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Sun H, Tian H, Hu Y, Cui Y, Chen X, Xu M, Wang X, Zhou T. Bio-Plausible Multimodal Learning with Emerging Neuromorphic Devices. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024:e2406242. [PMID: 39258724 DOI: 10.1002/advs.202406242] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/06/2024] [Revised: 08/02/2024] [Indexed: 09/12/2024]
Abstract
Multimodal machine learning, as a prospective advancement in artificial intelligence, endeavors to emulate the brain's multimodal learning abilities with the objective to enhance interactions with humans. However, this approach requires simultaneous processing of diverse types of data, leading to increased model complexity, longer training times, and higher energy consumption. Multimodal neuromorphic devices have the capability to preprocess spatio-temporal information from various physical signals into unified electrical signals with high information density, thereby enabling more biologically plausible multimodal learning with low complexity and high energy-efficiency. Here, this work conducts a comparison between the expression of multimodal machine learning and multimodal neuromorphic computing, followed by an overview of the key characteristics associated with multimodal neuromorphic devices. The bio-plausible operational principles and the multimodal learning abilities of emerging devices are examined, which are classified into heterogeneous and homogeneous multimodal neuromorphic devices. Subsequently, this work provides a detailed description of the multimodal learning capabilities demonstrated by neuromorphic circuits and their respective applications. Finally, this work highlights the limitations and challenges of multimodal neuromorphic computing in order to hopefully provide insight into potential future research directions.
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Affiliation(s)
- Haonan Sun
- School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China
- State Key Laboratory of Electronic Thin Film and Integrated Devices, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Haoxiang Tian
- State Key Laboratory of Electronic Thin Film and Integrated Devices, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Yihao Hu
- School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China
- State Key Laboratory of Electronic Thin Film and Integrated Devices, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Yi Cui
- State Key Laboratory of Electronic Thin Film and Integrated Devices, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Xinrui Chen
- State Key Laboratory of Electronic Thin Film and Integrated Devices, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Minyi Xu
- State Key Laboratory of Electronic Thin Film and Integrated Devices, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Xianfu Wang
- State Key Laboratory of Electronic Thin Film and Integrated Devices, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Tao Zhou
- School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China
- State Key Laboratory of Electronic Thin Film and Integrated Devices, University of Electronic Science and Technology of China, Chengdu, 611731, China
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Park T, Han Y, Lee S, Kim YH, Yoo H. Wavelength-Dependent Multistate Programmability and Optoelectronic Logic-in-Memory Operation from the Narrow Bandgap pNDI-SVS Floating Gate. NANO LETTERS 2024; 24:9544-9552. [PMID: 38968419 DOI: 10.1021/acs.nanolett.4c01998] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/07/2024]
Abstract
This study introduces wavelength-dependent multistate programmable optoelectronic logic-in-memory (OLIM) operation using a broadband photoresponsive pNDI-SVS floating gate. The distinct optical absorption of the relatively large bandgap DNTT channel (2.6 eV) and the narrow bandgap pNDI-SVS floating gate (1.37 eV) lead to varying light-induced charge carrier accumulation across different wavelengths. In the proposed OLIM device comprising the p-type pNDI-SVS-based optoelectronic memory (POEM) transistor and an IGZO n-type transistor, we achieve controllable output voltage signals by modulating the pull-up performance through optical wavelength and applied bias manipulation. Real-time OLIM operation yields four discernible output values. The device's high mechanical flexibility and seamless surface integration among the paper substrate, pNDI-SVS, parylene gate dielectric, and DNTT region render it compatible for integration into paper-based optoelectronics. Our flexible POEM device on name card substrates demonstrates stable operational performance, with minimal variation (8%) after 100 cycles of repeated memory operation, remaining reliable across various angle measurements.
