Multimodal Artificial Neurological Sensory–Memory System Based on Flexible Carbon Nanotube Synaptic Transistor

Memory is believed to occur in the human brain as a result of two types of synaptic plasticity: short-term plasticity (STP) and long-term potentiation (LTP; refs 1-4). In neuromorphic engineering, emulation of known neural behaviour has proven to be difficult to implement in software because of the highly complex interconnected nature of thought processes. Here we report the discovery of a Ag(2)S inorganic synapse, which emulates the synaptic functions of both STP and LTP characteristics through the use of input pulse repetition time. The structure known as an atomic switch, operating at critical voltages, stores information as STP with a spontaneous decay of conductance level in response to intermittent input stimuli, whereas frequent stimulation results in a transition to LTP. The Ag(2)S inorganic synapse has interesting characteristics with analogies to an individual biological synapse, and achieves dynamic memorization in a single device without the need of external preprogramming. A psychological model related to the process of memorizing and forgetting is also demonstrated using the inorganic synapses. Our Ag(2)S element indicates a breakthrough in mimicking synaptic behaviour essential for the further creation of artificial neural systems that emulate characteristics of human memory.

Flexible nanogenerators that efficiently convert mechanical energy into electrical energy have been extensively studied because of their great potential for driving low-power personal electronics and self-powered sensors. Integration of flexibility and stretchability to nanogenerator has important research significance that enables applications in flexible/stretchable electronics, organic optoelectronics, and wearable electronics. Progress in nanogenerators for mechanical energy harvesting is reviewed, mainly including two key technologies: flexible piezoelectric nanogenerators (PENGs) and flexible triboelectric nanogenerators (TENGs). By means of material classification, various approaches of PENGs based on ZnO nanowires, lead zirconate titanate (PZT), poly(vinylidene fluoride) (PVDF), 2D materials, and composite materials are introduced. For flexible TENG, its structural designs and factors determining its output performance are discussed, as well as its integration, fabrication and applications. The latest representative achievements regarding the hybrid nanogenerator are also summarized. Finally, some perspectives and challenges in this field are discussed.