external stimuli. Herein, a high-performance optoelectronic synapse based on the stoichiometry-controlled LaAlO3/SrTiO3 (LAO/STO) heterostructure is developed. By increasing La/Al ratio up to 1.057:1, the PPC is effectively enhanced but suppressed the background conductivity at the LAO/STO interface, achieving strong synaptic behaviors. The spectral noise analyses reveal that the synaptic behaviors are attributed to the cation-related point defects and their charge compensation mechanism near the LAO/STO interface. The short-term and long-term plasticity is demonstrated, including the paired-pulse facilitation, in the La-rich LAO/STO device upon exposure to UV light pulses. As proof of concepts, two essential synaptic functionalities, the pulse-number-dependent plasticity and the self-noise cancellation, are emulated using the 5 × 5 array of La-rich LAO/STO synapses. Beyond the typical oxygen deficiency control, the results show how harnessing the cation stoichiometry can be used to design oxide heterostructures for advanced optoelectronic synapses and neuromorphic applications.
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Affiliation(s)
- Minkyung Lee
- Department of Physics, Ajou University, Suwon, 16499, Republic of Korea
- Department of Energy Systems Research, Ajou University, Suwon, 16499, Republic of Korea
| | - Youngmin Kim
- Department of Physics, Ajou University, Suwon, 16499, Republic of Korea
- Department of Energy Systems Research, Ajou University, Suwon, 16499, Republic of Korea
| | - Sang Hyeon Mo
- Department of Physics, Ajou University, Suwon, 16499, Republic of Korea
| | - Sungkyu Kim
- Department of Nanotechnology and Advanced Materials Engineering, Sejong University, Seoul, 05006, Republic of Korea
| | - Kitae Eom
- Department of Electronic Engineering, Gachon University, Seongnam, 13120, Republic of Korea
| | - Hyungwoo Lee
- Department of Physics, Ajou University, Suwon, 16499, Republic of Korea
- Department of Energy Systems Research, Ajou University, Suwon, 16499, Republic of Korea
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7
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Bag A, Ghosh G, Sultan MJ, Chouhdry HH, Hong SJ, Trung TQ, Kang GY, Lee NE. Bio-Inspired Sensory Receptors for Artificial-Intelligence Perception. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024:e2403150. [PMID: 38699932 DOI: 10.1002/adma.202403150] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/01/2024] [Revised: 04/16/2024] [Indexed: 05/05/2024]
Abstract
In the era of artificial intelligence (AI), there is a growing interest in replicating human sensory perception. Selective and sensitive bio-inspired sensory receptors with synaptic plasticity have recently gained significant attention in developing energy-efficient AI perception. Various bio-inspired sensory receptors and their applications in AI perception are reviewed here. The critical challenges for the future development of bio-inspired sensory receptors are outlined, emphasizing the need for innovative solutions to overcome hurdles in sensor design, integration, and scalability. AI perception can revolutionize various fields, including human-machine interaction, autonomous systems, medical diagnostics, environmental monitoring, industrial optimization, and assistive technologies. As advancements in bio-inspired sensing continue to accelerate, the promise of creating more intelligent and adaptive AI systems becomes increasingly attainable, marking a significant step forward in the evolution of human-like sensory perception.
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Affiliation(s)
- Atanu Bag
- School of Advanced Materials Science & Engineering, Sungkyunkwan University, Suwon, Gyeonggi-do, 16419, Republic of Korea
- Research Centre for Advanced Materials Technology, Sungkyunkwan University, Suwon, Gyeonggi-do, 16419, Republic of Korea
| | - Gargi Ghosh
- School of Advanced Materials Science & Engineering, Sungkyunkwan University, Suwon, Gyeonggi-do, 16419, Republic of Korea
| | - M Junaid Sultan
- School of Advanced Materials Science & Engineering, Sungkyunkwan University, Suwon, Gyeonggi-do, 16419, Republic of Korea
| | - Hamna Haq Chouhdry
- SKKU Advanced Institute of Nanotechnology (SAINT), Sungkyunkwan University, Suwon, Gyeonggi-do, 16419, Republic of Korea
| | - Seok Ju Hong
- School of Advanced Materials Science & Engineering, Sungkyunkwan University, Suwon, Gyeonggi-do, 16419, Republic of Korea
| | - Tran Quang Trung
- School of Advanced Materials Science & Engineering, Sungkyunkwan University, Suwon, Gyeonggi-do, 16419, Republic of Korea
| | - Geun-Young Kang
- School of Advanced Materials Science & Engineering, Sungkyunkwan University, Suwon, Gyeonggi-do, 16419, Republic of Korea
| | - Nae-Eung Lee
- School of Advanced Materials Science & Engineering, Sungkyunkwan University, Suwon, Gyeonggi-do, 16419, Republic of Korea
- Research Centre for Advanced Materials Technology, Sungkyunkwan University, Suwon, Gyeonggi-do, 16419, Republic of Korea
- SKKU Advanced Institute of Nanotechnology (SAINT), Sungkyunkwan University, Suwon, Gyeonggi-do, 16419, Republic of Korea
- Samsung Advanced Institute for Health Sciences & Technology (SAIHST), Institute of Quantum Biophysics (IQB) and Biomedical Institute for Convergence at SKKU (BICS), Sungkyunkwan University, Suwon, Gyeonggi-do, 16419, Republic of Korea
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8
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Deng Y, Liu S, Ma X, Guo S, Zhai B, Zhang Z, Li M, Yu Y, Hu W, Yang H, Kapitonov Y, Han J, Wu J, Li Y, Zhai T. Intrinsic Defect-Driven Synergistic Synaptic Heterostructures for Gate-Free Neuromorphic Phototransistors. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024; 36:e2309940. [PMID: 38373410 DOI: 10.1002/adma.202309940] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Revised: 01/28/2024] [Indexed: 02/21/2024]
Abstract
The optoelectronic synaptic devices based on two-dimensional (2D) materials offer great advances for future neuromorphic visual systems with dramatically improved integration density and power efficiency. The effective charge capture and retention are considered as one vital prerequisite to realizing the synaptic memory function. However, the current 2D synaptic devices are predominantly relied on materials with artificially-engineered defects or intricate gate-controlled architectures to realize the charge trapping process. These approaches, unfortunately, suffer from the degradation of pristine materials, rapid device failure, and unnecessary complication of device structures. To address these challenges, an innovative gate-free heterostructure paradigm is introduced herein. The heterostructure presents a distinctive dome-like morphology wherein a defect-rich Fe7S8 core is enveloped snugly by a curved MoS2 dome shell (Fe7S8@MoS2), allowing the realization of effective photocarrier trapping through the intrinsic defects in the adjacent Fe7S8 core. The resultant neuromorphic devices exhibit remarkable light-tunable synaptic behaviors with memory time up to ≈800 s under single optical pulse, thus demonstrating great advances in simulating visual recognition system with significantly improved image recognition efficiency. The emergence of such heterostructures foreshadows a promising trajectory for underpinning future synaptic devices, catalyzing the realization of high-efficiency and intricate visual processing applications.
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Affiliation(s)
- Yao Deng
- 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
| | - 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
| | - Xiaoxi Ma
- 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
| | - Shuyang Guo
- School of Computer Science and Artificial Intelligence, Wuhan University of Technology, Wuhan, 430070, P. R. China
| | - Baoxing Zhai
- Institute of Semiconductors, Henan Academy of Sciences, Zhengzhou, 450046, P. R. China
| | - Zihan Zhang
- Department of Mechanics, School of Aerospace Engineering, Huazhong University of Science and Technology, Wuhan, 430074, P. R. China
| | - Manshi Li
- Wuhan National High Magnetic Field Centre, Department of Physics, Huazhong University of Science and Technology, Wuhan, 430074, P. R. China
| | - Yimeng Yu
- State Key Laboratory of Advanced Technology for Materials Synthesis and Processing, Nanostructure Research Center, Wuhan University of Technology, Wuhan, 430070, P. R. China
| | - Wenhua Hu
- School of Computer Science and Artificial Intelligence, Wuhan University of Technology, Wuhan, 430070, P. R. China
| | - Hui Yang
- Department of Mechanics, School of Aerospace Engineering, Huazhong University of Science and Technology, Wuhan, 430074, P. R. China
| | - Yury Kapitonov
- Department of Photonics, Saint Petersburg State University, Saint Petersburg, 199034, Russia
| | - Junbo Han
- Wuhan National High Magnetic Field Centre, Department of Physics, Huazhong University of Science and Technology, Wuhan, 430074, P. R. China
| | - Jinsong Wu
- State Key Laboratory of Advanced Technology for Materials Synthesis and Processing, Nanostructure Research Center, Wuhan University of Technology, Wuhan, 430070, 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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9
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Zhang X, Wang C, Sun Q, Wu J, Dai Y, Li E, Wu J, Chen H, Duan S, Hu W. Inorganic Halide Perovskite Nanowires/Conjugated Polymer Heterojunction-Based Optoelectronic Synaptic Transistors for Dynamic Machine Vision. NANO LETTERS 2024; 24:4132-4140. [PMID: 38534013 DOI: 10.1021/acs.nanolett.3c05092] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/28/2024]
Abstract
Inspired by the retina, artificial optoelectronic synapses have groundbreaking potential for machine vision. The field-effect transistor is a crucial platform for optoelectronic synapses that is highly sensitive to external stimuli and can modulate conductivity. On the basis of the decent optical absorption, perovskite materials have been widely employed for constructing optoelectronic synaptic transistors. However, the reported optoelectronic synaptic transistors focus on the static processing of independent stimuli at different moments, while the natural visual information consists of temporal signals. Here, we report CsPbBrI2 nanowire-based optoelectronic synaptic transistors to study the dynamic responses of artificial synaptic transistors to time-varying visual information for the first time. Moreover, on the basis of the dynamic synaptic behavior, a hardware system with an accuracy of 85% is built to the trajectory of moving objects. This work offers a new way to develop artificial optoelectronic synapses for the construction of dynamic machine vision systems.
