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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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Yan J, Armstrong JPK, Scarpa F, Perriman AW. Hydrogel-Based Artificial Synapses for Sustainable Neuromorphic Electronics. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024:e2403937. [PMID: 39087845 DOI: 10.1002/adma.202403937] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/17/2024] [Revised: 06/16/2024] [Indexed: 08/02/2024]
Abstract
Hydrogels find widespread applications in biomedicine because of their outstanding biocompatibility, biodegradability, and tunable material properties. Hydrogels can be chemically functionalized or reinforced to respond to physical or chemical stimulation, which opens up new possibilities in the emerging field of intelligent bioelectronics. Here, the state-of-the-art in functional hydrogel-based transistors and memristors is reviewed as potential artificial synapses. Within these systems, hydrogels can serve as semisolid dielectric electrolytes in transistors and as switching layers in memristors. These synaptic devices with volatile and non-volatile resistive switching show good adaptability to external stimuli for short-term and long-term synaptic memory effects, some of which are integrated into synaptic arrays as artificial neurons; although, there are discrepancies in switching performance and efficacy. By comparing different hydrogels and their respective properties, an outlook is provided on a new range of biocompatible, environment-friendly, and sustainable neuromorphic hardware. How potential energy-efficient information storage and processing can be achieved using artificial neural networks with brain-inspired architecture for neuromorphic computing is described. The development of hydrogel-based artificial synapses can significantly impact the fields of neuromorphic bionics, biometrics, and biosensing.
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Affiliation(s)
- Jiongyi Yan
- School of Cellular and Molecular Medicine, University of Bristol, Bristol, BS8 1TD, UK
| | - James P K Armstrong
- Department of Translational Health Sciences, Bristol Medical School, University of Bristol, Bristol, BS1 3NY, UK
| | - Fabrizio Scarpa
- Bristol Composites Institute, School of Civil, Aerospace and Design Engineering (CADE), University of Bristol, University Walk, Bristol, BS8 1TR, UK
| | - Adam W Perriman
- School of Cellular and Molecular Medicine, University of Bristol, Bristol, BS8 1TD, UK
- Research School of Chemistry, Australian National University, Canberra, Australian Capital Territory, 2601, Australia
- John Curtin School of Medical Research, Australian National University, Canberra, Australian Capital Territory, 2601, Australia
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Kundale SS, Pawar PS, Kumbhar DD, Devara IKG, Sharma I, Patil PR, Lestari WA, Shim S, Park J, Dongale TD, Nam SY, Heo J, Park JH. Multilevel Conductance States of Vapor-Transport-Deposited Sb 2S 3 Memristors Achieved via Electrical and Optical Modulation. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024:e2405251. [PMID: 38958496 DOI: 10.1002/advs.202405251] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/14/2024] [Revised: 06/17/2024] [Indexed: 07/04/2024]
Abstract
The pursuit of advanced brain-inspired electronic devices and memory technologies has led to explore novel materials by processing multimodal and multilevel tailored conductive properties as the next generation of semiconductor platforms, due to von Neumann architecture limits. Among such materials, antimony sulfide (Sb2S3) thin films exhibit outstanding optical and electronic properties, and therefore, they are ideal for applications such as thin-film solar cells and nonvolatile memory systems. This study investigates the conduction modulation and memory functionalities of Sb2S3 thin films deposited via the vapor transport deposition technique. Experimental results indicate that the Ag/Sb2S3/Pt device possesses properties suitable for memory applications, including low operational voltages, robust endurance, and reliable switching behavior. Further, the reproducibility and stability of these properties across different device batches validate the reliability of these devices for practical implementation. Moreover, Sb2S3-based memristors exhibit artificial neuroplasticity with prolonged stability, promising considerable advancements in neuromorphic computing. Leveraging the photosensitivity of Sb2S3 enables the Ag/Sb2S3/Pt device to exhibit significant low operating potential and conductivity modulation under optical stimulation for memory applications. This research highlights the potential applications of Sb2S3 in future memory devices and optoelectronics and in shaping electronics with versatility.
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Affiliation(s)
- Somnath S Kundale
- Department of Materials Engineering and Convergence Technology, Gyeongsang National University, Jinju, Gyeongsangnam-do, 52828, Republic of Korea
- Research Institute for Green Energy Convergence Technology, Gyeongsang National University, Jinju, 52828, Republic of Korea
| | - Pravin S Pawar
- Department of Materials Science and Engineering, and Optoelectronics Convergence Research Center, Chonnam National University, Gwangju, 61186, Republic of Korea
| | - Dhananjay D Kumbhar
- Computational Electronics and Nanoscience Research Laboratory, School of Nanoscience and Biotechnology, Shivaji University, Kolhapur, 416004, India
| | - I Ketut Gary Devara
- Department of Materials Engineering and Convergence Technology, Gyeongsang National University, Jinju, Gyeongsangnam-do, 52828, Republic of Korea
| | - Indu Sharma
- Department of Materials Science and Engineering, and Optoelectronics Convergence Research Center, Chonnam National University, Gwangju, 61186, Republic of Korea
| | - Parag R Patil
- Department of Materials Science and Engineering, and Optoelectronics Convergence Research Center, Chonnam National University, Gwangju, 61186, Republic of Korea
| | - Windy Ayu Lestari
- Department of Materials Engineering and Convergence Technology, Gyeongsang National University, Jinju, Gyeongsangnam-do, 52828, Republic of Korea
| | - Soobin Shim
- Department of Materials Engineering and Convergence Technology, Gyeongsang National University, Jinju, Gyeongsangnam-do, 52828, Republic of Korea
| | - Jihye Park
- Department of Materials Engineering and Convergence Technology, Gyeongsang National University, Jinju, Gyeongsangnam-do, 52828, Republic of Korea
| | - Tukaram D Dongale
- Computational Electronics and Nanoscience Research Laboratory, School of Nanoscience and Biotechnology, Shivaji University, Kolhapur, 416004, India
| | - Sang Yong Nam
- Department of Materials Engineering and Convergence Technology, Gyeongsang National University, Jinju, Gyeongsangnam-do, 52828, Republic of Korea
- Research Institute for Green Energy Convergence Technology, Gyeongsang National University, Jinju, 52828, Republic of Korea
| | - Jaeyeong Heo
- Department of Materials Science and Engineering, and Optoelectronics Convergence Research Center, Chonnam National University, Gwangju, 61186, Republic of Korea
| | - Jun Hong Park
- Department of Materials Engineering and Convergence Technology, Gyeongsang National University, Jinju, Gyeongsangnam-do, 52828, Republic of Korea
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Rokade KA, Kumbhar DD, Patil SL, Sutar SS, More KV, Dandge PB, Kamat RK, Dongale TD. CogniFiber: Harnessing Biocompatible and Biodegradable 1D Collagen Nanofibers for Sustainable Nonvolatile Memory and Synaptic Learning Applications. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024; 36:e2312484. [PMID: 38501916 DOI: 10.1002/adma.202312484] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Revised: 03/11/2024] [Indexed: 03/20/2024]
Abstract
Here, resistive switching (RS) devices are fabricated using naturally abundant, nontoxic, biocompatible, and biodegradable biomaterials. For this purpose, 1D chitosan nanofibers (NFs), collagen NFs, and chitosan-collagen NFs are synthesized by using an electrospinning technique. Among different NFs, the collagen-NFs-based device shows promising RS characteristics. In particular, the optimized Ag/collagen NFs/fluorine-doped tin oxide RS device shows a voltage-tunable analog memory behavior and good nonvolatile memory properties. Moreover, it can also mimic various biological synaptic learning properties and can be used for pattern classification applications with the help of the spiking neural network. The time series analysis technique is employed to model and predict the switching variations of the RS device. Moreover, the collagen NFs have shown good cytotoxicity and anticancer properties, suggesting excellent biocompatibility as a switching layer. The biocompatibility of collagen NFs is explored with the help of NRK-52E (Normal Rat Kidney cell line) and MCF-7 (Michigan Cancer Foundation-7 cancer cell line). Additionally, the biodegradability of the device is evaluated through a physical transient test. This work provides a vital step toward developing a biocompatible and biodegradable switching material for sustainable nonvolatile memory and neuromorphic computing applications.
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Affiliation(s)
- Kasturi A Rokade
- Computational Electronics and Nanoscience Research Laboratory, School of Nanoscience and Biotechnology, Shivaji University, Kolhapur, 416004, India
| | - Dhananjay D Kumbhar
- Computational Electronics and Nanoscience Research Laboratory, School of Nanoscience and Biotechnology, Shivaji University, Kolhapur, 416004, India
| | - Snehal L Patil
- Computational Electronics and Nanoscience Research Laboratory, School of Nanoscience and Biotechnology, Shivaji University, Kolhapur, 416004, India
| | - Santosh S Sutar
- Yashwantrao Chavan School of Rural Development, Shivaji University, Kolhapur, 416004, India
| | - Krantiveer V More
- Department of Chemistry, Shivaji University, Kolhapur, 416004, India
| | - Padma B Dandge
- Department of Biochemistry, Shivaji University, Kolhapur, 416004, India
| | - Rajanish K Kamat
- Department of Electronics, Shivaji University, Kolhapur, 416004, India
- The Institute of Science, Dr. Homi Bhabha State University, 15, Madam Cama Road, Mumbai, 400032, India
| | - Tukaram D Dongale
- Computational Electronics and Nanoscience Research Laboratory, School of Nanoscience and Biotechnology, Shivaji University, Kolhapur, 416004, India
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Bag A, Ghosh G, Sultan MJ, Chouhdry HH, Hong SJ, Trung TQ, Kang GY, Lee NE. Bio-Inspired Sensory Receptors for Artificial-Intelligence Perception. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024:e2403150. [PMID: 38699932 DOI: 10.1002/adma.202403150] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/01/2024] [Revised: 04/16/2024] [Indexed: 05/05/2024]
Abstract
In the era of artificial intelligence (AI), there is a growing interest in replicating human sensory perception. Selective and sensitive bio-inspired sensory receptors with synaptic plasticity have recently gained significant attention in developing energy-efficient AI perception. Various bio-inspired sensory receptors and their applications in AI perception are reviewed here. The critical challenges for the future development of bio-inspired sensory receptors are outlined, emphasizing the need for innovative solutions to overcome hurdles in sensor design, integration, and scalability. AI perception can revolutionize various fields, including human-machine interaction, autonomous systems, medical diagnostics, environmental monitoring, industrial optimization, and assistive technologies. As advancements in bio-inspired sensing continue to accelerate, the promise of creating more intelligent and adaptive AI systems becomes increasingly attainable, marking a significant step forward in the evolution of human-like sensory perception.
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Affiliation(s)
- Atanu Bag
- School of Advanced Materials Science & Engineering, Sungkyunkwan University, Suwon, Gyeonggi-do, 16419, Republic of Korea
- Research Centre for Advanced Materials Technology, Sungkyunkwan University, Suwon, Gyeonggi-do, 16419, Republic of Korea
| | - Gargi Ghosh
- School of Advanced Materials Science & Engineering, Sungkyunkwan University, Suwon, Gyeonggi-do, 16419, Republic of Korea
| | - M Junaid Sultan
- School of Advanced Materials Science & Engineering, Sungkyunkwan University, Suwon, Gyeonggi-do, 16419, Republic of Korea
| | - Hamna Haq Chouhdry
- SKKU Advanced Institute of Nanotechnology (SAINT), Sungkyunkwan University, Suwon, Gyeonggi-do, 16419, Republic of Korea
| | - Seok Ju Hong
- School of Advanced Materials Science & Engineering, Sungkyunkwan University, Suwon, Gyeonggi-do, 16419, Republic of Korea
| | - Tran Quang Trung
- School of Advanced Materials Science & Engineering, Sungkyunkwan University, Suwon, Gyeonggi-do, 16419, Republic of Korea
| | - Geun-Young Kang
- School of Advanced Materials Science & Engineering, Sungkyunkwan University, Suwon, Gyeonggi-do, 16419, Republic of Korea
| | - Nae-Eung Lee
- School of Advanced Materials Science & Engineering, Sungkyunkwan University, Suwon, Gyeonggi-do, 16419, Republic of Korea
- Research Centre for Advanced Materials Technology, Sungkyunkwan University, Suwon, Gyeonggi-do, 16419, Republic of Korea
- SKKU Advanced Institute of Nanotechnology (SAINT), Sungkyunkwan University, Suwon, Gyeonggi-do, 16419, Republic of Korea
- Samsung Advanced Institute for Health Sciences & Technology (SAIHST), Institute of Quantum Biophysics (IQB) and Biomedical Institute for Convergence at SKKU (BICS), Sungkyunkwan University, Suwon, Gyeonggi-do, 16419, Republic of Korea
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Wang K, Liao Y, Li W, Li J, Su H, Chen R, Park JH, Zhang Y, Zhou X, Wu C, Liu Z, Guo T, Kim TW. Memory-electroluminescence for multiple action-potentials combination in bio-inspired afferent nerves. Nat Commun 2024; 15:3505. [PMID: 38664383 PMCID: PMC11045776 DOI: 10.1038/s41467-024-47641-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Accepted: 04/05/2024] [Indexed: 04/28/2024] Open
Abstract
The development of optoelectronics mimicking the functions of the biological nervous system is important to artificial intelligence. This work demonstrates an optoelectronic, artificial, afferent-nerve strategy based on memory-electroluminescence spikes, which can realize multiple action-potentials combination through a single optical channel. The memory-electroluminescence spikes have diverse morphologies due to their history-dependent characteristics and can be used to encode distributed sensor signals. As the key to successful functioning of the optoelectronic, artificial afferent nerve, a driving mode for light-emitting diodes, namely, the non-carrier injection mode, is proposed, allowing it to drive nanoscale light-emitting diodes to generate a memory-electroluminescence spikes that has multiple sub-peaks. Moreover, multiplexing of the spikes can be obtained by using optical signals with different wavelengths, allowing for a large signal bandwidth, and the multiple action-potentials transmission process in afferent nerves can be demonstrated. Finally, sensor-position recognition with the bio-inspired afferent nerve is developed and shown to have a high recognition accuracy of 98.88%. This work demonstrates a strategy for mimicking biological afferent nerves and offers insights into the construction of artificial perception systems.
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Affiliation(s)
- Kun Wang
- College of Physics and Information Engineering, Fuzhou University, Fuzhou, 350108, China
| | - Yitao Liao
- College of Physics and Information Engineering, Fuzhou University, Fuzhou, 350108, China
| | - Wenhao Li
- College of Physics and Information Engineering, Fuzhou University, Fuzhou, 350108, China
| | - Junlong Li
- College of Physics and Information Engineering, Fuzhou University, Fuzhou, 350108, China
| | - Hao Su
- College of Physics and Information Engineering, Fuzhou University, Fuzhou, 350108, China
| | - Rong Chen
- Fujian Science & Technology Innovation Laboratory for Optoelectronic Information of China, Fuzhou, 350108, China
| | - Jae Hyeon Park
- Department of Electronic and Computer Engineering, Hanyang University, Seoul, 133-791, Korea
| | - Yongai Zhang
- College of Physics and Information Engineering, Fuzhou University, Fuzhou, 350108, China
- Fujian Science & Technology Innovation Laboratory for Optoelectronic Information of China, Fuzhou, 350108, China
| | - Xiongtu Zhou
- College of Physics and Information Engineering, Fuzhou University, Fuzhou, 350108, China
- Fujian Science & Technology Innovation Laboratory for Optoelectronic Information of China, Fuzhou, 350108, China
| | - Chaoxing Wu
- College of Physics and Information Engineering, Fuzhou University, Fuzhou, 350108, China.
