Introduction to Analog Testing of Resistive Random Access Memory (RRAM) Devices towards Scalable Analog Compute Technology for Deep Learning

Abstract

In this paper we demonstrate a novel methodology to electrically test and characterize resistive random-access memory (RRAM) single bit devices for deep learning application. We extract critical device performance metrics for validating and optimizing fabrication processes which feed into yield learning. We adopt the algorithm-based bias condition search methodology and extract forming and switching voltage parameters without overdriving the devices. This test methodology can be used for Technology Development Learning Cycle in a research and development environment.