Memristor modeling - static, statistical, and stochastic methodologies

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. In addition, the stochastic switching behaviors have been widely observed. To facilitate the investigation on memristor-based hardware implementation, we compare and summarize different memristor modeling methodologies, from the simple static model to statistical analysis by taking the impact of process variations into consideration and the stochastic behavior model based on the real experimental measurements. In this work, we use the most popular TiO2 thin-film device as an example to analyze the memristor’s electrical properties. Our proposed modeling methodologies can be easily extended to the other structures/materials with necessary modifications.

Duke Scholars

Author Hai "Helen" Li Electrical and Computer Engineering