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Affiliation(s)
- Xianghong Zhang
- Institute of Optoelectronic Display, National & Local United Engineering Lab of Flat Panel Display Technology, Fuzhou University, Fuzhou 350002, China
- Shanghai Frontiers Science Research Base of Intelligent Optoelectronics and Perception, Institute of Optoelectronics, Department of Materials Science, Fudan University, Shanghai 200433, China
| | - Congyong Wang
- Joint School of National University of Singapore and Tianjin University, International Campus of Tianjin University, Binhai New City, Fuzhou 350207, China
- Department of Chemistry, National University of Singapore, 3 Science Drive, Singapore 117543
| | - Qisheng Sun
- China Electronics Technology Group Corp 46th Research Institute, 26 Dongting Road, Tianjin 300220, P. R. China
| | - Jianxin Wu
- Institute of Optoelectronic Display, National & Local United Engineering Lab of Flat Panel Display Technology, Fuzhou University, Fuzhou 350002, China
- Fujian Science & Technology Innovation Laboratory for Optoelectronic Information of China, Fuzhou 350100, China
| | - Yan Dai
- Institute of Optoelectronic Display, National & Local United Engineering Lab of Flat Panel Display Technology, Fuzhou University, Fuzhou 350002, China
- Fujian Science & Technology Innovation Laboratory for Optoelectronic Information of China, Fuzhou 350100, China
| | - Enlong Li
- Shanghai Frontiers Science Research Base of Intelligent Optoelectronics and Perception, Institute of Optoelectronics, Department of Materials Science, Fudan University, Shanghai 200433, China
| | - Jishan Wu
- Department of Chemistry, National University of Singapore, 3 Science Drive, Singapore 117543
| | - Huipeng Chen
- Institute of Optoelectronic Display, National & Local United Engineering Lab of Flat Panel Display Technology, Fuzhou University, Fuzhou 350002, China
- Fujian Science & Technology Innovation Laboratory for Optoelectronic Information of China, Fuzhou 350100, China
| | - Shuming Duan
- Joint School of National University of Singapore and Tianjin University, International Campus of Tianjin University, Binhai New City, Fuzhou 350207, China
| | - Wenping Hu
- Joint School of National University of Singapore and Tianjin University, International Campus of Tianjin University, Binhai New City, Fuzhou 350207, China
- Key Laboratory of Organic Integrated Circuits, Ministry of Education & Tianjin Key Laboratory of Molecular Optoelectronic Sciences, Department of Chemistry, School of Science, Tianjin University, Tianjin 300072, China
- Collaborative Innovation Center of Chemical Science and Engineering (Tianjin), Tianjin 300072, China
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10
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Zhu S, Xie T, Lv Z, Leng YB, Zhang YQ, Xu R, Qin J, Zhou Y, Roy VAL, Han ST. Hierarchies in Visual Pathway: Functions and Inspired Artificial Vision. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024; 36:e2301986. [PMID: 37435995 DOI: 10.1002/adma.202301986] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Revised: 06/28/2023] [Accepted: 07/10/2023] [Indexed: 07/13/2023]
Abstract
The development of artificial intelligence has posed a challenge to machine vision based on conventional complementary metal-oxide semiconductor (CMOS) circuits owing to its high latency and inefficient power consumption originating from the data shuffling between memory and computation units. Gaining more insights into the function of every part of the visual pathway for visual perception can bring the capabilities of machine vision in terms of robustness and generality. Hardware acceleration of more energy-efficient and biorealistic artificial vision highly necessitates neuromorphic devices and circuits that are able to mimic the function of each part of the visual pathway. In this paper, we review the structure and function of the entire class of visual neurons from the retina to the primate visual cortex within reach (Chapter 2) are reviewed. Based on the extraction of biological principles, the recent hardware-implemented visual neurons located in different parts of the visual pathway are discussed in detail in Chapters 3 and 4. Furthermore, valuable applications of inspired artificial vision in different scenarios (Chapter 5) are provided. The functional description of the visual pathway and its inspired neuromorphic devices/circuits are expected to provide valuable insights for the design of next-generation artificial visual perception systems.
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Affiliation(s)
- Shirui Zhu
- Institute of Microscale Optoelectronics, Shenzhen University, Shenzhen, 518060, P. R. China
| | - Tao Xie
- Institute of Microscale Optoelectronics, Shenzhen University, Shenzhen, 518060, P. R. China
| | - Ziyu Lv
- College of Electronics and Information Engineering, Shenzhen University, Shenzhen, 518060, P. R. China
| | - Yan-Bing Leng
- Institute of Microscale Optoelectronics, Shenzhen University, Shenzhen, 518060, P. R. China
| | - Yu-Qi Zhang
- Institute of Microscale Optoelectronics, Shenzhen University, Shenzhen, 518060, P. R. China
| | - Runze Xu
- Institute of Microscale Optoelectronics, Shenzhen University, Shenzhen, 518060, P. R. China
| | - Jingrun Qin
- Institute of Microscale Optoelectronics, Shenzhen University, Shenzhen, 518060, P. R. China
| | - Ye Zhou
- Institute for Advanced Study, Shenzhen University, Shenzhen, 518060, P. R. China
| | - Vellaisamy A L Roy
- School of Science and Technology, Hong Kong Metropolitan University, Hong Kong, 999077, P. R. China
| | - Su-Ting Han
- College of Electronics and Information Engineering, Shenzhen University, Shenzhen, 518060, P. R. China
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11
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Kim J, Lee J, Lee JM, Facchetti A, Marks TJ, Park SK. Recent Advances in Low-Dimensional Nanomaterials for Photodetectors. SMALL METHODS 2024; 8:e2300246. [PMID: 37203281 DOI: 10.1002/smtd.202300246] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/25/2023] [Revised: 04/21/2023] [Indexed: 05/20/2023]
Abstract
New emerging low-dimensional such as 0D, 1D, and 2D nanomaterials have attracted tremendous research interests in various fields of state-of-the-art electronics, optoelectronics, and photonic applications due to their unique structural features and associated electronic, mechanical, and optical properties as well as high-throughput fabrication for large-area and low-cost production and integration. Particularly, photodetectors which transform light to electrical signals are one of the key components in modern optical communication and developed imaging technologies for whole application spectrum in the daily lives, including X-rays and ultraviolet biomedical imaging, visible light camera, and infrared night vision and spectroscopy. Today, diverse photodetector technologies are growing in terms of functionality and performance beyond the conventional silicon semiconductor, and low-dimensional nanomaterials have been demonstrated as promising potential platforms. In this review, the current states of progress on the development of these nanomaterials and their applications in the field of photodetectors are summarized. From the elemental combination for material design and lattice structure to the essential investigations of hybrid device architectures, various devices and recent developments including wearable photodetectors and neuromorphic applications are fully introduced. Finally, the future perspectives and challenges of the low-dimensional nanomaterials based photodetectors are also discussed.
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Affiliation(s)
- Jaehyun Kim
- Department of Chemistry and Materials Research Center, Northwestern University, Evanston, IL, 60208, USA
| | - Junho Lee
- Displays and Devices Research Lab. School of Electrical and Electronics Engineering, Chung-Ang University, Seoul, 06974, South Korea
| | - Jong-Min Lee
- Displays and Devices Research Lab. School of Electrical and Electronics Engineering, Chung-Ang University, Seoul, 06974, South Korea
| | - Antonio Facchetti
- Department of Chemistry and Materials Research Center, Northwestern University, Evanston, IL, 60208, USA
| | - Tobin J Marks
- Department of Chemistry and Materials Research Center, Northwestern University, Evanston, IL, 60208, USA
| | - Sung Kyu Park
- Displays and Devices Research Lab. School of Electrical and Electronics Engineering, Chung-Ang University, Seoul, 06974, South Korea
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12
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Wu G, Abid M, Zerara M, Cho J, Choi M, Ó Coileáin C, Hung KM, Chang CR, Shvets IV, Wu HC. Miniaturized spectrometer with intrinsic long-term image memory. Nat Commun 2024; 15:676. [PMID: 38263315 PMCID: PMC10805890 DOI: 10.1038/s41467-024-44884-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Accepted: 01/09/2024] [Indexed: 01/25/2024] Open
Abstract
Miniaturized spectrometers have great potential for use in portable optoelectronics and wearable sensors. However, current strategies for miniaturization rely on von Neumann architectures, which separate the spectral sensing, storage, and processing modules spatially, resulting in high energy consumption and limited processing speeds due to the storage-wall problem. Here, we present a miniaturized spectrometer that utilizes a single SnS2/ReSe2 van der Waals heterostructure, providing photodetection, spectrum reconstruction, spectral imaging, long-term image memory, and signal processing capabilities. Interface trap states are found to induce a gate-tunable and wavelength-dependent photogating effect and a non-volatile optoelectronic memory effect. Our approach achieves a footprint of 19 μm, a bandwidth from 400 to 800 nm, a spectral resolution of 5 nm, and a > 104 s long-term image memory. Our single-detector computational spectrometer represents a path beyond von Neumann architectures.
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Affiliation(s)
- Gang Wu
- School of Physics, Beijing Institute of Technology, Beijing, 100081, P. R. China
| | - Mohamed Abid
- School of Physics, Beijing Institute of Technology, Beijing, 100081, P. R. China
| | | | - Jiung Cho
- Western Seoul Cente, Korea Basic Science Institute, Seoul, 03579, Republic of Korea
- Department of Advanced Materials Engineering, Chung-Ang University, 4726, Seodong-daero, Daedeok-myeon, Anseong-si, Gyeonggi-do, 17546, Republic of Korea
| | - Miri Choi
- Chuncheon Center, Korea Basic Science Institute, Chuncheon, 24341, Republic of Korea
| | - Cormac Ó Coileáin
- Institute of Physics, Faculty of Electrical Engineering and Information Technology, University of the Bundeswehr Munich, Neubiberg, 85577, Germany
| | - Kuan-Ming Hung
- Department of Electronics Engineering, National Kaohsiung University of Science and Technology, Kaohsiung, 807, Taiwan, ROC
| | - Ching-Ray Chang
- Quantum Information Center, Chung Yuan Christian University, Taoyuan, 32023, Taiwan, ROC
- Department of Physics, National Taiwan University, Taipei, 106, Taiwan, ROC
| | - Igor V Shvets
- School of Physics, Trinity College Dublin, Dublin, Dublin 2, Ireland
| | - Han-Chun Wu
- School of Physics, Beijing Institute of Technology, Beijing, 100081, P. R. China.