- Fujian Science & Technology Innovation Laboratory for Optoelectronic Information of China, Fuzhou, 350108, China.
| | - Zhiqiang Liu
- Research and Development Center for Semiconductor Lighting Technology, Institute of Semiconductors, Chinese Academy of Sciences, Beijing, 100083, China.
| | - Tailiang Guo
- College of Physics and Information Engineering, Fuzhou University, Fuzhou, 350108, China.
- Fujian Science & Technology Innovation Laboratory for Optoelectronic Information of China, Fuzhou, 350108, China.
| | - Tae Whan Kim
- Department of Electronic and Computer Engineering, Hanyang University, Seoul, 133-791, Korea.
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Kim J, Lee S, Seo Y, Kim S. Emulating biological synaptic characteristics of HfOx/AlN-based 3D vertical resistive memory for neuromorphic systems. J Chem Phys 2024; 160:144703. [PMID: 38587228 DOI: 10.1063/5.0202610] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2024] [Accepted: 03/21/2024] [Indexed: 04/09/2024] Open
Abstract
Here, we demonstrate double-layer 3D vertical resistive random-access memory with a hole-type structure embedding Pt/HfOx/AlN/TiN memory cells, conduct analog resistive switching, and examine the potential of memristors for use in neuromorphic systems. The electrical characteristics, including resistive switching, retention, and endurance, of each layer are also obtained. Additionally, we investigate various synaptic characteristics, such as spike-timing dependent plasticity, spike-amplitude dependent plasticity, spike-rate dependent plasticity, spike-duration dependent plasticity, and spike-number dependent plasticity. This synapse emulation holds great potential for neuromorphic computing applications. Furthermore, potentiation and depression are manifested through identical pulses based on DC resistive switching. The pattern recognition rates within the neural network are evaluated, and based on the conductance changing linearly with incremental pulses, we achieve a pattern recognition accuracy of over 95%. Finally, the device's stability and synapse characteristics exhibit excellent potential for use in neuromorphic systems.
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Affiliation(s)
- Juri Kim
- Division of Electronics and Electrical Engineering, Dongguk University, Seoul 04620, South Korea
| | - Subaek Lee
- Division of Electronics and Electrical Engineering, Dongguk University, Seoul 04620, South Korea
| | - Yeongkyo Seo
- Department of Information and Communication Engineering, Inha University, Incheon 22212, Republic of Korea
| | - Sungjun Kim
- Division of Electronics and Electrical Engineering, Dongguk University, Seoul 04620, South Korea
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An YJ, Yan H, Yeom CM, Jeong JK, Eadi SB, Lee HD, Kwon HM. Effects of thermal annealing on analog resistive switching behavior in bilayer HfO 2/ZnO synaptic devices: the role of ZnO grain boundaries. NANOSCALE 2024; 16:4609-4619. [PMID: 38258994 DOI: 10.1039/d3nr04917e] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
The effects of thermal annealing on analog resistive switching behavior in bilayer HfO2/ZnO synaptic devices were investigated. The annealed active ZnO layer between the top Pd electrode and the HfO2 layer exhibited electroforming-free resistive switching. In particular, the switching uniformity, stability, and reliability of the synaptic devices were dramatically improved via thermal annealing at 600 °C atomic force microscopy and X-ray diffraction analyses revealed that active ZnO films demonstrated increased grain size upon annealing from 400 °C to 700 °C, whereas the ZnO film thickness and the annealing of the HfO2 layer in bilayer HfO2/ZnO synaptic devices did not profoundly affect the analog switching behavior. The optimized thermal annealing at 600 °C in bilayer HfO2/ZnO synaptic devices dramatically improved the nonlinearity of long-term potentiation/depression properties, the relative coefficient of variation of the asymmetry distribution σ/μ, and the asymmetry ratio, which approached 1. The results offer valuable insights into the implementation of highly robust synaptic devices in neural networks.
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Affiliation(s)
- Yeong-Jin An
- Department of Electronics Engineering, Chungnam National University, Daejeon, 34134, Republic of Korea.
| | - Han Yan
- Department of Electronics Engineering, Chungnam National University, Daejeon, 34134, Republic of Korea.
| | - Chae-Min Yeom
- Department of Electronics Engineering, Chungnam National University, Daejeon, 34134, Republic of Korea.
| | - Jun-Kyo Jeong
- Department of Electronics Engineering, Chungnam National University, Daejeon, 34134, Republic of Korea.
| | - Sunil Babu Eadi
- Department of Electronics Engineering, Chungnam National University, Daejeon, 34134, Republic of Korea.
| | - Hi-Deok Lee
- Department of Electronics Engineering, Chungnam National University, Daejeon, 34134, Republic of Korea.
| | - Hyuk-Min Kwon
- Department of Semiconductor Processing Equipment, Semiconductor Convergence Campus of Korea Polytechnic College, Anseong, Kyunggi-Do, 17550, Republic of Korea.
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9
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Wang W, Wang Y, Yin F, Niu H, Shin YK, Li Y, Kim ES, Kim NY. Tailoring Classical Conditioning Behavior in TiO 2 Nanowires: ZnO QDs-Based Optoelectronic Memristors for Neuromorphic Hardware. NANO-MICRO LETTERS 2024; 16:133. [PMID: 38411720 PMCID: PMC10899558 DOI: 10.1007/s40820-024-01338-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Accepted: 12/28/2023] [Indexed: 02/28/2024]
Abstract
Neuromorphic hardware equipped with associative learning capabilities presents fascinating applications in the next generation of artificial intelligence. However, research into synaptic devices exhibiting complex associative learning behaviors is still nascent. Here, an optoelectronic memristor based on Ag/TiO2 Nanowires: ZnO Quantum dots/FTO was proposed and constructed to emulate the biological associative learning behaviors. Effective implementation of synaptic behaviors, including long and short-term plasticity, and learning-forgetting-relearning behaviors, were achieved in the device through the application of light and electrical stimuli. Leveraging the optoelectronic co-modulated characteristics, a simulation of neuromorphic computing was conducted, resulting in a handwriting digit recognition accuracy of 88.9%. Furthermore, a 3 × 7 memristor array was constructed, confirming its application in artificial visual memory. Most importantly, complex biological associative learning behaviors were emulated by mapping the light and electrical stimuli into conditioned and unconditioned stimuli, respectively. After training through associative pairs, reflexes could be triggered solely using light stimuli. Comprehensively, under specific optoelectronic signal applications, the four features of classical conditioning, namely acquisition, extinction, recovery, and generalization, were elegantly emulated. This work provides an optoelectronic memristor with associative behavior capabilities, offering a pathway for advancing brain-machine interfaces, autonomous robots, and machine self-learning in the future.
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Affiliation(s)
- Wenxiao Wang
- School of Information Science and Engineering, University of Jinan, Jinan, 250022, People's Republic of China
- RFIC Centre, NDAC Centre, Kwangwoon University, Nowon-gu, Seoul, 139-701, South Korea
- Department of Electronics Engineering, Kwangwoon University, Nowon-Gu, Seoul, 139-701, South Korea
| | - Yaqi Wang
- School of Information Science and Engineering, University of Jinan, Jinan, 250022, People's Republic of China
| | - Feifei Yin
- RFIC Centre, NDAC Centre, Kwangwoon University, Nowon-gu, Seoul, 139-701, South Korea
- Department of Electronics Engineering, Kwangwoon University, Nowon-Gu, Seoul, 139-701, South Korea
| | - Hongsen Niu
- RFIC Centre, NDAC Centre, Kwangwoon University, Nowon-gu, Seoul, 139-701, South Korea
- Department of Electronics Engineering, Kwangwoon University, Nowon-Gu, Seoul, 139-701, South Korea
| | - Young-Kee Shin
- Department of Molecular Medicine and Biopharmaceutical Sciences, Seoul National University, Seoul, 08826, South Korea
| | - Yang Li
- School of Information Science and Engineering, University of Jinan, Jinan, 250022, People's Republic of China.
- School of Microelectronics, Shandong University, Jinan, 250101, People's Republic of China.
| | - Eun-Seong Kim
- RFIC Centre, NDAC Centre, Kwangwoon University, Nowon-gu, Seoul, 139-701, South Korea.
- Department of Electronics Engineering, Kwangwoon University, Nowon-Gu, Seoul, 139-701, South Korea.
| | - Nam-Young Kim
- RFIC Centre, NDAC Centre, Kwangwoon University, Nowon-gu, Seoul, 139-701, South Korea.
- Department of Electronics Engineering, Kwangwoon University, Nowon-Gu, Seoul, 139-701, South Korea.
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10
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Qiu E, Zhang YH, Ventra MD, Schuller IK. Reconfigurable Cascaded Thermal Neuristors for Neuromorphic Computing. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024; 36:e2306818. [PMID: 37770043 DOI: 10.1002/adma.202306818] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Revised: 09/25/2023] [Indexed: 10/03/2023]
Abstract
While the complementary metal-oxide semiconductor (CMOS) technology is the mainstream for the hardware implementation of neural networks, an alternative route is explored based on a new class of spiking oscillators called "thermal neuristors", which operate and interact solely via thermal processes. Utilizing the insulator-to-metal transition (IMT) in vanadium dioxide, a wide variety of reconfigurable electrical dynamics mirroring biological neurons is demonstrated. Notably, inhibitory functionality is achieved just in a single oxide device, and cascaded information flow is realized exclusively through thermal interactions. To elucidate the underlying mechanisms of the neuristors, a detailed theoretical model is developed, which accurately reflects the experimental results. This study establishes the foundation for scalable and energy-efficient thermal neural networks, fostering progress in brain-inspired computing.
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Affiliation(s)
- Erbin Qiu
- Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA, 92093, USA
- Department of Physics, University of California San Diego, La Jolla, CA, 92093, USA
| | - Yuan-Hang Zhang
- Department of Physics, University of California San Diego, La Jolla, CA, 92093, USA
| | | | - Ivan K Schuller
- Department of Physics, University of California San Diego, La Jolla, CA, 92093, USA
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11
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Kwon JY, Kim JE, Kim JS, Chun SY, Soh K, Yoon JH. Artificial sensory system based on memristive devices. EXPLORATION (BEIJING, CHINA) 2024; 4:20220162. [PMID: 38854486 PMCID: PMC10867403 DOI: 10.1002/exp.20220162] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Accepted: 10/16/2023] [Indexed: 06/11/2024]
Abstract
In the biological nervous system, the integration and cooperation of parallel system of receptors, neurons, and synapses allow efficient detection and processing of intricate and disordered external information. Such systems acquire and process environmental data in real-time, efficiently handling complex tasks with minimal energy consumption. Memristors can mimic typical biological receptors, neurons, and synapses by implementing key features of neuronal signal-processing functions such as selective adaption in receptors, leaky integrate-and-fire in neurons, and synaptic plasticity in synapses. External stimuli are sensitively detected and filtered by "artificial receptors," encoded into spike signals via "artificial neurons," and integrated and stored through "artificial synapses." The high operational speed, low power consumption, and superior scalability of memristive devices make their integration with high-performance sensors a promising approach for creating integrated artificial sensory systems. These integrated systems can extract useful data from a large volume of raw data, facilitating real-time detection and processing of environmental information. This review explores the recent advances in memristor-based artificial sensory systems. The authors begin with the requirements of artificial sensory elements and then present an in-depth review of such elements demonstrated by memristive devices. Finally, the major challenges and opportunities in the development of memristor-based artificial sensory systems are discussed.
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Affiliation(s)
- Ju Young Kwon
- Electronic Materials Research CenterKorea Institute of Science and Technology (KIST)SeoulRepublic of Korea
| | - Ji Eun Kim
- Electronic Materials Research CenterKorea Institute of Science and Technology (KIST)SeoulRepublic of Korea
- Department of Materials Science and EngineeringKorea UniversitySeoulRepublic of Korea
| | - Jong Sung Kim
- Electronic Materials Research CenterKorea Institute of Science and Technology (KIST)SeoulRepublic of Korea
- Department of Materials Science and EngineeringKorea UniversitySeoulRepublic of Korea
| | - Suk Yeop Chun
- Electronic Materials Research CenterKorea Institute of Science and Technology (KIST)SeoulRepublic of Korea
- KU‐KIST Graduate School of Converging Science and TechnologyKorea UniversitySeoulRepublic of Korea
| | - Keunho Soh
- Electronic Materials Research CenterKorea Institute of Science and Technology (KIST)SeoulRepublic of Korea
- Department of Materials Science and EngineeringKorea UniversitySeoulRepublic of Korea
| | - Jung Ho Yoon
- Electronic Materials Research CenterKorea Institute of Science and Technology (KIST)SeoulRepublic of Korea
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12
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Sunny MM, Thamankar R. Spike rate dependent synaptic characteristics in lamellar, multilayered alpha-MoO 3 based two-terminal devices - efficient way to control the synaptic amplification. RSC Adv 2024; 14:2518-2528. [PMID: 38226148 PMCID: PMC10788777 DOI: 10.1039/d3ra07757h] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Accepted: 12/19/2023] [Indexed: 01/17/2024] Open
Abstract
Brain-inspired computing systems require a rich variety of neuromorphic devices using multi-functional materials operating at room temperature. Artificial synapses which can be operated using optical and electrical stimuli are in high demand. In this regard, layered materials have attracted a lot of attention due to their tunable energy gap and exotic properties. In the current study, we report the growth of layered MoO3 using the chemical vapor deposition (CVD) technique. MoO3 has an energy gap of 3.22 eV and grows with a large aspect ratio, as seen through optical and scanning electron microscopy. We used transmission electron microscopy (TEM) and X-ray photoelectron spectroscopy for complete characterisation. The two-terminal devices using platinum (Pt/MoO3/Pt) exhibit superior memory with the high-resistance state (HRS) and low-resistance state (LRS) differing by a large resistance (∼MΩ). The devices also show excellent synaptic characteristics. Both optical and electrical pulses can be utilised to stimulate the synapse. Consistent learning (potentiation) and forgetting (depression) curves are measured. Transition from long term depression to long term potentiation can be achieved using the spike frequency dependent pulsing scheme. We have found that the amplification of postsynaptic current can be tuned using such frequency dependent spikes. This will help us to design neuromorphic devices with the required synaptic amplification.
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Affiliation(s)
- Meenu Maria Sunny
- Department of Physics, Vellore Institute of Technology Vellore TN India
- Centre for Functional Materials, Vellore Institute of Technology Vellore TN India
| | - R Thamankar
- Centre for Functional Materials, Vellore Institute of Technology Vellore TN India
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13
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Rodríguez-Azar PI, Mejía-Muñoz JM, Cruz-Mejía O, Torres-Escobar R, López LVR. Fog Computing for Control of Cyber-Physical Systems in Industry Using BCI. SENSORS (BASEL, SWITZERLAND) 2023; 24:149. [PMID: 38203012 PMCID: PMC10781321 DOI: 10.3390/s24010149] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Revised: 12/23/2023] [Accepted: 12/25/2023] [Indexed: 01/12/2024]
Abstract
Brain-computer interfaces use signals from the brain, such as EEG, to determine brain states, which in turn can be used to issue commands, for example, to control industrial machinery. While Cloud computing can aid in the creation and operation of industrial multi-user BCI systems, the vast amount of data generated from EEG signals can lead to slow response time and bandwidth problems. Fog computing reduces latency in high-demand computation networks. Hence, this paper introduces a fog computing solution for BCI processing. The solution consists in using fog nodes that incorporate machine learning algorithms to convert EEG signals into commands to control a cyber-physical system. The machine learning module uses a deep learning encoder to generate feature images from EEG signals that are subsequently classified into commands by a random forest. The classification scheme is compared using various classifiers, being the random forest the one that obtained the best performance. Additionally, a comparison was made between the fog computing approach and using only cloud computing through the use of a fog computing simulator. The results indicate that the fog computing method resulted in less latency compared to the solely cloud computing approach.
