Artificial synapses can boost neuromorphic computing to overcome the inherent limitations of von Neumann architecture. As a promising memristor candidate, ferroelectric tunnel junctions (FTJ) enable the authors to successfully emulate spike-timing-dependent synapses. However, the nonlinear and asymmetric synaptic weight update under repeated presynaptic stimulation hampers neuromorphic computing by favoring the runaway of synaptic weights during learning. Here, the authors demonstrate an FTJ whose conductivity varies linearly and symmetrically by judiciously combining ferroelectric domain switching and oxygen vacancy migration. The artificial neural network based on this FTJ-synapse achieves classification accuracy of 96.7% during supervised learning, which is the closest to the maximum theoretical value of 98% achieved to date. This artificial synapse also demonstrates stable unsupervised learning in a noisy environment for its well-balanced spike-timing-dependent plasticity response. The novel concept of controlling ionic migration in ferroelectric materials paves the way toward highly reliable and reproducible supervised and unsupervised learning strategies.

Low-energy switching of ferroelectrics has been intensively studied for energy-efficient nanoelectronics. Mechanical force is considered as a low-energy consumption technique for switching the polarization of ferroelectric films due to the flexoelectric effect. Reduced threshold force is always desirable for the considerations of energy saving, easy domain manipulation, and sample surface protection. In this work, the mechanical switching behaviors of BaTiO3/SrRuO3 epitaxial heterostructure grown on Nb:SrTiO3 (001) substrate are reported. Domain switching is found to be induced by an extremely low tip force of 320 nN (estimated pressure ∼0.09 GPa), which is the lowest value ever reported. This low mechanical threshold is attributed to the small compressive strain, the low oxygen vacancy concentration in BaTiO3 film, and the high conductivity of the SrRuO3 electrode. The flexoelectricity under both perpendicular mechanical load (point measurement) and sliding load (scanning measurement) are investigated. The sliding mode shows a much stronger flexoelectric field for its strong trailing field. The mechanical written domains show several advantages in comparison with the electrically written ones: low charge injection, low energy consumption, high density, and improved stability. The ultralow-pressure switching in this work presents opportunities for next-generation low-energy and high-density memory electronics.

Artificial synapses are electronic devices that simulate important functions of biological synapses, and therefore are the basic components of artificial neural morphological networks for brain-like computing. A most important objective for developing artificial synapses is to simulate the characteristics of biological synapses as much as possible, especially their self-adaptive ability to external stimuli. Here we have successfully developed an artificial synapse with multiple synaptic functions and highly adaptive characteristic based on a simple SrTiO3/Nb:SrTiO3 heterojunction type memristor. Diverse functions of synaptic learning, such as short-term/long-term plasticity, transition from short- to long-term plasticity, learning-forgetting-relearning behaviors, associative learning and dynamic filtering, are all bio-realistically implemented in a single device. The remarkable synaptic performance is attributed to the fascinating inherent dynamics of oxygen vacancy drift and diffusion, which give arise to the coexistence of volatile- and nonvolatile- type resistive switching. This work reports a multi-functional synaptic emulator with advanced computing capability based on a simple heterostructure, showing great application potential for a compact and low-power neuromorphic computing system.