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Khan R, Rahman NU, Hayat MF, Ghernaout D, Salih AAM, Ashraf GA, Samad A, Mahmood MA, Rahman N, Sohail M, Iqbal S, Abdullaev S, Khan A. Unveiling cutting-edge developments: architectures and nanostructured materials for application in optoelectronic artificial synapses. NANOSCALE 2024; 16:14589-14620. [PMID: 39011743 DOI: 10.1039/d4nr00904e] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/17/2024]
Abstract
One possible result of low-level characteristics in the traditional von Neumann formulation system is brain-inspired photonics technology based on human brain idea. Optoelectronic neural devices, which are accustomed to imitating the sensory role of biological synapses by adjusting connection measures, can be used to fabricate highly reliable neurologically calculating devices. In this case, nanosized materials and device designs are attracting attention since they provide numerous potential benefits in terms of limited cool contact, rapid transfer fluidity, and the capture of photocarriers. In addition, the combination of classic nanosized photodetectors with recently generated digital synapses offers promising results in a variety of practical applications, such as data processing and computation. Herein, we present the progress in constructing improved optoelectronic synaptic devices that rely on nanomaterials, for example, 0-dimensional (quantum dots), 1-dimensional, and 2-dimensional composites, besides the continuously developing mixed heterostructures. Furthermore, the challenges and potential prospects linked with this field of study are discussed in this paper.
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Affiliation(s)
- Rajwali Khan
- National Water and Energy Center, United Arab Emirates University, Al Ain, 15551, United Arab Emirates.
- Department of Physics, University of Lakki Marwat, Lakki Marwat, 2842, KP, Pakistan
| | - Naveed Ur Rahman
- National Water and Energy Center, United Arab Emirates University, Al Ain, 15551, United Arab Emirates.
- Department of Physics, University of Lakki Marwat, Lakki Marwat, 2842, KP, Pakistan
| | | | - Djamel Ghernaout
- Chemical Engineering Department, College of Engineering, University of Ha'il, PO Box 2440, Ha'il 81441, Saudi Arabia
- Chemical Engineering Department, Faculty of Engineering, University of Blida, PO Box 270, Blida 09000, Algeria
| | - Alsamani A M Salih
- Chemical Engineering Department, College of Engineering, University of Ha'il, PO Box 2440, Ha'il 81441, Saudi Arabia
- Department of Chemical Engineering, Faculty of Engineering, Al Neelain University, Khartoum 12702, Sudan
| | | | - Abdus Samad
- Department of Physics, University of Lakki Marwat, Lakki Marwat, 2842, KP, Pakistan
| | | | - Nasir Rahman
- Department of Physics, University of Lakki Marwat, Lakki Marwat, 2842, KP, Pakistan
| | - Mohammad Sohail
- Department of Physics, University of Lakki Marwat, Lakki Marwat, 2842, KP, Pakistan
| | - Shahid Iqbal
- Department of Physics, University of Wisconsin, La Crosse, WI 54601, USA
| | - Sherzod Abdullaev
- Senior Researcher, Engineering School, Central Asian University, Tashkent, Uzbekistan
- Senior Researcher, Scientific and Innovation Department, Tashkent State Pedagogical University, Uzbekistan
| | - Alamzeb Khan
- Yale University School of Medicine, New Haven, Connecticut, USA
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Shen W, Wang P, Wei G, Yuan S, Chen M, Su Y, Xu B, Li G. SiC@NiO Core-Shell Nanowire Networks-Based Optoelectronic Synapses for Neuromorphic Computing and Visual Systems at High Temperature. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2024; 20:e2400458. [PMID: 38607289 DOI: 10.1002/smll.202400458] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/19/2024] [Revised: 03/18/2024] [Indexed: 04/13/2024]
Abstract
1D nanowire networks, sharing similarities of structure, information transfer, and computation with biological neural networks, have emerged as a promising platform for neuromorphic systems. Based on brain-like structures of 1D nanowire networks, neuromorphic synaptic devices can overcome the von Neumann bottleneck, achieving intelligent high-efficient sensing and computing function with high information processing rates and low power consumption. Here, high-temperature neuromorphic synaptic devices based on SiC@NiO core-shell nanowire networks optoelectronic memristors (NNOMs) are developed. Experimental results demonstrate that NNOMs attain synaptic short/long-term plasticity and modulation plasticity under both electrical and optical stimulation, and exhibit advanced functions such as short/long-term memory and "learning-forgetting-relearning" under optical stimulation at both room temperature and 200 °C. Based on the advanced functions under light stimulus, the constructed 5 × 3 optoelectronic synaptic array devices exhibit a stable visual memory function up to 200 °C, which can be utilized to develop artificial visual systems. Additionally, when exposed to multiple electronic or optical stimuli, the NNOMs effectively replicate the principles of Pavlovian classical conditioning, achieving visual heterologous synaptic functionality and refining neural networks. Overall, with abundant synaptic characteristics and high-temperature thermal stability, these neuromorphic synaptic devices offer a promising route for advancing neuromorphic computing and visual systems.
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Affiliation(s)
- Weikang Shen
- Xi'an Key Laboratory of Compound Semiconductor Materials and Devices, School of Physics & Information Science, Shaanxi University of Science and Technology, Xi'an, Shaanxi, 710021, P. R. China
| | - Pan Wang
- Xi'an Key Laboratory of Compound Semiconductor Materials and Devices, School of Physics & Information Science, Shaanxi University of Science and Technology, Xi'an, Shaanxi, 710021, P. R. China
| | - Guodong Wei
- Xi'an Key Laboratory of Compound Semiconductor Materials and Devices, School of Physics & Information Science, Shaanxi University of Science and Technology, Xi'an, Shaanxi, 710021, P. R. China
- Shanxi-Zheda Institute of Advanced Materials and Chemical Engineering, Taiyuan, Shanxi, 030024, P. R. China
| | - Shuai Yuan
- Xi'an Key Laboratory of Compound Semiconductor Materials and Devices, School of Physics & Information Science, Shaanxi University of Science and Technology, Xi'an, Shaanxi, 710021, P. R. China
| | - Mi Chen
- Xi'an Key Laboratory of Compound Semiconductor Materials and Devices, School of Physics & Information Science, Shaanxi University of Science and Technology, Xi'an, Shaanxi, 710021, P. R. China
| | - Ying Su
- Xi'an Key Laboratory of Compound Semiconductor Materials and Devices, School of Physics & Information Science, Shaanxi University of Science and Technology, Xi'an, Shaanxi, 710021, P. R. China
| | - Bingshe Xu
- Xi'an Key Laboratory of Compound Semiconductor Materials and Devices, School of Physics & Information Science, Shaanxi University of Science and Technology, Xi'an, Shaanxi, 710021, P. R. China
- Shanxi-Zheda Institute of Advanced Materials and Chemical Engineering, Taiyuan, Shanxi, 030024, P. R. China
| | - Guoqiang Li
- Xi'an Key Laboratory of Compound Semiconductor Materials and Devices, School of Physics & Information Science, Shaanxi University of Science and Technology, Xi'an, Shaanxi, 710021, P. R. China
- The School of Integrated Circuits, State Key Laboratory of Luminescent Materials and Devices, South China University of Technology, Guangzhou, 510641, P. R. China
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Ren Q, Zhu C, Ma S, Wang Z, Yan J, Wan T, Yan W, Chai Y. Optoelectronic Devices for In-Sensor Computing. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024:e2407476. [PMID: 39004873 DOI: 10.1002/adma.202407476] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/26/2024] [Revised: 06/27/2024] [Indexed: 07/16/2024]
Abstract
The demand for accurate perception of the physical world leads to a dramatic increase in sensory nodes. However, the transmission of massive and unstructured sensory data from sensors to computing units poses great challenges in terms of power-efficiency, transmission bandwidth, data storage, time latency, and security. To efficiently process massive sensory data, it is crucial to achieve data compression and structuring at the sensory terminals. In-sensor computing integrates perception, memory, and processing functions within sensors, enabling sensory terminals to perform data compression and data structuring. Here, vision sensors are adopted as an example and discuss the functions of electronic, optical, and optoelectronic hardware for visual processing. Particularly, hardware implementations of optoelectronic devices for in-sensor visual processing that can compress and structure multidimensional vision information are examined. The underlying resistive switching mechanisms of volatile/nonvolatile optoelectronic devices and their processing operations are explored. Finally, a perspective on the future development of optoelectronic devices for in-sensor computing is provided.
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Affiliation(s)
- Qinqi Ren
- Department of Applied Physics, The Hong Kong Polytechnic University, Kowloon, Hong Kong, 999077, China
- Joint Research Centre of Microelectronics, The Hong Kong Polytechnic University, Kowloon, Hong Kong, 999077, China
| | - Chaoyi Zhu
- Department of Applied Physics, The Hong Kong Polytechnic University, Kowloon, Hong Kong, 999077, China
- Joint Research Centre of Microelectronics, The Hong Kong Polytechnic University, Kowloon, Hong Kong, 999077, China
| | - Sijie Ma
- Department of Applied Physics, The Hong Kong Polytechnic University, Kowloon, Hong Kong, 999077, China
- Joint Research Centre of Microelectronics, The Hong Kong Polytechnic University, Kowloon, Hong Kong, 999077, China
| | - Zhaoqing Wang
- Department of Applied Physics, The Hong Kong Polytechnic University, Kowloon, Hong Kong, 999077, China
- Joint Research Centre of Microelectronics, The Hong Kong Polytechnic University, Kowloon, Hong Kong, 999077, China
| | - Jianmin Yan
- Department of Applied Physics, The Hong Kong Polytechnic University, Kowloon, Hong Kong, 999077, China
- Joint Research Centre of Microelectronics, The Hong Kong Polytechnic University, Kowloon, Hong Kong, 999077, China
| | - Tianqing Wan
- Department of Applied Physics, The Hong Kong Polytechnic University, Kowloon, Hong Kong, 999077, China
- Joint Research Centre of Microelectronics, The Hong Kong Polytechnic University, Kowloon, Hong Kong, 999077, China
| | - Weicheng Yan
- Department of Applied Physics, The Hong Kong Polytechnic University, Kowloon, Hong Kong, 999077, China
- Joint Research Centre of Microelectronics, The Hong Kong Polytechnic University, Kowloon, Hong Kong, 999077, China
| | - Yang Chai
- Department of Applied Physics, The Hong Kong Polytechnic University, Kowloon, Hong Kong, 999077, China
- Joint Research Centre of Microelectronics, The Hong Kong Polytechnic University, Kowloon, Hong Kong, 999077, China
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Kim IJ, Choi J, Lee JS. Exploring Disturb Characteristics in 2D and 3D Ferroelectric NAND Memory Arrays for Next-Generation Memory Technology. ACS APPLIED MATERIALS & INTERFACES 2024; 16:33763-33770. [PMID: 38899561 DOI: 10.1021/acsami.4c03785] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/21/2024]
Abstract
Ferroelectric transistors are considered promising for next-generation 3D NAND technology due to their lower power consumption and faster operation compared to conventional charge-trap flash memories. However, ensuring their suitability for such applications requires a thorough investigation of array-scale reliability. This study specifically examines the suitability of hafnia-based ferroelectric transistors for advanced 3D NAND applications, with a specific focus on establishing a disturb-free voltage scheme to ensure the reliability of ferroelectric transistors within the array. Our key finding highlights the crucial role of optimal pass voltage in achieving disturb-free operation in both 2D and 3D ferroelectric NAND arrays. Additionally, the study indicates that read disturb remains negligible when an appropriate read voltage is applied. These insights provide a practical strategy for achieving reliable operation in 2D and 3D ferroelectric NAND, highlighting the potential of hafnia-based ferroelectric materials to meet the evolving requirements of high-density and reliable NAND flash memory applications.
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Affiliation(s)
- Ik-Jyae Kim
- Department of Materials Science and Engineering, Pohang University of Science and Technology (POSTECH), Pohang 37673, Korea
| | - Jiwoung Choi
- Department of Materials Science and Engineering, Pohang University of Science and Technology (POSTECH), Pohang 37673, Korea
| | - Jang-Sik Lee
- Department of Materials Science and Engineering, Pohang University of Science and Technology (POSTECH), Pohang 37673, Korea
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Leng K, Wan Y, Fu Y, Wang L, Wang Q. Si/CuO Heterojunction-Based Photomemristor for Reconfigurable, Non-Volatile, and Self-Powered In-Sensor Computing. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2024; 20:e2309945. [PMID: 38400705 DOI: 10.1002/smll.202309945] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Revised: 01/16/2024] [Indexed: 02/25/2024]
Abstract
In-sensor computing has attracted considerable interest as a solution for overcoming the energy efficiency and response time limitations of the traditional von Neumann architecture. Recently, emerging memristors based on transition-metal oxides (TMOs) have attracted attention as promising candidates for in-memory computing owing to their tunable conductance, high speed, and low operational energy. However, the poor photoresponse of TMOs presents challenges for integrating sensing and processing units into a single device. This integration is crucial for eliminating the need for a sensor/processor interface and achieving energy-efficient in-sensor computing systems. In this study, a Si/CuO heterojunction-based photomemristor is proposed that combines the reversible resistive switching behavior of CuO with the appropriate optical absorption bandgap of the Si substrate. The proposed photomemristor demonstrates a simultaneous reconfigurable, non-volatile, and self-powered photoresponse, producing a microampere-level photocurrent at zero bias. The controlled migration of oxygen vacancies in CuO result in distinct energy-band bending at the interface, enabling multiple levels of photoresponsivity. Additionally, the device exhibits high stability and ultrafast response speed to the built-in electric field. Furthermore, the prototype photomemristor can be trained to emulate the attention-driven nature of the human visual system, indicating the tremendous potential of TMO-based photomemristors as hardware foundations for in-sensor computing.
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Affiliation(s)
- Kangmin Leng
- Department of Physics, School of Physics and Materials Science, Nanchang University, Nanchang, 330031, China
| | - Yu Wan
- Department of Physics, School of Physics and Materials Science, Nanchang University, Nanchang, 330031, China
| | - Yao Fu
- Department of Materials, School of Physics and Materials Science, Nanchang University, Nanchang, 330031, China
| | - Li Wang
- Department of Physics, School of Physics and Materials Science, Nanchang University, Nanchang, 330031, China
| | - Qisheng Wang
- Department of Physics, School of Physics and Materials Science, Nanchang University, Nanchang, 330031, China
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Meng Y, Cheng G. Human somatosensory systems based on sensor-memory-integrated technology. NANOSCALE 2024; 16:11928-11958. [PMID: 38847091 DOI: 10.1039/d3nr06521a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/28/2024]
Abstract
As a representative artificial neural network (ANN) for incorporating sensing functions and memory functions into one system to achieve highly miniaturized and highly integrated devices or systems, artificial sensory systems (ASSs) can have a far-reaching influence on precise instrumentation, sensing, and automation engineering. Artificial sensory systems have enjoyed considerable progress in recent years, from low degree integrations to highly advanced sophisticated integrations, from single-modal perceptions to multimode-fused perceptions. However, there are issues around the large hardware area, power consumption, and communication bandwidth needed during the processes where multimodal sensing signals are converted into a digital mode before they can be processed by a digital processor. Therefore, deepening the research into sensory integration is of great importance. In this review, we briefly introduce fundamental knowledge about the memristor mechanism, describe some representative human somatosensory systems, and elucidate the relationship between the properties of memristor devices and the structure. The electronic character of the sensors, future prospects, and key challenges surrounding sensor-memory integrated technologies are also discussed.
