(Digital Presentation) Electrochemical Metallization Cell Based Memristive Neuron Chip Fabricated with 28nm CMOS Process for Real-Time Unsupervised Learning and Pattern Recognition

Abstract

Spiking neurons communicate with other neurons using sparse and binary signals in a human brain, so they can achieve real-time processing of the information with ultra-low power consumption[1,2]. Thereby, spiking neurons are essential elements for building an energy-efficient biomimetic spatiotemporal system. Recently, to emulate the behavior of biological neuron, many researches for memristive device-based neurons with peripheral circuits (i.e., sense-amplifier or reset circuit)[3] and complementary metal-oxide-semiconductor (CMOS) neurons with capacitors have been reported[4]. Most of the reported memristive device-based neurons required a high operation voltage (>1.2 V) for emulating integrate function of a biological neuron. In addition, complementary metal-oxide-semiconductor-based neurons could not achieve high neuronal density due to using a capacitor in emulating integrate function.In this study, therefore, we propose an electrochemical metallization cell based memristive neuron chip fabricated with 28-nm CMOS process having a low operation voltage (<0.7 V) for emulating integrate function. In addition, since the proposed electrochemical metallization cell based memristive neuron chip does not require a capacitor for emulating integrate function, it can realize a high neuronal density. The memristive neuron chip exhibited a typical integrate-and-fire function; particularly, the frequency of generating a spiking output signal exponentially increased with the input voltage amplitude. Moreover, a spiking neural network was designed using the memristive neuron chip and the software program consisting of a crossbar synaptic memristor array, simplified spike-timing-dependent-plasticity learning rule, and current-to-voltage converters. Using the co-designed spiking neural network with software and hardware, real-time unsupervised learning was realized. Finally, using the trained spiking neural network, a real-time classification for the MNIST hand-written image taken by a live webcam was successfully performed in an inference process. Acknowledgement This research was supported by National R&D Program through the National Research Foundation of Korea(NRF) funded by Ministry of Science and ICT(2021M3F3A2A01037733). Reference Mead, C. Neuromorphic Electronic Systems. Encycl. Comput. Neurosci. 78, 1979–1979 (2015).Douglas, R. Neuromorphic Analogue VLSI. Annu. Rev. Neurosci. 18, 255–281 (1995).Tuma, T., Pantazi, A., Le Gallo, M., Sebastian, A. & Eleftheriou, E. Stochastic phase-change neurons. Nat. Nanotechnol. 11, 693–699 (2016).Aamir, S. A., Müller, P., Hartel, A., Schemmel, J. & Meier, K. A highly tunable 65-nm CMOS LIF neuron for a large scale neuromorphic system. Eur. Solid-State Circuits Conf. 2016-Octob, 71–74 (2016). Figure 1