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Affiliation(s)
- Taehyun Park
- SDC Research Group, Department of Electronic Engineering, Gachon University, 1342 Seongnam-daero, Seongnam 13120, Republic of Korea
| | - Youngmin Han
- SDC Research Group, Department of Electronic Engineering, Gachon University, 1342 Seongnam-daero, Seongnam 13120, Republic of Korea
| | - Seonjeong Lee
- Department of Chemistry and RIMA, Gyeongsang National University, Jinju, 660-701 Republic of Korea
| | - Yun-Hi Kim
- Department of Chemistry and RIMA, Gyeongsang National University, Jinju, 660-701 Republic of Korea
| | - Hocheon Yoo
- SDC Research Group, Department of Electronic Engineering, Gachon University, 1342 Seongnam-daero, Seongnam 13120, Republic of Korea
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Li W, Li J, Mu T, Li J, Sun P, Dai M, Chen Y, Yang R, Chen Z, Wang Y, Wu Y, Wang S. The Nonvolatile Memory and Neuromorphic Simulation of ReS 2/h-BN/Graphene Floating Gate Devices Under Photoelectrical Hybrid Modulations. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2024; 20:e2311630. [PMID: 38470212 DOI: 10.1002/smll.202311630] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Revised: 02/02/2024] [Indexed: 03/13/2024]
Abstract
The floating gate devices, as a kind of nonvolatile memory, obtain great application potential in logic-in-memory chips. The 2D materials have been greatly studied due to atomically flat surfaces, higher carrier mobility, and excellent photoelectrical response. The 2D ReS2 flake is an excellent candidate for channel materials due to thickness-independent direct bandgap and outstanding optoelectronic response. In this paper, the floating gate devices are prepared with the ReS2/h-BN/Gr heterojunction. It obtains superior nonvolatile electrical memory characteristics, including a higher memory window ratio (81.82%), tiny writing/erasing voltage (±8 V/2 ms), long retention (>1000 s), and stable endurance (>1000 times) as well as multiple memory states. Meanwhile, electrical writing and optical erasing are achieved by applying electrical and optical pulses, and multilevel storage can easily be achieved by regulating light pulse parameters. Finally, due to the ideal long-time potentiation/depression synaptic weights regulated by light pulses and electrical pulses, the convolutional neural network (CNN) constructed by ReS2/h-BN/Gr floating gate devices can achieve image recognition with an accuracy of up to 98.15% for MNIST dataset and 91.24% for Fashion-MNIST dataset. The research work adds a powerful option for 2D materials floating gate devices to apply to logic-in-memory chips and neuromorphic computing.
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Affiliation(s)
- Wei Li
- School of Microelectronics, Northwestern Polytechnical University, 127 West Youyi Road, Beilin District, Xi'an, Shaanxi, 710072, P. R. China
| | - Jiaying Li
- School of Microelectronics, Northwestern Polytechnical University, 127 West Youyi Road, Beilin District, Xi'an, Shaanxi, 710072, P. R. China
| | - Tianhui Mu
- School of Microelectronics, Northwestern Polytechnical University, 127 West Youyi Road, Beilin District, Xi'an, Shaanxi, 710072, P. R. China
| | - Jiayao Li
- School of Statistics, Wuhan University of Science and Technology, 947 Heping Avenue, Qingshan District, Wuhan, Hubei, 430081, P. R. China
| | - Pengcheng Sun
- School of Microelectronics, Northwestern Polytechnical University, 127 West Youyi Road, Beilin District, Xi'an, Shaanxi, 710072, P. R. China
| | - Mingjian Dai
- School of Microelectronics, Northwestern Polytechnical University, 127 West Youyi Road, Beilin District, Xi'an, Shaanxi, 710072, P. R. China
| | - Yuhua Chen
- School of Microelectronics, Northwestern Polytechnical University, 127 West Youyi Road, Beilin District, Xi'an, Shaanxi, 710072, P. R. China
| | - Ruijing Yang
- School of Microelectronics, Northwestern Polytechnical University, 127 West Youyi Road, Beilin District, Xi'an, Shaanxi, 710072, P. R. China
| | - Zhao Chen
- School of Microelectronics, Northwestern Polytechnical University, 127 West Youyi Road, Beilin District, Xi'an, Shaanxi, 710072, P. R. China
| | - Yucheng Wang
- School of Microelectronics, Northwestern Polytechnical University, 127 West Youyi Road, Beilin District, Xi'an, Shaanxi, 710072, P. R. China
| | - Yupan Wu
- School of Microelectronics, Northwestern Polytechnical University, 127 West Youyi Road, Beilin District, Xi'an, Shaanxi, 710072, P. R. China
| | - Shaoxi Wang
