NSF Award
1202236
SMURFS: Statistical Modeling, SimUlation and Robust Design Techniques For MemriStors<br/>Abstract<br/><br/>The fourth fundamental passive circuit element − memristor, has demonstrated great potentials in massive data storage, neuromorphic computing, signal processing, biomedical lab-on-a-chip, sensing etc. The objective of this research is to investigate the design implications of process variations and environmental fluctuations to memristor-based VLSI systems, to exploit a fast statistical simulation technique, and to explore new circuit techniques to improve the memristive system reliability and robustness.<br/>Intellectual Merit: This research includes three integrated components: (1) The statistical device models for representative memristor technologies, i.e., the TiO2 thin-film and spintronic memristors, will be developed to facilitate the process variation aware design space explorations; (2) The fast Monte-Carlo simulation platform will be developed for memristor-based VLSI system designs and simulations; (3) By leveraging the statistical memristor models and simulation platform, robust circuit design techniques that can minimize the fluctuations of the electrical properties caused by process variations will be investigated.<br/>Broader Impacts: This research provides a comprehensive design package for efficiently integrating memristor into existing VLSI systems to offer better performance and power consumption. The device engineers and the circuit designers are well bridged and educated by the research innovations. The developed techniques can be directly transferred to industry applications under the close collaborations with leading industry partners, and directly impact the future memristor-based VLSI systems. The activities in the collaboration also include the tutorials in the major conferences on the technical aspects of the projects and new course development.
