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Sun Y, Wang H, Xie D. Recent Advance in Synaptic Plasticity Modulation Techniques for Neuromorphic Applications. NANO-MICRO LETTERS 2024; 16:211. [PMID: 38842588 PMCID: PMC11156833 DOI: 10.1007/s40820-024-01445-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/19/2024] [Accepted: 05/14/2024] [Indexed: 06/07/2024]
Abstract
Manipulating the expression of synaptic plasticity of neuromorphic devices provides fascinating opportunities to develop hardware platforms for artificial intelligence. However, great efforts have been devoted to exploring biomimetic mechanisms of plasticity simulation in the last few years. Recent progress in various plasticity modulation techniques has pushed the research of synaptic electronics from static plasticity simulation to dynamic plasticity modulation, improving the accuracy of neuromorphic computing and providing strategies for implementing neuromorphic sensing functions. Herein, several fascinating strategies for synaptic plasticity modulation through chemical techniques, device structure design, and physical signal sensing are reviewed. For chemical techniques, the underlying mechanisms for the modification of functional materials were clarified and its effect on the expression of synaptic plasticity was also highlighted. Based on device structure design, the reconfigurable operation of neuromorphic devices was well demonstrated to achieve programmable neuromorphic functions. Besides, integrating the sensory units with neuromorphic processing circuits paved a new way to achieve human-like intelligent perception under the modulation of physical signals such as light, strain, and temperature. Finally, considering that the relevant technology is still in the basic exploration stage, some prospects or development suggestions are put forward to promote the development of neuromorphic devices.
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Affiliation(s)
- Yilin Sun
- School of Integrated Circuits and Electronics, Beijing Institute of Technology, Beijing, 100081, People's Republic of China.
| | - Huaipeng Wang
- School of Integrated Circuits, Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing, 100084, People's Republic of China
| | - Dan Xie
- School of Integrated Circuits, Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing, 100084, People's Republic of China.
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Gosai J, Patel M, Liu L, Lokhandwala A, Thakkar P, Chee MY, Jain M, Lew WS, Chaudhari N, Solanki A. Control-Etched Ti 3C 2T x MXene Nanosheets for a Low-Voltage-Operating Flexible Memristor for Efficient Neuromorphic Computation. ACS APPLIED MATERIALS & INTERFACES 2024; 16:17821-17831. [PMID: 38536948 DOI: 10.1021/acsami.4c01364] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/12/2024]
Abstract
Hardware neural networks with mechanical flexibility are promising next-generation computing systems for smart wearable electronics. Overcoming the challenge of developing a fully synaptic plastic network, we demonstrate a low-operating-voltage PET/ITO/p-MXene/Ag flexible memristor device by controlling the etching of aluminum metal ions in Ti3C2Tx MXene. The presence of a small fraction of Al ions in partially etched MXene (p-Ti3C2Tx) significantly suppresses the operating voltage to 1 V compared to 7 V from fully Al etched MXene (f-Ti3C2Tx)-based devices. Former devices exhibit excellent non-volatile data storage properties, with a robust ∼103 ON/OFF ratio, high endurance of ∼104 cycles, multilevel resistance states, and long data retention measured up to ∼106 s. High mechanical stability up to ∼73° bending angle and environmental robustness are confirmed with consistent switching characteristics under increasing temperature and humid conditions. Furthermore, a p-Ti3C2Tx MXene memristor is employed to mimic the biological synapse by measuring the learning-forgetting pattern for ∼104 cycles as potentiation and depression. Spike time-dependent plasticity (STDP) based on Hebb's Learning rules is also successfully demonstrated. Moreover, a remarkable accuracy of ∼95% in recognizing modified patterns from the National Institute of Standards and Technology (MNIST) data set with just 29 training epochs is achieved in simulation. Ultimately, our findings underscore the potential of MXene-based flexible memristor devices as versatile components for data storage and neuromorphic computing.
