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 memristors. 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 Hf 0.57 Zr 0.43 O 2 (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 (1 %) in the tungsten oxide (WO x ) read out channel. By adjusting the channel thickness from 15nm to 8nm, the on/off ratio of the FeFET can be engineered from 1 % to 200 % with an on-resistance ideally >100 kΩ, depending on the channel geometry. The device concept is using earth-abundant materials, and is 1 arXiv:2001.06475v1 [cs.ET] 17 Jan 2020 compatible with a back end of line (BEOL) integration into complementary metal-oxidesemiconductor (CMOS) processes. It has therefore a great potential for the fabrication of high density, large-scale integrated arrays of artificial analog synapses.Keywords ferroelectric switching, hafnium zirconium oxide, tungsten oxide, BEOL, ferroelectric field-effect transistor, memristor