Brain-inspired computing is an emerging field, which intends to extend the capabilities of information technology beyond digital logic. The progress of the field relies on artificial synaptic devices as the building block for brainlike computing systems. Here, we report an electronic synapse based on a ferroelectric tunnel memristor, where its synaptic plasticity learning property can be controlled by nanoscale interface engineering. The effect of the interface engineering on the device performance was studied. Different memristor interfaces lead to an opposite virgin resistance state of the devices. More importantly, nanoscale interface engineering could tune the intrinsic band alignment of the ferroelectric/metal-semiconductor heterostructure over a large range of 1.28 eV, which eventually results in different memristive and spike-timing-dependent plasticity (STDP) properties of the devices. Bidirectional and unidirectional gradual resistance modulation of the devices could therefore be controlled by tuning the band alignment. This study gives useful insights on tuning device functionalities through nanoscale interface engineering. The diverse STDP forms of the memristors with different interfaces may play different specific roles in various spike neural networks.

An electro-photo-sensitive synapse based on a highly reliable InGaZnO thin-film transistor is demonstrated to mimic synaptic functions and pattern-recognition functions.

Memristors, acting as artificial synapses, have promised their prospects in neuromorphic systems that imitate the brain's computing paradigm. However, most studies focused on the understanding of the memristive mechanism and how to optimize the synaptic performance, and the implementations of higher‐order cognitive functions are quite limited. Here the experimental demonstration of a representative network level learning function, i.e., associative learning and extinction, in a compact memristive neuromorphic circuit with only one memristor is reported. The association of the conditioned and unconditioned stimulus is established within a temporal window through the spike‐timing‐dependent plasticity rule, whereas the extinction of the formed memory is due to the synaptic depression. The temporal contiguity consists with biological behaviors and reflects nature's cause and effect rule. An efficient methodology of integrating memristors into large‐scale neuromorphic systems for massively parallel computing, such as pattern recognition, is provided herein.