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Bonagiri A, Biswas D, Chakravarthy S. Coupled Memristor Oscillators for Neuromorphic Locomotion Control: Modeling and Analysis. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2024; 35:8638-8652. [PMID: 37018567 DOI: 10.1109/tnnls.2022.3231298] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
The recent surge of interest in brain-inspired architectures along with the development of nonlinear dynamical electronic devices and circuits has enabled energy-efficient hardware realizations of several important neurobiological systems and features. Central pattern generator (CPG) is one such neural system underlying the control of various rhythmic motor behaviors in animals. A CPG can produce spontaneous coordinated rhythmic output signals without any feedback mechanism, ideally realizable by a system of coupled oscillators. Bio-inspired robotics aims to use this approach to control the limb movement for synchronized locomotion. Hence, devising a compact and energy-efficient hardware platform to implement neuromorphic CPGs would be of great benefit for bio-inspired robotics. In this work, we demonstrate that four capacitively coupled vanadium dioxide (VO2) memristor-based oscillators can produce spatiotemporal patterns corresponding to the primary quadruped gaits. The phase relationships underlying the gait patterns are governed by four tunable bias voltages (or four coupling strengths) making the network programmable, reducing the complex problem of gait selection and dynamic interleg coordination to the choice of four control parameters. To this end, we first introduce a dynamical model for the VO2 memristive nanodevice, then perform analytical and bifurcation analysis of a single oscillator, and finally demonstrate the dynamics of coupled oscillators through extensive numerical simulations. We also show that adopting the presented model for a VO2 memristor reveals a striking resemblance between VO2 memristor oscillators and conductance-based biological neuron models such as the Morris-Lecar (ML) model. This can inspire and guide further research on implementation of neuromorphic memristor circuits that emulate neurobiological phenomena.
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Tan T, Guo H, Li Y, Wang Y, Cai W, Bao W, Zhou P, Feng X. Integration of MoS 2 Memtransistor Devices and Analogue Circuits for Sensor Fusion in Autonomous Vehicle Target Localization. ACS NANO 2024; 18:13652-13661. [PMID: 38751043 DOI: 10.1021/acsnano.4c00456] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
In contemporary autonomous driving systems relying on sensor fusion, traditional digital processors encounter challenges associated with analogue-to-digital conversion and iterative vector-matrix operations, which are encumbered by limitations in terms of response time and energy consumption. In this study, we present an analogue Kalman filter circuit based on molybdenum disulfide (MoS2) memtransistor, designed to accelerate sensor fusion for precise localization in autonomous vehicle applications. The nonvolatile memory characteristics of the memtransistor allow for the storage of a fixed Kalman gain, which eliminates the data convergence and thus accelerates the processing speeds. Additionally, the modulation of multiple conductance states by the gate terminal enables fast adaptability to diverse autonomous driving scenarios by tuning multiple Kalman filter gains. Our proposed analogue Kalman filter circuit accurately estimates the position coordinates of target vehicles by fusing sensor data from light detection and ranging (LiDAR), millimeter-wave radar (Radar), and camera, and it successfully solves real-word problems in a signal-free crossroad intersection. Notably, our system achieves a 1000-fold improvement in energy efficiency compared to that of digital circuits. This work underscores the viability of a memtransistor for achieving fast, energy-efficient real-time sensing, and continuous signal processing in advanced sensor fusion technology.
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Affiliation(s)
- Tian Tan
- School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Haoyue Guo
- School of Microelectronics, Southern University of Science and Technology, Shenzhen 518055, China
| | - Yida Li
- School of Microelectronics, Southern University of Science and Technology, Shenzhen 518055, China
| | - Yafei Wang
- School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Weiwei Cai
- School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Wenzhong Bao
- School of Microelectronics, Fudan University, Shanghai 200433, China
- Shaoxing Laboratory, Shaoxing 312300, China
| | - Peng Zhou
- School of Microelectronics, Fudan University, Shanghai 200433, China
| | - Xuewei Feng
- School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
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3
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Yang Y, Zhu F, Zhang X, Chen P, Wang Y, Zhu J, Ding Y, Cheng L, Li C, Jiang H, Wang Z, Lin P, Shi T, Wang M, Liu Q, Xu N, Liu M. Firing feature-driven neural circuits with scalable memristive neurons for robotic obstacle avoidance. Nat Commun 2024; 15:4318. [PMID: 38773067 PMCID: PMC11109161 DOI: 10.1038/s41467-024-48399-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Accepted: 04/30/2024] [Indexed: 05/23/2024] Open
Abstract
Neural circuits with specific structures and diverse neuronal firing features are the foundation for supporting intelligent tasks in biology and are regarded as the driver for catalyzing next-generation artificial intelligence. Emulating neural circuits in hardware underpins engineering highly efficient neuromorphic chips, however, implementing a firing features-driven functional neural circuit is still an open question. In this work, inspired by avoidance neural circuits of crickets, we construct a spiking feature-driven sensorimotor control neural circuit consisting of three memristive Hodgkin-Huxley neurons. The ascending neurons exhibit mixed tonic spiking and bursting features, which are used for encoding sensing input. Additionally, we innovatively introduce a selective communication scheme in biology to decode mixed firing features using two descending neurons. We proceed to integrate such a neural circuit with a robot for avoidance control and achieve lower latency than conventional platforms. These results provide a foundation for implementing real brain-like systems driven by firing features with memristive neurons and put constructing high-order intelligent machines on the agenda.
