Memristor-based Willshaw network: Capacity and robustness to noise in the presence of defects

The recent realization of memristors, nanodevices remarkably similar to biological synapses, opened the possibility to fabricate highly scalable artificial neural networks. While the physical implementation of such networks is still emerging, it is useful to perform simulations to determine the impact of non-ideal devices or device faults in the performance of memory networks. Here, we numerically evaluate a memristor-based Willshaw associative memory network, studying its capacity and robustness to noise as a function of defects probability and device parameter variations. Two types of defective memristors are addressed (stuck-at-0 and stuck-at-1) and Gaussian distributions are imposed to their threshold voltages, ON and OFF resistances. We conclude that the type and number of defects strongly determine how the network should be operated. The reading current threshold also plays a key role in determining the network's capacity and robustness to noise. Furthermore, there is a maximum defect percentage above which the network can no longer reliably store information. We also found that the memristor-based Willshaw network is more sensitive to resistance variance than to threshold voltage variance.