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Affiliation(s)
- Yanfang Meng
- Institute of Intelligent Flexible Mechatronics, School of Mechanical Engineering, Jiangsu University, Zhenjiang, No. 301 Xuefu Road, Zhenjiang, Jiangsu Province, 212013, China.
| | - Guanggui Cheng
- Institute of Intelligent Flexible Mechatronics, School of Mechanical Engineering, Jiangsu University, Zhenjiang, No. 301 Xuefu Road, Zhenjiang, Jiangsu Province, 212013, China.
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Lai XC, Tang Z, Fang J, Feng L, Yao DJ, Zhang L, Jiang YP, Liu QX, Tang XG, Zhou YC, Shang J, Zhong GK, Gao J. An adjustable multistage resistance switching behavior of a photoelectric artificial synaptic device with a ferroelectric diode effect for neuromorphic computing. MATERIALS HORIZONS 2024; 11:2886-2897. [PMID: 38563639 DOI: 10.1039/d4mh00064a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Neuromorphic computing, which mimics biological neural networks, is widely regarded as the optimal solution for addressing the limitations of traditional von Neumann computing architecture. In this work, an adjustable multistage resistance switching ferroelectric Bi2FeCrO6 diode artificial synaptic device was fabricated using a sol-gel method with a simple process. The device exhibits nonlinearity in its electrical characteristics, demonstrating tunable multistage resistance switching behavior and a strong ferroelectric diode effect through the manipulation of ferroelectric polarization. One of its salient advantages resides in its capacity to dynamically regulate its polarization state in response to an external electric field, thereby facilitating the fine-tuning of synaptic connection strength while maintaining synaptic stability. The device is capable of accurately simulating the fundamental properties of biological synapses, including long/short-term plasticity, paired-pulse facilitation, and spike-timing-dependent plasticity. Additionally, the device exhibits a distinctive photoelectric response and is capable of inducing synaptic plasticity by light signal activation. The utilization of a femtosecond laser for the scrutiny of carrier transport mechanisms imparts profound insights into the intricate dynamics governing the optical memory effect. Furthermore, utilizing a convolutional neural network (CNN) architecture, the recognition accuracy of the MNIST and fashion MNIST datasets was improved to 95.6% and 78%, respectively, through the implementation of improved random adaptive algorithms. These findings present a new opportunity for utilizing Bi2FeCrO6 materials in the development of artificial synapses for neuromorphic computation.
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Affiliation(s)
- Xi-Cai Lai
- School of Physics and Optoelectronic Engineering, Guangdong University of Technology, Guangzhou Higher Education Mega Center, Guangzhou, 510006, P. R. China.
| | - Zhenhua Tang
- School of Physics and Optoelectronic Engineering, Guangdong University of Technology, Guangzhou Higher Education Mega Center, Guangzhou, 510006, P. R. China.
| | - Junlin Fang
- School of Physics and Optoelectronic Engineering, Guangdong University of Technology, Guangzhou Higher Education Mega Center, Guangzhou, 510006, P. R. China.
| | - Leyan Feng
- School of Physics and Optoelectronic Engineering, Guangdong University of Technology, Guangzhou Higher Education Mega Center, Guangzhou, 510006, P. R. China.
| | - Di-Jie Yao
- School of Physics and Optoelectronic Engineering, Guangdong University of Technology, Guangzhou Higher Education Mega Center, Guangzhou, 510006, P. R. China.
| | - Li Zhang
- School of Physics and Optoelectronic Engineering, Guangdong University of Technology, Guangzhou Higher Education Mega Center, Guangzhou, 510006, P. R. China.
| | - Yan-Ping Jiang
- School of Physics and Optoelectronic Engineering, Guangdong University of Technology, Guangzhou Higher Education Mega Center, Guangzhou, 510006, P. R. China.
| | - Qiu-Xiang Liu
- School of Physics and Optoelectronic Engineering, Guangdong University of Technology, Guangzhou Higher Education Mega Center, Guangzhou, 510006, P. R. China.
| | - Xin-Gui Tang
- School of Physics and Optoelectronic Engineering, Guangdong University of Technology, Guangzhou Higher Education Mega Center, Guangzhou, 510006, P. R. China.
| | - Yi-Chun Zhou
- School of Advanced Materials and Nanotechnology, Xidian University, Xian 710126, China
| | - Jie Shang
- Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201, China
| | - Gao-Kuo Zhong
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Ju Gao
- Department of Physics, The University of Hong Kong, Hong Kong 999077, P. R. China
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Wu X, Chen S, Jiang L, Wang X, Qiu L, Zheng L. Highly Sensitive, Low-Energy-Consumption Biomimetic Olfactory Synaptic Transistors Based on the Aggregation of the Semiconductor Films. ACS Sens 2024; 9:2673-2683. [PMID: 38688032 DOI: 10.1021/acssensors.4c00616] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/02/2024]
Abstract
Artificial olfactory synaptic devices with low energy consumption and low detection limits are important for the further development of neuromorphic computing and intelligent robotics. In this work, an ultralow energy consumption and low detection limit imitation olfactory synaptic device based on organic field-effect transistors (OFETs) was prepared. The aggregation state of poly(diketopyrrolopyrrole-selenophene) (PTDPP) semiconductor films is modulated by adding unfavorable solvents and annealing treatments to obtain excellent charge transfer and gas synaptic properties. The regulated OFET device can execute basic biological synaptic functions, including excitatory postsynaptic currents (EPSCs), paired-pulse facilitation (PPF), and the transition from short-term to long-term plasticity, at an ultralow operating voltage of -0.0005 V. The ultralow energy consumption during the biomimetic simulation is in the range of 8.94-88 fJ per spike. Noteworthily, the gas detection limit of the device is as low as 50 ppb, well below normal human NO2 gas perception limits (100-1000 ppb). Additionally, high-pass filtering, Pavlovian conditioned reflexes, and decoding of "Morse code" were simulated. Finally, a grid-free conformal device with outstanding flexibility and stability was fabricated. In conclusion, the control of semiconductor thin-film aggregation provides effective guidance for preparing low-energy-consumption, highly sensitive olfactory nerve-mimicking devices and promoting the development of wearable electronics.
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Affiliation(s)
- Xiaocheng Wu
- National Engineering Lab of Special Display Technology, State Key Lab of Advanced Display Technology, Academy of Optoelectronic Technology, Hefei University of Technology, Hefei 230009, P. R. China
- Intelligent Interconnected Systems Laboratory of Anhui, Anhui Province Key Laboratory of Measuring Theory and Precision Instrument, School of Instrument Science and Optoelectronic Engineering, Hefei University of Technology, Hefei 230009, P. R. China
| | - Siyu Chen
- National Engineering Lab of Special Display Technology, State Key Lab of Advanced Display Technology, Academy of Optoelectronic Technology, Hefei University of Technology, Hefei 230009, P. R. China
- Intelligent Interconnected Systems Laboratory of Anhui, Anhui Province Key Laboratory of Measuring Theory and Precision Instrument, School of Instrument Science and Optoelectronic Engineering, Hefei University of Technology, Hefei 230009, P. R. China
| | - Longlong Jiang
- National Engineering Lab of Special Display Technology, State Key Lab of Advanced Display Technology, Academy of Optoelectronic Technology, Hefei University of Technology, Hefei 230009, P. R. China
- Intelligent Interconnected Systems Laboratory of Anhui, Anhui Province Key Laboratory of Measuring Theory and Precision Instrument, School of Instrument Science and Optoelectronic Engineering, Hefei University of Technology, Hefei 230009, P. R. China
| | - Xiaohong Wang
- National Engineering Lab of Special Display Technology, State Key Lab of Advanced Display Technology, Academy of Optoelectronic Technology, Hefei University of Technology, Hefei 230009, P. R. China
- Intelligent Interconnected Systems Laboratory of Anhui, Anhui Province Key Laboratory of Measuring Theory and Precision Instrument, School of Instrument Science and Optoelectronic Engineering, Hefei University of Technology, Hefei 230009, P. R. China
| | - Longzhen Qiu
- National Engineering Lab of Special Display Technology, State Key Lab of Advanced Display Technology, Academy of Optoelectronic Technology, Hefei University of Technology, Hefei 230009, P. R. China
- Intelligent Interconnected Systems Laboratory of Anhui, Anhui Province Key Laboratory of Measuring Theory and Precision Instrument, School of Instrument Science and Optoelectronic Engineering, Hefei University of Technology, Hefei 230009, P. R. China
| | - Lei Zheng
- School of Food and Biological Engineering, Hefei University of Technology, Hefei 230009, China
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9
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Wang Y, Zha Y, Bao C, Hu F, Di Y, Liu C, Xing F, Xu X, Wen X, Gan Z, Jia B. Monolithic 2D Perovskites Enabled Artificial Photonic Synapses for Neuromorphic Vision Sensors. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024; 36:e2311524. [PMID: 38275007 DOI: 10.1002/adma.202311524] [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: 11/01/2023] [Revised: 01/23/2024] [Indexed: 01/27/2024]
Abstract
Neuromorphic visual sensors (NVS) based on photonic synapses hold a significant promise to emulate the human visual system. However, current photonic synapses rely on exquisite engineering of the complex heterogeneous interface to realize learning and memory functions, resulting in high fabrication cost, reduced reliability, high energy consumption and uncompact architecture, severely limiting the up-scaled manufacture, and on-chip integration. Here a photo-memory fundamental based on ion-exciton coupling is innovated to simplify synaptic structure and minimize energy consumption. Due to the intrinsic organic/inorganic interface within the crystal, the photodetector based on monolithic 2D perovskite exhibits a persistent photocurrent lasting about 90 s, enabling versatile synaptic functions. The electrical power consumption per synaptic event is estimated to be≈1.45 × 10-16 J, one order of magnitude lower than that in a natural biological system. Proof-of-concept image preprocessing using the neuromorphic vision sensors enabled by photonic synapse demonstrates 4 times enhancement of classification accuracy. Furthermore, getting rid of the artificial neural network, an expectation-based thresholding model is put forward to mimic the human visual system for facial recognition. This conceptual device unveils a new mechanism to simplify synaptic structure, promising the transformation of the NVS and fostering the emergence of next generation neural networks.
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Affiliation(s)
- Yun Wang
- Center for Future Optoelectronic Functional Materials, School of Computer and Electronic Information/School of Artificial Intelligence, Nanjing Normal University, Nanjing, 210023, P. R. China
| | - Yanfang Zha
- Center for Future Optoelectronic Functional Materials, School of Computer and Electronic Information/School of Artificial Intelligence, Nanjing Normal University, Nanjing, 210023, P. R. China
| | - Chunxiong Bao
- National Laboratory of Solid State Microstructures, Nanjing University, Nanjing, 210093, P. R. China
| | - Fengrui Hu
- National Laboratory of Solid State Microstructures, Nanjing University, Nanjing, 210093, P. R. China
| | - Yunsong Di
- Center for Future Optoelectronic Functional Materials, School of Computer and Electronic Information/School of Artificial Intelligence, Nanjing Normal University, Nanjing, 210023, P. R. China
| | - Cihui Liu
- Center for Future Optoelectronic Functional Materials, School of Computer and Electronic Information/School of Artificial Intelligence, Nanjing Normal University, Nanjing, 210023, P. R. China
| | - Fangjian Xing
- Center for Future Optoelectronic Functional Materials, School of Computer and Electronic Information/School of Artificial Intelligence, Nanjing Normal University, Nanjing, 210023, P. R. China
| | - Xingyuan Xu
- State Key Laboratory of Information Photonics and Optical Communications, Beijing University of Posts and Telecommunications, Beijing, 100876, P. R. China
| | - Xiaoming Wen
- Centre for Atomaterials and Nanomanufacturing, School of Science, RMIT University, Melbourne, Victoria, 3000, Australia
| | - Zhixing Gan
- Center for Future Optoelectronic Functional Materials, School of Computer and Electronic Information/School of Artificial Intelligence, Nanjing Normal University, Nanjing, 210023, P. R. China
- National Laboratory of Solid State Microstructures, Nanjing University, Nanjing, 210093, P. R. China
- College of Materials Science and Engineering, Qingdao University of Science and Technology, Qingdao, 266042, P. R. China
| | - Baohua Jia
- Centre for Atomaterials and Nanomanufacturing, School of Science, RMIT University, Melbourne, Victoria, 3000, Australia
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10
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Luo X, Chen C, He Z, Wang M, Pan K, Dong X, Li Z, Liu B, Zhang Z, Wu Y, Ban C, Chen R, Zhang D, Wang K, Wang Q, Li J, Lu G, Liu J, Liu Z, Huang W. A bionic self-driven retinomorphic eye with ionogel photosynaptic retina. Nat Commun 2024; 15:3086. [PMID: 38600063 PMCID: PMC11006927 DOI: 10.1038/s41467-024-47374-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Accepted: 03/27/2024] [Indexed: 04/12/2024] Open
Abstract
Bioinspired bionic eyes should be self-driving, repairable and conformal to arbitrary geometries. Such eye would enable wide-field detection and efficient visual signal processing without requiring external energy, along with retinal transplantation by replacing dysfunctional photoreceptors with healthy ones for vision restoration. A variety of artificial eyes have been constructed with hemispherical silicon, perovskite and heterostructure photoreceptors, but creating zero-powered retinomorphic system with transplantable conformal features remains elusive. By combining neuromorphic principle with retinal and ionoelastomer engineering, we demonstrate a self-driven hemispherical retinomorphic eye with elastomeric retina made of ionogel heterojunction as photoreceptors. The receptor driven by photothermoelectric effect shows photoperception with broadband light detection (365 to 970 nm), wide field-of-view (180°) and photosynaptic (paired-pulse facilitation index, 153%) behaviors for biosimilar visual learning. The retinal photoreceptors are transplantable and conformal to any complex surface, enabling visual restoration for dynamic optical imaging and motion tracking.
