On the fault tolerance of a clustered single-electron neural network for differential enhancement

A clustered neural network, in which neuronal information is represented by a cluster (population of neurons), rather than a single neuron, is a possible solution to construct fault-tolerant singleelectron circuits. We designed single-electron circuits based on a clustered neural network that performs differential enhancement where differences between the cluster’s outputs receiving various magnitudes of inputs are enhanced after the processing. Simulation results showed that the degradation of the performance of the clustered single-electron neural network was significantly lower than that of a non-clustered network, which indicates that this approach is one possible way to construct fault-tolerant computing systems on nanodevices.