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Yang C, Wang H, Cao Z, Chen X, Zhou G, Zhao H, Wu Z, Zhao Y, Sun B. Memristor-Based Bionic Tactile Devices: Opening the Door for Next-Generation Artificial Intelligence. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2024; 20:e2308918. [PMID: 38149504 DOI: 10.1002/smll.202308918] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Revised: 11/13/2023] [Indexed: 12/28/2023]
Abstract
Bioinspired tactile devices can effectively mimic and reproduce the functions of the human tactile system, presenting significant potential in the field of next-generation wearable electronics. In particular, memristor-based bionic tactile devices have attracted considerable attention due to their exceptional characteristics of high flexibility, low power consumption, and adaptability. These devices provide advanced wearability and high-precision tactile sensing capabilities, thus emerging as an important research area within bioinspired electronics. This paper delves into the integration of memristors with other sensing and controlling systems and offers a comprehensive analysis of the recent research advancements in memristor-based bionic tactile devices. These advancements incorporate artificial nociceptors and flexible electronic skin (e-skin) into the category of bio-inspired sensors equipped with capabilities for sensing, processing, and responding to stimuli, which are expected to catalyze revolutionary changes in human-computer interaction. Finally, this review discusses the challenges faced by memristor-based bionic tactile devices in terms of material selection, structural design, and sensor signal processing for the development of artificial intelligence. Additionally, it also outlines future research directions and application prospects of these devices, while proposing feasible solutions to address the identified challenges.
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Affiliation(s)
- Chuan Yang
- School of Physical Science and Technology, Key Laboratory of Advanced Technology of Materials, Southwest Jiaotong University, Chengdu, Sichuan, 610031, China
| | - Hongyan Wang
- School of Physical Science and Technology, Key Laboratory of Advanced Technology of Materials, Southwest Jiaotong University, Chengdu, Sichuan, 610031, China
| | - Zelin Cao
- Frontier Institute of Science and Technology (FIST), Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, China
| | - Xiaoliang Chen
- Frontier Institute of Science and Technology (FIST), Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, China
| | - Guangdong Zhou
- College of Artificial Intelligence, Brain-inspired Computing & Intelligent Control of Chongqing Key Lab, Southwest University, Chongqing, 400715, China
| | - Hongbin Zhao
- State Key Laboratory of Advanced Materials for Smart Sensing, General Research Institute for Nonferrous Metals, Beijing, 100088, China
| | - Zhenhua Wu
- School of Mechanical Engineering, Shanghai Jiao Tong University, 800 DongChuan Rd, Shanghai, 200240, China
| | - Yong Zhao
- School of Physical Science and Technology, Key Laboratory of Advanced Technology of Materials, Southwest Jiaotong University, Chengdu, Sichuan, 610031, China
- Fujian Provincial Collaborative Innovation Center for Advanced High-Field Superconducting Materials and Engineering, Fujian Normal University, Fuzhou, Fujian, 350117, China
| | - Bai Sun
- Frontier Institute of Science and Technology (FIST), Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, China
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Dutta T, Chaturvedi P, Llamas-Garro I, Velázquez-González JS, Dubey R, Mishra SK. Smart materials for flexible electronics and devices: hydrogel. RSC Adv 2024; 14:12984-13004. [PMID: 38655485 PMCID: PMC11033831 DOI: 10.1039/d4ra01168f] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2024] [Accepted: 04/05/2024] [Indexed: 04/26/2024] Open
Abstract
In recent years, flexible conductive materials have attracted considerable attention for their potential use in flexible energy storage devices, touch panels, sensors, memristors, and other applications. The outstanding flexibility, electricity, and tunable mechanical properties of hydrogels make them ideal conductive materials for flexible electronic devices. Various synthetic strategies have been developed to produce conductive and environmentally friendly hydrogels for high-performance flexible electronics. In this review, we discuss the state-of-the-art applications of hydrogels in flexible electronics, such as energy storage, touch panels, memristor devices, and sensors like temperature, gas, humidity, chemical, strain, and textile sensors, and the latest synthesis methods of hydrogels. Describe the process of fabricating sensors as well. Finally, we discussed the challenges and future research avenues for flexible and portable electronic devices based on hydrogels.
