Electrically induced resistive switching in metal insulator-metal structures is a subject of increasing scientific interest because it is one of the alternatives that satisfies current requirements for universal non-volatile memories. However, the origin of the switching mechanism is still controversial. Here we report the fabrication of a resistive switching device inside a transmission electron microscope, made from a Pt/SiO 2 /a-Ta 2 O 5 À x /a-TaO 2 À x /Pt structure, which clearly shows reversible bipolar resistive switching behaviour. The currentvoltage measurements simultaneously confirm each of the resistance states (set, reset and breakdown). In situ scanning transmission electron microscope experiments verify, at the atomic scale, that the switching effects occur by the formation and annihilation of conducting channels between a top Pt electrode and a TaO 2 À x base layer, which consist of nanoscale TaO 1 À x filaments. Information on the structure and dimensions of conductive channels observed in situ offers great potential for designing resistive switching devices with the high endurance and large scalability.

Efforts to develop scalable learning algorithms for implementation of networks of spiking neurons in silicon have been hindered by the considerable footprints of learning circuits, which grow as the number of synapses increases. Recent developments in nanotechnologies provide an extremely compact device with low-power consumption.In particular, nanoscale resistive switching devices (resistive random-access memory (RRAM)) are regarded as a promising solution for implementation of biological synapses due to their nanoscale dimensions, capacity to store multiple bits and the low energy required to operate distinct states. In this paper, we report the fabrication, modeling and implementation of nanoscale RRAM with multi-level storage capability for an electronic synapse device. In addition, we first experimentally demonstrate the learning capabilities and predictable performance by a neuromorphic circuit composed of a nanoscale 1 kbit RRAM cross-point array of synapses and complementary metal-oxide-semiconductor neuron circuits. These developments open up possibilities for the development of ubiquitous ultra-dense, ultra-low-power cognitive computers.

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