Encoding, training and retrieval in ferroelectric tunnel junctions

Ferroelectric tunnel junctions (FTJs) are quantum nanostructures that have great potential in the hardware basis for future neuromorphic applications. Among recently proposed possibilities, the artificial cognition has high hopes, where encoding, training, memory solidification and retrieval constitute a whole chain that is inseparable. However, it is yet envisioned but experimentally unconfirmed. The poor retention or short-term store of tunneling electroresistance, in particular the intermediate states, is still a key challenge in FTJs. Here we report the encoding, training and retrieval in BaTiO3 FTJs, emulating the key features of information processing in terms of cognitive neuroscience. This is implemented and exemplified through processing characters. Using training inputs that are validated by the evolution of both barrier profile and domain configuration, accurate recalling of encoded characters in the retrieval stage is demonstrated.