Implementation of convolutional neural network and 8-bit reservoir computing in CMOS compatible VRRAM

We developed W/HfO2/TiN vertical resistive random-access memory (VRRAM) for neuromorphic computing. First, basic electrical properties, such as current-voltage curves, retention, and endurance, were determined. To examine the conduction mechanism, a device with a large switching area was fabricated, and its current level and that of the VRRAM were compared. Moreover, we analyzed the current behavior relative to the ambient temperature. Subsequently, the number of states upon potentiation and depression was linearly converted via conductance modulation due to an applied pulse. The practicality of the device was assessed using a convolu-tional neural network. Finally, 16-state reservoir computing was combined with multilevel characteristics to implement 8-bit reservoir computing with 256 states. We verified that in terms of time and power consumption, 8-bit reservoir computing is more efficient than 4-bit reservoir computing. Hence, we concluded that the W/ HfO2/TiN VRRAM cell is a promising volatile memory device.

Automated summary

In this study, we developed a W/HfO2/TiN vertical resistive random-access memory (VRRAM) for neuromorphic computing. The basic electrical properties, conduction mechanism, and current behavior relative to temperature were investigated. The practicality of the device was evaluated using a convolutional neural network, and 8-bit reservoir computing with higher efficiency was achieved.