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-COx-based) synaptic device having 32 multi-level conductance states is designed. The designed MnIC and α-COx-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.