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13
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Lai PT, Chen CY, Lin HC, Chuang BY, Kuo KH, Greve CR, Su TK, Tan GH, Li CF, Huang SW, Hsiao KY, Herzig EM, Lu MY, Huang YC, Wong KT, Lin HW. Harnessing 2D Ruddlesden-Popper Perovskite with Polar Organic Cation for Ultrasensitive Multibit Nonvolatile Transistor-Type Photomemristors. ACS NANO 2023; 17:25552-25564. [PMID: 38096149 DOI: 10.1021/acsnano.3c09595] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/27/2023]
Abstract
Photomemristors have been regarded as one of the most promising candidates for next-generation hardware-based neuromorphic computing due to their potentials of fast data transmission and low power consumption. However, intriguingly, so far, photomemristors seldom display truly nonvolatile memory characteristics with high light sensitivity. Herein, we demonstrate ultrasensitive photomemristors utilizing two-dimensional (2D) Ruddlesden-Popper (RP) perovskites with a highly polar donor-acceptor-type push-pull organic cation, 4-(5-(2-aminoethyl)thiophen-2-yl)benzonitrile+ (EATPCN+), as charge-trapping layers. High linearity and almost zero-decay retention are observed in (EATPCN)2PbI4 devices, which are very distinct from that of the traditional 2D RP perovskite devices consisting of nonpolar organic cations, such as phenethylamine+ (PEA+) and octylamine+ (OA+), and traditional 3D perovskite devices consisting of methylamine+ (MA+). The 2-fold advantages, including desirable spatial crystal arrangement and engineered energetic band alignment, clarify the mechanism of superior performance in (EATPCN)2PbI4 devices. The optimized (EATPCN)2PbI4 photomemristor also shows a memory window of 87.9 V and an on/off ratio of 106 with a retention time of at least 2.4 × 105 s and remains unchanged after >105 writing-reading-erasing-reading endurance cycles. Very low energy consumptions of 1.12 and 6 fJ for both light stimulation and the reading process of each status update are also demonstrated. The extremely low power consumption and high photoresponsivity were simultaneously achieved. The high photosensitivity surpasses that of a state-of-the-art commercial pulse energy meter by several orders of magnitude. With their outstanding linearity and retention, rabbit images have been rebuilt by (EATPCN)2PbI4 photomemristors, which truthfully render the image without fading over time. Finally, by utilizing the powerful ∼8 bits of nonvolatile potentiation and depression levels of (EATPCN)2PbI4 photomemristors, the accuracies of the recognition tasks of CIFAR-10 image classification and MNIST handwritten digit classification have reached 89% and 94.8%, respectively. This study represents the first report of utilizing a functional donor-acceptor type of organic cation in 2D RP perovskites for high-performance photomemristors with characteristics that are not found in current halide perovskites.
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Affiliation(s)
- Po-Ting Lai
- Department of Materials Science and Engineering, National Tsing Hua University, Hsinchu 30013, Taiwan
- Frontier Research Center on Fundamental and Applied Sciences of Matters, National Tsing Hua University, Hsinchu 30013, Taiwan
| | - Cheng-Yueh Chen
- Department of Materials Science and Engineering, National Tsing Hua University, Hsinchu 30013, Taiwan
- Frontier Research Center on Fundamental and Applied Sciences of Matters, National Tsing Hua University, Hsinchu 30013, Taiwan
| | - Hao-Cheng Lin
- Department of Materials Science and Engineering, National Tsing Hua University, Hsinchu 30013, Taiwan
- Frontier Research Center on Fundamental and Applied Sciences of Matters, National Tsing Hua University, Hsinchu 30013, Taiwan
| | - Bo-Yuan Chuang
- Department of Chemistry, National Taiwan University, Taipei 10617, Taiwan
| | - Kai-Hua Kuo
- Department of Chemistry, National Taiwan University, Taipei 10617, Taiwan
| | - Christopher R Greve
- Dynamics and Structure Formation─Herzig Group, University of Bayreuth, 95447 Bayreuth, Germany
| | - Tsung-Kai Su
- Department of Materials Science and Engineering, National Tsing Hua University, Hsinchu 30013, Taiwan
- Frontier Research Center on Fundamental and Applied Sciences of Matters, National Tsing Hua University, Hsinchu 30013, Taiwan
| | - Guang-Hsun Tan
- Department of Materials Science and Engineering, National Tsing Hua University, Hsinchu 30013, Taiwan
- Frontier Research Center on Fundamental and Applied Sciences of Matters, National Tsing Hua University, Hsinchu 30013, Taiwan
| | - Chia-Feng Li
- Department of Materials Engineering, Ming Chi University of Technology, New Taipei City 24301, Taiwan
| | - Sheng-Wen Huang
- Department of Materials Engineering, Ming Chi University of Technology, New Taipei City 24301, Taiwan
| | - Kai-Yuan Hsiao
- Department of Materials Science and Engineering, National Tsing Hua University, Hsinchu 30013, Taiwan
| | - Eva M Herzig
- Dynamics and Structure Formation─Herzig Group, University of Bayreuth, 95447 Bayreuth, Germany
| | - Ming-Yen Lu
- Department of Materials Science and Engineering, National Tsing Hua University, Hsinchu 30013, Taiwan
| | - Yu-Ching Huang
- Department of Materials Engineering, Ming Chi University of Technology, New Taipei City 24301, Taiwan
| | - Ken-Tsung Wong
- Department of Chemistry, National Taiwan University, Taipei 10617, Taiwan
- Department of Chemistry, National Taiwan University, Taipei 10617, Taiwan
| | - Hao-Wu Lin
- Department of Materials Science and Engineering, National Tsing Hua University, Hsinchu 30013, Taiwan
- Frontier Research Center on Fundamental and Applied Sciences of Matters, National Tsing Hua University, Hsinchu 30013, Taiwan
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14
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Chen Y, Huang Y, Zeng J, Kang Y, Tan Y, Xie X, Wei B, Li C, Fang L, Jiang T. Energy-Efficient ReS 2-Based Optoelectronic Synapse for 3D Object Reconstruction and Recognition. ACS APPLIED MATERIALS & INTERFACES 2023; 15:58631-58642. [PMID: 38054897 DOI: 10.1021/acsami.3c14958] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/07/2023]
Abstract
The neuromorphic vision system (NVS) equipped with optoelectronic synapses integrates perception, storage, and processing and is expected to address the issues of traditional machine vision. However, owing to their lack of stereo vision, existing NVSs focus on 2D image processing, which makes it difficult to solve problems such as spatial cognition errors and low-precision interpretation. Consequently, inspired by the human visual system, an NVS with stereo vision is developed to achieve 3D object recognition, depending on the prepared ReS2 optoelectronic synapse with 12.12 fJ ultralow power consumption. This device exhibits excellent optical synaptic plasticity derived from the persistent photoconductivity effect. As the cornerstone for 3D vision, color planar information is successfully discriminated and stored in situ at the sensor end, benefiting from its wavelength-dependent plasticity in the visible region. Importantly, the dependence of the channel conductance on the target distance is experimentally revealed, implying that the structure information on the object can be directly captured and stored by the synapse. The 3D image of the object is successfully reconstructed via fusion of its planar and depth images. Therefore, the proposed 3D-NVS based on ReS2 synapses for 3D objects achieves a recognition accuracy of 97.0%, which is much higher than that for 2D objects (32.6%), demonstrating its strong ability to prevent 2D-photo spoofing in applications such as face payment, entrance guard systems, and others.
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Affiliation(s)
- Yabo Chen
- Institute for Quantum Information & State Key Laboratory of High Performance Computing, College of Computer, National University of Defense Technology, Changsha 410073, P. R. China
| | - Yujie Huang
- Institute for Quantum Information & State Key Laboratory of High Performance Computing, College of Computer, National University of Defense Technology, Changsha 410073, P. R. China
| | - Junwei Zeng
- Institute for Quantum Information & State Key Laboratory of High Performance Computing, College of Computer, National University of Defense Technology, Changsha 410073, P. R. China
| | - Yan Kang
- College of Advanced Interdisciplinary Studies, National University of Defense Technology, Changsha 410073, P. R. China
| | - Yinlong Tan
- College of Advanced Interdisciplinary Studies, National University of Defense Technology, Changsha 410073, P. R. China
| | - Xiangnan Xie
- Institute of Quantum Information Science and Technology, College of Science, National University of Defense Technology, Changsha 410073, P. R. China
| | - Bo Wei
- Institute for Quantum Information & State Key Laboratory of High Performance Computing, College of Computer, National University of Defense Technology, Changsha 410073, P. R. China
| | - Cheng Li
- Institute of Quantum Information Science and Technology, College of Science, National University of Defense Technology, Changsha 410073, P. R. China
| | - Liang Fang
- Institute for Quantum Information & State Key Laboratory of High Performance Computing, College of Computer, National University of Defense Technology, Changsha 410073, P. R. China
| | - Tian Jiang
- Institute of Quantum Information Science and Technology, College of Science, National University of Defense Technology, Changsha 410073, P. R. China
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15
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Abstract
Efforts to design devices emulating complex cognitive abilities and response processes of biological systems have long been a coveted goal. Recent advancements in flexible electronics, mirroring human tissue's mechanical properties, hold significant promise. Artificial neuron devices, hinging on flexible artificial synapses, bioinspired sensors, and actuators, are meticulously engineered to mimic the biological systems. However, this field is in its infancy, requiring substantial groundwork to achieve autonomous systems with intelligent feedback, adaptability, and tangible problem-solving capabilities. This review provides a comprehensive overview of recent advancements in artificial neuron devices. It starts with fundamental principles of artificial synaptic devices and explores artificial sensory systems, integrating artificial synapses and bioinspired sensors to replicate all five human senses. A systematic presentation of artificial nervous systems follows, designed to emulate fundamental human nervous system functions. The review also discusses potential applications and outlines existing challenges, offering insights into future prospects. We aim for this review to illuminate the burgeoning field of artificial neuron devices, inspiring further innovation in this captivating area of research.
