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Zhu L, Lin J, Zhu Y, Wu J, Wan X, Sun H, Yu Z, Xu Y, Tan C. Flexible Organic Electrochemical Transistors for Energy-Efficient Neuromorphic Computing. NANOMATERIALS (BASEL, SWITZERLAND) 2024; 14:1195. [PMID: 39057872 PMCID: PMC11279808 DOI: 10.3390/nano14141195] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/18/2024] [Revised: 07/04/2024] [Accepted: 07/10/2024] [Indexed: 07/28/2024]
Abstract
Brain-inspired flexible neuromorphic devices are of great significance for next-generation high-efficiency wearable sensing and computing systems. In this paper, we propose a flexible organic electrochemical transistor using poly[(bithiophene)-alternate-(2,5-di(2-octyldodecyl)- 3,6-di(thienyl)-pyrrolyl pyrrolidone)] (DPPT-TT) as the organic semiconductor and poly(methyl methacrylate) (PMMA)/LiClO4 solid-state electrolyte as the gate dielectric layer. Under gate voltage modulation, an electric double layer (EDL) forms between the dielectric layer and the channel, allowing the device to operate at low voltages. Furthermore, by leveraging the double layer effect and electrochemical doping within the device, we successfully mimic various synaptic behaviors, including excitatory post-synaptic currents (EPSC), paired-pulse facilitation (PPF), high-pass filtering characteristics, transitions from short-term plasticity (STP) to long-term plasticity (LTP), and demonstrate its image recognition and storage capabilities in a 3 × 3 array. Importantly, the device's electrical performance remains stable even after bending, achieving ultra-low-power consumption of 2.08 fJ per synaptic event at -0.001 V. This research may contribute to the development of ultra-low-power neuromorphic computing, biomimetic robotics, and artificial intelligence.
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Affiliation(s)
- Li Zhu
- College of Integrated Circuit Science and Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210023, China; (L.Z.); (J.L.); (X.W.); (H.S.); (Z.Y.); (Y.X.)
| | - Junchen Lin
- College of Integrated Circuit Science and Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210023, China; (L.Z.); (J.L.); (X.W.); (H.S.); (Z.Y.); (Y.X.)
| | - Yixin Zhu
- Yongjiang Laboratory (Y-LAB), Ningbo 315202, China;
| | - Jie Wu
- College of Integrated Circuit Science and Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210023, China; (L.Z.); (J.L.); (X.W.); (H.S.); (Z.Y.); (Y.X.)
| | - Xiang Wan
- College of Integrated Circuit Science and Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210023, China; (L.Z.); (J.L.); (X.W.); (H.S.); (Z.Y.); (Y.X.)
| | - Huabin Sun
- College of Integrated Circuit Science and Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210023, China; (L.Z.); (J.L.); (X.W.); (H.S.); (Z.Y.); (Y.X.)
- Guangdong Greater Bay Area Institute of Integrated Circuit and System, Guangzhou 510535, China
| | - Zhihao Yu
- College of Integrated Circuit Science and Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210023, China; (L.Z.); (J.L.); (X.W.); (H.S.); (Z.Y.); (Y.X.)
- Guangdong Greater Bay Area Institute of Integrated Circuit and System, Guangzhou 510535, China
| | - Yong Xu
- College of Integrated Circuit Science and Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210023, China; (L.Z.); (J.L.); (X.W.); (H.S.); (Z.Y.); (Y.X.)
- Guangdong Greater Bay Area Institute of Integrated Circuit and System, Guangzhou 510535, China
| | - Cheeleong Tan
- College of Integrated Circuit Science and Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210023, China; (L.Z.); (J.L.); (X.W.); (H.S.); (Z.Y.); (Y.X.)
- Guangdong Greater Bay Area Institute of Integrated Circuit and System, Guangzhou 510535, China
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2
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Song J, Liu H, Zhao Z, Lin P, Yan F. Flexible Organic Transistors for Biosensing: Devices and Applications. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024; 36:e2300034. [PMID: 36853083 DOI: 10.1002/adma.202300034] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/02/2023] [Revised: 02/20/2023] [Indexed: 06/18/2023]
Abstract
Flexible and stretchable biosensors can offer seamless and conformable biological-electronic interfaces for continuously acquiring high-fidelity signals, permitting numerous emerging applications. Organic thin film transistors (OTFTs) are ideal transducers for flexible and stretchable biosensing due to their soft nature, inherent amplification function, biocompatibility, ease of functionalization, low cost, and device diversity. In consideration of the rapid advances in flexible-OTFT-based biosensors and their broad applications, herein, a timely and comprehensive review is provided. It starts with a detailed introduction to the features of various OTFTs including organic field-effect transistors and organic electrochemical transistors, and the functionalization strategies for biosensing, with a highlight on the seminal work and up-to-date achievements. Then, the applications of flexible-OTFT-based biosensors in wearable, implantable, and portable electronics, as well as neuromorphic biointerfaces are detailed. Subsequently, special attention is paid to emerging stretchable organic transistors including planar and fibrous devices. The routes to impart stretchability, including structural engineering and material engineering, are discussed, and the implementations of stretchable organic transistors in e-skin and smart textiles are included. Finally, the remaining challenges and the future opportunities in this field are summarized.
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Affiliation(s)
- Jiajun Song
- Department of Applied Physics, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, 999077, P. R. China
| | - Hong Liu
- Department of Applied Physics, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, 999077, P. R. China
| | - Zeyu Zhao
- Department of Applied Physics, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, 999077, P. R. China
| | - Peng Lin
- Shenzhen Key Laboratory of Special Functional Materials and Guangdong Research Center for Interfacial Engineering of Functional Materials, College of Materials Science and Engineering, Shenzhen University, Shenzhen, 518060, P. R. China
| | - Feng Yan
- Department of Applied Physics, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, 999077, P. R. China
- Research Institute of Intelligent Wearable Systems, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, 999077, P. R. China
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Wu X, Shi S, Liang B, Dong Y, Yang R, Ji R, Wang Z, Huang W. Ultralow-power optoelectronic synaptic transistors based on polyzwitterion dielectrics for in-sensor reservoir computing. SCIENCE ADVANCES 2024; 10:eadn4524. [PMID: 38630830 PMCID: PMC11023521 DOI: 10.1126/sciadv.adn4524] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Accepted: 03/13/2024] [Indexed: 04/19/2024]
Abstract
Bio-inspired transistor synapses use solid electrolytes to achieve low-power operation and rich synaptic behaviors via ion diffusion and trapping. While these neuromorphic devices hold great promise, they still suffer from challenges such as high leakage currents and power consumption, electrolysis risk, and irreversible conductance changes due to long-range ion migrations and permanent ion trapping. In addition, their response to light is generally limited because of "exciton-polaron quenching", which restricts their potential in in-sensor neuromorphic visions. To address these issues, we propose replacing solid electrolytes with polyzwitterions, where the cation and anion are covalently concatenated via a flexible alkyl chain, thus preventing long-range ion migrations while inducing good photoresponses to the transistors via interfacial charge trapping. Our detailed studies reveal that polyzwitterion-based transistors exhibit optoelectronic synaptic behavior with ultralow-power consumption (~250 aJ per spike) and enable high-performance in-sensor reservoir computing, achieving 95.56% accuracy in perceiving the trajectory of moving basketballs.
