Receive a weekly summary and discussion of the top papers of the week by leading researchers in the field.

In ACS applied materials & interfaces ; h5-index 147.0

Neuromorphic computing architectures enable the dense co-location of memory and processing elements within a single circuit. This co-location removes the communication bottleneck of transferring data between separate memory and computing units as in standard von Neuman architectures for data-critical applications including machine learning. The essential building blocks of neuromorphic systems are non-volatile synaptic elements such as a memristor. Key memristor properties include a suitable non-volatile resistance range, continuous linear resistance modulation and symmetric switching. In this work, we demonstrate voltage-controlled, symmetric and analog potentiation and depression of a ferroelectric Hf0.57Zr0.43O2 (HZO) field effect transistor (FeFET) with good linearity. Our FeFET operates with a low writing energy (fJ) and fast programming time (40 ns). Retention measurements have been done over 4-bits depth with low noise in the tungsten oxide (WOx) read out channel. By adjusting the channel thickness from 15 nm to 8 nm, the on/off ratio of the FeFET can be engineered from 1% to 200% with an on-resistance >100 kΩ, depending on the channel geometry. The device concept is using earth-abundant materials, and is compatible with a back end of line (BEOL) integration into complementary metal-oxide-semiconductor (CMOS) processes. It has therefore a great potential for the fabrication of high density, large-scale integrated arrays of artificial analog synapses.

Halter Mattia, B├ęgon-Lours Laura, Bragaglia Valeria, Sousa Marilyne, Offrein Bert, Abel Stefan, Luisier Mathieu, Fompeyrine Jean