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Affiliation(s)
- Jeny Gosai
- Advanced Hybrid Nanomaterial Laboratory, Department of Chemistry, School of Energy Technology, Pandit Deendayal Energy University, Gandhinagar 382426, Gujarat, India
- Flextronics Lab, Pandit Deendayal Energy University, Gandhinagar 382426, Gujarat, India
| | - Mansi Patel
- Flextronics Lab, Pandit Deendayal Energy University, Gandhinagar 382426, Gujarat, India
- Department of Physics, School of Energy Technology, Pandit Deendayal Energy University, Gandhinagar 382426, Gujarat, India
| | - Lingli Liu
- School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore 637371, Singapore
| | - Aziz Lokhandwala
- Flextronics Lab, Pandit Deendayal Energy University, Gandhinagar 382426, Gujarat, India
- Department of Physics, School of Energy Technology, Pandit Deendayal Energy University, Gandhinagar 382426, Gujarat, India
| | - Parth Thakkar
- Flextronics Lab, Pandit Deendayal Energy University, Gandhinagar 382426, Gujarat, India
- Department of Physics, School of Energy Technology, Pandit Deendayal Energy University, Gandhinagar 382426, Gujarat, India
| | - Mun Yin Chee
- School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore 637371, Singapore
| | - Muskan Jain
- Flextronics Lab, Pandit Deendayal Energy University, Gandhinagar 382426, Gujarat, India
- Department of Physics, School of Energy Technology, Pandit Deendayal Energy University, Gandhinagar 382426, Gujarat, India
| | - Wen Siang Lew
- School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore 637371, Singapore
| | - Nitin Chaudhari
- Advanced Hybrid Nanomaterial Laboratory, Department of Chemistry, School of Energy Technology, Pandit Deendayal Energy University, Gandhinagar 382426, Gujarat, India
| | - Ankur Solanki
- Flextronics Lab, Pandit Deendayal Energy University, Gandhinagar 382426, Gujarat, India
- Department of Physics, School of Energy Technology, Pandit Deendayal Energy University, Gandhinagar 382426, Gujarat, India
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Im IH, Baek JH, Kim SJ, Kim J, Park SH, Kim JY, Yang JJ, Jang HW. Halide Perovskites-Based Diffusive Memristors for Artificial Mechano-Nociceptive System. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024; 36:e2307334. [PMID: 37708845 DOI: 10.1002/adma.202307334] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Revised: 08/24/2023] [Indexed: 09/16/2023]
Abstract
Numerous efforts for emulating organ systems comprised of multiple functional units have driven substantial advancements in bio-realistic electronics and systems. The resistance change behavior observed in diffusive memristors shares similarities with the potential change in biological neurons. Here, the diffusive threshold switching phenomenon in Ag-incorporated organometallic halide perovskites is utilized to demonstrate the functions of afferent neurons. Halide perovskites-based diffusive memristors show a low threshold voltage of ≈0.2 V with little variation, attributed to the facile migration of Ag ions uniformly dispersed within the halide matrix. Based on the reversible and reliable volatile threshold switching, the memristors successfully demonstrate fundamental nociceptive functions including threshold firing, relaxation, and sensitization. Furthermore, to replicate the biological mechano-nociceptive phenomenon at a system level, an artificial mechano-nociceptive system is built by integrating a diffusive memristor with a force-sensing resistor. The presented system is capable of detecting and discerning the detrimental impact caused by a heavy steel ball, effectively exhibiting the corresponding sensitization response. By further extending the single nociceptive system into a 5 × 5 array, successful stereoscopic nociception of uneven impulses is achieved in the artificial skin system through array-scale sensitization. These results represent significant progress in the field of bio-inspired electronics and systems.