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Affiliation(s)
- Yue Yang
- State Key Laboratory of Integrated Chips and Systems, Frontier Institute of Chip and System, Fudan University, Shanghai, 200433, China
- Key Laboratory of Microelectronics Device & Integrated Technology, Institute of Microelectronics of Chinese Academy of Sciences, Beijing, 100029, China
| | - Fangduo Zhu
- State Key Laboratory of Integrated Chips and Systems, Frontier Institute of Chip and System, Fudan University, Shanghai, 200433, China
| | - Xumeng Zhang
- State Key Laboratory of Integrated Chips and Systems, Frontier Institute of Chip and System, Fudan University, Shanghai, 200433, China.
| | - Pei Chen
- State Key Laboratory of Integrated Chips and Systems, Frontier Institute of Chip and System, Fudan University, Shanghai, 200433, China
| | - Yongzhou Wang
- Key Laboratory of Microelectronics Device & Integrated Technology, Institute of Microelectronics of Chinese Academy of Sciences, Beijing, 100029, China
| | - Jiaxue Zhu
- Key Laboratory of Microelectronics Device & Integrated Technology, Institute of Microelectronics of Chinese Academy of Sciences, Beijing, 100029, China
| | - Yanting Ding
- State Key Laboratory of Integrated Chips and Systems, Frontier Institute of Chip and System, Fudan University, Shanghai, 200433, China
| | - Lingli Cheng
- State Key Laboratory of Integrated Chips and Systems, Frontier Institute of Chip and System, Fudan University, Shanghai, 200433, China
- Key Laboratory of Microelectronics Device & Integrated Technology, Institute of Microelectronics of Chinese Academy of Sciences, Beijing, 100029, China
| | - Chao Li
- State Key Laboratory of Integrated Chips and Systems, Frontier Institute of Chip and System, Fudan University, Shanghai, 200433, China
- Key Laboratory of Microelectronics Device & Integrated Technology, Institute of Microelectronics of Chinese Academy of Sciences, Beijing, 100029, China
| | - Hao Jiang
- State Key Laboratory of Integrated Chips and Systems, Frontier Institute of Chip and System, Fudan University, Shanghai, 200433, China
| | - Zhongrui Wang
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, 999077, China
| | - Peng Lin
- College of Computer Science and Technology, Zhejiang University, Zhejiang, 310027, China
| | - Tuo Shi
- Key Laboratory of Microelectronics Device & Integrated Technology, Institute of Microelectronics of Chinese Academy of Sciences, Beijing, 100029, China
| | - Ming Wang
- State Key Laboratory of Integrated Chips and Systems, Frontier Institute of Chip and System, Fudan University, Shanghai, 200433, China
| | - Qi Liu
- State Key Laboratory of Integrated Chips and Systems, Frontier Institute of Chip and System, Fudan University, Shanghai, 200433, China.
- Key Laboratory of Microelectronics Device & Integrated Technology, Institute of Microelectronics of Chinese Academy of Sciences, Beijing, 100029, China.
| | - Ningsheng Xu
- State Key Laboratory of Integrated Chips and Systems, Frontier Institute of Chip and System, Fudan University, Shanghai, 200433, China
| | - Ming Liu
- State Key Laboratory of Integrated Chips and Systems, Frontier Institute of Chip and System, Fudan University, Shanghai, 200433, China
- Key Laboratory of Microelectronics Device & Integrated Technology, Institute of Microelectronics of Chinese Academy of Sciences, Beijing, 100029, China
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Liu X, Dai S, Zhao W, Zhang J, Guo Z, Wu Y, Xu Y, Sun T, Li L, Guo P, Yang J, Hu H, Zhou J, Zhou P, Huang J. All-Photolithography Fabrication of Ion-Gated Flexible Organic Transistor Array for Multimode Neuromorphic Computing. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024; 36:e2312473. [PMID: 38385598 DOI: 10.1002/adma.202312473] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Revised: 02/17/2024] [Indexed: 02/23/2024]
Abstract
Organic ion-gated transistors (OIGTs) demonstrate commendable performance for versatile neuromorphic systems. However, due to the fragility of organic materials to organic solvents, efficient and reliable all-photolithography methods for scalable manufacturing of high-density OIGT arrays with multimode neuromorphic functions are still missing, especially when all active layers are patterned in high-density. Here, a flexible high-density (9662 devices per cm2) OIGT array with high yield and minimal device-to-device variation is fabricated by a modified all-photolithography method. The unencapsulated flexible array can withstand 1000 times' bending at a radius of 1 mm, and 3 months' storage test in air, without obvious performance degradation. More interesting, the OIGTs can be configured between volatile and nonvolatile modes, suitable for constructing reservoir computing systems to achieve high accuracy in classifying handwritten digits with low training costs. This work proposes a promising design of organic and flexible electronics for affordable neuromorphic systems, encompassing both array and algorithm aspects.
