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Wang L, Zhang T, Shen J, Huang J, Li W, Shi W, Huang W, Yi M. Flexibly Photo-Regulated Brain-Inspired Functions in Flexible Neuromorphic Transistors. ACS APPLIED MATERIALS & INTERFACES 2023; 15:13380-13392. [PMID: 36853974 DOI: 10.1021/acsami.2c22754] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
As an attractive prototype for neuromorphic computing, the difficultly attained three-terminal platforms have specific advantages in implementing the brain-inspired functions. Also, in these devices, the most utilized mechanisms are confined to the electrical gate-controlled ionic migrations, which are sensitive to the device defects and stoichiometric ratio. The resultant memristive responses have fluctuant characteristics, which have adverse influences on the neural emulations. Herein, we designed a specific transistor platform with light-regulated ambipolar memory characteristics. Also, based on its gentle processes of charge trapping, we obtain the impressive memristive performances featured by smooth responses and long-term endurable characteristics. The optoelectronic samples were also fabricated on flexible substrates successfully. Interestingly, based on the optoelectronic signals of the flexible devices, we endow the desirable optical processes with the brain-inspired emulations. We can flexibly emulate the light-inspired learning-memory functions in a synapse and further devise the advanced synapse array. More importantly, through this versatile platform, we investigate the mutual regulation of excitation and inhibition and implement their sensitive-mode transformations and the homeostasis property, which is conducive to ensuring the stability of overall neural activity. Furthermore, our flexible optoelectronic platform achieves high classification accuracy when implemented in artificial neural network simulations. This work demonstrates the advantages of the optoelectronic platform in implementing the significant brain-inspired functions and provides an insight into the future integration of visible sensing in flexible optoelectronic transistor platforms.
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Affiliation(s)
- Laiyuan Wang
- State Key Laboratory of Organic Electronics and Information Displays & Institute of Advanced Materials (IAM), Nanjing University of Posts & Telecommunications, 9 Wenyuan Road, Nanjing 210023, China
- Shaanxi Institute of Flexible Electronics (SIFE), Northwestern Polytechnical University (NPU), 127 West Youyi Road, Xi'an 710072, China
| | - Tao Zhang
- State Key Laboratory of Organic Electronics and Information Displays & Institute of Advanced Materials (IAM), Nanjing University of Posts & Telecommunications, 9 Wenyuan Road, Nanjing 210023, China
| | - Junhao Shen
- State Key Laboratory of Organic Electronics and Information Displays & Institute of Advanced Materials (IAM), Nanjing University of Posts & Telecommunications, 9 Wenyuan Road, Nanjing 210023, China
| | - Jin Huang
- State Key Laboratory of Organic Electronics and Information Displays & Institute of Advanced Materials (IAM), Nanjing University of Posts & Telecommunications, 9 Wenyuan Road, Nanjing 210023, China
| | - Wen Li
- State Key Laboratory of Organic Electronics and Information Displays & Institute of Advanced Materials (IAM), Nanjing University of Posts & Telecommunications, 9 Wenyuan Road, Nanjing 210023, China
| | - Wei Shi
- Key Laboratory of Flexible Electronics (KLOFE) & Institute of Advanced Materials (IAM), Nanjing Tech University (Nanjing Tech), 30 South Puzhu Road, Nanjing 211816, People's Republic of China
| | - Wei Huang
- State Key Laboratory of Organic Electronics and Information Displays & Institute of Advanced Materials (IAM), Nanjing University of Posts & Telecommunications, 9 Wenyuan Road, Nanjing 210023, China
- Shaanxi Institute of Flexible Electronics (SIFE), Northwestern Polytechnical University (NPU), 127 West Youyi Road, Xi'an 710072, China
- Key Laboratory of Flexible Electronics (KLOFE) & Institute of Advanced Materials (IAM), Nanjing Tech University (Nanjing Tech), 30 South Puzhu Road, Nanjing 211816, People's Republic of China
| | - Mingdong Yi
- State Key Laboratory of Organic Electronics and Information Displays & Institute of Advanced Materials (IAM), Nanjing University of Posts & Telecommunications, 9 Wenyuan Road, Nanjing 210023, China
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Nirmal KA, Nhivekar GS, Khot AC, Dongale TD, Kim TG. Unraveling the Effect of the Water Content in the Electrolyte on the Resistive Switching Properties of Self-Assembled One-Dimensional Anodized TiO 2 Nanotubes. J Phys Chem Lett 2022; 13:7870-7880. [PMID: 35979996 DOI: 10.1021/acs.jpclett.2c01075] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
The applied potential, time, and water content are crucial factors in the electrochemical anodization process because the growth of one-dimensional nanotubes can be accelerated by enhancing the corrosive effect. We investigated the effect of the water content on the resistive switching (RS) properties of Ti foils by anodizing the foils and varying the water content in an electrolyte (1-10 vol %). By increasing the water content, we facilitated a slow transition from nanopores to nanotubes and realized an increase in the tube wall diameter and tube length. All of the fabricated memristive devices exhibited a reliable and reproducible bipolar resistive switching effect. The optimized device exhibited bipolar RS properties with good dc endurance (104 cycles) and data retention capability (105 s). Our results suggest that as the water content increases to 5 vol %, the RS process improves; further increases in the water content impair the RS process.