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Affiliation(s)
- Ke He
- Innovative Centre for Flexible Devices (iFLEX), Max Planck-NTU Joint Lab for Artificial Senses, School of Materials Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore
| | - Cong Wang
- Innovative Centre for Flexible Devices (iFLEX), Max Planck-NTU Joint Lab for Artificial Senses, School of Materials Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore
| | - Yongli He
- Innovative Centre for Flexible Devices (iFLEX), Max Planck-NTU Joint Lab for Artificial Senses, School of Materials Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore
| | - Jiangtao Su
- Innovative Centre for Flexible Devices (iFLEX), Max Planck-NTU Joint Lab for Artificial Senses, School of Materials Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore
| | - Xiaodong Chen
- Innovative Centre for Flexible Devices (iFLEX), Max Planck-NTU Joint Lab for Artificial Senses, School of Materials Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore
- Institute for Digital Molecular Analytics and Science (IDMxS), Nanyang Technological University, 59 Nanyang Drive, Singapore 636921, Singapore
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16
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Chen P, Li D, Li Z, Xu X, Wang H, Zhou X, Zhai T. Programmable Physical Unclonable Functions Using Randomly Anisotropic Two-Dimensional Flakes. ACS NANO 2023. [PMID: 37982379 DOI: 10.1021/acsnano.3c08740] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/21/2023]
Abstract
Physical unclonable functions (PUFs) have been developed as promising strategies for secure authentication. Conventional strategies of PUFs have a limitation in the aspect of security for their static single channel. The introduction of polarization will endow a static PUF with many dynamic transformations based on polarized properties. Herein, high-security PUFs based on the polarized luminescence of chaotic luminescent patterns are fabricated by anisotropic rare earth (RE) material Er3O4Cl flakes. These derivatives under different polarizations show strong randomness (with similarity of the same PUF as high as 97%, while that for different PUFs is as low as 44%), which further widens the security and capacity of PUFs. Polarized luminescence control of Er3O4Cl patterns gives freedom to the PUFs and ensures a high encoding capacity of 2380000. Furthermore, we build a convolutional neural network (CNN) to realize fast intelligent authentication by extracting image features for convolution operation with a very high accuracy of 99.8% and fast classification speed in only 5 epochs.
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Affiliation(s)
- Ping Chen
- State Key Laboratory of Materials Processing and Die & Mould Technology, School of Materials Science and Engineering, Huazhong University of Science and Technology (HUST), Wuhan 430074, People's Republic of China
- School of Materials Science and Engineering, Hefei University of Technology, Hefei 230009, People's Republic of China
| | - Dongyan Li
- State Key Laboratory of Materials Processing and Die & Mould Technology, School of Materials Science and Engineering, Huazhong University of Science and Technology (HUST), Wuhan 430074, People's Republic of China
| | - Zexin Li
- State Key Laboratory of Materials Processing and Die & Mould Technology, School of Materials Science and Engineering, Huazhong University of Science and Technology (HUST), Wuhan 430074, People's Republic of China
| | - Xiang Xu
- State Key Laboratory of Materials Processing and Die & Mould Technology, School of Materials Science and Engineering, Huazhong University of Science and Technology (HUST), Wuhan 430074, People's Republic of China
| | - Haoyun Wang
- State Key Laboratory of Materials Processing and Die & Mould Technology, School of Materials Science and Engineering, Huazhong University of Science and Technology (HUST), Wuhan 430074, People's Republic of China
| | - Xing Zhou
- State Key Laboratory of Materials Processing and Die & Mould Technology, School of Materials Science and Engineering, Huazhong University of Science and Technology (HUST), Wuhan 430074, People's Republic of 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 (HUST), Wuhan 430074, People's Republic of China
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17
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Xu S, Momin M, Ahmed S, Hossain A, Veeramuthu L, Pandiyan A, Kuo CC, Zhou T. Illuminating the Brain: Advances and Perspectives in Optoelectronics for Neural Activity Monitoring and Modulation. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2023; 35:e2303267. [PMID: 37726261 DOI: 10.1002/adma.202303267] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/07/2023] [Revised: 05/30/2023] [Indexed: 09/21/2023]
Abstract
Optogenetic modulation of brain neural activity that combines optical and electrical modes in a unitary neural system has recently gained robust momentum. Controlling illumination spatial coverage, designing light-activated modulators, and developing wireless light delivery and data transmission are crucial for maximizing the use of optical neuromodulation. To this end, biocompatible electrodes with enhanced optoelectrical performance, device integration for multiplexed addressing, wireless transmission, and multimodal operation in soft systems have been developed. This review provides an outlook for uniformly illuminating large brain areas while spatiotemporally imaging the neural responses upon optoelectrical stimulation with little artifacts. Representative concepts and important breakthroughs, such as head-mounted illumination, multiple implanted optical fibers, and micro-light-delivery devices, are discussed. Examples of techniques that incorporate electrophysiological monitoring and optoelectrical stimulation are presented. Challenges and perspectives are posed for further research efforts toward high-density optoelectrical neural interface modulation, with the potential for nonpharmacological neurological disease treatments and wireless optoelectrical stimulation.
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Affiliation(s)
- Shumao Xu
- Department of Engineering Science and Mechanics, Center for Neural Engineering, The Pennsylvania State University, Pennsylvania, 16802, USA
| | - Marzia Momin
- Department of Engineering Science and Mechanics, Center for Neural Engineering, The Pennsylvania State University, Pennsylvania, 16802, USA
| | - Salahuddin Ahmed
- Department of Engineering Science and Mechanics, Center for Neural Engineering, The Pennsylvania State University, Pennsylvania, 16802, USA
| | - Arafat Hossain
- Department of Electrical Engineering, The Pennsylvania State University, Pennsylvania, 16802, USA
| | - Loganathan Veeramuthu
- Department of Molecular Science and Engineering, National Taipei University of Technology, Taipei, 10608, Republic of China
| | - Archana Pandiyan
- Department of Molecular Science and Engineering, National Taipei University of Technology, Taipei, 10608, Republic of China
| | - Chi-Ching Kuo
- Department of Molecular Science and Engineering, National Taipei University of Technology, Taipei, 10608, Republic of China
| | - Tao Zhou
- Department of Engineering Science and Mechanics, Center for Neural Engineering, The Pennsylvania State University, Pennsylvania, 16802, USA
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18
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Lee J, Jeong BH, Kamaraj E, Kim D, Kim H, Park S, Park HJ. Light-enhanced molecular polarity enabling multispectral color-cognitive memristor for neuromorphic visual system. Nat Commun 2023; 14:5775. [PMID: 37723149 PMCID: PMC10507016 DOI: 10.1038/s41467-023-41419-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2023] [Accepted: 09/01/2023] [Indexed: 09/20/2023] Open
Abstract
An optoelectronic synapse having a multispectral color-discriminating ability is an essential prerequisite to emulate the human retina for realizing a neuromorphic visual system. Several studies based on the three-terminal transistor architecture have shown its feasibility; however, its implementation with a two-terminal memristor architecture, advantageous to achieving high integration density as a simple crossbar array for an ultra-high-resolution vision chip, remains a challenge. Furthermore, regardless of the architecture, it requires specific material combinations to exhibit the photo-synaptic functionalities, and thus its integration into various systems is limited. Here, we suggest an approach that can universally introduce a color-discriminating synaptic functionality into a two-terminal memristor irrespective of the kinds of switching medium. This is possible by simply introducing the molecular interlayer with long-lasting photo-enhanced dipoles that can adjust the resistance of the memristor at the light-irradiation. We also propose the molecular design principle that can afford this feature. The optoelectronic synapse array having a color-discriminating functionality is confirmed to improve the inference accuracy of the convolutional neural network for the colorful image recognition tasks through a visual pre-processing. Additionally, the wavelength-dependent optoelectronic synapse can also be leveraged in the design of a light-programmable reservoir computing system.
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Affiliation(s)
- Jongmin Lee
- Department of Organic and Nano Engineering, Hanyang University, Seoul, 04763, Republic of Korea
- Human-Tech Convergence Program, Hanyang University, Seoul, 04763, Republic of Korea
| | - Bum Ho Jeong
- Department of Organic and Nano Engineering, Hanyang University, Seoul, 04763, Republic of Korea
- Human-Tech Convergence Program, Hanyang University, Seoul, 04763, Republic of Korea
| | - Eswaran Kamaraj
- Department of Chemistry, Kongju National University, Kongju, 32588, Republic of Korea
| | - Dohyung Kim
- Department of Organic and Nano Engineering, Hanyang University, Seoul, 04763, Republic of Korea
- Human-Tech Convergence Program, Hanyang University, Seoul, 04763, Republic of Korea
| | - Hakjun Kim
- Department of Organic and Nano Engineering, Hanyang University, Seoul, 04763, Republic of Korea
- Human-Tech Convergence Program, Hanyang University, Seoul, 04763, Republic of Korea
| | - Sanghyuk Park
- Department of Chemistry, Kongju National University, Kongju, 32588, Republic of Korea.
| | - Hui Joon Park
- Department of Organic and Nano Engineering, Hanyang University, Seoul, 04763, Republic of Korea.
- Human-Tech Convergence Program, Hanyang University, Seoul, 04763, Republic of Korea.
- Hanyang Institute of Smart Semiconductor, Seoul, 04763, Republic of Korea.
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19
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Kim H, Oh S, Choo H, Kang DH, Park JH. Tactile Neuromorphic System: Convergence of Triboelectric Polymer Sensor and Ferroelectric Polymer Synapse. ACS NANO 2023; 17:17332-17341. [PMID: 37611149 DOI: 10.1021/acsnano.3c05337] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/25/2023]
Abstract
Sensory neuromorphic systems are a promising technology, because they can replicate the way the human peripheral nervous system processes signals from the five sensory organs. Despite this potential, there are limited studies on how to implement these systems on a hardware neural network platform. In our research, we propose a tactile neuromorphic system that uses a poly(dimethylsiloxane) (PDMS)-based triboelectric sensor and a molybdenum disulfide (MoS2)/poly(vinylidene fluoride-trifluoro ethylene) (P(VDF-TrFE)) heterostructure-based ferroelectric synapse. The triboelectric sensor mimics a human tactile organ by converting tactile stimuli into electrical signals in real time. The ferroelectric synapse we developed demonstrates exceptional long-term potentiation/depression characteristics with a maximum dynamic range of 78 and a symmetrical value of 4.7. To assess the practicality of our proposed system, we conducted training and recognition simulations using Morse code alphabets and MNIST handwritten digits. The maximum recognition rate that we achieved was 96.17%.
