Analog synaptic behavior of mobile ion source-limited electrochemical RAM using CuO_x oxide electrode for deep learning accelerator

We demonstrate the synaptic characteristics of analogously modulated channel currents in Cu-ion-actuated electrochemical RAM (ECRAM) based on an HfO_x electrolyte and a WO_x channel. Uncontrolled synaptic response is found as a function of the gate pulse when a Cu-rich gate electrode delivers mobile ions, presumably due to many ions injected from the infinite ion reservoir. As a result, we propose a CuO_x oxide electrode to limit ion sources, which is indirectly validated by a physical examination of the degree of chemical bonding between Cu and oxygen, thereby boosting gate controllability over the channel. In addition, the HfO_x electrolyte needs to be designed to facilitate the adequate migration of Cu ions, considering thickness and film quality. Using material stack engineering, the channel current of optimized CuO_x/HfO_x/WO_x ECRAM can be steadily tuned via repeated identical gate pulses. The channel current and its change are proportional to the device area and the amount of migrated ions relevant to the gate pulse conditions, respectively. The homogeneous flow of ions across the entire area can, thus, be used to explain the obtained analog switching. The gate-controllable synaptic behavior of the ECRAM accelerates deep neural network training based on backpropagation algorithms. An improved pattern recognition accuracy of ∼88% for handwritten digits is achieved by linearly tuned multiple current states with more than 100 pulses and asymmetric gate voltage conditions in a three-layer neural network validated in simulation.