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Ko J, Kim D, Nguyen QH, Lee C, Kim N, Lee H, Eo J, Kwon JE, Jeon SY, Jang BC, Im SG, Joo Y. A nonconjugated radical polymer enables bimodal memory and in-sensor computing operation. SCIENCE ADVANCES 2024; 10:eadp0778. [PMID: 39121228 PMCID: PMC11313951 DOI: 10.1126/sciadv.adp0778] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/06/2024] [Accepted: 07/08/2024] [Indexed: 08/11/2024]
Abstract
This study reports intrinsic multimodal memristivity of a nonconjugated radical polymer with ambient stability. Organic memristive devices represent powerful candidates for biorealistic data storage and processing. However, there exists a substantial knowledge gap in realizing the synthetic biorealistic systems capable of effectively emulating the cooperative and multimodal activation processes in biological systems. In addition, conventional organic memristive materials are centered on conjugated small and macromolecules, making them synthetically challenging or difficult to process. In this work, we first describe the intrinsic resistive switching of the radical polymer that resulted in an exceptional state retention of >105 s and on/off ratio of >106. Next, we demonstrate its bimodal cooperative switching, in response to the proton accumulation as a biological input. Last, we expand our system toward an advanced in-sensor computing system. Our research demonstrates a nonconjugated radical polymer with intrinsic memristivity, which is directly applicable to future electronics including data storage, neuromorphics, and in-sensor computing.
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Affiliation(s)
- Jaehyoung Ko
- Institute of Advanced Composite Materials, Korea Institute of Science and Technology (KIST), Wanju-gun, Jeonbuk 55324, Republic of Korea
- Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea
| | - Daeun Kim
- Institute of Advanced Composite Materials, Korea Institute of Science and Technology (KIST), Wanju-gun, Jeonbuk 55324, Republic of Korea
- School of Materials Science and Engineering, Gwangju Institute of Science and Technology, Gwangju 61005, Republic of Korea
| | - Quynh H. Nguyen
- Institute of Advanced Composite Materials, Korea Institute of Science and Technology (KIST), Wanju-gun, Jeonbuk 55324, Republic of Korea
| | - Changhyeon Lee
- Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea
| | - Namju Kim
- School of Electronic and Electrical Engineering, Kyungpook National University, Daegu 41566, Republic of Korea
| | - Hoyeon Lee
- Institute of Advanced Composite Materials, Korea Institute of Science and Technology (KIST), Wanju-gun, Jeonbuk 55324, Republic of Korea
- Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea
| | - Joohwan Eo
- Institute of Advanced Composite Materials, Korea Institute of Science and Technology (KIST), Wanju-gun, Jeonbuk 55324, Republic of Korea
- Department of Materials Science and Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Republic of Korea
| | - Ji Eon Kwon
- Institute of Advanced Composite Materials, Korea Institute of Science and Technology (KIST), Wanju-gun, Jeonbuk 55324, Republic of Korea
- Department of JBNU-KIST Industry Academia Convergence Research, Jeonbuk National University, 567 Baekje-daero, Deokjin-gu, Jeonju, Jeonbuk 54896, Republic of Korea
| | - Seung-Yeol Jeon
- Institute of Advanced Composite Materials, Korea Institute of Science and Technology (KIST), Wanju-gun, Jeonbuk 55324, Republic of Korea
| | - Byung Chul Jang
- School of Electronic and Electrical Engineering, Kyungpook National University, Daegu 41566, Republic of Korea
| | - Sung Gap Im
- Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea
| | - Yongho Joo
- Institute of Advanced Composite Materials, Korea Institute of Science and Technology (KIST), Wanju-gun, Jeonbuk 55324, Republic of Korea
- Division of Nano and Information Technology, KIST School, Korea University of Science and Technology (UST), Jeonbuk 55324, Republic of Korea
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2
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Yang Q, Kang Y, Zhang C, Chen H, Zhang T, Bian Z, Su X, Xu W, Sun J, Wang P, Xu Y, Yu B, Zhao Y. A Plasmonic Optoelectronic Resistive Random-Access Memory for In-Sensor Color Image Cryptography. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2403043. [PMID: 38810136 PMCID: PMC11304321 DOI: 10.1002/advs.202403043] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/22/2024] [Revised: 05/17/2024] [Indexed: 05/31/2024]
Abstract
The optoelectronic resistive random-access memory (RRAM) with the integrated function of perception, storage and intrinsic randomness displays promising applications in the hardware level in-sensor image cryptography. In this work, 2D hexagonal boron nitride based optoelectronic RRAM is fabricated with semitransparent noble metal (Ag or Au) as top electrodes, which can simultaneous capture color image and generate physically unclonable function (PUF) key for in-sensor color image cryptography. Surface plasmons of noble metals enable the strong light absorption to realize an efficient modulation of filament growth at nanoscale. Resistive switching curves show that the optical stimuli can impede the filament aggregation and promote the filament annihilation, which originates from photothermal effects and photogenerated hot electrons in localized surface plasmon resonance of noble metals. By selecting noble metals, the optoelectronic RRAM array can respond to distinct wavelengths and mimic the biological dichromatic cone cells to perform the color perception. Due to the intrinsic and high-quality randomness, the optoelectronic RRAM can produce a PUF key in every exposure cycle, which can be applied in the reconfigurable cryptography. The findings demonstrate an effective strategy to build optoelectronic RRAM for in-sensor color image cryptography applications.
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Affiliation(s)
- Quan Yang
- College of Integrated CircuitsHangzhou Global Scientific and Technological Innovation CentreZhejiang University38 Zheda RoadHangzhou310027China
| | - Yu Kang
- College of Integrated CircuitsHangzhou Global Scientific and Technological Innovation CentreZhejiang University38 Zheda RoadHangzhou310027China
| | - Cheng Zhang
- College of Integrated CircuitsHangzhou Global Scientific and Technological Innovation CentreZhejiang University38 Zheda RoadHangzhou310027China
| | - Haohan Chen
- College of Integrated CircuitsHangzhou Global Scientific and Technological Innovation CentreZhejiang University38 Zheda RoadHangzhou310027China
| | - Tianjiao Zhang
- College of Integrated CircuitsHangzhou Global Scientific and Technological Innovation CentreZhejiang University38 Zheda RoadHangzhou310027China
| | - Zheng Bian
- College of Integrated CircuitsHangzhou Global Scientific and Technological Innovation CentreZhejiang University38 Zheda RoadHangzhou310027China
| | - Xiangwei Su
- College of Integrated CircuitsHangzhou Global Scientific and Technological Innovation CentreZhejiang University38 Zheda RoadHangzhou310027China
| | - Wei Xu
- Research Center for Frontier Fundamental StudiesZhejiang LabHangzhou311100China
| | - Jiabao Sun
- Micro‐Nano Fabrication CenterZhejiang University38 Zheda RoadHangzhou310027China
| | - Pan Wang
- College of Optical Science and EngineeringZhejiang UniversityHangzhou310027China
| | - Yang Xu
- College of Integrated CircuitsHangzhou Global Scientific and Technological Innovation CentreZhejiang University38 Zheda RoadHangzhou310027China
| | - Bin Yu
- College of Integrated CircuitsHangzhou Global Scientific and Technological Innovation CentreZhejiang University38 Zheda RoadHangzhou310027China
| | - Yuda Zhao
- College of Integrated CircuitsHangzhou Global Scientific and Technological Innovation CentreZhejiang University38 Zheda RoadHangzhou310027China
- Key Laboratory of Optoelectronic Chemical Materials and Devices of Ministry of EducationJianghan UniversityWuhan430056China
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Lian M, Gao C, Lin Z, Shan L, Chen C, Zou Y, Cheng E, Liu C, Guo T, Chen W, Chen H. Towards mixed physical node reservoir computing: light-emitting synaptic reservoir system with dual photoelectric output. LIGHT, SCIENCE & APPLICATIONS 2024; 13:179. [PMID: 39085198 PMCID: PMC11291830 DOI: 10.1038/s41377-024-01516-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/25/2024] [Revised: 05/19/2024] [Accepted: 06/29/2024] [Indexed: 08/02/2024]
Abstract
Memristor-based physical reservoir computing holds significant potential for efficiently processing complex spatiotemporal data, which is crucial for advancing artificial intelligence. However, owing to the single physical node mapping characteristic of traditional memristor reservoir computing, it inevitably induces high repeatability of eigenvalues to a certain extent and significantly limits the efficiency and performance of memristor-based reservoir computing for complex tasks. Hence, this work firstly reports an artificial light-emitting synaptic (LES) device with dual photoelectric output for reservoir computing, and a reservoir system with mixed physical nodes is proposed. The system effectively transforms the input signal into two eigenvalue outputs using a mixed physical node reservoir comprising distinct physical quantities, namely optical output with nonlinear optical effects and electrical output with memory characteristics. Unlike previously reported memristor-based reservoir systems, which pursue rich reservoir states in one physical dimension, our mixed physical node reservoir system can obtain reservoir states in two physical dimensions with one input without increasing the number and types of devices. The recognition rate of the artificial light-emitting synaptic reservoir system can achieve 97.22% in MNIST recognition. Furthermore, the recognition task of multichannel images can be realized through the nonlinear mapping of the photoelectric dual reservoir, resulting in a recognition accuracy of 99.25%. The mixed physical node reservoir computing proposed in this work is promising for implementing the development of photoelectric mixed neural networks and material-algorithm collaborative design.
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Affiliation(s)
- Minrui Lian
- Institute of Optoelectronic Display, National & Local United Engineering Lab of Flat Panel Display Technology, Fuzhou University, Fuzhou, 350002, China
- Fujian Science & Technology Innovation Laboratory for Optoelectronic Information of China, Fuzhou, 350100, China
- Joint School of National University of Singapore and Tianjin University, International Campus of Tianjin University, Fuzhou, 350207, China
| | - Changsong Gao
- Institute of Optoelectronic Display, National & Local United Engineering Lab of Flat Panel Display Technology, Fuzhou University, Fuzhou, 350002, China
- Fujian Science & Technology Innovation Laboratory for Optoelectronic Information of China, Fuzhou, 350100, China
| | - Zhenyuan Lin
- Institute of Optoelectronic Display, National & Local United Engineering Lab of Flat Panel Display Technology, Fuzhou University, Fuzhou, 350002, China
- Fujian Science & Technology Innovation Laboratory for Optoelectronic Information of China, Fuzhou, 350100, China
- Joint School of National University of Singapore and Tianjin University, International Campus of Tianjin University, Fuzhou, 350207, China
| | - Liuting Shan
- Institute of Optoelectronic Display, National & Local United Engineering Lab of Flat Panel Display Technology, Fuzhou University, Fuzhou, 350002, China
- Fujian Science & Technology Innovation Laboratory for Optoelectronic Information of China, Fuzhou, 350100, China
| | - Cong Chen
- Institute of Optoelectronic Display, National & Local United Engineering Lab of Flat Panel Display Technology, Fuzhou University, Fuzhou, 350002, China
- Fujian Science & Technology Innovation Laboratory for Optoelectronic Information of China, Fuzhou, 350100, China
| | - Yi Zou
- Institute of Optoelectronic Display, National & Local United Engineering Lab of Flat Panel Display Technology, Fuzhou University, Fuzhou, 350002, China
- Fujian Science & Technology Innovation Laboratory for Optoelectronic Information of China, Fuzhou, 350100, China
| | - Enping Cheng
- Institute of Optoelectronic Display, National & Local United Engineering Lab of Flat Panel Display Technology, Fuzhou University, Fuzhou, 350002, China
- Fujian Science & Technology Innovation Laboratory for Optoelectronic Information of China, Fuzhou, 350100, China
| | - Changfei Liu
- Institute of Optoelectronic Display, National & Local United Engineering Lab of Flat Panel Display Technology, Fuzhou University, Fuzhou, 350002, China
- Fujian Science & Technology Innovation Laboratory for Optoelectronic Information of China, Fuzhou, 350100, China
- Joint School of National University of Singapore and Tianjin University, International Campus of Tianjin University, Fuzhou, 350207, China
| | - Tailiang Guo
- Institute of Optoelectronic Display, National & Local United Engineering Lab of Flat Panel Display Technology, Fuzhou University, Fuzhou, 350002, China
- Fujian Science & Technology Innovation Laboratory for Optoelectronic Information of China, Fuzhou, 350100, China
| | - Wei Chen
- Joint School of National University of Singapore and Tianjin University, International Campus of Tianjin University, Fuzhou, 350207, China
- Department of Chemistry, National University of Singapore, 3 Science Drive 3, Singapore, 117543, Singapore
- Department of Physics, National University of Singapore, 3 Science Drive 3, Singapore, 117543, Singapore
| | - Huipeng Chen
- Institute of Optoelectronic Display, National & Local United Engineering Lab of Flat Panel Display Technology, Fuzhou University, Fuzhou, 350002, China.
- Fujian Science & Technology Innovation Laboratory for Optoelectronic Information of China, Fuzhou, 350100, China.
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Li P, Song H, Sa Z, Liu F, Wang M, Wang G, Wan J, Zang Z, Jiang J, Yang ZX. Tunable synaptic behaviors of solution-processed InGaO films for artificial visual systems. MATERIALS HORIZONS 2024. [PMID: 39072692 DOI: 10.1039/d4mh00396a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/30/2024]
Abstract
Due to their persistent photoconductivity, amorphous metal oxide thin films are promising for construction of artificial visual systems. In this work, large-scale, uniformly distributed amorphous InGaO thin films with an adjustable In/Ga ratio and thickness are prepared successfully by a low-cost environmentally friendly and easy-to-handle solution process for constructing artificial visual systems. With the increase of the In/Ga ratio and film thickness, the number of oxygen vacancies increases, along with the increase of post-synaptic current triggered by illumination, benefiting the transition of short-term plasticity to long-term plasticity. With an optimal In/Ga ratio and film thickness, the conductance response difference at a decay of 0 s between the 1st and the 10th views of a 5 × 5 array InGaO thin film transistor is up to 2.88 μA, along with an increase in the Idecay 30s/Idecay 0s ratio from 45.24% to 53.24%, resulting in a high image clarity and non-volatile artificial visual memory. Furthermore, a three-layer artificial vision network is constructed to evaluate the image recognition capability, exhibiting an accuracy of up to 91.32%. All results promise low-cost and easy-to-handle amorphous InGaO thin films for future visual information processing and image recognition.
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Affiliation(s)
- Pengsheng Li
- School of Physics, Shandong University, Jinan 2510100, China.
| | - Honglin Song
- Hunan Key Laboratory of Nanophotonics and Devices, School of Physics, Central South University, Changsha 410082, China.
| | - Zixu Sa
- School of Physics, Shandong University, Jinan 2510100, China.
| | - Fengjing Liu
- School of Physics, Shandong University, Jinan 2510100, China.
| | - Mingxu Wang
- School of Physics, Shandong University, Jinan 2510100, China.
| | - Guangcan Wang
- School of Physics, Shandong University, Jinan 2510100, China.
| | - Junchen Wan
- School of Physics, Shandong University, Jinan 2510100, China.
| | - Zeqi Zang
- School of Physics, Shandong University, Jinan 2510100, China.
| | - Jie Jiang
- Hunan Key Laboratory of Nanophotonics and Devices, School of Physics, Central South University, Changsha 410082, China.
| | - Zai-Xing Yang
- School of Physics, Shandong University, Jinan 2510100, China.
