Toward More Realistic Synaptic Mimicry in Non‐Volatile RRAM Devices: A Novel Experimental Approach Focused on Synaptic Forgetting

ABSTRACT Synaptic emulation using memristive devices has advanced neuromorphic computing by enabling energy‐efficient and scalable architectures. Here, we report a non‐volatile TiN/Al/TiN/Ti/TiO x /Al 2 O 3 /Pt resistive random‐access memory (RRAM) device featuring an oxygen‐deficient TiO x switching layer. The device exhibits reliable long‐term memory characteristics with stable multi‐level current modulation. Neuromorphic functionalities such as pattern learning and classification using the EMNIST dataset, as well as 4‐bit edge computing, are successfully demonstrated, with the classification achieving an accuracy of 91.18%. While prior studies predominantly focused on excitatory synaptic behaviors, this work introduces a hardware‐level approach to emulate synaptic forgetting, an essential but underexplored aspect of biological memory processing. To implement forgetting, we propose three experimental methodologies: (1) inhibitory postsynaptic current (IPSC) for synaptic suppression, (2) reversed Pavlovian conditioning to emulate de‐learning, and (3) activity‐dependent synaptic selection (ADSS) mimicking biologically realistic synaptic pruning. These strategies enable selective synaptic weakening based on firing strength and frequency, closely resembling natural forgetting mechanisms. Our findings establish a new paradigm in neuromorphic hardware that balances learning and forgetting using non‐volatile devices. This direction not only enhances biological plausibility but also broadens the functional capabilities of memristive systems for adaptive and efficient edge AI applications.