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Chen X, Chen L, Zhou J, Wu J, Wang Z, Wei L, Yuan S, Zhang Q. Self-Adhesive, Stretchable, and Thermosensitive Iontronic Hydrogels for Highly Sensitive Neuromorphic Sensing-Synaptic Systems. NANO LETTERS 2024. [PMID: 39116304 DOI: 10.1021/acs.nanolett.4c02614] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/10/2024]
Abstract
Artificial sensory afferent nerves that emulate receptor nanochannel perception and synaptic ionic information processing in chemical environments are highly desirable for bioelectronics. However, challenges persist in achieving life-like nanoscale conformal contact, agile multimodal sensing response, and synaptic feedback with ions. Here, a precisely tuned phase transition poly(N-isopropylacrylamide) (PNIPAM) hydrogel is introduced through the water molecule reservoir strategy. The resulting hydrogel with strongly cross-linked networks exhibits excellent mechanical performance (∼2000% elongation) and robust adhesive strength. Importantly, the hydrogel's enhanced ionic conductance and heterogeneous structure of the temperature-sensitive component enable highly sensitive strain information perception (GFmax = 7.94, response time ∼ 87 ms), temperature information perception (TCRmax = -1.974%/°C, response time ∼ 270 ms), and low energy consumption synaptic plasticity (42.2 fJ/spike). As a demonstration, a neuromorphic sensing-synaptic system is constructed integrating iontronic strain/temperature sensors with fiber synapses for real-time information sensing, discrimination, and feedback. This work holds enormous potential in bioinspired robotics and bioelectronics.
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Affiliation(s)
- Xuedan Chen
- School of Materials Science and Engineering, East China University of Science and Technology, Shanghai 200237, China
- Key Laboratory of Multifunctional Nanomaterials and Smart Systems, Suzhou Institute of Nano-Tech and Nano-Bionics, Chinese Academy of Sciences, Suzhou 215123, China
| | - Long Chen
- Key Laboratory of Multifunctional Nanomaterials and Smart Systems, Suzhou Institute of Nano-Tech and Nano-Bionics, Chinese Academy of Sciences, Suzhou 215123, China
- School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798, Singapore
| | - Jianxian Zhou
- Key Laboratory of Multifunctional Nanomaterials and Smart Systems, Suzhou Institute of Nano-Tech and Nano-Bionics, Chinese Academy of Sciences, Suzhou 215123, China
| | - Jiajun Wu
- Key Laboratory of Multifunctional Nanomaterials and Smart Systems, Suzhou Institute of Nano-Tech and Nano-Bionics, Chinese Academy of Sciences, Suzhou 215123, China
| | - Zhixun Wang
- School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798, Singapore
| | - Lei Wei
- School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798, Singapore
| | - Shuanglong Yuan
- School of Materials Science and Engineering, East China University of Science and Technology, Shanghai 200237, China
| | - Qichong Zhang
- Key Laboratory of Multifunctional Nanomaterials and Smart Systems, Suzhou Institute of Nano-Tech and Nano-Bionics, Chinese Academy of Sciences, Suzhou 215123, China
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Yan J, Armstrong JPK, Scarpa F, Perriman AW. Hydrogel-Based Artificial Synapses for Sustainable Neuromorphic Electronics. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024:e2403937. [PMID: 39087845 DOI: 10.1002/adma.202403937] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/17/2024] [Revised: 06/16/2024] [Indexed: 08/02/2024]
Abstract
Hydrogels find widespread applications in biomedicine because of their outstanding biocompatibility, biodegradability, and tunable material properties. Hydrogels can be chemically functionalized or reinforced to respond to physical or chemical stimulation, which opens up new possibilities in the emerging field of intelligent bioelectronics. Here, the state-of-the-art in functional hydrogel-based transistors and memristors is reviewed as potential artificial synapses. Within these systems, hydrogels can serve as semisolid dielectric electrolytes in transistors and as switching layers in memristors. These synaptic devices with volatile and non-volatile resistive switching show good adaptability to external stimuli for short-term and long-term synaptic memory effects, some of which are integrated into synaptic arrays as artificial neurons; although, there are discrepancies in switching performance and efficacy. By comparing different hydrogels and their respective properties, an outlook is provided on a new range of biocompatible, environment-friendly, and sustainable neuromorphic hardware. How potential energy-efficient information storage and processing can be achieved using artificial neural networks with brain-inspired architecture for neuromorphic computing is described. The development of hydrogel-based artificial synapses can significantly impact the fields of neuromorphic bionics, biometrics, and biosensing.
