The recent surge of interest in brain-inspired computing and power-efficient electronics has dramatically bolstered development of computation and communication using neuron-like spiking signals. Devices that can produce rapid and energy-efficient spiking could significantly advance these applications. Here we demonstrate DC-current or voltage-driven periodic spiking with sub-20 ns pulse widths from a single device composed of a thin VO2 film with a metallic carbon nanotube as a nanoscale heater. Compared with VO2-only devices, adding the nanotube heater dramatically decreases the transient duration and pulse energy, and increases the spiking frequency, by up to three orders of magnitude. This is caused by heating and cooling of the VO2 across its insulator-metal transition being localized to a nanoscale conduction channel in an otherwise bulk medium. This result provides an important component of energy-efficient neuromorphic computing systems, and a lithography-free technique for power-scaling of electronic devices that operate via bulk mechanisms.The emergence of artificial intelligence and data-intensive tasks has necessitated a revamp of computing hardware beyond transistor-based Boolean logic and the von Neumann architecture. 1,2 Within this revamping effort lies the broad domain of neuromorphic computing which aims to exploit biologically-inspired processes, namely computing, communicating, and operating a neural network using electrical spiking. [3][4][5][6][7][8] In order to improve the energy-efficiency and speed of such systems it is desirable to control the pulse width and energy, and to produce the spiking using single scalable devices. 4,[9][10][11] For instance, adjusting the analog node weights of a neural network by small increments in order to enable high precision will require precise and tunable low energy pulses, especially in networks that use memristors such as phase change memory or oxide ionic resistive switches. 12,13 Partly owing to the absence of compact circuits that can produce such tunable low-energy pulses, even the best memristor-based neural networks have had to implement elaborate transistor-based circuits at every node of very large networks, making the system's efficiency far from ideal. 14 Instead, compact spiking systems without transistors can be constructed by exploiting transient dynamics and/or electronic instabilities, for instance, the temporally abrupt resistance changes during a Mott insulator-

Vanadium dioxide (VO2) has been widely studied for its rich physics and potential applications, undergoing a prominent insulator-metal transition (IMT) near room temperature. The transition mechanism remains highly debated, and little is known about the IMT at nanoscale dimensions.To shed light on this problem, here we use ~1 nm wide carbon nanotube (CNT) heaters to trigger the IMT in VO2. Single metallic CNTs switch the adjacent VO2 at less than half the voltage and power required by control devices without a CNT, with switching power as low as ~85 μW at 300 nm device lengths. We also obtain potential and temperature maps of devices during operation using Kelvin Probe Microscopy (KPM) and Scanning Thermal Microscopy (SThM). Comparing these with three-dimensional electrothermal simulations, we find that the local heating of the VO2 by the CNT play a key role in the IMT. These results demonstrate the ability to trigger IMT in VO2 using nanoscale heaters, and highlight the significance of thermal engineering to improve device behaviour.

Translating the surging interest in neuromorphic electronic components, such as those based on nonlinearities near Mott transitions, into large‐scale commercial deployment faces steep challenges in the current lack of means to identify and design key material parameters. These issues are exemplified by the difficulties in connecting measurable material properties to device behavior via circuit element models. Here, the principle of local activity is used to build a model of VO2/SiN Mott threshold switches by sequentially accounting for constraints from a minimal set of quasistatic and dynamic electrical and high‐spatial‐resolution thermal data obtained via in situ thermoreflectance mapping. By combining independent data sets for devices with varying dimensions, the model is distilled to measurable material properties, and device scaling laws are established. The model can accurately predict electrical and thermal conductivities and capacitances and locally active dynamics (especially persistent spiking self‐oscillations). The systematic procedure by which this model is developed has been a missing link in predictively connecting neuromorphic device behavior with their underlying material properties, and should enable rapid screening of material candidates before employing expensive manufacturing processes and testing procedures.

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