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Affiliation(s)
- Kiran A Nirmal
- School of Electrical Engineering, Korea University, Anam-ro 145, Seongbuk-gu, Seoul 02841, Republic of Korea
| | - Ganesh S Nhivekar
- Department of Electronics, Yashavantrao Chavan Institute of Science, Satara 415 001, India
| | - Atul C Khot
- School of Electrical Engineering, Korea University, Anam-ro 145, Seongbuk-gu, Seoul 02841, Republic of Korea
| | - Tukaram D Dongale
- Computational Electronics and Nanoscience Research Laboratory, School of Nanoscience and Biotechnology, Shivaji University, Kolhapur 416 004, India
| | - Tae Geun Kim
- School of Electrical Engineering, Korea University, Anam-ro 145, Seongbuk-gu, Seoul 02841, Republic of Korea
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Li M, Hong Q, Wang X. Memristor-based circuit implementation of Competitive Neural Network based on online unsupervised Hebbian learning rule for pattern recognition. Neural Comput Appl 2022. [DOI: 10.1007/s00521-021-06361-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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Curado EMF, Melgar NB, Nobre FD. External Stimuli on Neural Networks: Analytical and Numerical Approaches. ENTROPY 2021; 23:e23081034. [PMID: 34441174 PMCID: PMC8393424 DOI: 10.3390/e23081034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/08/2021] [Revised: 08/03/2021] [Accepted: 08/05/2021] [Indexed: 11/26/2022]
Abstract
Based on the behavior of living beings, which react mostly to external stimuli, we introduce a neural-network model that uses external patterns as a fundamental tool for the process of recognition. In this proposal, external stimuli appear as an additional field, and basins of attraction, representing memories, arise in accordance with this new field. This is in contrast to the more-common attractor neural networks, where memories are attractors inside well-defined basins of attraction. We show that this procedure considerably increases the storage capabilities of the neural network; this property is illustrated by the standard Hopfield model, which reveals that the recognition capacity of our model may be enlarged, typically, by a factor 102. The primary challenge here consists in calibrating the influence of the external stimulus, in order to attenuate the noise generated by memories that are not correlated with the external pattern. The system is analyzed primarily through numerical simulations. However, since there is the possibility of performing analytical calculations for the Hopfield model, the agreement between these two approaches can be tested—matching results are indicated in some cases. We also show that the present proposal exhibits a crucial attribute of living beings, which concerns their ability to react promptly to changes in the external environment. Additionally, we illustrate that this new approach may significantly enlarge the recognition capacity of neural networks in various situations; with correlated and non-correlated memories, as well as diluted, symmetric, or asymmetric interactions (synapses). This demonstrates that it can be implemented easily on a wide diversity of models.
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Wang J, Zhuge X, Zhuge F. Hybrid oxide brain-inspired neuromorphic devices for hardware implementation of artificial intelligence. SCIENCE AND TECHNOLOGY OF ADVANCED MATERIALS 2021; 22:326-344. [PMID: 34025215 PMCID: PMC8128179 DOI: 10.1080/14686996.2021.1911277] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/09/2023]
Abstract
The state-of-the-art artificial intelligence technologies mainly rely on deep learning algorithms based on conventional computers with classical von Neumann computing architectures, where the memory and processing units are separated resulting in an enormous amount of energy and time consumed in the data transfer process. Inspired by the human brain acting like an ultra-highly efficient biological computer, neuromorphic computing is proposed as a technology for hardware implementation of artificial intelligence. Artificial synapses are the main component of a neuromorphic computing architecture. Memristors are considered to be a relatively ideal candidate for artificial synapse applications due to their high scalability and low power consumption. Oxides are most widely used in memristors due to the ease of fabrication and high compatibility with complementary metal-oxide-semiconductor processes. However, oxide memristors suffer from unsatisfactory stability and reliability. Oxide-based hybrid structures can effectively improve the device stability and reliability, therefore providing a promising prospect for the application of oxide memristors to neuromorphic computing. This work reviews the recent advances in the development of hybrid oxide memristive synapses. The discussion is organized according to the blending schemes as well as the working mechanisms of hybrid oxide memristors.
