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• Multi-level operation of Al 2 O 3 /TiO x memristor devices for neuromorphic system • Analyzing analogue-grade weight modulation by adjusting compliance current up to 64 levels • Precisely controlled device state with compliance current and off-chip learning verification • Evaluating the recognition performance of convolutional neural network considering the number of quantization level and accuracy 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 Al 2 O 3 /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. [ABSTRACT FROM AUTHOR]