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Affiliation(s)
- Jiongyi Yan
- School of Cellular and Molecular Medicine, University of Bristol, Bristol, BS8 1TD, UK
| | - James P K Armstrong
- Department of Translational Health Sciences, Bristol Medical School, University of Bristol, Bristol, BS1 3NY, UK
| | - Fabrizio Scarpa
- Bristol Composites Institute, School of Civil, Aerospace and Design Engineering (CADE), University of Bristol, University Walk, Bristol, BS8 1TR, UK
| | - Adam W Perriman
- School of Cellular and Molecular Medicine, University of Bristol, Bristol, BS8 1TD, UK
- Research School of Chemistry, Australian National University, Canberra, Australian Capital Territory, 2601, Australia
- John Curtin School of Medical Research, Australian National University, Canberra, Australian Capital Territory, 2601, Australia
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3
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Wu T, Gao S, Li Y. IGZO/WO 3-x-Heterostructured Artificial Optoelectronic Synaptic Devices Mimicking Image Segmentation and Motion Capture. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2024; 20:e2309857. [PMID: 38258604 DOI: 10.1002/smll.202309857] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Revised: 01/11/2024] [Indexed: 01/24/2024]
Abstract
Currently, artificial neural networks (ANNs) based on memristors are limited to recognizing static images of objects when simulating human visual system, preventing them from performing high-dimensional information perception, and achieving more complex biomimetic functions is subject to certain limitations. In this work, indium gallium zinc oxide (IGZO)/tungsten oxide (WO3-x)-heterostructured artificial optoelectronic synaptic devices mimicking image segmentation and motion capture exhibiting high-performance optoelectronic synaptic responses are proposed and demonstrated. Upon electrical and optical stimulations, the device shows a variety of fundamental and advanced electrical and optical synaptic plasticity. Most importantly, outstanding and repeatable linear synaptic weight changes are attained by the developed memristor. By taking advantage of the notable linear synaptic weight changes, ANNs have been constructed and successfully utilized to demonstrate two applications in the field of computer vision, including image segmentation and object tracking. The accuracy attained by the memristor-based ANNs is similar to that of the computer algorithms, while its power has been significantly reduced by 105 orders of magnitude. With successful emulations of the human brain reactions when observing objects, the demonstrated memristor and related ANNs can be effectively utilized in constructing artificial optoelectronic synaptic devices and show promising potential in emulating human visual perception.
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Affiliation(s)
- Tong Wu
- Shandong Provincial Key Laboratory of Network Based Intelligent Computing, School of Information Science and Engineering, University of Jinan, Jinan, 250022, China
| | - Song Gao
- Shandong Provincial Key Laboratory of Network Based Intelligent Computing, School of Information Science and Engineering, University of Jinan, Jinan, 250022, China
| | - Yang Li
- Shandong Provincial Key Laboratory of Network Based Intelligent Computing, School of Information Science and Engineering, University of Jinan, Jinan, 250022, China
- School of Microelectronics, Shandong University, Jinan, 250101, China
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Zhuge C, Zhang Y, Jiang J, Li X, Zhao Y, Fu Y, Wang Q, He D. Reliable Low-Current and Multilevel Memristive Electrochemical Neuromorphic Devices with Semi-Metal Sb Filament. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2024:e2400599. [PMID: 38860549 DOI: 10.1002/smll.202400599] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/25/2024] [Revised: 06/01/2024] [Indexed: 06/12/2024]
Abstract
Memristors are used in artificial neural networks owing to their exceptional integration capabilities and scalability. However, traditional memristors are hampered by limited resistance states and randomness, which curtails their application. The migration of metal ions critically influences the number of conductance states and the linearity of weight updates. Semi-metal filaments can provide subquantum conductance changes to the memristors due to the smaller single-atom conductance, such as Sb (≈0.01 G0 = 7.69 × 10-7 S). Here, a memristor featuring an active electrode composed of semi-metal Sb is introduced for the first time. This memristor demonstrates precise conductance control, a large on/off ratio, consistent switching, and prolonged retention exceeding 105 s. Density functional theory (DFT) calculations and characterization methods reveal the formation of Sb filaments during a set process. The interaction between Sb and O within the dielectric layer facilitates the Sb filaments' ability to preserve their morphology in the absence of electric fields.