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Affiliation(s)
- Xu Luo
- Key Laboratory of Flexible Electronics (KLoFE) & Institute of Advanced Materials (IAM), School of Flexible Electronics (Future Technologies), Nanjing Tech University (NanjingTech), Nanjing, China
| | - Chen Chen
- Key Laboratory of Flexible Electronics (KLoFE) & Institute of Advanced Materials (IAM), School of Flexible Electronics (Future Technologies), Nanjing Tech University (NanjingTech), Nanjing, China
| | - Zixi He
- Key Laboratory of Flexible Electronics (KLoFE) & Institute of Advanced Materials (IAM), School of Flexible Electronics (Future Technologies), Nanjing Tech University (NanjingTech), Nanjing, China
| | - Min Wang
- Key Laboratory of Flexible Electronics (KLoFE) & Institute of Advanced Materials (IAM), School of Flexible Electronics (Future Technologies), Nanjing Tech University (NanjingTech), Nanjing, China
| | - Keyuan Pan
- Key Laboratory of Flexible Electronics (KLoFE) & Institute of Advanced Materials (IAM), School of Flexible Electronics (Future Technologies), Nanjing Tech University (NanjingTech), Nanjing, China
| | - Xuemei Dong
- Key Laboratory of Flexible Electronics (KLoFE) & Institute of Advanced Materials (IAM), School of Flexible Electronics (Future Technologies), Nanjing Tech University (NanjingTech), Nanjing, China
| | - Zifan Li
- Key Laboratory of Flexible Electronics (KLoFE) & Institute of Advanced Materials (IAM), School of Flexible Electronics (Future Technologies), Nanjing Tech University (NanjingTech), Nanjing, China
| | - Bin Liu
- Key Laboratory of Flexible Electronics (KLoFE) & Institute of Advanced Materials (IAM), School of Flexible Electronics (Future Technologies), Nanjing Tech University (NanjingTech), Nanjing, China
| | - Zicheng Zhang
- Key Laboratory of Flexible Electronics (KLoFE) & Institute of Advanced Materials (IAM), School of Flexible Electronics (Future Technologies), Nanjing Tech University (NanjingTech), Nanjing, China
| | - Yueyue Wu
- Key Laboratory of Flexible Electronics (KLoFE) & Institute of Advanced Materials (IAM), School of Flexible Electronics (Future Technologies), Nanjing Tech University (NanjingTech), Nanjing, China
| | - Chaoyi Ban
- Key Laboratory of Flexible Electronics (KLoFE) & Institute of Advanced Materials (IAM), School of Flexible Electronics (Future Technologies), Nanjing Tech University (NanjingTech), Nanjing, China
| | - Rong Chen
- Key Laboratory of Flexible Electronics (KLoFE) & Institute of Advanced Materials (IAM), School of Flexible Electronics (Future Technologies), Nanjing Tech University (NanjingTech), Nanjing, China
| | - Dengfeng Zhang
- Key Laboratory of Flexible Electronics (KLoFE) & Institute of Advanced Materials (IAM), School of Flexible Electronics (Future Technologies), Nanjing Tech University (NanjingTech), Nanjing, China
| | - Kaili Wang
- Key Laboratory of Flexible Electronics (KLoFE) & Institute of Advanced Materials (IAM), School of Flexible Electronics (Future Technologies), Nanjing Tech University (NanjingTech), Nanjing, China
| | - Qiye Wang
- Key Laboratory of Flexible Electronics (KLoFE) & Institute of Advanced Materials (IAM), School of Flexible Electronics (Future Technologies), Nanjing Tech University (NanjingTech), Nanjing, China
| | - Junyue Li
- Key Laboratory of Flexible Electronics (KLoFE) & Institute of Advanced Materials (IAM), School of Flexible Electronics (Future Technologies), Nanjing Tech University (NanjingTech), Nanjing, China
| | - Gang Lu
- Key Laboratory of Flexible Electronics (KLoFE) & Institute of Advanced Materials (IAM), School of Flexible Electronics (Future Technologies), Nanjing Tech University (NanjingTech), Nanjing, China
| | - Juqing Liu
- Key Laboratory of Flexible Electronics (KLoFE) & Institute of Advanced Materials (IAM), School of Flexible Electronics (Future Technologies), Nanjing Tech University (NanjingTech), Nanjing, China.
| | - Zhengdong Liu
- Key Laboratory of Flexible Electronics (KLoFE) & Institute of Advanced Materials (IAM), School of Flexible Electronics (Future Technologies), Nanjing Tech University (NanjingTech), Nanjing, China.
| | - Wei Huang
- Key Laboratory of Flexible Electronics (KLoFE) & Institute of Advanced Materials (IAM), School of Flexible Electronics (Future Technologies), Nanjing Tech University (NanjingTech), Nanjing, China.
- Frontiers Science Center for Flexible Electronics, Institute of Flexible Electronics (IFE), Northwestern Polytechnical University, Xi'an, China.
- State Key Laboratory of Organic Electronics and Information Displays, Nanjing University of Posts and Telecommunications, Nanjing, China.
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11
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Kim IJ, Lee JS. Dopant Engineering of Hafnia-Based Ferroelectrics for Long Data Retention and High Thermal Stability. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2023:e2306871. [PMID: 37967323 DOI: 10.1002/smll.202306871] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Revised: 10/30/2023] [Indexed: 11/17/2023]
Abstract
Hafnia-based ferroelectrics have gained much attention because they can be used in highly scaled, advanced complementary metal-oxide semiconductor (CMOS) memory devices. However, thermal stability should be considered when integrating hafnia-based ferroelectric transistors in advanced CMOS devices, as they can be exposed to high-temperature processes. This work proposed that doping of Al in hafnia-based ferroelectric material can lead to high thermal stability. A ferroelectric capacitor based on Al-doped hafnia, which can be used for one-transistor-one-capacitor applications, exhibits stable operation even after annealing at 900 °C. Moreover, it demonstrates that the ferroelectric transistors based on Al-doped hafnia for one-transistor applications, such as ferroelectric NAND, retain their memory states for 10 years at 100 °C. This study presents a practical method to achieve thermally stable ferroelectric memories capable of enduring high-temperature processes and operation conditions.
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Affiliation(s)
- Ik-Jyae Kim
- Department of Materials Science and Engineering, Pohang University of Science and Technology (POSTECH), Pohang, 37673, Republic of Korea
| | - Jang-Sik Lee
- Department of Materials Science and Engineering, Pohang University of Science and Technology (POSTECH), Pohang, 37673, Republic of Korea
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12
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Park JY, Choe DH, Lee DH, Yu GT, Yang K, Kim SH, Park GH, Nam SG, Lee HJ, Jo S, Kuh BJ, Ha D, Kim Y, Heo J, Park MH. Revival of Ferroelectric Memories Based on Emerging Fluorite-Structured Ferroelectrics. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2023; 35:e2204904. [PMID: 35952355 DOI: 10.1002/adma.202204904] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Revised: 07/25/2022] [Indexed: 06/15/2023]
Abstract
Over the last few decades, the research on ferroelectric memories has been limited due to their dimensional scalability and incompatibility with complementary metal-oxide-semiconductor (CMOS) technology. The discovery of ferroelectricity in fluorite-structured oxides revived interest in the research on ferroelectric memories, by inducing nanoscale nonvolatility in state-of-the-art gate insulators by minute doping and thermal treatment. The potential of this approach has been demonstrated by the fabrication of sub-30 nm electronic devices. Nonetheless, to realize practical applications, various technical limitations, such as insufficient reliability including endurance, retention, and imprint, as well as large device-to-device-variation, require urgent solutions. Furthermore, such limitations should be considered based on targeting devices as well as applications. Various types of ferroelectric memories including ferroelectric random-access-memory, ferroelectric field-effect-transistor, and ferroelectric tunnel junction should be considered for classical nonvolatile memories as well as emerging neuromorphic computing and processing-in-memory. Therefore, from the viewpoint of materials science, this review covers the recent research focusing on ferroelectric memories from the history of conventional approaches to future prospects.
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Affiliation(s)
- Ju Yong Park
- Research Institute of Advanced Materials, Seoul National University, Seoul, 08826, Republic of Korea
| | - Duk-Hyun Choe
- Beyond Silicon Lab, Samsung Advanced Institute of Technology (SAIT), Suwon, 16678, Republic of Korea
| | - Dong Hyun Lee
- Department of Materials Science and Engineering and Inter-University Semiconductor Research Center, College of Engineering, Seoul National University, Seoul, 08826, Republic of Korea
| | - Geun Taek Yu
- School of Materials Science and Engineering, Pusan National University, Busan, 46241, Republic of Korea
| | - Kun Yang
- Department of Materials Science and Engineering and Inter-University Semiconductor Research Center, College of Engineering, Seoul National University, Seoul, 08826, Republic of Korea
| | - Se Hyun Kim
- Department of Materials Science and Engineering and Inter-University Semiconductor Research Center, College of Engineering, Seoul National University, Seoul, 08826, Republic of Korea
| | - Geun Hyeong Park
- Department of Materials Science and Engineering and Inter-University Semiconductor Research Center, College of Engineering, Seoul National University, Seoul, 08826, Republic of Korea
| | - Seung-Geol Nam
- Beyond Silicon Lab, Samsung Advanced Institute of Technology (SAIT), Suwon, 16678, Republic of Korea
| | - Hyun Jae Lee
- Beyond Silicon Lab, Samsung Advanced Institute of Technology (SAIT), Suwon, 16678, Republic of Korea
| | - Sanghyun Jo
- Beyond Silicon Lab, Samsung Advanced Institute of Technology (SAIT), Suwon, 16678, Republic of Korea
| | - Bong Jin Kuh
- Semiconductor Research and Development Center, Samsung Electronics, Hwaseong, 18448, Republic of Korea
| | - Daewon Ha
- Semiconductor Research and Development Center, Samsung Electronics, Hwaseong, 18448, Republic of Korea
| | - Yongsung Kim
- Beyond Silicon Lab, Samsung Advanced Institute of Technology (SAIT), Suwon, 16678, Republic of Korea
| | - Jinseong Heo
- Beyond Silicon Lab, Samsung Advanced Institute of Technology (SAIT), Suwon, 16678, Republic of Korea
| | - Min Hyuk Park
- Research Institute of Advanced Materials, Seoul National University, Seoul, 08826, Republic of Korea
- Department of Materials Science and Engineering and Inter-University Semiconductor Research Center, College of Engineering, Seoul National University, Seoul, 08826, Republic of Korea
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13
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Kim T, Choi CH, Hur JS, Ha D, Kuh BJ, Kim Y, Cho MH, Kim S, Jeong JK. Progress, Challenges, and Opportunities in Oxide Semiconductor Devices: A Key Building Block for Applications Ranging from Display Backplanes to 3D Integrated Semiconductor Chips. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2023; 35:e2204663. [PMID: 35862931 DOI: 10.1002/adma.202204663] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Revised: 07/04/2022] [Indexed: 06/15/2023]
Abstract
As Si has faced physical limits on further scaling down, novel semiconducting materials such as 2D transition metal dichalcogenides and oxide semiconductors (OSs) have gained tremendous attention to continue the ever-demanding downscaling represented by Moore's law. Among them, OS is considered to be the most promising alternative material because it has intriguing features such as modest mobility, extremely low off-current, great uniformity, and low-temperature processibility with conventional complementary-metal-oxide-semiconductor-compatible methods. In practice, OS has successfully replaced hydrogenated amorphous Si in high-end liquid crystal display devices and has now become a standard backplane electronic for organic light-emitting diode displays despite the short time since their invention in 2004. For OS to be implemented in next-generation electronics such as back-end-of-line transistor applications in monolithic 3D integration beyond the display applications, however, there is still much room for further study, such as high mobility, immune short-channel effects, low electrical contact properties, etc. This study reviews the brief history of OS and recent progress in device applications from a material science and device physics point of view. Simultaneously, remaining challenges and opportunities in OS for use in next-generation electronics are discussed.
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Affiliation(s)
- Taikyu Kim
- Department of Electronic Engineering, Hanyang University, Seoul, 04763, Republic of Korea
| | - Cheol Hee Choi
- Department of Electronic Engineering, Hanyang University, Seoul, 04763, Republic of Korea
| | - Jae Seok Hur
- Department of Electronic Engineering, Hanyang University, Seoul, 04763, Republic of Korea
| | - Daewon Ha
- Semiconductor R&D Center, Samsung Electronics, Hwaseong, Gyeonggi-do, 18848, Republic of Korea
| | - Bong Jin Kuh
- Semiconductor R&D Center, Samsung Electronics, Hwaseong, Gyeonggi-do, 18848, Republic of Korea
| | - Yongsung Kim
- Samsung Advanced Institute of Technology, Samsung Electronics, Suwon, Gyeonggi-do, 16678, Republic of Korea
| | - Min Hee Cho
- Semiconductor R&D Center, Samsung Electronics, Hwaseong, Gyeonggi-do, 18848, Republic of Korea
| | - Sangwook Kim
- Samsung Advanced Institute of Technology, Samsung Electronics, Suwon, Gyeonggi-do, 16678, Republic of Korea
| | - Jae Kyeong Jeong
- Department of Electronic Engineering, Hanyang University, Seoul, 04763, Republic of Korea
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14
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Wang X, Yang S, Qin Z, Hu B, Bu L, Lu G. Enhanced Multiwavelength Response of Flexible Synaptic Transistors for Human Sunburned Skin Simulation and Neuromorphic Computation. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2023; 35:e2303699. [PMID: 37358823 DOI: 10.1002/adma.202303699] [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: 04/20/2023] [Revised: 06/22/2023] [Indexed: 06/27/2023]
Abstract
In biological species, optogenetics and bioimaging work together to regulate the function of neurons. Similarly, the light-controlled artificial synaptic system not only enhances computational speed but also simulates complex synaptic functions. However, reported synaptic properties are mainly limited to mimicking simple biological functions and single-wavelength responses. Therefore, the development of flexible synaptic devices with multiwavelength optical signal response and multifunctional simulation remains a challenge. Here, flexible organic light-stimulated synaptic transistors (LSSTs) enabled by alumina oxide (AlOX ), with a simple fabrication process, are reported. By embedding AlOX nanoparticles, the excitons separation efficiency is improved, allowing for multiple wavelength responses. Optimized LSSTs can respond to multiple optical and electrical signals in a highly synaptic manner. Multiwavelength optical synaptic plasticity, electrical synaptic plasticity, sunburned skin simulation, learning efficiency model controlled by photoelectric cooperative stimulation, neural network computing, "deer" picture learning and memory functions are successfully proposed, which promote the development for future artificial intelligent systems. Furthermore, as prepared flexible transistors exhibit mechanical flexibility with bending radius down to 2.5 mm and improved photosynaptic plasticity, which facilitating development of neuromorphic computing and multifunction integration systems at the device-level.
