Analog high resistance bilayer RRAM device for hardware acceleration of neuromorphic computation

Analog nonvolatile resistive switching phenomena in metal oxides can potentially be used as a synaptic weight in hardware based neuromorphic computing accelerators. Single layer resistive randomaccess memory (RRAM) devices have switching currents in the greater than 1 mA range, effectively requiring too much energy for integration in a crossbar array based neural accelerator. This study details the fabrication and characterization of a bilayer RRAM device consisting of a Pt-TaOx-Al2O3-TiN stack which is designed for low current operation. This high resistance bilayer device reduces switching energy to similar to 8 pJ during RESET and 15 pJ during SET, at the expense of increased operational noise. Noise increase is expected in this higher resistance device due to electron trapping in levels created by vacancies piling up at the interface between the Al2O3 and TaOx layer. As a result, the simulated performance of these devices used in training a neuromorphic accelerator on the MNIST dataset was 80%, significantly lower than required. Using the difference in current between two devices to represent a digit and using two digits per weight with a technique called periodic carry (for a total of 4 devices), a training accuracy of 93% could be achieved. The device and methods detailed here represent a necessary step toward the realization of energy efficient neuromorphic accelerators. Published by AIP Publishing.