Analysis of electro-chemical RAM synaptic array for energy-efficient weight update - Kang, Heebum; Kim, Nayeon; Jeon, Seonuk; Kim, Hyun Wook; Hong, Eunryeong; Kim, Seyoung; Woo, Jiyong (Frontiers in Nanotechnology, 2022)
While electro-chemical RAM (ECRAM)-based cross-point synaptic arrays are considered to be promising candidates for energy-efficient neural network computational hardware, array-level analyses to achieve energy-efficient update operations have not yet been performed. In this work, we fabricated CuOx/HfOx/WOx ECRAM arrays and demonstrated linear and symmetrical weight update capabilities in both fully parallel and sequential update operations. Based on the experimental measurements, we showed that the source-drain leakage current (ISD) through the unselected ECRAM cells and resultant energy consumption—which had been neglected thus far—contributed a large portion to the total update energy. We showed that both device engineering to reduce ISD and the selection of an update scheme—for example, column-by-column—that avoided ISD intervention via unselected cells were key to enable energy-efficient neuromorphic computing.