The development of reliable memory devices on flexible substrates is vital for wearable electronics with bio-inspired computing capabilities. This work presents a comprehensive experimental study of parylene-based memristors fabricated on flexible polyethylene naphthalate substrates, focusing on the impact of mechanical bending deformations on resistive switching (RS) characteristics. The bending test methodologies included in-situ investigation under constant mechanical stress (constant-curvature mode) and analysis after cyclic bending in undeformed state (deformation-free mode). The current-voltage characteristics demonstrate stability of resistive switching and its key parameters (e.g., set and reset voltages, RON and ROFF resistances in the low and high resistive states, respectively) for the bending radius down to 0.5cm. Importantly, the intermediate resistive states stabilize at each bending level, enabling precise analog control. The devices also support biologically plausible spike-timing-dependent plasticity learning protocol. In addition, the possibility of flexible memristor implementation in neural networks for pattern recognition was analyzed. This study provides an experimental understanding of RS characteristics under mechanical deformations and allows the use of parylene memristors as a reliable circuit element for flexible neuromorphic electronics.

We employ systematic interface engineering to enhance the reliability and reduce stochasticity in HfOx-based memristors, which is crucial for neuromorphic computing. We achieve targeted control over switching parameters by integrating thermally evaporated Au, Al, and magnetron-sputtered TiN interlayers in six distinct configurations. Key findings include the identification of optimal stacks (e.g., Au/Ti + Al), enabling stable multi-level resistive switching and the discovery of a unique polarity-reversal endurance mode in TiN/Au devices. Experimental long-term potentiation/depression characteristics of the engineered devices, when utilized in a neuromorphic simulation, yield high classification accuracy (> 96%) on the MNIST benchmark. These results demonstrate a viable pathway towards fabricating reliable HfOx memristors for hardware neural networks.

This study investigates memristive and synaptic properties of parylene (PPX)-based memristors in crossbar geometry for brain-inspired neuromorphic computing applications. We demonstrate Cu/PPX/Au memristors exhibiting stable resistive switching (Ron/Roff > 7 × 105), multilevel operation (16 resistive states), and synaptic plasticity emulation capabilities. The devices successfully implement biosimilar learning rules, demonstrating spike-timing-dependent plasticity (STDP) with up to 1400

Memristive devices have proven themselves as synaptic elements with rich internal dynamics and stochasticity for bio-inspired neuromorphic computing systems. Memristors based on parylene are of special interest due to their promising memristive properties, biocompatibility and ease of production. However, their synaptic behavior has not yet been fully demonstrated. In addition, the previously proposed model of resistive switching (RS) of these memristors was of a qualitative character and therefore important switching nuances turned out to be hidden. In this paper, a phenomenological model of resistive switching in parylene memristors is developed, based on the electromigration of metal cations from the top electrode, taking into account the stochastic nature of the RS process and conductivity of the parylene gap between the filament and the electrode, which determines various resistive states (plasticity) of the structures. The model is confirmed by a good correspondence between the calculated and measured current-voltage characteristics of the memristors. We also demonstrate various forms of bio-inspired plasticity of the structures, such as paired pulse facilitation/depression, long-term potentiation/depression and spike timing/amplitude/width/rate dependent plasticity. In the case of rate-based plasticity it resembles the theoretically and experimentally thoroughly studied BCM plasticity rule. The results obtained show the possibility of using such structures in the development of next-generation neuromorphic computing systems with promising calculating and learning capabilities.

This paper investigates the effect of alpha-particle irradiation on the memristive properties of titanium oxide-based structures. Multilayer TiOx/Ti structures were fabricated by magnetron sputtering and subjected to alpha-particle irradiation with a fluence of 2 × 1012 ions/cm2. Defect formation was modeled using the Monte Carlo method. The memristive characteristics of the structures were studied before and after bombardment. Ion bombardment was found to increase the number of stable resistive states by nearly three times, extend the number of switching cycles by 1.5 times, and significantly enhance the ROFF/RON ratio. This optimization of memristive parameters is attributed to the formation of locally created defects.