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Affiliation(s)
- Xu 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
| | - Weidong Zhao
- School of Electronic and Information 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
| | - Yue Wu
- School of Materials Science and Engineering, Tongji University, Shanghai, 201804, P. R. China
| | - Yutong Xu
- School of Materials Science and Engineering, Tongji University, Shanghai, 201804, P. R. China
| | - Tongrui Sun
- School of Materials Science and Engineering, Tongji University, Shanghai, 201804, P. R. China
| | - Li Li
- School of Materials Science and Engineering, Tongji University, Shanghai, 201804, P. R. China
| | - Pu Guo
- School of Materials Science and Engineering, Tongji University, Shanghai, 201804, P. R. China
| | - Jie Yang
- School of Materials Science and Engineering, Tongji University, Shanghai, 201804, P. R. China
| | - Huawei Hu
- State Key Laboratory for Modification of Chemical Fibers and Polymer Materials, College of Materials Science and Engineering, Donghua University, Shanghai, 201620, P. R. China
| | - Junhe Zhou
- School of Electronic and Information Engineering, Tongji University, Shanghai, 201804, P. R. China
| | - Peng Zhou
- State Key Laboratory of ASIC and System, School of Microelectronics, Zhangjiang Fudan International Innovation Center, Fudan University, Shanghai, 200433, P. R. China
| | - Jia Huang
- School of Materials Science and Engineering, Tongji University, Shanghai, 201804, P. R. China
- National Key Laboratory of Autonomous Intelligent Unmanned Systems, Tongji University, Shanghai, 201804, P. R. China
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Bong JH, Grebenchuk S, Nikolaev KG, Chee CPT, Yang K, Chen S, Baranov D, Woods CR, Andreeva DV, Novoselov KS. Graphene oxide-DNA/graphene oxide-PDDA sandwiched membranes with neuromorphic function. NANOSCALE HORIZONS 2024; 9:863-872. [PMID: 38533738 DOI: 10.1039/d3nh00570d] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/28/2024]
Abstract
The behavior of polyelectrolytes in confined spaces has direct relevance to the protein mediated ion transport in living organisms. In this paper, we govern lithium chloride transport by the interface provided by polyelectrolytes, polycation, poly(diallyldimethylammonium chloride) (PDDA) and, polyanion, double stranded deoxyribonucleic acid (dsDNA), in confined graphene oxide (GO) membranes. Polyelectrolyte-GO interfaces demonstrate neuromorphic functions that were successfully applied with nanochannel ion interactions contributed, resulting in ion memory effects. Excitatory and inhibitory post-synaptic currents were tuned continuously as the number of pulses applied increased accordingly, increasing decay times. Furthermore, we demonstrated the short-term memory of a trained vs untrained device in computation. On account of its simple and safe production along with its robustness and stability, we anticipate our device to be a low dimensional building block for arrays to embed artificial neural networks in hardware for neuromorphic computing. Additionally, incorporating such devices with sensing and actuating parts for a complete feedback loop produces robotics with its own ability to learn by modifying actuation based on sensing data.
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Affiliation(s)
- Jia Hui Bong
- Institute for Functional Intelligent Materials, National University of Singapore, 117544, Singapore.
- Department of Materials Science and Engineering, National University of Singapore, 117575, Singapore
| | - Sergey Grebenchuk
- Institute for Functional Intelligent Materials, National University of Singapore, 117544, Singapore.
- Department of Materials Science and Engineering, National University of Singapore, 117575, Singapore
| | - Konstantin G Nikolaev
- Institute for Functional Intelligent Materials, National University of Singapore, 117544, Singapore.
| | - Celestine P T Chee
- Department of Materials Science and Engineering, National University of Singapore, 117575, Singapore
| | - Kou Yang
- Institute for Functional Intelligent Materials, National University of Singapore, 117544, Singapore.
| | - Siyu Chen
- Institute for Functional Intelligent Materials, National University of Singapore, 117544, Singapore.
- Department of Materials Science and Engineering, National University of Singapore, 117575, Singapore
| | - Denis Baranov
- Institute for Functional Intelligent Materials, National University of Singapore, 117544, Singapore.
| | - Colin R Woods
- Institute for Functional Intelligent Materials, National University of Singapore, 117544, Singapore.
- Department of Materials Science and Engineering, National University of Singapore, 117575, Singapore
| | - Daria V Andreeva
- Institute for Functional Intelligent Materials, National University of Singapore, 117544, Singapore.
- Department of Materials Science and Engineering, National University of Singapore, 117575, Singapore
| | - Kostya S Novoselov
- Institute for Functional Intelligent Materials, National University of Singapore, 117544, Singapore.
- Department of Materials Science and Engineering, National University of Singapore, 117575, Singapore
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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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7
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Rehman S, Khan MF, Kim HD, Kim S. A self-tuning PID controller based on analog-digital hybrid computing with a double-gate SnS 2 memtransistor. NANOSCALE 2023; 15:13675-13684. [PMID: 37554054 DOI: 10.1039/d2nr06853b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/10/2023]
Abstract
Most commercial drones utilize a traditional proportional-integral-derivative (PID) controller because of its design simplicity. However, the traditional PID controller has certain limitations in terms of optimality and robustness; it is difficult to actively adjust the PID gains under some disturbances. In this study, we demonstrated an analog-digital hybrid computing platform based on double-gate SnS2 memtransistors to implement a self-tuning/energy-efficient PID controller in drones. The customized analog circuit with memtransistors executes the PID control algorithm with low power consumption; we experimentally verified that the energy consumption of the proposed hybrid computing-based PID controller is only 63% of that of the traditional PID controller. In addition, the precise tunability of analog conductance states in the memtransistor proved to be capable of reconfiguring the performance of the PID controller, where the developed self-tuning algorithm can automatically find the optimal PID control performance.