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Affiliation(s)
- In Hyuk Im
- Department of Materials Science and Engineering, Research Institute of Advanced Materials, Seoul National University, Seoul, 08826, Republic of Korea
| | - Ji Hyun Baek
- Department of Materials Science and Engineering, Research Institute of Advanced Materials, Seoul National University, Seoul, 08826, Republic of Korea
| | - Seung Ju Kim
- Department of Materials Science and Engineering, Research Institute of Advanced Materials, Seoul National University, Seoul, 08826, Republic of Korea
- Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, 90089, USA
| | - Jaehyun Kim
- Department of Materials Science and Engineering, Research Institute of Advanced Materials, Seoul National University, Seoul, 08826, Republic of Korea
| | - Sung Hyuk Park
- Department of Materials Science and Engineering, Research Institute of Advanced Materials, Seoul National University, Seoul, 08826, Republic of Korea
| | - Jae Young Kim
- Department of Materials Science and Engineering, Research Institute of Advanced Materials, Seoul National University, Seoul, 08826, Republic of Korea
| | - J Joshua Yang
- Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, 90089, USA
| | - Ho Won Jang
- Department of Materials Science and Engineering, Research Institute of Advanced Materials, Seoul National University, Seoul, 08826, Republic of Korea
- Advanced Institute of Convergence Technology, Seoul National University, Suwon, 16229, Republic of Korea
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Xu M, Chen X, Guo Y, Wang Y, Qiu D, Du X, Cui Y, Wang X, Xiong J. Reconfigurable Neuromorphic Computing: Materials, Devices, and Integration. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2023; 35:e2301063. [PMID: 37285592 DOI: 10.1002/adma.202301063] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Revised: 05/15/2023] [Indexed: 06/09/2023]
Abstract
Neuromorphic computing has been attracting ever-increasing attention due to superior energy efficiency, with great promise to promote the next wave of artificial general intelligence in the post-Moore era. Current approaches are, however, broadly designed for stationary and unitary assignments, thus encountering reluctant interconnections, power consumption, and data-intensive computing in that domain. Reconfigurable neuromorphic computing, an on-demand paradigm inspired by the inherent programmability of brain, can maximally reallocate finite resources to perform the proliferation of reproducibly brain-inspired functions, highlighting a disruptive framework for bridging the gap between different primitives. Although relevant research has flourished in diverse materials and devices with novel mechanisms and architectures, a precise overview remains blank and urgently desirable. Herein, the recent strides along this pursuit are systematically reviewed from material, device, and integration perspectives. At the material and device level, one comprehensively conclude the dominant mechanisms for reconfigurability, categorized into ion migration, carrier migration, phase transition, spintronics, and photonics. Integration-level developments for reconfigurable neuromorphic computing are also exhibited. Finally, a perspective on the future challenges for reconfigurable neuromorphic computing is discussed, definitely expanding its horizon for scientific communities.
