Understanding and Manipulating Electronic Noise in Electrochemical Random Access Memory for Neuromorphic Computing

Brain inspired in-memory computing using networks of analog non-volatile memory (NVM) elements has emerged as a promising, more energy efficient alternative to digital computing for data intensive applications [1] . Since information processing is performed in the analog domain, control of electrical noise in the NVM elements becomes critical for achieving optimum accuracy [2] [3] . Here, we compare the noise characteristics of two NVM device types with programming mechanisms that depend on ion motion: electrochemical random access memory (ECRAM) and filamentary resistive random access memory (ReRAM). [4] [5] [6] . Furthermore, we demonstrate that ECRAM with a VO 2 channel, a material that exhibits an insulator-to-metal transition (IMT), enables manipulation of the noise characteristics, which can be utilized for hardware implementation of Bayesian Neural Network [7] .