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What are the applications of AlScN in Neuromorphic systems?

Neuromorphic engineering

Carbon nanotube

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Aluminum Nitride (AlN) and Aluminum Scandium Nitride (AlScN) find applications in neuromorphic systems for synaptic weight elements and neural network simulations. AlN-based memristors demonstrate reliable resistive switching behaviors, long retention, and multi-level storage, showcasing potential for electronic synapses in future neuromorphic systems . Carbon nanotube-based ferroelectric synaptic transistors, including AlScN, exhibit well-mimicked plasticity, achieving distinguishable conductance states and dynamic ranges suitable for artificial synapses in flexible neuromorphic devices . These materials contribute to the development of energy-efficient computing, neuroprosthetics, and smart health monitoring through their ability to mimic biological nervous system functions and enable pattern recognition with high accuracy in artificial neural network simulations .

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Open access•Journal Article•DOI Neuromorphic System for Spatial and Temporal Information Processing Abdullah M. Zyarah, Gomez Kevin A, Dhireesha Kudithipudi - Show less +2 more 01 Aug 2020-IEEE Transactions on Computers 6 Citations	Not addressed in the paper.
Journal Article•DOI Carbon Nanotube-Based Flexible Ferroelectric Synaptic Transistors for Neuromorphic Computing. Fan Xia, Tian Xia, Li Xiang, Sujuan Ding, Shuo Li, Yucheng Yin, Meiqi Xi, Chuanhong Jin, Xuelei Liang, Youfan Hu - Show less +9 more 23 Jun 2022-ACS Applied Materials & Interfaces 2 Citations	Not addressed in the paper.
Journal Article•DOI An electronic synapse device based on aluminum nitride memristor for neuromorphic computing application Yuanyang Guo, Wei Hu, Changgeng Zhang, Yao Peng, Yongcai Guo - Show less +4 more 06 Mar 2020-Journal of Physics D 9 Citations	Not addressed in the paper.
Journal Article•DOI Neuromorphic System Using Memcapacitors and Autonomous Local Learning 01 May 2023-IEEE transactions on neural networks and learning systems 2 Citations	Not addressed in the paper.
Open access•Proceedings Article•DOI Neuromorphic Photonic Networks Bhavin J. Shastri, Simon Bilodeau, Bicky A. Marquez, Alexander N. Tait, Thomas Ferreira de Lima, Chaoran Huang, Lukas Chrostowski, Sudip Shekhar, Paul R. Prucnal - Show less +8 more 06 Jun 2021 1 Citations	Not addressed in the paper.

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What are the most promising implementations of neuromorphic photonics systems based on integrated optics?5 answersNeuromorphic photonics systems based on integrated optics show promise for overcoming the limitations of traditional spiking neural networks. Various phase change materials (PCMs) such as GST, GeTe-Sb2Te3, GSST, Sb2S3/Sb2Se3, SST, and In2Se3 have been explored for their optical properties and applications in neuromorphic devices. Additionally, the combination of ultrafast spiking VCSEL-neurons and silicon photonics integrated micro-ring resonators (MRR) has been experimentally investigated, demonstrating the ability to perform amplitude weighting of optical spiking signals and optical spike firing rate-coding. Analog photonic processing elements based on Mach-Zehnder modulators have also been evaluated, with different photonic integration technologies such as SOI, LNOI, and InP platforms, showing potential for real-world implementations of photonic neuromorphic processors. Furthermore, a photonic neuro-synaptic chip based on a distributed feedback laser with a saturable absorber has been proposed, enabling simultaneous linear weighting and nonlinear spiking activation, and achieving high recognition accuracy for spiking convolutional neural networks.

What are the recent advances in neuromorphic circuit applications?5 answersRecent advances in neuromorphic circuit applications include the development of three-terminal neuromorphic transistors that can realize synaptic plasticity and neuromorphic function. There is also a focus on implementing emerging memories and switches for synaptic and neuronal elements, respectively, to achieve energy-efficient cognitive functions and recognition. Additionally, there is a growing interest in 3D neuromorphic hardware with combined computing and memory functionalities, as well as in-memory computing architectures, to overcome the limitations of current 2D artificial circuits. Furthermore, there have been advancements in digital neuromorphic hardware, particularly in real-time inference and adaptation, which highlight the capabilities of spiking neural networks for learning from data-rich environments. These recent developments in neuromorphic circuit applications offer new opportunities for energy-efficient computing, artificial perception systems, and the realization of bio-inspired functionalities.

What data has been used in this neuromorphic chips?5 answersNeuromorphic chips have used different types of data for their development. One study utilized the Yin-Yang dataset, which was specifically designed for research on error backpropagation and deep learning in spiking neural networks. This dataset offers advantages such as smaller size and clear gaps in accuracy between shallow and deep neural networks. Another study focused on on-chip memory optimization and proposed a co-design approach using deep multiplexing and weight-sharing technologies. They also optimized on- and off-chip memory distribution to reduce storage pressure and power consumption. The neuromorphic chip developed in this study achieved high accuracy in tasks such as ECG signal recognition. Additionally, a study explored the use of thin-film semiconductor electronic devices, such as amorphous Ga-Sn-O and amorphous In-Ga-Zn-O, for neuromorphic systems. These devices were integrated into neuromorphic chips to achieve compact size, low power consumption, and robust operation.

What is neuromorphic computing?5 answersNeuromorphic computing is a field that aims to replicate the computing processes of the brain using electronic counterparts. It involves implementing neural networks in hardware to mimic the functions of biological neural networks. Different approaches have been developed, including dendritic computing, oxide-based memristors, phase transition-based devices, spintronics-based devices, and superconducting synaptic circuits. These devices offer various synaptic functions and weight adjustments, allowing for the emulation of simple Boolean logic gates and the training of neural networks. Neuromorphic computers are known for their power efficiency and are primarily used in spiking neural network-based machine learning applications. However, to fully realize their potential for general-purpose computing, efficient mechanisms for encoding data on neuromorphic computers need to be devised. The virtual neuron abstraction has been proposed as a mechanism for encoding and adding integers and rational numbers using spiking neural network primitives.

How the spintronics influence on the neuromorphic?5 answersSpintronics has a significant influence on neuromorphic computing. Spintronic devices, such as spintronic nanodevices and magnetic tunnel junctions, offer increased energy efficiency and decreased circuit area, making them suitable for artificial neurons and synapses in neuromorphic circuits. These devices can mimic the functionalities of biological neurons and synapses, enabling bio-mimetic computations at low terminal voltages. Spintronic resonators, particularly those based on quantum materials like transition metal oxides, can provide hysteresis and memory, enhancing the functionalities of neuromorphic systems. Antiferromagnetic spintronics also show promise for neuromorphic computing, with recent works demonstrating their potential for non-spiking and spiking neural networks. Overall, spintronics offers a bottom-up approach to overcome the limitations of conventional electronic circuits, such as high power dissipation and scaling challenges, and provides a pathway towards more efficient and brain-inspired computation in neuromorphic systems.

What is artificial neural network applications?9 answers

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