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Affiliation(s)
- Paula Ivone Rodríguez-Azar
- Departamento de Ingeniería Industrial y Manufactura, Instituto de Ingeniería y Tecnología, Universidad Autónoma de Ciudad Juárez, Ciudad Juárez 32310, Mexico
| | - Jose Manuel Mejía-Muñoz
- Departamento de Ingeniería Eléctrica, Instituto de Ingenieria y Tecnologia, Universidad Autónoma de Ciudad Juárez, Ciudad Juárez 32310, Mexico;
| | - Oliverio Cruz-Mejía
- Departamento de Ingeniería Industrial, FES Aragón, Universidad Nacional Autónoma de México, Mexico 57171, Mexico;
| | | | - Lucero Verónica Ruelas López
- Departamento de Ingeniería Eléctrica, Instituto de Ingenieria y Tecnologia, Universidad Autónoma de Ciudad Juárez, Ciudad Juárez 32310, Mexico;
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14
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Abstract
Efforts to design devices emulating complex cognitive abilities and response processes of biological systems have long been a coveted goal. Recent advancements in flexible electronics, mirroring human tissue's mechanical properties, hold significant promise. Artificial neuron devices, hinging on flexible artificial synapses, bioinspired sensors, and actuators, are meticulously engineered to mimic the biological systems. However, this field is in its infancy, requiring substantial groundwork to achieve autonomous systems with intelligent feedback, adaptability, and tangible problem-solving capabilities. This review provides a comprehensive overview of recent advancements in artificial neuron devices. It starts with fundamental principles of artificial synaptic devices and explores artificial sensory systems, integrating artificial synapses and bioinspired sensors to replicate all five human senses. A systematic presentation of artificial nervous systems follows, designed to emulate fundamental human nervous system functions. The review also discusses potential applications and outlines existing challenges, offering insights into future prospects. We aim for this review to illuminate the burgeoning field of artificial neuron devices, inspiring further innovation in this captivating area of research.
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Affiliation(s)
- Ke He
- Innovative Centre for Flexible Devices (iFLEX), Max Planck-NTU Joint Lab for Artificial Senses, School of Materials Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore
| | - Cong Wang
- Innovative Centre for Flexible Devices (iFLEX), Max Planck-NTU Joint Lab for Artificial Senses, School of Materials Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore
| | - Yongli He
- Innovative Centre for Flexible Devices (iFLEX), Max Planck-NTU Joint Lab for Artificial Senses, School of Materials Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore
| | - Jiangtao Su
- Innovative Centre for Flexible Devices (iFLEX), Max Planck-NTU Joint Lab for Artificial Senses, School of Materials Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore
| | - Xiaodong Chen
- Innovative Centre for Flexible Devices (iFLEX), Max Planck-NTU Joint Lab for Artificial Senses, School of Materials Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore
- Institute for Digital Molecular Analytics and Science (IDMxS), Nanyang Technological University, 59 Nanyang Drive, Singapore 636921, Singapore
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15
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Xu M, Chen X, Guo Y, Wang Y, Qiu D, Du X, Cui Y, Wang X, Xiong J. Reconfigurable Neuromorphic Computing: Materials, Devices, and Integration. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2023; 35:e2301063. [PMID: 37285592 DOI: 10.1002/adma.202301063] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Revised: 05/15/2023] [Indexed: 06/09/2023]
Abstract
Neuromorphic computing has been attracting ever-increasing attention due to superior energy efficiency, with great promise to promote the next wave of artificial general intelligence in the post-Moore era. Current approaches are, however, broadly designed for stationary and unitary assignments, thus encountering reluctant interconnections, power consumption, and data-intensive computing in that domain. Reconfigurable neuromorphic computing, an on-demand paradigm inspired by the inherent programmability of brain, can maximally reallocate finite resources to perform the proliferation of reproducibly brain-inspired functions, highlighting a disruptive framework for bridging the gap between different primitives. Although relevant research has flourished in diverse materials and devices with novel mechanisms and architectures, a precise overview remains blank and urgently desirable. Herein, the recent strides along this pursuit are systematically reviewed from material, device, and integration perspectives. At the material and device level, one comprehensively conclude the dominant mechanisms for reconfigurability, categorized into ion migration, carrier migration, phase transition, spintronics, and photonics. Integration-level developments for reconfigurable neuromorphic computing are also exhibited. Finally, a perspective on the future challenges for reconfigurable neuromorphic computing is discussed, definitely expanding its horizon for scientific communities.
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Affiliation(s)
- Minyi Xu
- State Key Laboratory of Electronic Thin Film and Integrated Devices, School of Physics, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Xinrui Chen
- State Key Laboratory of Electronic Thin Film and Integrated Devices, School of Physics, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Yehao Guo
- State Key Laboratory of Electronic Thin Film and Integrated Devices, School of Physics, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Yang Wang
- State Key Laboratory of Electronic Thin Film and Integrated Devices, School of Physics, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Dong Qiu
- State Key Laboratory of Electronic Thin Film and Integrated Devices, School of Physics, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Xinchuan Du
- State Key Laboratory of Electronic Thin Film and Integrated Devices, School of Physics, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Yi Cui
- State Key Laboratory of Electronic Thin Film and Integrated Devices, School of Physics, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Xianfu Wang
- State Key Laboratory of Electronic Thin Film and Integrated Devices, School of Physics, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Jie Xiong
- State Key Laboratory of Electronic Thin Film and Integrated Devices, School of Physics, University of Electronic Science and Technology of China, Chengdu, 610054, China
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16
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Minnekhanov A, Matsukatova A, Trofimov A, Nesmelov A, Zavyalov S, Demin V, Emelyanov A. Reliable Memristive Synapses Based on Parylene-MoO x Nanocomposites for Neuromorphic Applications. ACS APPLIED MATERIALS & INTERFACES 2023; 15:54996-55008. [PMID: 37962902 DOI: 10.1021/acsami.3c13956] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Memristive devices, known for their nonvolatile resistive switching, are promising components for next-generation neuromorphic computing systems, which mimic the brain's neural architecture. Specifically, these devices are well-suited for functioning as artificial synapses due to their analogue tunability and low energy consumption. However, the improvement of their performance and reliability remains a pressing challenge. In this study, we report the development and comprehensive characterization of memristive devices based on a parylene-MoOx (PPX-Mo) nanocomposite layer, which exhibit improved characteristics over their parylene-based counterparts: lower switching voltage and energy, smaller dispersion, and better resistive plasticity. A robust statistical analysis identified the optimal synthesis parameters for these devices, providing valuable insights for future device optimization. The most probable resistive switching mechanism of the devices is proposed. By successfully integrating these memristors into a neuromorphic computing model and showcasing their scalability in crossbar geometry, we demonstrate their potential as functional artificial synapses. The results obtained from this study can be useful for the development of hardware-brain-inspired computational systems.
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Affiliation(s)
| | - Anna Matsukatova
- National Research Centre Kurchatov Institute, Moscow 123182, Russia
- Lomonosov Moscow State University, Moscow 119991, Russia
| | - Andrey Trofimov
- National Research Centre Kurchatov Institute, Moscow 123182, Russia
- Moscow Institute of Physics and Technology (National Research University), Dolgoprudny, Moscow 141701, Russia
| | | | - Sergey Zavyalov
- National Research Centre Kurchatov Institute, Moscow 123182, Russia
| | - Vyacheslav Demin
- National Research Centre Kurchatov Institute, Moscow 123182, Russia
- Moscow Institute of Physics and Technology (National Research University), Dolgoprudny, Moscow 141701, Russia
| | - Andrey Emelyanov
- National Research Centre Kurchatov Institute, Moscow 123182, Russia
- Moscow Institute of Physics and Technology (National Research University), Dolgoprudny, Moscow 141701, Russia
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17
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Zhao J, Tong L, Niu J, Fang Z, Pei Y, Zhou Z, Sun Y, Wang Z, Wang H, Lou J, Yan X. A bidirectional thermal sensory leaky integrate-and-fire (LIF) neuron model based on bipolar NbO x volatile threshold devices with ultra-low operating current. NANOSCALE 2023; 15:17599-17608. [PMID: 37874690 DOI: 10.1039/d3nr03034b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/26/2023]
Abstract
Brain-like artificial intelligence (AI) will become the main form and important platform in future computing. It will play an important and unique role in simulating brain functions, efficiently implementing AI algorithms, and improving computing power. Developing artificial neurons that can send facilitation/depression signals to artificial synapses, sense, and process temperature information is of great significance for achieving more efficient and compact brain-like computing systems. Herein, we have constructed a NbOx bipolar volatile threshold memristor, which could be operated by 1 μA ultra-low current and up to ∼104 switching ratios. By using a leaky integrate-and-fire (LIF) artificial neuron model, a bipolar LIF artificial neuron is constructed, which can realize the conventional threshold-driven firing, all-or-nothing spiking, refractory periods, and intensity-modulated frequency response bidirectionally at the positive/negative voltage stimulation, which will give the artificial synapse facilitation/depression signals. Furthermore, this bipolar LIF neuron can also explore different temperatures to output different signals, which could be constructed as a more compact thermal sensory neuron to avoid external harm to artificial robots. This study is of great significance for improving the computational efficiency of the system more effectively, achieving high integration density and low energy consumption artificial neural networks to meet the needs of brain-like neural computing.
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Affiliation(s)
- Jianhui Zhao
- Key Laboratory of Brain-Like Neuromorphic Devices and Systems of Hebei Province, College of Electronic and Information Engineering, Institute of Life Science and Green Development, Hebei University, Baoding 071002, China.
| | - Liang Tong
- Key Laboratory of Brain-Like Neuromorphic Devices and Systems of Hebei Province, College of Electronic and Information Engineering, Institute of Life Science and Green Development, Hebei University, Baoding 071002, China.
| | - Jiangzhen Niu
- Key Laboratory of Brain-Like Neuromorphic Devices and Systems of Hebei Province, College of Electronic and Information Engineering, Institute of Life Science and Green Development, Hebei University, Baoding 071002, China.
| | - Ziliang Fang
- Key Laboratory of Brain-Like Neuromorphic Devices and Systems of Hebei Province, College of Electronic and Information Engineering, Institute of Life Science and Green Development, Hebei University, Baoding 071002, China.
| | - Yifei Pei
- Key Laboratory of Brain-Like Neuromorphic Devices and Systems of Hebei Province, College of Physics Science and Technology, Institute of Life Science and Green Development, Hebei University, Baoding 071002, China
| | - Zhenyu Zhou
- Key Laboratory of Brain-Like Neuromorphic Devices and Systems of Hebei Province, College of Electronic and Information Engineering, Institute of Life Science and Green Development, Hebei University, Baoding 071002, China.
| | - Yong Sun
- Key Laboratory of Brain-Like Neuromorphic Devices and Systems of Hebei Province, College of Electronic and Information Engineering, Institute of Life Science and Green Development, Hebei University, Baoding 071002, China.
| | - Zhongrong Wang
- Key Laboratory of Brain-Like Neuromorphic Devices and Systems of Hebei Province, College of Electronic and Information Engineering, Institute of Life Science and Green Development, Hebei University, Baoding 071002, China.
| | - Hong Wang
- Key Laboratory of Brain-Like Neuromorphic Devices and Systems of Hebei Province, College of Electronic and Information Engineering, Institute of Life Science and Green Development, Hebei University, Baoding 071002, China.
| | - Jianzhong Lou
- Key Laboratory of Brain-Like Neuromorphic Devices and Systems of Hebei Province, College of Electronic and Information Engineering, Institute of Life Science and Green Development, Hebei University, Baoding 071002, China.
| | - Xiaobing Yan
- Key Laboratory of Brain-Like Neuromorphic Devices and Systems of Hebei Province, College of Electronic and Information Engineering, Institute of Life Science and Green Development, Hebei University, Baoding 071002, China.
- Key Laboratory of Brain-Like Neuromorphic Devices and Systems of Hebei Province, College of Physics Science and Technology, Institute of Life Science and Green Development, Hebei University, Baoding 071002, China
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18
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Luo J, Tian G, Zhang DG, Zhang XC, Lu ZN, Zhang ZD, Cai JW, Zhong YN, Xu JL, Gao X, Wang SD. Voltage-Mode Ferroelectric Synapse for Neuromorphic Computing. ACS APPLIED MATERIALS & INTERFACES 2023; 15:48452-48461. [PMID: 37802499 DOI: 10.1021/acsami.3c09506] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/10/2023]
Abstract
Ferroelectric materials with a modulable polarization extent hold promise for exploring voltage-driven neuromorphic hardware, in which direct current flow can be minimized. Utilizing a single active layer of an insulating ferroelectric polymer, we developed a voltage-mode ferroelectric synapse that can continuously and reversibly update its states. The device states are straightforwardly manifested in the form of variable output voltage, enabling large-scale direct cascading of multiple ferroelectric synapses to build a deep physical neural network. Such a neural network based on potential superposition rather than current flow is analogous to the biological counterpart driven by action potentials in the brain. A high accuracy of over 97% for the simulation of handwritten digit recognition is achieved using the voltage-mode neural network. The controlled ferroelectric polarization, revealed by piezoresponse force microscopy, turns out to be responsible for the synaptic weight updates in the ferroelectric synapses. The present work demonstrates an alternative strategy for the design and construction of emerging artificial neural networks.
