Academic literature on the topic 'Neuromorphic devices'
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Journal articles on the topic "Neuromorphic devices"
Ielmini, Daniele, and Stefano Ambrogio. "Emerging neuromorphic devices." Nanotechnology 31, no. 9 (December 9, 2019): 092001. http://dx.doi.org/10.1088/1361-6528/ab554b.
Full textGuo, Zhonghao. "Synaptic device-based neuromorphic computing in artificial intelligence." Applied and Computational Engineering 65, no. 1 (May 23, 2024): 253–59. http://dx.doi.org/10.54254/2755-2721/65/20240511.
Full textPark, Jisoo, Jihyun Shin, and Hocheon Yoo. "Heterostructure-Based Optoelectronic Neuromorphic Devices." Electronics 13, no. 6 (March 14, 2024): 1076. http://dx.doi.org/10.3390/electronics13061076.
Full textHuang, Wen, Huixing Zhang, Zhengjian Lin, Pengjie Hang, and Xing’ao Li. "Transistor-Based Synaptic Devices for Neuromorphic Computing." Crystals 14, no. 1 (January 9, 2024): 69. http://dx.doi.org/10.3390/cryst14010069.
Full textLim, Jung Wook, Su Jae Heo, Min A. Park, and Jieun Kim. "Synaptic Transistors Exhibiting Gate-Pulse-Driven, Metal-Semiconductor Transition of Conduction." Materials 14, no. 24 (December 7, 2021): 7508. http://dx.doi.org/10.3390/ma14247508.
Full textDiao, Yu, Yaoxuan Zhang, Yanran Li, and Jie Jiang. "Metal-Oxide Heterojunction: From Material Process to Neuromorphic Applications." Sensors 23, no. 24 (December 12, 2023): 9779. http://dx.doi.org/10.3390/s23249779.
Full textFeng, Chenyin, Wenwei Wu, Huidi Liu, Junke Wang, Houzhao Wan, Guokun Ma, and Hao Wang. "Emerging Opportunities for 2D Materials in Neuromorphic Computing." Nanomaterials 13, no. 19 (October 7, 2023): 2720. http://dx.doi.org/10.3390/nano13192720.
Full textKim, Dongshin, Ik-Jyae Kim, and Jang-Sik Lee. "Memory Devices for Flexible and Neuromorphic Device Applications." Advanced Intelligent Systems 3, no. 5 (January 25, 2021): 2000206. http://dx.doi.org/10.1002/aisy.202000206.
Full textHuang, Yi, Fatemeh Kiani, Fan Ye, and Qiangfei Xia. "From memristive devices to neuromorphic systems." Applied Physics Letters 122, no. 11 (March 13, 2023): 110501. http://dx.doi.org/10.1063/5.0133044.
Full textMachado, Pau, Salvador Manich, Álvaro Gómez-Pau, Rosa Rodríguez-Montañés, Mireia Bargalló González, Francesca Campabadal, and Daniel Arumí. "Programming Techniques of Resistive Random-Access Memory Devices for Neuromorphic Computing." Electronics 12, no. 23 (November 27, 2023): 4803. http://dx.doi.org/10.3390/electronics12234803.
Full textDissertations / Theses on the topic "Neuromorphic devices"
Islam, Rabiul. "Fabrication and Electrical Characterization of Organic Neuromorphic Memory Devices." Master's thesis, Department of Materials Science, TU Darmstadt, 2019. https://tuprints.ulb.tu-darmstadt.de/9208/1/Final%20Thesis%20Report_Rabiul%20Islam_2997810.pdf.
Full textHirtzlin, Tifenn. "Digital Implementation of Neuromorphic systems using Emerging Memory devices." Thesis, université Paris-Saclay, 2020. http://www.theses.fr/2020UPAST071.
Full textWhile electronics has prospered inexorably for several decades, its leading source of progress will stop in the next coming years, due to the fundamental technological limits of transistors. Nevertheless, microelectronics is currently offering a major breakthrough: in recent years, memory technologies have undergone incredible progress, opening the way for multiple research venues in embedded systems. Additionally, a major feature for future years will be the ability to integrate different technologies on the same chip. new emerging memory devices that can be embedded in the core of the CMOS, such as Resistive Random Access Memory (RRAM) or Spin Torque Magnetic Tunnel Junction (STMRAM) based on naturally intelligent inmemory-computing architecture. Three braininspired algorithms are carefully examined: Bayesian reasoning binarized neural networks, and an approach that further exploits the intrinsic behavior of components, population coding of neurons. Each of these approaches explores different aspects of in-memory computing
Lai, Qianxi. "Electrically configurable materials and devices for intelligent neuromorphic applications." Diss., Restricted to subscribing institutions, 2009. http://proquest.umi.com/pqdweb?did=1872061101&sid=1&Fmt=2&clientId=1564&RQT=309&VName=PQD.
