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Kumar D, Li H, Kumbhar DD, Rajbhar MK, Das UK, Syed AM, Melinte G, El-Atab N. Highly Efficient Back-End-of-Line Compatible Flexible Si-Based Optical Memristive Crossbar Array for Edge Neuromorphic Physiological Signal Processing and Bionic Machine Vision. NANO-MICRO LETTERS 2024; 16:238. [PMID: 38976105 PMCID: PMC11231128 DOI: 10.1007/s40820-024-01456-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/01/2024] [Accepted: 06/11/2024] [Indexed: 07/09/2024]
Abstract
The emergence of the Internet-of-Things is anticipated to create a vast market for what are known as smart edge devices, opening numerous opportunities across countless domains, including personalized healthcare and advanced robotics. Leveraging 3D integration, edge devices can achieve unprecedented miniaturization while simultaneously boosting processing power and minimizing energy consumption. Here, we demonstrate a back-end-of-line compatible optoelectronic synapse with a transfer learning method on health care applications, including electroencephalogram (EEG)-based seizure prediction, electromyography (EMG)-based gesture recognition, and electrocardiogram (ECG)-based arrhythmia detection. With experiments on three biomedical datasets, we observe the classification accuracy improvement for the pretrained model with 2.93% on EEG, 4.90% on ECG, and 7.92% on EMG, respectively. The optical programming property of the device enables an ultra-low power (2.8 × 10-13 J) fine-tuning process and offers solutions for patient-specific issues in edge computing scenarios. Moreover, the device exhibits impressive light-sensitive characteristics that enable a range of light-triggered synaptic functions, making it promising for neuromorphic vision application. To display the benefits of these intricate synaptic properties, a 5 × 5 optoelectronic synapse array is developed, effectively simulating human visual perception and memory functions. The proposed flexible optoelectronic synapse holds immense potential for advancing the fields of neuromorphic physiological signal processing and artificial visual systems in wearable applications.
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Affiliation(s)
- Dayanand Kumar
- Smart, Advanced Memory Devices and Applications (SAMA) Laboratory, Electrical and Computer Engineering, Computer Electrical Mathematical Science and Engineering, King Abdullah University of Science and Technology (KAUST), 23955-6900, Thuwal, Saudi Arabia
| | - Hanrui Li
- Smart, Advanced Memory Devices and Applications (SAMA) Laboratory, Electrical and Computer Engineering, Computer Electrical Mathematical Science and Engineering, King Abdullah University of Science and Technology (KAUST), 23955-6900, Thuwal, Saudi Arabia
| | - Dhananjay D Kumbhar
- Smart, Advanced Memory Devices and Applications (SAMA) Laboratory, Electrical and Computer Engineering, Computer Electrical Mathematical Science and Engineering, King Abdullah University of Science and Technology (KAUST), 23955-6900, Thuwal, Saudi Arabia
| | - Manoj Kumar Rajbhar
- Smart, Advanced Memory Devices and Applications (SAMA) Laboratory, Electrical and Computer Engineering, Computer Electrical Mathematical Science and Engineering, King Abdullah University of Science and Technology (KAUST), 23955-6900, Thuwal, Saudi Arabia
| | - Uttam Kumar Das
- Smart, Advanced Memory Devices and Applications (SAMA) Laboratory, Electrical and Computer Engineering, Computer Electrical Mathematical Science and Engineering, King Abdullah University of Science and Technology (KAUST), 23955-6900, Thuwal, Saudi Arabia
| | - Abdul Momin Syed
- Smart, Advanced Memory Devices and Applications (SAMA) Laboratory, Electrical and Computer Engineering, Computer Electrical Mathematical Science and Engineering, King Abdullah University of Science and Technology (KAUST), 23955-6900, Thuwal, Saudi Arabia
| | - Georgian Melinte
- Smart, Advanced Memory Devices and Applications (SAMA) Laboratory, Electrical and Computer Engineering, Computer Electrical Mathematical Science and Engineering, King Abdullah University of Science and Technology (KAUST), 23955-6900, Thuwal, Saudi Arabia
| | - Nazek El-Atab
- Smart, Advanced Memory Devices and Applications (SAMA) Laboratory, Electrical and Computer Engineering, Computer Electrical Mathematical Science and Engineering, King Abdullah University of Science and Technology (KAUST), 23955-6900, Thuwal, Saudi Arabia.
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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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Xiong C, Yang Z, Shen J, Tang F, He Q, Li Y, Xu M, Miao X. Nano t-Se Peninsulas Embedded in Natively Oxidized 2D TiSe 2 Enable Uniform and Fast Memristive Switching. ACS APPLIED MATERIALS & INTERFACES 2023; 15:23371-23379. [PMID: 37155833 DOI: 10.1021/acsami.3c00818] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/10/2023]
Abstract
Memristive devices, regardless of their potential applications in memory and computing scenarios, still suffer from large cycle-to-cycle and device-to-device variations due to the stochastic growth of conductive filaments (CFs). In this work, we fabricated a crossbar memristor using the 2D TiSe2 material and then oxidized it into TiO2 in the atmosphere at a moderate temperature. Such a mild oxidation approach fails to evaporate all Se into the air, and after further annealing using thermal or electrical stimulations, the remnant Se atoms gather near the interfaces and grow into nanosized crystals with relatively high conductivity. The resulting peninsula-shaped nanocrystals distort the electric field, forcing CFs to grow on them, which could largely confine the location and length of CFs. As a result, this two-terminal TiSe2/TiO2/TiSe2 device exhibits excellent resistive switching performance with a fairly low threshold voltage (Vset < 0.8 V, Vreset > 0.55 V) and high cycle-to-cycle consistency, enabling resistive switching at narrow operating variations, e.g., 500 ± 48 and 845 ± 39 mV. Our work offers a new approach to minimize the cycle-to-cycle stochasticity of the memristive device, paving the way for its applications in data storage and brain-inspired computing.
