Statistical Memristor Model and it's Applications in Neuromorphic Computing.

摘要

More than forty years ago, Professor Chua predicted the existence of the memristor to complete the set of passive devices that previously includes only resistor, capacitor, and inductor. However, till 2008 the first physical realization of memristors was demonstrated by HP Lab. The unique properties of memristor create great opportunities in future system design. For instance, the memristor has demonstrated the similar function as synapse, which makes it promising to utilize memristor in neuromorphic circuits design. However, as a nano-scale device, the process variation control in the manufacturing of memristors is very difficult. The impact of the process variations on a neural network system that relies on the continuous (analog) states of the memristor could be significant due to the deviation of the memristor state from the designed value. So a complete process variation analysis on memristor is necessary for the application in neural network. Due to the different physical mechanisms, TiO2-based memristor and spintronic memristor demonstrate very different electrical characteristics even when exposing the two types of devices to the same excitations and under the same process variation conditions. In this work, the impact of different geometry variations on the electrical properties of these two different types of memristors was evaluated by conducting the analytic modeling analysis and Monte-Carlo simulations.