- School of Microelectronics, Northwestern Polytechnical University, 127 West Youyi Road, Beilin District, Xi'an, Shaanxi, 710072, P. R. China
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Chen J, Liu X, Liu C, Tang L, Bu T, Jiang B, Qing Y, Xie Y, Wang Y, Shan Y, Li R, Ye C, Liao L. Reconfigurable Ag/HfO 2/NiO/Pt Memristors with Stable Synchronous Synaptic and Neuronal Functions for Renewable Homogeneous Neuromorphic Computing System. NANO LETTERS 2024; 24:5371-5378. [PMID: 38647348 DOI: 10.1021/acs.nanolett.4c01319] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/25/2024]
Abstract
Artificial synapses and bionic neurons offer great potential in highly efficient computing paradigms. However, complex requirements for specific electronic devices in neuromorphic computing have made memristors face the challenge of process simplification and universality. Herein, reconfigurable Ag/HfO2/NiO/Pt memristors are designed for feasible switching between volatile and nonvolatile modes by compliance current controlled Ag filaments, which enables stable and reconfigurable synaptic and neuronal functions. A neuromorphic computing system effectively replicates the biological synaptic weight alteration and continuously accomplishes excitation and reset of artificial neurons, which consist of bionic synapses and artificial neurons based on isotype Ag/HfO2/NiO/Pt memristors. This reconfigurable electrical performance of the Ag/HfO2/NiO/Pt memristors takes advantage of simplified hardware design and delivers integrated circuits with high density, which exhibits great potency for future neural networks.
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Affiliation(s)
- Jiaqi Chen
- Key Laboratory of Intelligent Sensing System and Security, Ministry of Education, School of Microelectronics, Hubei University, Wuhan 430062, China
| | - Xingqiang Liu
- Changsha Semiconductor Technology and Application Research Institute, Engineering Research Center of Advanced Semiconductor Technology, College of Semiconductor (College of Integrated Circuit), Hunan University, Changsha 410082, China
| | - Chang Liu
- Key Laboratory for Micro/Nano Optoelectronic Devices of Ministry of Education & Hunan Provincial Key Laboratory of Low-Dimensional Structural Physics and Devices, School of Physics and Electronics, Hunan University, Changsha 410082, China
| | - Lin Tang
- Key Laboratory for Micro/Nano Optoelectronic Devices of Ministry of Education & Hunan Provincial Key Laboratory of Low-Dimensional Structural Physics and Devices, School of Physics and Electronics, Hunan University, Changsha 410082, China
| | - Tong Bu
- Key Laboratory for Micro/Nano Optoelectronic Devices of Ministry of Education & Hunan Provincial Key Laboratory of Low-Dimensional Structural Physics and Devices, School of Physics and Electronics, Hunan University, Changsha 410082, China
| | - Bei Jiang
- Key Laboratory of Intelligent Sensing System and Security, Ministry of Education, School of Microelectronics, Hubei University, Wuhan 430062, China
- Changsha Semiconductor Technology and Application Research Institute, Engineering Research Center of Advanced Semiconductor Technology, College of Semiconductor (College of Integrated Circuit), Hunan University, Changsha 410082, China
| | - Yahui Qing
- Key Laboratory of Intelligent Sensing System and Security, Ministry of Education, School of Microelectronics, Hubei University, Wuhan 430062, China
| | - Yulu Xie
- Key Laboratory of Intelligent Sensing System and Security, Ministry of Education, School of Microelectronics, Hubei University, Wuhan 430062, China
| | - Yong Wang
- Key Laboratory of Intelligent Sensing System and Security, Ministry of Education, School of Microelectronics, Hubei University, Wuhan 430062, China
| | - Yongtao Shan
- Key Laboratory of Intelligent Sensing System and Security, Ministry of Education, School of Microelectronics, Hubei University, Wuhan 430062, China
| | - Ruxin Li
- Key Laboratory of Intelligent Sensing System and Security, Ministry of Education, School of Microelectronics, Hubei University, Wuhan 430062, China
| | - Cong Ye
- Key Laboratory of Intelligent Sensing System and Security, Ministry of Education, School of Microelectronics, Hubei University, Wuhan 430062, China
| | - Lei Liao
- Changsha Semiconductor Technology and Application Research Institute, Engineering Research Center of Advanced Semiconductor Technology, College of Semiconductor (College of Integrated Circuit), Hunan University, Changsha 410082, China