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Affiliation(s)
- Changying Xiong
- Wuhan National Laboratory for Optoelectronics, School of Integrated Circuits, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Zhe Yang
- Wuhan National Laboratory for Optoelectronics, School of Integrated Circuits, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Jiahao Shen
- Wuhan National Laboratory for Optoelectronics, School of Integrated Circuits, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Feiyu Tang
- Wuhan National Laboratory for Optoelectronics, School of Integrated Circuits, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Qiang He
- Wuhan National Laboratory for Optoelectronics, School of Integrated Circuits, Huazhong University of Science and Technology, Wuhan 430074, China
- Hubei Yangtze Memory Laboratories, Wuhan 430205, China
| | - Yi Li
- Wuhan National Laboratory for Optoelectronics, School of Integrated Circuits, Huazhong University of Science and Technology, Wuhan 430074, China
- Hubei Yangtze Memory Laboratories, Wuhan 430205, China
| | - Ming Xu
- Wuhan National Laboratory for Optoelectronics, School of Integrated Circuits, Huazhong University of Science and Technology, Wuhan 430074, China
- Hubei Yangtze Memory Laboratories, Wuhan 430205, China
| | - Xiangshui Miao
- Wuhan National Laboratory for Optoelectronics, School of Integrated Circuits, Huazhong University of Science and Technology, Wuhan 430074, China
- Hubei Yangtze Memory Laboratories, Wuhan 430205, China
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Yang R, Yin L, Lu J, Lu B, Pi X, Li S, Zhuge F, Lu Y, Shao W, Ye Z. Optoelectronic Artificial Synaptic Device Based on Amorphous InAlZnO Films for Learning Simulations. ACS APPLIED MATERIALS & INTERFACES 2022; 14:46866-46875. [PMID: 36194768 DOI: 10.1021/acsami.2c14029] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Neuromorphic computing, which mimics brain function, can address the shortcomings of the "von Neumann" system and is one of the critical components of next-generation computing. The use of light to stimulate artificial synapses has the advantages of low power consumption, low latency, and high stability. We demonstrate amorphous InAlZnO-based light-stimulated artificial synaptic devices with a thin-film transistor structure. The devices exhibit fundamental synaptic properties, including excitatory postsynaptic current, paired-pulse facilitation (PPF), and short-term plasticity to long-term plasticity conversion under light stimulation. The PPF index stimulated by 375 nm light is 155.9% when the time interval is 0.1 s. The energy consumption of each synaptic event is 2.3 pJ, much lower than that of ordinary MOS devices and other optical-controlled synaptic devices. The relaxation time constant reaches 277 s after only 10 light spikes, which shows the great synaptic plasticity of the device. In addition, we simulated the learning-forgetting-relearning-forgetting behavior and learning efficiency of human beings under different moods by changing the gate voltage. This work is expected to promote the development of high-performance optoelectronic synaptic devices for neuromorphic computing.
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Affiliation(s)
- Ruqi Yang
- State Key Laboratory of Silicon Materials, School of Materials Science and Engineering, Zhejiang University, Hangzhou310027, China
| | - Lei Yin
- State Key Laboratory of Silicon Materials, School of Materials Science and Engineering, Zhejiang University, Hangzhou310027, China
| | - Jianguo Lu
- State Key Laboratory of Silicon Materials, School of Materials Science and Engineering, Zhejiang University, Hangzhou310027, China
- Wenzhou Key Laboratory of Novel Optoelectronic and Nano Materials, Institute of Wenzhoum, Zhejiang University, Wenzhou325006, China
| | - Bojing Lu
- State Key Laboratory of Silicon Materials, School of Materials Science and Engineering, Zhejiang University, Hangzhou310027, China
| | - Xiaodong Pi
- State Key Laboratory of Silicon Materials, School of Materials Science and Engineering, Zhejiang University, Hangzhou310027, China
| | - Siqin Li
- State Key Laboratory of Silicon Materials, School of Materials Science and Engineering, Zhejiang University, Hangzhou310027, China
| | - Fei Zhuge
- Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo315201, China
| | - Yangdan Lu
- State Key Laboratory of Silicon Materials, School of Materials Science and Engineering, Zhejiang University, Hangzhou310027, China
| | - Wenyi Shao
- State Key Laboratory of Silicon Materials, School of Materials Science and Engineering, Zhejiang University, Hangzhou310027, China
| | - Zhizhen Ye
- State Key Laboratory of Silicon Materials, School of Materials Science and Engineering, Zhejiang University, Hangzhou310027, China
- Wenzhou Key Laboratory of Novel Optoelectronic and Nano Materials, Institute of Wenzhoum, Zhejiang University, Wenzhou325006, China
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