Artificial neurons and synapses are critical units for processing intricate information in neuromorphic systems. Memristors are frequently engineered as artificial synapses due to their simple structures, gradually changing conductance and high-density integration. However, few studies have designed memristors as artificial neurons. In this letter, we demonstrate an integration-and-fire artificial neuron based on a Ag/SiO2/Au threshold switching memristor. This neuron displays four critical features for action-potential-based computing: the all-or-nothing spiking of an action potential, threshold-driven spiking, a refractory period, and a strength-modulated frequency response. As a post-synaptic neuron, the designed neuron was demonstrated to be applicable to digit recognition. These results demonstrate that the developed artificial neuron can realize the basic functions of spiking neurons and has great potential for neuromorphic computing.

Detection and recognition of latent fingerprints play crucial roles in identification and security. However, the separation of sensor, memory, and processor in conventional ex-situ fingerprint recognition system seriously deteriorates the latency of decision-making and inevitably increases the overall computing power. In this work, a photoelectronic reservoir computing (RC) system, consisting of DUV photo-synapses and nonvolatile memristor array, is developed to detect and recognize the latent fingerprint with in-sensor and parallel in-memory computing. Through the Ga-rich design, we achieve amorphous GaO_x (a-GaO_x) photo-synapses with an enhanced persistent photoconductivity (PPC) effect. The PPC effect, which induces nonlinearly tunable conductivity, renders the a-GaO_x photo-synapses an ideal deep ultraviolet (DUV) photoelectronic reservoir, thus mapping the complex input vector into a dimensionality-reduced output vector. Connecting the reservoirs and a memristor array, we further construct an in-sensor RC system for latent fingerprint identification. The system maintains over 90% recognition accuracy for latent fingerprint within 15% stochastic noise level via the proposed dual-feature strategy. This work provides a subversive prototype system of DUV in-sensor RC for highly efficient recognition of latent fingerprints.

Neuromorphic computing based on spikes offers great potential in highly efficient computing paradigms. Recently, several hardware implementations of spiking neural networks based on traditional complementary metal-oxide semiconductor technology or memristors have been developed. However, an interface (called an afferent nerve in biology) with the environment, which converts the analog signal from sensors into spikes in spiking neural networks, is yet to be demonstrated. Here we propose and experimentally demonstrate an artificial spiking afferent nerve based on highly reliable NbO_x Mott memristors for the first time. The spiking frequency of the afferent nerve is proportional to the stimuli intensity before encountering noxiously high stimuli, and then starts to reduce the spiking frequency at an inflection point. Using this afferent nerve, we further build a power-free spiking mechanoreceptor system with a passive piezoelectric device as the tactile sensor. The experimental results indicate that our afferent nerve is promising for constructing self-aware neurorobotics in the future.

This article provides a review of current development and challenges in brain-inspired computing with memristors. We review the mechanisms of various memristive devices that can mimic synaptic and neuronal functionalities and survey the progress of memristive spiking and artificial neural networks. Different architectures are compared, including spiking neural networks, fully connected artificial neural networks, convolutional neural networks, and Hopfield recurrent neural networks. Challenges and strategies for nanoelectronic brain-inspired computing systems, including device variations, training, and testing algorithms, are also discussed.

In this letter, effects of top electrodes (TEs) on ferroelectric properties of Hf0.5Zr0.5O2 (HZO) thin films are examined systematically. The remnant polarization (Pr) of HZO thin films increases by altering TEs with lower thermal expansions coefficient (α). The largest 2Pr value of 38.72 μC/cm2 is observed for W TE with α=4.5×10-6/K, while the 2Pr value is only 22.83 μC/cm2 for Au TE with α=14.2×10-6/K. Meanwhile, coercive field (Ec) shifts along electric field axis and the offset is found to be dependent on the difference of workfunctions (WFs) between TE and TiN bottom electrode (BE). Ec shifts toward negative/positive direction, when the WF of TE is larger/smaller (Pt, Pd, Au/W, Al, Ta) than TiN BE. This work provides an effective way to modulate HfO2-based device performance for different requirements in actual application.