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Affiliation(s)
- Minyi Xu
- State Key Laboratory of Electronic Thin Film and Integrated Devices, School of Physics, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Xinrui Chen
- State Key Laboratory of Electronic Thin Film and Integrated Devices, School of Physics, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Yehao Guo
- State Key Laboratory of Electronic Thin Film and Integrated Devices, School of Physics, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Yang Wang
- State Key Laboratory of Electronic Thin Film and Integrated Devices, School of Physics, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Dong Qiu
- State Key Laboratory of Electronic Thin Film and Integrated Devices, School of Physics, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Xinchuan Du
- State Key Laboratory of Electronic Thin Film and Integrated Devices, School of Physics, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Yi Cui
- State Key Laboratory of Electronic Thin Film and Integrated Devices, School of Physics, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Xianfu Wang
- State Key Laboratory of Electronic Thin Film and Integrated Devices, School of Physics, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Jie Xiong
- State Key Laboratory of Electronic Thin Film and Integrated Devices, School of Physics, University of Electronic Science and Technology of China, Chengdu, 610054, China
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Schofield P, Bradicich A, Gurrola RM, Zhang Y, Brown TD, Pharr M, Shamberger PJ, Banerjee S. Harnessing the Metal-Insulator Transition of VO 2 in Neuromorphic Computing. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2023; 35:e2205294. [PMID: 36036767 DOI: 10.1002/adma.202205294] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/12/2022] [Revised: 08/02/2022] [Indexed: 06/15/2023]
Abstract
Future-generation neuromorphic computing seeks to overcome the limitations of von Neumann architectures by colocating logic and memory functions, thereby emulating the function of neurons and synapses in the human brain. Despite remarkable demonstrations of high-fidelity neuronal emulation, the predictive design of neuromorphic circuits starting from knowledge of material transformations remains challenging. VO2 is an attractive candidate since it manifests a near-room-temperature, discontinuous, and hysteretic metal-insulator transition. The transition provides a nonlinear dynamical response to input signals, as needed to construct neuronal circuit elements. Strategies for tuning the transformation characteristics of VO2 based on modification of material properties, interfacial structure, and field couplings, are discussed. Dynamical modulation of transformation characteristics through in situ processing is discussed as a means of imbuing synaptic function. Mechanistic understanding of site-selective modification; external, epitaxial, and chemical strain; defect dynamics; and interfacial field coupling in modifying local atomistic structure, the implications therein for electronic structure, and ultimately, the tuning of transformation characteristics, is emphasized. Opportunities are highlighted for inverse design and for using design principles related to thermodynamics and kinetics of electronic transitions learned from VO2 to inform the design of new Mott materials, as well as to go beyond energy-efficient computation to manifest intelligence.
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Affiliation(s)
- Parker Schofield
- Department of Chemistry, Texas A&M University, College Station, TX, 77843, USA
- Department of Materials Science and Engineering, Texas A&M University, College Station, TX, 77843, USA
| | - Adelaide Bradicich
- Department of Materials Science and Engineering, Texas A&M University, College Station, TX, 77843, USA
| | - Rebeca M Gurrola
- Department of Materials Science and Engineering, Texas A&M University, College Station, TX, 77843, USA
| | - Yuwei Zhang
- Department of Mechanical Engineering, Texas A&M University, College Station, TX, 77843, USA
| | | | - Matt Pharr
- Department of Mechanical Engineering, Texas A&M University, College Station, TX, 77843, USA
| | - Patrick J Shamberger
- Department of Materials Science and Engineering, Texas A&M University, College Station, TX, 77843, USA
| | - Sarbajit Banerjee
- Department of Chemistry, Texas A&M University, College Station, TX, 77843, USA
- Department of Materials Science and Engineering, Texas A&M University, College Station, TX, 77843, USA
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Patil AR, Dongale TD, Namade LD, Mohite SV, Kim Y, Sutar SS, Kamat RK, Rajpure KY. Sprayed FeWO4 thin film-based memristive device with negative differential resistance effect for non-volatile memory and synaptic learning applications. J Colloid Interface Sci 2023; 642:540-553. [PMID: 37028161 DOI: 10.1016/j.jcis.2023.03.189] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Revised: 03/21/2023] [Accepted: 03/29/2023] [Indexed: 04/04/2023]
