Indium-Gallium-Zinc Oxide-Based Synaptic Charge Trap Flash for Spiking Neural Network-Restricted Boltzmann Machine.

Recently, neuromorphic computing has been proposed to overcome the drawbacks of the current von Neumann computing architecture. Especially, spiking neural network (SNN) has received significant attention due to its ability to mimic the spike-driven behavior of biological neurons and synapses, potentially leading to low-power consumption and other advantages. In this work, we designed the indium-gallium-zinc oxide (IGZO) channel charge-trap flash (CTF) synaptic device based on a HfO2/Al2O3/Si3N4/Al2O3 layer. Our IGZO-based CTF device exhibits synaptic functions with 128 levels of synaptic weight states and spike-timing-dependent plasticity. The SNN-restricted Boltzmann machine was used to simulate the fabricated CTF device to evaluate the efficiency for the SNN system, achieving the high pattern-recognition accuracy of 83.9%. We believe that our results show the suitability of the fabricated IGZO CTF device as a synaptic device for neuromorphic computing.

Large Memory Window of van der Waals Heterostructure Devices Based on MOCVD-Grown 2D Layered Ge4 Se9.

Van der Waals (vdW) heterostructures have drawn much interest over the last decade owing to their absence of dangling bonds and their intriguing low-dimensional properties. The emergence of 2D materials has enabled the achievement of significant progress in both the discovery of physical phenomena and the realization of superior devices. In this work, the group IV metal chalcogenide 2D-layered Ge4 Se9 is introduced as a new selection of insulating vdW material. 2D-layered Ge4 Se9 is synthesized with a rectangular shape using the metalcorganic chemical vapor deposition system using a liquid germanium precursor at 240 °C. By stacking the Ge4 Se9 and MoS2 , vdW heterostructure devices are fabricated with a giant memory window of 129 V by sweeping back gate range of ±80 V. The gate-independent decay time reveals that the large hysteresis is induced by the interfacial charge transfer, which originates from the low band offset. Moreover, repeatable conductance changes are observed over the 2250 pulses with low non-linearity values of 0.26 and 0.95 for potentiation and depression curves, respectively. The energy consumption of the MoS2 /Ge4 Se9 device is about 15 fJ for operating energy and the learning accuracy of image classification reaches 88.3%, which further proves the great potential of artificial synapses.