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Affiliation(s)
- Xiaosong Wu
- State Key Laboratory of Structural Chemistry, Fujian Institute of Research on the Structure of Matter, Chinese Academy of Sciences, Fuzhou, Fujian 350002, P. R. China
- Fujian Science & Technology Innovation Laboratory for Optoelectronic Information of China, Fuzhou, Fujian 350002, P. R. China
- University of Chinese Academy of Sciences, 19A Yuquan Road, Beijing 100049, P. R. China
| | - Shuhui Shi
- Department of Electrical and Electronic Engineering, University of Hong Kong, Pokfulam Road, Hong Kong SAR, P. R. China
| | - Baoshuai Liang
- State Key Laboratory of Structural Chemistry, Fujian Institute of Research on the Structure of Matter, Chinese Academy of Sciences, Fuzhou, Fujian 350002, P. R. China
- Fujian Science & Technology Innovation Laboratory for Optoelectronic Information of China, Fuzhou, Fujian 350002, P. R. China
- University of Chinese Academy of Sciences, 19A Yuquan Road, Beijing 100049, P. R. China
| | - Yu Dong
- State Key Laboratory of Structural Chemistry, Fujian Institute of Research on the Structure of Matter, Chinese Academy of Sciences, Fuzhou, Fujian 350002, P. R. China
- Fujian Science & Technology Innovation Laboratory for Optoelectronic Information of China, Fuzhou, Fujian 350002, P. R. China
- Department of Electrical and Electronic Engineering, University of Hong Kong, Pokfulam Road, Hong Kong SAR, P. R. China
| | - Rumeng Yang
- State Key Laboratory of Structural Chemistry, Fujian Institute of Research on the Structure of Matter, Chinese Academy of Sciences, Fuzhou, Fujian 350002, P. R. China
- Fujian Science & Technology Innovation Laboratory for Optoelectronic Information of China, Fuzhou, Fujian 350002, P. R. China
- University of Chinese Academy of Sciences, 19A Yuquan Road, Beijing 100049, P. R. China
| | - Ruiduan Ji
- State Key Laboratory of Structural Chemistry, Fujian Institute of Research on the Structure of Matter, Chinese Academy of Sciences, Fuzhou, Fujian 350002, P. R. China
- Fujian Science & Technology Innovation Laboratory for Optoelectronic Information of China, Fuzhou, Fujian 350002, P. R. China
- University of Chinese Academy of Sciences, 19A Yuquan Road, Beijing 100049, P. R. China
| | - Zhongrui Wang
- Department of Electrical and Electronic Engineering, University of Hong Kong, Pokfulam Road, Hong Kong SAR, P. R. China
| | - Weiguo Huang
- State Key Laboratory of Structural Chemistry, Fujian Institute of Research on the Structure of Matter, Chinese Academy of Sciences, Fuzhou, Fujian 350002, P. R. China
- Fujian Science & Technology Innovation Laboratory for Optoelectronic Information of China, Fuzhou, Fujian 350002, P. R. China
- University of Chinese Academy of Sciences, 19A Yuquan Road, Beijing 100049, P. R. China
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Liu X, Sun C, Ye X, Zhu X, Hu C, Tan H, He S, Shao M, Li RW. Neuromorphic Nanoionics for Human-Machine Interaction: From Materials to Applications. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024:e2311472. [PMID: 38421081 DOI: 10.1002/adma.202311472] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Revised: 02/06/2024] [Indexed: 03/02/2024]
Abstract
Human-machine interaction (HMI) technology has undergone significant advancements in recent years, enabling seamless communication between humans and machines. Its expansion has extended into various emerging domains, including human healthcare, machine perception, and biointerfaces, thereby magnifying the demand for advanced intelligent technologies. Neuromorphic computing, a paradigm rooted in nanoionic devices that emulate the operations and architecture of the human brain, has emerged as a powerful tool for highly efficient information processing. This paper delivers a comprehensive review of recent developments in nanoionic device-based neuromorphic computing technologies and their pivotal role in shaping the next-generation of HMI. Through a detailed examination of fundamental mechanisms and behaviors, the paper explores the ability of nanoionic memristors and ion-gated transistors to emulate the intricate functions of neurons and synapses. Crucial performance metrics, such as reliability, energy efficiency, flexibility, and biocompatibility, are rigorously evaluated. Potential applications, challenges, and opportunities of using the neuromorphic computing technologies in emerging HMI technologies, are discussed and outlooked, shedding light on the fusion of humans with machines.
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Affiliation(s)
- Xuerong Liu
- CAS Key Laboratory of Magnetic Materials and Devices, and Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China
- Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China
- College of Materials Sciences and Opto-Electronic Technology, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Cui Sun
- CAS Key Laboratory of Magnetic Materials and Devices, and Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China
- Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China
| | - Xiaoyu Ye
- CAS Key Laboratory of Magnetic Materials and Devices, and Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China
- Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China
| | - Xiaojian Zhu
- CAS Key Laboratory of Magnetic Materials and Devices, and Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China
- Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China
| | - Cong Hu
- CAS Key Laboratory of Magnetic Materials and Devices, and Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China
- Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China
| | - Hongwei Tan
- Department of Applied Physics, Aalto University, Aalto, FI-00076, Finland
| | - Shang He
- CAS Key Laboratory of Magnetic Materials and Devices, and Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China
- Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China
| | - Mengjie Shao
- CAS Key Laboratory of Magnetic Materials and Devices, and Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China
- Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China
| | - Run-Wei Li
- CAS Key Laboratory of Magnetic Materials and Devices, and Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China
- Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China
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5
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Li X, Ou X, Chen G, Bi R, Li Z, Xie Z, Yue W, Guo SZ. Ultrasoft and High-Adhesion Block Copolymers for Neuromorphic Computing. ACS APPLIED MATERIALS & INTERFACES 2024. [PMID: 38412379 DOI: 10.1021/acsami.3c19350] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/29/2024]
Abstract
The "von Neumann bottleneck" is a formidable challenge in conventional computing, driving exploration into artificial synapses. Organic semiconductor materials show promise but are hindered by issues such as poor adhesion and a high elastic modulus. Here, we combine polyisoindigo-bithiophene (PIID-2T) with grafted poly(dimethylsiloxane) (PDMS) to synthesize the triblock-conjugated polymer (PIID-2T-PDMS). The polymer exhibited substantial enhancements in adhesion (4.8-68.8 nN) and reductions in elastic modulus (1.6-0.58 GPa) while maintaining the electrical characteristics of PIID-2T. The three-terminal organic synaptic transistor (three-terminal p-type organic artificial synapse (TPOAS)), constructed using PIID-2T-PDMS, exhibits an unprecedented analog switching range of 276×, surpassing previous records, and a remarkable memory on-off ratio of 106. Moreover, the device displays outstanding operational stability, retaining 99.6% of its original current after 1600 write-read events in the air. Notably, TPOAS replicates key biological synaptic behaviors, including paired-pulse facilitation (PPF), short-term plasticity (STP), and long-term plasticity (LTP). Simulations using handwritten digital data sets reveal an impressive recognition accuracy of 91.7%. This study presents a polyisoindigo-bithiophene-based block copolymer that offers enhanced adhesion, reduced elastic modulus, and high-performance artificial synapses, paving the way for the next generation of neuromorphic computing systems.
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Affiliation(s)
- Xiaohong Li
- Guangzhou Key Laboratory of Flexible Electronic Materials and Wearable Devices, Key Laboratory for Polymeric Composite and Functional Materials of Ministry of Education, State Key Laboratory of Optoelectronic Materials and Technologies, School of Materials Science and Engineering, Sun Yat-sen University, Guangzhou 510275, P. R. China
| | - Xingcheng Ou
- Guangzhou Key Laboratory of Flexible Electronic Materials and Wearable Devices, Key Laboratory for Polymeric Composite and Functional Materials of Ministry of Education, State Key Laboratory of Optoelectronic Materials and Technologies, School of Materials Science and Engineering, Sun Yat-sen University, Guangzhou 510275, P. R. China
| | - Guoliang Chen
- Guangzhou Key Laboratory of Flexible Electronic Materials and Wearable Devices, Key Laboratory for Polymeric Composite and Functional Materials of Ministry of Education, State Key Laboratory of Optoelectronic Materials and Technologies, School of Materials Science and Engineering, Sun Yat-sen University, Guangzhou 510275, P. R. China
| | - Ran Bi
- Guangzhou Key Laboratory of Flexible Electronic Materials and Wearable Devices, Key Laboratory for Polymeric Composite and Functional Materials of Ministry of Education, State Key Laboratory of Optoelectronic Materials and Technologies, School of Materials Science and Engineering, Sun Yat-sen University, Guangzhou 510275, P. R. China
| | - Ziqian Li
- Guangzhou Key Laboratory of Flexible Electronic Materials and Wearable Devices, Key Laboratory for Polymeric Composite and Functional Materials of Ministry of Education, State Key Laboratory of Optoelectronic Materials and Technologies, School of Materials Science and Engineering, Sun Yat-sen University, Guangzhou 510275, P. R. China
| | - Zhuang Xie
- Guangzhou Key Laboratory of Flexible Electronic Materials and Wearable Devices, Key Laboratory for Polymeric Composite and Functional Materials of Ministry of Education, State Key Laboratory of Optoelectronic Materials and Technologies, School of Materials Science and Engineering, Sun Yat-sen University, Guangzhou 510275, P. R. China
| | - Wan Yue
- Guangzhou Key Laboratory of Flexible Electronic Materials and Wearable Devices, Key Laboratory for Polymeric Composite and Functional Materials of Ministry of Education, State Key Laboratory of Optoelectronic Materials and Technologies, School of Materials Science and Engineering, Sun Yat-sen University, Guangzhou 510275, P. R. China
| | - Shuang-Zhuang Guo
- Guangzhou Key Laboratory of Flexible Electronic Materials and Wearable Devices, Key Laboratory for Polymeric Composite and Functional Materials of Ministry of Education, State Key Laboratory of Optoelectronic Materials and Technologies, School of Materials Science and Engineering, Sun Yat-sen University, Guangzhou 510275, P. R. China
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Xu Y, Liu D, Dai S, Zhang J, Guo Z, Liu X, Xiong L, Huang J. Stretchable and neuromorphic transistors for pain perception and sensitization emulation. MATERIALS HORIZONS 2024; 11:958-968. [PMID: 38099601 DOI: 10.1039/d3mh01766d] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/20/2024]
Abstract
Pain perception nociceptors (PPN), an important type of sensory neuron, are capable of sending out alarm signals when the human body is exposed to destructive stimuli. Simulating the human ability to perceive the external environment and spontaneously avoid injury is a critical function of neural sensing of artificial intelligence devices. The demand for developing artificial PPN has subsequently increased. However, due to the application scenarios of bionic electronic devices such as human skin, electronic prostheses, and robot bodies, where a certain degree of surface deformation constantly occurs, the ideal artificial PPN should have the stretchability to adapt to real scenarios. Here, an organic semiconductor nanofiber artificial pain perception nociceptor (NAPPN) based on a pre-stretching strategy is demonstrated to achieve key pain aspects such as threshold, sensitization, and desensitization. Remarkably, while stretching up to 50%, the synaptic behaviors and injury warning ability of NAPPN can be retained. To verify the wearability of the device, NAPPN was attached to a curved human finger joint, on which PPN behaviors were successfully mimicked. This provides a promising strategy for realizing neural sensing function on either deformed or mobile electronic devices.