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Affiliation(s)
- Xin Wang
- Frontier Institute of Science and Technology, State Key Laboratory of Electrical Insulation and Power Equipment, Xi'an Jiaotong University, Xi'an, 710054, China
| | - Shuting Yang
- School of Chemistry, Xi'an Jiaotong University, Xi'an, 710049, China
| | - Zongze Qin
- Frontier Institute of Science and Technology, State Key Laboratory of Electrical Insulation and Power Equipment, Xi'an Jiaotong University, Xi'an, 710054, China
| | - Bin Hu
- Frontier Institute of Science and Technology, State Key Laboratory of Electrical Insulation and Power Equipment, Xi'an Jiaotong University, Xi'an, 710054, China
| | - Laju Bu
- School of Chemistry, Xi'an Jiaotong University, Xi'an, 710049, China
| | - Guanghao Lu
- Frontier Institute of Science and Technology, State Key Laboratory of Electrical Insulation and Power Equipment, Xi'an Jiaotong University, Xi'an, 710054, China
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15
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Wang X, Ran Y, Li X, Qin X, Lu W, Zhu Y, Lu G. Bio-inspired artificial synaptic transistors: evolution from innovative basic units to system integration. MATERIALS HORIZONS 2023; 10:3269-3292. [PMID: 37312536 DOI: 10.1039/d3mh00216k] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
The investigation of transistor-based artificial synapses in bioinspired information processing is undergoing booming exploration, and is the stable building block for brain-like computing. Given that the storage and computing separation architecture of von Neumann construction is not conducive to the current explosive information processing, it is critical to accelerate the connection between hardware systems and software simulations of intelligent synapses. So far, various works based on a transistor-based synaptic system successfully simulated functions similar to biological nerves in the human brain. However, the influence of the semiconductor and the device structural design on synaptic properties is still poorly linked. This review concretely emphasizes the recent advances in the novel structure design of semiconductor materials and devices used in synaptic transistors, not only from a single multifunction synaptic device but also to system application with various connected routes and related working mechanisms. Finally, crises and opportunities in transistor-based synaptic interconnection are discussed and predicted.
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Affiliation(s)
- Xin Wang
- Frontier Institute of Science and Technology, State Key Laboratory of Electrical Insulation and Power Equipment, Xi'an Jiaotong University, Xi'an 710054, P. R. China.
| | - Yixin Ran
- Frontier Institute of Science and Technology, State Key Laboratory of Electrical Insulation and Power Equipment, Xi'an Jiaotong University, Xi'an 710054, P. R. China.
| | - Xiaoqian Li
- Shandong Technology Center of Nanodevices and Integration, School of Microelectronics, Shandong University, Jinan, Shandong Province, 250100, P. R. China
| | - Xinsu Qin
- Frontier Institute of Science and Technology, State Key Laboratory of Electrical Insulation and Power Equipment, Xi'an Jiaotong University, Xi'an 710054, P. R. China.
| | - Wanlong Lu
- Frontier Institute of Science and Technology, State Key Laboratory of Electrical Insulation and Power Equipment, Xi'an Jiaotong University, Xi'an 710054, P. R. China.
| | - Yuanwei Zhu
- Frontier Institute of Science and Technology, State Key Laboratory of Electrical Insulation and Power Equipment, Xi'an Jiaotong University, Xi'an 710054, P. R. China.
| | - Guanghao Lu
- Frontier Institute of Science and Technology, State Key Laboratory of Electrical Insulation and Power Equipment, Xi'an Jiaotong University, Xi'an 710054, P. R. China.
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16
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Kim MK, Kim IJ, Lee JS. Defect Engineering of Hafnia-Based Ferroelectric Materials for High-Endurance Memory Applications. ACS OMEGA 2023; 8:18180-18185. [PMID: 37251138 PMCID: PMC10210041 DOI: 10.1021/acsomega.3c01561] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Accepted: 04/24/2023] [Indexed: 05/31/2023]
Abstract
Zirconium-doped hafnium oxide (HfZrOx) is one of the promising ferroelectric materials for next-generation memory applications. To realize high-performance HfZrOx for next-generation memory applications, the formation of defects in HfZrOx, including oxygen vacancies and interstitials, needs to be optimized, as it can affect the polarization and endurance characteristics of HfZrOx. In this study, we investigated the effects of ozone exposure time during the atomic layer deposition (ALD) process on the polarization and endurance characteristics of 16-nm-thick HfZrOx. HfZrOx films showed different polarization and endurance characteristics depending on the ozone exposure time. HfZrOx deposited using the ozone exposure time of 1 s showed small polarization and large defect concentration. The increase of the ozone exposure time to 2.5 s could reduce the defect concentration and improve the polarization characteristics of HfZrOx. When the ozone exposure time further increased to 4 s, a reduction of polarization was observed in HfZrOx due to the formation of oxygen interstitials and non-ferroelectric monoclinic phases. HfZrOx, with an ozone exposure time of 2.5 s, exhibited the most stable endurance characteristics because of the low initial defect concentration in HfZrOx, which was confirmed by the leakage current analysis. This study shows that the ozone exposure time of ALD needs to be controlled to optimize the formation of defects in HfZrOx films for the improvement of polarization and endurance characteristics.
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17
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Lee M, Seung H, Kwon JI, Choi MK, Kim DH, Choi C. Nanomaterial-Based Synaptic Optoelectronic Devices for In-Sensor Preprocessing of Image Data. ACS OMEGA 2023; 8:5209-5224. [PMID: 36816688 PMCID: PMC9933102 DOI: 10.1021/acsomega.3c00440] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/21/2023] [Accepted: 01/27/2023] [Indexed: 06/18/2023]
Abstract
With the advance in information technologies involving machine vision applications, the demand for energy- and time-efficient acquisition, transfer, and processing of a large amount of image data has rapidly increased. However, current architectures of the machine vision system have inherent limitations in terms of power consumption and data latency owing to the physical isolation of image sensors and processors. Meanwhile, synaptic optoelectronic devices that exhibit photoresponse similar to the behaviors of the human synapse enable in-sensor preprocessing, which makes the front-end part of the image recognition process more efficient. Herein, we review recent progress in the development of synaptic optoelectronic devices using functional nanomaterials and their unique interfacial characteristics. First, we provide an overview of representative functional nanomaterials and device configurations for the synaptic optoelectronic devices. Then, we discuss the underlying physics of each nanomaterial in the synaptic optoelectronic device and explain related device characteristics that allow for the in-sensor preprocessing. We also discuss advantages achieved by the application of the synaptic optoelectronic devices to image preprocessing, such as contrast enhancement and image filtering. Finally, we conclude this review and present a short prospect.
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Affiliation(s)
- Minkyung Lee
- Center
for Optoelectronic Materials and Devices, Post-silicon Semiconductor
Institute, Korea Institute of Science and
Technology (KIST), Seoul 02792, Republic of Korea
| | - Hyojin Seung
- Center
for Nanoparticle Research, Institute for
Basic Science (IBS), Seoul 08826, Republic of Korea
- School
of Chemical and Biological Engineering, Institute of Chemical Processes, Seoul National University, Seoul 08826, Republic
of Korea
| | - Jong Ik Kwon
- School
of Materials Science and Engineering, Ulsan
National Institute of Science and Technology (UNIST), Ulsan 44919, Republic of Korea
| | - Moon Kee Choi
- Center
for Nanoparticle Research, Institute for
Basic Science (IBS), Seoul 08826, Republic of Korea
- School
of Materials Science and Engineering, Ulsan
National Institute of Science and Technology (UNIST), Ulsan 44919, Republic of Korea
| | - Dae-Hyeong Kim
- Center
for Nanoparticle Research, Institute for
Basic Science (IBS), Seoul 08826, Republic of Korea
- School
of Chemical and Biological Engineering, Institute of Chemical Processes, Seoul National University, Seoul 08826, Republic
of Korea
- Department
of Materials Science and Engineering, Seoul
National University, Seoul 08826, Republic of Korea
| | - Changsoon Choi
- Center
for Optoelectronic Materials and Devices, Post-silicon Semiconductor
Institute, Korea Institute of Science and
Technology (KIST), Seoul 02792, Republic of Korea
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18
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Highly-scaled and fully-integrated 3-dimensional ferroelectric transistor array for hardware implementation of neural networks. Nat Commun 2023; 14:504. [PMID: 36720868 PMCID: PMC9889761 DOI: 10.1038/s41467-023-36270-0] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Accepted: 01/20/2023] [Indexed: 02/02/2023] Open
Abstract
Hardware-based neural networks (NNs) can provide a significant breakthrough in artificial intelligence applications due to their ability to extract features from unstructured data and learn from them. However, realizing complex NN models remains challenging because different tasks, such as feature extraction and classification, should be performed at different memory elements and arrays. This further increases the required number of memory arrays and chip size. Here, we propose a three-dimensional ferroelectric NAND (3D FeNAND) array for the area-efficient hardware implementation of NNs. Vector-matrix multiplication is successfully demonstrated using the integrated 3D FeNAND arrays, and excellent pattern classification is achieved. By allocating each array of vertical layers in 3D FeNAND as the hidden layer of NN, each layer can be used to perform different tasks, and the classification of color-mixed patterns is achieved. This work provides a practical strategy to realize high-performance and highly efficient NN systems by stacking computation components vertically.
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19
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Shen R, Jiang Y, Li Z, Tian J, Li S, Li T, Chen Q. Near-Infrared Artificial Optical Synapse Based on the P(VDF-TrFE)-Coated InAs Nanowire Field-Effect Transistor. MATERIALS (BASEL, SWITZERLAND) 2022; 15:8247. [PMID: 36431733 PMCID: PMC9698720 DOI: 10.3390/ma15228247] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Revised: 10/27/2022] [Accepted: 11/08/2022] [Indexed: 06/16/2023]
Abstract
Optical synapse is the basic component for optical neuromorphic computing and is attracting great attention, mainly due to its great potential in many fields, such as image recognition, artificial intelligence and artificial visual perception systems. However, optical synapse with infrared (IR) response has rarely been reported. InAs nanowires (NWs) have a direct narrow bandgap and a large surface to volume ratio, making them a promising material for IR detection. Here, we demonstrate a near-infrared (NIR) (750 to 1550 nm) optical synapse for the first time based on a poly(vinylidene fluoride-trifluoroethylene) (P(VDF-TrFE))-coated InAs NW field-effect transistor (FET). The responsivity of the P(VDF-TrFE)-coated InAs NW FET reaches 839.3 A/W under 750 nm laser illumination, demonstrating the advantage of P(VDF-TrFE) coverage. The P(VDF-TrFE)-coated InAs NW device exhibits optical synaptic behaviors in response to NIR light pulses, including excitatory postsynaptic current (EPSC), paired-pulse facilitation (PPF) and a transformation from short-term plasticity (STP) to long-term plasticity (LTP). The working mechanism is attributed to the polarization effect in the ferroelectric P(VDF-TrFE) layer, which dominates the trapping and de-trapping characteristics of photogenerated holes. These findings have significant implications for the development of artificial neural networks.
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Affiliation(s)
- Rui Shen
- Key Laboratory for the Physics and Chemistry of Nanodevices, School of Electronics, Peking University, Beijing 100871, China
| | - Yifan Jiang
- Key Laboratory for the Physics and Chemistry of Nanodevices, School of Electronics, Peking University, Beijing 100871, China
| | - Zhiwei Li
- Key Laboratory for the Physics and Chemistry of Nanodevices, School of Electronics, Peking University, Beijing 100871, China
| | - Jiamin Tian
- Key Laboratory for the Physics and Chemistry of Nanodevices, School of Electronics, Peking University, Beijing 100871, China
| | - Shuo Li
- Key Laboratory for the Physics and Chemistry of Nanodevices, School of Electronics, Peking University, Beijing 100871, China
| | - Tong Li
- Key Laboratory for the Physics and Chemistry of Nanodevices, School of Electronics, Peking University, Beijing 100871, China
- Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China
| | - Qing Chen
- Key Laboratory for the Physics and Chemistry of Nanodevices, School of Electronics, Peking University, Beijing 100871, China
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20
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Programmable ferroelectric bionic vision hardware with selective attention for high-precision image classification. Nat Commun 2022; 13:7019. [PMID: 36384983 PMCID: PMC9669032 DOI: 10.1038/s41467-022-34565-2] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2022] [Accepted: 10/28/2022] [Indexed: 11/18/2022] Open
Abstract
Selective attention is an efficient processing strategy to allocate computational resources for pivotal optical information. However, the hardware implementation of selective visual attention in conventional intelligent system is usually bulky and complex along with high computational cost. Here, programmable ferroelectric bionic vision hardware to emulate the selective attention is proposed. The tunneling effect of photogenerated carriers are controlled by dynamic variation of energy barrier, enabling the modulation of memory strength from 9.1% to 47.1% without peripheral storage unit. The molecular polarization of ferroelectric P(VDF-TrFE) layer enables a single device not only multiple nonvolatile states but also the implementation of selective attention. With these ferroelectric devices are arrayed together, UV light information can be selectively recorded and suppressed the with high current decibel level. Furthermore, the device with positive polarization exhibits high wavelength dependence in the image attention processing, and the fabricated ferroelectric sensory network exhibits high accuracy of 95.7% in the pattern classification for multi-wavelength images. This study can enrich the neuromorphic functions of bioinspired sensing devices and pave the way for profound implications of future bioinspired optoelectronics.
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21
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Cho SW, Jo C, Kim YH, Park SK. Progress of Materials and Devices for Neuromorphic Vision Sensors. NANO-MICRO LETTERS 2022; 14:203. [PMID: 36242681 PMCID: PMC9569410 DOI: 10.1007/s40820-022-00945-y] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Accepted: 09/08/2022] [Indexed: 05/31/2023]
Abstract
The latest developments in bio-inspired neuromorphic vision sensors can be summarized in 3 keywords: smaller, faster, and smarter. (1) Smaller: Devices are becoming more compact by integrating previously separated components such as sensors, memory, and processing units. As a prime example, the transition from traditional sensory vision computing to in-sensor vision computing has shown clear benefits, such as simpler circuitry, lower power consumption, and less data redundancy. (2) Swifter: Owing to the nature of physics, smaller and more integrated devices can detect, process, and react to input more quickly. In addition, the methods for sensing and processing optical information using various materials (such as oxide semiconductors) are evolving. (3) Smarter: Owing to these two main research directions, we can expect advanced applications such as adaptive vision sensors, collision sensors, and nociceptive sensors. This review mainly focuses on the recent progress, working mechanisms, image pre-processing techniques, and advanced features of two types of neuromorphic vision sensors based on near-sensor and in-sensor vision computing methodologies.