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Affiliation(s)
- Jingrui Wang
- School of Electronic and Information Engineering, Ningbo University of Technology, Ningbo, China
- Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China
| | - Xia Zhuge
- School of Electronic and Information Engineering, Ningbo University of Technology, Ningbo, China
| | - Fei Zhuge
- Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China
- Center of Materials Science and Optoelectronics Engineering, University of Chinese Academy of Sciences, Beijing, China
- Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China
- CONTACT Fei Zhuge Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo315201, China
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Covi E, Donati E, Liang X, Kappel D, Heidari H, Payvand M, Wang W. Adaptive Extreme Edge Computing for Wearable Devices. Front Neurosci 2021; 15:611300. [PMID: 34045939 PMCID: PMC8144334 DOI: 10.3389/fnins.2021.611300] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2020] [Accepted: 03/24/2021] [Indexed: 11/13/2022] Open
Abstract
Wearable devices are a fast-growing technology with impact on personal healthcare for both society and economy. Due to the widespread of sensors in pervasive and distributed networks, power consumption, processing speed, and system adaptation are vital in future smart wearable devices. The visioning and forecasting of how to bring computation to the edge in smart sensors have already begun, with an aspiration to provide adaptive extreme edge computing. Here, we provide a holistic view of hardware and theoretical solutions toward smart wearable devices that can provide guidance to research in this pervasive computing era. We propose various solutions for biologically plausible models for continual learning in neuromorphic computing technologies for wearable sensors. To envision this concept, we provide a systematic outline in which prospective low power and low latency scenarios of wearable sensors in neuromorphic platforms are expected. We successively describe vital potential landscapes of neuromorphic processors exploiting complementary metal-oxide semiconductors (CMOS) and emerging memory technologies (e.g., memristive devices). Furthermore, we evaluate the requirements for edge computing within wearable devices in terms of footprint, power consumption, latency, and data size. We additionally investigate the challenges beyond neuromorphic computing hardware, algorithms and devices that could impede enhancement of adaptive edge computing in smart wearable devices.
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Affiliation(s)
| | - Elisa Donati
- Institute of Neuroinformatics, University of Zurich, Eidgenössische Technische Hochschule Zürich (ETHZ), Zurich, Switzerland
| | - Xiangpeng Liang
- Microelectronics Lab, James Watt School of Engineering, University of Glasgow, Glasgow, United Kingdom
| | - David Kappel
- Bernstein Center for Computational Neuroscience, III Physikalisches Institut–Biophysik, Georg-August Universität, Göttingen, Germany
| | - Hadi Heidari
- Microelectronics Lab, James Watt School of Engineering, University of Glasgow, Glasgow, United Kingdom
| | - Melika Payvand
- Institute of Neuroinformatics, University of Zurich, Eidgenössische Technische Hochschule Zürich (ETHZ), Zurich, Switzerland
| | - Wei Wang
- The Andrew and Erna Viterbi Department of Electrical Engineering, Technion–Israel Institute of Technology, Haifa, Israel
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Park S, Klett S, Ivanov T, Knauer A, Doell J, Ziegler M. Engineering Method for Tailoring Electrical Characteristics in TiN/TiOx/HfOx/Au Bi-Layer Oxide Memristive Devices. FRONTIERS IN NANOTECHNOLOGY 2021. [DOI: 10.3389/fnano.2021.670762] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023] Open
Abstract
Memristive devices have led to an increased interest in neuromorphic systems. However, different device requirements are needed for the multitude of computation schemes used there. While linear and time-independent conductance modulation is required for machine learning, non-linear and time-dependent properties are necessary for neurobiologically realistic learning schemes. In this context, an adaptation of the resistance switching characteristic is necessary with regard to the desired application. Recently, bi-layer oxide memristive systems have proven to be a suitable device structure for this purpose, as they combine the possibility of a tailored memristive characteristic with low power consumption and uniformity of the device performance. However, this requires technological solutions that allow for precise adjustment of layer thicknesses, defect densities in the oxide layers, and suitable area sizes of the active part of the devices. For this purpose, we have investigated the bi-layer oxide system TiN/TiOx/HfOx/Au with respect to tailored I-V non-linearity, the number of resistance states, electroforming, and operating voltages. Therefore, a 4-inch full device wafer process was used. This process allows a systematic investigation, i.e., the variation of physical device parameters across the wafer as well as a statistical evaluation of the electrical properties with regard to the variability from device to device and from cycle to cycle. For the investigation, the thickness of the HfOx layer was varied between 2 and 8 nm, and the size of the active area of devices was changed between 100 and 2,500 µm2. Furthermore, the influence of the HfOx deposition condition was investigated, which influences the conduction mechanisms from a volume-based, filamentary to an interface-based resistive switching mechanism. Our experimental results are supported by numerical simulations that show the contribution of the HfOx film in the bi-layer memristive system and guide the development of a targeting device.