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Affiliation(s)
- Yutong Xu
- School of Materials Science and Engineering, Tongji University, Shanghai 201804, P. R. China.
| | - Dapeng Liu
- School of Materials Science and Engineering, Tongji University, Shanghai 201804, P. R. China.
| | - Shilei Dai
- School of Materials Science and Engineering, Tongji University, Shanghai 201804, P. R. China.
| | - Junyao Zhang
- School of Materials Science and Engineering, Tongji University, Shanghai 201804, P. R. China.
| | - Ziyi Guo
- School of Materials Science and Engineering, Tongji University, Shanghai 201804, P. R. China.
| | - Xu Liu
- School of Materials Science and Engineering, Tongji University, Shanghai 201804, P. R. China.
| | - Lize Xiong
- Translational Research Institute of Brain and Brain-Like Intelligence, Shanghai Key Laboratory of Anesthesiology and Brain Functional Modulation, Shanghai Fourth People's Hospital Affiliated to Tongji University, Tongji University, Shanghai, 200434, P. R. China.
| | - Jia Huang
- Translational Research Institute of Brain and Brain-Like Intelligence, Shanghai Key Laboratory of Anesthesiology and Brain Functional Modulation, Shanghai Fourth People's Hospital Affiliated to Tongji University, Tongji University, Shanghai, 200434, P. R. China.
- School of Materials Science and Engineering, Tongji University, Shanghai 201804, P. R. China.
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Zhou X, Wang Z, Xiong T, He B, Wang Z, Zhang H, Hu D, Liu Y, Yang C, Li Q, Chen M, Zhang Q, Wei L. Fiber Crossbars: An Emerging Architecture of Smart Electronic Textiles. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2023; 35:e2300576. [PMID: 37042804 DOI: 10.1002/adma.202300576] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Revised: 03/18/2023] [Indexed: 06/19/2023]
Abstract
Smart wearables have a significant impact on people's daily lives, enabling personalized motion monitoring, realizing the Internet of Things, and even reshaping the next generation of telemedicine systems. Fiber crossbars (FCs), constructed by crossing two fibers, have become an emerging architecture among the accessible structures of state-of-the-art smart electronic textiles. The mechanical, chemical, and electrical interactions between crossing fibers result in extensive functionalities, leading to the significant development of innovative electronic textiles employing FCs as their basic units. This review provides a timely and comprehensive overview of the structure designs, material selections, and assembly techniques of FC-based devices. The recent advances in FC-based devices are summarized, including multipurpose sensing, multiple-mode computing, high-resolution display, high-efficient power supply, and large-scale textile systems. Finally, current challenges, potential solutions, and future perspectives for FC-based systems are discussed for their further development in scale-up production and commercial applications.
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Affiliation(s)
- Xuhui Zhou
- School of Electrical and Electronic Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798, Singapore
| | - Zhe Wang
- School of Electrical and Electronic Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798, Singapore
| | - Ting Xiong
- School of Electrical and Electronic Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798, Singapore
| | - Bing He
- School of Electrical and Electronic Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798, Singapore
| | - Zhixun Wang
- School of Electrical and Electronic Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798, Singapore
| | - Haozhe Zhang
- School of Electrical and Electronic Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798, Singapore
| | - Dongmei Hu
- Key Laboratory of Multifunctional Nanomaterials and Smart Systems, Suzhou Institute of Nano-Tech and Nano-Bionics, Chinese Academy of Sciences, Suzhou, 215123, P. R. China
| | - Yanting Liu
- School of Electrical and Electronic Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798, Singapore
| | - Chunlei Yang
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, P. R. China
| | - Qingwen Li
- Key Laboratory of Multifunctional Nanomaterials and Smart Systems, Suzhou Institute of Nano-Tech and Nano-Bionics, Chinese Academy of Sciences, Suzhou, 215123, P. R. China
| | - Ming Chen
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, P. R. China
| | - Qichong Zhang
- Key Laboratory of Multifunctional Nanomaterials and Smart Systems, Suzhou Institute of Nano-Tech and Nano-Bionics, Chinese Academy of Sciences, Suzhou, 215123, P. R. China
| | - Lei Wei
- School of Electrical and Electronic Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798, Singapore
- The Institute for Digital Molecular Analytics and Science (IDMxS), Nanyang Technological University, 59 Nanyang Drive, Singapore, 636921, Singapore
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8
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Mallik S, Tsuruoka T, Tsuchiya T, Terabe K. Effects of Mg Doping to a LiCoO 2 Channel on the Synaptic Plasticity of Li Ion-Gated Transistors. ACS APPLIED MATERIALS & INTERFACES 2023; 15:47184-47195. [PMID: 37768881 DOI: 10.1021/acsami.3c07833] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/30/2023]
Abstract
Artificial synapses with ideal functionalities are essential in hardware neural networks to allow for energy-efficient analog computing. However, the realization of linear and symmetric weight updates in real synaptic devices has proven challenging and ultimately limits the online training capabilities of neural network systems. Herein, we investigate the effect of Mg doping on a LiCoO2 (LCO) channel in a Li ion-gated synaptic transistor, so as to improve long-term and short-term plasticity. Two transistor structures, based on a lithium phosphorus oxynitride electrolyte, were examined by using undoped LCO and Mg-doped LCO as the channel material between the source and drain electrodes. It was found that Mg doping increased the initial channel conductance by 3 orders of magnitude, which is probably due to the substitution of Co3+ by Mg2+ and the compensation of hole creation. It was further found that the doped channel transistor showed good retention characteristics and better linearity of long-term potentiation and depression when voltage pulses were applied to the gate electrode. The improved retention and linearity are attributed to an extended range of the insulator-to-conductor transition by Mg doping and Li-ion extraction/insertion cooperated in the LCO channel. Using the obtained synaptic weight update, artificial neural network simulations demonstrated that the doped channel transistor shows an image recognition accuracy of ∼80% for handwritten digits, which is higher than ∼65% exhibited by the undoped channel transistor. Mg doping also improved short-term plasticity such as paired-pulse facilitation/depression and Hebbian spike timing-dependent plasticity. These results indicate that elemental doping to the channel of Li ion-gated synaptic transistors could be a useful procedure for realizing robust neuromorphic systems based on analog computing.
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Affiliation(s)
- Samapika Mallik
- Research Center for Materials Nanoarchitectonics (MANA), National Institute for Materials Science, Namiki 1-1, Tsukuba, 305-0044, Japan
| | - Tohru Tsuruoka
- Research Center for Materials Nanoarchitectonics (MANA), National Institute for Materials Science, Namiki 1-1, Tsukuba, 305-0044, Japan
| | - Takashi Tsuchiya
- Research Center for Materials Nanoarchitectonics (MANA), National Institute for Materials Science, Namiki 1-1, Tsukuba, 305-0044, Japan
| | - Kazuya Terabe
- Research Center for Materials Nanoarchitectonics (MANA), National Institute for Materials Science, Namiki 1-1, Tsukuba, 305-0044, Japan
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Zhang GX, Zhang ZC, Chen XD, Kang L, Li Y, Wang FD, Shi L, Shi K, Liu ZB, Tian JG, Lu TB, Zhang J. Broadband sensory networks with locally stored responsivities for neuromorphic machine vision. SCIENCE ADVANCES 2023; 9:eadi5104. [PMID: 37713483 PMCID: PMC10881039 DOI: 10.1126/sciadv.adi5104] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/30/2023] [Accepted: 08/14/2023] [Indexed: 09/17/2023]
Abstract
As the most promising candidates for the implementation of in-sensor computing, retinomorphic vision sensors can constitute built-in neural networks and directly implement multiply-and-accumulation operations using responsivities as the weights. However, existing retinomorphic vision sensors mainly use a sustained gate bias to maintain the responsivity due to its volatile nature. Here, we propose an ion-induced localized-field strategy to develop retinomorphic vision sensors with nonvolatile tunable responsivity in both positive and negative regimes and construct a broadband and reconfigurable sensory network with locally stored weights to implement in-sensor convolutional processing in spectral range of 400 to 1800 nanometers. In addition to in-sensor computing, this retinomorphic device can implement in-memory computing benefiting from the nonvolatile tunable conductance, and a complete neuromorphic visual system involving front-end in-sensor computing and back-end in-memory computing architectures has been constructed, executing supervised and unsupervised learning tasks as demonstrations. This work paves the way for the development of high-speed and low-power neuromorphic machine vision for time-critical and data-intensive applications.