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Affiliation(s)
- Taposhree Dutta
- Department of Chemistry, Indian Institute of Engineering Science and Technology Shibpur Howrah W.B. - 711103 India
| | - Pavan Chaturvedi
- Department of Physics, Vanderbilt University 3414 Murphy Rd, Apt#4 Nashville TN-37203 USA +575-650-4595
| | - Ignacio Llamas-Garro
- Navigation and Positioning Research Unit, Centre Tecnològic de Telecomunicacions de Catalunya Castelldefels Spain
| | | | - Rakesh Dubey
- Instiute of Physics, University of Szczecin Poland
| | - Satyendra Kumar Mishra
- Space and Reslinent Research Unit, Centre Tecnològic de Telecomunicacions de Catalunya Castelldefels Spain
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3
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Li Z, Lin X, Wei J, Shan X, Wu Z, Luo Y, Wang Y, Feng Y, Ren T, Song Z, Wang F, Zhang K. An Artificial LiSiO x Nociceptor with Neural Blockade and Self-Protection Abilities. ACS APPLIED MATERIALS & INTERFACES 2024; 16:19205-19213. [PMID: 38591860 DOI: 10.1021/acsami.4c01406] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/10/2024]
Abstract
An artificial nociceptor, as a critical and special bionic receptor, plays a key role in a bioelectronic device that detects stimuli and provides warnings. However, fully exploiting bioelectronic applications remains a major challenge due to the lack of the methods of implementing basic nociceptor functions and nociceptive blockade in a single device. In this work, we developed a Pt/LiSiOx/TiN artificial nociceptor. It had excellent stability under the 104 endurance test with pulse stimuli and exhibited a significant threshold current of 1 mA with 1 V pulse stimuli. Other functions such as relaxation, inadaptation, and sensitization were all realized in a single device. Also, the pain blockade function was first achieved in this nociceptor with over a 25% blocking degree, suggesting a self-protection function. More importantly, an obvious depression was activated by a stimulus over 1.6 V due to the cooperative effects of both lithium ions and oxygen ions in LiSiOx and the dramatic accumulation of Joule heat. The conducting channel ruptured partially under sequential potentiation, thus achieving nociceptive blockade, besides basic functions in one single nociceptor, which was rarely reported. These results provided important guidelines for constructing high-performance memristor-based artificial nociceptors and opened up an alternative approach to the realization of bioelectronic systems for artificial intelligence.
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Affiliation(s)
- Zewen Li
- Tianjin Key Laboratory of Film Electronic & Communication Devices, School of Integrated Circuit Science and Engineering, Tianjin University of Technology, Tianjin 300384, China
| | - Xin Lin
- School of Materials Science and Engineering, Tianjin University of Technology, Tianjin 300384, China
| | - Junqing Wei
- Tianjin Key Laboratory of Film Electronic & Communication Devices, School of Integrated Circuit Science and Engineering, Tianjin University of Technology, Tianjin 300384, China
| | - Xin Shan
- School of Materials Science and Engineering, Tianjin University of Technology, Tianjin 300384, China
| | - Zeyu Wu
- Tianjin Key Laboratory of Film Electronic & Communication Devices, School of Integrated Circuit Science and Engineering, Tianjin University of Technology, Tianjin 300384, China
| | - Yu Luo
- Tianjin Key Laboratory of Film Electronic & Communication Devices, School of Integrated Circuit Science and Engineering, Tianjin University of Technology, Tianjin 300384, China
| | - Yuchan Wang
- Tianjin Key Laboratory of Film Electronic & Communication Devices, School of Integrated Circuit Science and Engineering, Tianjin University of Technology, Tianjin 300384, China
| | - Yulin Feng
- Key Laboratory of the Ministry of Education for Optoelectronic Measurement Technology and Instrument, Beijing Information Science and Technology University, Beijing 100192, China
| | - Tianling Ren
- Beijing National Research Center for Information Science and Technology, Institute of Microelectronics, Tsinghua University, Beijing 100084, China