Нafnium oxide is currently considered one of the most promising metal-oxide materials for creating memristive structures. Memristive structures find their application in many areas of science and technology; for example, with their help, the biosimilar emulation of synapses in neuromorphic computing systems is possible. One of the important obstacles to the industrial use of memristors is the variability of resistive switching. Nonstoichiometry in memristor structure can be an important tool for controlling resistive switching. Therefore, in this work, memristors based on hafnium oxide in a metal–insulator–metal sandwich structure are synthesized by electron-beam deposition, which makes it possible to create nonstoichiometric films. The effect of resistive switching is studied depending on the material of the upper electrode and the thickness of the hafnium-oxide layer. The synthesis parameters are determined to achieve a balance between the main memristive characteristics.

The key elements of neuromorphic computing systems (NCS) are memristors—resistors with a memory effect—that can be used for simultaneous processing and storage of information. It is promising to create them in crossbar geometry, where memristors are located at the intersections of the transverse electrode buses. In this work, the influence of the area and geometry of contacts on the main memristive characteristics of parylene-based structures is investigated. The results obtained indicate the independence of such memristive characteristics as the switching voltage into the low-resistance (Uset) and high-resistance states (Ureset), as well as the resistance of the samples in the low-resistance (Ron) state, from the contact area. At the same time, resistances in the high-resistance (Roff) state increase with decreasing area, which confirms the single-filament model of resistive switching, and also makes it possible to increase the window of resistance in such structures.

Memristors are among the most promising elements for modern microelectronics, having unique properties such as quasi-continuous change of conductance and long-term storage of resistive states. However, identifying the physical mechanisms of resistive switching and evolution of conductive filaments in such structures still remains a major challenge. In this work, aiming at a better understanding of these phenomena, we experimentally investigate an unusual effect of enhanced conductive filament stability in memristors with copper filaments under the applied voltage and present a simplified theoretical model of the effect of a quantum current through a filament on its shape. Our semi-quantitative, continuous model predicts, indeed, that for a thin filament, the "quantum pressure" exerted on its walls by the recoil of charge carriers can well compete with the surface tension and crucially affect the evolution of the filament profile at the voltages around 1V. At lower voltages, the quantum pressure is expected to provide extra stability to the filaments supporting quantized conductance, which we also reveal experimentally using a novel methodology focusing on retention statistics. Our results indicate that the recoil effects could potentially be important for resistive switching in memristive devices with metallic filaments and that taking them into account in rational design of memristors could help achieve their better retention and plasticity characteristics.

Currently, there is growing interest in wearable and biocompatible smart computing and information processing systems that are safe for the human body. Memristive devices are promising for solving such problems due to a number of their attractive properties, such as low power consumption, scalability, and the multilevel nature of resistive switching (plasticity). The multilevel plasticity allows memristors to emulate synapses in hardware neuromorphic computing systems (NCSs). The aim of this work was to study Cu/poly-p-xylylene(PPX)/Au memristive elements fabricated in the crossbar geometry. In developing the technology for manufacturing such samples, we took into account their characteristics, in particular stable and multilevel resistive switching (at least 10 different states) and low operating voltage (<2 V), suitable for NCSs. Experiments on cycle to cycle (C2C) switching of a single memristor and device to device (D2D) switching of several memristors have shown high reproducibility of resistive switching (RS) voltages. Based on the obtained memristors, a formal hardware neuromorphic network was created that can be trained to classify simple patterns.

Nowadays there is a growing interest in wearable and biocompatible computing systems that are safe for the human body. Memristive devices are prospective for such tasks owing to a number of their attractive properties, in particular, the multilevel character of resistive switching, or plasticity, which allows them to emulate synapses in hardware neuromorphic networks (NNs). The use of local learning rules for such NNs, for example, bioinspired spike-timing-dependent plasticity (STDP), has firmly established itself in recent years. In biological systems the basic STDP can be modified in the presence of neuromodulators (e.g. dopamine). This effect is believed to be essential for important biological functions such as reinforcement learning (RL), memory and others. The goal of this work was to demonstrate that such dopamine-like modulated STDP can be used in memristors based on a biocompatible polymer, parylene (poly-p-xylylene, or PPX). We have studied memristors both in the form of single Cu/PPX/ITO devices and in the form of crossbar Cu/PPX/Au structures. It was found that, in addition to stable memristive characteristics suitable for NNs, these devices can also change their conductance by means of bioinspired STDP rules, including dopamine-like modulated STDP window realized by introducing the coefficients for neuron spike amplitudes. The amplitude coefficients from −1 (inhibitory mode) to 1 (excitatory mode) of pre- and post-spikes, reflecting the 'dopamine' concentration, in various combinations allow observing the STDP window not only of the usual shape, but also of the anti-STDP, bell and anti-bell shapes. The obtained results demonstrate that the development of memristors based on PPX provides prospects for hardware realization of bio-inspired spiking NNs with RL ability.