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Affiliation(s)
- Guo-Xin Zhang
- MOE International Joint Laboratory of Materials Microstructure, Institute for New Energy Materials and Low Carbon Technologies, School of Material Science and Engineering, Tianjin University of Technology, Tianjin 300384, China
| | - Zhi-Cheng Zhang
- The Key Laboratory of Weak Light Nonlinear Photonics, Ministry of Education, School of Physics, Nankai University, Tianjin 300071, China
| | - Xu-Dong Chen
- MOE International Joint Laboratory of Materials Microstructure, Institute for New Energy Materials and Low Carbon Technologies, School of Material Science and Engineering, Tianjin University of Technology, Tianjin 300384, China
- The Key Laboratory of Weak Light Nonlinear Photonics, Ministry of Education, School of Physics, Nankai University, Tianjin 300071, China
| | - Lixing Kang
- Key Laboratory of Multifunctional Nanomaterials and Smart Systems Division of Advanced Material, Suzhou Institute of Nano-Tech and Nano-Bionics, Chinese Academy of Sciences, Suzhou 215123, China
| | - Yuan Li
- The Key Laboratory of Weak Light Nonlinear Photonics, Ministry of Education, School of Physics, Nankai University, Tianjin 300071, China
| | - Fu-Dong Wang
- MOE International Joint Laboratory of Materials Microstructure, Institute for New Energy Materials and Low Carbon Technologies, School of Material Science and Engineering, Tianjin University of Technology, Tianjin 300384, China
| | - Lei Shi
- MOE International Joint Laboratory of Materials Microstructure, Institute for New Energy Materials and Low Carbon Technologies, School of Material Science and Engineering, Tianjin University of Technology, Tianjin 300384, China
| | - Ke Shi
- MOE International Joint Laboratory of Materials Microstructure, Institute for New Energy Materials and Low Carbon Technologies, School of Material Science and Engineering, Tianjin University of Technology, Tianjin 300384, China
| | - Zhi-Bo Liu
- The Key Laboratory of Weak Light Nonlinear Photonics, Ministry of Education, School of Physics, Nankai University, Tianjin 300071, China
| | - Jian-Guo Tian
- The Key Laboratory of Weak Light Nonlinear Photonics, Ministry of Education, School of Physics, Nankai University, Tianjin 300071, China
| | - Tong-Bu Lu
- MOE International Joint Laboratory of Materials Microstructure, Institute for New Energy Materials and Low Carbon Technologies, School of Material Science and Engineering, Tianjin University of Technology, Tianjin 300384, China
| | - Jin Zhang
- Center for Nanochemistry, Beijing Science and Engineering Center for Nanocarbons, Beijing National Laboratory for Molecular Sciences, College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China
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10
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Dai S, Liu X, Liu Y, Xu Y, Zhang J, Wu Y, Cheng P, Xiong L, Huang J. Emerging Iontronic Neural Devices for Neuromorphic Sensory Computing. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2023; 35:e2300329. [PMID: 36891745 DOI: 10.1002/adma.202300329] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Revised: 02/22/2023] [Indexed: 06/18/2023]
Abstract
Living organisms have a very mysterious and powerful sensory computing system based on ion activity. Interestingly, studies on iontronic devices in the past few years have proposed a promising platform for simulating the sensing and computing functions of living organisms, because: 1) iontronic devices can generate, store, and transmit a variety of signals by adjusting the concentration and spatiotemporal distribution of ions, which analogs to how the brain performs intelligent functions by alternating ion flux and polarization; 2) through ionic-electronic coupling, iontronic devices can bridge the biosystem with electronics and offer profound implications for soft electronics; 3) with the diversity of ions, iontronic devices can be designed to recognize specific ions or molecules by customizing the charge selectivity, and the ionic conductivity and capacitance can be adjusted to respond to external stimuli for a variety of sensing schemes, which can be more difficult for electron-based devices. This review provides a comprehensive overview of emerging neuromorphic sensory computing by iontronic devices, highlighting representative concepts of both low-level and high-level sensory computing and introducing important material and device breakthroughs. Moreover, iontronic devices as a means of neuromorphic sensing and computing are discussed regarding the pending challenges and future directions.
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Affiliation(s)
- Shilei Dai
- Shanghai Key Laboratory of Anesthesiology and Brain Functional Modulation, Translational Research Institute of Brain and Brain-Like Intelligence, Shanghai Fourth People's Hospital, Tongji University, Shanghai, 200434, P. R. China
- Interdisciplinary Materials Research Center, School of Materials Science and Engineering, Tongji University, Shanghai, 201804, P. R. China
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Pokfulam, Hong Kong, 999077, China
| | - Xu Liu
- Interdisciplinary Materials Research Center, School of Materials Science and Engineering, Tongji University, Shanghai, 201804, P. R. China
| | - Youdi Liu
- Department of Engineering Science and Mechanics, Pennsylvania State University, University Park, State College, PA, 16802, USA
| | - Yutong Xu
- Interdisciplinary Materials Research Center, School of Materials Science and Engineering, Tongji University, Shanghai, 201804, P. R. China
| | - Junyao Zhang
- Interdisciplinary Materials Research Center, School of Materials Science and Engineering, Tongji University, Shanghai, 201804, P. R. China
| | - Yue Wu
- Interdisciplinary Materials Research Center, School of Materials Science and Engineering, Tongji University, Shanghai, 201804, P. R. China
| | - Ping Cheng
- Pritzker School of Molecular Engineering, The University of Chicago, Chicago, IL, 60637, USA
| | - Lize Xiong
- Shanghai Key Laboratory of Anesthesiology and Brain Functional Modulation, Translational Research Institute of Brain and Brain-Like Intelligence, Shanghai Fourth People's Hospital, Tongji University, Shanghai, 200434, P. R. China
| | - Jia Huang
- Shanghai Key Laboratory of Anesthesiology and Brain Functional Modulation, Translational Research Institute of Brain and Brain-Like Intelligence, Shanghai Fourth People's Hospital, Tongji University, Shanghai, 200434, P. R. China
- Interdisciplinary Materials Research Center, School of Materials Science and Engineering, Tongji University, Shanghai, 201804, P. R. China
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11
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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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12
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He L, Yang Z, Wang Z, Leydecker T, Orgiu E. Organic multilevel (opto)electronic memories towards neuromorphic applications. NANOSCALE 2023. [PMID: 37378458 DOI: 10.1039/d3nr01311a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/29/2023]
Abstract
In the past decades, neuromorphic computing has attracted the interest of the scientific community due to its potential to circumvent the von Neumann bottleneck. Organic materials, owing to their fine tunablility and their ability to be used in multilevel memories, represent a promising class of materials to fabricate neuromorphic devices with the key requirement of operation with synaptic weight. In this review, recent studies of organic multilevel memory are presented. The operating principles and the latest achievements obtained with devices exploiting the main approaches to reach multilevel operation are discussed, with emphasis on organic devices using floating gates, ferroelectric materials, polymer electrets and photochromic molecules. The latest results obtained using organic multilevel memories for neuromorphic circuits are explored and the major advantages and drawbacks of the use of organic materials for neuromorphic applications are discussed.
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Affiliation(s)
- Lin He
- Institute of Fundamental and Frontier Sciences (IFFS), University of Electronic Science and Technology of China, Chengdu 610054, China.
| | - Zuchong Yang
- Institut national de la recherche scientifique (INRS), Centre Énergie Matériaux Télécommunications, 1650 Boul. Lionel Boulet, Varennes J3X 1S2, Canada.
| | - Zhiming Wang
- Institute of Fundamental and Frontier Sciences (IFFS), University of Electronic Science and Technology of China, Chengdu 610054, China.
| | - Tim Leydecker
- Institute of Fundamental and Frontier Sciences (IFFS), University of Electronic Science and Technology of China, Chengdu 610054, China.
| | - Emanuele Orgiu
- Institut national de la recherche scientifique (INRS), Centre Énergie Matériaux Télécommunications, 1650 Boul. Lionel Boulet, Varennes J3X 1S2, Canada.
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13
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Sun C, Liu X, Jiang Q, Ye X, Zhu X, Li RW. Emerging electrolyte-gated transistors for neuromorphic perception. SCIENCE AND TECHNOLOGY OF ADVANCED MATERIALS 2023; 24:2162325. [PMID: 36684849 PMCID: PMC9848240 DOI: 10.1080/14686996.2022.2162325] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Revised: 12/18/2022] [Accepted: 12/21/2022] [Indexed: 05/31/2023]
Abstract
With the rapid development of intelligent robotics, the Internet of Things, and smart sensor technologies, great enthusiasm has been devoted to developing next-generation intelligent systems for the emulation of advanced perception functions of humans. Neuromorphic devices, capable of emulating the learning, memory, analysis, and recognition functions of biological neural systems, offer solutions to intelligently process sensory information. As one of the most important neuromorphic devices, Electrolyte-gated transistors (EGTs) have shown great promise in implementing various vital neural functions and good compatibility with sensors. This review introduces the materials, operating principle, and performances of EGTs, followed by discussing the recent progress of EGTs for synapse and neuron emulation. Integrating EGTs with sensors that faithfully emulate diverse perception functions of humans such as tactile and visual perception is discussed. The challenges of EGTs for further development are given.
