Computers based on the von-Neumann architecture have become extremely inefficient to process the huge amount of information collected with big data. The continuous data transfer between memory and processor results in energy and speed inefficiency, also known as von-Neumann bottleneck. A transition to novel computing paradigms and architectures, such as bioinspired analog in-memory computing concept is therefore paramount. The crossbar array is an architecture enabling the colocation of memory and processing. It consists of an interconnected array of memory devices with tunable conductance, also known as memristors. This configuration allows to perform vector-matrix-multiplications in a single operation. VMMs are extensively used in AI algorithms of training and inference of neural networks (NNs). The fully connected NN training requires the mapping of the network weights into the array conductance. It also requires adjusting the weights in a monotonic and symmetric multi-step manner by applying identical pulses. Memristors based on Hf O2 ReRAM are promising candidates f or in-memory computing. The scalable 2-terminal structure is key to integration into highly dense crossbars, in addition to their CMOS compatibility. Conventional ReRAM are based on HfO2 stacked in between a bottom inert electrode and a top electrode with an oxygen scavenging f unction. After an electroforming operation, a filament of oxygen vacancies allows current to flow through the top and bottom electrodes. The transition from the high to the low resistive state (set) occurs abruptly during programming and it is due to a self-accelerated oxygen exchange between the HfO2 filament and the top electrode. In literature it has been shown that conventional HfO2 technology performances improve with a bilayer ReRAM stack. In this concept, the metallic top scavenging layer is replaced by a properly designed conductive-metal-oxide (CMO) material and engineered to be part of the device layer stack. Although bilayer ReRAM have already shown remarkable capabilities to implement neuromorphic systems, the resistive switching mechanism, as well as the role of the top electrode, is not fully understood. In our work we developed multiple CMO/HfO2 ReRAM concepts which show gradual bidirectional resistive switching and improved symmetry, which approaches the required specs for training a network of memristive devices. To explain the switching properties of our CMO/HfO2 based devices we modelled with an equivalent electrical circuit the f orming, set and reset operations within a dedicated Impedance Spectroscopy experiment. Our bilayer ReRAM represents an attractive and flexible memory element f or large-scale integration, considering its excellent granular switching properties and CMOS compatibility. References  Gokmen T. and Vlasov Y. Front. Neurosci., 10 p333 (2016).  J. Woo, S. Yu, IEEE Nanotechnology Magazine, vol. 12, pp. 36-44, 2018.  S. Kim, Y. Abbas, Y. Jeon, A. Sokolov, B. Ku, C. Choi, vol. 29, p. 415204, 2018.