M2CA: Modular Memristive Crossbar Arrays

The memristor crossbar array architecture can find a wide range of applications in the design of neuromorphic computing systems. The scalability of the arrays is important to extend the use in complex cognitive tasks. However, the creation of large-sized arrays is limited by a sneak-path problem reducing noise margins and accuracy. In this paper, we perform a large scale analysis of a sneak path problem in crossbar arrays using HSPICE simulation models. This allows for developing a realistic mathematical model for simulating large scale crossbar arrays. The performance analysis and impact of sneak paths for neural network implemented on a crossbar array is tested using the MNIST character recognition database. Also, in this work we provide a possible solution to suppress the influence of the sneak path current on the network. The suppressing effect is achieved by dividing a large memristive crossbar array into smaller arrays. These crossbars are simulated in HSPICE as well, examined and compared to the originally constructed crossbar.