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Affiliation(s)
- Shania Rehman
- Department of Semiconductor Systems Engineering and Convergence Engineering for Intelligent Drone, Sejong University, Seoul, 05006, Korea.
| | | | - Hee-Dong Kim
- Department of Semiconductor Systems Engineering and Convergence Engineering for Intelligent Drone, Sejong University, Seoul, 05006, Korea.
| | - Sungho Kim
- Department of Semiconductor Systems Engineering and Convergence Engineering for Intelligent Drone, Sejong University, Seoul, 05006, Korea.
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Memristor devices denoised to achieve thousands of conductance levels. Nature 2023:10.1038/d41586-023-00724-8. [PMID: 36991051 DOI: 10.1038/d41586-023-00724-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/31/2023]
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Rao M, Tang H, Wu J, Song W, Zhang M, Yin W, Zhuo Y, Kiani F, Chen B, Jiang X, Liu H, Chen HY, Midya R, Ye F, Jiang H, Wang Z, Wu M, Hu M, Wang H, Xia Q, Ge N, Li J, Yang JJ. Thousands of conductance levels in memristors integrated on CMOS. Nature 2023; 615:823-829. [PMID: 36991190 DOI: 10.1038/s41586-023-05759-5] [Citation(s) in RCA: 28] [Impact Index Per Article: 28.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2022] [Accepted: 01/25/2023] [Indexed: 03/31/2023]
Abstract
Neural networks based on memristive devices1-3 have the ability to improve throughput and energy efficiency for machine learning4,5 and artificial intelligence6, especially in edge applications7-21. Because training a neural network model from scratch is costly in terms of hardware resources, time and energy, it is impractical to do it individually on billions of memristive neural networks distributed at the edge. A practical approach would be to download the synaptic weights obtained from the cloud training and program them directly into memristors for the commercialization of edge applications. Some post-tuning in memristor conductance could be done afterwards or during applications to adapt to specific situations. Therefore, in neural network applications, memristors require high-precision programmability to guarantee uniform and accurate performance across a large number of memristive networks22-28. This requires many distinguishable conductance levels on each memristive device, not only laboratory-made devices but also devices fabricated in factories. Analog memristors with many conductance states also benefit other applications, such as neural network training, scientific computing and even 'mortal computing'25,29,30. Here we report 2,048 conductance levels achieved with memristors in fully integrated chips with 256 × 256 memristor arrays monolithically integrated on complementary metal-oxide-semiconductor (CMOS) circuits in a commercial foundry. We have identified the underlying physics that previously limited the number of conductance levels that could be achieved in memristors and developed electrical operation protocols to avoid such limitations. These results provide insights into the fundamental understanding of the microscopic picture of memristive switching as well as approaches to enable high-precision memristors for various applications. Fig. 1 HIGH-PRECISION MEMRISTOR FOR NEUROMORPHIC COMPUTING.: a, Proposed scheme of the large-scale application of memristive neural networks for edge computing. Neural network training is performed in the cloud. The obtained weights are downloaded and accurately programmed into a massive number of memristor arrays distributed at the edge, which imposes high-precision requirements on memristive devices. b, An eight-inch wafer with memristors fabricated by a commercial semiconductor manufacturer. c, High-resolution transmission electron microscopy image of the cross-section view of a memristor. Pt and Ta serve as the bottom electrode (BE) and top electrode (TE), respectively. Scale bars, 1 μm and 100 nm (inset). d, Magnification of the memristor material stack. Scale bar, 5 nm. e, As-programmed (blue) and after-denoising (red) currents of a memristor are read by a constant voltage (0.2 V). The denoising process eliminated the large-amplitude RTN observed in the as-programmed state (see Methods). f, Magnification of three nearest-neighbour states after denoising. The current of each state was read by a constant voltage (0.2 V). No large-amplitude RTN was observed, and all of the states can be clearly distinguished. g, An individual memristor on the chip was tuned into 2,048 resistance levels by high-resolution off-chip driving circuitry, and each resistance level was read by a d.c. voltage sweeping from 0 to 0.2 V. The target resistance was set from 50 µS to 4,144 µS with a 2-µS interval between neighbouring levels. All readings at 0.2 V are less than 1 µS from the target conductance. Bottom inset, magnification of the resistance levels. Top inset, experimental results of an entire 256 × 256 array programmed by its 6-bit on-chip circuitry into 64 32 × 32 blocks, and each block is programmed into one of the 64 conductance levels. Each of the 256 × 256 memristors has been previously switched over one million cycles, demonstrating the high endurance and robustness of the devices.
