Ultrafast and stable phase transition realized in MoTe2-based memristive devices.

Phase engineering of two-dimensional transition metal dichalcogenides has received increasing attention in recent years due to its atomically thin nature and polymorphism. Here, we first realize an electric-field-induced controllable phase transition between semiconducting 2H and metallic 1T' phases in MoTe2 memristive devices. The device performs stable bipolar resistive switching with a cycling endurance of over 105, an excellent retention characteristic of over 105 s at an elevated temperature of 85 °C and an ultrafast switching of ∼5 ns for SET and ∼10 ns for RESET. More importantly, the device works in different atmospheres including air, vacuum and oxygen, and even works with no degradation after being placed in air for one year, indicating excellent surrounding and time stability. In situ Raman analysis reveals that the stable resistive switching originates from a controllable phase transition between 2H and 1T' phases. Density functional theory calculations reveal that the Te vacancy facilitates the phase transition in MoTe2 through decreasing the barrier between 2H and 1T' phases, and serving as nucleation sites due to the elimination of repulsive forces. This electric-field-induced controllable phase transition in MoTe2 devices offers new opportunities for developing reliable and ultrafast phase transition devices based on atomically thin membranes.

Threshold switching memristor-based stochastic neurons for probabilistic computing.

Biological neurons exhibit dynamic excitation behavior in the form of stochastic firing, rather than stiffly giving out spikes upon reaching a fixed threshold voltage, which empowers the brain to perform probabilistic inference in the face of uncertainty. However, owing to the complexity of the stochastic firing process in biological neurons, the challenge of fabricating and applying stochastic neurons with bio-realistic dynamics to probabilistic scenarios remains to be fully addressed. In this work, a novel CuS/GeSe conductive-bridge threshold switching memristor is fabricated and singled out to realize electronic stochastic neurons, which is ascribed to the similarity between the stochastic switching behavior observed in the device and that of biological ion channels. The corresponding electric circuit of a stochastic neuron is then constructed and the probabilistic firing capacity of the neuron is utilized to implement Bayesian inference in a spiking neural network (SNN). The application prospects are demonstrated on the example of a tumor diagnosis task, where common fatal diagnostic errors of a conventional artificial neural network are successfully circumvented. Moreover, in comparison to deterministic neuron-based SNNs, the stochastic neurons enable SNNs to deliver an estimate of the uncertainty in their predictions, and the fidelity of the judgement is drastically improved by 81.2%.