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Huang Z, Shi W, Wu S, Wang Y, Yang S, Chen H. Pre-sensor computing with compact multilayer optical neural network. SCIENCE ADVANCES 2024; 10:eado8516. [PMID: 39058775 PMCID: PMC11277373 DOI: 10.1126/sciadv.ado8516] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/23/2024] [Accepted: 06/25/2024] [Indexed: 07/28/2024]
Abstract
Moving computation units closer to sensors is becoming a promising approach to addressing bottlenecks in computing speed, power consumption, and data storage. Pre-sensor computing with optical neural networks (ONNs) allows extensive processing. However, the lack of nonlinear activation and dependence on laser input limits the computational capacity, practicality, and scalability. A compact and passive multilayer ONN (MONN) is proposed, which has two convolution layers and an inserted nonlinear layer, performing pre-sensor computations with designed passive masks and a quantum dot film for incoherent light. MONN has an optical length as short as 5 millimeters, two orders of magnitude smaller than state-of-the-art lens-based ONNs. MONN outperforms linear single-layer ONN across various vision tasks, off-loading up to 95% of computationally expensive operations into optics from electronics. Motivated by MONN, a paradigm is emerging for mobile vision, fulfilling the demands for practicality, miniaturization, and low power consumption.
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Affiliation(s)
- Zheng Huang
- Department of Electronic Engineering, Tsinghua University, Beijing 100084, China
- Beijing National Research Center for Information Science and Technology, Beijing 100084, China
| | - Wanxin Shi
- Department of Electronic Engineering, Tsinghua University, Beijing 100084, China
- Beijing National Research Center for Information Science and Technology, Beijing 100084, China
| | - Shukai Wu
- Department of Electronic Engineering, Tsinghua University, Beijing 100084, China
- Beijing National Research Center for Information Science and Technology, Beijing 100084, China
| | - Yaode Wang
- Department of Electronic Engineering, Tsinghua University, Beijing 100084, China
- Beijing National Research Center for Information Science and Technology, Beijing 100084, China
| | - Sigang Yang
- Department of Electronic Engineering, Tsinghua University, Beijing 100084, China
- Beijing National Research Center for Information Science and Technology, Beijing 100084, China
| | - Hongwei Chen
- Department of Electronic Engineering, Tsinghua University, Beijing 100084, China
- Beijing National Research Center for Information Science and Technology, Beijing 100084, China
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Zhang QR, Ouyang WL, Wang XM, Yang F, Chen JG, Wen ZX, Liu JX, Wang G, Liu Q, Liu FC. Dynamic memristor for physical reservoir computing. NANOSCALE 2024; 16:13847-13860. [PMID: 38984618 DOI: 10.1039/d4nr01445f] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/11/2024]
Abstract
Reservoir computing (RC) has attracted considerable attention for its efficient handling of temporal signals and lower training costs. As a nonlinear dynamic system, RC can map low-dimensional inputs into high-dimensional spaces and implement classification using a simple linear readout layer. The memristor exhibits complex dynamic characteristics due to its internal physical processes, which renders them an ideal choice for the implementation of physical reservoir computing (PRC) systems. This review focuses on PRC systems based on memristors, explaining the resistive switching mechanism at the device level and emphasizing the tunability of their dynamic behavior. The development of memristor-based reservoir computing systems is highlighted, along with discussions on the challenges faced by this field and potential future research directions.
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Affiliation(s)
- Qi-Rui Zhang
- Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou 313099, China.
- School of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Wei-Lun Ouyang
- School of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Xue-Mei Wang
- School of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Fan Yang
- School of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Jian-Gang Chen
- School of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Zhi-Xing Wen
- Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou 313099, China.
- School of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Jia-Xin Liu
- School of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Ge Wang
- School of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Qing Liu
- School of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Fu-Cai Liu
- Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou 313099, China.
- School of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China
- State Key Laboratory of Electronic Thin Films and Integrated Devices, University of Electronic Science and Technology of China, Chengdu 610054, China
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Chang KC, Feng X, Duan X, Liu H, Liu Y, Peng Z, Lin X, Li L. Integrating ultraviolet sensing and memory functions in gallium nitride-based optoelectronic devices. NANOSCALE HORIZONS 2024; 9:1166-1174. [PMID: 38668875 DOI: 10.1039/d3nh00560g] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/25/2024]
Abstract
Optoelectronic devices present a promising avenue for emulating the human visual system. However, existing devices struggle to maintain optical image information after removing external stimuli, preventing the integration of image perception and memory. The development of optoelectronic memory devices offers a feasible solution to bridge this gap. Simultaneously, the artificial vision for perceiving and storing ultraviolet (UV) images is particularly important because UV light carries information imperceptible to the naked eye. This study introduces a multi-level UV optoelectronic memory based on gallium nitride (GaN), seamlessly integrating UV sensing and memory functions within a single device. The embedded SiO2 side-gates around source and drain regions effectively extend the lifetime of photo-generated carriers, enabling dual-mode storage of UV signals in terms of threshold voltage and ON-state current. The optoelectronic memory demonstrates excellent robustness with the retention time exceeding 4 × 104 s and programming/erasing cycles surpassing 1 × 105. Adjusting the gate voltage achieves five distinct storage states, each characterized by excellent retention, and efficiently modulates erasure times for rapid erasure. Furthermore, the integration of the GaN optoelectronic memory array successfully captures and stably stores specific UV images for over 7 days. The study marks a significant stride in optoelectronic memories, showcasing their potential in applications requiring prolonged retention.
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Affiliation(s)
- Kuan-Chang Chang
- School of Electronic and Computer Engineering, Peking University Shenzhen Graduate School, Shenzhen, 518055, China.
| | - Xibei Feng
- School of Electronic and Computer Engineering, Peking University Shenzhen Graduate School, Shenzhen, 518055, China.
| | - Xinqing Duan
- School of Electronic and Computer Engineering, Peking University Shenzhen Graduate School, Shenzhen, 518055, China.
| | - Huangbai Liu
- School of Electronic and Computer Engineering, Peking University Shenzhen Graduate School, Shenzhen, 518055, China.
| | - Yanxin Liu
- School of Electronic and Computer Engineering, Peking University Shenzhen Graduate School, Shenzhen, 518055, China.
| | - Zehui Peng
- School of Electronic and Computer Engineering, Peking University Shenzhen Graduate School, Shenzhen, 518055, China.
| | - Xinnan Lin
- Anhui Engineering Research Center of Vehicle Display Integrated Systems, Joint Discipline Key Laboratory of Touch Display Materials and Devices, School of Integrated Circuits, Anhui Polytechnic University, Wuhu 241000, China.
| | - Lei Li
- School of Electronic and Computer Engineering, Peking University Shenzhen Graduate School, Shenzhen, 518055, China.
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Lv Z, Zhu S, Wang Y, Ren Y, Luo M, Wang H, Zhang G, Zhai Y, Zhao S, Zhou Y, Jiang M, Leng YB, Han ST. Development of Bio-Voltage Operated Humidity-Sensory Neurons Comprising Self-Assembled Peptide Memristors. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024:e2405145. [PMID: 38877385 DOI: 10.1002/adma.202405145] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/10/2024] [Revised: 06/11/2024] [Indexed: 06/16/2024]
Abstract
Biomimetic humidity sensors offer a low-power approach for respiratory monitoring in early lung-disease diagnosis. However, balancing miniaturization and energy efficiency remains challenging. This study addresses this issue by introducing a bioinspired humidity-sensing neuron comprising a self-assembled peptide nanowire (NW) memristor with unique proton-coupled ion transport. The proposed neuron shows a low Ag+ activation energy owing to the NW and redox activity of the tyrosine (Tyr)-rich peptide in the system, facilitating ultralow electric-field-driven threshold switching and a high energy efficiency. Additionally, Ag+ migration in the system can be controlled by a proton source owing to the hydrophilic nature of the phenolic hydroxyl group in Tyr, enabling the humidity-based control of the conductance state of the memristor. Furthermore, a memristor-based neuromorphic perception neuron that can encode humidity signals into spikes is proposed. The spiking characteristics of this neuron can be modulated to emulate the strength-modulated spike-frequency characteristics of biological neurons. A three-layer spiking neural network with input neurons comprising these highly tunable humidity perception neurons shows an accuracy of 92.68% in lung-disease diagnosis. This study paves the way for developing bioinspired self-assembly strategies to construct neuromorphic perception systems, bridging the gap between artificial and biological sensing and processing paradigms.
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Affiliation(s)
- Ziyu Lv
- College of Electronics and Information Engineering, Shenzhen University, Shenzhen, 518060, P. R. China
| | - Shirui Zhu
- Institute of Microscale Optoelectronics, Shenzhen University, Shenzhen, 518060, P. R. China
| | - Yan Wang
- School of Microelectronics, Hefei University of Technology, Hefei, 230009, P. R. China
| | - Yanyun Ren
- 2020 X-Lab, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai, 200050, China
| | - Mingtao Luo
- College of Electronics and Information Engineering, Shenzhen University, Shenzhen, 518060, P. R. China
| | - Hanning Wang
- College of Electronics and Information Engineering, Shenzhen University, Shenzhen, 518060, P. R. China
| | - Guohua Zhang
- College of Electronics and Information Engineering, Shenzhen University, Shenzhen, 518060, P. R. China
| | - Yongbiao Zhai
- College of Electronics and Information Engineering, Shenzhen University, Shenzhen, 518060, P. R. China
| | - Shilong Zhao
- School of Electronic Information Engineering, Foshan University, Foshan, 528000, P. R. China
| | - Ye Zhou
- Institute for Advanced Study, Shenzhen University, Shenzhen, 518060, P. R. China
| | - Minghao Jiang
- College of Electronics and Information Engineering, Shenzhen University, Shenzhen, 518060, P. R. China
| | - Yan-Bing Leng
- Institute of Microscale Optoelectronics, Shenzhen University, Shenzhen, 518060, P. R. China
| | - Su-Ting Han
- Department of Applied Biology and Chemical Technology, The Hong Kong Polytechnic University, Kowloon, Hong Kong, 999077, P. R. China
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Xia J, Gao C, Peng C, Liu Y, Chen PA, Wei H, Jiang L, Liao L, Chen H, Hu Y. Multidimensional Deep Ultraviolet (DUV) Synapses Based on Organic/Perovskite Semiconductor Heterojunction Transistors for Antispoofing Facial Recognition Systems. NANO LETTERS 2024; 24:6673-6682. [PMID: 38779991 DOI: 10.1021/acs.nanolett.4c01356] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/25/2024]
Abstract
Reliably discerning real human faces from fake ones, known as antispoofing, is crucial for facial recognition systems. While neuromorphic systems offer integrated sensing-memory-processing functions, they still struggle with efficient antispoofing techniques. Here we introduce a neuromorphic facial recognition system incorporating multidimensional deep ultraviolet (DUV) optoelectronic synapses to address these challenges. To overcome the complexity and high cost of producing DUV synapses using traditional wide-bandgap semiconductors, we developed a low-temperature (≤70 °C) solution process for fabricating DUV synapses based on PEA2PbBr4/C8-BTBT heterojunction field-effect transistors. This method enables the large-scale (4-in.), uniform, and transparent production of DUV synapses. These devices respond to both DUV and visible light, showing multidimensional features. Leveraging the unique ability of the multidimensional DUV synapse (MDUVS) to discriminate real human skin from artificial materials, we have achieved robust neuromorphic facial recognition with antispoofing capability, successfully identifying genuine human faces with an accuracy exceeding 92%.
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Affiliation(s)
- Jiangnan Xia
- International Science and Technology Innovation Cooperation Base for Advanced Display Technologies of Hunan Province School of Physics and Electronics, Hunan University, Changsha 410082, China
- Changsha Semiconductor Technology and Application Innovation Research Institute College of Semiconductors (College of Integrated Circuits), Hunan University, Changsha 410082, China
| | - Changsong Gao
- Institute of Optoelectronic Display National and Local United Engineering Lab of Flat Panel Display Technology, Fuzhou University, Fuzhou 350002, China
| | - Chengyuan Peng
- International Science and Technology Innovation Cooperation Base for Advanced Display Technologies of Hunan Province School of Physics and Electronics, Hunan University, Changsha 410082, China
- Changsha Semiconductor Technology and Application Innovation Research Institute College of Semiconductors (College of Integrated Circuits), Hunan University, Changsha 410082, China
- Shenzhen Research Institute of Hunan University, Shenzhen 518063, China
| | - Yu Liu
- International Science and Technology Innovation Cooperation Base for Advanced Display Technologies of Hunan Province School of Physics and Electronics, Hunan University, Changsha 410082, China
- Changsha Semiconductor Technology and Application Innovation Research Institute College of Semiconductors (College of Integrated Circuits), Hunan University, Changsha 410082, China
- Shenzhen Research Institute of Hunan University, Shenzhen 518063, China
| | - Ping-An Chen
- International Science and Technology Innovation Cooperation Base for Advanced Display Technologies of Hunan Province School of Physics and Electronics, Hunan University, Changsha 410082, China
- Changsha Semiconductor Technology and Application Innovation Research Institute College of Semiconductors (College of Integrated Circuits), Hunan University, Changsha 410082, China
- Shenzhen Research Institute of Hunan University, Shenzhen 518063, China
| | - Huan Wei
- International Science and Technology Innovation Cooperation Base for Advanced Display Technologies of Hunan Province School of Physics and Electronics, Hunan University, Changsha 410082, China
- Changsha Semiconductor Technology and Application Innovation Research Institute College of Semiconductors (College of Integrated Circuits), Hunan University, Changsha 410082, China
- Shenzhen Research Institute of Hunan University, Shenzhen 518063, China
| | - Lang Jiang
- Beijing National Laboratory for Molecular Sciences, Institute of Chemistry Chinese Academy of Sciences, Beijing 100190, China
| | - Lei Liao
- International Science and Technology Innovation Cooperation Base for Advanced Display Technologies of Hunan Province School of Physics and Electronics, Hunan University, Changsha 410082, China
| | - Huipeng Chen
- Institute of Optoelectronic Display National and Local United Engineering Lab of Flat Panel Display Technology, Fuzhou University, Fuzhou 350002, China
| | - Yuanyuan Hu
- International Science and Technology Innovation Cooperation Base for Advanced Display Technologies of Hunan Province School of Physics and Electronics, Hunan University, Changsha 410082, China
- Changsha Semiconductor Technology and Application Innovation Research Institute College of Semiconductors (College of Integrated Circuits), Hunan University, Changsha 410082, China
- Shenzhen Research Institute of Hunan University, Shenzhen 518063, China
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10
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Wang Z, Li M, Yang H, Shao S, Li J, Deng M, Kang K, Fang Y, Wang H, Zhao J. Enhancement-Mode Carbon Nanotube Optoelectronic Synaptic Transistors with Large and Controllable Threshold Voltage Modulation Window for Broadband Flexible Vision Systems. ACS NANO 2024; 18:14298-14311. [PMID: 38787538 DOI: 10.1021/acsnano.4c00166] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/25/2024]
Abstract
The development of large-scale integration of optoelectronic neuromorphic devices with ultralow power consumption and broadband responses is essential for high-performance bionics vision systems. In this work, we developed a strategy to construct large-scale (40 × 30) enhancement-mode carbon nanotube optoelectronic synaptic transistors with ultralow power consumption (33.9 aJ per pulse) and broadband responses (from 365 to 620 nm) using low-work function yttrium (Y)-gate electrodes and the mixture of eco-friendly photosensitive Ag2S quantum dots (QDs) and ionic liquids (ILs)-cross-linking-poly(4-vinylphenol) (PVP) (ILs-c-PVP) as the dielectric layers. Solution-processable carbon nanotube thin-film transistors (TFTs) showed enhancement-mode characteristics with the wide and controllable threshold voltage window (-1 V∼0 V) owing to use of the low-work-function Y-gate electrodes. It is noted that carbon nanotube optoelectronic synaptic transistors exhibited high on/off ratios (>106), small hysteresis and low operating voltage (≤2 V), and enhancement mode even under the illumination of ultraviolet (UV, 365 nm), blue (450 nm), and green (550 nm) to red (620 nm) pulse lights when introducing eco-friendly Ag2S QDs in dielectric layers, demonstrating that they have the strong fault-tolerant ability for the threshold voltage drifts caused by various manufacturing scenarios. Furthermore, some important bionic functions including a high paired pulse facilitation index (PPF index, up to 290%), learning and memory function with the long duration (200 s), and rapid recovery (2 s). Pavlov's dog experiment (retention time up to 20 min) and visual memory forgetting experiments (the duration of high current for 180 s) are also demonstrated. Significantly, the optoelectronic synaptic transistors can be used to simulate the adaptive process of vision in varying light conditions, and we demonstrated the dynamic transition of light adaptation to dark adaptation based on light-induced conditional behavior. This work undoubtedly provides valuable insights for the future development of artificial vision systems.