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Affiliation(s)
- Mingyi Rao
- TetraMem, Fremont, CA, USA
- Department of Electrical and Computer Engineering, University of Massachusetts, Amherst, MA, USA
| | - Hao Tang
- Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Jiangbin Wu
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, USA
| | - Wenhao Song
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, USA
| | | | | | - Ye Zhuo
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, USA
| | - Fatemeh Kiani
- Department of Electrical and Computer Engineering, University of Massachusetts, Amherst, MA, USA
| | - Benjamin Chen
- Department of Electrical and Computer Engineering, University of Massachusetts, Amherst, MA, USA
| | | | - Hefei Liu
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, USA
| | - Hung-Yu Chen
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, USA
| | - Rivu Midya
- Department of Electrical and Computer Engineering, University of Massachusetts, Amherst, MA, USA
| | - Fan Ye
- Department of Electrical and Computer Engineering, University of Massachusetts, Amherst, MA, USA
| | - Hao Jiang
- Department of Electrical and Computer Engineering, University of Massachusetts, Amherst, MA, USA
| | - Zhongrui Wang
- Department of Electrical and Computer Engineering, University of Massachusetts, Amherst, MA, USA
| | | | | | - Han Wang
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, USA
| | - Qiangfei Xia
- TetraMem, Fremont, CA, USA
- Department of Electrical and Computer Engineering, University of Massachusetts, Amherst, MA, USA
| | | | - Ju Li
- Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - J Joshua Yang
- TetraMem, Fremont, CA, USA.
- Department of Electrical and Computer Engineering, University of Massachusetts, Amherst, MA, USA.
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, USA.
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10
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Kim JP, Kim SK, Park S, Kuk SH, Kim T, Kim BH, Ahn SH, Cho YH, Jeong Y, Choi SY, Kim S. Dielectric-Engineered High-Speed, Low-Power, Highly Reliable Charge Trap Flash-Based Synaptic Device for Neuromorphic Computing beyond Inference. NANO LETTERS 2023; 23:451-461. [PMID: 36637103 DOI: 10.1021/acs.nanolett.2c03453] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
The coming of the big-data era brought a need for power-efficient computing that cannot be realized in the Von Neumann architecture. Neuromorphic computing which is motivated by the human brain can greatly reduce power consumption through matrix multiplication, and a device that mimics a human synapse plays an important role. However, many synaptic devices suffer from limited linearity and symmetry without using incremental step pulse programming (ISPP). In this work, we demonstrated a charge-trap flash (CTF)-based synaptic transistor using trap-level engineered Al2O3/Ta2O5/Al2O3 gate stack for successful neuromorphic computing. This novel gate stack provided precise control of the conductance with more than 6 bits. We chose the appropriate bias for highly linear and symmetric modulation of conductance and realized it with very short (25 ns) identical pulses at low voltage, resulting in low power consumption and high reliability. Finally, we achieved high learning accuracy in the training of 60000 MNIST images.
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Affiliation(s)
- Joon Pyo Kim
- School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon34141, Republic of Korea
| | - Seong Kwang Kim
- School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon34141, Republic of Korea
| | - Seohak Park
- School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon34141, Republic of Korea
| | - Song-Hyeon Kuk
- School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon34141, Republic of Korea
| | - Taeyoon Kim
- Korea Institute of Science and Technology (KIST), Seoul02792, Republic of Korea
| | - Bong Ho Kim
- School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon34141, Republic of Korea
| | - Seong-Hun Ahn
- Department of Physics, Korea Advanced Institute of Science and Technology (KAIST), Daejeon34141, Republic of Korea
| | - Yong-Hoon Cho
- Department of Physics, Korea Advanced Institute of Science and Technology (KAIST), Daejeon34141, Republic of Korea
| | - YeonJoo Jeong
- Korea Institute of Science and Technology (KIST), Seoul02792, Republic of Korea
| | - Sung-Yool Choi
- School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon34141, Republic of Korea
| | - Sanghyeon Kim
- School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon34141, Republic of Korea
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11
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Roe DG, Ho DH, Choi YY, Choi YJ, Kim S, Jo SB, Kang MS, Ahn JH, Cho JH. Humanlike spontaneous motion coordination of robotic fingers through spatial multi-input spike signal multiplexing. Nat Commun 2023; 14:5. [PMID: 36596783 PMCID: PMC9810717 DOI: 10.1038/s41467-022-34324-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Accepted: 10/19/2022] [Indexed: 01/05/2023] Open
Abstract
With advances in robotic technology, the complexity of control of robot has been increasing owing to fundamental signal bottlenecks and limited expressible logic state of the von Neumann architecture. Here, we demonstrate coordinated movement by a fully parallel-processable synaptic array with reduced control complexity. The synaptic array was fabricated by connecting eight ion-gel-based synaptic transistors to an ion gel dielectric. Parallel signal processing and multi-actuation control could be achieved by modulating the ionic movement. Through the integration of the synaptic array and a robotic hand, coordinated movement of the fingers was achieved with reduced control complexity by exploiting the advantages of parallel multiplexing and analog logic. The proposed synaptic control system provides considerable scope for the advancement of robotic control systems.