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Affiliation(s)
- Cui Sun
- CAS Key Laboratory of Magnetic Materials and Devices, and Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China
- Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China
| | - Xuerong Liu
- CAS Key Laboratory of Magnetic Materials and Devices, and Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China
- Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China
| | - Qian Jiang
- CAS Key Laboratory of Magnetic Materials and Devices, and Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China
- Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China
- College of Materials Sciences and Opto-Electronic Technology, University of Chinese Academy of Sciences, Beijing, China
| | - Xiaoyu Ye
- CAS Key Laboratory of Magnetic Materials and Devices, and Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China
- Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China
- College of Materials Sciences and Opto-Electronic Technology, University of Chinese Academy of Sciences, Beijing, China
| | - Xiaojian Zhu
- CAS Key Laboratory of Magnetic Materials and Devices, and Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China
- Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China
- College of Materials Sciences and Opto-Electronic Technology, University of Chinese Academy of Sciences, Beijing, China
| | - Run-Wei Li
- CAS Key Laboratory of Magnetic Materials and Devices, and Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China
- Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China
- College of Materials Sciences and Opto-Electronic Technology, University of Chinese Academy of Sciences, Beijing, China
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14
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Park B, Hwang Y, Kwon O, Hwang S, Lee JA, Choi DH, Lee SK, Kim AR, Cho B, Kwon JD, Lee JI, Kim Y. Robust 2D MoS 2 Artificial Synapse Device Based on a Lithium Silicate Solid Electrolyte for High-Precision Analogue Neuromorphic Computing. ACS APPLIED MATERIALS & INTERFACES 2022; 14:53038-53047. [PMID: 36394301 DOI: 10.1021/acsami.2c14080] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
High-precision artificial synaptic devices compatible with existing CMOS technology are essential for realizing robust neuromorphic hardware systems with reliable parallel analogue computation beyond the von Neumann serial digital computing architecture. However, critical issues related to reliability and variability, such as nonlinearity and asymmetric weight updates, have been great challenges in the implementation of artificial synaptic devices in practical neuromorphic hardware systems. Herein, a robust three-terminal two-dimensional (2D) MoS2 artificial synaptic device combined with a lithium silicate (LSO) solid-state electrolyte thin film is proposed. The rationally designed synaptic device exhibits excellent linearity and symmetry upon electrical potentiation and depression, benefiting from the reversible intercalation of Li ions into the MoS2 channel. In particular, extremely low cycle-to-cycle variations (3.01%) during long-term potentiation and depression processes over 500 pulses are achieved, causing statistical analogue discrete states. Thus, a high classification accuracy of 96.77% (close to the software baseline of 98%) is demonstrated in the Modified National Institute of Standards and Technology (MNIST) simulations. These results provide a future perspective for robust synaptic device architecture of lithium solid-state electrolytes stacked with 2D van der Waals layered channels for high-precision analogue neuromorphic computing systems.
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Affiliation(s)
- Byeongjin Park
- Department of Energy and Electronic Materials, Nanosurface Materials Division, Korea Institute of Materials Science (KIMS), 797 Changwondaero, Sungsan-gu, Changwon51508, Gyeongnam, Republic of Korea
- School of Materials Science and Engineering, Pusan National University, 2 Busandaehak-ro 63-beon-gil, Geumjeong-gu, Busan46241, Republic of Korea
| | - Yunjeong Hwang
- Department of Energy and Electronic Materials, Nanosurface Materials Division, Korea Institute of Materials Science (KIMS), 797 Changwondaero, Sungsan-gu, Changwon51508, Gyeongnam, Republic of Korea
| | - Ojun Kwon
- Department of Advanced Material Engineering, Chungbuk National University, 1 Chungdae-ro, Seowon-gu, Cheongju28644, Chungbuk, Republic of Korea
| | - Seungkwon Hwang
- Department of Energy and Electronic Materials, Nanosurface Materials Division, Korea Institute of Materials Science (KIMS), 797 Changwondaero, Sungsan-gu, Changwon51508, Gyeongnam, Republic of Korea
- School of Materials Science and Engineering, Pusan National University, 2 Busandaehak-ro 63-beon-gil, Geumjeong-gu, Busan46241, Republic of Korea
| | - Ju Ah Lee
- Department of Energy and Electronic Materials, Nanosurface Materials Division, Korea Institute of Materials Science (KIMS), 797 Changwondaero, Sungsan-gu, Changwon51508, Gyeongnam, Republic of Korea
- School of Materials Science and Engineering, Pusan National University, 2 Busandaehak-ro 63-beon-gil, Geumjeong-gu, Busan46241, Republic of Korea
| | - Dong-Hyeong Choi
- Department of Energy and Electronic Materials, Nanosurface Materials Division, Korea Institute of Materials Science (KIMS), 797 Changwondaero, Sungsan-gu, Changwon51508, Gyeongnam, Republic of Korea
- School of Materials Science and Engineering, Pusan National University, 2 Busandaehak-ro 63-beon-gil, Geumjeong-gu, Busan46241, Republic of Korea
| | - Seoung-Ki Lee
- School of Materials Science and Engineering, Pusan National University, 2 Busandaehak-ro 63-beon-gil, Geumjeong-gu, Busan46241, Republic of Korea
| | - Ah Ra Kim
- Department of Energy and Electronic Materials, Nanosurface Materials Division, Korea Institute of Materials Science (KIMS), 797 Changwondaero, Sungsan-gu, Changwon51508, Gyeongnam, Republic of Korea
| | - Byungjin Cho
- Department of Advanced Material Engineering, Chungbuk National University, 1 Chungdae-ro, Seowon-gu, Cheongju28644, Chungbuk, Republic of Korea
| | - Jung-Dae Kwon
- Department of Energy and Electronic Materials, Nanosurface Materials Division, Korea Institute of Materials Science (KIMS), 797 Changwondaero, Sungsan-gu, Changwon51508, Gyeongnam, Republic of Korea
| | - Je In Lee
- School of Materials Science and Engineering, Pusan National University, 2 Busandaehak-ro 63-beon-gil, Geumjeong-gu, Busan46241, Republic of Korea
| | - Yonghun Kim
- Department of Energy and Electronic Materials, Nanosurface Materials Division, Korea Institute of Materials Science (KIMS), 797 Changwondaero, Sungsan-gu, Changwon51508, Gyeongnam, Republic of Korea
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15
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Wang X, Lu W, Wei P, Qin Z, Qiao N, Qin X, Zhang M, Zhu Y, Bu L, Lu G. Artificial Tactile Recognition Enabled by Flexible Low-Voltage Organic Transistors and Low-Power Synaptic Electronics. ACS APPLIED MATERIALS & INTERFACES 2022; 14:48948-48959. [PMID: 36269162 DOI: 10.1021/acsami.2c14625] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
The advancement of self-powered intelligent strain systems for human-computer interaction is crucial toward wearable and energy-saving applications. Simultaneously, lowering operating voltage and thus reducing power consumption are of particular interests. A brain-like smart synaptic hardware system is considered as a promising candidate for low-power, parallel computing and learning processes. However, the combination of low-voltage organic transistors and energy efficient smart synapse hardware systems driven by a tactile signal has been hindered by the limited materials and technology. Here, by employing an elastomeric copolymer poly(vinylidene fluoride-co-hexafluoropropylene) (PVDF-HFP) with a high HFP content of 25 mol %, flexible, low-voltage transistors (|VG| ≤ 3 V) and a low energy consumption synapse ≤ 9.2 × 10-17 J are devised simultaneously, along with the lowest quality factor (R = Pw × VG, 2.76 × 10-16 J V). Furthermore, based on the low voltage and low power consumption characteristics, flexible artificial tactile recognition system and Morse code recognition are established without any computing supporting. Mechanical flexibility, cycling stability, image contrast enhancement functions, and simulated pattern recognition accuracy of the multilayer perceptron neural network are also simulated. This work recommends a route of exploiting low voltage, low power consumption synaptic systems and smart human-machine interfaces with low energy loss based on flexible organic synaptic transistors.
