One-Dimensional Covalent Organic Framework-Based Multilevel Memristors for Neuromorphic Computing

Memristors are essential components of neuromorphic systems that mimic the synaptic plasticity observed in biological neurons. In this study, a novel approach employing one-dimensional covalent organic framework (1D COF) films was explored to enhance the performance of memristors. The unique structural and electronic properties of two 1D COF films (COF-4,4'-methylenedianiline (MDA) and COF-4,4'-oxydianiline (ODA)) offer advantages for multilevel resistive switching, which is a key feature in neuromorphic computing applications. By further introducing a TiO2 layer on the COF-ODA film, a built-in electric field between the COF-TiO2 interfaces could be generated, demonstrating the feasibility of utilizing COFs as a platform for constructing memristors with tunable resistive states. The 1D nanochannels of these COF structures contributed to the efficient modulation of electrical conductance, enabling precise control over synaptic weights in neuromorphic circuits. This study also investigated the potential of these COF-based memristors to achieve energy-efficient and high-density memory devices.

Control-Etched Ti3C2Tx MXene Nanosheets for a Low-Voltage-Operating Flexible Memristor for Efficient Neuromorphic Computation

Hardware neural networks with mechanical flexibility are promising next-generation computing systems for smart wearable electronics. Overcoming the challenge of developing a fully synaptic plastic network, we demonstrate a low-operating-voltage PET/ITO/p-MXene/Ag flexible memristor device by controlling the etching of aluminum metal ions in Ti3C2Tx MXene. The presence of a small fraction of Al ions in partially etched MXene (p-Ti3C2Tx) significantly suppresses the operating voltage to 1 V compared to 7 V from fully Al etched MXene (f-Ti3C2Tx)-based devices. Former devices exhibit excellent non-volatile data storage properties, with a robust ∼103 ON/OFF ratio, high endurance of ∼104 cycles, multilevel resistance states, and long data retention measured up to ∼106 s. High mechanical stability up to ∼73° bending angle and environmental robustness are confirmed with consistent switching characteristics under increasing temperature and humid conditions. Furthermore, a p-Ti3C2Tx MXene memristor is employed to mimic the biological synapse by measuring the learning-forgetting pattern for ∼104 cycles as potentiation and depression. Spike time-dependent plasticity (STDP) based on Hebb's Learning rules is also successfully demonstrated. Moreover, a remarkable accuracy of ∼95% in recognizing modified patterns from the National Institute of Standards and Technology (MNIST) data set with just 29 training epochs is achieved in simulation. Ultimately, our findings underscore the potential of MXene-based flexible memristor devices as versatile components for data storage and neuromorphic computing.

Insights into synaptic functionality and resistive switching in lead iodide flexible memristor devices

Neuromorphic platforms are gaining popularity due to their superior efficiency, low power consumption, and adaptable parallel signal processing capabilities, overcoming the limitations of traditional von Neumann architecture. We conduct an in-depth investigation into the factors influencing the resistive switching mechanism in memristor devices utilizing lead iodide (PbI2). We establish correlations between device performance and morphological features, unveiling synaptic like behaviour of device making it suitable for range of flexible neuromorphic applications. Notably, a highly reliable unipolar switching mechanism is identified, exhibiting stability even under mechanical strain (with a bending radius of approximately 4 mm) and in high humidity environment (at 75% relative humidity) without the need for encapsulation. The investigation delves into the complex interplay of charge transport, ion migration and the active interface, elucidating the factors contributing to the remarkable resistive switching observed in PbI2-based memristors. The detailed findings highlight synaptic behaviors akin to the modulation of synaptic strengths, with an impressive potentiation and depression of 2 × 104 cycles, emphasizing the role of spike time-dependent plasticity (STDP). The flexible platform demonstrates exceptional performance, achieving a simulated accuracy rate of 95.06% in recognizing modified patterns from the National Institute of Standards and Technology (MNIST) dataset with just 30 training epochs. Ultimately, this research underscores the potential of PbI2-based flexible memristor devices as versatile component for neuromorphic computing. Moreover, it demonstrate the robustness of PbI2 memristors in terms of their resistive switching capabilities, showcasing resilience both mechanically and electrically. This underscores their potential in replicating synaptic functions for advanced information processing systems.

Hollow spherical Mn0.5Zn0.5Fe2O4 nanoparticles with a magnetic vortex configuration for enhanced magnetic hyperthermia efficacy

Conventional magnetic nanoagents in cancer hyperthermia therapy suffer from a low magnetic heating efficiency. To address this issue, researchers have pursued magnetic nanoparticles with topological magnetic domain structures, such as the vortex-domain structure, to enhance the magnetic heating performance of conventional nanoparticles while maintaining excellent biocompatibility. In this study, we synthesized hollow spherical Mn0.5Zn0.5Fe2O4 (MZF-HS) nanoparticles using a straightforward solvothermal method, yielding samples with an average outer diameter of approximately 350 nm and an average inner diameter of about 220 nm. The heating efficiency of the nanoparticles was experimentally verified, and the specific absorption rate (SAR) value of the hollow MZF was found to be approximately 1.5 times that of solid MZF. The enhanced heating performance is attributed to the vortex states in the hollow MZF structure as validated with micromagnetic simulation studies. In vitro studies demonstrated the lower cell viability of breast cancer cells (MCF-7, BT549, and 4T1) after MHT in the presence of MZF-HS. The synthesized MZF caused 51% cell death after MHT, while samples of MZF-HS resulted in 77% cell death. Our findings reveal that magnetic particles with a vortex state demonstrate superior heating efficiency, highlighting the potential of hollow spherical particles as effective heat generators for MHT applications.