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Affiliation(s)
- Zebin Wang
- Institute of Nano Science and Technology, University of Science and Technology of China, No. 166 Ren Ai Road, Suzhou Industrial Park, Suzhou, Jiangsu Province 215123, PR China
- School of Nano-Tech and Nano-Bionics, University of Science and Technology of China, No. 398 Ruoshui Road, Suzhou Industrial Park, Suzhou, Jiangsu Province 215123, PR China
- Division of Nanodevices and Related Nanomaterials, Suzhou Institute of Nano-Tech and Nano-Bionics, Chinese Academy of Sciences, No. 398 Ruoshui Road, Suzhou Industrial Park, Suzhou, Jiangsu Province 215123, PR China
| | - Min Li
- School of Nano-Tech and Nano-Bionics, University of Science and Technology of China, No. 398 Ruoshui Road, Suzhou Industrial Park, Suzhou, Jiangsu Province 215123, PR China
- Division of Nanodevices and Related Nanomaterials, Suzhou Institute of Nano-Tech and Nano-Bionics, Chinese Academy of Sciences, No. 398 Ruoshui Road, Suzhou Industrial Park, Suzhou, Jiangsu Province 215123, PR China
| | - Hongchao Yang
- School of Nano-Tech and Nano-Bionics, University of Science and Technology of China, No. 398 Ruoshui Road, Suzhou Industrial Park, Suzhou, Jiangsu Province 215123, PR China
| | - Shuangshuang Shao
- School of Nano-Tech and Nano-Bionics, University of Science and Technology of China, No. 398 Ruoshui Road, Suzhou Industrial Park, Suzhou, Jiangsu Province 215123, PR China
- Division of Nanodevices and Related Nanomaterials, Suzhou Institute of Nano-Tech and Nano-Bionics, Chinese Academy of Sciences, No. 398 Ruoshui Road, Suzhou Industrial Park, Suzhou, Jiangsu Province 215123, PR China
| | - Jiaqi Li
- School of Nano-Tech and Nano-Bionics, University of Science and Technology of China, No. 398 Ruoshui Road, Suzhou Industrial Park, Suzhou, Jiangsu Province 215123, PR China
- Division of Nanodevices and Related Nanomaterials, Suzhou Institute of Nano-Tech and Nano-Bionics, Chinese Academy of Sciences, No. 398 Ruoshui Road, Suzhou Industrial Park, Suzhou, Jiangsu Province 215123, PR China
| | - Meng Deng
- Institute of Nano Science and Technology, University of Science and Technology of China, No. 166 Ren Ai Road, Suzhou Industrial Park, Suzhou, Jiangsu Province 215123, PR China
- School of Nano-Tech and Nano-Bionics, University of Science and Technology of China, No. 398 Ruoshui Road, Suzhou Industrial Park, Suzhou, Jiangsu Province 215123, PR China
- Division of Nanodevices and Related Nanomaterials, Suzhou Institute of Nano-Tech and Nano-Bionics, Chinese Academy of Sciences, No. 398 Ruoshui Road, Suzhou Industrial Park, Suzhou, Jiangsu Province 215123, PR China
| | - Kaixiang Kang
- School of Nano-Tech and Nano-Bionics, University of Science and Technology of China, No. 398 Ruoshui Road, Suzhou Industrial Park, Suzhou, Jiangsu Province 215123, PR China
- Division of Nanodevices and Related Nanomaterials, Suzhou Institute of Nano-Tech and Nano-Bionics, Chinese Academy of Sciences, No. 398 Ruoshui Road, Suzhou Industrial Park, Suzhou, Jiangsu Province 215123, PR China
| | - Yuxiao Fang
- School of Nano-Tech and Nano-Bionics, University of Science and Technology of China, No. 398 Ruoshui Road, Suzhou Industrial Park, Suzhou, Jiangsu Province 215123, PR China
- Division of Nanodevices and Related Nanomaterials, Suzhou Institute of Nano-Tech and Nano-Bionics, Chinese Academy of Sciences, No. 398 Ruoshui Road, Suzhou Industrial Park, Suzhou, Jiangsu Province 215123, PR China
| | - Hua Wang
- Key Laboratory of Interface Science and Engineering in Advanced Materials of Ministry of Education, Taiyuan University of Technology, NO.79, Yingze West Main Street, Taiyuan, Shanxi Province 030024, P.R. China
| | - Jianwen Zhao
- School of Nano-Tech and Nano-Bionics, University of Science and Technology of China, No. 398 Ruoshui Road, Suzhou Industrial Park, Suzhou, Jiangsu Province 215123, PR China
- Division of Nanodevices and Related Nanomaterials, Suzhou Institute of Nano-Tech and Nano-Bionics, Chinese Academy of Sciences, No. 398 Ruoshui Road, Suzhou Industrial Park, Suzhou, Jiangsu Province 215123, PR China
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11
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Hou X, Liu Y, Bai S, Yu S, Huang H, Yang K, Li C, Peng Z, Zhao X, Zhou X, Xu G, Long S. Pyroelectric Photoconductive Diode for Highly Sensitive and Fast DUV Detection. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024; 36:e2314249. [PMID: 38564779 DOI: 10.1002/adma.202314249] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/27/2023] [Revised: 02/29/2024] [Indexed: 04/04/2024]
Abstract
Detecting high-energy photons from the deep ultraviolet (DUV) to X-rays is vital in security, medicine, industry, and science. Wide bandgap (WBG) semiconductors exhibit great potential for detecting high-energy photons. However, the implementation of highly sensitive and high-speed detectors based on WBG semiconductors has been a huge challenge due to the inevitable deep level traps and the lack of appropriate device structure engineering. Here, a sensitive and fast pyroelectric photoconductive diode (PPD), which couples the interface pyroelectric effect with the photoconductive effect based on tailored polycrystal Ga-rich GaOx (PGR-GaOx) Schottky photodiode, is first proposed. The PPD device exhibits ultrahigh detection performance for DUV and X-ray light. The responsivity for DUV light and sensitivity for X-ray are up to 104 A W-1 and 105 µC Gyair -1 cm-2, respectively. Especially, the interface pyroelectric effect induced by polar symmetry in the depletion region of the PGR-GaOx can significantly improve the response speed of the device by 105 times. Furthermore, the potential of the device is demonstrated for imaging enhancement systems with low power consumption and high sensitivity. This work fully excavates the potential of the pyroelectric effect for detectors and provides a novel design strategy to achieve sensitive and high-speed detectors.
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Affiliation(s)
- Xiaohu Hou
- School of Microelectronics, University of Science and Technology of China, Hefei, 230026, China
| | - Yan Liu
- School of Microelectronics, University of Science and Technology of China, Hefei, 230026, China
| | - Shiyu Bai
- School of Microelectronics, University of Science and Technology of China, Hefei, 230026, China
| | - Shunjie Yu
- School of Microelectronics, University of Science and Technology of China, Hefei, 230026, China
| | - Hong Huang
- School of Microelectronics, University of Science and Technology of China, Hefei, 230026, China
| | - Kai Yang
- School of Microelectronics, University of Science and Technology of China, Hefei, 230026, China
| | - Chen Li
- School of Microelectronics, University of Science and Technology of China, Hefei, 230026, China
| | - Zhixin Peng
- School of Microelectronics, University of Science and Technology of China, Hefei, 230026, China
| | - Xiaolong Zhao
- School of Microelectronics, University of Science and Technology of China, Hefei, 230026, China
| | - Xuanze Zhou
- School of Microelectronics, University of Science and Technology of China, Hefei, 230026, China
| | - Guangwei Xu
- School of Microelectronics, University of Science and Technology of China, Hefei, 230026, China
| | - Shibing Long
- School of Microelectronics, University of Science and Technology of China, Hefei, 230026, China
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12
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Liu S, Zhong X, Li Y, Guo B, He Z, Wu Z, Liu S, Guo Y, Shi X, Chen W, Duan H, Zeng J, Liu G. A Self-Oscillated Organic Synapse for In-Memory Two-Factor Authentication. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2401080. [PMID: 38520711 DOI: 10.1002/advs.202401080] [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: 01/29/2024] [Revised: 03/02/2024] [Indexed: 03/25/2024]
Abstract
Entering the era of AI 2.0, bio-inspired target recognition facilitates life. However, target recognition may suffer from some risks when the target is hijacked. Therefore, it is significantly important to provide an encryption process prior to neuromorphic computing. In this work, enlightened from time-varied synaptic rule, an in-memory asymmetric encryption as pre-authentication is utilized with subsequent convolutional neural network (ConvNet) for target recognition, achieving in-memory two-factor authentication (IM-2FA). The unipolar self-oscillated synaptic behavior is adopted to function as in-memory asymmetric encryption, which can greatly decrease the complexity of the peripheral circuit compared to bipolar stimulation. Results show that without passing the encryption process with suitable weights at the correct time, the ConvNet for target recognition will not work properly with an extremely low accuracy lower than 0.86%, thus effectively blocking out the potential risks of involuntary access. When a set of correct weights is evolved at a suitable time, a recognition rate as high as 99.82% can be implemented for target recognition, which verifies the effectiveness of the IM-2FA strategy.
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Affiliation(s)
- Shuzhi Liu
- Department of Micro/Nano Electronics, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China
- School of Chemistry and Chemical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Xiaolong Zhong
- Department of Micro/Nano Electronics, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Yuxuan Li
- School of Chemistry and Chemical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Bingjie Guo
- School of Chemistry and Chemical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Zhilong He
- School of Chemistry and Chemical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Zhixin Wu
- Department of Micro/Nano Electronics, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Sixian Liu
- Department of Micro/Nano Electronics, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Yanbo Guo
- Department of Micro/Nano Electronics, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Xiaoling Shi
- Department of Micro/Nano Electronics, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Weilin Chen
- Department of Micro/Nano Electronics, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Hongxiao Duan
- Department of Micro/Nano Electronics, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Jianmin Zeng
- Department of Micro/Nano Electronics, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Gang Liu
- Department of Micro/Nano Electronics, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China
- School of Chemistry and Chemical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China
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13
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Zhang X, Liu D, Liu S, Cai Y, Shan L, Chen C, Chen H, Liu Y, Guo T, Chen H. Toward Intelligent Display with Neuromorphic Technology. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024; 36:e2401821. [PMID: 38567884 DOI: 10.1002/adma.202401821] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/02/2024] [Revised: 03/19/2024] [Indexed: 04/16/2024]
Abstract
In the era of the Internet and the Internet of Things, display technology has evolved significantly toward full-scene display and realistic display. Incorporating "intelligence" into displays is a crucial technical approach to meet the demands of this development. Traditional display technology relies on distributed hardware systems to achieve intelligent displays but encounters challenges stemming from the physical separation of sensing, processing, and light-emitting modules. The high energy consumption and data transformation delays limited the development of intelligence display, breaking the physical separation is crucial to overcoming the bottlenecks of intelligence display technology. Inspired by the biological neural system, neuromorphic technology with all-in-one features is widely employed across various fields. It proves effective in reducing system power consumption, facilitating frequent data transformation, and enabling cross-scene integration. Neuromorphic technology shows great potential to overcome display technology bottlenecks, realizing the full-scene display and realistic display with high efficiency and low power consumption. This review offers a comprehensive summary of recent advancements in the application of neuromorphic technology in displays, with a focus on interoperability. This work delves into its state-of-the-art designs and potential future developments aimed at revolutionizing display technology.
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Affiliation(s)
- Xianghong Zhang
- Institute of Optoelectronic Display, National and Local United Engineering Lab of Flat Panel Display Technology, Fuzhou University, Fuzhou, 350002, China
- Fujian Science and Technology Innovation Laboratory for Optoelectronic Information of China, Fuzhou, Fujian, 350100, China
| | - Di Liu
- Institute of Optoelectronic Display, National and Local United Engineering Lab of Flat Panel Display Technology, Fuzhou University, Fuzhou, 350002, China
- Fujian Science and Technology Innovation Laboratory for Optoelectronic Information of China, Fuzhou, Fujian, 350100, China
| | - Shuai Liu
- Institute of Optoelectronic Display, National and Local United Engineering Lab of Flat Panel Display Technology, Fuzhou University, Fuzhou, 350002, China
- Fujian Science and Technology Innovation Laboratory for Optoelectronic Information of China, Fuzhou, Fujian, 350100, China
| | - Yongjie Cai
- Institute of Optoelectronic Display, National and Local United Engineering Lab of Flat Panel Display Technology, Fuzhou University, Fuzhou, 350002, China
- Fujian Science and Technology Innovation Laboratory for Optoelectronic Information of China, Fuzhou, Fujian, 350100, China
| | - Liuting Shan
- Institute of Optoelectronic Display, National and Local United Engineering Lab of Flat Panel Display Technology, Fuzhou University, Fuzhou, 350002, China
- Fujian Science and Technology Innovation Laboratory for Optoelectronic Information of China, Fuzhou, Fujian, 350100, China
| | - Cong Chen
- Institute of Optoelectronic Display, National and Local United Engineering Lab of Flat Panel Display Technology, Fuzhou University, Fuzhou, 350002, China
- Fujian Science and Technology Innovation Laboratory for Optoelectronic Information of China, Fuzhou, Fujian, 350100, China
| | - Huimei Chen
- Institute of Optoelectronic Display, National and Local United Engineering Lab of Flat Panel Display Technology, Fuzhou University, Fuzhou, 350002, China
- Fujian Science and Technology Innovation Laboratory for Optoelectronic Information of China, Fuzhou, Fujian, 350100, China
| | - Yaqian Liu
- School of Electronics and Information, Zhengzhou University of Light Industry, Zhengzhou, Henan, 450002, China
| | - Tailiang Guo
- Institute of Optoelectronic Display, National and Local United Engineering Lab of Flat Panel Display Technology, Fuzhou University, Fuzhou, 350002, China
- Fujian Science and Technology Innovation Laboratory for Optoelectronic Information of China, Fuzhou, Fujian, 350100, China
| | - Huipeng Chen
- Institute of Optoelectronic Display, National and Local United Engineering Lab of Flat Panel Display Technology, Fuzhou University, Fuzhou, 350002, China
- Fujian Science and Technology Innovation Laboratory for Optoelectronic Information of China, Fuzhou, Fujian, 350100, China
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14
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Liu Y, Yu S, Zhang Z, Hou X, Ding M, Zhao X, Xu G, Zhou X, Long S. Reservoir Computing Based on Oxygen-Vacancy-Mediated X-ray Optical Synaptic Device for Medical CT Bone Diagnosis. ACS APPLIED MATERIALS & INTERFACES 2024. [PMID: 38696352 DOI: 10.1021/acsami.4c01255] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2024]
Abstract
Recognition and judgment of X-ray computed tomography (CT) images play a crucial role in medical diagnosis and disease prevention. However, the storage and calculation of the X-ray imaging system applied in the traditional CT diagnosis is separate, and the pathological judgment is based on doctors' experience, which will affect the timeliness and accuracy of decision-making. In this paper, a simple-structured reservoir computing network (RC) is proposed based on Ga2O3 X-ray optical synaptic devices to recognize medical skeletal CT images with high accuracy. Through oxygen vacancy engineering, Ga2O3 X-ray optical synaptic devices with adjustable photocurrent gain and a persistent photoconductivity effect were obtained. By using the Ga2O3 X-ray optical synaptic device as a reservoir, we constructed an RC network for medical skeletal CT diagnosis and verified its image recognition capability using the MNIST data set with an accuracy of 78.08%. In the elbow skeletal CT image recognition task, the recognition rate is as high as 100%. This work constructs a simple-structured RC network for X-ray image recognition, which is of great significance in applications in medical fields.
