ECRAM as Scalable Synaptic Cell for High-Speed, Low-Power Neuromorphic Computing

Abstract

We demonstrate a nonvolatile Electro-Chemical Random-Access Memory (ECRAM) based on lithium (Li) ion intercalation in tungsten oxide (WO3) for high-speed, low-power neuromorphic computing. Symmetric and linear update on the channel conductance is achieved using gate current pulses, where up to 1000 discrete states with large dynamic range and good retention are demonstrated. MNIST simulation based on the experimental data shows an accuracy of 96%. For the first time, high-speed programming with pulse width down to 5 ns and device operation at scales down to 300×300 nm2 are shown, confirming the technological relevance of ECRAM for neuromorphic array implementation. It is also verified that the conductance change scales linearly with pulse width, amplitude and charge, projecting an ultralow switching energy ~1 fJ for 100×100 nm2 devices.