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Real‐Time Unsupervised Learning and Image Recognition via Memristive Neural Integrated Chip Based on Negative Differential Resistance of Electrochemical Metallization Cell Neuron Device
Author(s) -
Woo DaeSeong,
Kim JaeKyeong,
Park GwangHo,
Lee WooGuk,
Han MinJong,
Jin SooMin,
Shim TaeHun,
Kim JaeJoon,
Park Jinsub,
Park JeaGun
Publication year - 2025
Publication title -
small
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.785
H-Index - 236
eISSN - 1613-6829
pISSN - 1613-6810
DOI - 10.1002/smll.202407612
Subject(s) - neuromorphic engineering , spiking neural network , materials science , transistor , computer science , chip , artificial neural network , memristor , integrated circuit , nanotechnology , optoelectronics , voltage , electronic engineering , artificial intelligence , electrical engineering , engineering , telecommunications
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 2 chalcogenide layer, with a nonlinear increase in the action potential in a manner similar to a human sensory system. Moreover, to construct a fully‐structured spiking neural network (SNN), an oxygenated amorphous carbon‐based (α‐CO x ‐based) synaptic device having 32 multi‐level conductance states is designed. The designed MnIC and α‐CO x ‐based synaptic device demonstrate real‐time unsupervised learning via a spike‐timing‐dependent plasticity learning rule with an SNN. Using the trained SNN, the real‐time hand‐written digit image of a cell phone obtained from a live webcam is successfully classified, which suggests practical applications for brain‐like neuromorphic chips.