Memristor crossbar arrays can compose the efficient hardware for artificial intelligent applications. However, the requirements for a linear and symmetric synaptic weight update and low cycle‐to‐cycle (C2C) and device‐to‐device variability as well as the sneak‐path current issue have been delaying its further development. This study reports on a thin‐film amorphous oxide‐based 4×4 1‐transistor 1‐memristor (1T1M) crossbar. The a‐IGZO crossbar is built on a flexible polyimide substrate, enabling IoT and wearable applications. In the novel framework, the thin‐film transistor and memristor are fabricated at the same level, with the same processing steps and sharing the same materials for all layers. The 1T1M cells show linear and symmetrical plasticity characteristic with low C2C variability. The memristor performs like an analog dot product engine and vector–matrix multiplications in the 4×4 crossbars is demonstrated experimentally, in which the sneak‐path current issue is successfully suppressed, resulting in a proof‐of‐concept for a cost‐effective, flexible artificial neural networks hardware.
This study explores the resistive switching phenomena present in 4 µm2 amorphous Indium–Gallium–Zinc Oxide (IGZO) memristors. Despite being extensively reported in the literature, not many studies detail the mechanisms that dominate conduction on the different states of IGZO-based devices. In this article, we demonstrate that resistive switching occurs due to the modulation of the Schottky barrier present at the bottom interface of the device. Furthermore, thermionic field emission and field emission regimes are identified as the dominant conduction mechanisms at the high resistive state of the device, while the bulk-limited ohmic conduction is found at the low resistive state. Due to the high complexity associated with creating compact models of resistive switching, a data-driven model is drafted taking systematic steps.
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