| | - Zhitang Song
- Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai 200050, China
| | - Fang Wang
- Tianjin Key Laboratory of Film Electronic & Communication Devices, School of Integrated Circuit Science and Engineering, Tianjin University of Technology, Tianjin 300384, China
| | - Kailiang Zhang
- Tianjin Key Laboratory of Film Electronic & Communication Devices, School of Integrated Circuit Science and Engineering, Tianjin University of Technology, Tianjin 300384, China
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Wang Y, Wang Y, Mushtaq RT, Wei Q. Advancements in Soft Robotics: A Comprehensive Review on Actuation Methods, Materials, and Applications. Polymers (Basel) 2024; 16:1087. [PMID: 38675005 PMCID: PMC11054840 DOI: 10.3390/polym16081087] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2024] [Revised: 04/10/2024] [Accepted: 04/11/2024] [Indexed: 04/28/2024] Open
Abstract
The flexibility and adaptability of soft robots enable them to perform various tasks in changing environments, such as flower picking, fruit harvesting, in vivo targeted treatment, and information feedback. However, these fulfilled functions are discrepant, based on the varied working environments, driving methods, and materials. To further understand the working principle and research emphasis of soft robots, this paper summarized the current research status of soft robots from the aspects of actuating methods (e.g., humidity, temperature, PH, electricity, pressure, magnetic field, light, biological, and hybrid drive), materials (like hydrogels, shape-memory materials, and other flexible materials) and application areas (camouflage, medical devices, electrical equipment, and grippers, etc.). Finally, we provided some opinions on the technical difficulties and challenges of soft robots to comprehensively comprehend soft robots, lucubrate their applications, and improve the quality of our lives.
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Affiliation(s)
- Yanmei Wang
- Industry Engineering Department, School of Mechanical Engineering, Northwestern Polytechnical University, Xi’an 710072, China; (R.T.M.); (Q.W.)
| | - Yanen Wang
- Industry Engineering Department, School of Mechanical Engineering, Northwestern Polytechnical University, Xi’an 710072, China; (R.T.M.); (Q.W.)
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Rehman NU, Ullah A, Mahmood MA, Rahman N, Sohail M, Iqbal S, Juraev N, Althubeiti K, Al Otaibi S, Khan R. Cobalt-doped zinc oxide based memristors with nociceptor characteristics for bio-inspired technology. RSC Adv 2024; 14:11797-11810. [PMID: 38617576 PMCID: PMC11009837 DOI: 10.1039/d4ra01250j] [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: 02/18/2024] [Accepted: 04/01/2024] [Indexed: 04/16/2024] Open
Abstract
Neuromorphic computing is a new field of information technology, which is inspired by the biomimetic properties of the memristor as an electronic synapse and neuron. If there are electronic receptors that can transmit exterior impulses to the internal nervous system, then the use of memristors can be expanded to artificial nerves. In this study, a layer type memristor is used to build an artificial nociceptor in a very feasible and straightforward manner. An artificial nociceptor is demonstrated here through the fabrication and characterization of a cobalt-doped zinc oxide (CZO)/Au based memristor. In order to increase threshold switching performance, the surface effects of the CZO layer are eliminated by adding cobalt cobalt-doped zinc oxide (CZO) layer between the P++-Si and Au electrodes. Allodynia, hyperalgesia, threshold, and relaxation are the four distinct nociceptive behaviours that the device displays based on the strength, rate of relapse, and duration of the external stimuli. The electrons that are trapped in or released from the CZO layer's traps are responsible for these nociceptive behaviours. A multipurpose nociceptor performance is produced by this type of CZO-based device, which is crucial for artificial intelligence system applications such as neural integrated devices with nanometer-sized characteristics.