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Carlos E, Branquinho R, Martins R, Kiazadeh A, Fortunato E. Recent Progress in Solution-Based Metal Oxide Resistive Switching Devices. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2021; 33:e2004328. [PMID: 33314334 DOI: 10.1002/adma.202004328] [Citation(s) in RCA: 33] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/25/2020] [Revised: 09/08/2020] [Indexed: 06/12/2023]
Abstract
Metal oxide resistive switching memories have been a crucial component for the requirements of the Internet of Things, which demands ultra-low power and high-density devices with new computing principles, exploiting low cost green products and technologies. Most of the reported resistive switching devices use conventional methods (physical and chemical vapor deposition), which are quite expensive due to their up-scale production. Solution-processing methods have been improved, being now a reliable technology that offers many advantages for resistive random-access memory (RRAM) such as high versatility, large area uniformity, transparency, low-cost and a simple fabrication of two-terminal structures. Solution-based metal oxide RRAM devices are emergent and promising non-volatile memories for future electronics. In this review, a brief history of non-volatile memories is highlighted as well as the present status of solution-based metal oxide resistive random-access memory (S-RRAM). Then, a focus on describing the solution synthesis parameters of S-RRAMs which induce a massive influence in the overall performance of these devices is discussed. Next, a precise analysis is performed on the metal oxide thin film and electrode interface and the recent advances on S-RRAM that will allow their large-area manufacturing. Finally, the figures of merit and the main challenges in S-RRAMs are discussed and future trends are proposed.
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Affiliation(s)
- Emanuel Carlos
- CENIMAT/i3N Departamento de Ciência dos Materiais, Faculdade de Ciências e Tecnologia (FCT), Universidade NOVA de Lisboa (UNL), and CEMOP/UNINOVA, Caparica, 2829-516, Portugal
| | - Rita Branquinho
- CENIMAT/i3N Departamento de Ciência dos Materiais, Faculdade de Ciências e Tecnologia (FCT), Universidade NOVA de Lisboa (UNL), and CEMOP/UNINOVA, Caparica, 2829-516, Portugal
| | - Rodrigo Martins
- CENIMAT/i3N Departamento de Ciência dos Materiais, Faculdade de Ciências e Tecnologia (FCT), Universidade NOVA de Lisboa (UNL), and CEMOP/UNINOVA, Caparica, 2829-516, Portugal
| | - Asal Kiazadeh
- CENIMAT/i3N Departamento de Ciência dos Materiais, Faculdade de Ciências e Tecnologia (FCT), Universidade NOVA de Lisboa (UNL), and CEMOP/UNINOVA, Caparica, 2829-516, Portugal
| | - Elvira Fortunato
- CENIMAT/i3N Departamento de Ciência dos Materiais, Faculdade de Ciências e Tecnologia (FCT), Universidade NOVA de Lisboa (UNL), and CEMOP/UNINOVA, Caparica, 2829-516, Portugal
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Zarrin PS, Zahari F, Mahadevaiah MK, Perez E, Kohlstedt H, Wenger C. Neuromorphic on-chip recognition of saliva samples of COPD and healthy controls using memristive devices. Sci Rep 2020; 10:19742. [PMID: 33184439 PMCID: PMC7661727 DOI: 10.1038/s41598-020-76823-7] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2020] [Accepted: 10/26/2020] [Indexed: 11/09/2022] Open
Abstract
Chronic Obstructive Pulmonary Disease (COPD) is a life-threatening lung disease, affecting millions of people worldwide. Implementation of Machine Learning (ML) techniques is crucial for the effective management of COPD in home-care environments. However, shortcomings of cloud-based ML tools in terms of data safety and energy efficiency limit their integration with low-power medical devices. To address this, energy efficient neuromorphic platforms can be used for the hardware-based implementation of ML methods. Therefore, a memristive neuromorphic platform is presented in this paper for the on-chip recognition of saliva samples of COPD patients and healthy controls. Results of its performance evaluations showed that the digital neuromorphic chip is capable of recognizing unseen COPD samples with accuracy and sensitivity values of 89% and 86%, respectively. Integration of this technology into personalized healthcare devices will enable the better management of chronic diseases such as COPD.
