Memristor devices for use in neuromorphic systems

This paper describes the fabrication and characterization process used to develop memristors that are strong candidates for use in neuromorphic systems. A common approach for the development of memristor-based neuromorphic circuits is to store synaptic weight values within memristors as resistance values. This requires use of the continuous resistance range available in the memristors to store the weight matrix produced by a learning algorithm. More specifically, these devices will be used in systems that implement supervised learning algorithms such as single and multilayer perceptrons. It is important to be able to iteratively program a target resistance through a number of feedback controlled voltage pulses as opposed to abruptly switching the device between two binary states. This paper shows how TiO2memristor devices placed in a crossbar arrangement are capable of implementing Boolean logic functions using a perceptron algorithm. Resistances within a memristor crossbar can be tuned and reconfigured so that a neuron circuit based on this crossbar can represent multiple different functions.