Impact of Program Accuracy and Random Telegraph Noise on the Performance of a NOR Flash-based Neuromorphic Classifier

In this work, we investigate the impact of program accuracy and of the time instabilities in cell threshold-voltage (V
T
) arising from random telegraph noise (RTN) on the performance of a neuromorphic digit classifier exploiting NOR Flash arrays as artificial synaptic arrays. First, by modeling cell V
T
placement resulting from a program-and-verify algorithm based on incremental step pulse programming (ISPP) in the presence of program noise, the classifier truthfulness is investigated as a function of the discretization step of the verify level and of the cell control-gate–to–floating-gate capacitance. Then, the degradation of the classifier accuracy due to RTN fluctuations displacing cell V
T
from its programmed value is addressed as a function of the most relevant RTN statistical parameter, i.e., the average value of the single-trap fluctuation amplitude. Results highlight some quantitative criteria to determine how scaled NOR Flash cells and arrays can be when targeting neuromorphic applications.