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Affiliation(s)
- Hyeongjun Kim
- Department of Electrical and Computer Engineering, Sungkyunkwan University (SKKU), Suwon 16419, South Korea
| | - Seyong Oh
- Division of Electrical Engineering, Hanyang University ERICA, Ansan 15588, South Korea
| | - Hyongsuk Choo
- Department of Electrical and Computer Engineering, Sungkyunkwan University (SKKU), Suwon 16419, South Korea
| | - Dong-Ho Kang
- School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology (GIST), Gwangju 61005, South Korea
| | - Jin-Hong Park
- Department of Electrical and Computer Engineering, Sungkyunkwan University (SKKU), Suwon 16419, South Korea
- SKKU Advanced Institute of Nanotechnology (SAINT), Sungkyunkwan University (SKKU), Suwon 16417, Korea
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20
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Ra HS, Kim TW, Taylor DA, Lee JJ, Song S, Ahn J, Jang J, Taniguchi T, Watanabe K, Shim JW, Lee JS, Hwang DK. Probing Optical Multi-Level Memory Effects in Single Core-Shell Quantum Dots and Application Through 2D-0D Hybrid Inverters. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2023; 35:e2303664. [PMID: 37465946 DOI: 10.1002/adma.202303664] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Revised: 07/14/2023] [Accepted: 07/17/2023] [Indexed: 07/20/2023]
Abstract
Challenges in the development of a multi-level memory (MM) device for multinary arithmetic computers have posed an obstacle to low-power, ultra-high-speed operation. For the effective transfer of a huge amount of data between arithmetic and storage devices, optical communication technology represents a compelling solution. Here, by replicating a floating gate architecture with CdSe/ZnS type-I core/shell quantum dots (QDs), a 2D-0D hybrid optical multi-level memory (OMM) device operated is demonstrated by laser pulses. In the device, laser pulses create linear optically trapped currents with MM characteristics, while conversely, voltage pulses reset all the trapped currents at once. Assuming electron transfer via the energy band alignment between MoS2 and CdSe, the study also establishes the mechanism of the OMM effect. Analysis of the designed device led to a new hypothesis that charge transfer is difficult for laterally adjacent QDs facing a double ZnS shell, which is tested by separately stimulating different positions on the 2D-0D hybrid structure with finely focused laser pulses. Results indicate that each laser pulse induced independent MM characteristics in the 2D-0D hybrid architecture. Based on this phenomenon, we propose a MM inverter to produce MM effects, such as programming and erasing, solely through the use of laser pulses. Finally, the feasibility of a fully optically-controlled intelligent system based on the proposed OMM inverters is evaluated through a CIFAR-10 pattern recognition task using a convolutional neural network.
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Affiliation(s)
- Hyun-Soo Ra
- Center for Opto-Electronic Materials and Devices, Post-Silicon Semiconductor Institute, Korea Institute of Science and Technology (KIST), Seoul, 02792, Republic of Korea
| | - Tae Wook Kim
- Center for Opto-Electronic Materials and Devices, Post-Silicon Semiconductor Institute, Korea Institute of Science and Technology (KIST), Seoul, 02792, Republic of Korea
- School of Electrical Engineering, Korea University, Seoul, 02841, Republic of Korea
| | - Derrick Allan Taylor
- Department of Energy Science and Engineering, Daegu Gyeongbuk Institute of Science and Technology (DGIST), Daegu, 42988, Republic of Korea
| | - Je-Jun Lee
- Center for Spintronics, Korea Institute of Science and Technology (KIST), Seoul, 02792, Republic of Korea
| | - Seungho Song
- Center for Opto-Electronic Materials and Devices, Post-Silicon Semiconductor Institute, Korea Institute of Science and Technology (KIST), Seoul, 02792, Republic of Korea
| | - Jongtae Ahn
- Center for Opto-Electronic Materials and Devices, Post-Silicon Semiconductor Institute, Korea Institute of Science and Technology (KIST), Seoul, 02792, Republic of Korea
| | - Jisu Jang
- Center for Opto-Electronic Materials and Devices, Post-Silicon Semiconductor Institute, Korea Institute of Science and Technology (KIST), Seoul, 02792, Republic of Korea
| | - Takashi Taniguchi
- Advanced Materials Laboratory, National Institute for Materials Science, Tsukuba, 305-0044, Japan
| | - Kenji Watanabe
- Advanced Materials Laboratory, National Institute for Materials Science, Tsukuba, 305-0044, Japan
| | - Jae Won Shim
- School of Electrical Engineering, Korea University, Seoul, 02841, Republic of Korea
| | - Jong-Soo Lee
- Department of Energy Science and Engineering, Daegu Gyeongbuk Institute of Science and Technology (DGIST), Daegu, 42988, Republic of Korea
| | - Do Kyung Hwang
- Center for Opto-Electronic Materials and Devices, Post-Silicon Semiconductor Institute, Korea Institute of Science and Technology (KIST), Seoul, 02792, Republic of Korea
- Division of Nanoscience & Technology, KIST School, University of Science and Technology (UST), Seoul, 02792, Republic of Korea
- KU-KIST Graduate School of Converging Science and Technology, Korea University, Seoul, 02841, Republic of Korea
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21
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Bae S, Jeong TY, Raebiger H, Yee KJ, Kim YH. Localized coherent phonon generation in monolayer MoSe 2 from ultrafast exciton trapping at shallow traps. NANOSCALE HORIZONS 2023; 8:1282-1287. [PMID: 37470115 DOI: 10.1039/d3nh00194f] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/21/2023]
Abstract
We report spectroscopic evidence for the ultrafast trapping of band edge excitons at defects and the subsequent generation of defect-localized coherent phonons (CPs) in monolayer MoSe2. While the photoluminescence measurement provides signals of exciton recombination at both shallow and deep traps, our time-resolved pump-probe spectroscopy on the sub-picosecond time scale detects localized CPs only from the ultrafast exciton trapping at shallow traps. Based on occupation-constrained density functional calculations, we identify the Se vacancy and the oxygen molecule adsorbed on a Se vacancy as the atomistic origins of deep and shallow traps, respectively. Establishing the correlations between the defect-induced ultrafast exciton trapping and the generation of defect-localized CPs, our work could open up new avenues to engineer photoexcited carriers through lattice defects in two-dimensional materials.
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Affiliation(s)
- Soungmin Bae
- Department of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Korea.
- Department of Physics, Yokohama National University, Yokohama, Japan
| | - Tae Young Jeong
- Department of Physics, Chungnam National University, Daejeon 34134, Korea.
| | - Hannes Raebiger
- Department of Physics, Yokohama National University, Yokohama, Japan
| | - Ki-Ju Yee
- Department of Physics, Chungnam National University, Daejeon 34134, Korea.
| | - Yong-Hoon Kim
- Department of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Korea.
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22
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Cao Z, Sun B, Zhou G, Mao S, Zhu S, Zhang J, Ke C, Zhao Y, Shao J. Memristor-based neural networks: a bridge from device to artificial intelligence. NANOSCALE HORIZONS 2023; 8:716-745. [PMID: 36946082 DOI: 10.1039/d2nh00536k] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/31/2023]
Abstract
Since the beginning of the 21st century, there is no doubt that the importance of artificial intelligence has been highlighted in many fields, among which the memristor-based artificial neural network technology is expected to break through the limitation of von Neumann so as to realize the replication of the human brain by enabling strong parallel computing ability and efficient data processing and become an important way towards the next generation of artificial intelligence. A new type of nanodevice, namely memristor, which is based on the variability of its resistance value, not only has very important applications in nonvolatile information storage, but also presents obsessive progressiveness in highly integrated circuits, making it one of the most promising circuit components in the post-Moore era. In particular, memristors can effectively simulate neural synapses and build neural networks; thus, they can be applied for the preparation of various artificial intelligence systems. This study reviews the research progress of memristors in artificial neural networks in detail and highlights the structural advantages and frontier applications of neural networks based on memristors. Finally, some urgent problems and challenges in current research are summarized and corresponding solutions and future development trends are put forward.
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Affiliation(s)
- Zelin Cao
- Frontier Institute of Science and Technology (FIST), Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China.
- Shaanxi International Joint Research Center for Applied Technology of Controllable Neutron Source, School of Science, Xijing University, Xi'an 710123, China
| | - Bai Sun
- Frontier Institute of Science and Technology (FIST), Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China.
| | - Guangdong Zhou
- College of Artificial Intelligence, Brain-inspired Computing & Intelligent Control of Chongqing Key Lab, Southwest University, Chongqing 400715, China
| | - Shuangsuo Mao
- Fujian Provincial Collaborative Innovation Center for Advanced High-Field Superconducting Materials and Engineering, Fujian Normal University, Fuzhou, Fujian 350117, China
| | - Shouhui Zhu
- School of Physical Science and Technology, Key Laboratory of Advanced Technology of Materials, Southwest Jiaotong University, Chengdu, Sichuan 610031, China
| | - Jie Zhang
- School of Electrical Engineering, Southwest Jiaotong University, Chengdu, Sichuan 610031, China
| | - Chuan Ke
- School of Electrical Engineering, Southwest Jiaotong University, Chengdu, Sichuan 610031, China
| | - Yong Zhao
- Fujian Provincial Collaborative Innovation Center for Advanced High-Field Superconducting Materials and Engineering, Fujian Normal University, Fuzhou, Fujian 350117, China
- School of Physical Science and Technology, Key Laboratory of Advanced Technology of Materials, Southwest Jiaotong University, Chengdu, Sichuan 610031, China
- School of Electrical Engineering, Southwest Jiaotong University, Chengdu, Sichuan 610031, China
| | - Jinyou Shao
- Frontier Institute of Science and Technology (FIST), Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China.
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23
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Liu X, Wang S, Di Z, Wu H, Liu C, Zhou P. An Optoelectronic Synapse Based on Two-Dimensional Violet Phosphorus Heterostructure. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2023:e2301851. [PMID: 37229772 PMCID: PMC10401094 DOI: 10.1002/advs.202301851] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Revised: 04/26/2023] [Indexed: 05/27/2023]
Abstract
Neuromorphic computing can efficiently handle data-intensive tasks and address the redundant interaction required by von Neumann architectures. Synaptic devices are essential components for neuromorphic computation. 2D phosphorene, such as violet phosphorene, show great potential in optoelectronics due to their strong light-matter interactions, while current research is mainly focused on synthesis and characterization, its application in photoelectric devices is vacant. Here, the authors combined violet phosphorene and molybdenum disulfide to demonstrate an optoelectronic synapse with a light-to-dark ratio of 106 , benefiting from a significant threshold shift due to charge transfer and trapping in the heterostructure. Remarkable synaptic properties are demonstrated, including a dynamic range (DR) of > 60 dB, 128 (7-bit) distinguishable conductance states, electro-optical dependent plasticity, short-term paired-pulse facilitation, and long-term potentiation/depression. Thanks to the excellent DR and multi-states, high-precision image classification with accuracies of 95.23% and 79.65% is achieved for the MNIST and complex Fashion-MNIST datasets, which is close to the ideal device (95.47%, 79.95%). This work opens the way for the use of emerging phosphorene in optoelectronics and provides a new strategy for building synaptic devices for high-precision neuromorphic computing.