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Affiliation(s)
- Sung Woon Cho
- Department of Advanced Components and Materials Engineering, Sunchon National University, Sunchŏn, Jeonnam, 57922, Republic of Korea
| | - Chanho Jo
- Department of Electrical and Electronics Engineering, Chung-Ang University, Seoul, 06974, Republic of Korea
| | - Yong-Hoon Kim
- School of Advanced Materials Science and Engineering, Sungkyunkwan University, Suwon, 16419, Republic of Korea.
- SKKU Advanced Institute of Nanotechnology (SAINT), Sungkyunkwan University, Suwon, 16419, Republic of Korea.
| | - Sung Kyu Park
- Department of Electrical and Electronics Engineering, Chung-Ang University, Seoul, 06974, Republic of Korea.
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22
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Kim D, Lee JS. Emulating the Signal Transmission in a Neural System Using Polymer Membranes. ACS APPLIED MATERIALS & INTERFACES 2022; 14:42308-42316. [PMID: 36069456 DOI: 10.1021/acsami.2c12166] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Neurons are vital components of the brain. When stimulated by neurotransmitters at the dendrites, neurons deliver signals as changes in the membrane potential by ion movement. The signal transmission of a nervous system exhibits a high energy efficiency. These characteristics of neurons are being exploited to develop efficient neuromorphic computing systems. In this study, we develop chemical synapses for neuromorphic devices and emulate the signaling processes in a nervous system using a polymer membrane, in which the ionic permeability can be controlled. The polymer membrane comprises poly(diallyl-dimethylammonium chloride) and poly(3-sulfopropyl acrylate potassium salt), which have positive and negative charges, respectively. The ionic permeability of the polymer membrane is controlled by the injection of a neurotransmitter solution. This device emulates the signal transmission behavior of biological neurons depending on the concentration of the injected neurotransmitter solution. The proposed artificial neuronal signaling device can facilitate the development of bio-realistic neuromorphic devices.
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Affiliation(s)
- Dongshin Kim
- Department of Materials Science and Engineering, Pohang University of Science and Technology (POSTECH), Pohang 37673, Korea
| | - Jang-Sik Lee
- Department of Materials Science and Engineering, Pohang University of Science and Technology (POSTECH), Pohang 37673, Korea
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23
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Co-assembled perylene/graphene oxide photosensitive heterobilayer for efficient neuromorphics. Nat Commun 2022; 13:4996. [PMID: 36008407 PMCID: PMC9411554 DOI: 10.1038/s41467-022-32725-y] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Accepted: 08/11/2022] [Indexed: 11/09/2022] Open
Abstract
Neuromorphic electronics, which use artificial photosensitive synapses, can emulate biological nervous systems with in-memory sensing and computing abilities. Benefiting from multiple intra/interactions and strong light-matter coupling, two-dimensional heterostructures are promising synaptic materials for photonic synapses. Two primary strategies, including chemical vapor deposition and physical stacking, have been developed for layered heterostructures, but large-scale growth control over wet-chemical synthesis with comprehensive efficiency remains elusive. Here we demonstrate an interfacial coassembly heterobilayer films from perylene and graphene oxide (GO) precursors, which are spontaneously formed at the interface, with uniform bilayer structure of single-crystal perylene and well-stacked GO over centimeters in size. The planar heterostructure device exhibits an ultrahigh specific detectivity of 3.1 × 1013 Jones and ultralow energy consumption of 10−9 W as well as broadband photoperception from 365 to 1550 nm. Moreover, the device shows outstanding photonic synaptic behaviors with a paired-pulse facilitation (PPF) index of 214% in neuroplasticity, the heterosynapse array has the capability of information reinforcement learning and recognition. Layered heterostructures are promising photosensitive materials for advanced optoelectronics. Here, the authors introduce an interfacial coassembly method to construct large-scale perylene/grahene oxide (GO) heterobilayer for broadband photoreception and efficient neuromorphics.
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24
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Li Q, Wang T, Fang Y, Hu X, Tang C, Wu X, Zhu H, Ji L, Sun QQ, Zhang DW, Chen L. Ultralow Power Wearable Organic Ferroelectric Device for Optoelectronic Neuromorphic Computing. NANO LETTERS 2022; 22:6435-6443. [PMID: 35737934 DOI: 10.1021/acs.nanolett.2c01768] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
In order to imitate brain-inspired biological information processing systems, various neuromorphic computing devices have been proposed, most of which were prepared on rigid substrates and have energy consumption levels several orders of magnitude higher than those of biological synapses (∼10 fJ per spike). Herein, a new type of wearable organic ferroelectric artificial synapse is proposed, which has two modulation modes (optical and electrical modulation). Because of the high photosensitivity of organic semiconductors and the ultrafast polarization switching of ferroelectric materials, the synaptic device has an ultrafast operation speed of 30 ns and an ultralow power consumption of 0.0675 aJ per synaptic event. Under combined photoelectric modulation, the artificial synapse realizes associative learning. The proposed artificial synapse with ultralow power consumption demonstrates good synaptic plasticity under different bending strains. This provides new avenues for the construction of ultralow power artificial intelligence system and the development of future wearable devices.
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Affiliation(s)
- Qingxuan Li
- State Key Laboratory of ASIC and System, School of Microelectronics, Fudan University, Shanghai 200433, P.R. China
| | - Tianyu Wang
- State Key Laboratory of ASIC and System, School of Microelectronics, Fudan University, Shanghai 200433, P.R. China
- Zhangjiang Fudan International Innovation Center, Shanghai 201203, China
| | - Yuqing Fang
- State Key Laboratory of ASIC and System, School of Microelectronics, Fudan University, Shanghai 200433, P.R. China
| | - Xuemeng Hu
- State Key Laboratory of ASIC and System, School of Microelectronics, Fudan University, Shanghai 200433, P.R. China
| | - Chengkang Tang
- State Key Laboratory of ASIC and System, School of Microelectronics, Fudan University, Shanghai 200433, P.R. China
| | - Xiaohan Wu
- State Key Laboratory of ASIC and System, School of Microelectronics, Fudan University, Shanghai 200433, P.R. China
- Zhangjiang Fudan International Innovation Center, Shanghai 201203, China
| | - Hao Zhu
- State Key Laboratory of ASIC and System, School of Microelectronics, Fudan University, Shanghai 200433, P.R. China
- Zhangjiang Fudan International Innovation Center, Shanghai 201203, China
| | - Li Ji
- State Key Laboratory of ASIC and System, School of Microelectronics, Fudan University, Shanghai 200433, P.R. China
- Zhangjiang Fudan International Innovation Center, Shanghai 201203, China
| | - Qing-Qing Sun
- State Key Laboratory of ASIC and System, School of Microelectronics, Fudan University, Shanghai 200433, P.R. China
- Zhangjiang Fudan International Innovation Center, Shanghai 201203, China
| | - David Wei Zhang
- State Key Laboratory of ASIC and System, School of Microelectronics, Fudan University, Shanghai 200433, P.R. China
- Zhangjiang Fudan International Innovation Center, Shanghai 201203, China
- National Integrated Circuit Innovation Center, Shanghai 201203, China
| | - Lin Chen
- State Key Laboratory of ASIC and System, School of Microelectronics, Fudan University, Shanghai 200433, P.R. China
- Zhangjiang Fudan International Innovation Center, Shanghai 201203, China
- National Integrated Circuit Innovation Center, Shanghai 201203, China
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25
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Islam MM, Krishnaprasad A, Dev D, Martinez-Martinez R, Okonkwo V, Wu B, Han SS, Bae TS, Chung HS, Touma J, Jung Y, Roy T. Multiwavelength Optoelectronic Synapse with 2D Materials for Mixed-Color Pattern Recognition. ACS NANO 2022; 16:10188-10198. [PMID: 35612988 DOI: 10.1021/acsnano.2c01035] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/25/2023]
Abstract
Neuromorphic visual systems emulating biological retina functionalities have enormous potential for in-sensor computing, with prospects of making artificial intelligence ubiquitous. Conventionally, visual information is captured by an image sensor, stored by memory units, and eventually processed by the machine learning algorithm. Here, we present an optoelectronic synapse device with multifunctional integration of all the processes required for real time object identification. Ultraviolet-visible wavelength-sensitive MoS2 FET channel with infrared sensitive PtTe2/Si gate electrode enables the device to sense, store, and process optical data for a wide range of the electromagnetic spectrum, while maintaining a low dark current. The device exhibits optical stimulation-controlled short-term and long-term potentiation, electrically driven long-term depression, synaptic weight update for multiple wavelengths of light ranging from 300 nm in ultraviolet to 2 μm in infrared. An artificial neural network developed using the extracted weight update parameters of the device can be trained to identify both single wavelength and mixed wavelength patterns. This work demonstrates a device that could potentially be used for realizing a multiwavelength neuromorphic visual system for pattern recognition and object identification.
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Affiliation(s)
- Molla Manjurul Islam
- NanoScience Technology Center, University of Central Florida, Orlando, Florida 32826, United States
- Department of Physics, University of Central Florida, Orlando, Florida 32816, United States
| | - Adithi Krishnaprasad
- NanoScience Technology Center, University of Central Florida, Orlando, Florida 32826, United States
- Department of Electrical and Computer Engineering, University of Central Florida, Orlando, Florida 32816, United States
| | - Durjoy Dev
- NanoScience Technology Center, University of Central Florida, Orlando, Florida 32826, United States
- Department of Electrical and Computer Engineering, University of Central Florida, Orlando, Florida 32816, United States
| | - Ricardo Martinez-Martinez
- NanoScience Technology Center, University of Central Florida, Orlando, Florida 32826, United States
- Department of Electrical and Computer Engineering, University of Central Florida, Orlando, Florida 32816, United States
| | - Victor Okonkwo
- NanoScience Technology Center, University of Central Florida, Orlando, Florida 32826, United States
| | - Benjamin Wu
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, New York 11794, United States
| | - Sang Sub Han
- NanoScience Technology Center, University of Central Florida, Orlando, Florida 32826, United States
| | - Tae-Sung Bae
- Analytical Research Division, Korea Basic Science Institute, Jeonju 54907, South Korea
| | - Hee-Suk Chung
- Analytical Research Division, Korea Basic Science Institute, Jeonju 54907, South Korea
| | - Jimmy Touma
- Air Force Research Lab, Eglin Air Force Base, Florida 32542, United States
| | - Yeonwoong Jung
- NanoScience Technology Center, University of Central Florida, Orlando, Florida 32826, United States
- Department of Electrical and Computer Engineering, University of Central Florida, Orlando, Florida 32816, United States
- Department of Materials Science and Engineering, University of Central Florida, Orlando, Florida 32816, United States
| | - Tania Roy
- NanoScience Technology Center, University of Central Florida, Orlando, Florida 32826, United States
- Department of Physics, University of Central Florida, Orlando, Florida 32816, United States
- Department of Electrical and Computer Engineering, University of Central Florida, Orlando, Florida 32816, United States
- Department of Materials Science and Engineering, University of Central Florida, Orlando, Florida 32816, United States
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26
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Fu Y, Chan YT, Jiang YP, Chang KH, Wu HC, Lai CS, Wang JC. Polarity-Differentiated Dielectric Materials in Monolayer Graphene Charge-Regulated Field-Effect Transistors for an Artificial Reflex Arc and Pain-Modulation System of the Spinal Cord. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2022; 34:e2202059. [PMID: 35619163 DOI: 10.1002/adma.202202059] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Revised: 04/28/2022] [Indexed: 06/15/2023]
Abstract
The nervous system is a vital part of organisms to survive and it endows them with remarkable abilities, such as perception, recognition, regulation, learning, and decision-making, by intertwining myriad neurons. To realize such outstanding efficacies and functions, many artificial devices and systems have been investigated to emulate the operating principles of the nervous system. Here, an artificial reflex arc (ARA) and artificial pain modulation system (APMS) are proposed to imitate the unconscious behaviors of the spinal cord. Gdx Oy - and Alx Oy -based charge-regulated field-effect transistors (CRFETs) with a monolayer graphene channel are fabricated and adopted as inhibitory and excitatory synapses, respectively, under the same pulse signals to mimic the biological reflex arc through a connection with a poly(vinylidene fluoride-co-trifluoroethylene)-based actuator. Additionally, a memristor is integrated with a CRFET as the interneuron to regulate the Dirac point by controlling the voltage drop on the graphene channel, analogous to the descending pain-inhibition system in the spinal cord, to prevent excessive pain perception. The proposed ARA and APMS provide a significant step forward to realizing the functions of the nervous system, giving promising potential for developing future intelligent alarm systems, neuroprosthetics, and neurorobotics.
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Affiliation(s)
- Yi Fu
- Department of Electronic Engineering, Chang Gung University, Guishan Dist, Taoyuan, 33302, Taiwan
| | - Ya-Ting Chan
- Department of Electronic Engineering, Chang Gung University, Guishan Dist, Taoyuan, 33302, Taiwan
| | - Yi-Pei Jiang
- Department of Electronic Engineering, Chang Gung University, Guishan Dist, Taoyuan, 33302, Taiwan
| | - Kuo-Hsuan Chang
- Department of Neurology, Chang Gung Memorial Hospital, Linkou, Guishan Dist, Taoyuan, 33305, Taiwan
- College of Medicine, Chang Gung University, Guishan Dist, Taoyuan, 33302, Taiwan
| | - Hsiu-Chuan Wu
- Department of Neurology, Chang Gung Memorial Hospital, Linkou, Guishan Dist, Taoyuan, 33305, Taiwan
- College of Medicine, Chang Gung University, Guishan Dist, Taoyuan, 33302, Taiwan
| | - Chao-Sung Lai
- Department of Electronic Engineering, Chang Gung University, Guishan Dist, Taoyuan, 33302, Taiwan
- Green Technology Research Center, Chang Gung University, Guishan Dist, Taoyuan, 33302, Taiwan
- Department of Nephrology, Chang Gung Memorial Hospital, Linkou, Guishan Dist, Taoyuan, 33305, Taiwan
- Department of Materials Engineering, Ming Chi University of Technology, Taishan Dist, New Taipei City, 243303, Taiwan
| | - Jer-Chyi Wang
- Department of Electronic Engineering, Chang Gung University, Guishan Dist, Taoyuan, 33302, Taiwan
- Green Technology Research Center, Chang Gung University, Guishan Dist, Taoyuan, 33302, Taiwan
- Department of Neurosurgery, Chang Gung Memorial Hospital, Linkou, Guishan Dist, Taoyuan, 33305, Taiwan
- Department of Electronic Engineering, Ming Chi University of Technology, Taishan Dist, New Taipei City, 243303, Taiwan
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27
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Liang K, Wang R, Huo B, Ren H, Li D, Wang Y, Tang Y, Chen Y, Song C, Li F, Ji B, Wang H, Zhu B. Fully Printed Optoelectronic Synaptic Transistors Based on Quantum Dot-Metal Oxide Semiconductor Heterojunctions. ACS NANO 2022; 16:8651-8661. [PMID: 35451308 DOI: 10.1021/acsnano.2c00439] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Optoelectronic synaptic transistors with hybrid heterostructure channels have been extensively developed to construct artificial visual systems, inspired by the human visual system. However, optoelectronic transistors taking full advantages of superior optoelectronic synaptic behaviors, low-cost processes, low-power consumption, and environmental benignity remained a challenge. Herein, we report a fully printed, high-performance optoelectronic synaptic transistor based on hybrid heterostructures of heavy-metal-free InP/ZnSe core/shell quantum dots (QDs) and n-type SnO2 amorphous oxide semiconductors (AOSs). The elaborately designed heterojunction improves the separation efficiency of photoexcited charges, leading to high photoresponsivity and tunable synaptic weight changes. Under the coordinated modulation of electrical and optical modes, important biological synaptic behaviors, including excitatory postsynaptic current, short/long-term plasticity, and paired-pulse facilitation, were demonstrated with a low power consumption (∼5.6 pJ per event). The InP/ZnSe QD/SnO2 based artificial vision system illustrated a significantly improved accuracy of 91% in image recognition, compared to that of bare SnO2 based counterparts (58%). Combining the outstanding synaptic characteristics of both AOS materials and heterojunction structures, this work provides a printable, low-cost, and high-efficiency strategy to achieve advanced optoelectronic synapses for neuromorphic electronics and artificial intelligence.