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Affiliation(s)
- Dong Gue Roe
- School of Electrical and Electronic Engineering, Yonsei University, Seoul, 03722, Republic of Korea
| | - Dong Hae Ho
- Department of Chemical and Biomolecular Engineering, Yonsei University, Seoul, 03722, Republic of Korea
| | - Yoon Young Choi
- Department of Mechanical Science and Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA
| | - Young Jin Choi
- Department of Chemical and Biomolecular Engineering, Yonsei University, Seoul, 03722, Republic of Korea
| | - Seongchan Kim
- SKKU Advanced Institute of Nanotechnology (SAINT), Sungkyunkwan University, Suwon, 16419, Republic of Korea
| | - Sae Byeok Jo
- School of Chemical Engineering, Sungkyunkwan University, Suwon, 16419, Republic of Korea
| | - Moon Sung Kang
- Department of Chemical and Biomolecular Engineering, Institute of Emergent Materials, Sogang University, Seoul, 04107, Republic of Korea
| | - Jong-Hyun Ahn
- School of Electrical and Electronic Engineering, Yonsei University, Seoul, 03722, Republic of Korea
| | - Jeong Ho Cho
- Department of Chemical and Biomolecular Engineering, Yonsei University, Seoul, 03722, Republic of Korea.
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12
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Chen M, Ouyang J, Jian A, Liu J, Li P, Hao Y, Gong Y, Hu J, Zhou J, Wang R, Wang J, Hu L, Wang Y, Ouyang J, Zhang J, Hou C, Wei L, Zhou H, Zhang D, Tao G. Imperceptible, designable, and scalable braided electronic cord. Nat Commun 2022; 13:7097. [PMID: 36402785 PMCID: PMC9675780 DOI: 10.1038/s41467-022-34918-x] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Accepted: 11/11/2022] [Indexed: 11/21/2022] Open
Abstract
Flexible sensors, friendly interfaces, and intelligent recognition are important in the research of novel human-computer interaction and the development of smart devices. However, major challenges are still encountered in designing user-centered smart devices with natural, convenient, and efficient interfaces. Inspired by the characteristics of textile-based flexible electronic sensors, in this article, we report a braided electronic cord with a low-cost, and automated fabrication to realize imperceptible, designable, and scalable user interfaces. The braided electronic cord is in a miniaturized form, which is suitable for being integrated with various occasions in life. To achieve high-precision interaction, a multi-feature fusion algorithm is designed to recognize gestures of different positions, different contact areas, and different movements performed on a single braided electronic cord. The recognized action results are fed back to varieties of interactive terminals, which show the diversity of cord forms and applications. Our braided electronic cord with the features of user friendliness, excellent durability and rich interaction mode will greatly promote the development of human-machine integration in the future.
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Affiliation(s)
- Min Chen
- grid.33199.310000 0004 0368 7223Wuhan National Laboratory for Optoelectronics and School of Computer Science and Technology, Huazhong University of Science and Technology, 430074 Wuhan, China
| | - Jingyu Ouyang
- grid.33199.310000 0004 0368 7223Wuhan National Laboratory for Optoelectronics and School of Computer Science and Technology, Huazhong University of Science and Technology, 430074 Wuhan, China
| | - Aijia Jian
- grid.33199.310000 0004 0368 7223Wuhan National Laboratory for Optoelectronics and School of Computer Science and Technology, Huazhong University of Science and Technology, 430074 Wuhan, China
| | - Jia Liu
- grid.33199.310000 0004 0368 7223Wuhan National Laboratory for Optoelectronics and School of Computer Science and Technology, Huazhong University of Science and Technology, 430074 Wuhan, China
| | - Pan Li
- grid.33199.310000 0004 0368 7223Wuhan National Laboratory for Optoelectronics and School of Computer Science and Technology, Huazhong University of Science and Technology, 430074 Wuhan, China
| | - Yixue Hao
- grid.33199.310000 0004 0368 7223Wuhan National Laboratory for Optoelectronics and School of Computer Science and Technology, Huazhong University of Science and Technology, 430074 Wuhan, China
| | - Yuchen Gong
- grid.33199.310000 0004 0368 7223Wuhan National Laboratory for Optoelectronics and School of Computer Science and Technology, Huazhong University of Science and Technology, 430074 Wuhan, China
| | - Jiayu Hu
- grid.33199.310000 0004 0368 7223Wuhan National Laboratory for Optoelectronics and School of Computer Science and Technology, Huazhong University of Science and Technology, 430074 Wuhan, China
| | - Jing Zhou
- grid.33199.310000 0004 0368 7223Wuhan National Laboratory for Optoelectronics and School of Computer Science and Technology, Huazhong University of Science and Technology, 430074 Wuhan, China
| | - Rui Wang
- grid.33199.310000 0004 0368 7223Wuhan National Laboratory for Optoelectronics and School of Computer Science and Technology, Huazhong University of Science and Technology, 430074 Wuhan, China
| | - Jiaxi Wang
- grid.33199.310000 0004 0368 7223Wuhan National Laboratory for Optoelectronics and School of Computer Science and Technology, Huazhong University of Science and Technology, 430074 Wuhan, China
| | - Long Hu
- grid.33199.310000 0004 0368 7223Wuhan National Laboratory for Optoelectronics and School of Computer Science and Technology, Huazhong University of Science and Technology, 430074 Wuhan, China
| | - Yuwei Wang
- grid.33199.310000 0004 0368 7223Wuhan National Laboratory for Optoelectronics and School of Computer Science and Technology, Huazhong University of Science and Technology, 430074 Wuhan, China
| | - Ju Ouyang
- grid.33199.310000 0004 0368 7223Wuhan National Laboratory for Optoelectronics and School of Computer Science and Technology, Huazhong University of Science and Technology, 430074 Wuhan, China
| | - Jing Zhang
- grid.503241.10000 0004 1760 9015School of Mechanical Engineering and Electronic Information, China University of Geosciences (Wuhan), 430074 Wuhan, China
| | - Chong Hou
- grid.33199.310000 0004 0368 7223Wuhan National Laboratory for Optoelectronics and School of Computer Science and Technology, Huazhong University of Science and Technology, 430074 Wuhan, China ,grid.33199.310000 0004 0368 7223School of Optical and Electronic Information, Huazhong University of Science and Technology, 430074 Wuhan, China