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Affiliation(s)
- Naveed Ur Rehman
- Department of Physics, University of Lakki Marwat Lakki Marwat 2842 KP Pakistan
| | - Aziz Ullah
- Department of Physics, University of Lakki Marwat Lakki Marwat 2842 KP Pakistan
| | | | - Nasir Rahman
- Department of Physics, University of Lakki Marwat Lakki Marwat 2842 KP Pakistan
| | - Mohammad Sohail
- Department of Physics, University of Lakki Marwat Lakki Marwat 2842 KP Pakistan
| | - Shahid Iqbal
- Department of Physics, University of Wisconsin La Crosse WI 54601 USA
| | - Nizomiddin Juraev
- Faculty of Chemical Engineering, New Uzbekistan University Tashkent Uzbekistan
- Scientific and Innovation Department, Tashkent State Pedagogical University Tashkent Uzbekistan
| | - Khaled Althubeiti
- Department of Chemistry, College of Science, Taif University P.O. BOX. 110 21944 Taif Saudi Arabia
| | - Sattam Al Otaibi
- Department of Electrical Engineering, College of Engineering Taif University P.O. Box 11099 Taif 21944 Saudi Arabia
| | - Rajwali Khan
- Department of Physics, University of Lakki Marwat Lakki Marwat 2842 KP Pakistan
- Department of Physics, United Arab Emirates University Al Ain 15551 Abu Dhabi UAE
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6
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Ahmad I, Khan MN, Hayat K, Ahmad T, Shams DF, Khan W, Tirth V, Rehman G, Muhammad W, Elhadi M, Alotaibi A, Shah SK. Investigating the Antibacterial and Anti-inflammatory Potential of Polyol-Synthesized Silver Nanoparticles. ACS OMEGA 2024; 9:13208-13216. [PMID: 38524435 PMCID: PMC10956083 DOI: 10.1021/acsomega.3c09851] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Revised: 02/23/2024] [Accepted: 02/27/2024] [Indexed: 03/26/2024]
Abstract
Silver nanoparticles (Ag-NPs) were synthesized by using the polyol method. The structural and morphological characteristics of Ag-NPs were studied by using X-ray diffraction (XRD) and field-emission scanning electron microscopy (FE-SEM). The XRD analysis revealed the formation of single-phase polycrystalline Ag-NPs with an average crystallite size and lattice constant of ∼23 nm and 4.07 Å, respectively, while the FE-SEM shows the formation of a uniform and spherical morphology. Energy-dispersive X-ray spectroscopy confirmed the formation of single-phase Ag-NPs, and no extra elements were detected. A strong absorption peak at ∼427 nm was observed in the UV-vis spectrum, which reflects the surface plasmon resonance (SPR) behavior characteristic of Ag-NPs with a spherical morphology. Fourier-transform infrared (FTIR) spectra also supported the XRD and EDX results with regard to the purity of the prepared Ag-NPs. Anti-inflammatory activity was tested using HRBCs membrane stabilization and heat-induced hemolysis assays. The antibacterial activity of Ag-NPs was evaluated against four different types of pathogenic bacteria by using the disc diffusion method (DDM). The Gram-negative bacterial strains used in this study are Escherichia coli (E. coli), Klebsiella, Shigella, and Salmonella. The analysis suggested that the antibacterial activities of Ag-NPs have an influential role in inhibiting the growth of the tested Gram-negative bacteria, and thus Ag-NPs can find a potential application in the pharmaceutical industry.
