Programming Protocol Optimization for Analog Weight Tuning in Resistive Memories

Analog weight tuning in resistive memories is attractive for multilevel operation and neuro-inspired computing. To tune the device conductance to the desired states as fast as possible without sacrificing the accuracy, we propose an optimization programming protocol by adjusting the pulse amplitude incremental steps, the pulsewidth incremental steps, and the start voltages. Our experimental results on HfO_x-based resistive memories indicate that avoiding over-reset by appropriate programming parameters is critical for fast convergence of the conductance tuning. The over-reset behavior is caused by the stochastic nature of filament formation and rupture, as simulated by a 1-D filament model.