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Affiliation(s)
- Yan Liu
- School of Microelectronics, University of Science and Technology of China, Hefei, Anhui 230026, China
| | - Shunjie Yu
- School of Microelectronics, University of Science and Technology of China, Hefei, Anhui 230026, China
| | - Zhongfang Zhang
- School of Microelectronics, University of Science and Technology of China, Hefei, Anhui 230026, China
| | - Xiaohu Hou
- School of Microelectronics, University of Science and Technology of China, Hefei, Anhui 230026, China
| | - Mengfan Ding
- School of Microelectronics, University of Science and Technology of China, Hefei, Anhui 230026, China
| | - Xiaolong Zhao
- School of Microelectronics, University of Science and Technology of China, Hefei, Anhui 230026, China
| | - Guangwei Xu
- School of Microelectronics, University of Science and Technology of China, Hefei, Anhui 230026, China
| | - Xuanze Zhou
- School of Microelectronics, University of Science and Technology of China, Hefei, Anhui 230026, China
| | - Shibing Long
- School of Microelectronics, University of Science and Technology of China, Hefei, Anhui 230026, China
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15
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Chen J, Liu X, Liu C, Tang L, Bu T, Jiang B, Qing Y, Xie Y, Wang Y, Shan Y, Li R, Ye C, Liao L. Reconfigurable Ag/HfO 2/NiO/Pt Memristors with Stable Synchronous Synaptic and Neuronal Functions for Renewable Homogeneous Neuromorphic Computing System. NANO LETTERS 2024; 24:5371-5378. [PMID: 38647348 DOI: 10.1021/acs.nanolett.4c01319] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/25/2024]
Abstract
Artificial synapses and bionic neurons offer great potential in highly efficient computing paradigms. However, complex requirements for specific electronic devices in neuromorphic computing have made memristors face the challenge of process simplification and universality. Herein, reconfigurable Ag/HfO2/NiO/Pt memristors are designed for feasible switching between volatile and nonvolatile modes by compliance current controlled Ag filaments, which enables stable and reconfigurable synaptic and neuronal functions. A neuromorphic computing system effectively replicates the biological synaptic weight alteration and continuously accomplishes excitation and reset of artificial neurons, which consist of bionic synapses and artificial neurons based on isotype Ag/HfO2/NiO/Pt memristors. This reconfigurable electrical performance of the Ag/HfO2/NiO/Pt memristors takes advantage of simplified hardware design and delivers integrated circuits with high density, which exhibits great potency for future neural networks.
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Affiliation(s)
- Jiaqi Chen
- Key Laboratory of Intelligent Sensing System and Security, Ministry of Education, School of Microelectronics, Hubei University, Wuhan 430062, China
| | - Xingqiang Liu
- Changsha Semiconductor Technology and Application Research Institute, Engineering Research Center of Advanced Semiconductor Technology, College of Semiconductor (College of Integrated Circuit), Hunan University, Changsha 410082, China
| | - Chang Liu
- Key Laboratory for Micro/Nano Optoelectronic Devices of Ministry of Education & Hunan Provincial Key Laboratory of Low-Dimensional Structural Physics and Devices, School of Physics and Electronics, Hunan University, Changsha 410082, China
| | - Lin Tang
- Key Laboratory for Micro/Nano Optoelectronic Devices of Ministry of Education & Hunan Provincial Key Laboratory of Low-Dimensional Structural Physics and Devices, School of Physics and Electronics, Hunan University, Changsha 410082, China
| | - Tong Bu
- Key Laboratory for Micro/Nano Optoelectronic Devices of Ministry of Education & Hunan Provincial Key Laboratory of Low-Dimensional Structural Physics and Devices, School of Physics and Electronics, Hunan University, Changsha 410082, China
| | - Bei Jiang
- Key Laboratory of Intelligent Sensing System and Security, Ministry of Education, School of Microelectronics, Hubei University, Wuhan 430062, China
- Changsha Semiconductor Technology and Application Research Institute, Engineering Research Center of Advanced Semiconductor Technology, College of Semiconductor (College of Integrated Circuit), Hunan University, Changsha 410082, China
| | - Yahui Qing
- Key Laboratory of Intelligent Sensing System and Security, Ministry of Education, School of Microelectronics, Hubei University, Wuhan 430062, China
| | - Yulu Xie
- Key Laboratory of Intelligent Sensing System and Security, Ministry of Education, School of Microelectronics, Hubei University, Wuhan 430062, China
| | - Yong Wang
- Key Laboratory of Intelligent Sensing System and Security, Ministry of Education, School of Microelectronics, Hubei University, Wuhan 430062, China
| | - Yongtao Shan
- Key Laboratory of Intelligent Sensing System and Security, Ministry of Education, School of Microelectronics, Hubei University, Wuhan 430062, China
| | - Ruxin Li
- Key Laboratory of Intelligent Sensing System and Security, Ministry of Education, School of Microelectronics, Hubei University, Wuhan 430062, China
| | - Cong Ye
- Key Laboratory of Intelligent Sensing System and Security, Ministry of Education, School of Microelectronics, Hubei University, Wuhan 430062, China
| | - Lei Liao
- Changsha Semiconductor Technology and Application Research Institute, Engineering Research Center of Advanced Semiconductor Technology, College of Semiconductor (College of Integrated Circuit), Hunan University, Changsha 410082, China
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16
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Li P, Zhang M, Zhou Q, Zhang Q, Xie D, Li G, Liu Z, Wang Z, Guo E, He M, Wang C, Gu L, Yang G, Jin K, Ge C. Reconfigurable optoelectronic transistors for multimodal recognition. Nat Commun 2024; 15:3257. [PMID: 38627413 PMCID: PMC11021444 DOI: 10.1038/s41467-024-47580-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Accepted: 04/05/2024] [Indexed: 04/19/2024] Open
Abstract
Biological nervous system outperforms in both dynamic and static information perception due to their capability to integrate the sensing, memory and processing functions. Reconfigurable neuromorphic transistors, which can be used to emulate different types of biological analogues in a single device, are important for creating compact and efficient neuromorphic computing networks, but their design remains challenging due to the need for opposing physical mechanisms to achieve different functions. Here we report a neuromorphic electrolyte-gated transistor that can be reconfigured to perform physical reservoir and synaptic functions. The device exhibits dynamics with tunable time-scales under optical and electrical stimuli. The nonlinear volatile property is suitable for reservoir computing, which can be used for multimodal pre-processing. The nonvolatility and programmability of the device through ion insertion/extraction achieved via electrolyte gating, which are required to realize synaptic functions, are verified. The device's superior performance in mimicking human perception of dynamic and static multisensory information based on the reconfigurable neuromorphic functions is also demonstrated. The present study provides an exciting paradigm for the realization of multimodal reconfigurable devices and opens an avenue for mimicking biological multisensory fusion.
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Affiliation(s)
- Pengzhan Li
- Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, Beijing, China
- Key Laboratory of Terahertz Optoelectronics, Ministry of Education, Department of Physics, Capital Normal University, Beijing, China
| | - Mingzhen Zhang
- Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, Beijing, China
- School of Physical Sciences, University of Chinese Academy of Science, Beijing, China
| | - Qingli Zhou
- Key Laboratory of Terahertz Optoelectronics, Ministry of Education, Department of Physics, Capital Normal University, Beijing, China
| | - Qinghua Zhang
- Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, Beijing, China
- Yangtze River Delta Physics Research Center Co. Ltd., Liyang, China
| | - Donggang Xie
- Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, Beijing, China
- School of Physical Sciences, University of Chinese Academy of Science, Beijing, China
| | - Ge Li
- Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, Beijing, China
- School of Physical Sciences, University of Chinese Academy of Science, Beijing, China
| | - Zhuohui Liu
- Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, Beijing, China
- College of Materials Science and Opto-Electronic Technology, University of Chinese Academy of Sciences, Beijing, China
| | - Zheng Wang
- Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, Beijing, China
- School of Physical Sciences, University of Chinese Academy of Science, Beijing, China
| | - Erjia Guo
- Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, Beijing, China
- School of Physical Sciences, University of Chinese Academy of Science, Beijing, China
| | - Meng He
- Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, Beijing, China
| | - Can Wang
- Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, Beijing, China
- School of Physical Sciences, University of Chinese Academy of Science, Beijing, China
| | - Lin Gu
- Beijing National Center for Electron Microscopy and Laboratory of Advanced Materials, Department of Materials Science and Engineering, Tsinghua University, Beijing, China
| | - Guozhen Yang
- Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, Beijing, China
| | - Kuijuan Jin
- Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, Beijing, China.
- School of Physical Sciences, University of Chinese Academy of Science, Beijing, China.
| | - Chen Ge
- Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, Beijing, China.
- School of Physical Sciences, University of Chinese Academy of Science, Beijing, China.
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17
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Zhao L, Wu W, Gao B, Zhao Z, An B, Xu Q. CO 2 Stress-Driven Room Temperature Ferromagnetism of Ultrathin 2D Gallium Oxide. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2024; 20:e2308187. [PMID: 38016073 DOI: 10.1002/smll.202308187] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Revised: 10/25/2023] [Indexed: 11/30/2023]
Abstract
Spintronic devices work by manipulating the spin of electrons other than charge transfer, which is of revolutionary significance and can largely reduce energy consumption in the future. Herein, ultrathin two-dimensional (2D) non-van der Waals (non-vdW) γ-Ga2O3 with room temperature ferromagnetism is successfully obtained by using supercritical CO2 (SC CO2). The stress effect of SC CO2 under different pressures selectively modulates the orientation and strength of covalent bonds, leading to the change of atomic structure including lattice expansion, introduction of O vacancy, and transition of Ga-O coordination (GaO4 and GaO6). Magnetic measurements show that pristine γ-Ga2O3 is nonferromagnetic, whereas the SC CO2 treated γ-Ga2O3 exhibits obvious ferromagnetic behavior with an optimal magnetization of 0.025 emu g-1 and a Curie temperature of 300 K.
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Affiliation(s)
- Lanyu Zhao
- Henan Institute of Advanced Technology, Zhengzhou University, Zhengzhou, 450001, China
| | - Wenzhuo Wu
- Henan Institute of Advanced Technology, Zhengzhou University, Zhengzhou, 450001, China
| | - Bo Gao
- College of Materials Science and Engineering, Zhengzhou University, Zhengzhou, 450052, China
| | - Zhiliang Zhao
- Henan Institute of Advanced Technology, Zhengzhou University, Zhengzhou, 450001, China
| | - Bin An
- Henan Institute of Advanced Technology, Zhengzhou University, Zhengzhou, 450001, China
| | - Qun Xu
- Henan Institute of Advanced Technology, Zhengzhou University, Zhengzhou, 450001, China
- College of Materials Science and Engineering, Zhengzhou University, Zhengzhou, 450052, China
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18
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Jiang S, Peng L, Li L, Dai Q, Pei M, Wu C, Su J, Gu D, Zhang H, Guo H, Qiu J, Li Y. Task-Adaptive Neuromorphic Computing Using Reconfigurable Organic Neuristors with Tunable Plasticity and Logic-in-Memory Operations. J Phys Chem Lett 2024; 15:2301-2310. [PMID: 38386516 DOI: 10.1021/acs.jpclett.4c00284] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/24/2024]
Abstract
The brain's function can be dynamically reconfigured through a unified neuron-synapse architecture, enabling task-adaptive network-level topology for energy-efficient learning and inferencing. Here, we demonstrate an organic neuristor utilizing a ferroelectric-electrolyte dielectric interface. This neuristor enables tunable short- to long-term plasticity and reconfigurable logic-in-memory functions by controlling the interfacial interaction between electrolyte ions and ferroelectric dipoles. Notably, the short-term plasticity of the organic neuristor allows for power-efficient reservoir computing in edge-computing scenarios, exhibiting impressive recognition accuracy, including images (90.6%) and acoustic signals (97.7%). For high-performance computing tasks, the neuristor based on long-term plasticity and logic-in-memory operations can construct all of the hardware circuits of a binarized neural network (BNN) within a unified framework. The BNN demonstrates excellent noise tolerance, achieving high recognition accuracies of 99.2% and 86.4% on the MNIST and CIFAR-10 data sets, respectively. Consequently, our research sheds light on the development of power-efficient artificial intelligence systems.
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Affiliation(s)
- Sai Jiang
- School of Microelectronics and Control Engineering, Changzhou University, Changzhou, Jiangsu 213164, P. R. China
- National Laboratory of Solid-State Microstructures, School of Electronic Science and Engineering, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, Jiangsu 210093, P. R. China
| | - Lichao Peng
- School of Microelectronics and Control Engineering, Changzhou University, Changzhou, Jiangsu 213164, P. R. China
| | - Longfei Li
- National Laboratory of Solid-State Microstructures, School of Electronic Science and Engineering, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, Jiangsu 210093, P. R. China
| | - Qinyong Dai
- National Laboratory of Solid-State Microstructures, School of Electronic Science and Engineering, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, Jiangsu 210093, P. R. China
| | - Mengjiao Pei
- National Laboratory of Solid-State Microstructures, School of Electronic Science and Engineering, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, Jiangsu 210093, P. R. China
| | - Chaoran Wu
- School of Microelectronics and Control Engineering, Changzhou University, Changzhou, Jiangsu 213164, P. R. China
| | - Jian Su
- School of Microelectronics and Control Engineering, Changzhou University, Changzhou, Jiangsu 213164, P. R. China
| | - Ding Gu
- School of Microelectronics and Control Engineering, Changzhou University, Changzhou, Jiangsu 213164, P. R. China
| | - Han Zhang
- School of Microelectronics and Control Engineering, Changzhou University, Changzhou, Jiangsu 213164, P. R. China
| | - Huafei Guo
- School of Microelectronics and Control Engineering, Changzhou University, Changzhou, Jiangsu 213164, P. R. China
| | - Jianhua Qiu
- School of Microelectronics and Control Engineering, Changzhou University, Changzhou, Jiangsu 213164, P. R. China
| | - Yun Li
- National Laboratory of Solid-State Microstructures, School of Electronic Science and Engineering, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, Jiangsu 210093, P. R. China
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19
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Wang J, Ilyas N, Ren Y, Ji Y, Li S, Li C, Liu F, Gu D, Ang KW. Technology and Integration Roadmap for Optoelectronic Memristor. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024; 36:e2307393. [PMID: 37739413 DOI: 10.1002/adma.202307393] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Revised: 09/10/2023] [Indexed: 09/24/2023]
Abstract
Optoelectronic memristors (OMs) have emerged as a promising optoelectronic Neuromorphic computing paradigm, opening up new opportunities for neurosynaptic devices and optoelectronic systems. These OMs possess a range of desirable features including minimal crosstalk, high bandwidth, low power consumption, zero latency, and the ability to replicate crucial neurological functions such as vision and optical memory. By incorporating large-scale parallel synaptic structures, OMs are anticipated to greatly enhance high-performance and low-power in-memory computing, effectively overcoming the limitations of the von Neumann bottleneck. However, progress in this field necessitates a comprehensive understanding of suitable structures and techniques for integrating low-dimensional materials into optoelectronic integrated circuit platforms. This review aims to offer a comprehensive overview of the fundamental performance, mechanisms, design of structures, applications, and integration roadmap of optoelectronic synaptic memristors. By establishing connections between materials, multilayer optoelectronic memristor units, and monolithic optoelectronic integrated circuits, this review seeks to provide insights into emerging technologies and future prospects that are expected to drive innovation and widespread adoption in the near future.