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Affiliation(s)
- Ibrar Ahmad
- Department
of Basic and Applied Sciences for Engineering, Sapienza University of Rome, Rome 00185, Italy
| | - Muhammad Nadeem Khan
- Department
of Biotechnology, Abdul Wali Khan University
Mardan, Mardan 23200, Khyber Pakhtunkhwa, Pakistan
| | - Khizar Hayat
- Department
of Physics, Abdul Wali Khan University Mardan, Mardan 23200, Khyber Pakhtunkhwa, Pakistan
| | - Tanveer Ahmad
- Department
of Physics, Abdul Wali Khan University Mardan, Mardan 23200, Khyber Pakhtunkhwa, Pakistan
| | - Dilawar Farhan Shams
- Department
of Environmental Sciences, Abdul Wali Khan
University Mardan, Mardan 23200, Khyber Pakhtunkhwa, Pakistan
| | - Waliullah Khan
- Department
of Chemistry, Abdul Wali Khan University, Mardan 23200, Khyber Pakhtunkhwa, Pakistan
| | - Vineet Tirth
- Mechanical
Engineering Department, College of Engineering, King Khalid University, Abha 61421, Asir, Kingdom of Saudi Arabia
- Research
Center for Advanced Materials Science (RCAMS), King Khalid University, Guraiger, P.O. Box 9004, Abha 61413, Asir, Kingdom of Saudi Arabia
| | - Gauhar Rehman
- Department
of Zoology, Abdul Wali Khan University, Mardan 23200, Khyber Pakhtunkhwa, Pakistan
| | - Wazir Muhammad
- Department
of Physics, Florida Atlantic University, Boca Raton, Florida 33431, United States
| | - Muawya Elhadi
- Department
of Physics, Faculty of Science and Humanities, Shaqra University, P.O. Box 1040, Ad-Dawadimi 11911, Saudi Arabia
| | - Afraa Alotaibi
- Department of Physics, College of Science, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
| | - Said Karim Shah
- Department
of Physics, Abdul Wali Khan University Mardan, Mardan 23200, Khyber Pakhtunkhwa, Pakistan
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7
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Jang YH, Han JK, Moon S, Shim SK, Han J, Cheong S, Lee SH, Hwang CS. A high-dimensional in-sensor reservoir computing system with optoelectronic memristors for high-performance neuromorphic machine vision. MATERIALS HORIZONS 2024; 11:499-509. [PMID: 37966888 DOI: 10.1039/d3mh01584j] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2023]
Abstract
In-sensor reservoir computing (RC) is a promising technology to reduce power consumption and training costs of machine vision systems by processing optical signals temporally. This study demonstrates a high-dimensional in-sensor RC system with optoelectronic memristors to enhance the performance of the in-sensor RC system. Because optoelectronic memristors can respond to both optical and electrical stimuli, optical and electrical masks are proposed to improve the dimensionality and performance of the in-sensor RC system. An optical mask is employed to regulate the wavelength of light, while an electrical mask is used to control the initial conductance of zinc oxide optoelectronic memristors. The distinct characteristics of these two masks contribute to the representation of various distinguishable reservoir states, making it possible to implement diverse reservoir configurations with minimal correlation and to increase the dimensionality of the in-sensor RC system. Using the high-dimensional in-sensor RC system, handwritten digits are successfully classified with an accuracy of 94.1%. Furthermore, human action pattern recognition is achieved with a high accuracy of 99.4%. These high accuracies are achieved with the use of a single-layer readout network, which can significantly reduce the network size and training costs.
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Affiliation(s)
- Yoon Ho Jang
- Department of Materials Science and Engineering and Inter-university Semiconductor Research Center, College of Engineering, Seoul National University, Seoul, 08826, Republic of Korea.
| | - Joon-Kyu Han
- Department of Materials Science and Engineering and Inter-university Semiconductor Research Center, College of Engineering, Seoul National University, Seoul, 08826, Republic of Korea.
| | - Sangik Moon
- Department of Materials Science and Engineering and Inter-university Semiconductor Research Center, College of Engineering, Seoul National University, Seoul, 08826, Republic of Korea.
| | - Sung Keun Shim
- Department of Materials Science and Engineering and Inter-university Semiconductor Research Center, College of Engineering, Seoul National University, Seoul, 08826, Republic of Korea.
| | - Janguk Han
- Department of Materials Science and Engineering and Inter-university Semiconductor Research Center, College of Engineering, Seoul National University, Seoul, 08826, Republic of Korea.
| | - Sunwoo Cheong
- Department of Materials Science and Engineering and Inter-university Semiconductor Research Center, College of Engineering, Seoul National University, Seoul, 08826, Republic of Korea.
| | - Soo Hyung Lee
- Department of Materials Science and Engineering and Inter-university Semiconductor Research Center, College of Engineering, Seoul National University, Seoul, 08826, Republic of Korea.
| | - Cheol Seong Hwang
- Department of Materials Science and Engineering and Inter-university Semiconductor Research Center, College of Engineering, Seoul National University, Seoul, 08826, Republic of Korea.
