Real‐Time Unsupervised Learning and Image Recognition via Memristive Neural Integrated Chip Based on Negative Differential Resistance of Electrochemical Metallization Cell Neuron Device

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 GeS₂ 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.