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Affiliation(s)
- Jinyong Wang
- School of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, P. R. China
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, 117576, Singapore
| | - Nasir Ilyas
- School of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, P. R. China
| | - Yujing Ren
- Department of Chemical and Biomolecular Engineering, National University of Singapore, Singapore, 117585, Singapore
| | - Yun Ji
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, 117576, Singapore
| | - Sifan Li
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, 117576, Singapore
| | - Changcun Li
- School of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, P. R. China
| | - Fucai Liu
- School of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, P. R. China
| | - Deen Gu
- School of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, P. R. China
- State Key Laboratory of Electronic Thin Films and Integrated Devices, University of Electronic Science and Technology of China, Chengdu, 611731, P. R. China
| | - Kah-Wee Ang
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, 117576, Singapore
- Institute of Materials Research and Engineering, A*STAR, Singapore, 138634, Singapore
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20
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Wan C, Pei M, Shi K, Cui H, Long H, Qiao L, Xing Q, Wan Q. Toward a Brain-Neuromorphics Interface. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024:e2311288. [PMID: 38339866 DOI: 10.1002/adma.202311288] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Revised: 01/17/2024] [Indexed: 02/12/2024]
Abstract
Brain-computer interfaces (BCIs) that enable human-machine interaction have immense potential in restoring or augmenting human capabilities. Traditional BCIs are realized based on complementary metal-oxide-semiconductor (CMOS) technologies with complex, bulky, and low biocompatible circuits, and suffer with the low energy efficiency of the von Neumann architecture. The brain-neuromorphics interface (BNI) would offer a promising solution to advance the BCI technologies and shape the interactions with machineries. Neuromorphic devices and systems are able to provide substantial computation power with extremely high energy-efficiency by implementing in-materia computing such as in situ vector-matrix multiplication (VMM) and physical reservoir computing. Recent progresses on integrating neuromorphic components with sensing and/or actuating modules, give birth to the neuromorphic afferent nerve, efferent nerve, sensorimotor loop, and so on, which has advanced the technologies for future neurorobotics by achieving sophisticated sensorimotor capabilities as the biological system. With the development on the compact artificial spiking neuron and bioelectronic interfaces, the seamless communication between a BNI and a bioentity is reasonably expectable. In this review, the upcoming BNIs are profiled by introducing the brief history of neuromorphics, reviewing the recent progresses on related areas, and discussing the future advances and challenges that lie ahead.
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Affiliation(s)
- Changjin Wan
- Yongjiang Laboratory (Y-LAB), Ningbo, Zhejiang, 315202, China
- School of Electronic Science and Engineering, National Laboratory of Solid-State Microstructures, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, 210093, China
- Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China
| | - Mengjiao Pei
- School of Electronic Science and Engineering, National Laboratory of Solid-State Microstructures, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, 210093, China
| | - Kailu Shi
- School of Electronic Science and Engineering, National Laboratory of Solid-State Microstructures, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, 210093, China
| | - Hangyuan Cui
- School of Electronic Science and Engineering, National Laboratory of Solid-State Microstructures, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, 210093, China
| | - Haotian Long
- School of Electronic Science and Engineering, National Laboratory of Solid-State Microstructures, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, 210093, China
| | - Lesheng Qiao
- School of Electronic Science and Engineering, National Laboratory of Solid-State Microstructures, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, 210093, China
| | - Qianye Xing
- School of Electronic Science and Engineering, National Laboratory of Solid-State Microstructures, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, 210093, China
| | - Qing Wan
- Yongjiang Laboratory (Y-LAB), Ningbo, Zhejiang, 315202, China
- School of Electronic Science and Engineering, National Laboratory of Solid-State Microstructures, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, 210093, China
- Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China
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21
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Wang J, Pan X, Zhao Z, Xie Y, Luo W, Xie Q, Zeng H, Shuai Y, Song Z, Wu C, Zhang W. An Infrared Near-Sensor Reservoir Computing System Based on Large-Dynamic-Space Memristor with Tens of Thousands of States for Dynamic Gesture Perception. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2307359. [PMID: 38145361 PMCID: PMC10853732 DOI: 10.1002/advs.202307359] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Revised: 11/21/2023] [Indexed: 12/26/2023]
Abstract
To efficiently process the massive amount of sensor data, it is demanding to develop a new paradigm. Inspired by neurobiological systems, an infrared near-senor reservoir computing (RC) system, consisting of infrared sensors and memristors based on single-crystalline LiTaO3 and LiNbO3 (LN) thin film respectively, is demonstrated. The analog memristor is used as a reservoir in the RC system to process sensor signals with spatiotemporal characteristics. LN crystal structure stacked with oxygen octahedra provides favorable conditions for reliable Mott variable-range hopping conduction, which provides the memristor with tens of thousands of reservoir states within a large dynamic range. With the characteristics, the analog sensor signals with high data fidelity can be directly fed to the memristive reservoir, and the spatiotemporal features can be separated and mapped. The system demonstrated a dynamic gesture perception task, achieving an accuracy of 99.6%, which highlights the great application potential of the memristor in signal sensor processing and will advance the application of artificial intelligence in sensor systems. Crystal ion slicing techniques are used to fabricate a single-crystalline thin film for both the memristor and sensor, which opens up the possibility of realizing monolithic integration of a memristor-based near-sensor computing system.
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Affiliation(s)
- Jiejun Wang
- School of Integrated Circuit Science and EngineeringUniversity of Electronic Science and Technology of ChinaChengdu611731China
- National Key Laboratory of Electronic Thin Film and Integrated DevicesUniversity of Electronic Science and Technology of ChinaChengdu611731China
| | - Xinqiang Pan
- School of Integrated Circuit Science and EngineeringUniversity of Electronic Science and Technology of ChinaChengdu611731China
- National Key Laboratory of Electronic Thin Film and Integrated DevicesUniversity of Electronic Science and Technology of ChinaChengdu611731China
| | - Zebin Zhao
- School of Integrated Circuit Science and EngineeringUniversity of Electronic Science and Technology of ChinaChengdu611731China
- National Key Laboratory of Electronic Thin Film and Integrated DevicesUniversity of Electronic Science and Technology of ChinaChengdu611731China
| | - Yiduo Xie
- School of Integrated Circuit Science and EngineeringUniversity of Electronic Science and Technology of ChinaChengdu611731China
- National Key Laboratory of Electronic Thin Film and Integrated DevicesUniversity of Electronic Science and Technology of ChinaChengdu611731China
| | - Wenbo Luo
- School of Integrated Circuit Science and EngineeringUniversity of Electronic Science and Technology of ChinaChengdu611731China
- National Key Laboratory of Electronic Thin Film and Integrated DevicesUniversity of Electronic Science and Technology of ChinaChengdu611731China
| | - Qin Xie
- School of Integrated Circuit Science and EngineeringUniversity of Electronic Science and Technology of ChinaChengdu611731China
- National Key Laboratory of Electronic Thin Film and Integrated DevicesUniversity of Electronic Science and Technology of ChinaChengdu611731China
| | - Huizhong Zeng
- School of Integrated Circuit Science and EngineeringUniversity of Electronic Science and Technology of ChinaChengdu611731China
- National Key Laboratory of Electronic Thin Film and Integrated DevicesUniversity of Electronic Science and Technology of ChinaChengdu611731China
| | - Yao Shuai
- School of Integrated Circuit Science and EngineeringUniversity of Electronic Science and Technology of ChinaChengdu611731China
- National Key Laboratory of Electronic Thin Film and Integrated DevicesUniversity of Electronic Science and Technology of ChinaChengdu611731China
| | - Zeqian Song
- School of Integrated Circuit Science and EngineeringUniversity of Electronic Science and Technology of ChinaChengdu611731China
- National Key Laboratory of Electronic Thin Film and Integrated DevicesUniversity of Electronic Science and Technology of ChinaChengdu611731China
| | - Chuangui Wu
- School of Integrated Circuit Science and EngineeringUniversity of Electronic Science and Technology of ChinaChengdu611731China
- National Key Laboratory of Electronic Thin Film and Integrated DevicesUniversity of Electronic Science and Technology of ChinaChengdu611731China
| | - Wanli Zhang
- School of Integrated Circuit Science and EngineeringUniversity of Electronic Science and Technology of ChinaChengdu611731China
- National Key Laboratory of Electronic Thin Film and Integrated DevicesUniversity of Electronic Science and Technology of ChinaChengdu611731China
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22
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Zhu S, Xie T, Lv Z, Leng YB, Zhang YQ, Xu R, Qin J, Zhou Y, Roy VAL, Han ST. Hierarchies in Visual Pathway: Functions and Inspired Artificial Vision. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024; 36:e2301986. [PMID: 37435995 DOI: 10.1002/adma.202301986] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Revised: 06/28/2023] [Accepted: 07/10/2023] [Indexed: 07/13/2023]
Abstract
The development of artificial intelligence has posed a challenge to machine vision based on conventional complementary metal-oxide semiconductor (CMOS) circuits owing to its high latency and inefficient power consumption originating from the data shuffling between memory and computation units. Gaining more insights into the function of every part of the visual pathway for visual perception can bring the capabilities of machine vision in terms of robustness and generality. Hardware acceleration of more energy-efficient and biorealistic artificial vision highly necessitates neuromorphic devices and circuits that are able to mimic the function of each part of the visual pathway. In this paper, we review the structure and function of the entire class of visual neurons from the retina to the primate visual cortex within reach (Chapter 2) are reviewed. Based on the extraction of biological principles, the recent hardware-implemented visual neurons located in different parts of the visual pathway are discussed in detail in Chapters 3 and 4. Furthermore, valuable applications of inspired artificial vision in different scenarios (Chapter 5) are provided. The functional description of the visual pathway and its inspired neuromorphic devices/circuits are expected to provide valuable insights for the design of next-generation artificial visual perception systems.
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Affiliation(s)
- Shirui Zhu
- Institute of Microscale Optoelectronics, Shenzhen University, Shenzhen, 518060, P. R. China
| | - Tao Xie
- Institute of Microscale Optoelectronics, Shenzhen University, Shenzhen, 518060, P. R. China
| | - Ziyu Lv
- College of Electronics and Information Engineering, Shenzhen University, Shenzhen, 518060, P. R. China
| | - Yan-Bing Leng
- Institute of Microscale Optoelectronics, Shenzhen University, Shenzhen, 518060, P. R. China
| | - Yu-Qi Zhang
- Institute of Microscale Optoelectronics, Shenzhen University, Shenzhen, 518060, P. R. China
| | - Runze Xu
- Institute of Microscale Optoelectronics, Shenzhen University, Shenzhen, 518060, P. R. China
| | - Jingrun Qin
- Institute of Microscale Optoelectronics, Shenzhen University, Shenzhen, 518060, P. R. China
| | - Ye Zhou
- Institute for Advanced Study, Shenzhen University, Shenzhen, 518060, P. R. China
| | - Vellaisamy A L Roy
- School of Science and Technology, Hong Kong Metropolitan University, Hong Kong, 999077, P. R. China
| | - Su-Ting Han
- College of Electronics and Information Engineering, Shenzhen University, Shenzhen, 518060, P. R. China
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23
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Gao C, Liu D, Xu C, Xie W, Zhang X, Bai J, Lin Z, Zhang C, Hu Y, Guo T, Chen H. Toward grouped-reservoir computing: organic neuromorphic vertical transistor with distributed reservoir states for efficient recognition and prediction. Nat Commun 2024; 15:740. [PMID: 38272878 PMCID: PMC10810880 DOI: 10.1038/s41467-024-44942-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Accepted: 01/10/2024] [Indexed: 01/27/2024] Open
Abstract
Reservoir computing has attracted considerable attention due to its low training cost. However, existing neuromorphic hardware, focusing mainly on shallow-reservoir computing, faces challenges in providing adequate spatial and temporal scales characteristic for effective computing. Here, we report an ultra-short channel organic neuromorphic vertical transistor with distributed reservoir states. The carrier dynamics used to map signals are enriched by coupled multivariate physics mechanisms, while the vertical architecture employed greatly increases the feedback intensity of the device. Consequently, the device as a reservoir, effectively mapping sequential signals into distributed reservoir state space with 1152 reservoir states, and the range ratio of temporal and spatial characteristics can simultaneously reach 2640 and 650, respectively. The grouped-reservoir computing based on the device can simultaneously adapt to different spatiotemporal task, achieving recognition accuracy over 94% and prediction correlation over 95%. This work proposes a new strategy for developing high-performance reservoir computing networks.
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Affiliation(s)
- Changsong Gao
- Institute of Optoelectronic Display, National & Local United Engineering Lab of Flat Panel Display Technology, Fuzhou University, 350002, Fuzhou, China
- Fujian Science & Technology Innovation Laboratory for Optoelectronic Information of China, 350100, Fuzhou, China
| | - Di Liu
- Institute of Optoelectronic Display, National & Local United Engineering Lab of Flat Panel Display Technology, Fuzhou University, 350002, Fuzhou, China
- Fujian Science & Technology Innovation Laboratory for Optoelectronic Information of China, 350100, Fuzhou, China
| | - Chenhui Xu
- Institute of Optoelectronic Display, National & Local United Engineering Lab of Flat Panel Display Technology, Fuzhou University, 350002, Fuzhou, China
- Fujian Science & Technology Innovation Laboratory for Optoelectronic Information of China, 350100, Fuzhou, China
| | - Weidong Xie
- Institute of Optoelectronic Display, National & Local United Engineering Lab of Flat Panel Display Technology, Fuzhou University, 350002, Fuzhou, China
- Fujian Science & Technology Innovation Laboratory for Optoelectronic Information of China, 350100, Fuzhou, China
| | - Xianghong Zhang
- Institute of Optoelectronic Display, National & Local United Engineering Lab of Flat Panel Display Technology, Fuzhou University, 350002, Fuzhou, China
- Fujian Science & Technology Innovation Laboratory for Optoelectronic Information of China, 350100, Fuzhou, China
| | - Junhua Bai
- Joint School of National University of Singapore and Tianjin University, International Campus of Tianjin University, Binhai New City, 350207, Fuzhou, China
| | - Zhixian Lin
- Institute of Optoelectronic Display, National & Local United Engineering Lab of Flat Panel Display Technology, Fuzhou University, 350002, Fuzhou, China
- School of Advanced Manufacturing, Fuzhou University, 362200, Quanzhou, China
| | - Cheng Zhang
- Department of Physics, Fuzhou University, 350108, Fuzhou, China
| | - Yuanyuan Hu
- Changsha Semiconductor Technology and Application Innovation Research Institute, College of Semiconductors (College of Integrated Circuits), Hunan University, 410082, Changsha, China
| | - Tailiang Guo
- Institute of Optoelectronic Display, National & Local United Engineering Lab of Flat Panel Display Technology, Fuzhou University, 350002, Fuzhou, China
- Fujian Science & Technology Innovation Laboratory for Optoelectronic Information of China, 350100, Fuzhou, China
| | - Huipeng Chen
- Institute of Optoelectronic Display, National & Local United Engineering Lab of Flat Panel Display Technology, Fuzhou University, 350002, Fuzhou, China.
- Fujian Science & Technology Innovation Laboratory for Optoelectronic Information of China, 350100, Fuzhou, China.
