Real‐Time Unsupervised Learning and Image Recognition via Memristive Neural Integrated Chip Based on Negative Differential Resistance of Electrochemical Metallization Cell Neuron Device (Small 21/2025)
Source abstract
Abstract
Memristive-Neural Integrated Chip The human brain can process spatiotemporal information more efficiently than contemporary computers. This image illustrates empirical evidence of biological brain functions via capacitorless memristive-neural integrated chip (MnIC) and an oxygenated-amorphous-carbon-based synaptic device. The MnIC successfully demonstrates real-time unsupervised learning and image recognition in a spiking neural network, showcasing a significant advancement in practical applications. More in article number 2407612, Jinsub Park, Jea-Gun Park, and co-workers.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Neural Networks and Applications · Machine Learning and ELM · Advanced Memory and Neural Computing
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Affiliations
Department of Nano‐scale Semiconductor Engineering Hanyang University Seoul 04763 Republic of Korea
Department of Electronic Engineering Hanyang University Seoul 04763 Republic of Korea
SK Hynix Inc. Icheon Kyunggi‐do 17336 Republic of Korea
Advanced Semiconductor Materials and Devices Development Center Hanyang University Seoul 04763 Republic of Korea
Department of Electrical and Computer Engineering Seoul National University Seoul 08826 Republic of Korea
References
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