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Affiliation(s)
- Jie Luo
- Institute of Functional Nano & Soft Materials (FUNSOM), Jiangsu Key Laboratory for Carbon-Based Functional Materials & Devices, Soochow University, Suzhou, Jiangsu 215123, P. R. China
| | - Guo Tian
- Institute for Advanced Materials and Guangdong Provincial Key Laboratory of Optical Information Materials and Technology, South China Academy of Advanced Optoelectronics, South China Normal University, Guangzhou 510006, P. R. China
| | - Ding-Guo Zhang
- Institute of Functional Nano & Soft Materials (FUNSOM), Jiangsu Key Laboratory for Carbon-Based Functional Materials & Devices, Soochow University, Suzhou, Jiangsu 215123, P. R. China
| | - Xing-Chen Zhang
- Institute for Advanced Materials and Guangdong Provincial Key Laboratory of Optical Information Materials and Technology, South China Academy of Advanced Optoelectronics, South China Normal University, Guangzhou 510006, P. R. China
| | - Zhen-Ni Lu
- Institute of Functional Nano & Soft Materials (FUNSOM), Jiangsu Key Laboratory for Carbon-Based Functional Materials & Devices, Soochow University, Suzhou, Jiangsu 215123, P. R. China
| | - Zhong-Da Zhang
- Institute of Functional Nano & Soft Materials (FUNSOM), Jiangsu Key Laboratory for Carbon-Based Functional Materials & Devices, Soochow University, Suzhou, Jiangsu 215123, P. R. China
| | - Jia-Wei Cai
- Institute of Functional Nano & Soft Materials (FUNSOM), Jiangsu Key Laboratory for Carbon-Based Functional Materials & Devices, Soochow University, Suzhou, Jiangsu 215123, P. R. China
| | - Ya-Nan Zhong
- Institute of Functional Nano & Soft Materials (FUNSOM), Jiangsu Key Laboratory for Carbon-Based Functional Materials & Devices, Soochow University, Suzhou, Jiangsu 215123, P. R. China
| | - Jian-Long Xu
- Institute of Functional Nano & Soft Materials (FUNSOM), Jiangsu Key Laboratory for Carbon-Based Functional Materials & Devices, Soochow University, Suzhou, Jiangsu 215123, P. R. China
| | - Xu Gao
- Institute of Functional Nano & Soft Materials (FUNSOM), Jiangsu Key Laboratory for Carbon-Based Functional Materials & Devices, Soochow University, Suzhou, Jiangsu 215123, P. R. China
| | - Sui-Dong Wang
- Institute of Functional Nano & Soft Materials (FUNSOM), Jiangsu Key Laboratory for Carbon-Based Functional Materials & Devices, Soochow University, Suzhou, Jiangsu 215123, P. R. China
- Macao Institute of Materials Science and Engineering (MIMSE), MUST-SUDA Joint Research Center for Advanced Functional Materials, Macau University of Science and Technology, Taipa, Macao 999078, P. R. China
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19
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Han MJ, Tsukruk VV. Trainable Bilingual Synaptic Functions in Bio-enabled Synaptic Transistors. ACS NANO 2023; 17:18883-18892. [PMID: 37721448 PMCID: PMC10569090 DOI: 10.1021/acsnano.3c04113] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Accepted: 09/14/2023] [Indexed: 09/19/2023]
Abstract
The signal transmission of the nervous system is regulated by neurotransmitters. Depending on the type of neurotransmitter released by presynaptic neurons, neuron cells can either be excited or inhibited. Maintaining a balance between excitatory and inhibitory synaptic responses is crucial for the nervous system's versatility, elasticity, and ability to perform parallel computing. On the way to mimic the brain's versatility and plasticity traits, creating a preprogrammed balance between excitatory and inhibitory responses is required. Despite substantial efforts to investigate the balancing of the nervous system, a complex circuit configuration has been suggested to simulate the interaction between excitatory and inhibitory synapses. As a meaningful approach, an optoelectronic synapse for balancing the excitatory and inhibitory responses assisted by light mediation is proposed here by deploying humidity-sensitive chiral nematic phases of known polysaccharide cellulose nanocrystals. The environment-induced pitch tuning changes the polarization of the helicoidal organization, affording different hysteresis effects with the subsequent excitatory and inhibitory nonvolatile behavior in the bio-electrolyte-gated transistors. By applying voltage pulses combined with stimulation of chiral light, the artificial optoelectronic synapse tunes not only synaptic functions but also learning pathways and color recognition. These multifunctional bio-based synaptic field-effect transistors exhibit potential for enhanced parallel neuromorphic computing and robot vision technology.
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Affiliation(s)
- Moon Jong Han
- Department
of Electronic Engineering, Gachon University, Seongnam 13120, Republic of Korea
| | - Vladimir V. Tsukruk
- School
of Materials Science and Engineering, Georgia
Institute of Technology, Atlanta, Georgia 30332, United States
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20
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Zhao J, Ran Y, Pei Y, Wei Y, Sun J, Zhang Z, Wang J, Zhou Z, Wang Z, Sun Y, Yan X. Memristors based on NdNiO 3 nanocrystals film as sensory neurons for neuromorphic computing. MATERIALS HORIZONS 2023; 10:4521-4531. [PMID: 37555245 DOI: 10.1039/d3mh00835e] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/10/2023]
Abstract
By mimicking the behavior of the human brain, artificial neural systems offer the possibility to further improve computing efficiency and solve the von Neumann bottleneck. In particular, neural systems with perceptual capability expand the application field and lay a good foundation for the construction of perceptual storage and computational systems. However, research on neurons with perceptual functions is still relatively scarce, with most works focusing on optoelectronic synapses. The neuron is important for neuromorphic computing systems because neurons output excitatory or inhibitory stimuli to regulate the weight of synapses. Therefore, the construction of sensory neurons is crucial to expand the application range of brain-like neural computing. Here, an artificial sensory neuron is proposed, which is constructed using a photosensitive bipolar threshold switching memristor based on NdNiO3 (NNO) nanocrystals. These metallic phase nanocrystals can not only enhance the local electric field, but also act as a reservoir for defects (VoS) to guide the growth of conductive filaments and stabilize the performance of the device. They present stable bipolar threshold switching behavior with a low 120 nW set power, and the operating voltages decreased in light due to photocarrier action. A leaky integrate firing (LIF) neuron has been realized, which achieved key biological neuron functions, such as all-or-nothing spiking, threshold-driven firing, refractory period, and spiking frequency modulation. The LIF neurons receiving optical inputs have the properties of an artificial sensory neuron. It could regulate the spiking output frequency at different light densities, which could be used for a ship approaching a port. This work provides a promising hardware implementation towards constructing high-performance artificial intelligence to assist ships at night in a sensory system.
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Affiliation(s)
- Jianhui Zhao
- Key Laboratory of Brain-Like Neuromorphic Devices and Systems of Hebei Province, College of Electronic and Information Engineering, Hebei University, Baoding 071002, People's Republic of China.
| | - Yunfeng Ran
- Key Laboratory of Brain-Like Neuromorphic Devices and Systems of Hebei Province, College of Electronic and Information Engineering, Hebei University, Baoding 071002, People's Republic of China.
| | - Yifei Pei
- Hebei Key Laboratory of Optic-Electronic Information Materials, College of Physics Science and Technology, Hebei University, Baoding 071002, People's Republic of China
| | - Yiheng Wei
- Key Laboratory of Brain-Like Neuromorphic Devices and Systems of Hebei Province, College of Electronic and Information Engineering, Hebei University, Baoding 071002, People's Republic of China.
| | - Jiameng Sun
- Key Laboratory of Brain-Like Neuromorphic Devices and Systems of Hebei Province, College of Electronic and Information Engineering, Hebei University, Baoding 071002, People's Republic of China.
| | - Zixuan Zhang
- Key Laboratory of Brain-Like Neuromorphic Devices and Systems of Hebei Province, College of Electronic and Information Engineering, Hebei University, Baoding 071002, People's Republic of China.
| | - Jiacheng Wang
- Key Laboratory of Brain-Like Neuromorphic Devices and Systems of Hebei Province, College of Electronic and Information Engineering, Hebei University, Baoding 071002, People's Republic of China.
| | - Zhenyu Zhou
- Key Laboratory of Brain-Like Neuromorphic Devices and Systems of Hebei Province, College of Electronic and Information Engineering, Hebei University, Baoding 071002, People's Republic of China.
| | - Zhongrong Wang
- Key Laboratory of Brain-Like Neuromorphic Devices and Systems of Hebei Province, College of Electronic and Information Engineering, Hebei University, Baoding 071002, People's Republic of China.
| | - Yong Sun
- Key Laboratory of Brain-Like Neuromorphic Devices and Systems of Hebei Province, College of Electronic and Information Engineering, Hebei University, Baoding 071002, People's Republic of China.
| | - Xiaobing Yan
- Key Laboratory of Brain-Like Neuromorphic Devices and Systems of Hebei Province, College of Electronic and Information Engineering, Hebei University, Baoding 071002, People's Republic of China.
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21
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Ye F, Kiani F, Huang Y, Xia Q. Diffusive Memristors with Uniform and Tunable Relaxation Time for Spike Generation in Event-Based Pattern Recognition. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2023; 35:e2204778. [PMID: 36036786 DOI: 10.1002/adma.202204778] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Revised: 08/05/2022] [Indexed: 06/15/2023]
Abstract
A diffusive memristor is a promising building block for brain-inspired computing hardware. However, the randomness in the device relaxation dynamics limits the wide-range adoption of diffusive memristors in large arrays. In this work, the device stack is engineered to achieve a much-improved uniformity in the relaxation time (standard deviation σ reduced from ≈12 to ≈0.32 ms). The memristor is further connected with a resistor or a capacitor and the relaxation time is tuned between 1.13 µs and 1.25 ms, ranging from three orders of magnitude. The hierarchy of time surfaces (HOTS) algorithm, to utilize the tunable and uniform relaxation behavior for spike generation, is implemented. An accuracy of 77.3% is achieved in recognizing moving objects in the neuromorphic MNIST (N-MNIST) dataset. The work paves the way for building emerging neuromorphic computing hardware systems with ultralow power consumption.
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Affiliation(s)
- Fan Ye
- Department of Electrical and Computer Engineering, University of Massachusetts Amherst, Amherst, MA, 01003, USA
| | - Fatemeh Kiani
- Department of Electrical and Computer Engineering, University of Massachusetts Amherst, Amherst, MA, 01003, USA
| | - Yi Huang
- Department of Electrical and Computer Engineering, University of Massachusetts Amherst, Amherst, MA, 01003, USA
| | - Qiangfei Xia
- Department of Electrical and Computer Engineering, University of Massachusetts Amherst, Amherst, MA, 01003, USA
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22
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Wang X, Ran Y, Li X, Qin X, Lu W, Zhu Y, Lu G. Bio-inspired artificial synaptic transistors: evolution from innovative basic units to system integration. MATERIALS HORIZONS 2023; 10:3269-3292. [PMID: 37312536 DOI: 10.1039/d3mh00216k] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
The investigation of transistor-based artificial synapses in bioinspired information processing is undergoing booming exploration, and is the stable building block for brain-like computing. Given that the storage and computing separation architecture of von Neumann construction is not conducive to the current explosive information processing, it is critical to accelerate the connection between hardware systems and software simulations of intelligent synapses. So far, various works based on a transistor-based synaptic system successfully simulated functions similar to biological nerves in the human brain. However, the influence of the semiconductor and the device structural design on synaptic properties is still poorly linked. This review concretely emphasizes the recent advances in the novel structure design of semiconductor materials and devices used in synaptic transistors, not only from a single multifunction synaptic device but also to system application with various connected routes and related working mechanisms. Finally, crises and opportunities in transistor-based synaptic interconnection are discussed and predicted.
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Affiliation(s)
- Xin Wang
- Frontier Institute of Science and Technology, State Key Laboratory of Electrical Insulation and Power Equipment, Xi'an Jiaotong University, Xi'an 710054, P. R. China.
| | - Yixin Ran
- Frontier Institute of Science and Technology, State Key Laboratory of Electrical Insulation and Power Equipment, Xi'an Jiaotong University, Xi'an 710054, P. R. China.
| | - Xiaoqian Li
- Shandong Technology Center of Nanodevices and Integration, School of Microelectronics, Shandong University, Jinan, Shandong Province, 250100, P. R. China
| | - Xinsu Qin
- Frontier Institute of Science and Technology, State Key Laboratory of Electrical Insulation and Power Equipment, Xi'an Jiaotong University, Xi'an 710054, P. R. China.
| | - Wanlong Lu
- Frontier Institute of Science and Technology, State Key Laboratory of Electrical Insulation and Power Equipment, Xi'an Jiaotong University, Xi'an 710054, P. R. China.
| | - Yuanwei Zhu
- Frontier Institute of Science and Technology, State Key Laboratory of Electrical Insulation and Power Equipment, Xi'an Jiaotong University, Xi'an 710054, P. R. China.
| | - Guanghao Lu
- Frontier Institute of Science and Technology, State Key Laboratory of Electrical Insulation and Power Equipment, Xi'an Jiaotong University, Xi'an 710054, P. R. China.
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23
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Jiang T, Zeng BF, Zhang B, Tang L. Single-molecular protein-based bioelectronics via electronic transport: fundamentals, devices and applications. Chem Soc Rev 2023; 52:5968-6002. [PMID: 37498342 DOI: 10.1039/d2cs00519k] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/28/2023]
Abstract
Biomolecular electronics is a rapidly growing multidisciplinary field that combines biology, nanoscience, and engineering to bridge the two important fields of life sciences and molecular electronics. Proteins are remarkable for their ability to recognize molecules and transport electrons, making the integration of proteins into electronic devices a long sought-after goal and leading to the emergence of the field of protein-based bioelectronics, also known as proteotronics. This field seeks to design and create new biomolecular electronic platforms that allow for the understanding and manipulation of protein-mediated electronic charge transport and related functional applications. In recent decades, there have been numerous reports on protein-based bioelectronics using a variety of nano-gapped electrical devices and techniques at the single molecular level, which are not achievable with conventional ensemble approaches. This review focuses on recent advances in physical electron transport mechanisms, device fabrication methodologies, and various applications in protein-based bioelectronics. We discuss the most recent progress of the single or few protein-bridged electrical junction fabrication strategies, summarise the work on fundamental and functional applications of protein bioelectronics that enable high and dynamic electron transport, and highlight future perspectives and challenges that still need to be addressed. We believe that this specific review will stimulate the interdisciplinary research of topics related to protein-related bioelectronics, and open up new possibilities for single-molecule biophysics and biomedicine.
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Affiliation(s)
- Tao Jiang
- State Key Laboratory of Modern Optical Instrumentation, College of Optical Science and Engineering, Zhejiang University, Hangzhou 310027, China.
| | - Biao-Feng Zeng
- State Key Laboratory of Modern Optical Instrumentation, College of Optical Science and Engineering, Zhejiang University, Hangzhou 310027, China.
| | - Bintian Zhang
- Shenzhen Key Laboratory of Precision Measurement and Early Warning Technology for Urban Environmental Health Risks, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China.
| | - Longhua Tang
- State Key Laboratory of Modern Optical Instrumentation, College of Optical Science and Engineering, Zhejiang University, Hangzhou 310027, China.
- Institute of Quantum Sensing, Interdisciplinary Centre for Quantum Information, Zhejiang University, Hangzhou 310027, China
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24
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Aboumerhi K, Güemes A, Liu H, Tenore F, Etienne-Cummings R. Neuromorphic applications in medicine. J Neural Eng 2023; 20:041004. [PMID: 37531951 DOI: 10.1088/1741-2552/aceca3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Accepted: 08/02/2023] [Indexed: 08/04/2023]
Abstract
In recent years, there has been a growing demand for miniaturization, low power consumption, quick treatments, and non-invasive clinical strategies in the healthcare industry. To meet these demands, healthcare professionals are seeking new technological paradigms that can improve diagnostic accuracy while ensuring patient compliance. Neuromorphic engineering, which uses neural models in hardware and software to replicate brain-like behaviors, can help usher in a new era of medicine by delivering low power, low latency, small footprint, and high bandwidth solutions. This paper provides an overview of recent neuromorphic advancements in medicine, including medical imaging and cancer diagnosis, processing of biosignals for diagnosis, and biomedical interfaces, such as motor, cognitive, and perception prostheses. For each section, we provide examples of how brain-inspired models can successfully compete with conventional artificial intelligence algorithms, demonstrating the potential of neuromorphic engineering to meet demands and improve patient outcomes. Lastly, we discuss current struggles in fitting neuromorphic hardware with non-neuromorphic technologies and propose potential solutions for future bottlenecks in hardware compatibility.
