Harnessing a WOx-based flexible transparent memristor synapse with a hafnium oxide layer for neuromorphic computing

Abstract

Transparent memristor-based neuromorphic synapses are expected to be specialised devices for high-speed information transmission and processing. The synaptic linearity and potentiation/depression cycles are imperative issues for the application of memristors. This work explores a memristor for improving switching uniformity by introducing a thin HfO_x interfacial layer as a diffusion-limiting layer sandwiched between WO_x and ITO bottom electrodes. An optimized HfO_x thickness not only provides the best switching properties but also shows superior synaptic properties. The optimized 15 nm thin WO_x layer can retain the memristor's excellence in P/D linearity, a cycling stability of 494 epochs and image recognition up to 3 mm bending, making it suitable for flexible devices. The artificial synapse is capable of reversible short-term and long-term learning behaviors confirmed by spike-timing-dependent-plasticity (STDP) results. X-ray photoelectron spectroscopy confirms the device composition and provides the oxygen vacancy concentration at the WO_x/HfO_x interface to realize the switching mechanism. The thicknesses of the different layers are estimated from the high-resolution transmission electron microscopy observations. The fabricated device exhibits 92.2% transparency, as confirmed by the UV-Vis spectrum.