A bi-functional three-terminal memristor applicable as an artificial synapse and neuron

Due to their significant resemblance to the biological brain, spiking neural networks (SNNs) show promise in handling spatiotemporal information with high time and energy efficiency. Two-terminal memristors have the capability to achieve both synaptic and neuronal functions; however, such memristors face asynchronous programming/reading operation issues. Here, a three-terminal memristor (3TM) based on oxygen ion migration is developed to function as both a synapse and a neuron. We demonstrate short-term plasticity such as pair-pulse facilitation and high-pass dynamic filtering in our devices. Additionally, a 'learning-forgetting-relearning' behavior is successfully mimicked, with lower power required for the relearning process than the first learning. Furthermore, by leveraging the short-term dynamics, the leaky-integrate-and-fire neuronal model is emulated by the 3TM without adopting an external capacitor to obtain the leakage property. The proposed bi-functional 3TM offers more process compatibility for integrating synaptic and neuronal components in the hardware implementation of an SNN.

Proton-Assisted Redox-Based Three-Terminal Memristor for Synaptic Device Applications

Emerging technologies, i.e., spintronics, 2D materials, and memristive devices, have been widely investigated as the building block of neuromorphic computing systems. Three-terminal memristor (3TM) is specifically designed to mitigate the challenges encountered by its two-terminal counterpart as it can concurrently execute signal transmission and memory operations. In this work, we present a complementary metal-oxide-semiconductor-compatible 3TM with highly linear weight update characteristics and a dynamic range of ∼15. The switching mechanism is governed by the migration of oxygen ions and protons in and out of the channel under an external gate electric field. The involvement of the protonic defects in the electrochemical reactions is proposed based on the bipolar pulse trains required to initiate the oxidation process and the device electrical characteristics under different humidity levels. For the synaptic operation, an excellent endurance performance with over 256k synaptic weight updates was demonstrated while maintaining a stable dynamic range. Additionally, the synaptic performance of the 3TM is simulated and implemented into a four-layer neural network (NN) model, achieving an accuracy of ∼92% in MNIST handwritten digit recognition. With such desirable conductance modulation characteristics, our proposed 3T-memristor is a promising synaptic device candidate to realize the hardware implementation of the artificial NN.

The impact of oxygen vacancy defect density on the nonlinearity and short-term plasticity of TiO2-based exponential selector

The readout margin of the one selector-one RRAM (1S1R) crossbar array architecture is strongly dependent on the nonlinearity of the selector device. In this work, we demonstrated that the nonlinearity of Pt/TiO2/Pt exponential selectors increases with decreasing oxygen vacancy defect density. The defect density is controlled by modulating the sputtering pressure in the oxide deposition process. Our results reveal that the dominant conduction mechanisms of the Pt/TiO2/Pt structure transit from Schottky emission to Poole-Frenkel emission with the increase of sputtering pressure. Such transition is attributed to the rise of oxygen vacancy concentration. In addition, the short-term plasticity feature of the Pt/TiO2/Pt selector is shown to be enhanced with a lower defect density. These results suggest that low defect density is necessary for improved exponential selector performances.

Switching the memory behaviour from binary to ternary by triggering S62- relaxation in polysulfide-bearing zinc-organic complex molecular memories

The use of crystalline metal-organic complexes with definite structures as multilevel memories can enable explicit structure-property correlations, which is significant for designing the next generation of memories. Here, four Zn-polysulfide complexes with different degrees of conjugation have been fabricated as memory devices. ZnS₆(L)₂-based memories (L = pyridine and 3-methylpyridine) can exhibit only bipolar binary memory performances, but ZnS₆(L)-based memories (L = 2,2'-bipyridine and 1,10-phenanthroline) illustrate non-volatile ternary memory performances with high ON2/ON1/OFF ratios (10^4.22/10^2.27/1 and 10^4.85/10^2.58/1) and ternary yields (74% and 78%). Their ON1 states stem from the packing adjustments of organic ligands upon the injection of carriers, and the ON2 states are a result of the ring-to-chain relaxation of S₆^2- anions. The lower conjugated degrees in ZnS₆(L)₂ result in less compact packing; consequently, the adjacent S₆^2- rings are too long to trigger the S₆^2- relaxation. The deep structure-property correlation in this work provides a new strategy for implementing multilevel memory by triggering polysulfide relaxation based on the conjugated degree regulation of organic ligands.

Frequency-Dependent Synapse Weight Tuning in 1S1R with a Short-Term Plasticity TiOx-Based Exponential Selector

Short-term plasticity (STP) is an important synaptic characteristic in the hardware implementation of artificial neural networks (ANN), as it enables the temporal information processing (TIP) capability. However, the STP feature is rather challenging to reproduce from a single nonvolatile resistive random-access memory (RRAM) element, as it requires a certain degree of volatility. In this work, a Pt/TiO_x/Pt exponential selector is introduced not only to suppress the sneak current but also to enable the TIP feature in a one selector-one RRAM (1S1R) synaptic device. Our measurements reveal that the exponential selector exhibits the STP characteristic, while a Pt/HfO_x/Ti RRAM enables the long-term memory capability of the synapse. Thereafter, we experimentally demonstrated pulse frequency-dependent multilevel switching in the 1S1R device, exhibiting the TIP capability of the developed 1S1R synapse. The observed STP of the selector is strongly influenced by the bottom metal-oxide interface, in which Ar plasma treatment on the bottom Pt electrode resulted in the annihilation of the STP feature in the selector. A mechanism is thus proposed to explain the observed STP, using the local electric field enhancement induced at the metal-oxide interface coupled with the drift-diffusion model of mobile O^2- and Ti³⁺ ions. This work therefore provides a reliable means of producing the STP feature in a 1S1R device, which demonstrates the TIP capability sought after in hardware-based ANN.