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Xu B, Guo D, Dong W, Gao H, Zhu P, Wang Z, Watanabe K, Taniguchi T, Luo Z, Zheng F, Zheng S, Zhou J. Gap State-Modulated Van Der Waals Short-Term Memory with Broad Band Negative Photoconductance. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2024; 20:e2309626. [PMID: 38098431 DOI: 10.1002/smll.202309626] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Revised: 11/05/2023] [Indexed: 05/25/2024]
Abstract
Floating gate memory (FGM), composed of van der Waals (vdW) junctions with an atomically thin floating layer for charge storage, is widely employed to develop logic-in memories and in-sensor computing devices. Most research efforts of FGM are spent on achieving long-term charge storage and fast reading/writing for flash and random-access memory. However, dynamic modulation of memory time via a tunneling barrier and vdW interfaces, which is critical for synaptic computing and machine vision, is still lacking. Here, a van der Waals short-term memory with tunable memory windows and retention times from milliseconds to thousands of seconds is reported, which is approximately exponentially proportional to the thickness h-BN (hexagonal boron nitride) barrier. The specific h-BN barrier with fruitful gap states provides charge release channels for trapped charges, making the vdW device switchable between positive photoconductance and negative photoconductance with a broadband light from IR to UV range. The dynamic short-term memory with tunable photo response highlights the design strategy of novel vdW memory vis interface engineering for further intelligent information storage and optoelectronic detection.
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Affiliation(s)
- Boyu Xu
- Centre for Quantum Physics, Key Laboratory of Advanced Optoelectronic Quantum Architecture and Measurement (MOE), School of Physics, Beijing Institute of Technology, Beijing, 100081, China
| | - Dan Guo
- Centre for Quantum Physics, Key Laboratory of Advanced Optoelectronic Quantum Architecture and Measurement (MOE), School of Physics, Beijing Institute of Technology, Beijing, 100081, China
| | - Weikang Dong
- Centre for Quantum Physics, Key Laboratory of Advanced Optoelectronic Quantum Architecture and Measurement (MOE), School of Physics, Beijing Institute of Technology, Beijing, 100081, China
| | - Huiying Gao
- Centre for Quantum Physics, Key Laboratory of Advanced Optoelectronic Quantum Architecture and Measurement (MOE), School of Physics, Beijing Institute of Technology, Beijing, 100081, China
| | - Peng Zhu
- Centre for Quantum Physics, Key Laboratory of Advanced Optoelectronic Quantum Architecture and Measurement (MOE), School of Physics, Beijing Institute of Technology, Beijing, 100081, China
| | - Zhiwei Wang
- Centre for Quantum Physics, Key Laboratory of Advanced Optoelectronic Quantum Architecture and Measurement (MOE), School of Physics, Beijing Institute of Technology, Beijing, 100081, China
| | - Kenji Watanabe
- National Institute for Materials Science, 1-1 Namiki, Tsukuba, 303-0044, Japan
| | - Takashi Taniguchi
- National Institute for Materials Science, 1-1 Namiki, Tsukuba, 303-0044, Japan
| | - Zhaochu Luo
- State Key Laboratory of Artificial Microstructure and Mesoscopic Physics, School of Physics, Peking University, Beijing, 100871, China
| | - Fawei Zheng
- Centre for Quantum Physics, Key Laboratory of Advanced Optoelectronic Quantum Architecture and Measurement (MOE), School of Physics, Beijing Institute of Technology, Beijing, 100081, China
| | - Shoujun Zheng
- Centre for Quantum Physics, Key Laboratory of Advanced Optoelectronic Quantum Architecture and Measurement (MOE), School of Physics, Beijing Institute of Technology, Beijing, 100081, China
| | - Jiadong Zhou
- Centre for Quantum Physics, Key Laboratory of Advanced Optoelectronic Quantum Architecture and Measurement (MOE), School of Physics, Beijing Institute of Technology, Beijing, 100081, China
- Advanced Research Institute of Multidisciplinary Science, Beijing Institute of Technology, Beijing, 100081, China
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Duan X, Cao Z, Gao K, Yan W, Sun S, Zhou G, Wu Z, Ren F, Sun B. Memristor-Based Neuromorphic Chips. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024; 36:e2310704. [PMID: 38168750 DOI: 10.1002/adma.202310704] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/14/2023] [Revised: 12/15/2023] [Indexed: 01/05/2024]
Abstract
