In this letter, we experimentally demonstrated a novel resistive device with a hybrid switching mode that can be alternated between volatile threshold switching and non-volatile resistive switching. The device consists of dual-functional layers VO2/HfO2 sandwiched by symmetrical TiN electrodes. A >20 unified ratio for selectivity and memory window is obtained. Owing to the stable resistive behavior of HfO2 and insulator-metal transition of VO2, the device shows excellent uniform switching parameters in both switching modes with a high on-state current density (1E4 A/cm2) and fast switching/recovery speed (<30 ns). This self-selective resistive memory is of great potential in the high-density crossbar array, particularly for the future 3D-Vertical resistive random access memory (RRAM) integration.

Neuromorphic computing systems have shown powerful capability in tasks, such as recognition, learning, classification and decision-making, which are both challenging and inefficient in using the traditional computation architecture. The key elements including synapses and neurons, and their feasible hardware implementation are essential for practical neuromorphic computing. However, most existing synaptic devices used to emulate functions of a single synapse and the synapse-based networks are more energy intensive and less sustainable than their biological counterparts. The dendritic functions such as integration of spatiotemporal signals and spike-frequency coding characteristics have not been well implemented in a single synaptic device and thus play an imperative role in future practical hardware-based spiking neural networks. Moreover, most emerging synaptic transistors are fabricated by nanofabrication processes without CMOS compatibility for further wafer-scale integration. Herein, we demonstrate a novel ionic-gated silicon nanowire synaptic field-effect transistor (IGNWFET) with low power consumption (<400 fJ per switching event) based on the standard CMOS process platform. For the first time, the dendritic integration and dual-synaptic dendritic computations (such as “Add” and “Subtraction”) could be realized by processing frequency coded spikes using a single device. Meanwhile, multi-functional characteristics of artificial synapses including the short-term and long-term synaptic plasticity, paired pulse facilitation and high-pass filtering were also successfully demonstrated based on 40 nm wide IGNWFETs. The migration of ions in polymer electrolyte and trapping in high-k dielectric were also experimentally studied in-depth to understand the short-term plasticity and long-term plasticity. Combined with statistical uniformity across a 4-inch wafer, the comprehensive performance of IGNWFET demonstrates its potential application in future biologically emulated neuromorphic systems.

Retina shows an extremely high signal processing efficiency because of its specific signal processing strategy which called computing in sensor. In retina, photoreceptor cells encode light signals into spikes and ganglion cells finish the shape perception process. In order to realize the neuromorphic vision sensor, the one-transistor-one-memristor (1T1M) structure which formed by one memristor and one MOSFET in serial is used to construct photoreceptor cell and ganglion cell. The voltage changes between two terminals of memristor and MOSFET can mimic the changes of membrane potential caused by spikes and illumination respectively. In this paper, the tunable memristive neurons with 1T1M structures are built. According to the concept of receptive field of ganglion cells (GCs) in the retina, the artificial shape perception retina network is constructed with these memristive neurons. The final results show that the artificial retina can extract shape information from the image and transfer it into spike frequency realizing the function of computing in sensor.