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24
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Xu K, Peng B, Mao H, Wang Z, Gong H, Fu C, Ren FF, Yang Y, Wan C, Wan Q, Ye J. Ga 2O 3 Bipolar Heterojunction-Based Optoelectronic Synapse Array with Visual Attention. J Phys Chem Lett 2024; 15:556-564. [PMID: 38198134 DOI: 10.1021/acs.jpclett.3c02898] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2024]
Abstract
The human brain efficiently processes only a fraction of visual information, a phenomenon termed attentional control, resulting in energy savings and heightened adaptability. Translating this mechanism into artificial visual neurons holds promise for constructing energy-efficient, bioinspired visual systems. Here, we propose a self-rectifying artificial visual neuron (SEVN) based on a NiO/Ga2O3 bipolar heterojunction with attentional control on patterns with a target color. The device exhibits short-term potentiation (STP) with quantum point contact (QPC) traits at low bias and transitions to long-term potentiation (LTP) at high bias, particularly facilitated by electron capture in deep defects upon ultraviolet (UV) exposure. With the utilization of two wavelengths of light upon the target and interference part of CAPTCHA to simulate top-down attentional control, the recognition accuracy is enhanced from 74 to 84%. These findings have the potential to augment the visual capability of neuromorphic systems with implications for diverse applications, including cybersecurity, healthcare, and machine vision.
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Affiliation(s)
- Ke Xu
- School of Electronic Science and Engineering, Nanjing University, Nanjing, Jiangsu 210093, People's Republic of China
| | - Baocheng Peng
- School of Electronic Science and Engineering, Nanjing University, Nanjing, Jiangsu 210093, People's Republic of China
| | - Huiwu Mao
- School of Electronic Science and Engineering, Nanjing University, Nanjing, Jiangsu 210093, People's Republic of China
| | - Zhengpeng Wang
- School of Electronic Science and Engineering, Nanjing University, Nanjing, Jiangsu 210093, People's Republic of China
| | - Hehe Gong
- School of Electronic Science and Engineering, Nanjing University, Nanjing, Jiangsu 210093, People's Republic of China
| | - Chuanyu Fu
- School of Electronic Science and Engineering, Nanjing University, Nanjing, Jiangsu 210093, People's Republic of China
| | - Fang-Fang Ren
- School of Electronic Science and Engineering, Nanjing University, Nanjing, Jiangsu 210093, People's Republic of China
| | - Yi Yang
- School of Electronic Science and Engineering, Nanjing University, Nanjing, Jiangsu 210093, People's Republic of China
| | - Changjin Wan
- School of Electronic Science and Engineering, Nanjing University, Nanjing, Jiangsu 210093, People's Republic of China
| | - Qing Wan
- School of Electronic Science and Engineering, Nanjing University, Nanjing, Jiangsu 210093, People's Republic of China
- Yongjiang Laboratory (Y-LAB), Ningbo, Zhejiang 315202, People's Republic of China
| | - Jiandong Ye
- School of Electronic Science and Engineering, Nanjing University, Nanjing, Jiangsu 210093, People's Republic of China
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25
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Guo G, Ray A, Izydorczak M, Goldfeder J, Lipson H, Xu W. Unveiling intra-person fingerprint similarity via deep contrastive learning. SCIENCE ADVANCES 2024; 10:eadi0329. [PMID: 38215200 PMCID: PMC10786417 DOI: 10.1126/sciadv.adi0329] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Accepted: 12/15/2023] [Indexed: 01/14/2024]
Abstract
Fingerprint biometrics are integral to digital authentication and forensic science. However, they are based on the unproven assumption that no two fingerprints, even from different fingers of the same person, are alike. This renders them useless in scenarios where the presented fingerprints are from different fingers than those on record. Contrary to this prevailing assumption, we show above 99.99% confidence that fingerprints from different fingers of the same person share very strong similarities. Using deep twin neural networks to extract fingerprint representation vectors, we find that these similarities hold across all pairs of fingers within the same person, even when controlling for spurious factors like sensor modality. We also find evidence that ridge orientation, especially near the fingerprint center, explains a substantial part of this similarity, whereas minutiae used in traditional methods are almost nonpredictive. Our experiments suggest that, in some situations, this relationship can increase forensic investigation efficiency by almost two orders of magnitude.
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Affiliation(s)
- Gabe Guo
- Department of Computer Science, Columbia University, New York, NY 10027, USA
| | - Aniv Ray
- Department of Computer Science, Columbia University, New York, NY 10027, USA
| | - Miles Izydorczak
- Department of Computer Science, Tufts University, Medford, MA 02155, USA
| | - Judah Goldfeder
- Department of Computer Science, Columbia University, New York, NY 10027, USA
| | - Hod Lipson
- Department of Mechanical Engineering, Columbia University, New York, NY 10027, USA
| | - Wenyao Xu
- Department of Computer Science and Engineering, SUNY Buffalo, Buffalo, NY 14260, USA
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26
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Chen Y, Huang Y, Zeng J, Kang Y, Tan Y, Xie X, Wei B, Li C, Fang L, Jiang T. Energy-Efficient ReS 2-Based Optoelectronic Synapse for 3D Object Reconstruction and Recognition. ACS APPLIED MATERIALS & INTERFACES 2023; 15:58631-58642. [PMID: 38054897 DOI: 10.1021/acsami.3c14958] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/07/2023]
Abstract
The neuromorphic vision system (NVS) equipped with optoelectronic synapses integrates perception, storage, and processing and is expected to address the issues of traditional machine vision. However, owing to their lack of stereo vision, existing NVSs focus on 2D image processing, which makes it difficult to solve problems such as spatial cognition errors and low-precision interpretation. Consequently, inspired by the human visual system, an NVS with stereo vision is developed to achieve 3D object recognition, depending on the prepared ReS2 optoelectronic synapse with 12.12 fJ ultralow power consumption. This device exhibits excellent optical synaptic plasticity derived from the persistent photoconductivity effect. As the cornerstone for 3D vision, color planar information is successfully discriminated and stored in situ at the sensor end, benefiting from its wavelength-dependent plasticity in the visible region. Importantly, the dependence of the channel conductance on the target distance is experimentally revealed, implying that the structure information on the object can be directly captured and stored by the synapse. The 3D image of the object is successfully reconstructed via fusion of its planar and depth images. Therefore, the proposed 3D-NVS based on ReS2 synapses for 3D objects achieves a recognition accuracy of 97.0%, which is much higher than that for 2D objects (32.6%), demonstrating its strong ability to prevent 2D-photo spoofing in applications such as face payment, entrance guard systems, and others.
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Affiliation(s)
- Yabo Chen
- Institute for Quantum Information & State Key Laboratory of High Performance Computing, College of Computer, National University of Defense Technology, Changsha 410073, P. R. China
| | - Yujie Huang
- Institute for Quantum Information & State Key Laboratory of High Performance Computing, College of Computer, National University of Defense Technology, Changsha 410073, P. R. China
| | - Junwei Zeng
- Institute for Quantum Information & State Key Laboratory of High Performance Computing, College of Computer, National University of Defense Technology, Changsha 410073, P. R. China
| | - Yan Kang
- College of Advanced Interdisciplinary Studies, National University of Defense Technology, Changsha 410073, P. R. China
| | - Yinlong Tan
- College of Advanced Interdisciplinary Studies, National University of Defense Technology, Changsha 410073, P. R. China
| | - Xiangnan Xie
- Institute of Quantum Information Science and Technology, College of Science, National University of Defense Technology, Changsha 410073, P. R. China
| | - Bo Wei
- Institute for Quantum Information & State Key Laboratory of High Performance Computing, College of Computer, National University of Defense Technology, Changsha 410073, P. R. China
| | - Cheng Li
- Institute of Quantum Information Science and Technology, College of Science, National University of Defense Technology, Changsha 410073, P. R. China
| | - Liang Fang
- Institute for Quantum Information & State Key Laboratory of High Performance Computing, College of Computer, National University of Defense Technology, Changsha 410073, P. R. China
| | - Tian Jiang
- Institute of Quantum Information Science and Technology, College of Science, National University of Defense Technology, Changsha 410073, P. R. China
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27
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Yaremkevich DD, Scherbakov AV, De Clerk L, Kukhtaruk SM, Nadzeyka A, Campion R, Rushforth AW, Savel'ev S, Balanov AG, Bayer M. On-chip phonon-magnon reservoir for neuromorphic computing. Nat Commun 2023; 14:8296. [PMID: 38097654 PMCID: PMC10721880 DOI: 10.1038/s41467-023-43891-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Accepted: 11/22/2023] [Indexed: 12/17/2023] Open
Abstract
Reservoir computing is a concept involving mapping signals onto a high-dimensional phase space of a dynamical system called "reservoir" for subsequent recognition by an artificial neural network. We implement this concept in a nanodevice consisting of a sandwich of a semiconductor phonon waveguide and a patterned ferromagnetic layer. A pulsed write-laser encodes input signals into propagating phonon wavepackets, interacting with ferromagnetic magnons. The second laser reads the output signal reflecting a phase-sensitive mix of phonon and magnon modes, whose content is highly sensitive to the write- and read-laser positions. The reservoir efficiently separates the visual shapes drawn by the write-laser beam on the nanodevice surface in an area with a size comparable to a single pixel of a modern digital camera. Our finding suggests the phonon-magnon interaction as a promising hardware basis for realizing on-chip reservoir computing in future neuromorphic architectures.
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Affiliation(s)
- Dmytro D Yaremkevich
- Experimentelle Physik 2, Technische Universität Dortmund, D-44227, Dortmund, Germany
| | - Alexey V Scherbakov
- Experimentelle Physik 2, Technische Universität Dortmund, D-44227, Dortmund, Germany.
| | - Luke De Clerk
- Department of Physics, Loughborough University, Loughborough, LE11 3TU, UK
- Machine Learning Development, SS&C Technologies, 128 Queen Victoria Street, London, EC4V 4BJ, UK
| | - Serhii M Kukhtaruk
- Department of Theoretical Physics, V. E. Lashkaryov Institute of Semiconductor Physics, 03028, Kyiv, Ukraine
| | | | - Richard Campion
- School of Physics and Astronomy, University of Nottingham, Nottingham, NG7 2RD, UK
| | - Andrew W Rushforth
- School of Physics and Astronomy, University of Nottingham, Nottingham, NG7 2RD, UK
| | - Sergey Savel'ev
- Department of Physics, Loughborough University, Loughborough, LE11 3TU, UK
| | | | - Manfred Bayer
- Experimentelle Physik 2, Technische Universität Dortmund, D-44227, Dortmund, Germany
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28
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Zhu K, Yan B. Multifunctional Eu(III)-modified HOFs: roxarsone and aristolochic acid carcinogen monitoring and latent fingerprint identification based on artificial intelligence. MATERIALS HORIZONS 2023; 10:5782-5795. [PMID: 37814901 DOI: 10.1039/d3mh01253k] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/11/2023]
Abstract
The exploration of multifunctional materials and intelligent technologies used for fluorescence sensing and latent fingerprint (LFP) identification is a research hotspot of material science. In this study, an emerging crystalline luminescent material, Eu3+-functionalized hydrogen-bonded organic framework (Eu@HOF-BTB, Eu@1), is fabricated successfully. Eu@1 can emit purple red fluorescence with a high photoluminescence quantum yield of 36.82%. Combined with artificial intelligence (AI) algorithms including support vector machine, principal component analysis, and hierarchical clustering analysis, Eu@1 as a sensor can concurrently distinguish two carcinogens, roxarsone and aristolochic acid, based on different mechanisms. The sensing process exhibits high selectivity, high efficiency, and excellent anti-interference. Meanwhile, Eu@1 is also an excellent eikonogen for LFP identification with high-resolution and high-contrast. Based on an automatic fingerprint identification system, the simultaneous differentiation of two fingerprint images is achieved. Moreover, a simulation experiment of criminal arrest is conducted. By virtue of the Alexnet-based fingerprint analysis platform of AI, unknown LFPs can be compared with a database to identify the criminal within one second with over 90% recognition accuracy. With AI technology, HOFs are applied for the first time in the LFP identification field, which provides a new material and solution for investigators to track criminal clues and handle cases efficiently.
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Affiliation(s)
- Kai Zhu
- Shanghai Key Lab of Chemical Assessment and Sustainability, School of Chemical Science and Engineering, Tongji University, Siping Road 1239, Shanghai 200092, China.
| | - Bing Yan
- Shanghai Key Lab of Chemical Assessment and Sustainability, School of Chemical Science and Engineering, Tongji University, Siping Road 1239, Shanghai 200092, China.
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29
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Kalateh A, Jalali A, Kamali Ashtiani MJ, Mohammadimasoudi M, Bastami H, Mohseni M. Resistive switching transparent SnO 2 thin film sensitive to light and humidity. Sci Rep 2023; 13:20036. [PMID: 37973907 PMCID: PMC10654523 DOI: 10.1038/s41598-023-45790-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Accepted: 10/24/2023] [Indexed: 11/19/2023] Open
Abstract
Designing and manufacturing memristor devices with simple and less complicated methods is highly promising for their future development. Here, an Ag/SnO2/FTO(F-SnO2) structure is used through the deposition of the SnO2 layer attained by its sol via the air-brush method on an FTO substrate. This structure was investigated in terms of the memristive characteristics. The negative differential resistance (NDR) effect was observed in environment humidity conditions. In this structure, valance change memory and electrometalization change memory mechanisms cause the current peak in the NDR region by forming an OH- conductive filament. In addition, the photoconductivity effect was found under light illumination and this structure shows the positive photoconductance effect by increasing the conductivity. Memristivity was examined for up to 100 cycles and significant stability was observed as a valuable advantage for neuromorphic computing. Our study conveys a growth mechanism of an optical memristor that is sensitive to light and humidity suitable for sensing applications.
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Affiliation(s)
- Asiyeh Kalateh
- Electrical Engineering Department, Shahid Beheshti University, Tehran, Iran
| | - Ali Jalali
- Electrical Engineering Department, Shahid Beheshti University, Tehran, Iran.
| | | | - Mohammad Mohammadimasoudi
- Nano-Bio-Photonics Lab, Faculty of New Sciences and Technologies, University of Tehran, Tehran, Iran
| | - Hajieh Bastami
- Department of Materials and Metallurgical Engineering, Technical and Vocational University, Tehran, Iran
| | - Majid Mohseni
- Physics Department, Shahid Beheshti University, Tehran, Iran
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30
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Jiang T, Wang Y, Huang W, Ling H, Tian G, Deng Y, Geng Y, Ji D, Hu W. Retina-inspired organic neuromorphic vision sensor with polarity modulation for decoding light information. LIGHT, SCIENCE & APPLICATIONS 2023; 12:264. [PMID: 37932276 PMCID: PMC10628194 DOI: 10.1038/s41377-023-01310-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Revised: 10/07/2023] [Accepted: 10/16/2023] [Indexed: 11/08/2023]
Abstract
The neuromorphic vision sensor (NeuVS), which is based on organic field-effect transistors (OFETs), uses polar functional groups (PFGs) in polymer dielectrics as interfacial units to control charge carriers. However, the mechanism of modulating charge transport on basis of PFGs in devices is unclear. Here, the carboxyl group is introduced into polymer dielectrics in this study, and it can induce the charge transfer process at the semiconductor/dielectric interfaces for effective carrier transport, giving rise to the best device mobility up to 20 cm2 V-1 s-1 at a low operating voltage of -1 V. Furthermore, the polarity modulation effect could further increase the optical figures of merit in NeuVS devices by at least an order of magnitude more than the devices using carboxyl group-free polymer dielectrics. Additionally, devices containing carboxyl groups improved image sensing for light information decoding with 52 grayscale signals and memory capabilities at an incredibly low power consumption of 1.25 fJ/spike. Our findings provide insight into the production of high-performance polymer dielectrics for NeuVS devices.
