Memristive devices are promising enablers for computing-in-memory architectures, offering reduced latency and energy consumption compared to conventional designs. Among these, the memristive device-based Cellular Nonlinear Network (M-CNN) provides a compact framework for universal computing, including image processing and neuromorphic computing. In this work, we investigate the use of IGZO-based devices exhibiting gradual switching as core elements of M-CNN cells. A simulation approach based on measured I-V-characteristics is developed to evaluate device–circuit interactions. We first analyze the limitations of the conventional M-CNN cell core, where asymmetric I-V-characteristics restrict voltage levels and accelerate device degradation. To address these issues, we propose a symmetrized cell that mitigates asymmetry, intrinsically limits cell voltage, and supports differential readout. The results demonstrate that gradual switching enables reliable distinction of input current levels while ensuring stable operation and reduced power consumption, thus paving the way for robust IGZO-based M-CNN implementations.

We have successfully fabricated one of the earliest array-scale prototypes of a Memristive Cellular Nonlinear Network (M-CNN) with interconnected cells. In this live demonstration, we will showcase the operation of this 4x4 M-CNN array performing an edge detection task according to our previous work [1]. A user-defined input will be applied to the network, and the computing results will be visualized alongside the simulated operation of a standard CNN for comparison.

In this paper, the design of an isolated Memristor Cellular Nonlinear Network (CNN) cell with discrete electronic elements is presented. The proposed versatile circuit allows for adjustable cell dynamical characteristics, controlled by design parameters, while the discrete element approach enables simple on-board implementation without the need for large-scale integration, which is necessary for testing hardware with individual fabricated memristors. A voltage-mode approach, that makes use of the diversity of operational amplifiers, is preferred here over a current-mode one that necessitates a large number of individual transistors. The dynamical properties of the system are initially investigated through the calculation of equilibrium points and further illustrated applying the concept of State Dynamic Routes (SDRs) for the cell assuming that the memristor dynamics are much slower than the capacitor voltage dynamics. Moreover, the effect of design parameters on the cell dynamics is being investigated, showing how the scaling of the operating voltage, as well as a plethora of CNN variants -i.e., the Chua-Yang and Full Range models-, can be implemented within the same design. Finally, the nonlinear conductance properties of real memristor devices are incorporated into the study, demonstrating interesting bifurcation phenomena between the cell monostability and bistability for specific parameter values.

Cellular Nonlinear Networks (CNN) as a powerful paradigm is highly suitable for signal processing of multiple tasks, since they can execute cascaded processing operations in a one-layer array via real-time template updating. Their VLSI implementation by using the conventional CMOS-based integration technology, however, remains a big challenge. The memristive CNN (M-CNN) offers several merits over conventional CNN, such as compactness, nonvolatility, versatility. This paper presents a direct comparison of computing performance between the M-CNN and the conventional CNN for the implementation of a LOGAND operation template using circuit simulation. Our findings show that the M-CNN implementation offers rapid attainment of equilibrium state compared to the CNN implementation. In addition, the result is stored in a non-volatile manner in the M-CNN whereas the CNN only offers a volatile storage.