As conventional computers based on von Neumann architecture approach their physical and performance limits, unconventional computing paradigms such as Cellular Neural Networks (CellNNs) have emerged as promising platforms for real-time, massively parallel analog computation. However, conventional analog CellNNs suffer from scalability and power constraints due to large cell hardware overhead. This work investigates the integration of memristor-based crossbar arrays into CellNN architectures to address these limitations by exploiting their analog tunability, high density, and low power operation. A 1-Transistor-1-Memristor (1T1R) crossbar is proposed for implementing the coupling weights defining the CellNN gene. Device nonlinearity, asymmetry and stochastic variability are incorporated using the physics-based JART VCM memristor model, enabling accurate mapping of target weights onto memristor conductances through numerical optimization and differential-pair encoding. Simulations of edge detection tasks confirm high functional accuracy and robustness, while Monte Carlo analysis reveals variability’s impact, underscoring the need for variability-aware design of reliable memristor-CNN hardware.

In this work, we introduce a high speed and area efficient Cellular Nonlinear Network (CNN) cell, featuring two circuit variants that utilize threshold switches. The threshold switch (TS) model employed represents a current-controlled nanoscale negative differential resistance (NDR) device which exhibits an S-shaped DC I-V curve as a fingerprint. The proposed cell can be considered as the dual of the standard isolated CNN cell where the bistable cell characteristics, originating from the N-shaped voltage-controlled resistor, is implemented through the S-shaped current-controlled TSs. Similarly, the dynamics induced by the parallel capacitor accompanying the nonlinear resistor in the standard cell version are implemented through the internal inductive dynamics of the TSs, resulting in area and speed efficiency. The proposed CNN cell employs a DC voltage source, two bias resistors and 2 TSs, and essentially, features a differential-mode operation which helps to endow it with a symmetric DC I-V characteristic, as is the case for the standard CNN cell. The differential-mode approach further introduces design flexibility as the cell DC I-V characteristic can be adjusted by tuning circuit parameters. We demonstrate the functionality of the proposed cell by implementing image processing tasks ranging from edge detection and thresholding to logic AND and OR operations.

The electrical engineering community, interested to develop bio-inspired circuits, approaching the efficiency of the neural networks, is searching passionately for accurate yet simple electronic neurons, or neuristors for short. In recent years, the advent of volatile memristor devices, typically referred to as threshold switches, which admit a negative differential resistance under suitable polarization, similarly as the sodium and potassium ion channels across neuronal axon membranes, has opened up new exciting opportunities in neuromorphic circuit design, enabling innovative analogue electronic cells, capable to reproduce closely the intricate dynamical behaviors of biological neurons without requiring a disproportionate use of resources. The study, presented in this manuscript, achieves an important milestone in this area of research, demonstrating, through a circuit design approach based upon concepts and techniques from Dynamical System Theory, how to leverage the rich dynamics of a threshold switch, capable to boost a periodic sine-wave current signal of infinitesimal amplitude, while acting as a source of local energy, when poised on a suitable bias point, lying along the negative differential resistance branch of the respective S-shaped DC current-voltage characteristic, to induce, one after the other, the three fundamental bifurcations, governing the evolution of an electrical voltage spike from birth to extinction via the All-to-None effect across a biological axon membrane under a reverse sweep in the net synaptic current, according to the fourth-order Hodgkin-Huxley neuron model, in a second-order three-element circuit of unprecedented simplicity, as the current, generated by a DC source, appearing in parallel to a linear capacitor as well as to the volatile locally-active memristor, is subject to a monotonic increase.

This manuscript sheds light into the fundamental importance of the Principles of Local Activity and Edge of Chaos for the future design of innovative circuits, which, employing biomimetic memristive devices, are ideally suited for the development of energy-efficient artificially-intelligent technical systems. The focus of the work is the design of a Second-Order Reactance-Less Oscillator, across which oscillations may develop if and only if at least one of its two different volatile thermally-activated memristor physical realizations is biased along a negative differential resistance branch of the respective DC locus, which turns it into a source of local energy. Very importantly, the proposed cell is first found to lock in the oscillatory mode out of a local Hopf Supercritical Bifurcation when its design parameters are chosen from the Edge of Chaos region, providing clear evidence for the high degree of excitability it acquires as a result.