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Affiliation(s)
- Khaled Aboumerhi
- Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD, United States of America
| | - Amparo Güemes
- Electrical Engineering Division, Department of Engineering, University of Cambridge, 9 JJ Thomson Ave, Cambridge CB3 0FA, United Kingdom
| | - Hongtao Liu
- Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD, United States of America
| | - Francesco Tenore
- Research and Exploratory Development Department, The Johns Hopkins University Applied Physics Laboratory, Laurel, MD, United States of America
| | - Ralph Etienne-Cummings
- Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD, United States of America
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25
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Zhou K, Jia Z, Zhou Y, Ding G, Ma XQ, Niu W, Han ST, Zhao J, Zhou Y. Covalent Organic Frameworks for Neuromorphic Devices. J Phys Chem Lett 2023; 14:7173-7192. [PMID: 37540588 DOI: 10.1021/acs.jpclett.3c01711] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/06/2023]
Abstract
Neuromorphic computing could enable the potential to break the inherent limitations of conventional von Neumann architectures, which has led to widespread research interest in developing novel neuromorphic memory devices, such as memristors and bioinspired artificial synaptic devices. Covalent organic frameworks (COFs), as crystalline porous polymers, have tailorable skeletons and pores, providing unique platforms for the interplay with photons, excitons, electrons, holes, ions, spins, and molecules. Such features encourage the rising research interest in COF materials in neuromorphic electronics. To develop high-performance COF-based neuromorphic memory devices, it is necessary to comprehensively understand materials, devices, and applications. Therefore, this Perspective focuses on discussing the use of COF materials for neuromorphic memory devices in terms of molecular design, thin-film processing, and neuromorphic applications. Finally, we provide an outlook for future directions and potential applications of COF-based neuromorphic electronics.
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Affiliation(s)
- Kui Zhou
- Institute for Advanced Study, Shenzhen University, 3688 Nanhai Avenue, Shenzhen 518060, P. R. China
| | - Ziqi Jia
- Institute for Advanced Study, Shenzhen University, 3688 Nanhai Avenue, Shenzhen 518060, P. R. China
| | - Yao Zhou
- College of Materials Science and Engineering, Shenzhen University, 3688 Nanhai Avenue, Shenzhen 518060, P. R. China
| | - Guanglong Ding
- Institute for Advanced Study, Shenzhen University, 3688 Nanhai Avenue, Shenzhen 518060, P. R. China
| | - Xin-Qi Ma
- Institute for Advanced Study, Shenzhen University, 3688 Nanhai Avenue, Shenzhen 518060, P. R. China
| | - Wenbiao Niu
- Institute for Advanced Study, Shenzhen University, 3688 Nanhai Avenue, Shenzhen 518060, P. R. China
| | - Su-Ting Han
- College of Electronics and Information Engineering, Shenzhen University, 3688 Nanhai Avenue, Shenzhen 518060, P. R. China
| | - Jiyu Zhao
- State Key Laboratory of Fine Chemicals, Dalian University of Technology, 2 Linggong Road, Dalian 116024, P. R. China
| | - Ye Zhou
- Institute for Advanced Study, Shenzhen University, 3688 Nanhai Avenue, Shenzhen 518060, P. R. China
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26
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Gebicke-Haerter PJ. The computational power of the human brain. Front Cell Neurosci 2023; 17:1220030. [PMID: 37608987 PMCID: PMC10441807 DOI: 10.3389/fncel.2023.1220030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Accepted: 07/05/2023] [Indexed: 08/24/2023] Open
Abstract
At the end of the 20th century, analog systems in computer science have been widely replaced by digital systems due to their higher computing power. Nevertheless, the question keeps being intriguing until now: is the brain analog or digital? Initially, the latter has been favored, considering it as a Turing machine that works like a digital computer. However, more recently, digital and analog processes have been combined to implant human behavior in robots, endowing them with artificial intelligence (AI). Therefore, we think it is timely to compare mathematical models with the biology of computation in the brain. To this end, digital and analog processes clearly identified in cellular and molecular interactions in the Central Nervous System are highlighted. But above that, we try to pinpoint reasons distinguishing in silico computation from salient features of biological computation. First, genuinely analog information processing has been observed in electrical synapses and through gap junctions, the latter both in neurons and astrocytes. Apparently opposed to that, neuronal action potentials (APs) or spikes represent clearly digital events, like the yes/no or 1/0 of a Turing machine. However, spikes are rarely uniform, but can vary in amplitude and widths, which has significant, differential effects on transmitter release at the presynaptic terminal, where notwithstanding the quantal (vesicular) release itself is digital. Conversely, at the dendritic site of the postsynaptic neuron, there are numerous analog events of computation. Moreover, synaptic transmission of information is not only neuronal, but heavily influenced by astrocytes tightly ensheathing the majority of synapses in brain (tripartite synapse). At least at this point, LTP and LTD modifying synaptic plasticity and believed to induce short and long-term memory processes including consolidation (equivalent to RAM and ROM in electronic devices) have to be discussed. The present knowledge of how the brain stores and retrieves memories includes a variety of options (e.g., neuronal network oscillations, engram cells, astrocytic syncytium). Also epigenetic features play crucial roles in memory formation and its consolidation, which necessarily guides to molecular events like gene transcription and translation. In conclusion, brain computation is not only digital or analog, or a combination of both, but encompasses features in parallel, and of higher orders of complexity.
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Affiliation(s)
- Peter J. Gebicke-Haerter
- Institute of Psychopharmacology, Central Institute of Mental Health, Faculty of Medicine, University of Heidelberg, Mannheim, Germany
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27
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Kwon D, Woo SY, Lee KH, Hwang J, Kim H, Park SH, Shin W, Bae JH, Kim JJ, Lee JH. Reconfigurable neuromorphic computing block through integration of flash synapse arrays and super-steep neurons. SCIENCE ADVANCES 2023; 9:eadg9123. [PMID: 37467329 PMCID: PMC10355823 DOI: 10.1126/sciadv.adg9123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/29/2023] [Accepted: 06/15/2023] [Indexed: 07/21/2023]
Abstract
Neuromorphic computing (NC) architecture inspired by biological nervous systems has been actively studied to overcome the limitations of conventional von Neumann architectures. In this work, we propose a reconfigurable NC block using a flash-type synapse array, emerging positive feedback (PF) neuron devices, and CMOS peripheral circuits, and integrate them on the same substrate to experimentally demonstrate the operations of the proposed NC block. Conductance modulation in the flash memory enables the NC block to be easily calibrated for output signals. In addition, the proposed NC block uses a reduced number of devices for analog-to-digital conversions due to the super-steep switching characteristics of the PF neuron device, substantially reducing the area overhead of NC block. Our NC block shows high energy efficiency (37.9 TOPS/W) with high accuracy for CIFAR-10 image classification (91.80%), outperforming prior works. This work shows the high engineering potential of integrating synapses and neurons in terms of system efficiency and high performance.
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Affiliation(s)
- Dongseok Kwon
- Department of Electrical and Computer Engineering and Inter-university Semiconductor Research Center, Seoul National University, Seoul 08826, Republic of Korea
| | - Sung Yun Woo
- Kyungbook National University, Daegu, Republic of Korea
| | - Kyu-Ho Lee
- Department of Electrical and Computer Engineering and Inter-university Semiconductor Research Center, Seoul National University, Seoul 08826, Republic of Korea
| | - Joon Hwang
- Department of Electrical and Computer Engineering and Inter-university Semiconductor Research Center, Seoul National University, Seoul 08826, Republic of Korea
| | - Hyeongsu Kim
- Department of Electrical and Computer Engineering and Inter-university Semiconductor Research Center, Seoul National University, Seoul 08826, Republic of Korea
| | - Sung-Ho Park
- Department of Electrical and Computer Engineering and Inter-university Semiconductor Research Center, Seoul National University, Seoul 08826, Republic of Korea
| | - Wonjun Shin
- Department of Electrical and Computer Engineering and Inter-university Semiconductor Research Center, Seoul National University, Seoul 08826, Republic of Korea
| | - Jong-Ho Bae
- School of Electrical Engineering, Kookmin University, Seoul 02707, Republic of Korea
| | - Jae-Joon Kim
- Department of Electrical and Computer Engineering and Inter-university Semiconductor Research Center, Seoul National University, Seoul 08826, Republic of Korea
| | - Jong-Ho Lee
- Ministry of Science and ICT, Sejong, Republic of Korea
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28
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López C. Artificial Intelligence and Advanced Materials. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2023; 35:e2208683. [PMID: 36560859 DOI: 10.1002/adma.202208683] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Revised: 12/01/2022] [Indexed: 06/09/2023]
Abstract
Artificial intelligence (AI) is gaining strength, and materials science can both contribute to and profit from it. In a simultaneous progress race, new materials, systems, and processes can be devised and optimized thanks to machine learning (ML) techniques, and such progress can be turned into innovative computing platforms. Future materials scientists will profit from understanding how ML can boost the conception of advanced materials. This review covers aspects of computation from the fundamentals to directions taken and repercussions produced by computation to account for the origins, procedures, and applications of AI. ML and its methods are reviewed to provide basic knowledge of its implementation and its potential. The materials and systems used to implement AI with electric charges are finding serious competition from other information-carrying and processing agents. The impact these techniques have on the inception of new advanced materials is so deep that a new paradigm is developing where implicit knowledge is being mined to conceive materials and systems for functions instead of finding applications to found materials. How far this trend can be carried is hard to fathom, as exemplified by the power to discover unheard of materials or physical laws buried in data.
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Affiliation(s)
- Cefe López
- Instituto de Ciencia de Materiales de Madrid (ICMM), Consejo Superior de Investigaciones Científicas (CSIC), Calle Sor Juana Inés de la Cruz 3, Madrid, 28049, Spain
- Donostia International Physics Centre (DIPC), Paseo Manuel de Lardizábal 4, San Sebastián, 20018, España
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29
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Li J, Qian Y, Li W, Yu S, Ke Y, Qian H, Lin YH, Hou CH, Shyue JJ, Zhou J, Chen Y, Xu J, Zhu J, Yi M, Huang W. Polymeric Memristor Based Artificial Synapses with Ultra-Wide Operating Temperature. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2023; 35:e2209728. [PMID: 36972150 DOI: 10.1002/adma.202209728] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Revised: 03/12/2023] [Indexed: 06/09/2023]
Abstract
Neuromorphic electronics, being inspired by how the brain works, hold great promise to the successful implementation of smart artificial systems. Among several neuromorphic hardware issues, a robust device functionality under extreme temperature is of particular importance for practical applications. Given that the organic memristors for artificial synapse applications are demonstrated under room temperature, achieving a robust device performance at extremely low or high temperature is still utterly challenging. In this work, the temperature issue is addressed by tuning the functionality of the solution-based organic polymeric memristor. The optimized memristor demonstrates a reliable performance under both the cryogenic and high-temperature environments. The unencapsulated organic polymeric memristor shows a robust memristive response under test temperature ranging from 77 to 573 K. Utilizing X-ray photoelectron spectroscopy (XPS) and time-of-flight secondary-ion mass spectrometry (ToF-SIMS) depth profiling, the device working mechanism is unveiled by comparing the compositional profiles of the fresh and written organic polymeric memristors. A reversible ion migration induced by an applied voltage contributes to the characteristic switching behavior of the memristor. Herein, both the robust memristive response achieved at extreme temperatures and the verified device working mechanism will remarkably accelerate the development of memristors in neuromorphic systems.
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Affiliation(s)
- Jiayu Li
- State Key Laboratory of Organic Electronics and Information Displays, Jiangsu Key Laboratory for Biosensors, Institute of Advanced Materials (IAM), Nanjing University of Posts & Telecommunications (NUPT), Nanjing, 210023, P. R. China
| | - Yangzhou Qian
- State Key Laboratory of Organic Electronics and Information Displays, Jiangsu Key Laboratory for Biosensors, Institute of Advanced Materials (IAM), Nanjing University of Posts & Telecommunications (NUPT), Nanjing, 210023, P. R. China
| | - Wen Li
- State Key Laboratory of Organic Electronics and Information Displays, Jiangsu Key Laboratory for Biosensors, Institute of Advanced Materials (IAM), Nanjing University of Posts & Telecommunications (NUPT), Nanjing, 210023, P. R. China
| | - Songcheng Yu
- State Key Laboratory of Organic Electronics and Information Displays, Jiangsu Key Laboratory for Biosensors, Institute of Advanced Materials (IAM), Nanjing University of Posts & Telecommunications (NUPT), Nanjing, 210023, P. R. China
| | - Yunxin Ke
- State Key Laboratory of Organic Electronics and Information Displays, Jiangsu Key Laboratory for Biosensors, Institute of Advanced Materials (IAM), Nanjing University of Posts & Telecommunications (NUPT), Nanjing, 210023, P. R. China
| | - Haowen Qian
- State Key Laboratory of Organic Electronics and Information Displays, Jiangsu Key Laboratory for Biosensors, Institute of Advanced Materials (IAM), Nanjing University of Posts & Telecommunications (NUPT), Nanjing, 210023, P. R. China
| | - Yen-Hung Lin
- Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Hong Kong SAR, 999077, P. R. China
| | - Cheng-Hung Hou
- Research Center for Applied Sciences, Academia Sinica, Taipei, 11529, Taiwan
| | - Jing-Jong Shyue
- Research Center for Applied Sciences, Academia Sinica, Taipei, 11529, Taiwan
| | - Jia Zhou
- State Key Laboratory of Organic Electronics and Information Displays, Jiangsu Key Laboratory for Biosensors, Institute of Advanced Materials (IAM), Nanjing University of Posts & Telecommunications (NUPT), Nanjing, 210023, P. R. China
| | - Ye Chen
- State Key Laboratory of Organic Electronics and Information Displays, Jiangsu Key Laboratory for Biosensors, Institute of Advanced Materials (IAM), Nanjing University of Posts & Telecommunications (NUPT), Nanjing, 210023, P. R. China
| | - Jiangping Xu
- Key Lab of Materials Chemistry for Energy Conversion & Storage of Ministry of Education, School of Chemistry & Chemical Engineering, Huazhong University of Science and Technology (HUST), Wuhan, 430074, P. R. China
| | - Jintao Zhu
- Key Lab of Materials Chemistry for Energy Conversion & Storage of Ministry of Education, School of Chemistry & Chemical Engineering, Huazhong University of Science and Technology (HUST), Wuhan, 430074, P. R. China
| | - Mingdong Yi
- State Key Laboratory of Organic Electronics and Information Displays, Jiangsu Key Laboratory for Biosensors, Institute of Advanced Materials (IAM), Nanjing University of Posts & Telecommunications (NUPT), Nanjing, 210023, P. R. China
| | - Wei Huang
- State Key Laboratory of Organic Electronics and Information Displays, Jiangsu Key Laboratory for Biosensors, Institute of Advanced Materials (IAM), Nanjing University of Posts & Telecommunications (NUPT), Nanjing, 210023, P. R. China
- Institute of Flexible Electronics (IFE), Northwestern Polytechnical University, Xi'an, 710072, P. R. China
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30
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Wang L, Zuo Z, Wen D. Realization of Artificial Nerve Synapses Based on Biological Threshold Resistive Random Access Memory. Adv Biol (Weinh) 2023; 7:e2200298. [PMID: 36650948 DOI: 10.1002/adbi.202200298] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Revised: 12/09/2022] [Indexed: 01/19/2023]
Abstract
A one-selector one resistor (1S1R) array formed of a selector and resistive random access memory (RRAM) is an important way to achieve high-density storage and neuromorphic computing. However, the low durability and poor consistency of the selector limit its practical application. The fabrication of a selector based on egg albumen (EA) is reported in this paper. The device exhibits excellent bidirectional threshold switching characteristics, including a low leakage current (10-7 A), a high ON/OFF current ratio (106 ), and good endurance (>700 days). It is used as a selector to form a 1S1R unit in combination with an EA-based RRAM to effectively solve the leakage current in a crossbar array. A feasible solution is provided for the realization of a protein-based 1S1R array to achieve high-density storage. The 1S1R unit shows characteristics similar to those of synapses in the human brain under impulse excitation and has great potential in simulating the human brain for neuromorphic calculations.).