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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'an710054, China
| | - Wanlong Lu
- Frontier Institute of Science and Technology, State Key Laboratory of Electrical Insulation and Power Equipment, Xi'an Jiaotong University, Xi'an710054, China
| | - Peng Wei
- Frontier Institute of Science and Technology, State Key Laboratory of Electrical Insulation and Power Equipment, Xi'an Jiaotong University, Xi'an710054, China
| | - Zongze Qin
- Frontier Institute of Science and Technology, State Key Laboratory of Electrical Insulation and Power Equipment, Xi'an Jiaotong University, Xi'an710054, China
| | - Nan Qiao
- Frontier Institute of Science and Technology, State Key Laboratory of Electrical Insulation and Power Equipment, Xi'an Jiaotong University, Xi'an710054, China
| | - Xinsu Qin
- School of Chemistry, Xi'an Jiaotong University, Xi'an710049, China
| | - Meng Zhang
- Frontier Institute of Science and Technology, State Key Laboratory of Electrical Insulation and Power Equipment, Xi'an Jiaotong University, Xi'an710054, China
| | - Yuanwei Zhu
- Frontier Institute of Science and Technology, State Key Laboratory of Electrical Insulation and Power Equipment, Xi'an Jiaotong University, Xi'an710054, China
| | - Laju Bu
- School of Chemistry, Xi'an Jiaotong University, Xi'an710049, China
| | - Guanghao Lu
- Frontier Institute of Science and Technology, State Key Laboratory of Electrical Insulation and Power Equipment, Xi'an Jiaotong University, Xi'an710054, China
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16
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Go GT, Lee Y, Seo DG, Lee TW. Organic Neuroelectronics: From Neural Interfaces to Neuroprosthetics. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2022; 34:e2201864. [PMID: 35925610 DOI: 10.1002/adma.202201864] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/26/2022] [Revised: 07/17/2022] [Indexed: 06/15/2023]
Abstract
Requirements and recent advances in research on organic neuroelectronics are outlined herein. Neuroelectronics such as neural interfaces and neuroprosthetics provide a promising approach to diagnose and treat neurological diseases. However, the current neural interfaces are rigid and not biocompatible, so they induce an immune response and deterioration of neural signal transmission. Organic materials are promising candidates for neural interfaces, due to their mechanical softness, excellent electrochemical properties, and biocompatibility. Also, organic nervetronics, which mimics functional properties of the biological nerve system, is being developed to overcome the limitations of the complex and energy-consuming conventional neuroprosthetics that limit long-term implantation and daily-life usage. Examples of organic materials for neural interfaces and neural signal recordings are reviewed, recent advances of organic nervetronics that use organic artificial synapses are highlighted, and then further requirements for neuroprosthetics are discussed. Finally, the future challenges that must be overcome to achieve ideal organic neuroelectronics for next-generation neuroprosthetics are discussed.
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Affiliation(s)
- Gyeong-Tak Go
- Department of Materials Science and Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul, 08826, Republic of Korea
| | - Yeongjun Lee
- Department of Chemical Engineering, Stanford University, Stanford, CA, 94305, USA
| | - Dae-Gyo Seo
- Department of Materials Science and Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul, 08826, Republic of Korea
| | - Tae-Woo Lee
- Department of Materials Science and Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul, 08826, Republic of Korea
- Institute of Engineering Research, Research Institute of Advanced Materials, Soft Foundry, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul, 08826, Republic of Korea
- School of Chemical and Biological Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul, 08826, Republic of Korea
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17
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Li Q, Wang T, Fang Y, Hu X, Tang C, Wu X, Zhu H, Ji L, Sun QQ, Zhang DW, Chen L. Ultralow Power Wearable Organic Ferroelectric Device for Optoelectronic Neuromorphic Computing. NANO LETTERS 2022; 22:6435-6443. [PMID: 35737934 DOI: 10.1021/acs.nanolett.2c01768] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
In order to imitate brain-inspired biological information processing systems, various neuromorphic computing devices have been proposed, most of which were prepared on rigid substrates and have energy consumption levels several orders of magnitude higher than those of biological synapses (∼10 fJ per spike). Herein, a new type of wearable organic ferroelectric artificial synapse is proposed, which has two modulation modes (optical and electrical modulation). Because of the high photosensitivity of organic semiconductors and the ultrafast polarization switching of ferroelectric materials, the synaptic device has an ultrafast operation speed of 30 ns and an ultralow power consumption of 0.0675 aJ per synaptic event. Under combined photoelectric modulation, the artificial synapse realizes associative learning. The proposed artificial synapse with ultralow power consumption demonstrates good synaptic plasticity under different bending strains. This provides new avenues for the construction of ultralow power artificial intelligence system and the development of future wearable devices.
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Affiliation(s)
- Qingxuan Li
- State Key Laboratory of ASIC and System, School of Microelectronics, Fudan University, Shanghai 200433, P.R. China
| | - Tianyu Wang
- State Key Laboratory of ASIC and System, School of Microelectronics, Fudan University, Shanghai 200433, P.R. China
- Zhangjiang Fudan International Innovation Center, Shanghai 201203, China
| | - Yuqing Fang
- State Key Laboratory of ASIC and System, School of Microelectronics, Fudan University, Shanghai 200433, P.R. China
| | - Xuemeng Hu
- State Key Laboratory of ASIC and System, School of Microelectronics, Fudan University, Shanghai 200433, P.R. China
| | - Chengkang Tang
- State Key Laboratory of ASIC and System, School of Microelectronics, Fudan University, Shanghai 200433, P.R. China
| | - Xiaohan Wu
- State Key Laboratory of ASIC and System, School of Microelectronics, Fudan University, Shanghai 200433, P.R. China
- Zhangjiang Fudan International Innovation Center, Shanghai 201203, China
| | - Hao Zhu
- State Key Laboratory of ASIC and System, School of Microelectronics, Fudan University, Shanghai 200433, P.R. China
- Zhangjiang Fudan International Innovation Center, Shanghai 201203, China
| | - Li Ji
- State Key Laboratory of ASIC and System, School of Microelectronics, Fudan University, Shanghai 200433, P.R. China
- Zhangjiang Fudan International Innovation Center, Shanghai 201203, China
| | - Qing-Qing Sun
- State Key Laboratory of ASIC and System, School of Microelectronics, Fudan University, Shanghai 200433, P.R. China
- Zhangjiang Fudan International Innovation Center, Shanghai 201203, China
| | - David Wei Zhang
- State Key Laboratory of ASIC and System, School of Microelectronics, Fudan University, Shanghai 200433, P.R. China
- Zhangjiang Fudan International Innovation Center, Shanghai 201203, China
- National Integrated Circuit Innovation Center, Shanghai 201203, China
| | - Lin Chen
- State Key Laboratory of ASIC and System, School of Microelectronics, Fudan University, Shanghai 200433, P.R. China
- Zhangjiang Fudan International Innovation Center, Shanghai 201203, China
- National Integrated Circuit Innovation Center, Shanghai 201203, China
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18
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Electrospun Nanofibers for Integrated Sensing, Storage, and Computing Applications. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12094370] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Electrospun nanofibers have become the most promising building blocks for future high-performance electronic devices because of the advantages of larger specific surface area, higher porosity, more flexibility, and stronger mechanical strength over conventional film-based materials. Moreover, along with the properties of ease of fabrication and cost-effectiveness, a broad range of applications based on nanomaterials by electrospinning have sprung up. In this review, we aim to summarize basic principles, influence factors, and advanced methods of electrospinning to produce hundreds of nanofibers with different structures and arrangements. In addition, electrospun nanofiber based electronics composed of both two-terminal and three-terminal devices and their practical applications are discussed in the fields of sensing, storage, and computing, which give rise to the further integration to realize a comprehensive and brain-like system. Last but not least, the emulation of biological synapses through artificial synaptic transistors and additionally optoelectronics in recent years are included as an important step toward the construction of large-scale, multifunctional systems.
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19
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Zhang H, Wang Z, Wang Z, He B, Chen M, Qi M, Liu Y, Xin J, Wei L. Recent progress of fiber-based transistors: materials, structures and applications. FRONTIERS OF OPTOELECTRONICS 2022; 15:2. [PMID: 36637572 PMCID: PMC9756263 DOI: 10.1007/s12200-022-00002-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/11/2021] [Accepted: 07/24/2021] [Indexed: 06/17/2023]
Abstract
Wearable electronics on fibers or fabrics assembled with electronic functions provide a platform for sensors, displays, circuitry, and computation. These new conceptual devices are human-friendly and programmable, which makes them indispensable for modern electronics. Their unique properties such as being adaptable in daily life, as well as being lightweight and flexible, have enabled many promising applications in robotics, healthcare, and the Internet of Things (IoT). Transistors, one of the fundamental blocks in electronic systems, allow for signal processing and computing. Therefore, study leading to integration of transistors with fabrics has become intensive. Here, several aspects of fiber-based transistors are addressed, including materials, system structures, and their functional devices such as sensory, logical circuitry, memory devices as well as neuromorphic computation. Recently reported advances in development and challenges to realizing fully integrated electronic textile (e-textile) systems are also discussed.
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Affiliation(s)
- Haozhe Zhang
- School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, 639798, Singapore
| | - Zhe Wang
- School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, 639798, Singapore
| | - Zhixun Wang
- School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, 639798, Singapore
| | - Bing He
- School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, 639798, Singapore
| | - Mengxiao Chen
- School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, 639798, Singapore
| | - Miao Qi
- School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, 639798, Singapore
| | - Yanting Liu
- School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, 639798, Singapore
| | - Jiwu Xin
- School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, 639798, Singapore
| | - Lei Wei
- School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, 639798, Singapore.
