ArticleSmall (Weinheim an der Bergstrasse, Germany)2025
Real-Time Unsupervised Learning and Image Recognition via Memristive Neural Integrated Chip Based on Negative Differential Resistance of Electrochemical Metallization Cell Neuron Device.
Article in Small (Weinheim an der Bergstrasse, Germany), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- Real-Time Unsupervised Learning and Image Recognition via Memristive Neural Integrated Chip Based on Negative Differential Resistance of Electrochemical Metallization Cell Neuron Device.Small (Weinheim an der Bergstrasse, Germany) · 2025Article
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10 authors.
Funding
Abstract
Spiking neurons are essential for building energy-efficient biomimetic spatiotemporal systems because they communicate with other neurons using sparse and binary signals. However, the achievable high density of artificial neurons having a capacitor for emulating the integrate function of biological neurons has a limit. Furthermore, a low-voltage operation (<1.0 V) is essential for connecting with modern complementary metal-oxide-semiconductor-field-effect-transistor-based (C-MOSFET-based) integrated circuits. Here, a capacitorless memristive-neural integrated chip (MnIC) based on the negative differential resistance of the electrochemical metallization cell designed using a 28-nm C-MOSFET process in a foundry is reported. The fabricated MnIC exhibits extremely low-voltage operation (<0.7 V) via the rupture dynamics of Ag filaments formed in the GeS
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