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Affiliation(s)
- Xiaoxian Liu
- Shanghai Key Lab for Future Computing Hardware and System, School of Microelectronics, Fudan University, Shanghai, 200433, China
| | - Shuiyuan Wang
- Shanghai Key Lab for Future Computing Hardware and System, School of Microelectronics, Fudan University, Shanghai, 200433, China
| | - Ziye Di
- Shanghai Key Lab for Future Computing Hardware and System, School of Microelectronics, Fudan University, Shanghai, 200433, China
| | - Haoqi Wu
- Shanghai Key Lab for Future Computing Hardware and System, School of Microelectronics, Fudan University, Shanghai, 200433, China
| | - Chunsen Liu
- Frontier Institute of Chip and System & Qizhi Institute, Fudan University, Shanghai, 200433, China
| | - Peng Zhou
- Shanghai Key Lab for Future Computing Hardware and System, School of Microelectronics, Fudan University, Shanghai, 200433, China
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24
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Seok H, Son S, Jathar SB, Lee J, Kim T. Synapse-Mimetic Hardware-Implemented Resistive Random-Access Memory for Artificial Neural Network. SENSORS (BASEL, SWITZERLAND) 2023; 23:3118. [PMID: 36991829 PMCID: PMC10058286 DOI: 10.3390/s23063118] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Revised: 03/11/2023] [Accepted: 03/13/2023] [Indexed: 06/19/2023]
Abstract
Memristors mimic synaptic functions in advanced electronics and image sensors, thereby enabling brain-inspired neuromorphic computing to overcome the limitations of the von Neumann architecture. As computing operations based on von Neumann hardware rely on continuous memory transport between processing units and memory, fundamental limitations arise in terms of power consumption and integration density. In biological synapses, chemical stimulation induces information transfer from the pre- to the post-neuron. The memristor operates as resistive random-access memory (RRAM) and is incorporated into the hardware for neuromorphic computing. Hardware composed of synaptic memristor arrays is expected to lead to further breakthroughs owing to their biomimetic in-memory processing capabilities, low power consumption, and amenability to integration; these aspects satisfy the upcoming demands of artificial intelligence for higher computational loads. Among the tremendous efforts toward achieving human-brain-like electronics, layered 2D materials have demonstrated significant potential owing to their outstanding electronic and physical properties, facile integration with other materials, and low-power computing. This review discusses the memristive characteristics of various 2D materials (heterostructures, defect-engineered materials, and alloy materials) used in neuromorphic computing for image segregation or pattern recognition. Neuromorphic computing, the most powerful artificial networks for complicated image processing and recognition, represent a breakthrough in artificial intelligence owing to their enhanced performance and lower power consumption compared with von Neumann architectures. A hardware-implemented CNN with weight control based on synaptic memristor arrays is expected to be a promising candidate for future electronics in society, offering a solution based on non-von Neumann hardware. This emerging paradigm changes the computing algorithm using entirely hardware-connected edge computing and deep neural networks.
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Affiliation(s)
- Hyunho Seok
- SKKU Advanced Institute of Nanotechnology (SAINT), Sungkyunkwan University, Suwon 16419, Republic of Korea
- Department of Nano Science and Technology, Sungkyunkwan University, Suwon 16419, Republic of Korea
| | - Shihoon Son
- SKKU Advanced Institute of Nanotechnology (SAINT), Sungkyunkwan University, Suwon 16419, Republic of Korea
- Department of Nano Science and Technology, Sungkyunkwan University, Suwon 16419, Republic of Korea
| | - Sagar Bhaurao Jathar
- SKKU Advanced Institute of Nanotechnology (SAINT), Sungkyunkwan University, Suwon 16419, Republic of Korea
- Department of Nano Science and Technology, Sungkyunkwan University, Suwon 16419, Republic of Korea
| | - Jaewon Lee
- School of Mechanical Engineering, Sungkyunkwan University, Suwon 16419, Republic of Korea
| | - Taesung Kim
- SKKU Advanced Institute of Nanotechnology (SAINT), Sungkyunkwan University, Suwon 16419, Republic of Korea
- Department of Nano Science and Technology, Sungkyunkwan University, Suwon 16419, Republic of Korea
- School of Mechanical Engineering, Sungkyunkwan University, Suwon 16419, Republic of Korea
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25
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Chen H, Li H, Ma T, Han S, Zhao Q. Biological function simulation in neuromorphic devices: from synapse and neuron to behavior. SCIENCE AND TECHNOLOGY OF ADVANCED MATERIALS 2023; 24:2183712. [PMID: 36926202 PMCID: PMC10013381 DOI: 10.1080/14686996.2023.2183712] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/27/2022] [Revised: 02/06/2023] [Accepted: 02/11/2023] [Indexed: 06/18/2023]
Abstract
As the boom of data storage and processing, brain-inspired computing provides an effective approach to solve the current problem. Various emerging materials and devices have been reported to promote the development of neuromorphic computing. Thereinto, the neuromorphic device represented by memristor has attracted extensive research due to its outstanding property to emulate the brain's functions from synaptic plasticity, sensory-memory neurons to some intelligent behaviors of living creatures. Herein, we mainly review the progress of these brain functions mimicked by neuromorphic devices, concentrating on synapse (i.e. various synaptic plasticity trigger by electricity and/or light), neurons (including the various sensory nervous system) and intelligent behaviors (such as conditioned reflex represented by Pavlov's dog experiment). Finally, some challenges and prospects related to neuromorphic devices are presented.
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Affiliation(s)
- Hui Chen
- Heart Center of Henan Provincial People’s Hospital, Central China Fuwai Hospital, Central China Fuwai Hospital of Zhengzhou University, Zhengzhou, P. R. China
| | - Huilin Li
- Henan Key Laboratory of Photovoltaic Materials, Henan University, Kaifeng, P. R. China
| | - Ting Ma
- Henan Key Laboratory of Photovoltaic Materials, Henan University, Kaifeng, P. R. China
| | - Shuangshuang Han
- Henan Key Laboratory of Photovoltaic Materials, Henan University, Kaifeng, P. R. China
| | - Qiuping Zhao
- Heart Center of Henan Provincial People’s Hospital, Central China Fuwai Hospital, Central China Fuwai Hospital of Zhengzhou University, Zhengzhou, P. R. China
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26
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Xiao Y, Xiong C, Chen MM, Wang S, Fu L, Zhang X. Structure modulation of two-dimensional transition metal chalcogenides: recent advances in methodology, mechanism and applications. Chem Soc Rev 2023; 52:1215-1272. [PMID: 36601686 DOI: 10.1039/d1cs01016f] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
Together with the development of two-dimensional (2D) materials, transition metal dichalcogenides (TMDs) have become one of the most popular series of model materials for fundamental sciences and practical applications. Due to the ever-growing requirements of customization and multi-function, dozens of modulated structures have been introduced in TMDs. In this review, we present a systematic and comprehensive overview of the structure modulation of TMDs, including point, linear and out-of-plane structures, following and updating the conventional classification for silicon and related bulk semiconductors. In particular, we focus on the structural characteristics of modulated TMD structures and analyse the corresponding root causes. We also summarize the recent progress in modulating methods, mechanisms, properties and applications based on modulated TMD structures. Finally, we demonstrate challenges and prospects in the structure modulation of TMDs and forecast potential directions about what and how breakthroughs can be achieved.
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Affiliation(s)
- Yao Xiao
- Collaborative Innovation Centre for Advanced Organic Chemical Materials Co-Constructed by the Province and Ministry, Ministry of Education Key Laboratory for the Synthesis and Application of Organic Functional Molecules, College of Chemistry and Chemical Engineering, Hubei University, Wuhan 430062, P. R. China.
| | - Chengyi Xiong
- Collaborative Innovation Centre for Advanced Organic Chemical Materials Co-Constructed by the Province and Ministry, Ministry of Education Key Laboratory for the Synthesis and Application of Organic Functional Molecules, College of Chemistry and Chemical Engineering, Hubei University, Wuhan 430062, P. R. China.
| | - Miao-Miao Chen
- Collaborative Innovation Centre for Advanced Organic Chemical Materials Co-Constructed by the Province and Ministry, Ministry of Education Key Laboratory for the Synthesis and Application of Organic Functional Molecules, College of Chemistry and Chemical Engineering, Hubei University, Wuhan 430062, P. R. China.
| | - Shengfu Wang
- Collaborative Innovation Centre for Advanced Organic Chemical Materials Co-Constructed by the Province and Ministry, Ministry of Education Key Laboratory for the Synthesis and Application of Organic Functional Molecules, College of Chemistry and Chemical Engineering, Hubei University, Wuhan 430062, P. R. China.
| | - Lei Fu
- The Institute for Advanced Studies (IAS), Wuhan University, Wuhan 430072, P. R. China. .,College of Chemistry and Molecular Sciences, Wuhan University, Wuhan 430072, P. R. China.
| | - Xiuhua Zhang
- Collaborative Innovation Centre for Advanced Organic Chemical Materials Co-Constructed by the Province and Ministry, Ministry of Education Key Laboratory for the Synthesis and Application of Organic Functional Molecules, College of Chemistry and Chemical Engineering, Hubei University, Wuhan 430062, P. R. China.
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27
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Dong X, Li S, Sun H, Jian L, Wei W, Chen J, Zhao Y, Chen J, Zhang X, Li Y. Optoelectronic Memristive Synapse Behavior for the Architecture of Cu 2ZnSnS 4@BiOBr Embedded in Poly(methyl methacrylate). J Phys Chem Lett 2023; 14:1512-1520. [PMID: 36745109 DOI: 10.1021/acs.jpclett.2c03939] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
The great potential of artificial optoelectronic devices that are capable of mimicking biosynapse functions in brain-like neuromorphic computing applications has aroused extensive interest, and the architecture design is decisive yet challenging. Herein, a new architecture of p-type Cu2ZnSnS4@BiOBr nanosheets embedded in poly(methyl methacrylate) (PMMA) films (CZTS@BOB-PMMA) is presented acting as a switching layer, which not only shows the bipolar resistive switching features (SET/RESET voltages, ∼ -0.93/+1.35 V; retention, >104 s) and electrical- and near-infrared light-induced synapse plasticity but also demonstrates electrical-driven excitatory postsynaptic current, spiking-time-dependent plasticity, paired pulse facilitation, long-term plasticity, long- and short-term memory, and "learning-forgetting-learning" behaviors. The approach is a rewarding attempt to broaden the research of optoelectric controllable memristive devices for building neuromorphic architectures mimicking human brain functionalities.