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Affiliation(s)
- Pouya Soltani Zarrin
- IHP-Leibniz-Institut Fuer Innovative Mikroelektronik, 15236, Frankfurt an der Oder, Germany.
| | - Finn Zahari
- Nanoelectronics, Faculty of Engineering, Kiel University, 24143, Kiel, Germany
| | | | - Eduardo Perez
- IHP-Leibniz-Institut Fuer Innovative Mikroelektronik, 15236, Frankfurt an der Oder, Germany
| | - Hermann Kohlstedt
- Nanoelectronics, Faculty of Engineering, Kiel University, 24143, Kiel, Germany
| | - Christian Wenger
- IHP-Leibniz-Institut Fuer Innovative Mikroelektronik, 15236, Frankfurt an der Oder, Germany.,BTU Cottbus-Senftenberg, 01968, Cottbus, Germany
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Zhang M, Liu H, Cui Q, Han P, Yang S, Shi M, Zhang T, Zhang Z, Li Z. Tendon stem cell-derived exosomes regulate inflammation and promote the high-quality healing of injured tendon. Stem Cell Res Ther 2020; 11:402. [PMID: 32943109 PMCID: PMC7499865 DOI: 10.1186/s13287-020-01918-x] [Citation(s) in RCA: 56] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2020] [Revised: 07/30/2020] [Accepted: 09/01/2020] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND Tendon stem cells (TSCs) have been reported to hold promises for tendon repair and regeneration. However, less is known about the effects of exosomes derived from TSCs. Therefore, we aimed to clarify the healing effects of TSC-derived exosomes (TSC-Exos) on tendon injury. METHODS The Achilles tendons of Sprague-Dawley male rats were used for primary culture of TSCs and tenocytes, and exosomes were isolated from TSCs. The proliferation of tenocytes induced by TSC-Exos was analyzed using an EdU assay; cell migration was measured by cell scratch and transwell assays. We used western blot to analyze the role of the PI3K/AKT and MAPK/ERK1/2 signaling pathways. In vivo, Achilles tendon injury models were created in Sprague-Dawley rats. Rats (n = 54) were then randomly assigned to three groups: the TSC-Exos group, the GelMA group, and the control group. We used immunofluorescence to detect changes in the expression of inflammatory and apoptotic markers at 1 week after surgery. Histology and changes in expression of extracellular matrix (ECM)-related indices were assessed by hematoxylin-eosin (H&E) staining and immunohistochemistry at 2 and 8 weeks. The collagen fiber diameter of the healing tendon was analyzed at 8 weeks by transmission electron microscopy (TEM). RESULTS TSC-Exos were taken up by tenocytes, which promoted the proliferation and migration of cells in a dose-dependent manner; this process may depend on the activation of the PI3K/AKT and MAPK/ERK1/2 signaling pathways. At 1 week after surgery, we found that inflammation and apoptosis were significantly suppressed by TSC-Exos. At 2 and 8 weeks, tendons treated with TSC-Exos showed more continuous and regular arrangement in contrast to disorganized tendons in the GelMA and control groups, and TSC-Exos may help regulate ECM balance and inhibited scar formation. Further, at 8 weeks, the TSC-Exos group had a larger diameter of collagen compared to the control group. CONCLUSIONS Our data suggest that TSC-Exos could promote high-quality healing of injured tendon, which may be a promising therapeutic approach for tendon injury.