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8
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Sun B, Chen Y, Zhou G, Cao Z, Yang C, Du J, Chen X, Shao J. Memristor-Based Artificial Chips. ACS NANO 2024; 18:14-27. [PMID: 38153841 DOI: 10.1021/acsnano.3c07384] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/30/2023]
Abstract
Memristors, promising nanoelectronic devices with in-memory resistive switching behavior that is assembled with a physically integrated core processing unit (CPU) and memory unit and even possesses highly possible multistate electrical behavior, could avoid the von Neumann bottleneck of traditional computing devices and show a highly efficient ability of parallel computation and high information storage. These advantages position them as potential candidates for future data-centric computing requirements and add remarkable vigor to the research of next-generation artificial intelligence (AI) systems, particularly those that involve brain-like intelligence applications. This work provides an overview of the evolution of memristor-based devices, from their initial use in creating artificial synapses and neural networks to their application in developing advanced AI systems and brain-like chips. It offers a broad perspective of the key device primitives enabling their special applications from the view of materials, nanostructure, and mechanism models. We highlight these demonstrations of memristor-based nanoelectronic devices that have potential for use in the field of brain-like AI, point out the existing challenges of memristor-based nanodevices toward brain-like chips, and propose the guiding principle and promising outlook for future device promotion and system optimization in the biomedical AI field.
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Affiliation(s)
- Bai Sun
- National Local Joint Engineering Research Center for Precision Surgery & Regenerative Medicine, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi 710049, People's Republic of China
- Department of Hepatobiliary Surgery, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi 710049, People's Republic of China
- Micro-and Nano-technology Research Center, State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, People's Republic of China
- Frontier Institute of Science and Technology (FIST), Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China
| | - Yuanzheng Chen
- School of Physical Science and Technology, Key Laboratory of Advanced Technology of Materials, Southwest Jiaotong University, Chengdu, Sichuan 610031, People's Republic of China
| | - Guangdong Zhou
- College of Artificial Intelligence, Brain-inspired Computing & Intelligent Control of Chongqing Key Lab, Southwest University, Chongqing 400715, People's Republic of China
| | - Zelin Cao
- National Local Joint Engineering Research Center for Precision Surgery & Regenerative Medicine, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi 710049, People's Republic of China
- Department of Hepatobiliary Surgery, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi 710049, People's Republic of China
- Micro-and Nano-technology Research Center, State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, People's Republic of China
- Frontier Institute of Science and Technology (FIST), Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China
| | - Chuan Yang
- School of Physical Science and Technology, Key Laboratory of Advanced Technology of Materials, Southwest Jiaotong University, Chengdu, Sichuan 610031, People's Republic of China
| | - Junmei Du
- School of Physical Science and Technology, Key Laboratory of Advanced Technology of Materials, Southwest Jiaotong University, Chengdu, Sichuan 610031, People's Republic of China
| | - Xiaoliang Chen
- Micro-and Nano-technology Research Center, State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, People's Republic of China
- Frontier Institute of Science and Technology (FIST), Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China
| | - Jinyou Shao
- Micro-and Nano-technology Research Center, State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, People's Republic of China
- Frontier Institute of Science and Technology (FIST), Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China
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9
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Im IH, Baek JH, Kim SJ, Kim J, Park SH, Kim JY, Yang JJ, Jang HW. Halide Perovskites-Based Diffusive Memristors for Artificial Mechano-Nociceptive System. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024; 36:e2307334. [PMID: 37708845 DOI: 10.1002/adma.202307334] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Revised: 08/24/2023] [Indexed: 09/16/2023]
Abstract
Numerous efforts for emulating organ systems comprised of multiple functional units have driven substantial advancements in bio-realistic electronics and systems. The resistance change behavior observed in diffusive memristors shares similarities with the potential change in biological neurons. Here, the diffusive threshold switching phenomenon in Ag-incorporated organometallic halide perovskites is utilized to demonstrate the functions of afferent neurons. Halide perovskites-based diffusive memristors show a low threshold voltage of ≈0.2 V with little variation, attributed to the facile migration of Ag ions uniformly dispersed within the halide matrix. Based on the reversible and reliable volatile threshold switching, the memristors successfully demonstrate fundamental nociceptive functions including threshold firing, relaxation, and sensitization. Furthermore, to replicate the biological mechano-nociceptive phenomenon at a system level, an artificial mechano-nociceptive system is built by integrating a diffusive memristor with a force-sensing resistor. The presented system is capable of detecting and discerning the detrimental impact caused by a heavy steel ball, effectively exhibiting the corresponding sensitization response. By further extending the single nociceptive system into a 5 × 5 array, successful stereoscopic nociception of uneven impulses is achieved in the artificial skin system through array-scale sensitization. These results represent significant progress in the field of bio-inspired electronics and systems.
