Fast Spiking of a Mott VO₂–Carbon Nanotube Composite Device

Abstract

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-

Observation of Optically Addressable Nonvolatile Memory in VO2 at Room Temperature

“…is utilized for memory [15][16][17][19][20][21] and the negative differential resistance (NDR) regime of the VO 2 leads to voltage or current oscillations. [12,[22][23][24] Thus far, the VO 2 insulator-metal transition (IMT) due to a structural phase transition (SPT) has been volatile at room temperature, making a VO 2 memory cell power-inefficient. Nonvolatile phasechange has been observed in VO 2 on a piezoelectric substrate, but the nonvolatility is due to remnant strains in the piezoelectric material rather than the VO 2 itself.…”

“…In the O-state, the device exhibits NDR (see Figure 1d) and the coexistence of the both M 1 -phase and metallic-rutile phase (R-phase) induces voltage oscillations, due to the periodic discharging and charging of the VO 2 capacitance across its IMT. [12,24] After the writing with an optical pulse, the current bias near I T1 was turned off within a fall-time of <150 µs. When the memorywriting bias current was applied again with a sufficiently short rise-time of <180 µs, contrary to the expectation of the VO 2 returning to the M 1 -phase, the device was consistently found in the O-state characterized by persistent voltage oscillations.…”

Artificial Neural Networks Based on Memristive Devices: From Device to System

“…To increase the frequency of the spikes, carbon nanotubes were added into a VO 2 ‐based TS device. [ 126 ]…”

On Local Activity and Edge of Chaos in a NaMLab Memristor

“…Moreover, the use of appropriate materials in their fabrication allows to adopt them as basic building blocks of biomimetic neuromorphic circuits (Burr et al, 2015;Yi et al, 2018;Bohaichuk et al, 2019;Fuller et al, 2019;Serb et al, 2020). In this respect, given that the memory and learning capabilities of biological synapses may be rather accurately captured by nonvolatile memristor models (Chua, 2013), and that potassium and sodium ion channels in biological axon membranes essentially are volatile memristors (Ascoli et al, 2020a), as formulated in 1952 from Hodgkin and Huxley in a seminal paper (Hodgkin and Huxley, 1952), for which they were awarded the Nobel Prize in Physiology in 1961, and theoretically proved out in 2012 from Chua in a milestone manuscript (Chua et al, 2012), explaining several paradoxes that arose from their erroneous identification as time-varying resistances, we may conclude that resistance switching memories shall definitely play a fundamental role in the development of bio-realistic hardware implementations of the human brain in the incoming years.…”