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Affiliation(s)
- Mingzhao Zhang
- Department of Pediatric Surgery, The Second Affiliated Hospital of Harbin Medical University, No. 246, Xuefu Road, Nangang District, Harbin, 150001, China
| | - Hengchen Liu
- Department of Pediatric Surgery, The Second Affiliated Hospital of Harbin Medical University, No. 246, Xuefu Road, Nangang District, Harbin, 150001, China
| | - Qingbo Cui
- Department of Pediatric Surgery, The Second Affiliated Hospital of Harbin Medical University, No. 246, Xuefu Road, Nangang District, Harbin, 150001, China
| | - Peilin Han
- Department of Pediatric Surgery, The Second Affiliated Hospital of Harbin Medical University, No. 246, Xuefu Road, Nangang District, Harbin, 150001, China
| | - Shulong Yang
- Department of Pediatric Surgery, The Second Affiliated Hospital of Harbin Medical University, No. 246, Xuefu Road, Nangang District, Harbin, 150001, China
| | - Manyu Shi
- Department of Pediatric Surgery, The Second Affiliated Hospital of Harbin Medical University, No. 246, Xuefu Road, Nangang District, Harbin, 150001, China
| | - Tingting Zhang
- Department of Pediatric Surgery, The Second Affiliated Hospital of Harbin Medical University, No. 246, Xuefu Road, Nangang District, Harbin, 150001, China
| | - Zenan Zhang
- Department of Pediatric Surgery, The Second Affiliated Hospital of Harbin Medical University, No. 246, Xuefu Road, Nangang District, Harbin, 150001, China
| | - Zhaozhu Li
- Department of Pediatric Surgery, The Second Affiliated Hospital of Harbin Medical University, No. 246, Xuefu Road, Nangang District, Harbin, 150001, China.
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Analogue pattern recognition with stochastic switching binary CMOS-integrated memristive devices. Sci Rep 2020; 10:14450. [PMID: 32879397 PMCID: PMC7467933 DOI: 10.1038/s41598-020-71334-x] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2020] [Accepted: 08/14/2020] [Indexed: 11/29/2022] Open
Abstract
Biological neural networks outperform current computer technology in terms of power consumption and computing speed while performing associative tasks, such as pattern recognition. The analogue and massive parallel in-memory computing in biology differs strongly from conventional transistor electronics that rely on the von Neumann architecture. Therefore, novel bio-inspired computing architectures have been attracting a lot of attention in the field of neuromorphic computing. Here, memristive devices, which serve as non-volatile resistive memory, are employed to emulate the plastic behaviour of biological synapses. In particular, CMOS integrated resistive random access memory (RRAM) devices are promising candidates to extend conventional CMOS technology to neuromorphic systems. However, dealing with the inherent stochasticity of resistive switching can be challenging for network performance. In this work, the probabilistic switching is exploited to emulate stochastic plasticity with fully CMOS integrated binary RRAM devices. Two different RRAM technologies with different device variabilities are investigated in detail, and their potential applications in stochastic artificial neural networks (StochANNs) capable of solving MNIST pattern recognition tasks is examined. A mixed-signal implementation with hardware synapses and software neurons combined with numerical simulations shows that the proposed concept of stochastic computing is able to process analogue data with binary memory cells.
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Halter M, Bégon-Lours L, Bragaglia V, Sousa M, Offrein BJ, Abel S, Luisier M, Fompeyrine J. Back-End, CMOS-Compatible Ferroelectric Field-Effect Transistor for Synaptic Weights. ACS APPLIED MATERIALS & INTERFACES 2020; 12:17725-17732. [PMID: 32192333 DOI: 10.1021/acsami.0c00877] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Neuromorphic computing architectures enable the dense colocation of memory and processing elements within a single circuit. This colocation removes the communication bottleneck of transferring data between separate memory and computing units as in standard von Neuman architectures for data-critical applications including machine learning. The essential building blocks of neuromorphic systems are nonvolatile synaptic elements such as memristors. Key memristor properties include a suitable nonvolatile resistance range, continuous linear resistance modulation, and symmetric switching. In this work, we demonstrate voltage-controlled, symmetric and analog potentiation and depression of a ferroelectric Hf0.57Zr0.43O2 (HZO) field-effect transistor (FeFET) with good linearity. Our FeFET operates with low writing energy (fJ) and fast programming time (40 ns). Retention measurements have been performed over 4 bit depth with low noise (1%) in the tungsten oxide (WOx) readout channel. By adjusting the channel thickness from 15 to 8 nm, the on/off ratio of the FeFET can be engineered from 1 to 200% with an on-resistance ideally >100 kΩ, depending on the channel geometry. The device concept is using earth-abundant materials and is compatible with a back end of line (BEOL) integration into complementary metal-oxide-semiconductor (CMOS) processes. It has therefore a great potential for the fabrication of high-density, large-scale integrated arrays of artificial analog synapses.