Abstract
Resistive switching (RS) memories have attracted great attention as promising solutions to next-generation non-volatile memories and computing technologies because of their simple device configuration, high on/off ratio, low power consumption, fast switching, long retention, and significant cyclic stability. In this work, uniform and adherent iron tungstate (FeWO4) thin films were synthesized by the spray pyrolysis method with various precursor solution volumes, and these were tested as a switching layer for the fabrication of Ag/FWO/FTO memristive devices. The detailed structural investigation was done through various analytical and physio-chemical characterizations viz. X-ray diffraction (XRD) and its Rietveld refinement, Raman spectroscopy, and X-ray photoelectron spectroscopy (XPS) techniques. The results reveal the pure and single-phase FeWO4 compound thin film formation. Surface morphological study shows the spherical particle formation having a diameter in the range of 20 to 40 nm. The RS characteristics of the Ag/FWO/FTO memristive device demonstrate non-volatile memory characteristics with significant endurance and retention properties. Interestingly, the memory devices show stable and reproducible negative differential resistance (NDR) effects. The in-depth statistical analysis suggests the good operational uniformity of the device. Moreover, the switching voltages of the Ag/FWO/FTO memristive device were modeled using the time series analysis technique by utilizing Holt's Winter Exponential Smoothing (HWES) approach. Additionally, the device mimics bio-synaptic properties such as potentiation/depression, excitatory post-synaptic current (EPSC), and spike-timing-dependent plasticity (STDP) learning rules. For the present device, the space-charge-limited current (SCLC) and trap-controlled-SCLC effects dominated during positive and negative bias I-V characteristics, respectively. The RS mechanism dominated in the low resistance state (LRS), and the high resistance state (HRS) was explained based on the formation and rupture of conductive filament composed of Ag ions and oxygen vacancies. This work demonstrates the RS in the metal tungstate-based memristive devices and demonstrates a low-cost approach for fabricating memristive devices.
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Affiliation(s)
- Amitkumar R Patil
- Electrochemical Materials Laboratory, Department of Physics, Shivaji University, Kolhapur 416004, India
| | - Tukaram D Dongale
- Computational Electronics and Nanoscience Research Laboratory, School of Nanoscience and Biotechnology, Shivaji University, Kolhapur 416004, India
| | - Lahu D Namade
- Electrochemical Materials Laboratory, Department of Physics, Shivaji University, Kolhapur 416004, India
| | - Santosh V Mohite
- Department of Applied Chemistry, Konkuk University, Chungju 27478, Republic of Korea
| | - Yeonho Kim
- Department of Applied Chemistry, Konkuk University, Chungju 27478, Republic of Korea
| | - Santosh S Sutar
- Yashwantrao Chavan School of Rural Development, Shivaji University, Kolhapur 416004, India
| | - Rajanish K Kamat
- Department of Electronics, Shivaji University, Kolhapur 416004, India; Dr. Homi Bhabha State University, 15, Madam Cama Road, Mumbai 400032, India
| | - Keshav Y Rajpure
- Electrochemical Materials Laboratory, Department of Physics, Shivaji University, Kolhapur 416004, India.
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Zhou G, Ji X, Li J, Zhou F, Dong Z, Yan B, Sun B, Wang W, Hu X, Song Q, Wang L, Duan S. Second-order associative memory circuit hardware implemented by the evolution from battery-like capacitance to resistive switching memory. iScience 2022; 25:105240. [PMID: 36262310 PMCID: PMC9574501 DOI: 10.1016/j.isci.2022.105240] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Revised: 08/29/2022] [Accepted: 09/27/2022] [Indexed: 12/04/2022] Open
Abstract
Memristor-based Pavlov associative memory circuit presented today only realizes the simple condition reflex process. The secondary condition reflex endows the simple condition reflex process with more bionic, but it is only demonstrated in design and involves the large number of redundant circuits. A FeOx-based memristor exhibits an evolution process from battery-like capacitance (BLC) state to resistive switching (RS) memory as the I-V sweeping increase. The BLC is triggered by the active metal ion and hydroxide ion originated from water molecule splitting at different interfaces, while the RS memory behavior is dominated by the diffusion and migration of ion in the FeOx switching function layer. The evolution processes share the nearly same biophysical mechanism with the second-order conditioning. It enables a hardware-implemented second-order associative memory circuit to be feasible and simple. This work provides a novel path to realize the associative memory circuit with the second-order conditioning at hardware level.