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Affiliation(s)
- Chenyu Zhuge
- School of Materials and Energy, Lanzhou University, Lanzhou, 730000, China
| | - Yukun Zhang
- School of Materials and Energy, Lanzhou University, Lanzhou, 730000, China
| | - Jiandong Jiang
- School of Materials and Energy, Lanzhou University, Lanzhou, 730000, China
| | - Xiang Li
- School of Materials and Energy, Lanzhou University, Lanzhou, 730000, China
| | - Yanfei Zhao
- School of Materials and Energy, Lanzhou University, Lanzhou, 730000, China
| | - Yujun Fu
- School of Materials and Energy, Lanzhou University, Lanzhou, 730000, China
| | - Qi Wang
- School of Materials and Energy, Lanzhou University, Lanzhou, 730000, China
| | - Deyan He
- School of Materials and Energy, Lanzhou University, Lanzhou, 730000, China
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Chen K, Pan K, He S, Liu R, Zhou Z, Zhu D, Liu Z, He Z, Sun H, Wang M, Wang K, Tang M, Liu J. Mimicking Bidirectional Inhibitory Synapse Using a Porous-Confined Ionic Memristor with Electrolyte/Tris(4-aminophenyl)amine Neurotransmitter. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2400966. [PMID: 38483027 PMCID: PMC11109647 DOI: 10.1002/advs.202400966] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/26/2024] [Indexed: 05/23/2024]
Abstract
Ionic memristors can emulate brain-like functions of biological synapses for neuromorphic technologies. Apart from the widely studied excitatory-excitatory and excitatory-inhibitory synapses, reports on memristors with the inhibitory-inhibitory synaptic behaviors remain a challenge. Here, the first biaxially inhibited artificial synapse is demonstrated, consisting of a solid electrolyte and conjugated microporous polymers bilayer as neurotransmitter, with the former serving as an ion reservoir and the latter acting as a confined transport. Due to the migration, trapping, and de-trapping of ions within the nanoslits, the device poses inhibitory synaptic plasticity under both positive and negative stimuli. Remarkably, the artificial synapse is able to maintain a low level of stable nonvolatile memory over a long period of time (≈60 min) after multiple stimuli, with feature-inferencing/-training capabilities of neural node in neuromorphic computing. This work paves a reliable strategy for constructing nanochannel ionic memristive materials toward fully inhibitory synaptic devices.
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Affiliation(s)
- Kang Chen
- School of Materials Science and EngineeringXiangtan UniversityNorth Second Ring Road, YuhuXiangtanHunan411105China
| | - Keyuan Pan
- Key Laboratory of Flexible Electronics (KLOFE) & Institute of Advanced Materials (IAM)Nanjing Tech University (NanjingTech)30 South Puzhu RoadNanjing211816China
| | - Shang He
- School of Materials Science and EngineeringXiangtan UniversityNorth Second Ring Road, YuhuXiangtanHunan411105China
| | - Rui Liu
- School of Materials Science and EngineeringXiangtan UniversityNorth Second Ring Road, YuhuXiangtanHunan411105China
| | - Zhe Zhou
- Key Laboratory of Flexible Electronics (KLOFE) & Institute of Advanced Materials (IAM)Nanjing Tech University (NanjingTech)30 South Puzhu RoadNanjing211816China
| | - Duoyi Zhu
- Key Laboratory of Flexible Electronics (KLOFE) & Institute of Advanced Materials (IAM)Nanjing Tech University (NanjingTech)30 South Puzhu RoadNanjing211816China
| | - Zhengdong Liu
- Key Laboratory of Flexible Electronics (KLOFE) & Institute of Advanced Materials (IAM)Nanjing Tech University (NanjingTech)30 South Puzhu RoadNanjing211816China
| | - Zixi He
- Key Laboratory of Flexible Electronics (KLOFE) & Institute of Advanced Materials (IAM)Nanjing Tech University (NanjingTech)30 South Puzhu RoadNanjing211816China
| | - Hongchao Sun
- Key Laboratory of Flexible Electronics (KLOFE) & Institute of Advanced Materials (IAM)Nanjing Tech University (NanjingTech)30 South Puzhu RoadNanjing211816China
| | - Min Wang
- Key Laboratory of Flexible Electronics (KLOFE) & Institute of Advanced Materials (IAM)Nanjing Tech University (NanjingTech)30 South Puzhu RoadNanjing211816China
| | - Kaili Wang
- Key Laboratory of Flexible Electronics (KLOFE) & Institute of Advanced Materials (IAM)Nanjing Tech University (NanjingTech)30 South Puzhu RoadNanjing211816China
| | - Minghua Tang
