Deformation and fracture mechanisms of ultrathin Si nanowires (NWs), with diameters of down to ~9 nm, under uniaxial tension and bending were investigated by using in situ transmission electron microscopy and molecular dynamics simulations. It was revealed that the mechanical behavior of Si NWs had been closely related to the wire diameter, loading conditions, and stress states. Under tension, Si NWs deformed elastically until abrupt brittle fracture. The tensile strength showed a clear size dependence, and the greatest strength was up to 11.3 GPa. In contrast, under bending, the Si NWs demonstrated considerable plasticity. Under a bending strain of <14%, they could repeatedly be bent without cracking along with a crystalline-to-amorphous phase transition. Under a larger strain of >20%, the cracks nucleated on the tensed side and propagated from the wire surface, whereas on the compressed side a plastic deformation took place because of dislocation activities and an amorphous transition.

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Neuromorphic computing has the potential to accelerate high performance parallel and low power in-memory computation, artificial intelligence, and adaptive learning. Despite emulating the basic functions of biological synapses well, the existing artificial electronic synaptic devices have yet to match the softness, robustness, and ultralow power consumption of the brain. Here, we demonstrate an all-inorganic flexible artificial synapse enabled by a ferroelectric field effect transistor based on mica. The device not only exhibits excellent electrical pulse modulated conductance updating for synaptic functions but also shows remarkable mechanical flexibility and high temperature reliability, making robust neuromorphic computation possible under external disturbances such as stress and heating. Based on its linear, repeatable, and stable long-term plasticity, we simulate an artificial neural network for the Modified National Institute of Standards and Technology handwritten digit recognition with an accuracy of 94.4%. This work provides a promising way to enable flexible, low-power, robust, and highly efficient neuromorphic computation that mimics the brain.