M. J. Rozenberg, Schneegans, O., and Stoliar, P., “An ultra-compact leaky-integrate-and-fire model for building spiking neural networks”, Scientific Reports, vol. 9, p. 11123, 2019. WebsiteAbstract
We introduce an ultra-compact electronic circuit that realizes the leaky-integrate-and-fire model of artificial neurons. Our circuit has only three active devices, two transistors and a silicon controlled rectifier (SCR). We demonstrate the implementation of biologically realistic features, such as spike-frequency adaptation, a refractory period and voltage modulation of spiking rate. All characteristic times can be controlled by the resistive parameters of the circuit. We built the circuit with out-of-the-shelf components and demonstrate that our ultra-compact neuron is a modular block that can be associated to build multi-layer deep neural networks. We also argue that our circuit has low power requirements, as it is normally off except during spike generation. Finally, we discuss the ultimate ultra-compact limit, which may be achieved by further replacing the SCR circuit with Mott materials.
N. Ghenzi, et al., “One-transistor one-resistor (1T1R) cell for large-area electronics”, APPLIED PHYSICS LETTERS, vol. 113, p. 072108, 2018.Abstract
We developed a one-transistor one-resistor cell composed of one TiO2-based resistive switching (RS) device and one ZnO-based thin-film transistor (TFT). We study the electric characteristics of each component individually, and their interplay when both work together. We explored the direct control of bipolar RS devices, using our TFTs to drive current in both directions. We also report striking power implications when we swap the terminals of the RS device. The target of our work is the introduction of RS devices in large-area electronic (LAE) circuits. In this context, RS devices can be beneficial regarding functionality and energy consumption, when compared to other ways to introduce memory cells in LAE circuits. Published by AIP Publishing.