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Affiliation(s)
- Kun Liang
- College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310027, China
- Key Laboratory of 3D Micro/Nano Fabrication and Characterization of Zhejiang Province, School of Engineering, Westlake University, Hangzhou 310024, China
| | - Rui Wang
- Key Laboratory of Wide Band Gap Semiconductor Technology, School of Microelectronics, Xidian University, Xi'an 710071, China
| | - Bingbing Huo
- Key Laboratory of 3D Micro/Nano Fabrication and Characterization of Zhejiang Province, School of Engineering, Westlake University, Hangzhou 310024, China
- School of Materials and Engineering, Zhejiang University, Hangzhou 310027, China
| | - Huihui Ren
- Key Laboratory of 3D Micro/Nano Fabrication and Characterization of Zhejiang Province, School of Engineering, Westlake University, Hangzhou 310024, China
- School of Materials and Engineering, Zhejiang University, Hangzhou 310027, China
| | - Dingwei Li
- College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310027, China
- Key Laboratory of 3D Micro/Nano Fabrication and Characterization of Zhejiang Province, School of Engineering, Westlake University, Hangzhou 310024, China
| | - Yan Wang
- College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310027, China
- Key Laboratory of 3D Micro/Nano Fabrication and Characterization of Zhejiang Province, School of Engineering, Westlake University, Hangzhou 310024, China
| | - Yingjie Tang
- College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310027, China
- Key Laboratory of 3D Micro/Nano Fabrication and Characterization of Zhejiang Province, School of Engineering, Westlake University, Hangzhou 310024, China
| | - Yitong Chen
- Key Laboratory of 3D Micro/Nano Fabrication and Characterization of Zhejiang Province, School of Engineering, Westlake University, Hangzhou 310024, China
- School of Materials and Engineering, Zhejiang University, Hangzhou 310027, China
| | - Chunyan Song
- Key Laboratory of 3D Micro/Nano Fabrication and Characterization of Zhejiang Province, School of Engineering, Westlake University, Hangzhou 310024, China
| | - Fanfan Li
- Key Laboratory of 3D Micro/Nano Fabrication and Characterization of Zhejiang Province, School of Engineering, Westlake University, Hangzhou 310024, China
- Key Laboratory of Wide Band Gap Semiconductor Technology, School of Microelectronics, Xidian University, Xi'an 710071, China
| | - Botao Ji
- Key Laboratory of 3D Micro/Nano Fabrication and Characterization of Zhejiang Province, School of Engineering, Westlake University, Hangzhou 310024, China
- Institute of Advanced Technology, Westlake Institute for Advanced Study, Hangzhou 310024, China
| | - Hong Wang
- Key Laboratory of Wide Band Gap Semiconductor Technology, School of Microelectronics, Xidian University, Xi'an 710071, China
| | - Bowen Zhu
- Key Laboratory of 3D Micro/Nano Fabrication and Characterization of Zhejiang Province, School of Engineering, Westlake University, Hangzhou 310024, China
- Institute of Advanced Technology, Westlake Institute for Advanced Study, Hangzhou 310024, China
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28
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Shen J, Cheng Z, Zhou P. Optical and optoelectronic neuromorphic devices based on emerging memory technologies. NANOTECHNOLOGY 2022; 33:372001. [PMID: 35605580 DOI: 10.1088/1361-6528/ac723f] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/18/2022] [Accepted: 05/23/2022] [Indexed: 06/15/2023]
Abstract
As artificial intelligence continues its rapid development, inevitable challenges arise for the mainstream computing hardware to process voluminous data (Big data). The conventional computer system based on von Neumann architecture with separated processor unit and memory is approaching the limit of computational speed and energy efficiency. Thus, novel computing architectures such as in-memory computing and neuromorphic computing based on emerging memory technologies have been proposed. In recent years, light is incorporated into computational devices, beyond the data transmission in traditional optical communications, due to its innate superiority in speed, bandwidth, energy efficiency, etc. Thereinto, photo-assisted and photoelectrical synapses are developed for neuromorphic computing. Additionally, both the storage and readout processes can be implemented in optical domain in some emerging photonic devices to leverage unique properties of photonics. In this review, we introduce typical photonic neuromorphic devices rooted from emerging memory technologies together with corresponding operational mechanisms. In the end, the advantages and limitations of these devices originated from different modulation means are listed and discussed.
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Affiliation(s)
- Jiabin Shen
- State Key Laboratory of ASIC and Systems, School of Microelectronics, Fudan University, Shanghai 200433, People's Republic of China
- Zhangjiang Fudan International Innovation Center, Fudan University, Shanghai 201210, People's Republic of China
| | - Zengguang Cheng
- State Key Laboratory of ASIC and Systems, School of Microelectronics, Fudan University, Shanghai 200433, People's Republic of China
- Zhangjiang Fudan International Innovation Center, Fudan University, Shanghai 201210, People's Republic of China
| | - Peng Zhou
- State Key Laboratory of ASIC and Systems, School of Microelectronics, Fudan University, Shanghai 200433, People's Republic of China
- Zhangjiang Fudan International Innovation Center, Fudan University, Shanghai 201210, People's Republic of China
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29
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Seo D, Ryou H, Hong SW, Seo JH, Shin M, Hwang WS. Synaptic Current Response of a Liquid Ga Electrode via a Surface Electrochemical Redox Reaction in a NaOH Solution. ACS OMEGA 2022; 7:19872-19878. [PMID: 35721935 PMCID: PMC9202024 DOI: 10.1021/acsomega.2c01645] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Accepted: 05/20/2022] [Indexed: 06/15/2023]
Abstract
An ionic device using a liquid Ga electrode in a 1 M NaOH solution is proposed to generate artificial neural spike signals. The oxidation and reduction at the liquid Ga surface were investigated for different bias voltages at 50 °C. When the positive sweep voltage from the starting voltage (V S) of 1 V was applied to the Ga electrode, the oxidation current flowed immediately and decreased exponentially with time. The spike and decay current behavior resembled the polarization and depolarization at the influx and extrusion of Ca2+ in biological synapses. Different average decay times of ∼81 and ∼310 ms were implemented for V S of -2 and -5 V, respectively, to mimic the synaptic responses to short- and long-term plasticity; these decay states can be exploited for application in binary electrochemical memory devices. The oxidation mechanism of liquid Ga was studied. The differences in Ga ion concentration due to V S led to differences in oxidation behavior. Our device is beneficial for the organ cell-machine interface system because liquid Ga is biocompatible and flexible; thus, it can be applied in biocompatible and flexible neuromorphic device development for neuroprosthetics, human cell-machine interface formation, and personal health care monitoring.
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Affiliation(s)
- Dahee Seo
- Department
of Materials Science and Engineering, Korea
Aerospace University, Goyang 10540, Republic of Korea
- Smart
Drone Convergence, Korea Aerospace University, Goyang 10540, Republic of Korea
| | - Heejoong Ryou
- Department
of Materials Science and Engineering, Korea
Aerospace University, Goyang 10540, Republic of Korea
| | - Suck Won Hong
- Department
of Cogno-Mechatronics Engineering, Department of Optics and Mechatronics
Engineering, Pusan National University, Busan 46241, Republic of Korea
| | - Jong Hyun Seo
- Department
of Materials Science and Engineering, Korea
Aerospace University, Goyang 10540, Republic of Korea
| | - Myunghun Shin
- School
of Electronics and Information Engineering, Korea Aerospace University, Goyang 10540, Republic of Korea
| | - Wan Sik Hwang
- Department
of Materials Science and Engineering, Korea
Aerospace University, Goyang 10540, Republic of Korea
- Smart
Drone Convergence, Korea Aerospace University, Goyang 10540, Republic of Korea
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30
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Shin W, Yim J, Bae JH, Lee JK, Hong S, Kim J, Jeong Y, Kwon D, Koo RH, Jung G, Han C, Kim J, Park BG, Kwon D, Lee JH. Synergistic improvement of sensing performance in ferroelectric transistor gas sensors using remnant polarization. MATERIALS HORIZONS 2022; 9:1623-1630. [PMID: 35485256 DOI: 10.1039/d2mh00340f] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Gaseous pollutants, including nitrogen oxides, pose a severe threat to ecosystems and human health; therefore, developing reliable gas-sensing systems to detect them is becoming increasingly important. Among the various options, metal-oxide-based gas sensors have attracted attention due to their capability for real-time monitoring and large response. In particular, in the field of materials science, there has been extensive research into controlling the morphological properties of metal oxides. However, these approaches have limitations in terms of controlling the response, sensitivity, and selectivity after the sensing material is deposited. In this study, we propose a novel method to improve the gas-sensing performance by utilizing the remnant polarization of ferroelectric thin-film transistor (FeTFT) gas sensors. The proposed FeTFT gas sensor has IGZO and HZO as the conducting channel and ferroelectric layer, respectively. It is demonstrated that the response and sensitivity of FeTFT gas sensors can be modulated by engineering the polarization of the ferroelectric layer. The amount of reaction sites in IGZO, including electrons and oxygen vacancy-induced negatively charged oxygen, is changed depending on upward and downward polarization. The results of this study provide an essential foundation for further development of gas sensors with tunable sensing properties.
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Affiliation(s)
- Wonjun Shin
- Department of Electrical and Computer Engineering and Inter-university Semiconductor Research Center, Seoul National University, Seoul 08826, Republic of Korea.
| | - Jiyong Yim
- Department of Electrical Engineering, Inha University, Incheon, Korea.
| | - Jong-Ho Bae
- School of Electrical Engineering, Kookmin University, Seoul 02707, Korea
| | - Jung-Kyu Lee
- Department of Electrical and Computer Engineering and Inter-university Semiconductor Research Center, Seoul National University, Seoul 08826, Republic of Korea.
| | - Seongbin Hong
- Department of Electrical and Computer Engineering and Inter-university Semiconductor Research Center, Seoul National University, Seoul 08826, Republic of Korea.
| | - Jaehyeon Kim
- Department of Electrical and Computer Engineering and Inter-university Semiconductor Research Center, Seoul National University, Seoul 08826, Republic of Korea.
| | - Yujeong Jeong
- Department of Electrical and Computer Engineering and Inter-university Semiconductor Research Center, Seoul National University, Seoul 08826, Republic of Korea.
| | - Dongseok Kwon
- Department of Electrical and Computer Engineering and Inter-university Semiconductor Research Center, Seoul National University, Seoul 08826, Republic of Korea.
| | - Ryun-Han Koo
- Department of Electrical and Computer Engineering and Inter-university Semiconductor Research Center, Seoul National University, Seoul 08826, Republic of Korea.
| | - Gyuweon Jung
- Department of Electrical and Computer Engineering and Inter-university Semiconductor Research Center, Seoul National University, Seoul 08826, Republic of Korea.
| | - Changhyeon Han
- Department of Electrical Engineering, Inha University, Incheon, Korea.
| | - Jeonghan Kim
- Department of Electrical Engineering, Inha University, Incheon, Korea.
| | - Byung-Gook Park
- Department of Electrical and Computer Engineering and Inter-university Semiconductor Research Center, Seoul National University, Seoul 08826, Republic of Korea.
| | - Daewoong Kwon
- Department of Electrical Engineering, Inha University, Incheon, Korea.
| | - Jong-Ho Lee
- Department of Electrical and Computer Engineering and Inter-university Semiconductor Research Center, Seoul National University, Seoul 08826, Republic of Korea.
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Shi J, Jie J, Deng W, Luo G, Fang X, Xiao Y, Zhang Y, Zhang X, Zhang X. A Fully Solution-Printed Photosynaptic Transistor Array with Ultralow Energy Consumption for Artificial-Vision Neural Networks. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2022; 34:e2200380. [PMID: 35243701 DOI: 10.1002/adma.202200380] [Citation(s) in RCA: 36] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/13/2022] [Revised: 02/20/2022] [Indexed: 06/14/2023]
Abstract
Photosynaptic organic field-effect transistors (OFETs) represent a viable pathway to develop bionic optoelectronics. However, the high operating voltage and current of traditional photosynaptic OFETs lead to huge energy consumption greater than that of the real biological synapses, hindering their further development in new-generation visual prosthetics and artificial perception systems. Here, a fully solution-printed photosynaptic OFET (FSP-OFET) with substantial energy consumption reduction is reported, where a source Schottky barrier is introduced to regulate charge-carrier injection, and which operates with a fundamentally different mechanism from traditional devices. The FSP-OFET not only significantly lowers the working voltage and current but also provides extraordinary neuromorphic light-perception capabilities. Consequently, the FSP-OFET successfully emulates visual nervous responses to external light stimuli with ultralow energy consumption of 0.07-34 fJ per spike in short-term plasticity and 0.41-19.87 fJ per spike in long-term plasticity, both approaching the energy efficiency of biological synapses (1-100 fJ). Moreover, an artificial optic-neural network made from an 8 × 8 FSP-OFET array on a flexible substrate shows excellent image recognition and reinforcement abilities at a low energy cost. The designed FSP-OFET offers an opportunity to realize photonic neuromorphic functionality with extremely low energy consumption dissipation.