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Affiliation(s)
- In Hyuk Im
- Department of Materials Science and Engineering, Research Institute of Advanced Materials, Seoul National University, Seoul, 08826, Republic of Korea
| | - Ji Hyun Baek
- Department of Materials Science and Engineering, Research Institute of Advanced Materials, Seoul National University, Seoul, 08826, Republic of Korea
| | - Seung Ju Kim
- Department of Materials Science and Engineering, Research Institute of Advanced Materials, Seoul National University, Seoul, 08826, Republic of Korea
- Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, 90089, USA
| | - Jaehyun Kim
- Department of Materials Science and Engineering, Research Institute of Advanced Materials, Seoul National University, Seoul, 08826, Republic of Korea
| | - Sung Hyuk Park
- Department of Materials Science and Engineering, Research Institute of Advanced Materials, Seoul National University, Seoul, 08826, Republic of Korea
| | - Jae Young Kim
- Department of Materials Science and Engineering, Research Institute of Advanced Materials, Seoul National University, Seoul, 08826, Republic of Korea
| | - J Joshua Yang
- Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, 90089, USA
| | - Ho Won Jang
- Department of Materials Science and Engineering, Research Institute of Advanced Materials, Seoul National University, Seoul, 08826, Republic of Korea
- Advanced Institute of Convergence Technology, Seoul National University, Suwon, 16229, Republic of Korea
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10
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Diao Y, Zhang Y, Li Y, Jiang J. Metal-Oxide Heterojunction: From Material Process to Neuromorphic Applications. SENSORS (BASEL, SWITZERLAND) 2023; 23:9779. [PMID: 38139625 PMCID: PMC10747618 DOI: 10.3390/s23249779] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/29/2023] [Revised: 11/30/2023] [Accepted: 12/05/2023] [Indexed: 12/24/2023]
Abstract
As technologies like the Internet, artificial intelligence, and big data evolve at a rapid pace, computer architecture is transitioning from compute-intensive to memory-intensive. However, traditional von Neumann architectures encounter bottlenecks in addressing modern computational challenges. The emulation of the behaviors of a synapse at the device level by ionic/electronic devices has shown promising potential in future neural-inspired and compact artificial intelligence systems. To address these issues, this review thoroughly investigates the recent progress in metal-oxide heterostructures for neuromorphic applications. These heterostructures not only offer low power consumption and high stability but also possess optimized electrical characteristics via interface engineering. The paper first outlines various synthesis methods for metal oxides and then summarizes the neuromorphic devices using these materials and their heterostructures. More importantly, we review the emerging multifunctional applications, including neuromorphic vision, touch, and pain systems. Finally, we summarize the future prospects of neuromorphic devices with metal-oxide heterostructures and list the current challenges while offering potential solutions. This review provides insights into the design and construction of metal-oxide devices and their applications for neuromorphic systems.