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Affiliation(s)
- Lu Wang
- School of Electronic Engineering, Heilongjiang University, Harbin, 150080, P. R. China
| | - Ze Zuo
- School of Electronic Engineering, Heilongjiang University, Harbin, 150080, P. R. China
| | - Dianzhong Wen
- School of Electronic Engineering, Heilongjiang University, Harbin, 150080, P. R. China
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31
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Zhang ZC, Chen XD, Lu TB. Recent progress in neuromorphic and memory devices based on graphdiyne. SCIENCE AND TECHNOLOGY OF ADVANCED MATERIALS 2023; 24:2196240. [PMID: 37090847 PMCID: PMC10116926 DOI: 10.1080/14686996.2023.2196240] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Revised: 03/20/2023] [Accepted: 03/24/2023] [Indexed: 05/03/2023]
Abstract
Graphdiyne (GDY) is an emerging two-dimensional carbon allotrope featuring a direct bandgap and fascinating physical and chemical properties, and it has demonstrated its promising potential in applications of catalysis, energy conversion and storage, electrical/optoelectronic devices, etc. In particular, the recent breakthrough in the synthesis of large-area, high-quality and ultrathin GDY films provides a feasible approach to developing high-performance electrical devices based on GDY. Recently, various GDY-based electrical and optoelectronic devices including multibit optoelectronic memories, ultrafast nonvolatile memories, artificial synapses and memristors have been proposed, in which GDY plays a crucial role. It is essential to summarize the recent breakthrough of GDY in device applications as a guidance, especially considering that the existing GDY-related reviews mainly focus on the applications in catalysis and energy-related fields. Herein, we review GDY-based novel memory and neuromorphic devices and their applications in neuromorphic computing and artificial visual systems. This review will provide an insight into the design and preparation of GDY-based devices and broaden the application fields of GDY.
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Affiliation(s)
- Zhi-Cheng Zhang
- The Key Laboratory of Weak Light Nonlinear Photonics, Ministry of Education, School of Physics, Nankai University, Tianjin, China
| | - Xu-Dong Chen
- The Key Laboratory of Weak Light Nonlinear Photonics, Ministry of Education, School of Physics, Nankai University, Tianjin, China
- 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, 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, China
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Rahaman H, Kumar D, Chung HJ, Maddu R, Lim ST, Jin T, Piramanayagam SN. Diode Characteristics in Magnetic Domain Wall Devices via Geometrical Pinning for Neuromorphic Computing. ACS APPLIED MATERIALS & INTERFACES 2023; 15:15832-15838. [PMID: 36919974 DOI: 10.1021/acsami.2c20905] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
Neuromorphic computing (NC) is considered a potential vehicle for implementing energy-efficient artificial intelligence. To realize NC, several technologies are being investigated. Among them, the spin-orbit torque (SOT)-driven domain wall (DW) devices are one of the potential candidates. Researchers have proposed different device designs to achieve neurons and synapses, the building blocks of NC. However, the experimental realization of DW device-based NC is only at the primeval stage. Here, we have studied pine-tree DW devices, based on the Laplace pressure on the elastic DWs, for achieving synaptic functionalities and diode-like characteristics. We demonstrate an asymmetric pinning strength for DW motion in two opposite directions to show the potential of these devices as DW diodes. We have used micromagnetic simulations to understand the experimental findings and to estimate the Laplace pressure for various design parameters. The study provides a strategy to fabricate a multifunctional DW device, exhibiting synaptic properties and diode characteristics.
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Affiliation(s)
- Hasibur Rahaman
- School of Physical and Mathematical Sciences, Nanyang Technological University, 21 Nanyang Link, Singapore 637371
| | - Durgesh Kumar
- School of Physical and Mathematical Sciences, Nanyang Technological University, 21 Nanyang Link, Singapore 637371
| | - Hong Jing Chung
- Institute of Materials Research and Engineering, 2 Fusionopolis Way, Singapore 138634
| | - Ramu Maddu
- School of Physical and Mathematical Sciences, Nanyang Technological University, 21 Nanyang Link, Singapore 637371
| | - Sze Ter Lim
- Institute of Materials Research and Engineering, 2 Fusionopolis Way, Singapore 138634
| | - Tianli Jin
- School of Physical and Mathematical Sciences, Nanyang Technological University, 21 Nanyang Link, Singapore 637371
| | - S N Piramanayagam
- School of Physical and Mathematical Sciences, Nanyang Technological University, 21 Nanyang Link, Singapore 637371
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33
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Seok H, Son S, Jathar SB, Lee J, Kim T. Synapse-Mimetic Hardware-Implemented Resistive Random-Access Memory for Artificial Neural Network. SENSORS (BASEL, SWITZERLAND) 2023; 23:3118. [PMID: 36991829 PMCID: PMC10058286 DOI: 10.3390/s23063118] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Revised: 03/11/2023] [Accepted: 03/13/2023] [Indexed: 06/19/2023]
Abstract
Memristors mimic synaptic functions in advanced electronics and image sensors, thereby enabling brain-inspired neuromorphic computing to overcome the limitations of the von Neumann architecture. As computing operations based on von Neumann hardware rely on continuous memory transport between processing units and memory, fundamental limitations arise in terms of power consumption and integration density. In biological synapses, chemical stimulation induces information transfer from the pre- to the post-neuron. The memristor operates as resistive random-access memory (RRAM) and is incorporated into the hardware for neuromorphic computing. Hardware composed of synaptic memristor arrays is expected to lead to further breakthroughs owing to their biomimetic in-memory processing capabilities, low power consumption, and amenability to integration; these aspects satisfy the upcoming demands of artificial intelligence for higher computational loads. Among the tremendous efforts toward achieving human-brain-like electronics, layered 2D materials have demonstrated significant potential owing to their outstanding electronic and physical properties, facile integration with other materials, and low-power computing. This review discusses the memristive characteristics of various 2D materials (heterostructures, defect-engineered materials, and alloy materials) used in neuromorphic computing for image segregation or pattern recognition. Neuromorphic computing, the most powerful artificial networks for complicated image processing and recognition, represent a breakthrough in artificial intelligence owing to their enhanced performance and lower power consumption compared with von Neumann architectures. A hardware-implemented CNN with weight control based on synaptic memristor arrays is expected to be a promising candidate for future electronics in society, offering a solution based on non-von Neumann hardware. This emerging paradigm changes the computing algorithm using entirely hardware-connected edge computing and deep neural networks.
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Affiliation(s)
- Hyunho Seok
- SKKU Advanced Institute of Nanotechnology (SAINT), Sungkyunkwan University, Suwon 16419, Republic of Korea
- Department of Nano Science and Technology, Sungkyunkwan University, Suwon 16419, Republic of Korea
| | - Shihoon Son
- SKKU Advanced Institute of Nanotechnology (SAINT), Sungkyunkwan University, Suwon 16419, Republic of Korea
- Department of Nano Science and Technology, Sungkyunkwan University, Suwon 16419, Republic of Korea
| | - Sagar Bhaurao Jathar
- SKKU Advanced Institute of Nanotechnology (SAINT), Sungkyunkwan University, Suwon 16419, Republic of Korea
- Department of Nano Science and Technology, Sungkyunkwan University, Suwon 16419, Republic of Korea
| | - Jaewon Lee
- School of Mechanical Engineering, Sungkyunkwan University, Suwon 16419, Republic of Korea
| | - Taesung Kim
- SKKU Advanced Institute of Nanotechnology (SAINT), Sungkyunkwan University, Suwon 16419, Republic of Korea
- Department of Nano Science and Technology, Sungkyunkwan University, Suwon 16419, Republic of Korea
- School of Mechanical Engineering, Sungkyunkwan University, Suwon 16419, Republic of Korea
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34
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Chen H, Li H, Ma T, Han S, Zhao Q. Biological function simulation in neuromorphic devices: from synapse and neuron to behavior. SCIENCE AND TECHNOLOGY OF ADVANCED MATERIALS 2023; 24:2183712. [PMID: 36926202 PMCID: PMC10013381 DOI: 10.1080/14686996.2023.2183712] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/27/2022] [Revised: 02/06/2023] [Accepted: 02/11/2023] [Indexed: 06/18/2023]
Abstract
As the boom of data storage and processing, brain-inspired computing provides an effective approach to solve the current problem. Various emerging materials and devices have been reported to promote the development of neuromorphic computing. Thereinto, the neuromorphic device represented by memristor has attracted extensive research due to its outstanding property to emulate the brain's functions from synaptic plasticity, sensory-memory neurons to some intelligent behaviors of living creatures. Herein, we mainly review the progress of these brain functions mimicked by neuromorphic devices, concentrating on synapse (i.e. various synaptic plasticity trigger by electricity and/or light), neurons (including the various sensory nervous system) and intelligent behaviors (such as conditioned reflex represented by Pavlov's dog experiment). Finally, some challenges and prospects related to neuromorphic devices are presented.
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Affiliation(s)
- Hui Chen
- Heart Center of Henan Provincial People’s Hospital, Central China Fuwai Hospital, Central China Fuwai Hospital of Zhengzhou University, Zhengzhou, P. R. China
| | - Huilin Li
- Henan Key Laboratory of Photovoltaic Materials, Henan University, Kaifeng, P. R. China
| | - Ting Ma
- Henan Key Laboratory of Photovoltaic Materials, Henan University, Kaifeng, P. R. China
| | - Shuangshuang Han
- Henan Key Laboratory of Photovoltaic Materials, Henan University, Kaifeng, P. R. China
| | - Qiuping Zhao
- Heart Center of Henan Provincial People’s Hospital, Central China Fuwai Hospital, Central China Fuwai Hospital of Zhengzhou University, Zhengzhou, P. R. China
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35
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Li Z, Tang W, Zhang B, Yang R, Miao X. Emerging memristive neurons for neuromorphic computing and sensing. SCIENCE AND TECHNOLOGY OF ADVANCED MATERIALS 2023; 24:2188878. [PMID: 37090846 PMCID: PMC10120469 DOI: 10.1080/14686996.2023.2188878] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Abstract
Inspired by the principles of the biological nervous system, neuromorphic engineering has brought a promising alternative approach to intelligence computing with high energy efficiency and low consumption. As pivotal components of neuromorphic system, artificial spiking neurons are powerful information processing units and can achieve highly complex nonlinear computations. By leveraging the switching dynamic characteristics of memristive device, memristive neurons show rich spiking behaviors with simple circuit. This report reviews the memristive neurons and their applications in neuromorphic sensing and computing systems. The switching mechanisms that endow memristive devices with rich dynamics and nonlinearity are highlighted, and subsequently various nonlinear spiking neuron behaviors emulated in these memristive devices are reviewed. Then, recent development is introduced on neuromorphic system with memristive neurons for sensing and computing. Finally, we discuss challenges and outlooks of the memristive neurons toward high-performance neuromorphic hardware systems and provide an insightful perspective for the development of interactive neuromorphic electronic systems.
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Affiliation(s)
- Zhiyuan Li
- School of Integrated Circuits, School of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan, China
- Hubei Yangtze Memory Laboratories, Wuhan, China
| | - Wei Tang
- School of Integrated Circuits, School of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan, China
- Hubei Yangtze Memory Laboratories, Wuhan, China
| | - Beining Zhang
- School of Integrated Circuits, School of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan, China
- Hubei Yangtze Memory Laboratories, Wuhan, China
| | - Rui Yang
- School of Integrated Circuits, School of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan, China
- Hubei Yangtze Memory Laboratories, Wuhan, China
- CONTACT Rui Yang School of Integrated Circuits, Huazhong University of Science and Technology, Wuhan430074, China; Hubei Yangtze Memory Laboratories, Wuhan 430205, China
| | - Xiangshui Miao
- School of Integrated Circuits, School of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan, China
- Hubei Yangtze Memory Laboratories, Wuhan, China
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Zeng J, Zhao J, Bu T, Liu G, Qi Y, Zhou H, Dong S, Zhang C. A Flexible Tribotronic Artificial Synapse with Bioinspired Neurosensory Behavior. NANO-MICRO LETTERS 2022; 15:18. [PMID: 36580114 PMCID: PMC9800681 DOI: 10.1007/s40820-022-00989-0] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Accepted: 12/04/2022] [Indexed: 06/17/2023]
Abstract
As key components of artificial afferent nervous systems, synaptic devices can mimic the physiological synaptic behaviors, which have attracted extensive attentions. Here, a flexible tribotronic artificial synapse (TAS) with bioinspired neurosensory behavior is developed. The triboelectric potential generated by the external contact electrification is used as the ion-gel-gate voltage of the organic thin film transistor, which can tune the carriers transport through the migration/accumulation of ions. The TAS successfully demonstrates a series of synaptic behaviors by external stimuli, such as excitatory postsynaptic current, paired-pulse facilitation, and the hierarchical memory process from sensory memory to short-term memory and long-term memory. Moreover, the synaptic behaviors remained stable under the strain condition with a bending radius of 20 mm, and the TAS still exhibits excellent durability after 1000 bending cycles. Finally, Pavlovian conditioning has been successfully mimicked by applying force and vibration as food and bell, respectively. This work demonstrates a bioinspired flexible artificial synapse that will help to facilitate the development of artificial afferent nervous systems, which is great significance to the practical application of artificial limbs, robotics, and bionics in future.
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Affiliation(s)
- Jianhua Zeng
- CAS Center for Excellence in Nanoscience, Beijing Key Laboratory of Micro-Nano Energy and Sensor, Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing, 101400, People's Republic of China
- Center on Nanoenergy Research, School of Physical Science and Technology, Guangxi University, Nanning, 530004, People's Republic of China
| | - Junqing Zhao
- CAS Center for Excellence in Nanoscience, Beijing Key Laboratory of Micro-Nano Energy and Sensor, Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing, 101400, People's Republic of China
- School of Nanoscience and Technology, University of Chinese Academy of Sciences, Beijing, 100049, People's Republic of China
| | - Tianzhao Bu
- CAS Center for Excellence in Nanoscience, Beijing Key Laboratory of Micro-Nano Energy and Sensor, Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing, 101400, People's Republic of China
- School of Nanoscience and Technology, University of Chinese Academy of Sciences, Beijing, 100049, People's Republic of China
| | - Guoxu Liu
- CAS Center for Excellence in Nanoscience, Beijing Key Laboratory of Micro-Nano Energy and Sensor, Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing, 101400, People's Republic of China
- School of Nanoscience and Technology, University of Chinese Academy of Sciences, Beijing, 100049, People's Republic of China
| | - Youchao Qi
- CAS Center for Excellence in Nanoscience, Beijing Key Laboratory of Micro-Nano Energy and Sensor, Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing, 101400, People's Republic of China
- School of Nanoscience and Technology, University of Chinese Academy of Sciences, Beijing, 100049, People's Republic of China
| | - Han Zhou
- CAS Center for Excellence in Nanoscience, Beijing Key Laboratory of Micro-Nano Energy and Sensor, Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing, 101400, People's Republic of China
- Center on Nanoenergy Research, School of Physical Science and Technology, Guangxi University, Nanning, 530004, People's Republic of China
| | - Sicheng Dong
- CAS Center for Excellence in Nanoscience, Beijing Key Laboratory of Micro-Nano Energy and Sensor, Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing, 101400, People's Republic of China
- School of Nanoscience and Technology, University of Chinese Academy of Sciences, Beijing, 100049, People's Republic of China
| | - Chi Zhang
- CAS Center for Excellence in Nanoscience, Beijing Key Laboratory of Micro-Nano Energy and Sensor, Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing, 101400, People's Republic of China.
- Center on Nanoenergy Research, School of Physical Science and Technology, Guangxi University, Nanning, 530004, People's Republic of China.
- School of Nanoscience and Technology, University of Chinese Academy of Sciences, Beijing, 100049, People's Republic of China.