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Affiliation(s)
- Jialin Shi
- Institute of Functional Nano and Soft Materials (FUNSOM), Jiangsu Key Laboratory for Carbon-Based Functional Materials and Devices, Soochow University, Suzhou, Jiangsu, 215123, P. R. China
| | - Jiansheng Jie
- Institute of Functional Nano and Soft Materials (FUNSOM), Jiangsu Key Laboratory for Carbon-Based Functional Materials and Devices, Soochow University, Suzhou, Jiangsu, 215123, P. R. China
- Macao Institute of Materials Science and Engineering, Macau University of Science and Technology, Taipa, Macau SAR, 999078, P. R. China
| | - Wei Deng
- Institute of Functional Nano and Soft Materials (FUNSOM), Jiangsu Key Laboratory for Carbon-Based Functional Materials and Devices, Soochow University, Suzhou, Jiangsu, 215123, P. R. China
| | - Gan Luo
- Institute of Functional Nano and Soft Materials (FUNSOM), Jiangsu Key Laboratory for Carbon-Based Functional Materials and Devices, Soochow University, Suzhou, Jiangsu, 215123, P. R. China
| | - Xiaochen Fang
- Institute of Functional Nano and Soft Materials (FUNSOM), Jiangsu Key Laboratory for Carbon-Based Functional Materials and Devices, Soochow University, Suzhou, Jiangsu, 215123, P. R. China
| | - Yanling Xiao
- Institute of Functional Nano and Soft Materials (FUNSOM), Jiangsu Key Laboratory for Carbon-Based Functional Materials and Devices, Soochow University, Suzhou, Jiangsu, 215123, P. R. China
| | - Yujian Zhang
- Institute of Functional Nano and Soft Materials (FUNSOM), Jiangsu Key Laboratory for Carbon-Based Functional Materials and Devices, Soochow University, Suzhou, Jiangsu, 215123, P. R. China
| | - Xiujuan Zhang
- Institute of Functional Nano and Soft Materials (FUNSOM), Jiangsu Key Laboratory for Carbon-Based Functional Materials and Devices, Soochow University, Suzhou, Jiangsu, 215123, P. R. China
| | - Xiaohong Zhang
- Institute of Functional Nano and Soft Materials (FUNSOM), Jiangsu Key Laboratory for Carbon-Based Functional Materials and Devices, Soochow University, Suzhou, Jiangsu, 215123, P. R. China
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32
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Kwon O, Oh S, Park H, Jeong SH, Park W, Cho B. In-depth analysis on electrical parameters of floating gate IGZO synaptic transistor affecting pattern recognition accuracy. NANOTECHNOLOGY 2022; 33:215201. [PMID: 35147525 DOI: 10.1088/1361-6528/ac5444] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/29/2021] [Accepted: 02/09/2022] [Indexed: 06/14/2023]
Abstract
The reliable conductance modulation of synaptic devices is key when implementing high-performance neuromorphic systems. Herein, we propose a floating gate indium gallium zinc oxide (IGZO) synaptic device with an aluminum trapping layer to investigate the correlation between its diverse electrical parameters and pattern recognition accuracy. Basic synaptic properties such as excitatory postsynaptic current, paired pulse facilitation, long/short term memory, and long-term potentiation/depression are demonstrated in the IGZO synaptic transistor. The effects of pulse tuning conditions associated with the pulse voltage magnitude, interval, duration, and cycling number of the applied pulses on the conductance update are systematically investigated. It is discovered that both the nonlinearity of the conductance update and cycle-to-cycle variation should be critically considered using an artificial neural network simulator to ensure the high pattern recognition accuracy of Modified National Institute of Standards and Technology (MNIST) handwritten digit images. The highest recognition rate of the MNIST handwritten dataset is 94.06% for the most optimized pulse condition. Finally, a systematic study regarding the synaptic parameters must be performed to optimize the developed synapse device.
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Affiliation(s)
- Ojun Kwon
- Department of Advanced Material Engineering, Chungbuk National University, Chungdae-ro 1, Seowon-Gu, Cheongju, Chungbuk 28644, Republic of Korea
- Department of Urban, Energy, and Environmental Engineering, Chungbuk National University, Chungdae-ro 1, Seowon-Gu, Cheongju, Chungbuk 28644, Republic of Korea
| | - Seyoung Oh
- Department of Advanced Material Engineering, Chungbuk National University, Chungdae-ro 1, Seowon-Gu, Cheongju, Chungbuk 28644, Republic of Korea
- Department of Urban, Energy, and Environmental Engineering, Chungbuk National University, Chungdae-ro 1, Seowon-Gu, Cheongju, Chungbuk 28644, Republic of Korea
| | - Heejeong Park
- Department of Advanced Material Engineering, Chungbuk National University, Chungdae-ro 1, Seowon-Gu, Cheongju, Chungbuk 28644, Republic of Korea
- Department of Urban, Energy, and Environmental Engineering, Chungbuk National University, Chungdae-ro 1, Seowon-Gu, Cheongju, Chungbuk 28644, Republic of Korea
| | - Soo-Hong Jeong
- Department of Advanced Material Engineering, Chungbuk National University, Chungdae-ro 1, Seowon-Gu, Cheongju, Chungbuk 28644, Republic of Korea
- Department of Urban, Energy, and Environmental Engineering, Chungbuk National University, Chungdae-ro 1, Seowon-Gu, Cheongju, Chungbuk 28644, Republic of Korea
| | - Woojin Park
- Department of Advanced Material Engineering, Chungbuk National University, Chungdae-ro 1, Seowon-Gu, Cheongju, Chungbuk 28644, Republic of Korea
- Department of Urban, Energy, and Environmental Engineering, Chungbuk National University, Chungdae-ro 1, Seowon-Gu, Cheongju, Chungbuk 28644, Republic of Korea
| | - Byungjin Cho
- Department of Advanced Material Engineering, Chungbuk National University, Chungdae-ro 1, Seowon-Gu, Cheongju, Chungbuk 28644, Republic of Korea
- Department of Urban, Energy, and Environmental Engineering, Chungbuk National University, Chungdae-ro 1, Seowon-Gu, Cheongju, Chungbuk 28644, Republic of Korea
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33
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Kim HG, Hong DH, Yoo JH, Lee HC. Effect of Process Temperature on Density and Electrical Characteristics of Hf 0.5Zr 0.5O 2 Thin Films Prepared by Plasma-Enhanced Atomic Layer Deposition. NANOMATERIALS (BASEL, SWITZERLAND) 2022; 12:548. [PMID: 35159892 PMCID: PMC8839501 DOI: 10.3390/nano12030548] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Revised: 01/27/2022] [Accepted: 02/02/2022] [Indexed: 02/04/2023]
Abstract
HfxZr1-xO2 (HZO) thin films have excellent potential for application in various devices, including ferroelectric transistors and semiconductor memories. However, such applications are hindered by the low remanent polarization (Pr) and fatigue endurance of these films. To overcome these limitations, in this study, HZO thin films were fabricated via plasma-enhanced atomic layer deposition (PEALD), and the effects of the deposition and post-annealing temperatures on the density, crystallinity, and electrical properties of the thin films were analyzed. The thin films obtained via PEALD were characterized using cross-sectional transmission electron microscopy images and energy-dispersive spectroscopy analysis. An HZO thin film deposited at 180 °C exhibited the highest o-phase proportion as well as the highest density. By contrast, mixed secondary phases were observed in a thin film deposited at 280 °C. Furthermore, a post-annealing temperature of 600 °C yielded the highest thin film density, and the highest 2Pr value and fatigue endurance were obtained for the film deposited at 180 °C and post-annealed at 600 °C. In addition, we developed three different methods to further enhance the density of the films. Consequently, an enhanced maximum density and exceptional fatigue endurance of 2.5 × 107 cycles were obtained.
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Affiliation(s)
| | | | | | - Hee-Chul Lee
- Department of Advanced Materials Engineering, Korea Polytechnic University, Siheung 15073, Korea; (H.-G.K.); (D.-H.H.); (J.-H.Y.)
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34
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Recent Advances in Metal−Oxide Thin−Film Transistors: Flexible/Stretchable Devices, Integrated Circuits, Biosensors and Neuromorphic Applications. COATINGS 2022. [DOI: 10.3390/coatings12020204] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/04/2022]
Abstract
Thin−film transistors using metal oxides have been investigated extensively because of their high transparency, large area, and mass production of metal oxide semiconductors. Compatibility with conventional semiconductor processes, such as photolithography of the metal oxide offers the possibility to develop integrated circuits on a larger scale. In addition, combinations with other materials have enabled the development of sensor applications or neuromorphic devices in recent years. Here, this paper provides a timely overview of metal−oxide−based thin−film transistors focusing on emerging applications, including flexible/stretchable devices, integrated circuits, biosensors, and neuromorphic devices. This overview also revisits recent efforts on metal oxide−based thin−film transistors developed with high compatibility for integration to newly reported applications.
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35
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Lin YC, Yang WC, Chiang YC, Chen WC. Recent Advances in Organic Phototransistors: Nonvolatile Memory, Artificial Synapses, and Photodetectors. SMALL SCIENCE 2022. [DOI: 10.1002/smsc.202100109] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Affiliation(s)
- Yan-Cheng Lin
- Department of Chemical Engineering National Taiwan University Taipei 10617 Taiwan
- Advanced Research Center of Green Materials Science and Technology National Taiwan University Taipei 10617 Taiwan
| | - Wei-Chen Yang
- Department of Chemical Engineering National Taiwan University Taipei 10617 Taiwan
| | - Yun-Chi Chiang
- Department of Chemical Engineering National Taiwan University Taipei 10617 Taiwan
| | - Wen-Chang Chen
- Department of Chemical Engineering National Taiwan University Taipei 10617 Taiwan
- Advanced Research Center of Green Materials Science and Technology National Taiwan University Taipei 10617 Taiwan
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36
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Pei M, Wan C, Chang Q, Guo J, Jiang S, Zhang B, Wang X, Shi Y, Li Y. A Smarter Pavlovian Dog with Optically Modulated Associative Learning in an Organic Ferroelectric Neuromem. RESEARCH (WASHINGTON, D.C.) 2021; 2021:9820502. [PMID: 35024616 PMCID: PMC8715308 DOI: 10.34133/2021/9820502] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Accepted: 11/14/2021] [Indexed: 12/21/2022]
Abstract
Associative learning is a critical learning principle uniting discrete ideas and percepts to improve individuals' adaptability. However, enabling high tunability of the association processes as in biological counterparts and thus integration of multiple signals from the environment, ideally in a single device, is challenging. Here, we fabricate an organic ferroelectric neuromem capable of monadically implementing optically modulated associative learning. This approach couples the photogating effect at the interface with ferroelectric polarization switching, enabling highly tunable optical modulation of charge carriers. Our device acts as a smarter Pavlovian dog exhibiting adjustable associative learning with the training cycles tuned from thirteen to two. In particular, we obtain a large output difference (>103), which is very similar to the all-or-nothing biological sensory/motor neuron spiking with decrementless conduction. As proof-of-concept demonstrations, photoferroelectric coupling-based applications in cryptography and logic gates are achieved in a single device, indicating compatibility with biological and digital data processing.
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Affiliation(s)
- Mengjiao Pei
- National Laboratory of Solid-State Microstructures, School of Electronic Science and Engineering, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing 210093, China
| | - Changjin Wan
- National Laboratory of Solid-State Microstructures, School of Electronic Science and Engineering, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing 210093, China
| | - Qiong Chang
- School of Computing, Tokyo Institute of Technology, Tokyo 152-8550, Japan
| | - Jianhang Guo
- National Laboratory of Solid-State Microstructures, School of Electronic Science and Engineering, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing 210093, China
| | - Sai Jiang
- School of Microelectronics and Control Engineering, Changzhou University, Changzhou 213164, China
| | - Bowen Zhang
- National Laboratory of Solid-State Microstructures, School of Electronic Science and Engineering, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing 210093, China
| | - Xinran Wang
- National Laboratory of Solid-State Microstructures, School of Electronic Science and Engineering, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing 210093, China
| | - Yi Shi
- National Laboratory of Solid-State Microstructures, School of Electronic Science and Engineering, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing 210093, China
| | - Yun Li
- National Laboratory of Solid-State Microstructures, School of Electronic Science and Engineering, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing 210093, China
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37
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Jeong B, Gkoupidenis P, Asadi K. Solution-Processed Perovskite Field-Effect Transistor Artificial Synapses. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2021; 33:e2104034. [PMID: 34609764 DOI: 10.1002/adma.202104034] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/27/2021] [Revised: 09/30/2021] [Indexed: 06/13/2023]
Abstract
Metal halide perovskites are distinctive semiconductors that exhibit both ionic and electronic transport and are promising for artificial synapses. However, developing a 3-terminal transistor artificial synapse with the perovskite channel remains elusive due to the lack of a proper technique to regulate mobile ions in a non-volatile manner. Here, a solution-processed perovskite transistor is reported for artificial synapses through the implementation of a ferroelectric gate. The ferroelectric polarization provides a non-volatile electric field on the perovskite, leading to fixation of the mobile ions and hence modulation of the electronic conductance of the channel. Multi-state channel conductance is realized by partial ferroelectric polarization. The ferroelectric-gated perovskite transistor is successfully used as an artificial synapse that emulates basic synaptic functions such as long-term plasticity with excellent linearity, short-term as well as spike-timing-dependent plasticity. The strategy to regulate ion dynamics in the perovskites using the ferroelectric gate suggests a generic route to employ perovskites for synaptic electronics.
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Affiliation(s)
- Beomjin Jeong
- Max Planck Institute for Polymer Research, Ackermannweg 10, 55128, Mainz, Germany
- Department of Organic Material Science and Engineering, Pusan National University, Busandaehak-ro 63 beongil 2, Geumjeong-gu, Busan, 46241, Republic of Korea
| | | | - Kamal Asadi
- Max Planck Institute for Polymer Research, Ackermannweg 10, 55128, Mainz, Germany
- Department of Physics, University of Bath, Claverton Down, Bath, BA3 3YA, UK
- Centre for Therapeutic Innovation, University of Bath, Claverton Down, Bath, BA3 3YA, UK
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38
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Kwon SM, Kwak JY, Song S, Kim J, Jo C, Cho SS, Nam SJ, Kim J, Park GS, Kim YH, Park SK. Large-Area Pixelized Optoelectronic Neuromorphic Devices with Multispectral Light-Modulated Bidirectional Synaptic Circuits. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2021; 33:e2105017. [PMID: 34553426 DOI: 10.1002/adma.202105017] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Revised: 07/26/2021] [Indexed: 06/13/2023]
Abstract
The complete hardware implementation of an optoelectronic neuromorphic computing system is considered as one of the most promising solutions to realize energy-efficient artificial intelligence. Here, a fully light-driven and scalable optoelectronic neuromorphic circuit with metal-chalcogenide/metal-oxide heterostructure phototransistor and photovoltaic divider is proposed. To achieve wavelength-selective neural operation and hardware-based pattern recognition, multispectral light modulated bidirectional synaptic circuits are utilized as an individual pixel for highly accurate and large-area neuromorphic computing system. The wavelength selective control of photo-generated charges at the heterostructure interface enables the bidirectional synaptic modulation behaviors including the excitatory and inhibitory modulations. More importantly, a 7 × 7 neuromorphic pixel circuit array is demonstrated to show the viability of implementing highly accurate hardware-based pattern training. In both the pixel training and pattern recognition simulation, the neuromorphic circuit array with the bidirectional synaptic modulation exhibits lower training errors and higher recognition rates, respectively.