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Affiliation(s)
- Guangdong Zhou
- College of Artificial Intelligence, School of Materials and Energy, Southwest University, Chongqing 400715, PR China
| | - Xiaoyue Ji
- College of Electrical Engineering, Zhejiang University, Hangzhou 310027, PR China
| | - Jie Li
- Shenzhen-Hong Kong College of Microelectronics, Southern University of Science and Technology, Shenzhen 518055, China
| | - Feichi Zhou
- Shenzhen-Hong Kong College of Microelectronics, Southern University of Science and Technology, Shenzhen 518055, China
| | - Zhekang Dong
- College of Electrical Engineering, Zhejiang University, Hangzhou 310027, PR China
| | - Bingtao Yan
- College of Artificial Intelligence, School of Materials and Energy, Southwest University, Chongqing 400715, PR China
| | - Bai Sun
- Department of Mechanics and Mechatronics Engineering, Centre for Advanced Materials Joining, Waterloo Institute for Nanotechnology, University of Waterloo, Waterloo, ON N2L 3G1, Canada
| | - Wenhua Wang
- College of Artificial Intelligence, School of Materials and Energy, Southwest University, Chongqing 400715, PR China
| | - Xiaofang Hu
- College of Artificial Intelligence, School of Materials and Energy, Southwest University, Chongqing 400715, PR China
| | - Qunliang Song
- College of Artificial Intelligence, School of Materials and Energy, Southwest University, Chongqing 400715, PR China
| | - Lidan Wang
- College of Artificial Intelligence, School of Materials and Energy, Southwest University, Chongqing 400715, PR China
| | - Shukai Duan
- College of Artificial Intelligence, School of Materials and Energy, Southwest University, Chongqing 400715, PR China
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Choi SH, Park SO, Seo S, Choi S. Reliable multilevel memristive neuromorphic devices based on amorphous matrix via quasi-1D filament confinement and buffer layer. SCIENCE ADVANCES 2022; 8:eabj7866. [PMID: 35061541 PMCID: PMC8782456 DOI: 10.1126/sciadv.abj7866] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/03/2021] [Accepted: 11/30/2021] [Indexed: 05/19/2023]
Abstract
Conductive-bridging random access memory (CBRAM) has garnered attention as a building block of non-von Neumann architectures because of scalability and parallel processing on the crossbar array. To integrate CBRAM into the back-end-of-line (BEOL) process, amorphous switching materials have been investigated for practical usage. However, both the inherent randomness of filaments and disorders of amorphous material lead to poor reliability. In this study, a highly reliable nanoporous-defective bottom layer (NP-DBL) structure based on amorphous TiO2 is demonstrated (Ag/a-TiO2/a-TiOx/p-Si). The stoichiometries of DBL and the pore size can be manipulated to achieve the analog conductance updates and multilevel conductance by 300 states with 1.3% variation, and 10 levels, respectively. Compared with nonporous TiO2 CBRAM, endurance, retention, and uniformity can be improved by 106 pulses, 28 days at 85°C, and 6.7 times, respectively. These results suggest even amorphous-based systems, elaborately tuned structural variables, can help design more reliable CBRAMs.
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Dynamic Analysis of the Switched-Inductor Buck-Boost Converter Based on the Memristor. ELECTRONICS 2021. [DOI: 10.3390/electronics10040452] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The direct current (DC)–DC converter presents abundant nonlinear phenomena, such as periodic bifurcation and chaotic motion, under certain conditions. For a switched-inductor buck-boost (SIBB) converter with the memristive load, this paper constructs its state equation model under two operating statuses, investigates its chaotic dynamic characteristics, and draws and analyzes the bifurcation diagrams of the inductive current and phase portraits, under some parameter changing by the MATLAB simulation based on the state equation. Then, by applying certain minor perturbations to parameters, the chaotic phenomenon suppression method is explored by controlling peak current in continuous current mode (CCM) to keep the converter run normally. Finally, the power simulation (PSIM) verifies that the waveforms and the phase portraits controlling the corresponding parameters are consistent with those of the MATLAB simulation.
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