Full textAzam, Md Ali. "Energy Efficient Spintronic Device for Neuromorphic Computation." VCU Scholars Compass, 2019. https://scholarscompass.vcu.edu/etd/6036.
Full textZaman, Ayesha. "Modeling and Experimental Characterization of Memristor Devices for Neuromorphic Computing." University of Dayton / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=dayton159636782366637.
Full textMandal, Saptarshi. "Study of Mn doped HfO2 based Synaptic Devices for Neuromorphic Applications." University of Toledo / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1384535471.
Full textWenke, Sam. "Application and Simulation of Neuromorphic Devices for use in Neural Networks." University of Cincinnati / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1523635913955071.
Full textPedró, Puig Marta. "Implementation of unsupervised learning mechanisms on OxRAM devices for neuromorphic computing applications." Doctoral thesis, Universitat Autònoma de Barcelona, 2019. http://hdl.handle.net/10803/667894.
Full textThe present thesis compiles the results of the research oriented to provide a methodology for the electrical characterization, modeling and simulation of resistive switching devices, taking into consideration neuromorphic applications based on unsupervised learning This is widely demanded today as a low-consumption solution to the following issues: on the one hand, the speed limitations that take place in data transfer between the memory and processing units that takes place in conventional computer architectures. On the other hand, the growing need for low-power computational systems that perform tasks of classification, analysis and inference of massive amounts of data (for example, for Big Data applications), together with pattern recognition, prediction of behaviors and decision-making tasks (for applications focused on Internet-of-Things, among others). Specifically, Oxide-based Resistive Random Access Memory (OxRAM) devices are investigated as candidates for the electronic implementation of synapses in physical artificial neural networks, also referred to as neuromorphic architectures. First of all, a theoretical introduction to the different electronic technologies with resistive switching and non-volatile memory properties is provided. The figures of merit demonstrated and projected of each one of them are indicated according to the International Roadmap for Devices and Systems of 2018. With this first chapter, the intention is to provide the reader with the necessary background required to understand the results outlined in the following chapters. Next, and by using a bottom-up approach divided into the three following chapters, the procedures and results of the electrical characterization and modeling of the OxRAM devices studied for the implementation of analog electronic synapses are discussed. As a starting point, it is experimentally verified that the devices meet the requirements for the indicated application. In the following chapter, two fundamental learning rules are demonstrated experimentally in order to permit the execution of an autonomous (unsupervised) learning algorithm on a neuromorphic architecture based on the tested devices. The proven learning rules allow the devices to emulate certain processes and learning mechanisms reported in the neuroscience field, such as spike-timing dependent plasticity, or the classical conditioning phenomenon, for which Pavlov’s dog experiment is replicated as to establish the foundations of associative learning, to be implemented between two or more synaptic devices. To conclude this part related to analog electronic synapses, the hardware adaptation of an unsupervised learning algorithm is proposed. The designed algorithm provides the system with the property of self-organization, in such a way that, once trained, the physical neuronal network shows a topographical organization in its output layer, which is characteristic of the sensory processing areas of the biological brain. Furthermore, the proposed design and algorithm allow the concatenation of several neuronal networks, in order to execute cognitive tasks of a more complex nature, such as the association of different attributes to the same concept, related to hierarchical computation. The last chapter is dedicated to the study of OxRAM devices when a low-power mode is considered, for the implementation of binary synapses. Again using a bottom-up perspective, the chapter begins with the electrical characterization and modeling of the devices, which in this case constitute a neuromorphic chip. A probabilistic learning rule is demonstrated, which is then used in an unsupervised on-line learning algorithm designed for the inference and prediction of periodic temporal sequences. Finally, the differences and similarities between the two algorithms described in the thesis are discussed, and a proposal is made as to how each of these can be used in a joint and complementary way.
Ignatov, Marina [Verfasser]. "Emulation of Neural Dynamics in Neuromorphic Circuits Based on Memristive Devices / Marina Ignatov." Kiel : Universitätsbibliothek Kiel, 2018. http://d-nb.info/1156601932/34.