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Affiliation(s)
- Xiaofei Dong
- Key Laboratory of Atomic and Molecular Physics & Functional Materials of Gansu Province, College of Physics and Electronic Engineering, Northwest Normal University, Lanzhou730070, China
| | - Siyuan Li
- Key Laboratory of Atomic and Molecular Physics & Functional Materials of Gansu Province, College of Physics and Electronic Engineering, Northwest Normal University, Lanzhou730070, China
| | - Hao Sun
- Key Laboratory of Atomic and Molecular Physics & Functional Materials of Gansu Province, College of Physics and Electronic Engineering, Northwest Normal University, Lanzhou730070, China
| | - Lijuan Jian
- Key Laboratory of Atomic and Molecular Physics & Functional Materials of Gansu Province, College of Physics and Electronic Engineering, Northwest Normal University, Lanzhou730070, China
| | - Wenbin Wei
- Key Laboratory of Atomic and Molecular Physics & Functional Materials of Gansu Province, College of Physics and Electronic Engineering, Northwest Normal University, Lanzhou730070, China
| | - Jianbiao Chen
- Key Laboratory of Atomic and Molecular Physics & Functional Materials of Gansu Province, College of Physics and Electronic Engineering, Northwest Normal University, Lanzhou730070, China
| | - Yun Zhao
- Key Laboratory of Atomic and Molecular Physics & Functional Materials of Gansu Province, College of Physics and Electronic Engineering, Northwest Normal University, Lanzhou730070, China
| | - Jiangtao Chen
- Key Laboratory of Atomic and Molecular Physics & Functional Materials of Gansu Province, College of Physics and Electronic Engineering, Northwest Normal University, Lanzhou730070, China
| | - Xuqiang Zhang
- Key Laboratory of Atomic and Molecular Physics & Functional Materials of Gansu Province, College of Physics and Electronic Engineering, Northwest Normal University, Lanzhou730070, China
| | - Yan Li
- Key Laboratory of Atomic and Molecular Physics & Functional Materials of Gansu Province, College of Physics and Electronic Engineering, Northwest Normal University, Lanzhou730070, China
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28
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Zhu C, Liu H, Wang W, Xiang L, Jiang J, Shuai Q, Yang X, Zhang T, Zheng B, Wang H, Li D, Pan A. Optical synaptic devices with ultra-low power consumption for neuromorphic computing. LIGHT, SCIENCE & APPLICATIONS 2022; 11:337. [PMID: 36443284 PMCID: PMC9705294 DOI: 10.1038/s41377-022-01031-z] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Revised: 10/31/2022] [Accepted: 11/01/2022] [Indexed: 05/31/2023]
Abstract
Brain-inspired neuromorphic computing, featured by parallel computing, is considered as one of the most energy-efficient and time-saving architectures for massive data computing. However, photonic synapse, one of the key components, is still suffering high power consumption, potentially limiting its applications in artificial neural system. In this study, we present a BP/CdS heterostructure-based artificial photonic synapse with ultra-low power consumption. The device shows remarkable negative light response with maximum responsivity up to 4.1 × 108 A W-1 at VD = 0.5 V and light power intensity of 0.16 μW cm-2 (1.78 × 108 A W-1 on average), which further enables artificial synaptic applications with average power consumption as low as 4.78 fJ for each training process, representing the lowest among the reported results. Finally, a fully-connected optoelectronic neural network (FONN) is simulated with maximum image recognition accuracy up to 94.1%. This study provides new concept towards the designing of energy-efficient artificial photonic synapse and shows great potential in high-performance neuromorphic vision systems.
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Affiliation(s)
- Chenguang Zhu
- Key Laboratory for Micro-Nano Physics and Technology of Hunan Province, State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Materials Science and Engineering, Hunan University, 410082, Changsha, China
- Hunan Institute of Optoelectronic Integration, Hunan University, 410082, Changsha, China
| | - Huawei Liu
- Key Laboratory for Micro-Nano Physics and Technology of Hunan Province, State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Materials Science and Engineering, Hunan University, 410082, Changsha, China
- Hunan Institute of Optoelectronic Integration, Hunan University, 410082, Changsha, China
| | - Wenqiang Wang
- Key Laboratory for Micro-Nano Physics and Technology of Hunan Province, State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Materials Science and Engineering, Hunan University, 410082, Changsha, China
- Hunan Institute of Optoelectronic Integration, Hunan University, 410082, Changsha, China
| | - Li Xiang
- Key Laboratory for Micro-Nano Physics and Technology of Hunan Province, State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Materials Science and Engineering, Hunan University, 410082, Changsha, China.
- Hunan Institute of Optoelectronic Integration, Hunan University, 410082, Changsha, China.
| | - Jie Jiang
- School of Physics and Electronics, Central South University, 410083, Changsha, China
| | - Qin Shuai
- Key Laboratory for Micro-Nano Physics and Technology of Hunan Province, State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Materials Science and Engineering, Hunan University, 410082, Changsha, China
- Hunan Institute of Optoelectronic Integration, Hunan University, 410082, Changsha, China
| | - Xin Yang
- Key Laboratory for Micro-Nano Physics and Technology of Hunan Province, State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Materials Science and Engineering, Hunan University, 410082, Changsha, China
- Hunan Institute of Optoelectronic Integration, Hunan University, 410082, Changsha, China
| | - Tian Zhang
- Key Laboratory for Micro-Nano Physics and Technology of Hunan Province, State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Materials Science and Engineering, Hunan University, 410082, Changsha, China
- Hunan Institute of Optoelectronic Integration, Hunan University, 410082, Changsha, China
| | - Biyuan Zheng
- Key Laboratory for Micro-Nano Physics and Technology of Hunan Province, State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Materials Science and Engineering, Hunan University, 410082, Changsha, China
- Hunan Institute of Optoelectronic Integration, Hunan University, 410082, Changsha, China
| | - Hui Wang
- Key Laboratory for Micro-Nano Physics and Technology of Hunan Province, State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Materials Science and Engineering, Hunan University, 410082, Changsha, China
- Hunan Institute of Optoelectronic Integration, Hunan University, 410082, Changsha, China
| | - Dong Li
- Key Laboratory for Micro-Nano Physics and Technology of Hunan Province, State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Materials Science and Engineering, Hunan University, 410082, Changsha, China.
- Hunan Institute of Optoelectronic Integration, Hunan University, 410082, Changsha, China.
| | - Anlian Pan
- Key Laboratory for Micro-Nano Physics and Technology of Hunan Province, State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Materials Science and Engineering, Hunan University, 410082, Changsha, China.
- Hunan Institute of Optoelectronic Integration, Hunan University, 410082, Changsha, China.
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Zhang X, Zhang Y, Yu H, Zhao H, Cao Z, Zhang Z, Zhang Y. Van der Waals-Interface-Dominated All-2D Electronics. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2022:e2207966. [PMID: 36353883 DOI: 10.1002/adma.202207966] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Revised: 11/06/2022] [Indexed: 06/16/2023]
Abstract
The interface is the device. As the feature size rapidly shrinks, silicon-based electronic devices are facing multiple challenges of material performance decrease and interface quality degradation. Ultrathin 2D materials are considered as potential candidates in future electronics by their atomically flat surfaces and excellent immunity to short-channel effects. Moreover, due to naturally terminated surfaces and weak van der Waals (vdW) interactions between layers, 2D materials can be freely stacked without the lattice matching limit to form high-quality heterostructure interfaces with arbitrary components and twist angles. Controlled interlayer band alignment and optimized interfacial carrier behavior allow all-2D electronics based on 2D vdW interfaces to exhibit more comprehensive functionality and better performance. Especially, achieving the same computing capacity of multiple conventional devices with small footprint all-2D devices is considered to be the key development direction of future electronics. Herein, the unique properties of all-2D vdW interfaces and their construction methods are systematically reviewed and the main performance contributions of different vdW interfaces in 2D electronics are summarized, respectively. Finally, the recent progress and challenges for all-2D vdW electronics are discussed, and how to improve the compatibility of 2D material devices with silicon-based industrial technology is pointed out as a critical challenge.
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Affiliation(s)
- Xiankun Zhang
- Academy for Advanced Interdisciplinary Science and Technology, Beijing Advanced Innovation Center for Materials Genome Engineering, University of Science and Technology Beijing, Beijing, 100083, P. R. China
- Beijing Key Laboratory for Advanced Energy Materials and Technologies, School of Materials Science and Engineering, University of Science and Technology Beijing, Beijing, 100083, P. R. China
| | - Yanzhe Zhang
- Academy for Advanced Interdisciplinary Science and Technology, Beijing Advanced Innovation Center for Materials Genome Engineering, University of Science and Technology Beijing, Beijing, 100083, P. R. China
- Beijing Key Laboratory for Advanced Energy Materials and Technologies, School of Materials Science and Engineering, University of Science and Technology Beijing, Beijing, 100083, P. R. China
| | - Huihui Yu
- Academy for Advanced Interdisciplinary Science and Technology, Beijing Advanced Innovation Center for Materials Genome Engineering, University of Science and Technology Beijing, Beijing, 100083, P. R. China
- Beijing Key Laboratory for Advanced Energy Materials and Technologies, School of Materials Science and Engineering, University of Science and Technology Beijing, Beijing, 100083, P. R. China
| | - Hang Zhao
- Academy for Advanced Interdisciplinary Science and Technology, Beijing Advanced Innovation Center for Materials Genome Engineering, University of Science and Technology Beijing, Beijing, 100083, P. R. China
- Beijing Key Laboratory for Advanced Energy Materials and Technologies, School of Materials Science and Engineering, University of Science and Technology Beijing, Beijing, 100083, P. R. China
| | - Zhihong Cao
- Academy for Advanced Interdisciplinary Science and Technology, Beijing Advanced Innovation Center for Materials Genome Engineering, University of Science and Technology Beijing, Beijing, 100083, P. R. China
- Beijing Key Laboratory for Advanced Energy Materials and Technologies, School of Materials Science and Engineering, University of Science and Technology Beijing, Beijing, 100083, P. R. China
| | - Zheng Zhang
- Academy for Advanced Interdisciplinary Science and Technology, Beijing Advanced Innovation Center for Materials Genome Engineering, University of Science and Technology Beijing, Beijing, 100083, P. R. China
- Beijing Key Laboratory for Advanced Energy Materials and Technologies, School of Materials Science and Engineering, University of Science and Technology Beijing, Beijing, 100083, P. R. China
| | - Yue Zhang
- Academy for Advanced Interdisciplinary Science and Technology, Beijing Advanced Innovation Center for Materials Genome Engineering, University of Science and Technology Beijing, Beijing, 100083, P. R. China
- Beijing Key Laboratory for Advanced Energy Materials and Technologies, School of Materials Science and Engineering, University of Science and Technology Beijing, Beijing, 100083, P. R. China
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30
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Seo S, Cho JI, Jung KS, Andreev M, Lee JH, Ahn H, Jung S, Lee T, Kim B, Lee S, Kang J, Lee KB, Lee HJ, Kim KS, Yeom GY, Heo K, Park JH. A Van Der Waals Reconfigurable Multi-Valued Logic Device and Circuit Based on Tunable Negative-Differential-Resistance Phenomenon. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2022; 34:e2202799. [PMID: 35857340 DOI: 10.1002/adma.202202799] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/26/2022] [Revised: 06/26/2022] [Indexed: 06/15/2023]
Abstract
Multi-valued logic (MVL) technology that utilizes more than two logic states has recently been reconsidered because of the demand for greater power saving in current binary logic systems. Extensive efforts have been invested in developing MVL devices with multiple threshold voltages by adopting negative differential transconductance and resistance. In this study, a reconfigurable, multiple negative-differential-resistance (m-NDR) device with an electric-field-induced tunability of multiple threshold voltages is reported, which comprises a BP/ReS2 heterojunction and a ReS2 /h-BN/metal capacitor. Tunability for the m-NDR phenomenon is achieved via the resistance modulation of the ReS2 layer by electrical pulses applied to the capacitor region. Reconfigurability is verified in terms of the function of an MVL circuit composed of a reconfigurable m-NDR device and a load transistor, wherein staggered-type and broken-type double peak-NDR device operations are adopted for ternary inverter and latch circuits, respectively.