| | - Lei Wei
- grid.59025.3b0000 0001 2224 0361School of Electrical and Electronic Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798 Singapore
| | - Huamin Zhou
- grid.33199.310000 0004 0368 7223State Key Laboratory of Material Processing and Die & Mould Technology, School of Materials Science and Engineering, Huazhong University of Science and Technology, 430074 Wuhan, China
| | - Dingyu Zhang
- grid.507952.c0000 0004 1764 577XWuhan Jinyintan Hospital, 430048 Wuhan, Hubei China ,Hubei Provincial Health and Health Committee, 430015 Wuhan, Hubei China
| | - Guangming Tao
- grid.33199.310000 0004 0368 7223Wuhan National Laboratory for Optoelectronics and School of Computer Science and Technology, Huazhong University of Science and Technology, 430074 Wuhan, China ,grid.33199.310000 0004 0368 7223State Key Laboratory of Material Processing and Die & Mould Technology, School of Materials Science and Engineering, Huazhong University of Science and Technology, 430074 Wuhan, China
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13
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He K, Liu Y, Yu J, Guo X, Wang M, Zhang L, Wan C, Wang T, Zhou C, Chen X. Artificial Neural Pathway Based on a Memristor Synapse for Optically Mediated Motion Learning. ACS NANO 2022; 16:9691-9700. [PMID: 35587990 DOI: 10.1021/acsnano.2c03100] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Animals execute intelligent and efficient interactions with their surroundings through neural pathways, exhibiting learning, memory, and cognition. Artificial autonomous devices that generate self-optimizing feedback mimicking biological systems are essential in pursuing future intelligent robots. Here, we report an artificial neural pathway (ANP) based on a memristor synapse to emulate neuromorphic learning behaviors. In our ANP, optical stimulations are detected and converted into electrical signals through a flexible perovskite photoreceptor. The acquired electrical signals are further processed in a zeolitic imidazolate frameworks-8 (ZIF-8)-based memristor device. By controlling the growth of the ZIF-8 nanoparticles, the conductance of the memristor can be finely modulated with electrical stimulations to mimic the modulation of synaptic plasticity. The device is employed in the ANP to implement synaptic functions of learning and memory. Subsequently, the synaptic feedbacks are used to direct a robotic arm to perform responding motions. Upon repeatedly "reviewing" the optical stimulation, the ANP is able to learn, memorize, and complete the specific motions. This work provides a promising strategy toward the design of intelligent autonomous devices and bioinspired robots through memristor-based systems.
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Affiliation(s)
- Ke He
- Innovative Centre for Flexible Devices (iFLEX), Max Planck-NTU Joint Lab for Artificial Senses, School of Materials Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore
| | - Yaqing Liu
- Key Laboratory of Colloid and Interface Chemistry of the Ministry of Education, School of Chemistry and Chemical Engineering, Shandong University, Jinan, Shandong 250100, China
| | - Jiancan Yu
- Innovative Centre for Flexible Devices (iFLEX), Max Planck-NTU Joint Lab for Artificial Senses, School of Materials Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore
| | - Xintong Guo
- Innovative Centre for Flexible Devices (iFLEX), Max Planck-NTU Joint Lab for Artificial Senses, School of Materials Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore
| | - Ming Wang
- Innovative Centre for Flexible Devices (iFLEX), Max Planck-NTU Joint Lab for Artificial Senses, School of Materials Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore
| | - Liandong Zhang
- Advanced Robotics and Intelligent Control Centre, Singapore Polytechnic, 500 Dover Road, Singapore 139651, Singapore
| | - Changjin Wan
- Innovative Centre for Flexible Devices (iFLEX), Max Planck-NTU Joint Lab for Artificial Senses, School of Materials Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore
| | - Ting Wang
- Innovative Centre for Flexible Devices (iFLEX), Max Planck-NTU Joint Lab for Artificial Senses, School of Materials Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore
| | - Changjiu Zhou
- Advanced Robotics and Intelligent Control Centre, Singapore Polytechnic, 500 Dover Road, Singapore 139651, Singapore
| | - Xiaodong Chen
- Innovative Centre for Flexible Devices (iFLEX), Max Planck-NTU Joint Lab for Artificial Senses, School of Materials Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore
- Institute of Materials Research and Engineering, Agency for Science, Technology and Research, 2 Fusionopolis Way, Innovis, #08-03, Singapore 138634, Singapore
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14
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Rehman S, Khan MF, Kim HD, Kim S. Analog-digital hybrid computing with SnS 2 memtransistor for low-powered sensor fusion. Nat Commun 2022; 13:2804. [PMID: 35589720 PMCID: PMC9119935 DOI: 10.1038/s41467-022-30564-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2022] [Accepted: 05/04/2022] [Indexed: 11/29/2022] Open
Abstract
Algorithms for intelligent drone flights based on sensor fusion are usually implemented using conventional digital computing platforms. However, alternative energy-efficient computing platforms are required for robust flight control in a variety of environments to reduce the burden on both the battery and computing power. In this study, we demonstrated an analog–digital hybrid computing platform based on SnS2 memtransistors for low-power sensor fusion in drones. The analog Kalman filter circuit with memtransistors facilitates noise removal to accurately estimate the rotation of the drone by combining sensing data from the gyroscope and accelerometer. We experimentally verified that the power consumption of our hybrid computing-based Kalman filter is only 1/4th of that of the traditional software-based Kalman filter. Analog–digital hybrid computing based on SnS2 memtransistors is demonstrated for lowpower sensor fusion in drones, where a drone with hybrid computing performs sensor fusion with higher energy efficiency than that with only a digital processor.