- School of Materials Science and EngineeringXiangtan UniversityNorth Second Ring Road, YuhuXiangtanHunan411105China
| | - Juqing Liu
- Key Laboratory of Flexible Electronics (KLOFE) & Institute of Advanced Materials (IAM)Nanjing Tech University (NanjingTech)30 South Puzhu RoadNanjing211816China
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Li J, Fan F, Fu X, Liu M, Chen Y, Zhang B. Building Uniformly Structured Polymer Memristors via a 2D Conjugation Strategy for Neuromorphic Computing. Macromol Rapid Commun 2024:e2400172. [PMID: 38627960 DOI: 10.1002/marc.202400172] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2024] [Revised: 04/15/2024] [Indexed: 04/23/2024]
Abstract
Polymer memristors represent a highly promising avenue for the advancement of next-generation computing systems. However, the intrinsic structural heterogeneity characteristic of most polymers often results in organic polymer memristors displaying erratic resistive switching phenomena, which in turn lead to diminished production yields and compromised reliability. In this study, a 2D conjugated polymer, named PBDTT-BPQTPA, is synthesized by integrating the coplanar bis(thiophene)-4,8-dihydrobenzo[1,2-b:4,5-b]dithiophene (BDTT) as an electron-donating unit with a quinoxaline derivative serving as an electron-accepting unit. The incorporation of triphenylamine groups at the quinoxaline termini significantly enhances the polymer's conjugation and planarity, thereby facilitating more efficient charge transport. The fabricated polymer memristor with the structure of Al/PBDTT-BPQTPA/ITO exhibits typical non-volatile resistive switching behavior under high voltage conditions, along with history-dependent memristive properties at lower voltages. The unique memristive behavior of the device enables the simulation of synaptic enhancement/inhibition, learning algorithms, and memory operations. Additionally, the memristor demonstrates its capability for executing logical operations and handling decimal calculations. This study offers a promising and innovative approach for the development of artificial neuromorphic computing systems.
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Affiliation(s)
- Jinyong Li
- Key Laboratory for Advanced Materials and Joint International Research Laboratory of Precision Chemistry and Molecular Engineering, School of Chemistry and Molecular Engineering, East China University of Science and Technology, Shanghai, 200237, China
| | - Fei Fan
- Shanghai Key Laboratory of Crime Scene Evidence, Shanghai Research Institute of Criminal Science and Technology, Shanghai, 200083, China
| | - Xin Fu
- Key Laboratory for Advanced Materials and Joint International Research Laboratory of Precision Chemistry and Molecular Engineering, School of Chemistry and Molecular Engineering, East China University of Science and Technology, Shanghai, 200237, China
| | - Mingxing Liu
- Key Laboratory for Advanced Materials and Joint International Research Laboratory of Precision Chemistry and Molecular Engineering, School of Chemistry and Molecular Engineering, East China University of Science and Technology, Shanghai, 200237, China
| | - Yu Chen
- Key Laboratory for Advanced Materials and Joint International Research Laboratory of Precision Chemistry and Molecular Engineering, School of Chemistry and Molecular Engineering, East China University of Science and Technology, Shanghai, 200237, China
| | - Bin Zhang
- Key Laboratory for Advanced Materials and Joint International Research Laboratory of Precision Chemistry and Molecular Engineering, School of Chemistry and Molecular Engineering, East China University of Science and Technology, Shanghai, 200237, China
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7
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Li X, Ou X, Chen G, Bi R, Li Z, Xie Z, Yue W, Guo SZ. Ultrasoft and High-Adhesion Block Copolymers for Neuromorphic Computing. ACS APPLIED MATERIALS & INTERFACES 2024. [PMID: 38412379 DOI: 10.1021/acsami.3c19350] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/29/2024]
Abstract