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Affiliation(s)
- Ting Jiang
- Tianjin Key Laboratory of Molecular Optoelectronic Science, Department of Chemistry, Institute of Molecular Aggregation Science, Tianjin University, 300072, Tianjin, China
- Haihe Laboratory of Sustainable Chemical Transformations, 300192, Tianjin, China
| | - Yiru Wang
- State Key Laboratory of Organic Electronics and Information Displays & Institute of Advanced Materials, Nanjing University of Posts & Telecommunications, 210023, Nanjing, China
| | - Wanxin Huang
- State Key Laboratory of Organic Electronics and Information Displays & Institute of Advanced Materials, Nanjing University of Posts & Telecommunications, 210023, Nanjing, China
| | - Haifeng Ling
- State Key Laboratory of Organic Electronics and Information Displays & Institute of Advanced Materials, Nanjing University of Posts & Telecommunications, 210023, Nanjing, China
| | - Guofeng Tian
- State Key Laboratory of Chemical Resource Engineering, Beijing University of Chemical Technology, 100029, Beijing, China
| | - Yunfeng Deng
- School of Materials Science and Engineering, Tianjin University, 300072, Tianjin, China
| | - Yanhou Geng
- School of Materials Science and Engineering, Tianjin University, 300072, Tianjin, China
| | - Deyang Ji
- Tianjin Key Laboratory of Molecular Optoelectronic Science, Department of Chemistry, Institute of Molecular Aggregation Science, Tianjin University, 300072, Tianjin, China.
- Haihe Laboratory of Sustainable Chemical Transformations, 300192, Tianjin, China.
| | - Wenping Hu
- Haihe Laboratory of Sustainable Chemical Transformations, 300192, Tianjin, China
- Tianjin Key Laboratory of Molecular Optoelectronic Sciences, Department of Chemistry, School of Science, Tianjin University, 300072, Tianjin, China
- Collaborative Innovation Center of Chemical Science and Engineering, Tianjin University, 300072, Tianjin, China
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31
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Liu Z, Zhang Q, Xie D, Zhang M, Li X, Zhong H, Li G, He M, Shang D, Wang C, Gu L, Yang G, Jin K, Ge C. Interface-type tunable oxygen ion dynamics for physical reservoir computing. Nat Commun 2023; 14:7176. [PMID: 37935751 PMCID: PMC10630289 DOI: 10.1038/s41467-023-42993-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2023] [Accepted: 10/26/2023] [Indexed: 11/09/2023] Open
Abstract
Reservoir computing can more efficiently be used to solve time-dependent tasks than conventional feedforward network owing to various advantages, such as easy training and low hardware overhead. Physical reservoirs that contain intrinsic nonlinear dynamic processes could serve as next-generation dynamic computing systems. High-efficiency reservoir systems require nonlinear and dynamic responses to distinguish time-series input data. Herein, an interface-type dynamic transistor gated by an Hf0.5Zr0.5O2 (HZO) film was introduced to perform reservoir computing. The channel conductance of Mott material La0.67Sr0.33MnO3 (LSMO) can effectively be modulated by taking advantage of the unique coupled property of the polarization process and oxygen migration in hafnium-based ferroelectrics. The large positive value of the oxygen vacancy formation energy and negative value of the oxygen affinity energy resulted in the spontaneous migration of accumulated oxygen ions in the HZO films to the channel, leading to the dynamic relaxation process. The modulation of the channel conductance was found to be closely related to the current state, identified as the origin of the nonlinear response. In the time series recognition and prediction tasks, the proposed reservoir system showed an extremely low decision-making error. This work provides a promising pathway for exploiting dynamic ion systems for high-performance neural network devices.
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Affiliation(s)
- Zhuohui Liu
- Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, 100190, Beijing, China
- College of Materials Science and Opto-Electronic Technology, University of Chinese Academy of Sciences, 100049, Beijing, China
| | - Qinghua Zhang
- Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, 100190, Beijing, China
- Yangtze River Delta Physics Research Center Co. Ltd., 213300, Liyang, China
| | - Donggang Xie
- Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, 100190, Beijing, China
- School of Physical Sciences, University of Chinese Academy of Science, 100049, Beijing, China
| | - Mingzhen Zhang
- Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, 100190, Beijing, China
- School of Physical Sciences, University of Chinese Academy of Science, 100049, Beijing, China
| | - Xinyan Li
- Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, 100190, Beijing, China
- College of Materials Science and Opto-Electronic Technology, University of Chinese Academy of Sciences, 100049, Beijing, China
| | - Hai Zhong
- Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, 100190, Beijing, China
- School of Physics and Optoelectronics Engineering, Ludong University, 264025, Yantai, Shandong, China
| | - Ge Li
- Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, 100190, Beijing, China
- School of Physical Sciences, University of Chinese Academy of Science, 100049, Beijing, China
| | - Meng He
- Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, 100190, Beijing, China
| | - Dashan Shang
- Key Laboratory of Microelectronic Devices and Integrated Technology, Institute of Microelectronics, Chinese Academy of Sciences, 100029, Beijing, China
| | - Can Wang
- Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, 100190, Beijing, China
- School of Physical Sciences, University of Chinese Academy of Science, 100049, Beijing, China
| | - Lin Gu
- Beijing National Center for Electron Microscopy and Laboratory of Advanced Materials, Department of Materials Science and Engineering, Tsinghua University, 100084, Beijing, China
| | - Guozhen Yang
- Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, 100190, Beijing, China
| | - Kuijuan Jin
- Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, 100190, Beijing, China.
- School of Physical Sciences, University of Chinese Academy of Science, 100049, Beijing, China.
| | - Chen Ge
- Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, 100190, Beijing, China.
- School of Physical Sciences, University of Chinese Academy of Science, 100049, Beijing, China.
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32
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Zhang H, Qiu P, Lu Y, Ju X, Chi D, Yew KS, Zhu M, Wang S, Wei R, Hu W. In-Sensor Computing Realization Using Fully CMOS-Compatible TiN/HfO x-Based Neuristor Array. ACS Sens 2023; 8:3873-3881. [PMID: 37707324 DOI: 10.1021/acssensors.3c01418] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/15/2023]
Abstract
With the evolution of artificial intelligence, the explosive growth of data from sensory terminals gives rise to severe energy-efficiency bottleneck issues due to cumbersome data interactions among sensory, memory, and computing modules. Heterogeneous integration methods such as chiplet technology can significantly reduce unnecessary data movement; however, they fail to address the fundamental issue of the substantial time and energy overheads resulting from the physical separation of computing and sensory components. Brain-inspired in-sensor neuromorphic computing (ISNC) has plenty of room for such data-intensive applications. However, one key obstacle in developing ISNC systems is the lack of compatibility between material systems and manufacturing processes deployed in sensors and computing units. This study successfully addresses this challenge by implementing fully CMOS-compatible TiN/HfOx-based neuristor array. The developed ISNC system demonstrates several advantageous features, including multilevel analogue modulation, minimal dispersion, and no significant degradation in conductance (@125 °C). These characteristics enable stable and reproducible neuromorphic computing. Additionally, the device exhibits modulatable sensory and multi-store memory processes. Furthermore, the system achieves information recognition with a high accuracy rate of 93%, along with frequency selectivity and notable activity-dependent plasticity. This work provides a promising route to affordable and highly efficient sensory neuromorphic systems.
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Affiliation(s)
- Haizhong Zhang
- College of Physics and Information Engineering, Fuzhou University, Fuzhou 350116, China
- FZU-Jinjiang Joint Institute of Microelectronics, Jinjiang Science and Education Park, Fuzhou University, Jinjiang 362200, China
| | - Peng Qiu
- College of Physics and Information Engineering, Fuzhou University, Fuzhou 350116, China
| | - Yaoping Lu
- College of Physics and Information Engineering, Fuzhou University, Fuzhou 350116, China
| | - Xin Ju
- Institute of Materials Research and Engineering, 2 Fusionopolis Way, Innovis, #08-03, Agency for Science, Technology and Research, Singapore 138634, Singapore
| | - Dongzhi Chi
- Institute of Materials Research and Engineering, 2 Fusionopolis Way, Innovis, #08-03, Agency for Science, Technology and Research, Singapore 138634, Singapore
| | - Kwang Sing Yew
- Global Foundries, 60 Woodlands Industrial Park D Street 2, Singapore 738406, Singapore
| | - Minmin Zhu
- College of Physics and Information Engineering, Fuzhou University, Fuzhou 350116, China
- FZU-Jinjiang Joint Institute of Microelectronics, Jinjiang Science and Education Park, Fuzhou University, Jinjiang 362200, China
| | - Shaohao Wang
- College of Physics and Information Engineering, Fuzhou University, Fuzhou 350116, China
- FZU-Jinjiang Joint Institute of Microelectronics, Jinjiang Science and Education Park, Fuzhou University, Jinjiang 362200, China
| | - Rongshan Wei
- College of Physics and Information Engineering, Fuzhou University, Fuzhou 350116, China
- FZU-Jinjiang Joint Institute of Microelectronics, Jinjiang Science and Education Park, Fuzhou University, Jinjiang 362200, China
| | - Wei Hu
- College of Physics and Information Engineering, Fuzhou University, Fuzhou 350116, China
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33
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Huang PY, Jiang BY, Chen HJ, Xu JY, Wang K, Zhu CY, Hu XY, Li D, Zhen L, Zhou FC, Qin JK, Xu CY. Neuro-inspired optical sensor array for high-accuracy static image recognition and dynamic trace extraction. Nat Commun 2023; 14:6736. [PMID: 37872169 PMCID: PMC10593955 DOI: 10.1038/s41467-023-42488-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Accepted: 10/12/2023] [Indexed: 10/25/2023] Open
Abstract
Neuro-inspired vision systems hold great promise to address the growing demands of mass data processing for edge computing, a distributed framework that brings computation and data storage closer to the sources of data. In addition to the capability of static image sensing and processing, the hardware implementation of a neuro-inspired vision system also requires the fulfilment of detecting and recognizing moving targets. Here, we demonstrated a neuro-inspired optical sensor based on two-dimensional NbS2/MoS2 hybrid films, which featured remarkable photo-induced conductance plasticity and low electrical energy consumption. A neuro-inspired optical sensor array with 10 × 10 NbS2/MoS2 phototransistors enabled highly integrated functions of sensing, memory, and contrast enhancement capabilities for static images, which benefits convolutional neural network (CNN) with a high image recognition accuracy. More importantly, in-sensor trajectory registration of moving light spots was experimentally implemented such that the post-processing could yield a high restoration accuracy. Our neuro-inspired optical sensor array could provide a fascinating platform for the implementation of high-performance artificial vision systems.
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Affiliation(s)
- Pei-Yu Huang
- Sauvage Laboratory for Smart Materials, School of Materials Science and Engineering, Harbin Institute of Technology (Shenzhen), Shenzhen, 518055, China
| | - Bi-Yi Jiang
- School of Microelectronics, Southern University of Science and Technology, Shenzhen, 518055, China
- Department of Applied Physics, The Hong Kong Polytechnic University, Hong Kong, 999077, China
| | - Hong-Ji Chen
- Sauvage Laboratory for Smart Materials, School of Materials Science and Engineering, Harbin Institute of Technology (Shenzhen), Shenzhen, 518055, China
| | - Jia-Yi Xu
- School of Microelectronics, Southern University of Science and Technology, Shenzhen, 518055, China
| | - Kang Wang
- Key Laboratory of MEMS of the Ministry of Education, Southeast University, Nanjing, 210096, China
| | - Cheng-Yi Zhu
- Sauvage Laboratory for Smart Materials, School of Materials Science and Engineering, Harbin Institute of Technology (Shenzhen), Shenzhen, 518055, China
| | - Xin-Yan Hu
- School of Microelectronics, Southern University of Science and Technology, Shenzhen, 518055, China
| | - Dong Li
- Sauvage Laboratory for Smart Materials, School of Materials Science and Engineering, Harbin Institute of Technology (Shenzhen), Shenzhen, 518055, China
| | - Liang Zhen
- MOE Key Laboratory of Micro-Systems and Micro-Structures Manufacturing, Harbin Institute of Technology, Harbin, 150080, China
| | - Fei-Chi Zhou
- School of Microelectronics, Southern University of Science and Technology, Shenzhen, 518055, China.
| | - Jing-Kai Qin
- Sauvage Laboratory for Smart Materials, School of Materials Science and Engineering, Harbin Institute of Technology (Shenzhen), Shenzhen, 518055, China.
| | - Cheng-Yan Xu
- Sauvage Laboratory for Smart Materials, School of Materials Science and Engineering, Harbin Institute of Technology (Shenzhen), Shenzhen, 518055, China.
- MOE Key Laboratory of Micro-Systems and Micro-Structures Manufacturing, Harbin Institute of Technology, Harbin, 150080, China.
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34
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Bednarkiewicz A, Szalkowski M, Majak M, Korczak Z, Misiak M, Maćkowski S. All-Optical Data Processing with Photon-Avalanching Nanocrystalline Photonic Synapse. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2023; 35:e2304390. [PMID: 37572370 DOI: 10.1002/adma.202304390] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Revised: 08/01/2023] [Indexed: 08/14/2023]
Abstract
Data processing and storage in electronic devices are typically performed as a sequence of elementary binary operations. Alternative approaches, such as neuromorphic or reservoir computing, are rapidly gaining interest where data processing is relatively slow, but can be performed in a more comprehensive way or massively in parallel, like in neuronal circuits. Here, time-domain all-optical information processing capabilities of photon-avalanching (PA) nanoparticles at room temperature are discovered. Demonstrated functionality resembles properties found in neuronal synapses, such as: paired-pulse facilitation and short-term internal memory, in situ plasticity, multiple inputs processing, and all-or-nothing threshold response. The PA-memory-like behavior shows capability of machine-learning-algorithm-free feature extraction and further recognition of 2D patterns with simple 2 input artificial neural network. Additionally, high nonlinearity of luminescence intensity in response to photoexcitation mimics and enhances spike-timing-dependent plasticity that is coherent in nature with the way a sound source is localized in animal neuronal circuits. Not only are yet unexplored fundamental properties of photon-avalanche luminescence kinetics studied, but this approach, combined with recent achievements in photonics, light confinement and guiding, promises all-optical data processing, control, adaptive responsivity, and storage on photonic chips.
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Affiliation(s)
- Artur Bednarkiewicz
- Institute of Low Temperature and Structure Research, Polish Academy of Sciences, ul. Okólna 2, Wroclaw, 50-422, Poland
| | - Marcin Szalkowski
- Institute of Low Temperature and Structure Research, Polish Academy of Sciences, ul. Okólna 2, Wroclaw, 50-422, Poland
- Nanophotonics Group, Institute of Physics, Faculty of Physics, Astronomy and Informatics, Nicolaus Copernicus University in Toruń, 87-100, Toruń, ul. Grudziądzka 5, Poland
| | - Martyna Majak
- Institute of Low Temperature and Structure Research, Polish Academy of Sciences, ul. Okólna 2, Wroclaw, 50-422, Poland
| | - Zuzanna Korczak
- Institute of Low Temperature and Structure Research, Polish Academy of Sciences, ul. Okólna 2, Wroclaw, 50-422, Poland
| | - Małgorzata Misiak
- Institute of Low Temperature and Structure Research, Polish Academy of Sciences, ul. Okólna 2, Wroclaw, 50-422, Poland
| | - Sebastian Maćkowski
- Nanophotonics Group, Institute of Physics, Faculty of Physics, Astronomy and Informatics, Nicolaus Copernicus University in Toruń, 87-100, Toruń, ul. Grudziądzka 5, Poland
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35
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Zhang GX, Zhang ZC, Chen XD, Kang L, Li Y, Wang FD, Shi L, Shi K, Liu ZB, Tian JG, Lu TB, Zhang J. Broadband sensory networks with locally stored responsivities for neuromorphic machine vision. SCIENCE ADVANCES 2023; 9:eadi5104. [PMID: 37713483 PMCID: PMC10881039 DOI: 10.1126/sciadv.adi5104] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/30/2023] [Accepted: 08/14/2023] [Indexed: 09/17/2023]
Abstract
As the most promising candidates for the implementation of in-sensor computing, retinomorphic vision sensors can constitute built-in neural networks and directly implement multiply-and-accumulation operations using responsivities as the weights. However, existing retinomorphic vision sensors mainly use a sustained gate bias to maintain the responsivity due to its volatile nature. Here, we propose an ion-induced localized-field strategy to develop retinomorphic vision sensors with nonvolatile tunable responsivity in both positive and negative regimes and construct a broadband and reconfigurable sensory network with locally stored weights to implement in-sensor convolutional processing in spectral range of 400 to 1800 nanometers. In addition to in-sensor computing, this retinomorphic device can implement in-memory computing benefiting from the nonvolatile tunable conductance, and a complete neuromorphic visual system involving front-end in-sensor computing and back-end in-memory computing architectures has been constructed, executing supervised and unsupervised learning tasks as demonstrations. This work paves the way for the development of high-speed and low-power neuromorphic machine vision for time-critical and data-intensive applications.