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Affiliation(s)
- Seunghwan Seo
- Department of Electrical and Computer Engineering, Sungkyunkwan University (SKKU), Suwon, 16419, Korea
| | - Jeong-Ick Cho
- Department of Electrical and Computer Engineering, Sungkyunkwan University (SKKU), Suwon, 16419, Korea
| | - Kil-Su Jung
- Department of Semiconductor and Display Engineering, Sungkyunkwan University, Suwon, 16417, Korea
- Memory Technology Design Team, Samsung Electronics Co. Ltd, Hwasung, 18448, Korea
| | - Maksim Andreev
- Department of Electrical and Computer Engineering, Sungkyunkwan University (SKKU), Suwon, 16419, Korea
| | - Ju-Hee Lee
- Department of Electrical and Computer Engineering, Sungkyunkwan University (SKKU), Suwon, 16419, Korea
| | - Hogeun Ahn
- Department of Electrical and Computer Engineering, Sungkyunkwan University (SKKU), Suwon, 16419, Korea
| | - Sooyoung Jung
- Department of Electrical and Computer Engineering, Sungkyunkwan University (SKKU), Suwon, 16419, Korea
- Research Laboratory of Electronics, Massachusetts Institute of Technology (MIT), Cambridge, MA, 02139, USA
| | - Taeran Lee
- Department of Electrical and Computer Engineering, Sungkyunkwan University (SKKU), Suwon, 16419, Korea
| | - Byeongchan Kim
- Department of Electrical and Computer Engineering, Sungkyunkwan University (SKKU), Suwon, 16419, Korea
| | - Seojoo Lee
- Department of Electrical and Computer Engineering, Sungkyunkwan University (SKKU), Suwon, 16419, Korea
| | - Juncheol Kang
- Department of Electrical and Computer Engineering, Sungkyunkwan University (SKKU), Suwon, 16419, Korea
| | - Kyeong-Bae Lee
- Department of Electrical and Computer Engineering, Sungkyunkwan University (SKKU), Suwon, 16419, Korea
| | - Ho-Jun Lee
- Department of Electrical and Computer Engineering, Sungkyunkwan University (SKKU), Suwon, 16419, Korea
| | - Ki Seok Kim
- School of Advanced Materials Science and Engineering, Sungkyunkwan University, Suwon, 16417, Korea
- School of Semiconductor Science & Technology, Jeonbuk National University, Jeonju, 54896, Korea
| | - Geun Young Yeom
- School of Semiconductor Science & Technology, Jeonbuk National University, Jeonju, 54896, Korea
| | - Keun Heo
- Sungkyunkwan Advanced Institute of Nanotechnology (SAINT), Sungkyunkwan University, Suwon, 16417, Korea
| | - Jin-Hong Park
- Department of Electrical and Computer Engineering, Sungkyunkwan University (SKKU), Suwon, 16419, Korea
- Sungkyunkwan Advanced Institute of Nanotechnology (SAINT), Sungkyunkwan University, Suwon, 16417, Korea
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31
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Pham PV, Bodepudi SC, Shehzad K, Liu Y, Xu Y, Yu B, Duan X. 2D Heterostructures for Ubiquitous Electronics and Optoelectronics: Principles, Opportunities, and Challenges. Chem Rev 2022; 122:6514-6613. [PMID: 35133801 DOI: 10.1021/acs.chemrev.1c00735] [Citation(s) in RCA: 102] [Impact Index Per Article: 51.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
A grand family of two-dimensional (2D) materials and their heterostructures have been discovered through the extensive experimental and theoretical efforts of chemists, material scientists, physicists, and technologists. These pioneering works contribute to realizing the fundamental platforms to explore and analyze new physical/chemical properties and technological phenomena at the micro-nano-pico scales. Engineering 2D van der Waals (vdW) materials and their heterostructures via chemical and physical methods with a suitable choice of stacking order, thickness, and interlayer interactions enable exotic carrier dynamics, showing potential in high-frequency electronics, broadband optoelectronics, low-power neuromorphic computing, and ubiquitous electronics. This comprehensive review addresses recent advances in terms of representative 2D materials, the general fabrication methods, and characterization techniques and the vital role of the physical parameters affecting the quality of 2D heterostructures. The main emphasis is on 2D heterostructures and 3D-bulk (3D) hybrid systems exhibiting intrinsic quantum mechanical responses in the optical, valley, and topological states. Finally, we discuss the universality of 2D heterostructures with representative applications and trends for future electronics and optoelectronics (FEO) under the challenges and opportunities from physical, nanotechnological, and material synthesis perspectives.
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Affiliation(s)
- Phuong V Pham
- School of Micro-Nano Electronics, Hangzhou Global Scientific and Technological Innovation Center (HIC), Zhejiang University, Xiaoshan 311200, China.,State Key Laboratory of Silicon Materials, Zhejiang University, Hangzhou 310027, China.,ZJU-UIUC Joint Institute, Zhejiang University, Jiaxing 314400, China
| | - Srikrishna Chanakya Bodepudi
- School of Micro-Nano Electronics, Hangzhou Global Scientific and Technological Innovation Center (HIC), Zhejiang University, Xiaoshan 311200, China.,State Key Laboratory of Silicon Materials, Zhejiang University, Hangzhou 310027, China.,ZJU-UIUC Joint Institute, Zhejiang University, Jiaxing 314400, China
| | - Khurram Shehzad
- School of Micro-Nano Electronics, Hangzhou Global Scientific and Technological Innovation Center (HIC), Zhejiang University, Xiaoshan 311200, China.,State Key Laboratory of Silicon Materials, Zhejiang University, Hangzhou 310027, China.,ZJU-UIUC Joint Institute, Zhejiang University, Jiaxing 314400, China
| | - Yuan Liu
- School of Physics and Electronics, Hunan University, Hunan 410082, China
| | - Yang Xu
- School of Micro-Nano Electronics, Hangzhou Global Scientific and Technological Innovation Center (HIC), Zhejiang University, Xiaoshan 311200, China.,State Key Laboratory of Silicon Materials, Zhejiang University, Hangzhou 310027, China.,ZJU-UIUC Joint Institute, Zhejiang University, Jiaxing 314400, China
| | - Bin Yu
- School of Micro-Nano Electronics, Hangzhou Global Scientific and Technological Innovation Center (HIC), Zhejiang University, Xiaoshan 311200, China.,State Key Laboratory of Silicon Materials, Zhejiang University, Hangzhou 310027, China.,ZJU-UIUC Joint Institute, Zhejiang University, Jiaxing 314400, China
| | - Xiangfeng Duan
- Department of Chemistry and Biochemistry, University of California, Los Angeles (UCLA), Los Angeles, California 90095-1569, United States
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32
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Oh S, Lee JH, Seo S, Choo H, Lee D, Cho JI, Park JH. Electrolyte-Gated Vertical Synapse Array based on Van Der Waals Heterostructure for Parallel Computing. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2022; 9:e2103808. [PMID: 34957687 PMCID: PMC8867203 DOI: 10.1002/advs.202103808] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Revised: 11/11/2021] [Indexed: 06/01/2023]
Abstract
Recently, three-terminal synaptic devices, which separate read and write terminals, have attracted significant attention because they enable nondestructive read-out and parallel-access for updating synaptic weights. However, owing to their structural features, it is difficult to address the relatively high device density compared with two-terminal synaptic devices. In this study, a vertical synaptic device featuring remotely controllable weight updates via e-field-dependent movement of mobile ions in the ion-gel layer is developed. This synaptic device successfully demonstrates all essential synaptic characteristics, such as excitatory/inhibitory postsynaptic current (E/IPSC), paired-pulse facilitation (PPF), and long-term potentiation/depression (LTP/D) by electrical measurements, and exhibits competitive LTP/D characteristics with a dynamic range (Gmax /Gmin ) of 31.3, and asymmetry (AS) of 8.56. The stability of the LTP/D characteristics is also verified through repeated measurements over 50 cycles; the relative standard deviations (RSDs) of Gmax /Gmin and AS are calculated as 1.65% and 0.25%, respectively. These excellent synaptic properties enable a recognition rate of ≈99% in the training and inference tasks for acoustic and emotional information patterns. This study is expected to be an important foundation for the realization of future parallel computing networks for energy-efficient and high-speed data processing.
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Affiliation(s)
- Seyong Oh
- Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, 16419, Korea
| | - Ju-Hee Lee
- Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, 16419, Korea
| | - Seunghwan Seo
- Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, 16419, Korea
| | - Hyongsuk Choo
- Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, 16419, Korea
| | - Dongyoung Lee
- Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, 16419, Korea
| | - Jeong-Ick Cho
- Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, 16419, Korea
| | - Jin-Hong Park
- Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, 16419, Korea
- Sungkyunkwan Advanced Institute of Nanotechnology (SAINT), Sungkyunkwan University, Suwon, 16417, Korea
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