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37
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Zheng C, Liao Y, Wang J, Zhou Y, Han ST. Flexible Floating-Gate Electric-Double-Layer Organic Transistor for Neuromorphic Computing. ACS APPLIED MATERIALS & INTERFACES 2022; 14:57102-57112. [PMID: 36516355 DOI: 10.1021/acsami.2c20925] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
The key to the study of flexible neuromorphic computing is the excellent weight update characteristic of neuromorphic devices. Electric-double-layer transistors (EDLTs) include high transconductance, excellent stability of threshold voltage, linear weight updates, and repetitive ion-concentration-dependent switching properties. However, up to now, there is no report on a flexible EDLT that provides all the aforementioned performance characteristics. Here, a planar flexible floating-gate EDLT including an excellent linear/symmetric weight update, a large number (>800) of conductance states, repetitive switching endurance (>100 cycles), and low variation in weight update is reported. After 800 signal stimulations, it is found that the nonlinearity values of LTP are between 0.20 and 0.85, those of LTD fall between 0.66 and 1.55, the symmetricity values are between 120.7 and 639.8, and the dynamic range is between 150 and 352 nS. The study of 8 × 8 flexible floating-gate EDLT arrays shows that the average deviation and standard deviation between the experimental and theoretical values are 1.36 and 1.93, respectively, indicating that the conductance regulation in the array has a relatively small deviation. The different bending angles and the mechanical stability of the floating-gate EDLT are also studied, which exhibit the excellent bending properties. Furthermore, we studied the recognition of MNIST handwritten digit images by a three-layer perceptron artificial neural network with the experimental weight update, and the maximal recognition accuracy is up to 87.8%.
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Affiliation(s)
- Chaoyue Zheng
- Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou313001, P.R. China
- College of Electronics and Information Engineering, Shenzhen University, Shenzhen518060, P.R. China
| | - Yuan Liao
- Institute for Advanced Study, Shenzhen University, Shenzhen518060, P.R. China
| | - Junjie Wang
- College of Electronics and Information Engineering, Shenzhen University, Shenzhen518060, P.R. China
| | - Ye Zhou
- Institute for Advanced Study, Shenzhen University, Shenzhen518060, P.R. China
| | - Su-Ting Han
- College of Electronics and Information Engineering, Shenzhen University, Shenzhen518060, P.R. China
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38
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Li P, Bräuniger Y, Kunigkeit J, Zhou H, Ortega Vega MR, Zhang E, Grothe J, Brunner E, Kaskel S. Bioactive Ion-Based Switchable Supercapacitors. Angew Chem Int Ed Engl 2022; 61:e202212250. [PMID: 36260635 PMCID: PMC10100445 DOI: 10.1002/anie.202212250] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Indexed: 11/16/2022]
Abstract
Switchable supercapacitors (SCs) enable a reversible electrically-driven uptake/release of bioactive ions by polarizing porous carbon electrodes. Herein we demonstrate the first example of a bioactive ion-based switchable supercapacitor. Based on choline chloride and porous carbons we unravel the mechanism of physisorption vs. electrosorption by nuclear magnetic resonance, Raman, and impedance spectroscopy. Weak physisorption facilitates electrically-driven electrolyte depletion enabling the controllable uptake/release of electrolyte ions. A new 4-terminal device is proposed, with a main capacitor and a detective capacitor for monitoring bioactive ion adsorption in situ. Ion-concentration control in printed choline-based switchable SCs realizes switching down to 8.3 % residual capacitance. The exploration of adsorption mechanisms in printable microdevices will open an avenue of manipulating bioactive ions for the application of drug delivery, neuromodulation, or neuromorphic devices.
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Affiliation(s)
- Panlong Li
- Inorganic Chemistry ITechnische Universität DresdenBergstrasse 6601069DresdenGermany
| | - Yannik Bräuniger
- Inorganic Chemistry ITechnische Universität DresdenBergstrasse 6601069DresdenGermany
| | - Jonas Kunigkeit
- Bioanalytical ChemistryTechnische Universität DresdenBergstrasse 6601069DresdenGermany
| | - Hanfeng Zhou
- Inorganic Chemistry ITechnische Universität DresdenBergstrasse 6601069DresdenGermany
| | | | - En Zhang
- Inorganic Chemistry ITechnische Universität DresdenBergstrasse 6601069DresdenGermany
| | - Julia Grothe
- Inorganic Chemistry ITechnische Universität DresdenBergstrasse 6601069DresdenGermany
| | - Eike Brunner
- Bioanalytical ChemistryTechnische Universität DresdenBergstrasse 6601069DresdenGermany
| | - Stefan Kaskel
- Inorganic Chemistry ITechnische Universität DresdenBergstrasse 6601069DresdenGermany
- Fraunhofer IWSWinterbergstrasse 2801277DresdenGermany
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39
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A bioinspired flexible neuromuscular system based thermal-annealing-free perovskite with passivation. Nat Commun 2022; 13:7427. [PMID: 36460638 PMCID: PMC9718817 DOI: 10.1038/s41467-022-35092-w] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Accepted: 11/17/2022] [Indexed: 12/04/2022] Open
Abstract
Brain-inspired electronics require artificial synapses that have ultra-low energy consumption, high operating speed, and stable flexibility. Here, we demonstrate a flexible artificial synapse that uses a rapidly crystallized perovskite layer at room temperature. The device achieves a series of synaptic functions, including logical operations, temporal and spatial rules, and associative learning. Passivation using phenethyl-ammonium iodide eliminated defects and charge traps to reduce the energy consumption to 13.5 aJ per synaptic event, which is the world record for two-terminal artificial synapses. At this ultralow energy consumption, the device achieves ultrafast response frequency of up to 4.17 MHz; which is orders of magnitude magnitudes higher than previous perovskite artificial synapses. A multi-stimulus accumulative artificial neuromuscular system was then fabricated using the perovskite synapse as a key processing unit to control electrochemical artificial muscles, and realized muscular-fatigue warning. This artificial synapse will have applications in future bio-inspired electronics and neurorobots.
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40
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Milano G, Miranda E, Fretto M, Valov I, Ricciardi C. Experimental and Modeling Study of Metal-Insulator Interfaces to Control the Electronic Transport in Single Nanowire Memristive Devices. ACS APPLIED MATERIALS & INTERFACES 2022; 14:53027-53037. [PMID: 36396122 PMCID: PMC9716557 DOI: 10.1021/acsami.2c11022] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Accepted: 08/25/2022] [Indexed: 06/16/2023]
Abstract
Memristive devices relying on redox-based resistive switching mechanisms represent promising candidates for the development of novel computing paradigms beyond von Neumann architecture. Recent advancements in understanding physicochemical phenomena underlying resistive switching have shed new light on the importance of an appropriate selection of material properties required to optimize the performance of devices. However, despite great attention has been devoted to unveiling the role of doping concentration, impurity type, adsorbed moisture, and catalytic activity at the interfaces, specific studies concerning the effect of the counter electrode in regulating the electronic flow in memristive cells are scarce. In this work, the influence of the metal-insulator Schottky interfaces in electrochemical metallization memory (ECM) memristive cell model systems based on single-crystalline ZnO nanowires (NWs) is investigated following a combined experimental and modeling approach. By comparing and simulating the electrical characteristics of single NW devices with different contact configurations and by considering Ag and Pt electrodes as representative of electrochemically active and inert electrodes, respectively, we highlight the importance of an appropriate choice of electrode materials by taking into account the Schottky barrier height and interface chemistry at the metal-insulator interfaces. In particular, we show that a clever choice of metal-insulator interfaces allows to reshape the hysteretic conduction characteristics of the device and to increase the device performance by tuning its resistance window. These results obtained from single NW-based devices provide new insights into the selection criteria for materials and interfaces in connection with the design of advanced ECM cells.
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Affiliation(s)
- Gianluca Milano
- Advanced
Materials Metrology and Life Sciences Division, INRiM (Istituto Nazionale di Ricerca Metrologica), Strada delle Cacce 91, 10135Torino, Italy
| | - Enrique Miranda
- Departament
d’Enginyeria Electrònica, Universitat Autònoma de Barcelona (UAB), 08193Cerdanyola del Vallès, Spain
| | - Matteo Fretto
- Advanced
Materials Metrology and Life Sciences Division, INRiM (Istituto Nazionale di Ricerca Metrologica), Strada delle Cacce 91, 10135Torino, Italy
| | - Ilia Valov
- JARA—Fundamentals
for Future Information Technology, 52425Jülich, Germany
- Peter-Grünberg-Institut
(PGI 7), Forschungszentrum Jülich, Wilhelm-Johnen-Straße, 52425Jülich, Germany
| | - Carlo Ricciardi
- Department
of Applied Science and Technology, Politecnico
di Torino, C.so Duca degli Abruzzi 24, 10129Torino, Italy
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41
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Puri V, Kanojia N, Sharma A, Huanbutta K, Dheer D, Sangnim T. Natural product-based pharmacological studies for neurological disorders. Front Pharmacol 2022; 13:1011740. [PMID: 36419628 PMCID: PMC9676372 DOI: 10.3389/fphar.2022.1011740] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2022] [Accepted: 10/18/2022] [Indexed: 11/07/2023] Open
Abstract
Central nervous system (CNS) disorders and diseases are expected to rise sharply in the coming years, partly because of the world's aging population. Medicines for the treatment of the CNS have not been successfully made. Inadequate knowledge about the brain, pharmacokinetic and dynamic errors in preclinical studies, challenges with clinical trial design, complexity and variety of human brain illnesses, and variations in species are some potential scenarios. Neurodegenerative diseases (NDDs) are multifaceted and lack identifiable etiological components, and the drugs developed to treat them did not meet the requirements of those who anticipated treatments. Therefore, there is a great demand for safe and effective natural therapeutic adjuvants. For the treatment of NDDs and other memory-related problems, many herbal and natural items have been used in the Ayurvedic medical system. Anxiety, depression, Parkinson's, and Alzheimer's diseases (AD), as well as a plethora of other neuropsychiatric disorders, may benefit from the use of plant and food-derived chemicals that have antidepressant or antiepileptic properties. We have summarized the present level of knowledge about natural products based on topological evidence, bioinformatics analysis, and translational research in this review. We have also highlighted some clinical research or investigation that will help us select natural products for the treatment of neurological conditions. In the present review, we have explored the potential efficacy of phytoconstituents against neurological diseases. Various evidence-based studies and extensive recent investigations have been included, which will help pharmacologists reduce the progression of neuronal disease.
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Affiliation(s)
- Vivek Puri
- Chitkara School of Pharmacy, Chitkara University, Baddi, Himachal Pradesh, India
| | - Neha Kanojia
- Chitkara School of Pharmacy, Chitkara University, Baddi, Himachal Pradesh, India
| | - Ameya Sharma
- Chitkara School of Pharmacy, Chitkara University, Baddi, Himachal Pradesh, India
| | - Kampanart Huanbutta
- School of Pharmacy, Eastern Asia University, Rangsit, Pathum Thani, Thailand
| | - Divya Dheer
- Chitkara School of Pharmacy, Chitkara University, Baddi, Himachal Pradesh, India
| | - Tanikan Sangnim
- Faculty of Pharmaceutical Sciences, Burapha University, Muang, Chon Buri, Thailand
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42
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The EGM Model and the Winner-Takes-All (WTA) Mechanism for a Memristor-Based Neural Network. ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2022. [DOI: 10.1007/s13369-022-07290-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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43
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Rao G, Fang H, Zhou T, Zhao C, Shang N, Huang J, Liu Y, Du X, Li P, Jian X, Ma L, Wang J, Liu K, Wu J, Wang X, Xiong J. Robust Piezoelectricity with Spontaneous Polarization in Monolayer Tellurene and Multilayer Tellurium Film at Room Temperature for Reliable Memory. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2022; 34:e2204697. [PMID: 35793515 DOI: 10.1002/adma.202204697] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Revised: 06/22/2022] [Indexed: 06/15/2023]
Abstract
Robust neuromorphic computing in the Big Data era calls for long-term stable crossbar-array memory cells; however, the elemental segregation in the switch unit and memory unit that inevitably occurs upon cycling breaks the compositional and structural stability, making the whole memory cell a failure. Searching for a novel material without segregation that can be used for both switch and memory units is the major concern to fabricate robust and reliable nonvolatile cross-array memory cells. Tellurium (Te) is found recently to be the only peculiar material without segregation for switching, but the memory function has not been demonstrated yet. Herein, apparent piezoelectricity is experimentally confirmed with spontaneous polarization behaviors in elementary 2D Te, even in monolayer tellurene (0.4 nm), due to the highly oriented polarization of the molecular structure and the non-centrosymmetric lattice structure. A large memory window of 7000, a low working voltage of 2 V, and high on switching current up to 36.6 µA µm-1 are achieved in the as-fabricated Te-based memory device, revealing the great promise of Te for both switching and memory units in one cell without segregation. The piezoelectric Te with spontaneous polarization provides a platform to build robust, reliable, and high-density logic-in-memory chips in neuromorphic computing.
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Affiliation(s)
- Gaofeng Rao
- State Key Laboratory of Electronic Thin Film and Integrated Devices, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Hui Fang
- School of Physics, Southeast University, Nanjing, 211189, China
| | - Ting Zhou
- State Key Laboratory of Electronic Thin Film and Integrated Devices, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Chunlin Zhao
- Department of Materials Science, Sichuan University, Chengdu, 6110064, China
- College of Materials Science and Engineering, Fuzhou University, Fuzhou, 350108, China
| | - Nianze Shang
- State Key Laboratory for Mesoscopic Physics, Frontiers Science Center for Nano-optoelectronics, School of Physics, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, 100871, China
| | - Jianwen Huang
- State Key Laboratory of Electronic Thin Film and Integrated Devices, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Yuqing Liu
- State Key Laboratory of Electronic Thin Film and Integrated Devices, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Xinchuan Du
- State Key Laboratory of Electronic Thin Film and Integrated Devices, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Peng Li
- State Key Laboratory of Electronic Thin Film and Integrated Devices, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Xian Jian
- State Key Laboratory of Electronic Thin Film and Integrated Devices, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Liang Ma
- School of Physics, Southeast University, Nanjing, 211189, China
| | - Jinlan Wang
- School of Physics, Southeast University, Nanjing, 211189, China
| | - Kaihui Liu
- State Key Laboratory for Mesoscopic Physics, Frontiers Science Center for Nano-optoelectronics, School of Physics, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, 100871, China
| | - Jiagang Wu
- Department of Materials Science, Sichuan University, Chengdu, 6110064, China
| | - Xianfu Wang
- State Key Laboratory of Electronic Thin Film and Integrated Devices, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Jie Xiong
- State Key Laboratory of Electronic Thin Film and Integrated Devices, University of Electronic Science and Technology of China, Chengdu, 611731, China
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Guo J, Liu L, Bian B, Wang J, Zhao X, Zhang Y, Yan Y. Field-Created Coordinate Cation Bridges Enable Conductance Modulation and Artificial Synapse within Metal Nanoparticles. NANO LETTERS 2022; 22:6794-6801. [PMID: 35939405 DOI: 10.1021/acs.nanolett.2c02675] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
When metal nanoparticles are functionalized with charged ligands, the movement of counterions and conduction electrons is coupled, which enables us to develop electronic devices, including diodes, transistors, and logic gates, but dynamically modulating the conductivity of a synaptic device within these materials has proved challenging. Here we show that an artificial synapse can be created from thin films of functionalized metal nanoparticles using an active silver electrode. The electric-field-injected Ag+ coordinates with carboxyl ligands that sets up a conduction bridge to increase the nanoparticle conductivity by reducing the electron tunneling/hopping energy barriers. The dynamic modulation of conductivity allows us to implement several important synaptic functions such as potentiation/depression, paired-pulse facilitation, learning behaviors including short-term to long-term memory transition, self-learning, and massed leaning vs spaced learning. Finally, based on the nonvolatile characteristics, the metal nanoparticle synapse is used to build a single-layer hardware spiking neural network (SNN) for pattern recognition.