Full textHosseini, Peiman. "Phase-change and carbon based materials for advanced memory and computing devices." Thesis, University of Exeter, 2013. http://hdl.handle.net/10871/10122.
Full textBooks on the topic "Neuromorphic devices"
Yilmaz, Yalcin, Pinaki Mazumder, and Idongesit Ebong. Neuromorphic Circuits for Nanoscale Devices. New York: River Publishers, 2022. http://dx.doi.org/10.1201/9781003338918.
Full textSuri, Manan, ed. Advances in Neuromorphic Hardware Exploiting Emerging Nanoscale Devices. New Delhi: Springer India, 2017. http://dx.doi.org/10.1007/978-81-322-3703-7.
Full textNeuromorphic Circuits for Nanoscale Devices. River Publishers, 2019.
Find full textMazumder, Pinaki, Yalcin Yilmaz, and Idongesit Ebong. Neuromorphic Circuits for Nanoscale Devices. River Publishers, 2022.
Find full textMazumder, Pinaki, Yalcin Yilmaz, and Idongesit Ebong. Neuromorphic Circuits for Nanoscale Devices. River Publishers, 2022.
Find full textMazumder, Pinaki, Yalcin Yilmaz, and Idongesit Ebong. Neuromorphic Circuits for Nanoscale Devices. River Publishers, 2022.
Find full textDong, Yibo, Min Gu, Elena Goi, Yangyundou Wang, and Zhengfen Wan. Neuromorphic Photonic Devices and Applications. Elsevier, 2023.
Find full textMazumder, Pinaki, Yalcin Yilmaz, Idongesit Ebong, and Woo Hyung Lee. Neuromorphic Circuits for Nanoscale Devices. River Publishers, 2020.
Find full textWang, Jing, Min Gu, Elena Goi, Yangyundou Wang, and Zhengfen Wan. Neuromorphic Photonic Devices and Applications. Elsevier, 2023.
Find full textWan, Qing, and Yi Shi, eds. Neuromorphic Devices for Brain‐Inspired Computing. Wiley, 2022. http://dx.doi.org/10.1002/9783527835317.
Full textBook chapters on the topic "Neuromorphic devices"
Das, Sonali. "Perovskite Based Neuromorphic Devices." In Engineering Materials, 417–46. Cham: Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-031-57663-8_12.
Full textErokhin, Victor. "Memristive Devices and Circuits." In Fundamentals of Organic Neuromorphic Systems, 1–17. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-79492-7_1.
Full textZahari, Finn, Martin Ziegler, Pouya Doerwald, Christian Wenger, and Hermann Kohlstedt. "Neuromorphic Circuits with Redox-Based Memristive Devices." In Springer Series on Bio- and Neurosystems, 43–85. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-36705-2_2.
Full textEbong, Idongesit, and Pinaki Mazumder. "Neuromorphic Building Blocks with Memristors." In Neuromorphic Circuits for Nanoscale Devices, 145–68. New York: River Publishers, 2022. http://dx.doi.org/10.1201/9781003338918-5.
Full textDanial, Loai, Parul Damahe, Purvi Agrawal, Ruchi Dhamnani, and Shahar Kvatinsky. "Neuromorphic Data Converters Using Memristors." In Emerging Computing: From Devices to Systems, 245–90. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-7487-7_8.
Full textYilmaz, Yalcin, and Pinaki Mazumder. "Multi-Level Memory Architecture." In Neuromorphic Circuits for Nanoscale Devices, 117–44. New York: River Publishers, 2022. http://dx.doi.org/10.1201/9781003338918-4.
Full textYu, Yongbin, Lefei Men, Qingqing Hu, Shouming Zhong, Nyima Tashi, Pinaki Mazumder, Idongesit Ebong, Qishui Zhong, and Xingwen Liu. "Dynamic Analysis of Memristor-based Neural Network and its Application." In Neuromorphic Circuits for Nanoscale Devices, 303–49. New York: River Publishers, 2022. http://dx.doi.org/10.1201/9781003338918-12.
Full textMazumder, Pinaki, Sing-Rong Li, and Idongesit Ebong. "Tunneling-Based Cellular Nonlinear Network Architectures for Image Processing." In Neuromorphic Circuits for Nanoscale Devices, 183–203. New York: River Publishers, 2022. http://dx.doi.org/10.1201/9781003338918-7.