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Affiliation(s)
- Mattia Halter
- IBM Research GmbH-Zurich Research Laboratory, CH-8803 Rüschlikon, Switzerland
- Integrated Systems Laboratory, ETH Zurich, CH-8092 Zurich, Switzerland
| | - Laura Bégon-Lours
- IBM Research GmbH-Zurich Research Laboratory, CH-8803 Rüschlikon, Switzerland
| | - Valeria Bragaglia
- IBM Research GmbH-Zurich Research Laboratory, CH-8803 Rüschlikon, Switzerland
| | - Marilyne Sousa
- IBM Research GmbH-Zurich Research Laboratory, CH-8803 Rüschlikon, Switzerland
| | - Bert Jan Offrein
- IBM Research GmbH-Zurich Research Laboratory, CH-8803 Rüschlikon, Switzerland
| | - Stefan Abel
- IBM Research GmbH-Zurich Research Laboratory, CH-8803 Rüschlikon, Switzerland
| | - Mathieu Luisier
- Integrated Systems Laboratory, ETH Zurich, CH-8092 Zurich, Switzerland
| | - Jean Fompeyrine
- IBM Research GmbH-Zurich Research Laboratory, CH-8803 Rüschlikon, Switzerland
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Abstract
Artificial intelligence (AI) has the ability of revolutionizing our lives and society in a radical way, by enabling machine learning in the industry, business, health, transportation, and many other fields. The ability to recognize objects, faces, and speech, requires, however, exceptional computational power and time, which is conflicting with the current difficulties in transistor scaling due to physical and architectural limitations. As a result, to accelerate the progress of AI, it is necessary to develop materials, devices, and systems that closely mimic the human brain. In this work, we review the current status and challenges on the emerging neuromorphic devices for brain-inspired computing. First, we provide an overview of the memory device technologies which have been proposed for synapse and neuron circuits in neuromorphic systems. Then, we describe the implementation of synaptic learning in the two main types of neural networks, namely the deep neural network and the spiking neural network (SNN). Bio-inspired learning, such as the spike-timing dependent plasticity scheme, is shown to enable unsupervised learning processes which are typical of the human brain. Hardware implementations of SNNs for the recognition of spatial and spatio-temporal patterns are also shown to support the cognitive computation in silico. Finally, we explore the recent advances in reproducing bio-neural processes via device physics, such as insulating-metal transitions, nanoionics drift/diffusion, and magnetization flipping in spintronic devices. By harnessing the device physics in emerging materials, neuromorphic engineering with advanced functionality, higher density and better energy efficiency can be developed.
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Affiliation(s)
- Daniele Ielmini
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano and IU.NET, Piazza L. da Vinci 32 - 20133 Milano, Italy
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Emelyanov AV, Nikiruy KE, Serenko AV, Sitnikov AV, Presnyakov MY, Rybka RB, Sboev AG, Rylkov VV, Kashkarov PK, Kovalchuk MV, Demin VA. Self-adaptive STDP-based learning of a spiking neuron with nanocomposite memristive weights. NANOTECHNOLOGY 2020; 31:045201. [PMID: 31578002 DOI: 10.1088/1361-6528/ab4a6d] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Neuromorphic systems consisting of artificial neurons and memristive synapses could provide a much better performance and a significantly more energy-efficient approach to the implementation of different types of neural network algorithms than traditional hardware with the Von-Neumann architecture. However, the memristive weight adjustment in the formal neuromorphic networks by the standard back-propagation techniques suffers from poor device-to-device reproducibility. One of the most promising approaches to overcome this problem is to use local learning rules for spiking neuromorphic architectures which potentially could be adaptive to the variability issue mentioned above. Different kinds of local rules for learning spiking systems are mostly realized on a bio-inspired spike-time-dependent plasticity (STDP) mechanism, which is an improved type of classical Hebbian learning. Whereas the STDP-like mechanism has already been shown to emerge naturally in memristive devices, the demonstration of its self-adaptive learning property, potentially overcoming the variability problem, is more challenging and has yet to be reported. Here we experimentally demonstrate an STDP-based learning protocol that ensures self-adaptation of the memristor resistive states, after only a very few spikes, and makes the plasticity sensitive only to the input signal configuration, but neither to the initial state of the devices nor their device-to-device variability. Then, it is shown that the self-adaptive learning of a spiking neuron with memristive weights on rate-coded patterns could also be realized with hardware-based STDP rules. The experiments have been carried out with nanocomposite-based (Co40Fe40B20) х (LiNbO3-y )100-х memristive structures, but their results are believed to be applicable to a wide range of memristive devices. All the experimental data were supported and extended by numerical simulations. There is a hope that the obtained results pave the way for building up reliable spiking neuromorphic systems composed of partially unreliable analog memristive elements, with a more complex architecture and the capability of unsupervised learning.