In the era of information, characterized by an exponential growth in data volume and an escalating level of data abstraction, there has been a substantial focus on brain-like chips, which are known for their robust processing power and energy-efficient operation. Memristors are widely acknowledged as the optimal electronic devices for the realization of neuromorphic computing, due to their innate ability to emulate the interconnection and information transfer processes witnessed among neurons. This review paper focuses on memristor-based neuromorphic chips, which provide an extensive description of the working principle and characteristic features of memristors, along with their applications in the realm of neuromorphic chips. Subsequently, a thorough discussion of the memristor array, which serves as the pivotal component of the neuromorphic chip, as well as an examination of the present mainstream neural networks, is delved. Furthermore, the design of the neuromorphic chip is categorized into three crucial sections, including synapse-neuron cores, networks on chip (NoC), and neural network design. Finally, the key performance metrics of the chip is highlighted, as well as the key metrics related to the memristor devices are employed to realize both the synaptic and neuronal components.
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Affiliation(s)
- Xuegang Duan
- National Local Joint Engineering Research Center for Precision Surgery & Regenerative Medicine, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, China
- Department of hepatobiliary surgery, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, China
- Frontier Institute of Science and Technology (FIST), Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, China
- Micro-and Nano-technology Research Center, State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, China
| | - Zelin Cao
- National Local Joint Engineering Research Center for Precision Surgery & Regenerative Medicine, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, China
- Department of hepatobiliary surgery, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, China
- Frontier Institute of Science and Technology (FIST), Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, China
- Micro-and Nano-technology Research Center, State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, China
| | - Kaikai Gao
- National Local Joint Engineering Research Center for Precision Surgery & Regenerative Medicine, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, China
- Department of hepatobiliary surgery, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, China
- Frontier Institute of Science and Technology (FIST), Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, China
- Micro-and Nano-technology Research Center, State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, China
| | - Wentao Yan
- National Local Joint Engineering Research Center for Precision Surgery & Regenerative Medicine, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, China
- Department of hepatobiliary surgery, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, China
- Frontier Institute of Science and Technology (FIST), Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, China
- Micro-and Nano-technology Research Center, State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, China
| | - Siyu Sun
- Frontier Institute of Science and Technology (FIST), Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, China
- Micro-and Nano-technology Research Center, State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, China
| | - Guangdong Zhou
- College of Artificial Intelligence, Brain-inspired Computing & Intelligent Control of Chongqing Key Lab, Southwest University, Chongqing, 400715, China
| | - Zhenhua Wu
- School of Mechanical Engineering, Shanghai Jiao Tong University, 800 DongChuan Rd, Shanghai, 200240, China
| | - Fenggang Ren
- National Local Joint Engineering Research Center for Precision Surgery & Regenerative Medicine, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, China
- Department of hepatobiliary surgery, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, China
| | - Bai Sun
- National Local Joint Engineering Research Center for Precision Surgery & Regenerative Medicine, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, China
- Department of hepatobiliary surgery, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, China
- Frontier Institute of Science and Technology (FIST), Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, China
- Micro-and Nano-technology Research Center, State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, China
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