Multilevel switching memristor by compliance current adjustment for off-chip training of neuromorphic system

Multilevel operation is one of the most essential properties for synaptic devices to realize hardware artificial neural networks. Compliance current (I_cc) adjustment is a multilevel programming method that can be utilized for a large-scale one-transistor and one-resistor (1T1R) array. It protects the devices from permanent breakdown by regulating abrupt switching. However, according to the reported literature so far, the number of conductance states in the I_cc control method is insufficient to implement off-chip-trained neuromorphic systems. Therefore, we experimentally explore the feasibility of a larger number of conductance states using the I_cc control method. We fabricated an Al₂O₃/TiO_x-based resistive switching memory array, observed the conductance change while increasing I_cc during set operations, and 64-level conductance states were statistically demonstrated. Furthermore, we verified that the 64-level states showed recognition performance close to that of a software-based neural network through off-chip learning of the convolutional neural network (CNN) structure. The fabricated synaptic device array with the I_cc-control programming method is expected to contribute to the development of hardware neural network by reducing the information loss in the transfer process.