Executing the latest Artificial Intelligence (AI) tasks on modern computers based on the von-Neumann architecture is becoming more and more power-hungry, due to the increasing neural network size and volume of the data involved. Especially, the movement of data between memory and the processing units is a highly energy consuming operation and is generally the performance bottleneck. The path to the future of electronics for AI could embrace signal processing in analog memory devices based on the in-memory computing concept. When organized in crossbar arrays, resistive-switching technologies such as RRAMs or PCMs can be used to perform vector-matrix-multiplications (VMMs), the core calculation of AI’s inference and training tasks, in a single operation, by exploiting Ohm’s and Kirchoff’s law. Such technologies are scalable, two-terminal, and can be integrated in dense crossbars connected close to the Si-based transistors, to create a high performing synaptic analog signal processing accelerator. In recent technology developments , it was demonstrated that filamentary-based RRAMs with an engineered material stack, made of an oxide bilayer sandwiched between metallic electrodes, provide bidirectional gradual resistive switching to achieve the accumulative behavior of the conductive states, upon the sequential application of identical pulses, in both directions. This property makes such devices attractive for DNNs’ training applications, where symmetric conductance updates are an essential requirement of the training algorithm . In the oxide bilayer concept, the capping of the resistive switching insulator is done with a conductive- metal-oxide (CMO) material, to keep the forming voltages low. Although bilayer RRAMs have already shown remarkable capabilities to implement neuromorphic systems, the resistive switching mechanism, as well as the role of the CMO, is not yet fully understood . In our work we developed multiple CMO/HfO2 RRAM structures, changing the CMO properties such as the resistivity and thickness. Multiple electrode metals were used to understand the impact on the resistive switching properties. Furthermore, we provide an interpretation of the switching polarity and the gradual switching characteristics that are the main enhancements compared to conventional filamentary devices. To explain the switching properties of our CMO/HfO2-based devices we performed Impedance Spectroscopy experiments to establish an electrical equivalent circuit model describing the quasi-static forming, as well as the set and reset operations. Our filamentary bilayer ReRAMs represent a promising analog memory device for large-scale integration, considering its excellent granular switching properties and CMOS compatibility. References  P. Yao, H. Wu, B. Gao, S. B. Eryilmaz, X. Huang, W. Zhang, Q. Zhang,N. Deng, L. Shi, H.-S. P. Wong, H. Qian, Nature Communications 2017 8:1 2017, 8, 1 1.  Gokmen T. and Vlasov Y. Front. Neurosci., 10 p333 (2016).  S. Kim, Y. Abbas, Y. Jeon, A. Sokolov, B. Ku, C. Choi, vol. 29, p. 415204, 2018.