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Affiliation(s)
- Jiahui Guo
- CAS Key Laboratory of Nanosystem and Hierarchical Fabrication, CAS Center for Excellence in Nanoscience, National Center for Nanoscience and Technology, Beijing 100190, People's Republic of China
- University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
| | - Lin Liu
- CAS Key Laboratory of Nanosystem and Hierarchical Fabrication, CAS Center for Excellence in Nanoscience, National Center for Nanoscience and Technology, Beijing 100190, People's Republic of China
- University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
| | - Baoan Bian
- School of Science, Jiangnan University, Wuxi 214122, People's Republic of China
| | - Jingyu Wang
- CAS Key Laboratory of Nanosystem and Hierarchical Fabrication, CAS Center for Excellence in Nanoscience, National Center for Nanoscience and Technology, Beijing 100190, People's Republic of China
- University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
| | - Xing Zhao
- CAS Key Laboratory of Nanosystem and Hierarchical Fabrication, CAS Center for Excellence in Nanoscience, National Center for Nanoscience and Technology, Beijing 100190, People's Republic of China
| | - Yuchun Zhang
- CAS Key Laboratory of Nanosystem and Hierarchical Fabrication, CAS Center for Excellence in Nanoscience, National Center for Nanoscience and Technology, Beijing 100190, People's Republic of China
| | - Yong Yan
- CAS Key Laboratory of Nanosystem and Hierarchical Fabrication, CAS Center for Excellence in Nanoscience, National Center for Nanoscience and Technology, Beijing 100190, People's Republic of China
- University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
- School of Chemistry and Biological Engineering, University of Science and Technology Beijing, Beijing 100083, People's Republic of China
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45
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Kim SY, Yu JM, Lee GS, Yun DH, Kim MS, Kim JK, Kim DJ, Lee GB, Kim MS, Han JK, Seo M, Choi YK. Synaptic Segmented Transistor with Improved Linearity by Schottky Junctions and Accelerated Speed by Double-Layered Nitride. ACS APPLIED MATERIALS & INTERFACES 2022; 14:32261-32269. [PMID: 35797493 DOI: 10.1021/acsami.2c07975] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Neuromorphic devices have been extensively studied to overcome the limitations of a von Neumann system for artificial intelligence. A synaptic device is one of the most important components in the hardware integration for a neuromorphic system because a number of synaptic devices can be connected to a neuron with compactness as high as possible. Therefore, synaptic devices using silicon-based memory, which are advantageous for a high packing density and mass production due to matured fabrication technologies, have attracted considerable attention. In this study, a segmented transistor devoted to an artificial synapse is proposed for the first time to improve the linearity of the potentiation and depression (P/D). It is a complementary metal oxide semiconductor (CMOS)-compatible device that harnesses both non-ohmic Schottky junctions of the source and drain for improved weight linearity and double-layered nitride for enhanced speed. It shows three distinct and unique segments in drain current-gate voltage transfer characteristics induced by Schottky junctions. In addition, the different stoichiometries of SixNy for a double-layered nitride is utilized as a charge trap layer for boosting the operation speed. This work can bring the industry potentially one step closer to realizing the mass production of hardware-based synaptic devices in the future.
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Affiliation(s)
- Seong-Yeon Kim
- School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea
- SK Hynix Inc., Icheon 17336, Republic of Korea
| | - Ji-Man Yu
- School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea
| | - Gi Sung Lee
- National Nanofab Center (NNFC), 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea
| | - Dae-Hwan Yun
- School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea
- SK Hynix Inc., Icheon 17336, Republic of Korea
| | - Moon-Seok Kim
- School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea
| | - Jin-Ki Kim
- School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea
| | - Da-Jin Kim
- School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea
| | - Geon-Beom Lee
- School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea
| | - Myung-Su Kim
- School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea
| | - Joon-Kyu Han
- School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea
| | - Myungsoo Seo
- School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea
| | - Yang-Kyu Choi
- School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea
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46
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Fu Y, Chan YT, Jiang YP, Chang KH, Wu HC, Lai CS, Wang JC. Polarity-Differentiated Dielectric Materials in Monolayer Graphene Charge-Regulated Field-Effect Transistors for an Artificial Reflex Arc and Pain-Modulation System of the Spinal Cord. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2022; 34:e2202059. [PMID: 35619163 DOI: 10.1002/adma.202202059] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Revised: 04/28/2022] [Indexed: 06/15/2023]
Abstract
The nervous system is a vital part of organisms to survive and it endows them with remarkable abilities, such as perception, recognition, regulation, learning, and decision-making, by intertwining myriad neurons. To realize such outstanding efficacies and functions, many artificial devices and systems have been investigated to emulate the operating principles of the nervous system. Here, an artificial reflex arc (ARA) and artificial pain modulation system (APMS) are proposed to imitate the unconscious behaviors of the spinal cord. Gdx Oy - and Alx Oy -based charge-regulated field-effect transistors (CRFETs) with a monolayer graphene channel are fabricated and adopted as inhibitory and excitatory synapses, respectively, under the same pulse signals to mimic the biological reflex arc through a connection with a poly(vinylidene fluoride-co-trifluoroethylene)-based actuator. Additionally, a memristor is integrated with a CRFET as the interneuron to regulate the Dirac point by controlling the voltage drop on the graphene channel, analogous to the descending pain-inhibition system in the spinal cord, to prevent excessive pain perception. The proposed ARA and APMS provide a significant step forward to realizing the functions of the nervous system, giving promising potential for developing future intelligent alarm systems, neuroprosthetics, and neurorobotics.
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Affiliation(s)
- Yi Fu
- Department of Electronic Engineering, Chang Gung University, Guishan Dist, Taoyuan, 33302, Taiwan
| | - Ya-Ting Chan
- Department of Electronic Engineering, Chang Gung University, Guishan Dist, Taoyuan, 33302, Taiwan
| | - Yi-Pei Jiang
- Department of Electronic Engineering, Chang Gung University, Guishan Dist, Taoyuan, 33302, Taiwan
| | - Kuo-Hsuan Chang
- Department of Neurology, Chang Gung Memorial Hospital, Linkou, Guishan Dist, Taoyuan, 33305, Taiwan
- College of Medicine, Chang Gung University, Guishan Dist, Taoyuan, 33302, Taiwan
| | - Hsiu-Chuan Wu
- Department of Neurology, Chang Gung Memorial Hospital, Linkou, Guishan Dist, Taoyuan, 33305, Taiwan
- College of Medicine, Chang Gung University, Guishan Dist, Taoyuan, 33302, Taiwan
| | - Chao-Sung Lai
- Department of Electronic Engineering, Chang Gung University, Guishan Dist, Taoyuan, 33302, Taiwan
- Green Technology Research Center, Chang Gung University, Guishan Dist, Taoyuan, 33302, Taiwan
- Department of Nephrology, Chang Gung Memorial Hospital, Linkou, Guishan Dist, Taoyuan, 33305, Taiwan
- Department of Materials Engineering, Ming Chi University of Technology, Taishan Dist, New Taipei City, 243303, Taiwan
| | - Jer-Chyi Wang
- Department of Electronic Engineering, Chang Gung University, Guishan Dist, Taoyuan, 33302, Taiwan
- Green Technology Research Center, Chang Gung University, Guishan Dist, Taoyuan, 33302, Taiwan
- Department of Neurosurgery, Chang Gung Memorial Hospital, Linkou, Guishan Dist, Taoyuan, 33305, Taiwan
- Department of Electronic Engineering, Ming Chi University of Technology, Taishan Dist, New Taipei City, 243303, Taiwan
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47
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Perovskite-Based Memristor with 50-Fold Switchable Photosensitivity for In-Sensor Computing Neural Network. NANOMATERIALS 2022; 12:nano12132217. [PMID: 35808058 PMCID: PMC9268359 DOI: 10.3390/nano12132217] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/08/2022] [Revised: 06/23/2022] [Accepted: 06/27/2022] [Indexed: 02/06/2023]
Abstract
In-sensor computing can simultaneously output image information and recognition results through in-situ visual signal processing, which can greatly improve the efficiency of machine vision. However, in-sensor computing is challenging due to the requirement to controllably adjust the sensor’s photosensitivity. Herein, it is demonstrated a ternary cationic halide Cs0.05FA0.81MA0.14 Pb(I0.85Br0.15)3 (CsFAMA) perovskite, whose External quantum efficiency (EQE) value is above 80% in the entire visible region (400–750 nm), and peak responsibility value at 750 nm reaches 0.45 A/W. In addition, the device can achieve a 50-fold enhancement of the photoresponsibility under the same illumination by adjusting the internal ion migration and readout voltage. A proof-of-concept visually enhanced neural network system is demonstrated through the switchable photosensitivity of the perovskite sensor array, which can simultaneously optimize imaging and recognition results and improve object recognition accuracy by 17% in low-light environments.
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48
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From Theoretical Network to Bedside: Translational Application of Brain-Inspired Computing in Clinical Medicine. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12125788] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Advances in the brain-inspired computing space are growing at a rapid rate, and many of these emerging strategies are in the field of neuromorphic control, robotics, and sensor development, just to name a few. These innovations are disruptive in their own right and have numerous, multi-dimensional medical applications within precision medicine, telematics, device development, and informed clinical decision making. For this discussion, I will define brain-inspired computing in the scope of simulating the architecture of the brain and discuss the realization of integrating hardware and other technologies with the applications of medicine, along with the considerations for the regulatory pathway for approval and evaluating the risk/consequences of failure modes. This perspective is a call for continued discussion of the development of a pathway for translating these technologies into medical treatment and diagnostic strategies. The aim is to align with global regulatory bodies and ensure that regulation does not limit the capacity of these emerging innovations while ensuring patient safety and clinical efficacy. It is my perspective that it is and will continue to be critical that these technologies are correctly perceived and understood in the lens of multiple disciplines in order to reach their full potential for medical applications.
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49
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Shi J, Jie J, Deng W, Luo G, Fang X, Xiao Y, Zhang Y, Zhang X, Zhang X. A Fully Solution-Printed Photosynaptic Transistor Array with Ultralow Energy Consumption for Artificial-Vision Neural Networks. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2022; 34:e2200380. [PMID: 35243701 DOI: 10.1002/adma.202200380] [Citation(s) in RCA: 36] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/13/2022] [Revised: 02/20/2022] [Indexed: 06/14/2023]
Abstract
Photosynaptic organic field-effect transistors (OFETs) represent a viable pathway to develop bionic optoelectronics. However, the high operating voltage and current of traditional photosynaptic OFETs lead to huge energy consumption greater than that of the real biological synapses, hindering their further development in new-generation visual prosthetics and artificial perception systems. Here, a fully solution-printed photosynaptic OFET (FSP-OFET) with substantial energy consumption reduction is reported, where a source Schottky barrier is introduced to regulate charge-carrier injection, and which operates with a fundamentally different mechanism from traditional devices. The FSP-OFET not only significantly lowers the working voltage and current but also provides extraordinary neuromorphic light-perception capabilities. Consequently, the FSP-OFET successfully emulates visual nervous responses to external light stimuli with ultralow energy consumption of 0.07-34 fJ per spike in short-term plasticity and 0.41-19.87 fJ per spike in long-term plasticity, both approaching the energy efficiency of biological synapses (1-100 fJ). Moreover, an artificial optic-neural network made from an 8 × 8 FSP-OFET array on a flexible substrate shows excellent image recognition and reinforcement abilities at a low energy cost. The designed FSP-OFET offers an opportunity to realize photonic neuromorphic functionality with extremely low energy consumption dissipation.
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Affiliation(s)
- Jialin Shi
- Institute of Functional Nano and Soft Materials (FUNSOM), Jiangsu Key Laboratory for Carbon-Based Functional Materials and Devices, Soochow University, Suzhou, Jiangsu, 215123, P. R. China
| | - Jiansheng Jie
- Institute of Functional Nano and Soft Materials (FUNSOM), Jiangsu Key Laboratory for Carbon-Based Functional Materials and Devices, Soochow University, Suzhou, Jiangsu, 215123, P. R. China
- Macao Institute of Materials Science and Engineering, Macau University of Science and Technology, Taipa, Macau SAR, 999078, P. R. China
| | - Wei Deng
- Institute of Functional Nano and Soft Materials (FUNSOM), Jiangsu Key Laboratory for Carbon-Based Functional Materials and Devices, Soochow University, Suzhou, Jiangsu, 215123, P. R. China
| | - Gan Luo
- Institute of Functional Nano and Soft Materials (FUNSOM), Jiangsu Key Laboratory for Carbon-Based Functional Materials and Devices, Soochow University, Suzhou, Jiangsu, 215123, P. R. China
| | - Xiaochen Fang
- Institute of Functional Nano and Soft Materials (FUNSOM), Jiangsu Key Laboratory for Carbon-Based Functional Materials and Devices, Soochow University, Suzhou, Jiangsu, 215123, P. R. China
| | - Yanling Xiao
- Institute of Functional Nano and Soft Materials (FUNSOM), Jiangsu Key Laboratory for Carbon-Based Functional Materials and Devices, Soochow University, Suzhou, Jiangsu, 215123, P. R. China
| | - Yujian Zhang
- Institute of Functional Nano and Soft Materials (FUNSOM), Jiangsu Key Laboratory for Carbon-Based Functional Materials and Devices, Soochow University, Suzhou, Jiangsu, 215123, P. R. China
| | - Xiujuan Zhang
- Institute of Functional Nano and Soft Materials (FUNSOM), Jiangsu Key Laboratory for Carbon-Based Functional Materials and Devices, Soochow University, Suzhou, Jiangsu, 215123, P. R. China
| | - Xiaohong Zhang
- Institute of Functional Nano and Soft Materials (FUNSOM), Jiangsu Key Laboratory for Carbon-Based Functional Materials and Devices, Soochow University, Suzhou, Jiangsu, 215123, P. R. China
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50
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Kim MK, Kim IJ, Lee JS. CMOS-compatible compute-in-memory accelerators based on integrated ferroelectric synaptic arrays for convolution neural networks. SCIENCE ADVANCES 2022; 8:eabm8537. [PMID: 35394830 PMCID: PMC8993117 DOI: 10.1126/sciadv.abm8537] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/16/2021] [Accepted: 02/22/2022] [Indexed: 05/31/2023]
Abstract
Convolutional neural networks (CNNs) have gained much attention because they can provide superior complex image recognition through convolution operations. Convolution processes require repeated multiplication and accumulation operations, which are difficult tasks for conventional computing systems. Compute-in-memory (CIM) that uses parallel data processing is an ideal device structure for convolution operations. CIM based on two-terminal synaptic devices with a crossbar structure has been developed, but unwanted leakage current paths and the high-power consumption remain as the challenges. Here, we demonstrate integrated ferroelectric thin-film transistor (FeTFT) synaptic arrays that can provide efficient parallel programming and data processing for CNNs by the selective and accurate control of polarization in the ferroelectric layer. In addition, three-terminal FeTFTs can act as both nonvolatile memory and access device, which tackle issues from two-terminal devices. An integrated FeTFT synaptic array with parallel programming capabilities can perform convolution operations to extract image features with a high-recognition accuracy.
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