Full textHu, Xiaofang, Shukai Duan, Wenbo Song, Jiagui Wu, and Pinaki Mazumder. "Memristor-based Cellular Nonlinear/Neural Network: Design, Analysis and Applications." In Neuromorphic Circuits for Nanoscale Devices, 275–301. New York: River Publishers, 2022. http://dx.doi.org/10.1201/9781003338918-11.
Full textYilmaz, Yalcin, and Pinaki Mazumder. "Image Processing by a Programmable Artificial Retina Comprising Quantum Dots and Variable Resistance Devices." In Neuromorphic Circuits for Nanoscale Devices, 255–74. New York: River Publishers, 2022. http://dx.doi.org/10.1201/9781003338918-10.
Full textConference papers on the topic "Neuromorphic devices"
"Neuromorphic and Quantum Devices." In 2018 76th Device Research Conference (DRC). IEEE, 2018. http://dx.doi.org/10.1109/drc.2018.8443299.
Full textSantoro, Francesca. "Organic neuromorphic biointerfaces." In Bioelectronic Interfaces: Materials, Devices and Applications. València: FUNDACIO DE LA COMUNITAT VALENCIANA SCITO, 2024. http://dx.doi.org/10.29363/nanoge.cybioel.2024.047.
Full textNoheda, Beatriz. "Ferroelectrics for brain-inspired devices." In Neuromorphic Materials, Devices, Circuits and Systems. València: FUNDACIO DE LA COMUNITAT VALENCIANA SCITO, 2023. http://dx.doi.org/10.29363/nanoge.neumatdecas.2023.072.
Full textNeftci, Emre, Zhenming Yu, and Nathan Lereoux. "Training-to-Learn with Memristive Devices." In Neuromorphic Materials, Devices, Circuits and Systems. València: FUNDACIO DE LA COMUNITAT VALENCIANA SCITO, 2023. http://dx.doi.org/10.29363/nanoge.neumatdecas.2023.013.
Full textHam, Donhee. "Neuroelectronic interface and neuromorphic engineering." In Neuromorphic Materials, Devices, Circuits and Systems. València: FUNDACIO DE LA COMUNITAT VALENCIANA SCITO, 2023. http://dx.doi.org/10.29363/nanoge.neumatdecas.2023.058.
Full textWu, Lingxi, Rahul Sreekumar, Rasool Sharifi, Kevin Skadron, Mircea R. Stant, and Ashish Venkat. "Hardware Trojans in eNVM Neuromorphic Devices." In 2023 Design, Automation & Test in Europe Conference & Exhibition (DATE). IEEE, 2023. http://dx.doi.org/10.23919/date56975.2023.10136984.
Full textReita, C. "Memory devices in Neuromorphic Computing Systems." In 2017 International Conference on Solid State Devices and Materials. The Japan Society of Applied Physics, 2017. http://dx.doi.org/10.7567/ssdm.2017.m-2-01.
Full textZhu, Ruomin, Sam Lilak, Alon Loeffler, Joseph Lizier, Adam Stieg, James Gimzewski, and Zdenka Kuncic. "Reservoir Computing with Neuromorphic Nanowire Networks." In Neuromorphic Materials, Devices, Circuits and Systems. València: FUNDACIO DE LA COMUNITAT VALENCIANA SCITO, 2023. http://dx.doi.org/10.29363/nanoge.neumatdecas.2023.055.
Full textFarronato, Matteo, Piergiulio Mannocci, Saverio Ricci, Alessandro Bricalli, Margherita Melegari, Christian Monzio Compagnoni, and Daniele Ielmini. "Memtransistor devices based on MoS2 for neuromorphic computing." In Neuromorphic Materials, Devices, Circuits and Systems. València: FUNDACIO DE LA COMUNITAT VALENCIANA SCITO, 2023. http://dx.doi.org/10.29363/nanoge.neumatdecas.2023.042.
Full textVenkatesan, T. "Robust Resistive and Mem-devices for Neuromorphic Circuits." In Neuromorphic Materials, Devices, Circuits and Systems. València: FUNDACIO DE LA COMUNITAT VALENCIANA SCITO, 2023. http://dx.doi.org/10.29363/nanoge.neumatdecas.2023.007.
Full textReports on the topic "Neuromorphic devices"
Gall, W. E. Brain-Based Devices for Neuromorphic Computer Systems. Fort Belvoir, VA: Defense Technical Information Center, July 2013. http://dx.doi.org/10.21236/ada587348.
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