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Affiliation(s)
| | | | | | - Jie Jiang
- Hunan Key Laboratory of Nanophotonics and Devices, School of Physics, Central South University, 932 South Lushan Road, Changsha 410083, China
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Chen T, Ma Z, Hu H, Yang Y, Zhou C, Shen F, Xu H, Xu J, Xu L, Li W, Chen K. Artificial SiN z:H Synapse Crossbar Arrays with Gradual Conductive Pathway for High-Accuracy Neuromorphic Computing. NANOMATERIALS (BASEL, SWITZERLAND) 2023; 13:2362. [PMID: 37630946 PMCID: PMC10458972 DOI: 10.3390/nano13162362] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Revised: 08/05/2023] [Accepted: 08/14/2023] [Indexed: 08/27/2023]
Abstract
Inspired by its highly efficient capability to deal with big data, the brain-like computational system has attracted a great amount of attention for its ability to outperform the von Neumann computation paradigm. As the core of the neuromorphic computing chip, an artificial synapse based on the memristor, with a high accuracy in processing images, is highly desired. We report, for the first time, that artificial synapse arrays with a high accuracy in image recognition can be obtained through the fabrication of a SiNz:H memristor with a gradient Si/N ratio. The training accuracy of SiNz:H synapse arrays for image learning can reach 93.65%. The temperature-dependent I-V characteristic reveals that the gradual Si dangling bond pathway makes the main contribution towards improving the linearity of the tunable conductance. The thinner diameter and fixed disconnection point in the gradual pathway are of benefit in enhancing the accuracy of visual identification. The artificial SiNz:H synapse arrays display stable and uniform biological functions, such as the short-term biosynaptic functions, including spike-duration-dependent plasticity, spike-number-dependent plasticity, and paired-pulse facilitation, as well as the long-term ones, such as long-term potentiation, long-term depression, and spike-time-dependent plasticity. The highly efficient visual learning capability of the artificial SiNz:H synapse with a gradual conductive pathway for neuromorphic systems hold great application potential in the age of artificial intelligence (AI).
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Affiliation(s)
- Tong Chen
- School of Electronic Science and Engineering, Nanjing University, Nanjing 210093, China
- Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing 210093, China
- Jiangsu Provincial Key Laboratory of Photonic and Electronic Materials Sciences and Technology, Nanjing University, Nanjing 210093, China
| | - Zhongyuan Ma
- School of Electronic Science and Engineering, Nanjing University, Nanjing 210093, China
- Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing 210093, China
- Jiangsu Provincial Key Laboratory of Photonic and Electronic Materials Sciences and Technology, Nanjing University, Nanjing 210093, China
| | - Hongsheng Hu
- School of Electronic Science and Engineering, Nanjing University, Nanjing 210093, China
- Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing 210093, China
- Jiangsu Provincial Key Laboratory of Photonic and Electronic Materials Sciences and Technology, Nanjing University, Nanjing 210093, China
| | - Yang Yang
- School of Electronic Science and Engineering, Nanjing University, Nanjing 210093, China
- Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing 210093, China
- Jiangsu Provincial Key Laboratory of Photonic and Electronic Materials Sciences and Technology, Nanjing University, Nanjing 210093, China
| | - Chengfeng Zhou
- School of Electronic Science and Engineering, Nanjing University, Nanjing 210093, China
- Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing 210093, China
- Jiangsu Provincial Key Laboratory of Photonic and Electronic Materials Sciences and Technology, Nanjing University, Nanjing 210093, China
| | - Furao Shen
- School of Artificial Intelligence, Nanjing University, Nanjing 210093, China
| | - Haitao Xu
- Institute of Advanced Functional Materials and Devices, Shanxi University, Taiyuan 030031, China
- Institute of Carbon-Based Thin Film Electronics, Peking University, Taiyuan 030031, China
| | - Jun Xu
- School of Electronic Science and Engineering, Nanjing University, Nanjing 210093, China
- Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing 210093, China
- Jiangsu Provincial Key Laboratory of Photonic and Electronic Materials Sciences and Technology, Nanjing University, Nanjing 210093, China
| | - Ling Xu
- School of Electronic Science and Engineering, Nanjing University, Nanjing 210093, China
- Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing 210093, China
- Jiangsu Provincial Key Laboratory of Photonic and Electronic Materials Sciences and Technology, Nanjing University, Nanjing 210093, China
| | - Wei Li
- School of Electronic Science and Engineering, Nanjing University, Nanjing 210093, China
- Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing 210093, China
- Jiangsu Provincial Key Laboratory of Photonic and Electronic Materials Sciences and Technology, Nanjing University, Nanjing 210093, China
| | - Kunji Chen
- School of Electronic Science and Engineering, Nanjing University, Nanjing 210093, China
- Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing 210093, China
- Jiangsu Provincial Key Laboratory of Photonic and Electronic Materials Sciences and Technology, Nanjing University, Nanjing 210093, China
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