Neuromorphic circuits using emerging memory technologies have recently gained popularity since they facilitate dense integration with reduced design complexity. In this work, we introduce a simplified modeling approach for the analysis of threshold switch (TS) based neuron circuits where, under given constraints, we represent the TS device as a nonlinear resistor in series with a parasitic inductor. As a result, we define the current of the TS as its state variable. In order to demonstrate the feasibility of the proposed approach, we analyze the conventional Leaky Integrate and Fire (LIF) neuron circuit along with two of its modified variants. We validate the accuracy of the provided analysis and key predictions through numerical simulation results. As a significant contribution, we demonstrate the effectiveness of the proposed method in modifying the nullclines of the TS based LIF neuron and qualitatively align them with the nullclines of a 2ndorder biologically plausible neuron model.

Realized memristors exhibit a unique phenomenon called the fading memory effect, where the memristor response to an AC signal is determined by its characteristics (waveform, amplitude, frequency, and DC offset) rather than the memristor initial conditions. Recently, a method for programming Hewlett Packard’s TaOxmemristor to a target state was proposed, involving configuring the DC offset of a high-frequency square-wave AC voltage input. This served as a basic application example that exploits fading memory in non-volatile memristors, but didn’t consider non-ideal effects. Here, we assess the method applicability in a HfOx-based VCM resistive switch from Forschungszentrum Julich incorporating a variability-aware physics-based model.

As demand grows for efficient, localized processing in edge and in-sensor computing, novel architectural approaches are essential to meet low-power, high-density requirements. Memristor Cellular Nonlinear Networks (M-CNNs) offer a promising path forward, leveraging the unique properties of memristors for adaptable and scalable computation. This paper presents a study of novel M-CNN cell configurations designed to enhance computational versatility and address operational challenges in M-CNN-based systems. By leveraging memristor technology within CNN cells, we propose three distinct configurations: (1) incorporating parallel and series resistive elements for refined control over cell dynamics, (2) introducing a fixed bias voltage to expand computational capabilities, and (3) integrating the Full-Range CNN (FR-CNN) model into M-CNNs for the first time. The proposed topologies are evaluated through dynamic route maps (DRM) and vector field analysis to systematically assess stability and performance across varying design parameters.

Biological synapses behave as dynamically-rich nonlinear elements, participating in complicated computing tasks through their adaptation due to external stimuli. Such adaptivity constitutes an intrinsic property of non-volatile memristor devices, which are also able to maintain their internal state, under zero input, enabling novel bio-inspired learning operations. In this work, a synaptic element based on a memristive bridge, containing two resistors and two memristors, is studied, aiming to investigate complex memristor-based topologies that may result in rich synaptic dynamics. The proposed memristive bridge allows the realization of both positive and negative synaptic weights, while an asymmetric tuning of a weight, stemming from memristor's features and bridge topology, is demonstrated. In particular, by properly selecting the memristor's position and polarity within the bridge, different tuning behaviors have been observed, showcasing versatile learning properties of the topology. Along with the synaptic weight tuning, the read overall process of the synaptic weight, necessary for inference operations, is also investigated. We explore the dynamics of the bridge via numerical simulations.

Synchronization is one of the most challenging aspects of distributed systems in terms of their scalability. Minimal uncertainties can lead to problems or failures regarding data consistency in globally operating data centers or in distributed sensor arrays. Existing approaches to address these challenges are based on hierarchical synchronization concepts which are well understood and have reached technical maturity, but have the disadvantage of having a single point of failure. However, especially for critical infrastructure or backup more resilient solutions are required. Mutual synchronization where oscillators in a network are coupled bidirectionally without a reference have been considered. Due to the flat hierarchy such systems do not have a single point of failure. This work studies how hierarchical synchronization can be combined with architectures implementing mutual synchronization. A network of three mutually coupled 24 GHz oscillators is used to study how injecting a reference signal into one oscillator affects the dynamics. This can be quantified by analyzing in which range of frequencies the network of mutually coupled oscillators can follow the reference frequency. Measurements on a ring and chain network topology forced by an external reference oscillator shown here are in good agreement with the predictions of a nonlinear dynamical model.

This work presents synchronization of two bidirectionally delay-coupled phase locked loop (PLL) systems with voltage controlled oscillator frequencies of 24 GHz to validate a non-hierarchical clock distribution approach. For this purpose, a PLL architecture that allows mutual coupling between two such nodes is introduced. An existing phase domain model is extended to include the nonlinear response of the oscillator to the tuning signal. With this extension the frequencies and phase-relations of self-organized synchronized states can be precisely predicted. This is verified by measurements obtained from two synchronized PLLs for different time delays and division factors. The predictions of the model are in good agreement with the measurements. For time delays up to 14 ns it is shown that self-organized synchronization is feasible at microwave frequencies.