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Affiliation(s)
- A V Emelyanov
- National Research Center 'Kurchatov Institute', 123182 Moscow, Russia. Moscow Institute of Physics and Technology (State University), 141700 Dolgoprudny, Moscow Region, Russia
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Milo V, Malavena G, Monzio Compagnoni C, Ielmini D. Memristive and CMOS Devices for Neuromorphic Computing. MATERIALS (BASEL, SWITZERLAND) 2020; 13:E166. [PMID: 31906325 PMCID: PMC6981548 DOI: 10.3390/ma13010166] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/28/2019] [Revised: 12/17/2019] [Accepted: 12/18/2019] [Indexed: 11/17/2022]
Abstract
Neuromorphic computing has emerged as one of the most promising paradigms to overcome the limitations of von Neumann architecture of conventional digital processors. The aim of neuromorphic computing is to faithfully reproduce the computing processes in the human brain, thus paralleling its outstanding energy efficiency and compactness. Toward this goal, however, some major challenges have to be faced. Since the brain processes information by high-density neural networks with ultra-low power consumption, novel device concepts combining high scalability, low-power operation, and advanced computing functionality must be developed. This work provides an overview of the most promising device concepts in neuromorphic computing including complementary metal-oxide semiconductor (CMOS) and memristive technologies. First, the physics and operation of CMOS-based floating-gate memory devices in artificial neural networks will be addressed. Then, several memristive concepts will be reviewed and discussed for applications in deep neural network and spiking neural network architectures. Finally, the main technology challenges and perspectives of neuromorphic computing will be discussed.
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Affiliation(s)
| | | | | | - Daniele Ielmini
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano and Italian Universities Nanoelectronics Team (IU.NET), Piazza L. da Vinci 32, 20133 Milano, Italy; (V.M.); (G.M.); (C.M.C.)
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Memristor-CMOS Hybrid Circuit for Temporal-Pooling of Sensory and Hippocampal Responses of Cortical Neurons. MATERIALS 2019; 12:ma12060875. [PMID: 30875957 PMCID: PMC6470471 DOI: 10.3390/ma12060875] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/24/2019] [Revised: 03/09/2019] [Accepted: 03/13/2019] [Indexed: 11/24/2022]
Abstract
As a software framework, Hierarchical Temporal Memory (HTM) has been developed to perform the brain’s neocortical functions, such as spatial and temporal pooling. However, it should be realized with hardware not software not only to mimic the neocortical function but also to exploit its architectural benefit. To do so, we propose a new memristor-CMOS (Complementary Metal-Oxide-Semiconductor) hybrid circuit of temporal-pooling here, which is composed of the input-layer and output-layer neurons mimicking the neocortex. In the hybrid circuit, the input-layer neurons have the proximal and basal/distal dendrites to combine sensory information with the temporal/location information from the brain’s hippocampus. Using the same crossbar architecture, the output-layer neurons can perform a prediction by integrating the temporal information on the basal/distal dendrites. For training the proposed circuit, we used only simple Hebbian learning, not the complicated backpropagation algorithm. Due to the simple hardware of Hebbian learning, the proposed hybrid circuit can be very suitable to online learning. The proposed memristor-CMOS hybrid circuit has been verified by the circuit simulation using the real memristor model. The proposed circuit has been verified to predict both the ordinal and out-of-order sequences. In addition, the proposed circuit has been tested with the external noise and memristance variation.
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