Memristor, the fourth passive circuit element, has attracted increased attention since it was rediscovered by HP Lab in 2008. Its distinctive characteristic to record the historic profile of the voltage/current creates a great potential for future neuromorphic computing system design. However, at the nano-scale, process variation control in the manufacturing of memristor devices is very difficult. The impact of process variations on a memristive system that relies on the continuous (analog) states of the memristors could be significant. We use TiO 2-based memristor as an example to analyze the impact of geometry variations on the electrical properties. A simple algorithm was proposed to generate a large volume of geometry variation-aware three-dimensional device structures for Monte-Carlo simulations. A neuromorphic computing system based on memristor-based bidirectional synapse design is proposed as case study. We analyze and evaluate the robustness of the proposed system in pattern recognition based on massive Monte-Carlo simulations, after considering input defects and process variations. © 2012 ACM.
Statistical memristor modeling and case study in neuromorphic computing
Abstract
DOI
10.1145/2228360.2228466
Year