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Affiliation(s)
- Shania Rehman
- Department of Electrical Engineering and Convergence Engineering for Intelligent Drone, Sejong University, Seoul, 05006, Korea
| | - Muhammad Farooq Khan
- Department of Electrical Engineering and Convergence Engineering for Intelligent Drone, Sejong University, Seoul, 05006, Korea
| | - Hee-Dong Kim
- Department of Electrical Engineering and Convergence Engineering for Intelligent Drone, Sejong University, Seoul, 05006, Korea
| | - Sungho Kim
- Department of Electrical Engineering and Convergence Engineering for Intelligent Drone, Sejong University, Seoul, 05006, Korea.
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15
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Krauhausen I, Koutsouras DA, Melianas A, Keene ST, Lieberth K, Ledanseur H, Sheelamanthula R, Giovannitti A, Torricelli F, Mcculloch I, Blom PWM, Salleo A, van de Burgt Y, Gkoupidenis P. Organic neuromorphic electronics for sensorimotor integration and learning in robotics. SCIENCE ADVANCES 2021; 7:eabl5068. [PMID: 34890232 PMCID: PMC8664264 DOI: 10.1126/sciadv.abl5068] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/02/2023]
Abstract
In living organisms, sensory and motor processes are distributed, locally merged, and capable of forming dynamic sensorimotor associations. We introduce a simple and efficient organic neuromorphic circuit for local sensorimotor merging and processing on a robot that is placed in a maze. While the robot is exposed to external environmental stimuli, visuomotor associations are formed on the adaptable neuromorphic circuit. With this on-chip sensorimotor integration, the robot learns to follow a path to the exit of a maze, while being guided by visually indicated paths. The ease of processability of organic neuromorphic electronics and their unconventional form factors, in combination with education-purpose robotics, showcase a promising approach of an affordable, versatile, and readily accessible platform for exploring, designing, and evaluating behavioral intelligence through decentralized sensorimotor integration.
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Affiliation(s)
- Imke Krauhausen
- Max Planck Institute for Polymer Research, Mainz, Germany
- Microsystems, Institute for Complex Molecular Systems, Eindhoven University of Technology, Eindhoven, Netherlands
| | | | - Armantas Melianas
- Department of Materials Science and Engineering, Stanford University, Stanford, CA 94305, USA
- Exponent, 149 Commonwealth Dr, Menlo Park, CA 94025, USA
| | - Scott T. Keene
- Department of Engineering, University of Cambridge, Cambridge, UK
| | | | | | - Rajendar Sheelamanthula
- KAUST Solar Center, Physical Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
| | - Alexander Giovannitti
- Department of Materials Science and Engineering, Stanford University, Stanford, CA 94305, USA
| | - Fabrizio Torricelli
- Department of Information Engineering, University of Brescia, 25123 Brescia, Italy
| | - Iain Mcculloch
- KAUST Solar Center, Physical Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
- Department of Chemistry, University of Oxford, Oxford, UK
| | | | - Alberto Salleo
- Department of Materials Science and Engineering, Stanford University, Stanford, CA 94305, USA
- Corresponding author. (A.S.); (Y.v.d.B); (P.G.)
| | - Yoeri van de Burgt
- Microsystems, Institute for Complex Molecular Systems, Eindhoven University of Technology, Eindhoven, Netherlands
- Corresponding author. (A.S.); (Y.v.d.B); (P.G.)
| | - Paschalis Gkoupidenis
- Max Planck Institute for Polymer Research, Mainz, Germany
- Corresponding author. (A.S.); (Y.v.d.B); (P.G.)
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16
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Kassanos P. Analog-digital computing let robots go through the motions. Sci Robot 2020; 5:5/47/eabe6818. [PMID: 33087484 DOI: 10.1126/scirobotics.abe6818] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2020] [Accepted: 09/28/2020] [Indexed: 12/22/2022]
Abstract
A hybrid analog-digital computing platform using memristors enables faster, more energy-efficient, and adaptive real-time robot control.
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Affiliation(s)
- Panagiotis Kassanos
- Hamlyn Centre, Institute of Global Health Innovation, Imperial College London, London SW7 2AZ, UK.
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