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20
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Nikam RD, Lee J, Choi W, Banerjee W, Kwak M, Yadav M, Hwang H. Ionic Sieving Through One-Atom-Thick 2D Material Enables Analog Nonvolatile Memory for Neuromorphic Computing. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2021; 17:e2103543. [PMID: 34596963 DOI: 10.1002/smll.202103543] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/04/2021] [Revised: 08/17/2021] [Indexed: 06/13/2023]
Abstract
The first report on ion transport through atomic sieves of atomically thin 2D material is provided to solve critical limitations of electrochemical random-access memory (ECRAM) devices. Conventional ECRAMs have random and localized ion migration paths; as a result, the analog switching efficiency is inadequate to perform in-memory logic operations. Herein ion transport path scaled down to the one-atom-thick (≈0.33 nm) hexagonal boron nitride (hBN), and the ionic transport area is confined to a small pore (≈0.3 nm2 ) at the single-hexagonal ring. One-atom-thick hBN has ion-permeable pores at the center of each hexagonal ring due to weakened electron cloud and highly polarized B-N bond. The experimental evidence indicates that the activation energy barrier for H+ ion transport through single-layer hBN is ≈0.51 eV. Benefiting from the controlled ionic sieving through single-layer hBN, the ECRAMs exhibit superior nonvolatile analog switching with good memory retention and high endurance. The proposed approach enables atomically thin 2D material as an ion transport layer to regulate the switching of various ECRAM devices for artificial synaptic electronics.
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Affiliation(s)
- Revannath Dnyandeo Nikam
- Center for Single Atom-Based Semiconductor Device, Pohang University of Science and Technology (POSTECH), Pohang, 790-784, Republic of Korea
- Department of Material Science and Engineering, Pohang University of Science and Technology (POSTECH), Pohang, 790-784, Republic of Korea
| | - Jongwon Lee
- Center for Single Atom-Based Semiconductor Device, Pohang University of Science and Technology (POSTECH), Pohang, 790-784, Republic of Korea
- Department of Material Science and Engineering, Pohang University of Science and Technology (POSTECH), Pohang, 790-784, Republic of Korea
| | - Wooseok Choi
- Center for Single Atom-Based Semiconductor Device, Pohang University of Science and Technology (POSTECH), Pohang, 790-784, Republic of Korea
- Department of Material Science and Engineering, Pohang University of Science and Technology (POSTECH), Pohang, 790-784, Republic of Korea
| | - Writam Banerjee
- Center for Single Atom-Based Semiconductor Device, Pohang University of Science and Technology (POSTECH), Pohang, 790-784, Republic of Korea
- Department of Material Science and Engineering, Pohang University of Science and Technology (POSTECH), Pohang, 790-784, Republic of Korea
| | - Myonghoon Kwak
- Center for Single Atom-Based Semiconductor Device, Pohang University of Science and Technology (POSTECH), Pohang, 790-784, Republic of Korea
- Department of Material Science and Engineering, Pohang University of Science and Technology (POSTECH), Pohang, 790-784, Republic of Korea
| | - Manoj Yadav
- Center for Single Atom-Based Semiconductor Device, Pohang University of Science and Technology (POSTECH), Pohang, 790-784, Republic of Korea
- Department of Material Science and Engineering, Pohang University of Science and Technology (POSTECH), Pohang, 790-784, Republic of Korea
| | - Hyunsang Hwang
- Center for Single Atom-Based Semiconductor Device, Pohang University of Science and Technology (POSTECH), Pohang, 790-784, Republic of Korea
- Department of Material Science and Engineering, Pohang University of Science and Technology (POSTECH), Pohang, 790-784, Republic of Korea
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21
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Yang B, Wang Y, Hua Z, Zhang J, Li L, Hao D, Guo P, Xiong L, Huang J. Low-power consumption light-stimulated synaptic transistors based on natural carotene and organic semiconductors. Chem Commun (Camb) 2021; 57:8300-8303. [PMID: 34318806 DOI: 10.1039/d1cc03060d] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Developing synaptic devices with environment-friendly materials is a promising research direction. Here, light-stimulated synaptic transistors based on natural carotene and organic semiconductors were developed. Several important functions similar to biological synapses were realized and an ultra-low power consumption of 3.4 × 10-18 J was achieved.
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Affiliation(s)
- Ben Yang
- Interdisciplinary Materials Research Center, School of Materials Science and Engineering, Frontiers Science Center for Intelligent Autonomous Systems, Tongji University, Shanghai, 201804, P. R. China.
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22
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Qin W, Kang BH, Kim HJ. Flexible Artificial Synapses with a Biocompatible Maltose-Ascorbic Acid Electrolyte Gate for Neuromorphic Computing. ACS APPLIED MATERIALS & INTERFACES 2021; 13:34597-34604. [PMID: 34279076 DOI: 10.1021/acsami.1c07073] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
As constructing hardware technology is widely regarded as an important step toward realizing brain-like computers and artificial intelligence systems, the development of artificial synaptic electronics that can simulate biological synaptic functions is an emerging research field. Among the various types of artificial synapses, synaptic transistors using an electrolyte as the gate electrode have been implemented as the high capacitance of the electrolyte increases the driving current and lowers operating voltages. Here, transistors using maltose-ascorbic acid as the proton-conducting electrolyte are proposed. A novel electrolyte composed of maltose and ascorbic acid, both of which are biocompatible, enables the migration of protons. This allows the channel conductance of the transistors to be modulated with the gate input pulse voltage, and fundamental synaptic functions including excitatory postsynaptic current, paired-pulse facilitation, long-term potentiation, and long-term depression can be successfully emulated. Furthermore, the maltose-ascorbic acid electrolyte (MAE)-gated synaptic transistors exhibit high mechanical endurance, with near-linear conductivity modulation and repeatability after 1000 bending cycles under a curvature radius of 5 mm. Benefitting from its excellent biodegradability and biocompatibility, the proposed MAE has potential applications in environmentally friendly, economical, and high-performance neuromorphic electronics, which can be further applied to dermal electronics and implantable electronics in the future.
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Affiliation(s)
- Wei Qin
- School of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, Republic of Korea
| | - Byung Ha Kang
- School of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, Republic of Korea
| | - Hyun Jae Kim
- School of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, Republic of Korea
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23
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Kim SJ, Jeong JS, Jang HW, Yi H, Yang H, Ju H, Lim JA. Dendritic Network Implementable Organic Neurofiber Transistors with Enhanced Memory Cyclic Endurance for Spatiotemporal Iterative Learning. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2021; 33:e2100475. [PMID: 34028897 DOI: 10.1002/adma.202100475] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/19/2021] [Indexed: 06/12/2023]
Abstract
Dendritic network implementable organic neurofiber transistors with enhanced memory cyclic endurance for spatiotemporal iterative learning are proposed. The architecture of the fibrous organic electrochemical transistors consisting of a double-stranded assembly of electrode microfibers and an iongel gate insulator enables the highly sensitive multiple implementation of synaptic junctions via simple physical contact of gate-electrode microfibers, similar to the dendritic connections of a biological neuron fiber. In particular, carboxylic-acid-functionalized polythiophene as a semiconductor channel material provides stable gate-field-dependent multilevel memory characteristics with long-term stability and cyclic endurance, unlike the conventional poly(alkylthiophene)-based neuromorphic electrochemical transistors, which exhibit short retention and unstable endurance. The dissociation of the carboxylic acid of the polythiophene enables reversible doping and dedoping of the polythiophene channel by effectively stabilizing the ions that penetrate the channel during potentiation and depression cycles, leading to the reliable cyclic endurance of the device. The synaptic weight of the neurofiber transistors with a dendritic network maintains the state levels stably and is independently updated with each synapse connected with the presynaptic neuron to a specific state level. Finally, the neurofiber transistor demonstrates successful speech recognition based on iterative spiking neural network learning in the time domain, showing a substantial recognition accuracy of 88.9%.