The "von Neumann bottleneck" is a formidable challenge in conventional computing, driving exploration into artificial synapses. Organic semiconductor materials show promise but are hindered by issues such as poor adhesion and a high elastic modulus. Here, we combine polyisoindigo-bithiophene (PIID-2T) with grafted poly(dimethylsiloxane) (PDMS) to synthesize the triblock-conjugated polymer (PIID-2T-PDMS). The polymer exhibited substantial enhancements in adhesion (4.8-68.8 nN) and reductions in elastic modulus (1.6-0.58 GPa) while maintaining the electrical characteristics of PIID-2T. The three-terminal organic synaptic transistor (three-terminal p-type organic artificial synapse (TPOAS)), constructed using PIID-2T-PDMS, exhibits an unprecedented analog switching range of 276×, surpassing previous records, and a remarkable memory on-off ratio of 106. Moreover, the device displays outstanding operational stability, retaining 99.6% of its original current after 1600 write-read events in the air. Notably, TPOAS replicates key biological synaptic behaviors, including paired-pulse facilitation (PPF), short-term plasticity (STP), and long-term plasticity (LTP). Simulations using handwritten digital data sets reveal an impressive recognition accuracy of 91.7%. This study presents a polyisoindigo-bithiophene-based block copolymer that offers enhanced adhesion, reduced elastic modulus, and high-performance artificial synapses, paving the way for the next generation of neuromorphic computing systems.
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Affiliation(s)
- Xiaohong Li
- Guangzhou Key Laboratory of Flexible Electronic Materials and Wearable Devices, Key Laboratory for Polymeric Composite and Functional Materials of Ministry of Education, State Key Laboratory of Optoelectronic Materials and Technologies, School of Materials Science and Engineering, Sun Yat-sen University, Guangzhou 510275, P. R. China
| | - Xingcheng Ou
- Guangzhou Key Laboratory of Flexible Electronic Materials and Wearable Devices, Key Laboratory for Polymeric Composite and Functional Materials of Ministry of Education, State Key Laboratory of Optoelectronic Materials and Technologies, School of Materials Science and Engineering, Sun Yat-sen University, Guangzhou 510275, P. R. China
| | - Guoliang Chen
- Guangzhou Key Laboratory of Flexible Electronic Materials and Wearable Devices, Key Laboratory for Polymeric Composite and Functional Materials of Ministry of Education, State Key Laboratory of Optoelectronic Materials and Technologies, School of Materials Science and Engineering, Sun Yat-sen University, Guangzhou 510275, P. R. China
| | - Ran Bi
- Guangzhou Key Laboratory of Flexible Electronic Materials and Wearable Devices, Key Laboratory for Polymeric Composite and Functional Materials of Ministry of Education, State Key Laboratory of Optoelectronic Materials and Technologies, School of Materials Science and Engineering, Sun Yat-sen University, Guangzhou 510275, P. R. China
| | - Ziqian Li
- Guangzhou Key Laboratory of Flexible Electronic Materials and Wearable Devices, Key Laboratory for Polymeric Composite and Functional Materials of Ministry of Education, State Key Laboratory of Optoelectronic Materials and Technologies, School of Materials Science and Engineering, Sun Yat-sen University, Guangzhou 510275, P. R. China
| | - Zhuang Xie
- Guangzhou Key Laboratory of Flexible Electronic Materials and Wearable Devices, Key Laboratory for Polymeric Composite and Functional Materials of Ministry of Education, State Key Laboratory of Optoelectronic Materials and Technologies, School of Materials Science and Engineering, Sun Yat-sen University, Guangzhou 510275, P. R. China
| | - Wan Yue
- Guangzhou Key Laboratory of Flexible Electronic Materials and Wearable Devices, Key Laboratory for Polymeric Composite and Functional Materials of Ministry of Education, State Key Laboratory of Optoelectronic Materials and Technologies, School of Materials Science and Engineering, Sun Yat-sen University, Guangzhou 510275, P. R. China
| | - Shuang-Zhuang Guo
- Guangzhou Key Laboratory of Flexible Electronic Materials and Wearable Devices, Key Laboratory for Polymeric Composite and Functional Materials of Ministry of Education, State Key Laboratory of Optoelectronic Materials and Technologies, School of Materials Science and Engineering, Sun Yat-sen University, Guangzhou 510275, P. R. China
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Patel V, Patel M, Busupalli B, Solanki A. Interface Engineering Enables Multilevel Resistive Switching in Ultra-Low-Power Chemobrionic Copper Silicate. LANGMUIR : THE ACS JOURNAL OF SURFACES AND COLLOIDS 2024; 40:2311-2319. [PMID: 38232767 DOI: 10.1021/acs.langmuir.3c03431] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/19/2024]
