Metal-oxide based, CMOS-compatible ECRAM for Deep Learning Accelerator

Abstract

We demonstrate a CMOS-compatible, metal-oxide based Electro-Chemical Random-Access Memory (MO- ECRAM) for high-speed, low-power neuromorphic computing. The device demonstrates symmetric and linear conductance update, large on/off ratio and good retention while also withstanding high temperature treatments necessary for BEOL compatibility. Resistive switching in MO-ECRAM is observed with voltage pulses down to 10 ns and scales exponentially with voltage pulse amplitude, enabling parallel array operations without any selector/access devices. For the first time, we experimentally demonstrate fundamental techniques for fully- parallel array operations, stochastic update scheme and zero-shifting technique, and show a successful stochastic gradient descent algorithm demonstration in hardware using a MO- ECRAM array.