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Affiliation(s)
- Guo-Xin Zhang
- MOE International Joint Laboratory of Materials Microstructure, Institute for New Energy Materials and Low Carbon Technologies, School of Material Science and Engineering, Tianjin University of Technology, Tianjin 300384, China
| | - Zhi-Cheng Zhang
- The Key Laboratory of Weak Light Nonlinear Photonics, Ministry of Education, School of Physics, Nankai University, Tianjin 300071, China
| | - Xu-Dong Chen
- MOE International Joint Laboratory of Materials Microstructure, Institute for New Energy Materials and Low Carbon Technologies, School of Material Science and Engineering, Tianjin University of Technology, Tianjin 300384, China
- The Key Laboratory of Weak Light Nonlinear Photonics, Ministry of Education, School of Physics, Nankai University, Tianjin 300071, China
| | - Lixing Kang
- Key Laboratory of Multifunctional Nanomaterials and Smart Systems Division of Advanced Material, Suzhou Institute of Nano-Tech and Nano-Bionics, Chinese Academy of Sciences, Suzhou 215123, China
| | - Yuan Li
- The Key Laboratory of Weak Light Nonlinear Photonics, Ministry of Education, School of Physics, Nankai University, Tianjin 300071, China
| | - Fu-Dong Wang
- MOE International Joint Laboratory of Materials Microstructure, Institute for New Energy Materials and Low Carbon Technologies, School of Material Science and Engineering, Tianjin University of Technology, Tianjin 300384, China
| | - Lei Shi
- MOE International Joint Laboratory of Materials Microstructure, Institute for New Energy Materials and Low Carbon Technologies, School of Material Science and Engineering, Tianjin University of Technology, Tianjin 300384, China
| | - Ke Shi
- MOE International Joint Laboratory of Materials Microstructure, Institute for New Energy Materials and Low Carbon Technologies, School of Material Science and Engineering, Tianjin University of Technology, Tianjin 300384, China
| | - Zhi-Bo Liu
- The Key Laboratory of Weak Light Nonlinear Photonics, Ministry of Education, School of Physics, Nankai University, Tianjin 300071, China
| | - Jian-Guo Tian
- The Key Laboratory of Weak Light Nonlinear Photonics, Ministry of Education, School of Physics, Nankai University, Tianjin 300071, China
| | - Tong-Bu Lu
- MOE International Joint Laboratory of Materials Microstructure, Institute for New Energy Materials and Low Carbon Technologies, School of Material Science and Engineering, Tianjin University of Technology, Tianjin 300384, China
| | - Jin Zhang
- Center for Nanochemistry, Beijing Science and Engineering Center for Nanocarbons, Beijing National Laboratory for Molecular Sciences, College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China
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Dai S, Liu X, Liu Y, Xu Y, Zhang J, Wu Y, Cheng P, Xiong L, Huang J. Emerging Iontronic Neural Devices for Neuromorphic Sensory Computing. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2023; 35:e2300329. [PMID: 36891745 DOI: 10.1002/adma.202300329] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Revised: 02/22/2023] [Indexed: 06/18/2023]
Abstract
Living organisms have a very mysterious and powerful sensory computing system based on ion activity. Interestingly, studies on iontronic devices in the past few years have proposed a promising platform for simulating the sensing and computing functions of living organisms, because: 1) iontronic devices can generate, store, and transmit a variety of signals by adjusting the concentration and spatiotemporal distribution of ions, which analogs to how the brain performs intelligent functions by alternating ion flux and polarization; 2) through ionic-electronic coupling, iontronic devices can bridge the biosystem with electronics and offer profound implications for soft electronics; 3) with the diversity of ions, iontronic devices can be designed to recognize specific ions or molecules by customizing the charge selectivity, and the ionic conductivity and capacitance can be adjusted to respond to external stimuli for a variety of sensing schemes, which can be more difficult for electron-based devices. This review provides a comprehensive overview of emerging neuromorphic sensory computing by iontronic devices, highlighting representative concepts of both low-level and high-level sensory computing and introducing important material and device breakthroughs. Moreover, iontronic devices as a means of neuromorphic sensing and computing are discussed regarding the pending challenges and future directions.
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Affiliation(s)
- Shilei Dai
- Shanghai Key Laboratory of Anesthesiology and Brain Functional Modulation, Translational Research Institute of Brain and Brain-Like Intelligence, Shanghai Fourth People's Hospital, Tongji University, Shanghai, 200434, P. R. China
- Interdisciplinary Materials Research Center, School of Materials Science and Engineering, Tongji University, Shanghai, 201804, P. R. China
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Pokfulam, Hong Kong, 999077, China
| | - Xu Liu
- Interdisciplinary Materials Research Center, School of Materials Science and Engineering, Tongji University, Shanghai, 201804, P. R. China
| | - Youdi Liu
- Department of Engineering Science and Mechanics, Pennsylvania State University, University Park, State College, PA, 16802, USA
| | - Yutong Xu
- Interdisciplinary Materials Research Center, School of Materials Science and Engineering, Tongji University, Shanghai, 201804, P. R. China
| | - Junyao Zhang
- Interdisciplinary Materials Research Center, School of Materials Science and Engineering, Tongji University, Shanghai, 201804, P. R. China
| | - Yue Wu
- Interdisciplinary Materials Research Center, School of Materials Science and Engineering, Tongji University, Shanghai, 201804, P. R. China
| | - Ping Cheng
- Pritzker School of Molecular Engineering, The University of Chicago, Chicago, IL, 60637, USA
| | - Lize Xiong
- Shanghai Key Laboratory of Anesthesiology and Brain Functional Modulation, Translational Research Institute of Brain and Brain-Like Intelligence, Shanghai Fourth People's Hospital, Tongji University, Shanghai, 200434, P. R. China
| | - Jia Huang
- Shanghai Key Laboratory of Anesthesiology and Brain Functional Modulation, Translational Research Institute of Brain and Brain-Like Intelligence, Shanghai Fourth People's Hospital, Tongji University, Shanghai, 200434, P. R. China
- Interdisciplinary Materials Research Center, School of Materials Science and Engineering, Tongji University, Shanghai, 201804, P. R. China
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Yu Y, Niu Q, Li X, Xue J, Liu W, Lin D. A Review of Fingerprint Sensors: Mechanism, Characteristics, and Applications. MICROMACHINES 2023; 14:1253. [PMID: 37374839 DOI: 10.3390/mi14061253] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Revised: 06/07/2023] [Accepted: 06/09/2023] [Indexed: 06/29/2023]
Abstract
Identification technology based on biometrics is a branch of research that employs the unique individual traits of humans to authenticate identity, which is the most secure method of identification based on its exceptional high dependability and stability of human biometrics. Common biometric identifiers include fingerprints, irises, and facial sounds, among others. In the realm of biometric recognition, fingerprint recognition has gained success with its convenient operation and fast identif ication speed. Different fingerprint collecting techniques, which supply fingerprint information for fingerprint identification systems, have attracted a significant deal of interest in authentication technology regarding fingerprint identification systems. This work presents several fingerprint acquisition techniques, such as optical capacitive and ultrasonic, and analyzes acquisition types and structures. In addition, the pros and drawbacks of various sensor types, as well as the limits and benefits of optical, capacitive, and ultrasonic kinds, are discussed. It is the necessary stage for the application of the Internet of Things (IoT).
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Affiliation(s)
- Yirong Yu
- School of Optoelectronic Engineering, Xi'an Technological University, Xi'an 710032, China
| | - Qiming Niu
- School of Optoelectronic Engineering, Xi'an Technological University, Xi'an 710032, China
| | - Xuyang Li
- School of Optoelectronic Engineering, Xi'an Technological University, Xi'an 710032, China
| | - Jianshe Xue
- BOE Display Technology Co., Ltd., Beijing 100176, China
| | - Weiguo Liu
- School of Optoelectronic Engineering, Xi'an Technological University, Xi'an 710032, China
| | - Dabin Lin
- School of Optoelectronic Engineering, Xi'an Technological University, Xi'an 710032, China
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Wang X, Chen C, Zhu L, Shi K, Peng B, Zhu Y, Mao H, Long H, Ke S, Fu C, Zhu Y, Wan C, Wan Q. Vertically integrated spiking cone photoreceptor arrays for color perception. Nat Commun 2023; 14:3444. [PMID: 37301894 PMCID: PMC10257685 DOI: 10.1038/s41467-023-39143-8] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Accepted: 05/31/2023] [Indexed: 06/12/2023] Open
Abstract
The cone photoreceptors in our eyes selectively transduce the natural light into spiking representations, which endows the brain with high energy-efficiency color vision. However, the cone-like device with color-selectivity and spike-encoding capability remains challenging. Here, we propose a metal oxide-based vertically integrated spiking cone photoreceptor array, which can directly transduce persistent lights into spike trains at a certain rate according to the input wavelengths. Such spiking cone photoreceptors have an ultralow power consumption of less than 400 picowatts per spike in visible light, which is very close to biological cones. In this work, lights with three wavelengths were exploited as pseudo-three-primary colors to form 'colorful' images for recognition tasks, and the device with the ability to discriminate mixed colors shows better accuracy. Our results would enable hardware spiking neural networks with biologically plausible visual perception and provide great potential for the development of dynamic vision sensors.
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Affiliation(s)
- Xiangjing Wang
- School of Electronic Science and Engineering, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, 210093, China
| | - Chunsheng Chen
- School of Electronic Science and Engineering, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, 210093, China
| | - Li Zhu
- College of Integrated Circuit Science and Engineering, Nanjing University of Posts and Telecommunications, Nanjing, 210003, China
| | - Kailu Shi
- School of Electronic Science and Engineering, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, 210093, China
| | - Baocheng Peng
- School of Electronic Science and Engineering, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, 210093, China
| | - Yixin Zhu
- School of Electronic Science and Engineering, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, 210093, China
| | - Huiwu Mao
- School of Electronic Science and Engineering, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, 210093, China
| | - Haotian Long
- School of Electronic Science and Engineering, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, 210093, China
| | - Shuo Ke
- School of Electronic Science and Engineering, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, 210093, China
| | - Chuanyu Fu
- School of Electronic Science and Engineering, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, 210093, China
| | - Ying Zhu
- School of Electronic Science and Engineering, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, 210093, China
| | - Changjin Wan
- School of Electronic Science and Engineering, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, 210093, China.
| | - Qing Wan
- School of Electronic Science and Engineering, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, 210093, China.
- School of Micro Nanoelectronics, Zhejiang University, ZJU-Hangzhou Global Scientific and Technological Innovation Centre, 310027, Hangzhou, PR China.
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39
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Li C, Zhang X, Chen P, Zhou K, Yu J, Wu G, Xiang D, Jiang H, Wang M, Liu Q. Short-term synaptic plasticity in emerging devices for neuromorphic computing. iScience 2023; 26:106315. [PMID: 36950108 PMCID: PMC10025973 DOI: 10.1016/j.isci.2023.106315] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/06/2023] Open
Abstract
Neuromorphic computing is a promising computing paradigm toward building next-generation artificial intelligence machines, in which diverse types of synaptic plasticity play an active role in information processing. Compared to long-term plasticity (LTP) forming the foundation of learning and memory, short-term plasticity (STP) is essential for critical computational functions. So far, the practical applications of LTP have been widely investigated, whereas the implementation of STP in hardware is still elusive. Here, we review the development of STP by bridging the physics in emerging devices and biological behaviors. We explore the computational functions of various STP in biology and review their recent progress. Finally, we discuss the main challenges of introducing STP into synaptic devices and offer the potential approaches to utilize STP to enrich systems' capabilities. This review is expected to provide prospective ideas for implementing STP in emerging devices and may promote the construction of high-level neuromorphic machines.
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Affiliation(s)
- Chao Li
- State Key Laboratory of Integrated Chip and System, Frontier Institute of Chip and System, Fudan University, Shanghai 200433, China
- Key Laboratory of Microelectronics Device & Integrated Technology, Institute of Microelectronics of Chinese Academy of Sciences, Beijing 100029, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Xumeng Zhang
- State Key Laboratory of Integrated Chip and System, Frontier Institute of Chip and System, Fudan University, Shanghai 200433, China
- Zhangjiang Fudan International Innovation Center, Fudan University, Shanghai 200433, China
- Shanghai Qi Zhi Institute, Shanghai 200232, China
| | - Pei Chen
- State Key Laboratory of Integrated Chip and System, Frontier Institute of Chip and System, Fudan University, Shanghai 200433, China
| | - Keji Zhou
- State Key Laboratory of Integrated Chip and System, Frontier Institute of Chip and System, Fudan University, Shanghai 200433, China
- Zhangjiang Fudan International Innovation Center, Fudan University, Shanghai 200433, China
- Shanghai Qi Zhi Institute, Shanghai 200232, China
| | - Jie Yu
- State Key Laboratory of Integrated Chip and System, Frontier Institute of Chip and System, Fudan University, Shanghai 200433, China
- Zhangjiang Fudan International Innovation Center, Fudan University, Shanghai 200433, China
| | - Guangjian Wu
- State Key Laboratory of Integrated Chip and System, Frontier Institute of Chip and System, Fudan University, Shanghai 200433, China
- Zhangjiang Fudan International Innovation Center, Fudan University, Shanghai 200433, China
- Shanghai Qi Zhi Institute, Shanghai 200232, China
| | - Du Xiang
- State Key Laboratory of Integrated Chip and System, Frontier Institute of Chip and System, Fudan University, Shanghai 200433, China
- Zhangjiang Fudan International Innovation Center, Fudan University, Shanghai 200433, China
- Shanghai Qi Zhi Institute, Shanghai 200232, China
| | - Hao Jiang
- State Key Laboratory of Integrated Chip and System, Frontier Institute of Chip and System, Fudan University, Shanghai 200433, China
- Zhangjiang Fudan International Innovation Center, Fudan University, Shanghai 200433, China
- Shanghai Qi Zhi Institute, Shanghai 200232, China
| | - Ming Wang
- State Key Laboratory of Integrated Chip and System, Frontier Institute of Chip and System, Fudan University, Shanghai 200433, China
- Zhangjiang Fudan International Innovation Center, Fudan University, Shanghai 200433, China
- Shanghai Qi Zhi Institute, Shanghai 200232, China
| | - Qi Liu
- State Key Laboratory of Integrated Chip and System, Frontier Institute of Chip and System, Fudan University, Shanghai 200433, China
- Zhangjiang Fudan International Innovation Center, Fudan University, Shanghai 200433, China
- Shanghai Qi Zhi Institute, Shanghai 200232, China
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