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Affiliation(s)
- Soo Jin Kim
- Center for Opto-Electronic Materials and Devices, Korea Institute of Science and Technology, Seoul, 02792, Republic of Korea
- Department of Materials Science and Engineering, Seoul National University, Seoul, 08826, Republic of Korea
| | - Jae-Seung Jeong
- Center for Neuromorphic Engineering, Korea Institute of Science and Technology, Seoul, 02792, Republic of Korea
- Division of Nano and Information Technology, University of Science and Technology of Korea, Daejeon, 34113, Republic of Korea
| | - Ho Won Jang
- Department of Materials Science and Engineering, Seoul National University, Seoul, 08826, Republic of Korea
- Research Institute of Advanced Materials, Seoul National University, Seoul, 08826, Republic of Korea
| | - Hyunjung Yi
- Post-Silicon Semiconductor Institute, Korea Institute of Science and Technology, Seoul, 02792, Republic of Korea
- Department of Materials Science and Engineering, YU-KIST Institute, Yonsei University, Seoul, 03722, Republic of Korea
| | - Hoichang Yang
- Department of Chemical Engineering, Inha University, Incheon, 22212, Republic of Korea
| | - Hyunsu Ju
- Center for Opto-Electronic Materials and Devices, Korea Institute of Science and Technology, Seoul, 02792, Republic of Korea
| | - Jung Ah Lim
- Center for Opto-Electronic Materials and Devices, Korea Institute of Science and Technology, Seoul, 02792, Republic of Korea
- Division of Nano and Information Technology, University of Science and Technology of Korea, Daejeon, 34113, Republic of Korea
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24
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Ou Q, Yang B, Zhang J, Liu D, Chen T, Wang X, Hao D, Lu Y, Huang J. Degradable Photonic Synaptic Transistors Based on Natural Biomaterials and Carbon Nanotubes. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2021; 17:e2007241. [PMID: 33590701 DOI: 10.1002/smll.202007241] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/17/2020] [Revised: 01/10/2021] [Indexed: 06/12/2023]
Abstract
Artificial synaptic devices have potential for overcoming the bottleneck of von Neumann architecture and building artificial brain-like computers. Up to now, developing synaptic devices by utilizing biocompatible and biodegradable materials in electronic devices has been an interesting research direction due to the requirements of sustainable development. Here, a degradable photonic synaptic device is reported by combining biomaterials chlorophyll-a and single-walled carbon nanotubes (SWCNTs). Several basic synaptic functions, including excitatory postsynaptic current (EPSC), paired pulse facilitation (PPF), transition from short-term memory (STM) to long-term memory (LTM), and learning and forgetting behaviors, are successfully emulated through the chlorophyll-a/SWCNTs synaptic device. Furthermore, decent synaptic behaviors can still be achieved at a low drain voltage of -0.0001 V, which results in quite low energy consumption of 17.5 fJ per pulse. Finally, the degradability of this chlorophyll-a/SWCNTs transistor array is demonstrated, indicating that the device can be environmentally friendly. This work provides a new guide to the development of next-generation green and degradable neuromorphic computing electronics.
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Affiliation(s)
- Qingqing Ou
- Interdisciplinary Materials Research Center, School of Materials Science and Engineering, Shanghai Institute of Intelligent Science and Technology, Tongji University, Shanghai, 201804, P. R. China
| | - Ben Yang
- Interdisciplinary Materials Research Center, School of Materials Science and Engineering, Shanghai Institute of Intelligent Science and Technology, Tongji University, Shanghai, 201804, P. R. China
| | - Junyao Zhang
- Interdisciplinary Materials Research Center, School of Materials Science and Engineering, Shanghai Institute of Intelligent Science and Technology, Tongji University, Shanghai, 201804, P. R. China
| | - Dapeng Liu
- Interdisciplinary Materials Research Center, School of Materials Science and Engineering, Shanghai Institute of Intelligent Science and Technology, Tongji University, Shanghai, 201804, P. R. China
| | - Tianqi Chen
- Interdisciplinary Materials Research Center, School of Materials Science and Engineering, Shanghai Institute of Intelligent Science and Technology, Tongji University, Shanghai, 201804, P. R. China
| | - Xin Wang
- Interdisciplinary Materials Research Center, School of Materials Science and Engineering, Shanghai Institute of Intelligent Science and Technology, Tongji University, Shanghai, 201804, P. R. China
| | - Dandan Hao
- Interdisciplinary Materials Research Center, School of Materials Science and Engineering, Shanghai Institute of Intelligent Science and Technology, Tongji University, Shanghai, 201804, P. R. China
| | - Yang Lu
- Interdisciplinary Materials Research Center, School of Materials Science and Engineering, Shanghai Institute of Intelligent Science and Technology, Tongji University, Shanghai, 201804, P. R. China
| | - Jia Huang
- Interdisciplinary Materials Research Center, School of Materials Science and Engineering, Shanghai Institute of Intelligent Science and Technology, Tongji University, Shanghai, 201804, P. R. China
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25
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Lu K, Li X, Sun Q, Pang X, Chen J, Minari T, Liu X, Song Y. Solution-processed electronics for artificial synapses. MATERIALS HORIZONS 2021; 8:447-470. [PMID: 34821264 DOI: 10.1039/d0mh01520b] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/12/2023]
Abstract
Artificial synaptic devices and systems have become hot topics due to parallel computing, high plasticity, integration of storage, and processing to meet the challenges of the traditional Von Neumann computers. Currently, two-terminal memristors and three-terminal transistors have been mainly developed for high-density storage with high switching speed and high reliability because of the adjustable resistivity, controllable ion migration, and abundant choices of functional materials and fabrication processes. To achieve the low-cost, large-scale, and easy-process fabrication, solution-processed techniques have been extensively employed to develop synaptic electronics towards flexible and highly integrated three-dimensional (3D) neural networks. Herein, we have summarized and discussed solution-processed techniques in the fabrication of two-terminal memristors and three-terminal transistors for the application of artificial synaptic electronics mainly reported in the recent five years from the view of fabrication processes, functional materials, electronic operating mechanisms, and system applications. Furthermore, the challenges and prospects were discussed in depth to promote solution-processed techniques in the future development of artificial synapse with high performance and high integration.
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Affiliation(s)
- Kuakua Lu
- School of Materials Science and Engineering, The Key Laboratory of Material Processing and Mold of Ministry of Education, Henan Key Laboratory of Advanced Nylon Materials and Application, Zhengzhou University, Zhengzhou 450001, P. R. China.
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26
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Li Y, Lu J, Shang D, Liu Q, Wu S, Wu Z, Zhang X, Yang J, Wang Z, Lv H, Liu M. Oxide-Based Electrolyte-Gated Transistors for Spatiotemporal Information Processing. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2020; 32:e2003018. [PMID: 33079425 DOI: 10.1002/adma.202003018] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/04/2020] [Revised: 09/16/2020] [Indexed: 05/28/2023]
Abstract
Spiking neural networks (SNNs) sharing large similarity with biological nervous systems are promising to process spatiotemporal information and can provide highly time- and energy-efficient computational paradigms for the Internet-of-Things and edge computing. Nonvolatile electrolyte-gated transistors (EGTs) provide prominent analog switching performance, the most critical feature of synaptic element, and have been recently demonstrated as a promising synaptic device. However, high performance, large-scale EGT arrays, and EGT application for spatiotemporal information processing in an SNN are yet to be demonstrated. Here, an oxide-based EGT employing amorphous Nb2 O5 and Lix SiO2 is introduced as the channel and electrolyte gate materials, respectively, and integrated into a 32 × 32 EGT array. The engineered EGTs show a quasi-linear update, good endurance (106 ) and retention, a high switching speed of 100 ns, ultralow readout conductance (<100 nS), and ultralow areal switching energy density (20 fJ µm-2 ). The prominent analog switching performance is leveraged for hardware implementation of an SNN with the capability of spatiotemporal information processing, where spike sequences with different timings are able to be efficiently learned and recognized by the EGT array. Finally, this EGT-based spatiotemporal information processing is deployed to detect moving orientation in a tactile sensing system. These results provide an insight into oxide-based EGT devices for energy-efficient neuromorphic computing to support edge application.
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Affiliation(s)
- Yue Li
- Key Laboratory of Microelectronic Devices and Integrated Technology, Institute of Microelectronics, Chinese Academy of Sciences, Beijing, 100029, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Jikai Lu
- Key Laboratory of Microelectronic Devices and Integrated Technology, Institute of Microelectronics, Chinese Academy of Sciences, Beijing, 100029, China
- School of Microelectronics, University of Science and Technology of China, Hefei, Anhui, 230026, China
| | - Dashan Shang
- Key Laboratory of Microelectronic Devices and Integrated Technology, Institute of Microelectronics, Chinese Academy of Sciences, Beijing, 100029, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Qi Liu
- Key Laboratory of Microelectronic Devices and Integrated Technology, Institute of Microelectronics, Chinese Academy of Sciences, Beijing, 100029, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Shuyu Wu
- Key Laboratory of Microelectronic Devices and Integrated Technology, Institute of Microelectronics, Chinese Academy of Sciences, Beijing, 100029, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Zuheng Wu
- Key Laboratory of Microelectronic Devices and Integrated Technology, Institute of Microelectronics, Chinese Academy of Sciences, Beijing, 100029, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Xumeng Zhang
- Key Laboratory of Microelectronic Devices and Integrated Technology, Institute of Microelectronics, Chinese Academy of Sciences, Beijing, 100029, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Jianguo Yang
- Key Laboratory of Microelectronic Devices and Integrated Technology, Institute of Microelectronics, Chinese Academy of Sciences, Beijing, 100029, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Zhongrui Wang
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Pok Fu Lam Road, Hong Kong
- Department of Electrical and Computer Engineering, University of Massachusetts, Amherst, MA, 01003, USA
| | - Hangbing Lv
- Key Laboratory of Microelectronic Devices and Integrated Technology, Institute of Microelectronics, Chinese Academy of Sciences, Beijing, 100029, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Ming Liu
- Key Laboratory of Microelectronic Devices and Integrated Technology, Institute of Microelectronics, Chinese Academy of Sciences, Beijing, 100029, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
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