Accurate Weight Update in an Electrochemical Random-Access Memory Based Cross-Point Array Using Channel-High Half-Bias Scheme for Deep Learning Accelerator

Abstract

Recently cross-point arrays of synaptic memory devices have been intensively studied to accelerate deep neural network computations. Among various synaptic devices, electrochemical random-access memory (ECRAM) is emerging as a promising non-volatile memory candidate owing to its superior synaptic characteristics. However, an optimized update scheme for a three-terminal ECRAM-based cross-point array is yet to be developed. In this study, a metal-oxide-based ECRAM (MO-ECRAM) shows superior synaptic characteristics and the weight update of devices in the MO-ECRAM cross-point array is analyzed using the half-bias (HB) scheme. Additionally, A channel-high half-bias (CHB) scheme is proposed to overcome the degraded selectivity of the weight update caused by the three-terminal configuration of the ECRAM device. In the CHB scheme, the conductance change in the selected device can be increased considerably by applying a calculated additional voltage to the channel. Using the CHB scheme, parallel and selective updates are successfully performed in a 2 × 2 MO-ECRAM cross-point array. Finally, an experimental demonstration of the training algorithm shows the impact of selective updates when using the CHB scheme. This new update scheme is expected to improve training accuracy in ECRAM cross-point array-based deep learning accelerators.