Suppress variations of analog resistive memory for neuromorphic computing by localizing Vo formation

Reducing device to device variations of filamentary analog resistive random access memory (RRAM) is crucial for neuromorphic computing. Larger variations decrease the computing accuracy of the neuromorphic network. One of the main factors for the variations of filamentary RRAM is the random nature of filament formation. This work presents a defect engineering approach using the atomic layer deposition method to localize the oxygen vacancies (V-o) formation uniformly, which results in uniform multi-weak-filaments formed in RRAM devices. The variation of linearity and dynamic ON/OFF ratio in different devices can be suppressed using the proposed method. Besides the variation control in this work, the retention and read disturbance are also optimized by increasing the V-o migration barrier, which are also important for neuromorphic network. The analog RRAM array is demonstrated with good uniformity of analog switching behavior, fast speed, long retention, small read disturbance, which shows tremendous potential in developing large-scale RRAM based neural networks. Published by AIP Publishing.