Abstract
Memristor is assuming prominence due to its exceptionally low power consumption, adaptable, and parallel signal processing capabilities that address the limitations of the von Neumann architecture to meet the growing demand for advanced technologies such as artificial intelligence, Internet of Things (IoTs), and neuromorphic computation. In this work, we demonstrate resistive switching in copper silicate-based hollow tube-forming self-organized membrane structures belonging to the category of chemobrionics or chemical gardens to demonstrate cost-effective and highly efficient memristor devices. The device architecture is configured as ITO/PEDOT:PSS/active layer (copper silicate)/PMMA/Ag, an arrangement that serves to stabilize current-voltage hysteresis and exhibit a low SET voltage ∼0.2 V with a 0.8 nJ power consumption while manifesting robust data endurance and multilevel resistive switching. The inherent self-rectifying behavior, characterized by a high rectification ratio of 60, underscores the potential utility of these devices across a spectrum of electronic applications. To emulate the functionality of biological synapses, fundamental synaptic characteristics are assessed, including paired-pulse facilitation (PPF) and potentiation and depression (P&D). We validate the potential of copper silicate chemical garden-based memristor devices for applications that require real-time synaptic processing. Importantly, the fabrication of these devices was accomplished through a comprehensive solution-based, low-temperature process conducted under ambient environmental conditions, obviating the need for specialized glovebox facilities.
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Affiliation(s)
- Vipul Patel
- Department of Chemistry, School of Energy Technology, Pandit Deendayal Energy University, Gandhinagar, Gujarat 382426, India
| | - Mansi Patel
- Department of Physics, School of Energy Technology, Pandit Deendayal Energy University, Gandhinagar 382426, India
- Flextronics Lab, Pandit Deendayal Energy University, Gandhinagar, Gujarat 382426, India
| | - Balanagulu Busupalli
- Department of Chemistry, School of Energy Technology, Pandit Deendayal Energy University, Gandhinagar, Gujarat 382426, India
| | - Ankur Solanki
- Department of Physics, School of Energy Technology, Pandit Deendayal Energy University, Gandhinagar 382426, India
- Flextronics Lab, Pandit Deendayal Energy University, Gandhinagar, Gujarat 382426, India
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Hellenbrand M, MacManus-Driscoll J. Multi-level resistive switching in hafnium-oxide-based devices for neuromorphic computing. NANO CONVERGENCE 2023; 10:44. [PMID: 37710080 PMCID: PMC10501996 DOI: 10.1186/s40580-023-00392-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Accepted: 08/30/2023] [Indexed: 09/16/2023]
Abstract
In the growing area of neuromorphic and in-memory computing, there are multiple reviews available. Most of them cover a broad range of topics, which naturally comes at the cost of details in specific areas. Here, we address the specific area of multi-level resistive switching in hafnium-oxide-based devices for neuromorphic applications and summarize the progress of the most recent years. While the general approach of resistive switching based on hafnium oxide thin films has been very busy over the last decade or so, the development of hafnium oxide with a continuous range of programmable states per device is still at a very early stage and demonstrations are mostly at the level of individual devices with limited data provided. On the other hand, it is positive that there are a few demonstrations of full network implementations. We summarize the general status of the field, point out open questions, and provide recommendations for future work.
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Affiliation(s)
- Markus Hellenbrand
- Department of Materials Science & Metallurgy, University of Cambridge, 27 Charles Babbage Rd, Cambridge, CB3 0FS, UK.
| | - Judith MacManus-Driscoll
- Department of Materials Science & Metallurgy, University of Cambridge, 27 Charles Babbage Rd, Cambridge, CB3 0FS, UK
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