The paper presents the results of a study at room temperature of the quantization effect of the conductivity of memristive structures based on the organic material poly-p-xylylene with resistive switching. Measurement methods are shown and a comparative analysis of the manifestation of the effect when switching structures to a high-resistance and low-resistance state is carried out. The possibility of setting stable quantum states of conductivity in memristive structures based on poly-p-xylylene is demonstrated. It is shown that some of these states have short-term, and some long-term stability. The results obtained open up new possibilities for using the quantization effect of conductivity in the implementation of neuromorphic systems.

Исследованы эффекты резистивного переключения 2-го порядка в мемристорах на основе поли-n-ксилилена (PPX). Обнаружено уменьшение времени переключения, вызванное эффектами 2-го порядка. Результаты указывают на возможность использования обнаруженных эффектов в нейроморфных вычислительных системах (НВС).

Нейроморфные вычислительные сети (НВС) с синаптическими связями на основе мемристоров могут обеспечить значительно большую эффективность аппаратной реализации биоподобных спайковых нейронных сетей, чем цифровые синаптические элементы на основе комплементарной технологии. Для реализации энергоэффективных и в перспективе самообучаемых НВС необходимо, чтобы сопротивление мемристора, связывающего пре- и постсинаптический нейроны, могло быть изменено по локальным правилам, например по правилам пластичности, зависящей от времени прихода пре- и постсинаптического импульсов (STDP). На примере мемристивных структур Cu/поли-пара-ксилилен (РРХ)/оксид индия-олова (ITO), у которых верхний электрод (медь) выступал в качестве пресинаптического входа, а нижний (ITO) — в качестве постсинаптического, продемонстрирована возможность обучения мемристоров по правилам STDP. Найдены оптимальные значения амплитуды и длительности импульсов для прямоугольной и треугольной форм обучающих импульсов. Полученные результаты открывают перспективы создания автономных НВС, способных к обучению с учителем и без него для решения сложных когнитивных задач.

Flexible memristive structures based on poly-para-xylelene layers, which exhibit stable resistive switchings and are resistant to bendings with radii up to 10 mm, have been prepared and studied. A two-step scheme of setting the resistive state of the memristive structure is proposed, which is based on monitoring the compliance current passing through the structure. The results obtained allow memristive structures based on poly-para-xylelene layers to be used for neuromorphic computational systems and biocompatible “wearable” electronics.

—Neuromorphic computer networks (NCNs) with synaptic connections based on memristors can provide much greater efficiency in the hardware implementation of bio-inspired spiking neural networks than digital synaptic elements based on complementary technology. To achieve energy-efficient and, in the long-term, self-learning NCNs, the resistance of a memristor connecting pre- and postsynaptic neurons needs to be changeable according to local rules, e.g., according to the rules of spike-timing-dependent plasticity—STDP. The possibility of memristor training according to STDP rules was demonstrated by the example of Cu/poly- p -xylylene (PPX)/indium tin oxide (ITO) memristive structures, in which the top electrode (copper) acted as the presynaptic input, and the bottom (ITO), as the postsynaptic input. The optimal pulse amplitude and duration values are found for rectangular and triangular training pulses. The results open up prospects for creating autonomous NCNs capable of supervised and unsupervised learning to solve complex cognitive problems.

This work presents the results of the fabrication and investigation of flexible memristive structures based on parylene layers, which demonstrate stable resistive switching and are resistant to bends up to 10 mm radii. It is also proposed a two-step scheme for establishing the resistive state of the memristive structure, based on control of the value of the limiting current flowing through the structure. Obtained results open the possibility of using memristive structures based on parylene layers for neuromorphic computing systems and biocompatible "wearable" electronics.