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Affiliation(s)
- Sung Min Kwon
- School of Electrical and Electronics Engineering, Chung-Ang University, Seoul, 06974, Korea
| | - Jee Young Kwak
- School of Electrical and Electronics Engineering, Chung-Ang University, Seoul, 06974, Korea
| | - Seungho Song
- School of Advanced Materials Science and Engineering, Sungkyunkwan University, Suwon, 16419, Republic of Korea
| | - Jeehoon Kim
- School of Advanced Materials Science and Engineering, Sungkyunkwan University, Suwon, 16419, Republic of Korea
| | - Chanho Jo
- School of Electrical and Electronics Engineering, Chung-Ang University, Seoul, 06974, Korea
| | - Sung Soo Cho
- School of Electrical and Electronics Engineering, Chung-Ang University, Seoul, 06974, Korea
| | - Seung-Ji Nam
- School of Electrical and Electronics Engineering, Chung-Ang University, Seoul, 06974, Korea
| | - Jaehyun Kim
- Department of Chemistry and Materials Research Center, Northwestern University, 2145 Sheridan Road, Evanston, IL, 60208, USA
| | - Gyeong-Su Park
- Department of Material Science and Engineering, Seoul National University, Seoul, 08826, Korea
| | - Yong-Hoon Kim
- School of Advanced Materials Science and Engineering, Sungkyunkwan University, Suwon, 16419, Republic of Korea
| | - Sung Kyu Park
- School of Electrical and Electronics Engineering, Chung-Ang University, Seoul, 06974, Korea
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39
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Chen Y, Wang H, Yao Y, Wang Y, Ma C, Samorì P. Synaptic Plasticity Powering Long-Afterglow Organic Light-Emitting Transistors. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2021; 33:e2103369. [PMID: 34369012 DOI: 10.1002/adma.202103369] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/03/2021] [Revised: 06/03/2021] [Indexed: 06/13/2023]
Abstract
Long-lasting luminescence in optoelectronic devices is highly sought after for applications in optical data storage and display technology. While in light-emitting diodes this is achieved by exploiting long-afterglow organic materials as active components, such a strategy has never been pursued in light-emitting transistors, which are still rather unexplored and whose technological potential is yet to be demonstrated. Herein, the fabrication of long-afterglow organic light-emitting transistors (LAOLETs) is reported whose operation relies on an unprecedented strategy based on a photoinduced synaptic effect in an inorganic indium-gallium-zinc-oxide (IGZO) semiconducting channel layer, to power a persistent electroluminescence in organic light-emitting materials. Oxygen vacancies in the IGZO layer, produced by irradiation at λ = 312 nm, free electrons in excess yielding to a channel conductance increase. Due to the slow recombination kinetics of photogenerated electrons to oxygen vacancies in the channel layer, the organic material can be fueled by postsynaptic current and displays a long-lived light-emission (hundreds of seconds) after ceasing UV irradiation. As a proof-of-concept, the LAOLETs are integrated in active-matrix light-emitting arrays operating as visual UV sensors capable of long-lifetime green-light emission in the irradiated regions.
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Affiliation(s)
- Yusheng Chen
- Université de Strasbourg, CNRS, ISIS, 8 allée Gaspard Monge, Strasbourg, 67000, France
| | - Hanlin Wang
- Université de Strasbourg, CNRS, ISIS, 8 allée Gaspard Monge, Strasbourg, 67000, France
| | - Yifan Yao
- Université de Strasbourg, CNRS, ISIS, 8 allée Gaspard Monge, Strasbourg, 67000, France
| | - Ye Wang
- Université de Strasbourg, CNRS, ISIS, 8 allée Gaspard Monge, Strasbourg, 67000, France
| | - Chun Ma
- Université de Strasbourg, CNRS, ISIS, 8 allée Gaspard Monge, Strasbourg, 67000, France
| | - Paolo Samorì
- Université de Strasbourg, CNRS, ISIS, 8 allée Gaspard Monge, Strasbourg, 67000, France
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40
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Lee M, Nam S, Cho B, Kwon O, Lee HU, Hahm MG, Kim UJ, Son H. Accelerated Learning in Wide-Band-Gap AlN Artificial Photonic Synaptic Devices: Impact on Suppressed Shallow Trap Level. NANO LETTERS 2021; 21:7879-7886. [PMID: 34328342 DOI: 10.1021/acs.nanolett.1c01885] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Artificial synaptic platforms are promising for next-generation semiconductor computing devices; however, state-of-the-art optoelectronic approaches remain challenging, owing to their unstable charge trap states and limited integration. We demonstrate wide-band-gap (WBG) III-V materials for photoelectronic neural networks. Our experimental analysis shows that the enhanced crystallinity of WBG synapses promotes better synaptic characteristics, such as effective multilevel states, a wider dynamic range, and linearity, allowing the better power consumption, training, and recognition accuracy of artificial neural networks. Furthermore, light-frequency-dependent memory characteristics suggest that artificial optoelectronic synapses with improved crystallinity support the transition from short-term potentiation to long-term potentiation, implying a clear emulation of the psychological multistorage model. This is attributed to the charge trapping in deep-level states and suppresses fast decay and nonradiative recombination in shallow traps. We believe that the fingerprints of these WBG synaptic characteristics provide an effective strategy for establishing an artificial optoelectronic synaptic architecture for innovative neuromorphic computing.
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Affiliation(s)
- Moonsang Lee
- Department of Materials Science and Engineering, Inha University, 100 Inha-ro, Michuhol-gu, Incheon 22212, Republic of Korea
| | - Seunghyun Nam
- Department of Materials Science and Engineering, Inha University, 100 Inha-ro, Michuhol-gu, Incheon 22212, Republic of Korea
| | - Byungjin Cho
- Department of Advanced Material Engineering, Chungbuk National University, 1 Chungdae-ro, Seowon-gu, Cheongju, Chungbuk 28644, Republic of Korea
| | - Ojun Kwon
- Department of Advanced Material Engineering, Chungbuk National University, 1 Chungdae-ro, Seowon-gu, Cheongju, Chungbuk 28644, Republic of Korea
| | - Hyun Uk Lee
- Research Center for Materials Analysis, Korea Basic Science Institute (KBSI), Daejeon 34133, Republic of Korea
| | - Myung Gwan Hahm
- Department of Materials Science and Engineering, Inha University, 100 Inha-ro, Michuhol-gu, Incheon 22212, Republic of Korea
| | - Un Jeong Kim
- Imaging Device Laboratory, Samsung Advanced Institute of Technology, Suwon 443-803, Republic of Korea
| | - Hyungbin Son
- School of Integrative Engineering, Chung-Ang University, Seoul 06974, Republic of Korea
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41
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Qin W, Kang BH, Kim HJ. Flexible Artificial Synapses with a Biocompatible Maltose-Ascorbic Acid Electrolyte Gate for Neuromorphic Computing. ACS APPLIED MATERIALS & INTERFACES 2021; 13:34597-34604. [PMID: 34279076 DOI: 10.1021/acsami.1c07073] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
As constructing hardware technology is widely regarded as an important step toward realizing brain-like computers and artificial intelligence systems, the development of artificial synaptic electronics that can simulate biological synaptic functions is an emerging research field. Among the various types of artificial synapses, synaptic transistors using an electrolyte as the gate electrode have been implemented as the high capacitance of the electrolyte increases the driving current and lowers operating voltages. Here, transistors using maltose-ascorbic acid as the proton-conducting electrolyte are proposed. A novel electrolyte composed of maltose and ascorbic acid, both of which are biocompatible, enables the migration of protons. This allows the channel conductance of the transistors to be modulated with the gate input pulse voltage, and fundamental synaptic functions including excitatory postsynaptic current, paired-pulse facilitation, long-term potentiation, and long-term depression can be successfully emulated. Furthermore, the maltose-ascorbic acid electrolyte (MAE)-gated synaptic transistors exhibit high mechanical endurance, with near-linear conductivity modulation and repeatability after 1000 bending cycles under a curvature radius of 5 mm. Benefitting from its excellent biodegradability and biocompatibility, the proposed MAE has potential applications in environmentally friendly, economical, and high-performance neuromorphic electronics, which can be further applied to dermal electronics and implantable electronics in the future.
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Affiliation(s)
- Wei Qin
- School of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, Republic of Korea
| | - Byung Ha Kang
- School of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, Republic of Korea
| | - Hyun Jae Kim
- School of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, Republic of Korea
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42
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Meng JL, Wang TY, He ZY, Chen L, Zhu H, Ji L, Sun QQ, Ding SJ, Bao WZ, Zhou P, Zhang DW. Flexible boron nitride-based memristor for in situ digital and analogue neuromorphic computing applications. MATERIALS HORIZONS 2021; 8:538-546. [PMID: 34821269 DOI: 10.1039/d0mh01730b] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
The data processing efficiency of traditional computers is suffering from the intrinsic limitation of physically separated processing and memory units. Logic-in-memory and brain-inspired neuromorphic computing are promising in-memory computing paradigms for improving the computing efficiency and avoiding high power consumption caused by extra data movement. However, memristors that can conduct digital memcomputing and neuromorphic computing simultaneously are limited by the difference in the information form between digital data and analogue data. In order to solve this problem, this paper proposes a flexible low-dimensional memristor based on boron nitride (BN), which has ultralow-power non-volatile memory characteristic, reliable digital memcomputing capabilities, and integrated ultrafast neuromorphic computing capabilities in a single in situ computing system. The logic-in-memory basis, including FALSE, material implication (IMP), and NAND, are implemented successfully. The power consumption of the proposed memristor per synaptic event (198 fJ) can be as low as biology (fJ level) and the response time (1 μs) of the neuromorphic computing is four orders of magnitude shorter than that of the human brain (10 ms), paving the way for wearable ultrahigh efficient next-generation in-memory computing architectures.
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Affiliation(s)
- Jia-Lin Meng
- State Key Laboratory of ASIC and System, School of Microelectronics, Fudan University, Shanghai 200433, China.
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Kim K, Park JW, Lee D, Cho YH, Kim YS. Precise Turn-On Voltage Control of MIOSM Thin-Film Diodes with Amorphous Indium-Gallium-Zinc Oxide. ACS APPLIED MATERIALS & INTERFACES 2021; 13:878-886. [PMID: 33393755 DOI: 10.1021/acsami.0c17872] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Recently, metal-insulator-oxide semiconductor-metal (MIOSM) thin-film diodes (TFDs) have received attention as next-generation diodes due to their high rectification ratio and broad option on the operating voltage range. Nevertheless, precise turn-on voltage control of the MIOSM TFDs has been required for circuit design convenience. Here, we present a simple and accurate method of controlling the turn-on voltage of MIOSM TFDs. Studies on current-voltage characteristics reveal that controlling carrier injection into trap states in an insulator by oxygen vacancy variation of the oxide semiconductor plays a key role in the turn-on voltage shift of MIOSM TFDs. Moreover, by controlling the trap states in the insulator, the finely tuned turn-on voltages of the MIOSM TFDs are demonstrated for both low-voltage- and high-voltage-driving diodes. MIOSM TFDs with adjustable turn-on voltage, which can be built more efficiently and accurately, are expected to make oxide-based circuit designs more precise and straightforward.
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Affiliation(s)
- Kyungho Kim
- Program in Nano Science and Technology, Graduate School of Convergence Science and Technology, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Republic of Korea
- Samsung Display Company, Ltd, 1 Samsung-ro, Giheung-gu, Yongin-si, Gyeonggi-do 17113, Republic of Korea
| | - Jun-Woo Park
- Program in Nano Science and Technology, Graduate School of Convergence Science and Technology, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Republic of Korea
| | - Donggun Lee
- Program in Nano Science and Technology, Graduate School of Convergence Science and Technology, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Republic of Korea
| | - Yong Hyun Cho
- Program in Nano Science and Technology, Graduate School of Convergence Science and Technology, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Republic of Korea
| | - Youn Sang Kim
- Program in Nano Science and Technology, Graduate School of Convergence Science and Technology, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Republic of Korea
- School of Chemical & Biological Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Republic of Korea
- Advanced Institute of Convergence Technology, 145 Gwanggyo-ro, Yeongtong-gu, Suwon-si, Gyeonggi-do 16229, Republic of Korea
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Yoon SJ, Moon SE, Yoon SM. Implementation of an electrically modifiable artificial synapse based on ferroelectric field-effect transistors using Al-doped HfO 2 thin films. NANOSCALE 2020; 12:13421-13430. [PMID: 32614009 DOI: 10.1039/d0nr02401e] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Human brain-like synaptic behaviors of the ferroelectric field-effect transistors (FeFETs) were emulated by introducing the metal-ferroelectric-metal-insulator-semiconductor (MFMIS) gate stacks employing Al-doped HfO2 (Al:HfO2) ferroelectric thin films even at a low operation voltage. The synaptic plasticity of the MFMIS-FETs could be gradually modulated by the partial polarization characteristics of the Al:HfO2 thin films, which were examined to be dependent on the applied pulse conditions. Based on the ferroelectric polarization switching dynamics of the Al:HfO2 thin films, the proposed devices successfully emulate biological synaptic functions, including excitatory post-synaptic current (EPSC), paired-pulse facilitation (PPF), and spike timing-dependent plasticity (STDP). The channel conductance of the FeFETs could be controlled by partially switching the ferroelectric polarization of the Al:HfO2 gate insulators by means of pulse-number and pulse-amplitude modulations. Furthermore, the 3 × 3 array integrated with the Al:HfO2 MFMIS-FETs was also fabricated, in which electrically modifiable weighted-sum operation could be well verified in the 3 × 3 synapse array configuration.
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Affiliation(s)
- So-Jung Yoon
- Department of Advanced Materials Engineering for Information and Electronics, Kyung Hee University, Yongin, Gyeonggi-do 17104, Korea.
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